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IMPACTS OF GLOBLISATION AND TRADE
LIBERALISATION ON SMALL HOUSEHOLDS IN
VIETNAM LIVESTOCK SECTOR
Pham Thi Ngoc Linh
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
SCHOOL OF AGRICULTURAL AND RESOURCE ECONOMICS
Faculty of Natural and Agricultural Sciences
May 2011
ii
To my loved husband and son: Duc and Thanh
To my parents
iii
CERTIFICATION
The thesis is my own composition, all sources have been acknowledged and my
contribution is clearly identified in the thesis.
The thesis has been substantially completed during the course of enrolment in this
degree at UWA and has not previously been accepted for a degree at this or another
institution.
This thesis does not contain work that I have published, nor work under review for
publication.
Pham Thi Ngoc Linh
May 2011
iv
ACKNOWLEDGEMENTS
This thesis was completed with the valuable support of many people, to whom I am
indebted in one form or the other. However, all remaining shortcomings and mistakes in
this thesis are my own responsibility.
I would like to express my sincere thanks to Australian Centre for International
Agricultural Research (ACIAR) for granting me a John Allwright Scholarship to study
and complete my PhD at the University of Western Australia.
I would like to acknowledge my gratefulness to my principal supervisor, A. Prof. Dr.
Michael Burton, who has supported me in terms of knowledge, experience, material and
worthwhile comment. His tireless support has encouraged me to overcome difficulties
during my study. I remember his help in solving data problems and revising my papers
and thesis.
I take pleasure in offering my sincere appreciation to my co-supervisor, Dr. Greg
Hertzler, for his guidance and assistance. His critical comments and helpful suggestions
from the early stages of my study are acknowledged.
During the time of doing this thesis, I have also had external expert advice and
numerous supports from Dr. David Vanzetti from Crawford School of Economics and
Government, Australian National University. His excellent experience in dealing with
trade modelling helped me considerably in constructing and finalizing the trade part of
my thesis.
I whole-heartedly thank my sister, the best friend, Dr. Donna Brennan for gave me the
excellent opportunity of studying at UWA, and also for her continuous help and support
through my study time. Her experience and guidance helped me a lot, especially when
my supervisors were absent. Also, her sympathies and sharing both joy and sorrow with
me in the early stage of living far away from family was invaluable.
I would like to thank to School of Agricultural and Resource Economics, University of
Western Australia, for providing me the necessary facilities, excellent support and ideal
environment for undertaking research study. My thanks go to A. Prof. Dr. Ben White,
Sally Marsh, Helena Clayton, Jan Taylor and all teachers, staff and other colleges in
ARE, ACIAR scholarship officer Sharon Harvey, and AusAID officers at UWA:
v
Rhonda Haskell, Cathy Tang, and Christine Kerin who have been willing to help and
assisted me during my study at UWA. My thanks go to Dr. Christine Davies for her
English editing for the final thesis.
I wish to thank Dr. Dang Kim Son, my boss for both sending and encouraging me to
study. I sincerely appreciate his support in both my work and study time. My thanks
also go to my colleges: Nguyen Ngoc Que, Nguyen Do Anh Tuan, Tran Thi Quynh Chi,
Nguyen Le Hoa, Truong Thu Trang and others, for their continuous support and for
doing my work during my candidature.
I could not finish my hard study time without the support of my friends, Tran Cong
Thang, Nguyen Thi Bich Ngoc, Dang Lan Huong, Phan Nguyen Trung Khanh, Tran
Thanh Nhan, Bui Thu Ha, Tran Doc Lap, Nguyen Van Liem, Vu Le Tu, Pham Lan
Huong, Nguyen Thanh Hai and others. I would like to thank them for their friendship,
help and sharing, both physically and mentally, from small to big daily issues of life
during my time in Perth.
Especially, I would like to thank my family, my parents, and my mother in law, who
supported me by taking care my son and family, and encouraging me by giving me
comfortable conditions to study and finish my PhD. Thanks go to my dad and mum,
who always trust in and encourage me to follow up a research career. Last but not least,
I am indebted to my beloved husband Dinh Ngoc Duc, and my adorable son Dinh Duc
Thanh, who are always behind me, for their endless belief in me, for sharing with me all
sweet and hard things in those days. Without their boundless love, trust and
encouragement, I would not have made it this far.
Perth, May 2011
vi
ABSTRACT
Vietnam has negotiated a series of bilateral and multilateral trade agreements and has
made significant steps in integrating into the world economy. This integration is likely
to have both positive and negative effects on different stakeholders in the economy.
This study assesses the impacts of trade liberalisation on Vietnam’s small livestock
households on welfare, production and consumption behaviours, using a multi country
general equilibrium model (GTAP) at the macro level, linked with four, regionally
representative household models at the micro level.
A GTAP utility SplitCom is used to separate live pig and live poultry out of the general
livestock group prior to running several trade liberalisation scenarios. The household
model is linked to the trade model through changes in the prices of inputs and outputs
arising from different trade scenarios. Inside the household model, a recursive
household model is used, with a two-stage LES-AIDS model on the consumption side
and Cobb-Douglas functions on the production side.
The results of the simulations shows that Vietnam’s small households in the livestock
sector would benefit from almost all trade liberalisation scenarios considered here. The
largest benefit is generated by full liberalisation: by comparison there is almost no gain
from bilateral trade liberalisation with the USA. The voluntary trade liberalisation of
Vietnam in a unilateral liberalisation would generate some benefit for the country at the
national level without the need for negotiating with others, and also increase the welfare
of the households.
The changes in simulated prices lead to changes in the household’s welfare as a result of
changes in behaviour, including consumption, production (and hence profit), and also in
the time allocation decision of the household. In almost all trade liberalisation scenarios,
the decision to take more leisure as part of the utility maximising response contributes
about one quarter of the total increase in household’s welfare.
Assumptions that are made about how the labour market in structured, in both the
macro- and micro- models affect the impact on labour wages, and hence drive responses
of the household in the trade scenario simulations. The thesis reports the sensitivity of
the key findings of the research to these critical assumptions.
vii
TABLE OF CONTENTS
CERTIFICATION ...........................................................................................................iii
ACKNOWLEDGEMENTS .............................................................................................iv
ABSTRACT.....................................................................................................................vi
LIST OF FIGURES ..........................................................................................................x
LIST OF TABLES ..........................................................................................................xii
ABBREVIATIONS ........................................................................................................xv
CHAPTER 1 : INTRODUCTION ....................................................................................1
1.1. Introduction ..........................................................................................................1
1.2. Background Information of Vietnam Agriculture................................................1
1.3. General Methodology and Data for Study ...........................................................4
1.4. Contribution to Knowledge..................................................................................6
1.5. Outline of Thesis ..................................................................................................7
CHAPTER 2 : OVERVIEW OF LIVESTOCK SECTOR IN VIETNAM.....................10
2.1. The Development of Livestock Sector...............................................................10
2.1.1. Pig Husbandry .............................................................................................12
2.1.2. Poultry Husbandry .......................................................................................13
2.1.3. Cattle and Other Animal Husbandries.........................................................14
2.2. Issues in Development Process of the Livestock Sector in Vietnam.................15
2.2.1. Scale of Production of Livestock Husbandry ...............................................16
2.2.2. Quality and Productivity of Meat Production..............................................19
2.2.3. Price and Quality of Livestock Feed ............................................................19
2.2.4. Livestock Diseases and Animal Breeds........................................................22
2.2.5. Other Trade and Marketing Issues ..............................................................23
2.3. Policy Environment for Livestock Husbandry Development ............................26
2.4. Summary ............................................................................................................28
CHAPTER 3 : THEORETICAL FRAMEWORK OF AGRICULTURAL
HOUSEHOLD MODEL ...........................................................................................30
3.1. The Basic Model ................................................................................................32
3.2. Solving the General Model ................................................................................35
3.3. Role of Labour Market.......................................................................................36
3.4. Profit Effects ......................................................................................................40
viii
3.5. Summary ............................................................................................................42
CHAPTER 4 : THE ECONOMETRIC MODELS .........................................................44
4.1. Production of Agricultural Household ...............................................................45
4.1.1. Production Elasticity....................................................................................49
4.2. Consumption ......................................................................................................51
4.2.1. Linear Expenditure System (LES) Model .....................................................51
4.2.1.1. Adjusting the Wage of Agricultural Labour...........................................56
4.2.1.2. Estimation of LES Model .......................................................................59
4.2.2. Linear Approximately - Almost Ideal Demand System (LA-AIDS)
Model ............................................................................................................62
4.3. Summary ............................................................................................................67
CHAPTER 5 : VIETNAMESE HOUSEHOLD MODEL - INTERACTION
OF PRODUCTION AND CONSUMPTION DECISIONS .....................................68
5.1. Calibration of the Model ....................................................................................68
5.1.1. The Production Model..................................................................................69
5.1.2. The Consumption Model ..............................................................................70
5.1.3. Opportunity Cost of Each Working Day and Exogenous Income
in Base Year ..................................................................................................71
5.2. The Interaction of Production and Consumption Decisions ..............................72
5.3. Summary ............................................................................................................75
CHAPTER 6 : VIETNAM’S TRADE LIBERALISATION AND
SIMULATION MODEL...........................................................................................76
6.1. Brief of Vietnam’s Trade Liberalisation Process and Commitments ............77
6.2. Trade Liberalisation in GTAP Model ............................................................81
6.2.1. Previous studies on Vietnam trade liberalisation .....................................81
6.2.2. GTAP database .........................................................................................84
6.2.3. Using SplitCom and Introducing New Sectors into GTAP
Database .......................................................................................................86
6.2.4. Trade Scenarios of Trade Liberalisation Simulation ................................89
6.3. Simulations with Different Modifications of GTAP Closures.......................90
6.3.1 Closure A - Standard GTAP Closure ............................................................93
6.3.2 Closure B - Non Standard Closure in Labour Market ..................................98
6.3.3 Closure C - Modified Non-Standard Closure .............................................103
6.3.3.1. Estimation of R .....................................................................................103
6.3.4 Alternative Labour Assumptions and Real Wages ......................................108
ix
6.3.5 Changes of Vietnam’s Welfare under Alternative Closures .......................109
6.4. Summary ......................................................................................................111
CHAPTER 7 : IMPACTS OF TRADE LIBERALISATION ON SMALL
LIVESTOCK HOUSEHOLDS - LINKAGES BETWEEN TRADE AND
HOUSEHOLD MODELS.......................................................................................113
7.1. Price Changes in the Trade Scenario Simulations ...........................................113
7.2. Price and Wage Transmission..........................................................................116
7.3. Impacts of Trade Liberalisation on Households ..............................................118
7.3.1. Impacts of Global Trade Liberalisation on Households in the
South............................................................................................................120
7.3.2. Impacts of Global Trade Liberalisation on Households in
Different Regions ........................................................................................125
7.3.3. Impacts of Unilateral Trade Liberalisation on Households in
Different Regions ........................................................................................128
7.3.4 Impacts of Regional AFTA to Households in Different Regions.................131
7.3.5 Impacts of Expansion of the Regional AFTA (ASEAN plus China,
Korea and Japan) to Households in Different Regions ..............................134
7.3.6 Impacts of Bilateral Trade Liberalisation with the United State on
Households ..................................................................................................135
7.3.7 Impacts of Bilateral Trade with EU and Multilateral Trade
Liberalisation on Households in Different Regions....................................136
7.4. Impacts of Trade Scenarios, Opportunities and Threats in Each
Region ..........................................................................................................139
7.5. Summary ..........................................................................................................146
CHAPTER 8 : CONCLUSIONS ..................................................................................148
8.1. Summary, Conclusions, and Policy Implications ............................................148
8.2. Limitations of the Study and Potentials for Further Study ..............................153
APPENDICES 157
REFERENCES 205
x
LIST OF FIGURES
Figure 2-1: Herd Pig Number and Annual Growth Rate in Different Regions
of Vietnam......................................................................................................12
Figure 2-2: Herd Number of Poultry in Different Regions of Vietnam..........................13
Figure 2-3: Proportion of Beef and Buffalo Animals in the Total Herd in
Different Regions of Vietnam in 2007...........................................................15
Figure 2-4: Production Scale of Household Pig Raising ................................................17
Figure 2-5: Production Scale of Household Poultry Raising in 2006.............................18
Figure 2-6: Yield of Maize and Soybean (tons/ha) By Country in 2004........................20
Figure 2-7: Price of Maize in Vietnam and the World, 1998–2006 (USD/ton)..............21
Figure 2-8: Price of Soybean in Vietnam and the World, 1998–2006
(USD/ton).......................................................................................................21
Figure 2-9: Changes in Population, Income and Meat Consumption per capita ............23
Figure 2-10: Pig and Chicken Consumption per capita over Regions (kg) ....................24
Figure 3-1: Separation of Household Decisions .............................................................37
Figure 3-2: An Agricultural Household in Slack Season................................................38
Figure 6-1: Labour Market under GTAP Standard Closure - Closure A........................94
Figure 6-2: Labour Market of a Developing Country under GTAP Closure B ..............98
Figure 6-3: Unskilled Labour Market in Vietnam under Closure C .............................103
Figure 6-4: Changes in Real Wages under Scenario Simulations with
Different Possible Maximum Labour Supplies............................................108
Figure 6-5: Changes in Welfare under Alternative Trade Scenarios with
Different Closures ........................................................................................109
Figure 7-1: Changes in Agricultural Production of Household in the South
under Global Trade Scenario .......................................................................120
Figure 7-2: Changes in Relative Prices of Outputs and Inputs and Changes in
Agricultural Production of Household in the South under Global
Trade Scenario .............................................................................................121
Figure 7-3: Changes in Profit of Agricultural Production of Household in the
South under Global Trade Scenario .............................................................122
Figure 7-4: Disaggregate Effects of Price and Income to Changes in
Consumption of Household in the South under Global Trade
Scenario Compared with Baseline (percentage) ..........................................122
xi
Figure 7-5: Changes in Output Production and Farm Profit of Households in
Different Regions under Global Trade Scenario..........................................125
Figure 7-6: Changes in Consumption Quantities of Households in Different
Regions under Global Trade Scenario .........................................................127
Figure 7-7: Changes in Main Food Consumption of Households in Different
Regions under Global Trade Scenario .........................................................127
Figure 7-8: Changes in Agricultural Production of Households in Different
Regions under the Unilateral Trade Scenario ..............................................129
Figure 7-9: Changes in Food Consumption of Households in Different
Regions under Unilateral Trade Scenario (percentage) ...............................130
Figure 7-10: Changes in Quantity Consumption of Households in Different
Regions under AFTA in Comparison with Baseline (percentage) ..............132
Figure 7-11: Changes in Food Consumption of Households in Different
Regions under AFTA in Comparison with Baseline (percentage) ..............133
Figure 7-12: Changes in Production of Households in Different Regions
under AFTA+3.............................................................................................135
Figure 7-13: Changes in Production of Households in Different Regions
under Scenario of Bilateral Trade Liberalisation with EU...........................137
Figure 7-14: Changes in Production of Households in Different Regions
under Multilateral Trade Scenario ...............................................................137
Figure 7-15: Changes in Consumption of Households in Different Regions
under Scenario of Bilateral Trade Liberalisation with EU...........................137
Figure 7-16: Changes in Consumption of Households in Different Regions
under Multilateral Trade Scenario ...............................................................137
Figure 7-17: Changes in Main Food Consumption of Households in Different
Regions under Scenario of Bilateral Trade Liberalisation with EU ............138
Figure 7-18: Changes in Main Food Consumption of Households in Different
Regions under Multilateral Trade Scenario .................................................138
Figure 7-19: Welfare Changes of the South Household under Alternative
Scenarios with Different Assumptions of Labour Market ...........................145
Figure 7-20: Changes in Labour Allocation of Household in the South under
Alternative Scenarios with Different Assumptions on Labour
Market ..........................................................................................................145
xii
LIST OF TABLES
Table 2-1: Annual Growth Rate in Agricultural Sector (percent)...................................11
Table 2-2: GDP and Share of Agriculture and Livestock in the Economy.....................11
Table 2-3: Number of Cattle and Annual Percentage Growth Rates (2000 –
2006) ..............................................................................................................14
Table 2-4: Average Weight of Lean and De-boned Meat by Region (percent)..............19
Table 2-5: Share of Pig and Chicken for Sale by Household (percent) ..........................25
Table 3-1: Selected Response Agricultural Price Elasticity With and Without
Profit Effect....................................................................................................42
Table 4-1: Arithmetic Means of Inputs and Outputs for four Regions ...........................47
Table 4-2: Neutral Technological Efficiency Parameters in Different Regions .............48
Table 4-3: Elasticity of Output, Labour Demand and Profit with Respect to
Selected Variables in the Production Functions ............................................50
Table 4-4: Description, Means, and Standard Deviation of Variables of
Quantity Consumption and Price Indices in LES ..........................................55
Table 4-5: Household’s Production Function for Composite Agricultural
Commodity.....................................................................................................57
Table 4-6: Instrumental Variables Estimates of Shadow Wage......................................58
Table 4-7: Comparison of Ratio of Instrumental Estimated Shadow Wages to
(Local) Market Wages over Years .................................................................59
Table 4-8: Estimated Parameters of LES of Household .................................................60
Table 4-9: Elasticities for LES with Respect to Selected Variables, with Total
Expenditure Assumed Exogenous .................................................................62
Table 4-10: Uncompensated Elasticity of LA-AIDS Model for Main Food
Commodities ..................................................................................................66
Table 5-1: Calibration of Neutral Technological Efficiency Parameters........................69
Table 5-2: Ratio of Using Raw Feed/Total Feed for Pig and Chicken Raising
(percent) .........................................................................................................70
Table 5-3: Calibration of Constants in LA-AIDS...........................................................71
Table 5-4: Household Response Elasticities with Farm Profit Alternatively
Exogenous and Endogenous in RRD .............................................................74
Table 6-1: GTAP Regional Concordance .......................................................................85
Table 6-2: GTAP Sectoral Concordance ........................................................................88
xiii
Table 6-3: Alternative Trade Scenarios ..........................................................................90
Table 6-4: Vietnam’s Output and Trade Flows, 2001 (mill. USD) ................................92
Table 6-5: Initial Values (mill.USD) and Percentage Changes in Vietnamese
Outputs under Alternative GTAP Scenarios* with Closure A ......................95
Table 6-6: Price Changes in the Vietnamese Market under Alternative GTAP
Scenarios with Closure A (percentage)..........................................................97
Table 6-7: Changes in the Vietnamese Outputs and Unskilled Labour under
Alternative GTAP Scenarios with Closure B (percentage) ...........................99
Table 6-8: Price Changes in the Vietnamese Market under the Alternative
GTAP Scenarios with Closure B (percentage) ............................................100
Table 6-9: Price Changes of Consumption Commodities under Alternative
GTAP Scenarios with Closure B (percentage) ............................................102
Table 6-10: Percentage Changes in Vietnamese Market Prices under
Alternative GTAP Scenarios with Closure C (R=12%)...............................105
Table 6-11: Percentage Change in Consumption Commodity Price under
Alternative GTAP Scenarios with Closure C (R=12%)...............................107
Table 6-12: Number of Jobs Created for Unskilled Labour and its
Contribution to Total Social Welfare in Vietnam under Trade
Scenarios (percentage) .................................................................................110
Table 7-1: Matching between GTAP Sectors and Endowments in this Study
and their Concordance with Commodities and Goods in Vietnam’s
Household Models .......................................................................................115
Table 7-2: Average Wage of Labour at Current Price and Annual Growth
Rate ..............................................................................................................118
Table 7-3: Welfare Changes in Households in Different Regions under
Alternative Liberalisations Compare with Baseline (percentage) ...............119
Table 7-4: Disaggregated Changes in Main Food Consumption Quantity of
Household in the South due to Price and Expenditure Effects under
Global Trade Scenario (kg)..........................................................................124
Table 7-5: Ratio of Raw Feed and Price Change of Feed in Different
Households (percent) ...................................................................................126
Table 7-6: Changes in Labour Allocation of Households in Different Regions
under Global Trade Scenario Compared with Baseline
(percentage)..................................................................................................128
Table 7-7: Changes in Price and Consumption Quantity of Households in
Different Regions under the Unilateral Trade Scenario
(percentage)..................................................................................................130
xiv
Table 7-8: Changes in Time Allocation of Households in Different Regions
under Unilateral Trade Scenario Compare with Baseline
(percentage)..................................................................................................131
Table 7-9: Changes in Production Outputs and Total Farm Profit of
Households in Different Regions under AFTA Compare with
Baseline (kg) ................................................................................................132
Table 7-10: Changes in Time Allocation of Households in Different Regions
under AFTA Trade Scenario Compare with Baseline (days) ......................134
Table 7-11: Changes in Prices of Consumption Commodities and Production
Inputs and Outputs in Vietnam under Bilateral Trade
Liberalisation with USA (percentage) .........................................................136
Table 7-12: Changes in Time Allocation of Households in Different Regions
under Alternative Liberalisations (percentage)............................................139
Table 7-13: Welfare Changes of Households in Different Regions under
Alternative Liberalisations Compared with Baseline (percentage) .............140
Table 7-14: Production Changes of the Households in Different Regions
under Alternative Liberalisations Compared with Baseline
(percentage)..................................................................................................141
Table 7-15: Changes of Time Allocation of Households in Different Regions
under Alternative Liberalisations Compared with Baseline
(percentage)..................................................................................................142
Table 7-16: Change Unskilled Labour Wage under Alternative GTAP
Scenarios with Different Assumptions of Labour Market
(percentage)..................................................................................................144
xv
ABBREVIATIONS
ACFTA ASEAN-China Free Trade Area
ADB Asian Development Bank
AFR Africa
AFTA ASEAN Free Trade Area
AIDS Almost Ideal Demand System
AoA Agreement on Agriculture
ASEAN Association of South East Asia Nations
AUS Australia
B_T Beverages and Tobacco
BTA Bilateral Trade Agreement
CD Cobb-Douglas
CEE Central and East Europe
CEP Closer Economic Partnership
CEPT Common Effective Preferential Tariff
CGE Computable General Equilibrium
CH Central Highland
CHN China
CLMV Cambodia, Lao, Myanmar and Vietnam
CMT Beef and sheep meats
CPI Consumer Price Index
CRP Chemicals, rubber and plastic
CV Compensating Variation
CWGT Column users weights
EHP Early Harvest Program
ELE Electronic
EPA Economic Partnership Agreement
xvi
EU European Union
EV Equivalent Variation
FAO Food and Agriculture Organisation of the United Nations
FAOStat FAO Statistics
FMD Foods Mouths Diseases
FSH Fishing
GATT General Agreement on Tariffs and Trade
GDP Gross Domestic Product
GEL General Exclusion List
GSO General Statistical Office
GTAP Global Trade Analysis Project
HS Harmonize System
IDN Indonesia
IFPRI International Food Policy Research Institute
IL Inclusion List
IND India
JPN Japan
KOR Korea
LA-AIDS Linear Approximation Almost Ideal Demand System
LAM Latin America
LDCs Least Developed Countries
LES Linear Expenditure System
MAN Manufactures
MARD Ministry of Agriculture and Rural Development
Mill. Million
MLK Milk and dairy products
MRD Mekong River Delta
MYS Malaysia
NCC North Central Coast
xvii
NE North East
NES North East South
NL Normal List
NTBs Non-Tariff Barriers
NW North West
OAP Live animals sector
OCR Other crops
ODV Other developed countries
OFD Processed food
OLS Ordinary Least Square
OMT Pork, poultry, and other meats
OSO Oilseed and vegetable oil
PHL the Philippines
RES Natural resources, and petroleum product
RIC Paddy and processed rice
ROW Rest of the World
RRD Red River Delta
RWGT Row users weights
SAMs Social Accounting Matrices
SCC South Central Coast
SL Sensitive List
SPS Sanitary and Phytosanitary
STEs State Trading Enterprises
SUREG Seemingly Unrelated Regression
SVC Services
TBT Technical Barriers to Trade
TCN Transport, communication
TEL Temporary Exclusion List
THA Thailand
xviii
TRIPs Trade-Related Aspects of Intellectual Property Rights
TWGT Trade users weights
TXT Textile and apparel
UN United Nations
UNCTAD United Nations Conference on Trade and Development
UNDP United Nations Development Programme
USA United States of America
USD USA Dollar
USDA United States Department of Agriculture
VF Vegetable and fruit
VHLSS Vietnam Household Living Standard Survey
VLSS Vietnam Living Standard Survey
VND Vietnam Dong
VNM Vietnam
WB World Bank
WITS World Integrated Trade Solutions
WTO World Trade Organisation
XEA Rest of East Asia
XSA Rest of South Asia
XSE Rest of South East Asia
XWGT Cross users weights
1
CHAPTER 1 : INTRODUCTION
1.1. Introduction
The objective of this study is to analyse the implications of trade liberalisation on
Vietnam’s small-scale livestock producers. The study examines how household
production, consumption, leisure, working time and welfare are affected when prices
change due to trade liberalisation. The specific objectives are as follows:
• Evaluate the impact of trade liberalisation on Vietnam’s smallholder livestock
producers following Vietnam’s integration process into the world economy, by
connecting a computable general equilibrium (GTAP) with a household
production model for small livestock producers, simulating trade liberalisation
scenarios (in GTAP) and analysing changes to identify welfare effects at the
household level
• Investigate how the effects of trade policy changes impact on the national
economy as well as the livestock sector and their consequent effects on social
welfare.
• Assess impacts of trade liberalisation to changes in livestock producers'
household welfare. Analyse the reaction of smallholders that is represented by
their responsiveness in the supply of labour and demand for commodities and
leisure. By constructing a household model, behaviour of the household in
reaction to external impacts is captured, not only in terms of production
decisions, but also in term of consumption and household time allocation.
• Discuss opportunities and threats from trade liberalisation for smallholder
livestock producers.
1.2. Background Information of Vietnam Agriculture
Vietnam is located in the east of the Indo-Chinese peninsula (see map). The total natural
land resource of the country is 325 360 km2, of which 20-25 percent is used for
2
agricultural production. Vietnam is divided into 64 provinces and 8 agro-ecological
regions.
With Gross Domestic Product (GDP) per capita of about 800 USD (2007) Vietnam is
still considered one of the world's poorest countries. Nevertheless, many observers note
the rapidly growing economy since the Doi Moi1 (renovation) in 1986 which
corresponds with the beginning of the transformation from a centrally planned economy
to one which is market oriented.
With 72.5 percent of the population
living in rural areas and 59 percent of
labour working in the agricultural sector
(year 2007), the agricultural sector is the
foundation of the economy. Over the
recent past, GDP growth of the
agricultural sector has been relatively
stable at around 4.3 percent per year.
Vietnam has also progressed from a
national chronic food shortage to one of
the world’s leading exporters of
agricultural products including rice,
coffee, tea, and rubber. Economic
growth, national food security,
improvement of income and lives of the
majority of people, and the reduction in
the number of people living under the
poverty line were largely made possible
by the Doi Moi reforms. The remarkable
economic growth that resulted from
these reforms was based largely on rural
households which have become the new
unit of agricultural production.
1 Refers to the decollectivisation and market orientation of the Vietnamese agriculture sector
VIETNAM AGRO-ECOLOGICAL REGION MAP Note: NE: North East, NW: North West, RRD: Red River Delta, NNC: North Central Coast, SCC: South Central Coast, CH: Central Highland, NES: North East South, MRD: Mekong River Delta
3
In the agricultural sector, livestock is considered as one of the important pillars of a
sustainable development strategy (IFPRI 2001). Given limited prospects in the growth
of rice production and changing patterns of demand both in Vietnam and in the world
market, the livestock sector can help to achieve higher and more stable rural incomes,
reduce the incentive for the flow of migrants from rural to urban areas, make the
farming system sustainable in the long run, and contribute to the alleviation of rural
poverty, especially among ethnic groups in mountainous areas.
For many parts of Vietnam, large animals (i.e. bull, cow, and buffalo) are important
primarily not for their meat and milk products, but for the draught power they provide
for crop production and local transportation. Livestock raising (especially swine and
cattle) are a major source of fertiliser. In addition, livestock is important for providing
high quality protein to millions of smallholders thus enhancing their nutrition.
After the Doi Moi, the livestock industry has developed with an average growth rate of
5.4 percent per year, higher than the crop and service2 sectors. However, there are many
issues which constrain producer profitability in the livestock sector such as high feed
price, low productivity, and poor veterinary system.
Livestock in Vietnam are predominantly raised in small-scale household production
units. At present, smallholder producers supply the majority of meat in the market, with
most households operating individually in the production and marketing of livestock
and livestock products. They are constrained by poor access to markets, very low scale
operations, poor access to improved genetics, and high-quality forage and concentrate
feed, and poor animal husbandry and nutrition. However, for most of these households,
raising livestock is an important source of income providing at least 50 percent of cash
income in the household (Lapar et al. 2003).
Since 1995, Vietnam officially joined the world economy by becoming a member of the
Association of South East Asia Nations (ASEAN), signed a bilateral trade agreement
(BTA) with USA in 2000, and became a member of World Trade Organisation (WTO)
in 2007, after 11 years of negotiation. Implementation of multilateral and bilateral trade
agreements has opened a door for Vietnam to integrate into the global economy
providing both opportunities and threats to the agriculture sector. In the case of the 2 Refers to service subsector in agriculture
4
livestock industry, there may be both supply and demand side effects. For example,
income growth may increase demand for meat, but the domestic industry may also have
to compete with imported produce. Reducing protection on domestic maize may make
feed prices decrease, but the opportunity cost of labour in livestock production may
increase. Thus it is not clear whether livestock households, especially smallholders, will
be worse or better off as a result of trade liberalisation.
1.3. General Methodology and Data for Study
In order to assess the impacts on small households as a consequence of trade
liberalisation, an international trade model is linked with a household model. In general,
the modelling process of the study includes three main steps:
Step 1: At the micro level, a household model is built where the household utility is
optimised in the context of a number of constraints such as budget constraint, time
constraints and production technology.
Step 2: At the macro level, a multi-country general equilibrium model with its focus on
worldwide trade policy - Global Trade Analysis Project Model (GTAP) - is used to
simulate trade liberalisation.
Step 3: The trade model and household model are linked together and the results of
changes in welfare, production and consumption behaviours, etc. of the household are
presented. Given the aim of investigating the reaction of the household in trade scenario
simulations, price changes for consumption commodities as well as production factors
including labour in the agricultural sector, which are derived from the GTAP
simulation, are incorporated into the household model as policy shocks. In this step, the
study only examines the one-way effects of trade liberalisation on households, and not
their influence on trade.
A variety of empirical methods are used in the study. For example, econometric models
such as Linear Approximation Almost Ideal Demand System (LA-AIDS) and Linear
Expenditure System (LES) are applied in step 1 to estimate parameters for functions of
consumption, production, and utility of the household. In step 2, the GTAP software
SplitCom is used to separate pig and poultry out of the aggregate group of livestock in
5
the standard GTAP aggregation. Other statistical analysis is also applied to estimate the
price and wage transmission among regions within the country, when the two models
are linked together in step 3.
The study uses data from the Vietnam Household Living Standard Survey (VHLSS
2004) as the main source for constructing the econometric and household models. The
total survey sample was 45 900 households, of which 36 720 households were surveyed
for income only, the other 9180 households for both income and expenditure. The
sample covers and represents 3063 communes, over eight ecological regions (Red River
Delta, North East, North West, North Central Coast, South Central Coast, Central
Highland, North East South, and Mekong River Delta), and almost all provinces in the
country. Since the study especially focused on small-scale households in the livestock
sector, around 7000 of the 9180 households are chosen for analysis. Data from a survey
on the Vietnam livestock sector of the International Food Policy Research Institute
(IFPRI 2001) is also used for additional information about small households who raise
livestock and other criteria of economic and farming systems in Vietnam.
In constructing the household model, the whole country is divided into four regions:
Red River Delta, Northern upland (includes North East and North West), Central region
(includes North Central Coast, South Central Coast and Central Highland), and South
(includes Mekong River Delta and North East South). Each region represents an
ecological area where agro-ecological and economy conditions are largely similar, so
one household model is constructed to represent each region.
The GTAP model version 6.2, with a database for 2001 is used in step 2. The database
includes 96 countries/regions and 57 individual sectors. The database includes all data
on trade, production, consumption, tariff and etc. for Vietnam individually; therefore
the Vietnamese economy with all its factor and activity flows is represented in the
model. However, it does not identify the pig and chicken sectors separately, which are
subsumed within “livestock”. Generating new sectors of pig and chicken to introduce
into the GTAP database are based on extracting data from sources of United Nations
(UN): Comtrade, International Statistics, World Integrated Trade Solutions (WITS),
Statistics of Food and Agriculture Organisation (FAOStat), and Social Accounting
Matrices (SAMs) of various countries.
6
In addition, the study uses other sources of primary and secondary data and information
including government institutions and organisations in Vietnam, such as the Ministry of
Agriculture and Rural Development (MARD), General Statistical Office (GSO),
Research Institute for Market and Prices. Information also comes from documents and
databases of the United Nation’s and international organisations and financial
institutions: United Nations Development Programme (UNDP), United Nations
Conference on Trade and Development (UNCTAD), United States Department of
Agriculture (USDA), World Bank (WB), Asian Development Bank (ADB), IFPRI, etc.,
and other non-government organisations in Vietnam.
1.4. Contribution to Knowledge
The major contribution to knowledge derived from this study is development of a link
between the trade liberalisation GTAP results and a household model to examine
changes in welfare as well as behaviours in production and consumption of small
livestock producers in Vietnam following trade liberalisation. GTAP has been used
since 1992 with some previous applications to Vietnam (Nguyen & Ezaki 2005, Daude
2004, Nin et al. 2003, Holst 2004, Vanzetti & Pham 2006). Household models have
been developed for around 30 years and applied to many countries (Barnum & Squire
1979a, Chen & Ravallion 2002, Taffesse 1999, Kumar & Ramasamy 2003, Schnepf &
Senauer 1989, Janvry et al. 1995, Quazi 1992, Edmeades et al. 2004), but this is the
first application to the livestock sector in Vietnam, although there have been some other
microeconomic models developed (MacAulay et al. 2001, Hertzler & MacAulay 2000,
Seshan 2005, Holst 2004, Jensen & Tarp 2005). This is also the first application linking
a GTAP macro model to a household micro-level model for the livestock sector in
Vietnam.
By linking GTAP with a household model, the study examines how small livestock
households react to changes in economic policies, especially in the context of trade
liberalisation. This is especially important, given that livestock plays a very important
role in the agricultural sector and small households are dominant in livestock production
in Vietnam. Analytical results from the household model also allow one to see how
household behaviours change when they are both consumers and producers. Taking into
account how income is affected by production, via profit, thereby influencing
consumption, gives a more accurate assessment.
7
Using SplitCom to disaggregate pig and chicken sectors from the GTAP aggregate
database is the other contribution to knowledge, allowing for a more accurate measure
of change in household production to different price signals.
1.5. Outline of Thesis
The thesis contains eight chapters; brief summaries of subsequent chapters follow.
Chapter 2, “Overview of the Livestock Sector in Vietnam” reviews development of the
sector in recent years. The main characteristics of livestock production in Vietnam, such
as small-scale production, low productivity, low quality of both livestock products and
feed, poor state of animal health and veterinary services are then presented. The policy
environment and strategy of the Vietnamese government for future sector development
is also mentioned.
Chapter 3, “The Theoretical Framework of Agricultural Household Model” presents a
theoretical framework of a general model of an agricultural household, in which the
household chooses on-farm production, off-farm labour supply consumption of market
purchased goods, home produced goods, and leisure time in order to maximise its
utility.
The commodity and leisure demand of the household is defined by relations of prices
and income, and income is partly determined by the household’s production activities.
Since consumption behaviour is not independent of production behaviour, a recursive
property of the model is established. Conforming to the principles of a recursive model,
the assumptions in the household model are: household is price-taker in all markets and
all markets exist; commodities are homogeneous, including the labour market; decisions
relating to the total stock of land and labour are treated as given; and intertemporal
allocation and risk are omitted.
Chapter 4, “Econometric Models for Household Production and Consumption” includes
particular approaches to estimating production and consumption aspects of the
agricultural household. The production segment of the model is analysed employing a
Cobb-Douglas (CD) production function to estimate outputs and input demand. The
consumption side is specified using two stages: LES for a broad grouping of goods and
8
expenditures in the first stage, with the integration between demand for commodities
and the allocation of time for leisure and labour supply. In the second stage, expenditure
for each of individual commodities in the main food group is allocated using an LA-
AIDS model.
Econometric models are used to estimate parameters for functions of consumption,
production, and utility of the household. Data for the model estimation are primary data
of the VHLSS 2004, which represents all agro-ecological regions in the country. In
estimating the LES model, one has to deal with the issue of there being an incomplete
labour market, and hence market wages may not represent the opportunity cost of
family labour. To overcome this, a technique is used to estimate the “shadow wage” as
the opportunity cost of family’s labour.
Chapter 5, “Vietnamese Household Model - Interaction of Production and
Consumption Decisions” calibrates the household model. A representative household
model of each region, in which the model's elements and relationships are determined
based on the econometric results of Chapter 4, is presented. In the model construction,
the two sides (production and consumption) of the model are linked together. Hence the
reaction of the household to an exogenous change is presented. The whole process is
calibrated in Excel, and the utility maximising response to changes in conditions for the
household is determined using Excel solver.
By constructing the integrated household model, households' responses to policy
changes in both consumption and production are captured. The effects on consumption,
due to the “profit effect” arising from the change in farm production, yield more
realistic results than an examination of consumption in isolation.
Chapter 6, “Vietnam’s Trade Liberalisation and Simulation Model” briefly presents the
process and main commitments of Vietnam in the trade liberalisation process. Hence a
multi-country general equilibrium GTAP is used to simulate trade liberalisation
scenarios. Since the final objective of the study is to examine impacts of trade
liberalisation on small livestock households raising pigs and chicken, and this level of
detail is not present in the GTAP database, the software SplitCom is applied to
introduce live pig and chicken sectors into the GTAP database prior to running
simulations.
9
Results of trade liberalisation simulations under different closures in the GTAP model
are presented. The main changes in output, welfare, trade balance and prices are derived
from GTAP. Modifying closures in GTAP helps modelling become a more useful tool
in the analysis of economic impacts of policy changes, since it presents more accurately
the situation of resource allocation and usage of endowments, in this case of unskilled
labour, especially in the context of overwhelming unemployment in developing
countries.
Chapter 7, “Impacts of Trade Liberalisation on Small Livestock Household - Linkages
between Trade and Household Models” presents an analysis of how households would
be affected by changes in trade scenarios. By linking the trade and household models
together, households' changes in welfare, production and consumption behaviours as
well as time allocation between work and leisure are captured. Reviewing changes
under alternative assumed liberalisation scenarios and labour market conditions leads to
policy conclusions on the opportunities and threats from trade liberalisation for
smallholder livestock producers.
Chapter 8, “Conclusions” provides a summary of the contribution of the thesis. Some
conclusions and policy implications in the context of trade liberalisation for small
livestock producers are drawn. Limitations of the study and suggestions for further
study also are presented.
10
CHAPTER 2 : OVERVIEW OF LIVESTOCK SECTOR IN
VIETNAM
The agricultural sector, accounting for 57 percent of labour force in the country, is the
foundation of Vietnam’s economy. Since Doi Moi, the GDP growth of the agricultural
sector has been relatively stable increasing at around 5.03 percent per year on average
(GSO 2008). By allocating production factors, especially land to individual households,
liberalising economic relations, and opening the country to external markets, Doi Moi
helped Vietnam to progress from a nation of chronic food shortages to one of the
world's leading exporters of agricultural products (Castella & Dang 2002). The
fundamental changes in technical, economic and social issues that accompanied the
transition transformed agricultural production, resource management and land use, and
the institutions helped the economy in general and the agriculture sector in particular to
achieve a remarkable level of growth.
In order to have background information and understand the context of the study the
development progress of livestock sector since Doi Moi is reviewed here. This is
followed by emerging issues in that process, and finally, the policy environment and the
strategy of the Vietnamese government for future development of the livestock sector
are presented.
2.1. The Development of Livestock Sector
In the agriculture sector, livestock plays an important role and is considered an
important pillar of a sustainable development strategy. However, after successfully
implementing Doi Moi for some years, favouring development of the rice economy,
initial rapid economic growth has been slowing in recent years (World Bank 2000).
Given limited prospects in the growth of rice production and changing patterns of
demand both in Vietnam and the world market, the livestock sector may help to
increase and stabilise rural incomes, reduce incentives for flow of migrants from rural
to urban areas, make the farming system sustainable in the long run, and contribute to
the alleviation of rural poverty, especially among ethnic groups in mountainous areas
11
(IFPRI 2001). For low income producers, livestock serve as a store of wealth, it
provides at least 50 percent of cash income in small households. It also still provides
draught power in many regions of Vietnam, provides organic fertilizer, and acts as a
source of transportation (Lapar et al. 2003).
As a consequence of adopting an agricultural diversification program over recent years,
the development of the livestock sector expanded considerably and has grown faster
than agriculture as a whole (Table 2-1). In addition, development of the livestock sector
was faster in the last decade of the 20th century than from 1954 to 1990 (Lapar et al.
2003).
Table 2-1: Annual Growth Rate in Agricultural Sector (percent)
1986–1990 1991–1995 1996–2000 2001–2007
Agriculture 3.4 4.85 6.38 3.94
Cultivation 3.3 4.77 6.54 3.34
Livestock 3.5 5.38 6.32 6.77
Source: Calculation based on data of GSO 2008, the production value of sectors are fixed for the year
1994
Along with development of the agricultural sector, the output value of livestock has
significantly increased by a factor of 4 from 1995 to 2007.
Table 2-2: GDP and Share of Agriculture and Livestock in the Economy
1995 2000 2005 2007
Agricultural output (billion VND) 85,508 129,140 183,342 236,935
Livestock output (billion VND) 16,168 24,960 45,226 57,803
Agriculture share in GDP (%) 27.18 24.53 20.97 20.33
Livestock share in GDP (%) 5.14 4.74 5.17 4.96
Source: GSO 2008. Calculated at current price.
12
However, the contribution of livestock is still small, about 20 percent in the agricultural
sector, compared with 74 percent in the cultivation sub sector, and about 5 percent the
total economy in 2007
In the livestock sector, production has developed rapidly with the average liveweight of
livestock also growing at a remarkable rate annually. However, the increase in
aggregate liveweight was attributed mostly due to increases in the size of the national
herd, rather than improvement in productivity. Pig herd size increased by about 5
percent per year but average liveweight production increased by about 7 percent per
year. The size of the national poultry flock increased at an average rate of 5.9 percent,
and production (in liveweight) increased at an annual average rate of 6.3 percent (IFPRI
2001). Increases in the proportion of crossbred and exotic animals in livestock herds in
Vietnam were partially responsible for increases in off-take rates.
2.1.1. Pig Husbandry
The pig herd in Vietnam increased continuously over the last 20 years. The number of
pigs doubled from 12.3 million in 1990 to 24.9 million in 2003 and to 26.9 million in
2006, with the annual growth rate being 4.3 percent. The herd of sows expanded with an
average growth rate of 6.1 percent per year. In 2000, there were 2.8 million sows in the
country, increased to 4.33 million in 2006, making up more than 16 percent of the total
pig herd.
0
2
4
6
8
RRD NE NW NCC SCC CH NES MRD
regions
pig
herd
num
ber
(mill
hea
ds)
0%
2%
4%
6%
8%
10%
annu
al g
row
th r
ate
1995 2000 2007 annual grow th rate 1995-2000 annual grow th rate 2000-2007
Figure 2-1: Herd Pig Number and Annual Growth Rate in Different Regions of
Vietnam
Source: GSO 2008
13
Pig breeding has been vigorously developed in the Red River Delta (RRD), North East
(NE), North Central Coast (NCC) and the Mekong River Delta (MRD). However,
industrial farms that apply large-scaled, industrialised and commercialised husbandry
are often located in the South areas of MRD and (North East South) NES. In 2007, pig
herd numbers in RRD and MRD were 6.9 and 3.8 million, respectively, and the growth
rates were 3.9 and 3.06 percent per year (GSO 2008).
Total liveweight of pig has also increased, from 560 000 tons in 1985, 1 million tons in
1998, about 1.4 million tons in 2000 to 2.5 million tons in 2006.
2.1.2. Poultry Husbandry
By 2006, poultry herd size had increased to 226 million including chickens, ducks,
geese, and swans. Chickens accounted for over 75 percent in the total population. The
rapid expansion of poultry started in 1990 with about 107 million heads, and this trend
increased strongly in the following years, before the bird flu epidemic in 2004. From
2001 to 2003, the annual growth rate of herd size was 9 percent, with a poultry
population of 254 million individuals in 2003 (GSO 2008).
0
20
40
60
80
RRD NE NW NCC SCC CH NES MRD
regions
poul
try
herd
siz
e (m
ill h
eads
)
2000 2003 2007
Figure 2-2: Herd Number of Poultry in Different Regions of Vietnam
Source: GSO 2008
Most of the poultry in Vietnam are found in four regions: NE, the RRD, NCC, and the
MRD, whose total flock accounts for almost 80 percent of all poultry. Duck husbandry
is mainly in the two river deltas as intensive broiler production has been forming in
14
these regions to supply poultry meat to the biggest consumption centres of Ha Noi and
Ho Chi Minh City, and some big cities in the central region.
From 2001 to 2003, the poultry population had the highest growth, yielding 372.7 tons
and 4.85 billion eggs, accounting for 17 percent in total liveweight meats (MARD
2007). However, Figure 2-3 shows that in 2007 the poultry herd size was reduced
compared with 2003 in most regions due to the bird flu epidemics in the winter, since
year 2004.
2.1.3. Cattle and Other Animal Husbandries
In 2006, Vietnam had about 9.5 million cattle, of which 2.9 million were buffalo and
around 2 million were oxen. Due to the increase in demand for beef meat and milk, the
growth rate and expansion of the cattle herd has been high. From 2001 to 2006, the cow
herd’s growth was largest, with average annual growth at 22.4 percent. Beef increased
from 3.89 million in 2001 to 6.51 million in 2006 at an annual growth rate of 9.67
percent. In contrast, due to the mechanisation of agricultural production, increases in
buffalo numbers tended to slow down (MARD 2007).
Table 2-3: Number of Cattle and Annual Percentage Growth Rates (2000 – 2006)
2000 2001 2002 2003 2004 2005 2006
Beef (million heads) 4.13 3.89 4.06 4.39 4.91 5.54 6.51
Beef growth rate (%) -5.74 4.37 8.13 11.85 12.83 17.51
Cow (million heads) 0.035 0.041 0.055 0.079 0.095 0.104 0.113
Cow growth rate (%) 17.14 34.15 43.64 20.02 9.47 8.72
Buffalo (million heads) 2.87 2.81 2.81 2.83 2.87 2.92 2.92
Buffalo growth rate (%) -2.09 0.00 0.71 1.41 1.74 0.00
Source: MARD 2007;
Cattle breeding is quite developed in the central provinces and in the NE. The NCC and
(South Central Coast) SCC have the largest cattle herds, accounting for 40 percent of
the beef industry in Vietnam. Buffalo is also concentrated in the NCC with 26 percent,
just after the NE, with 42 percent of total buffalo herds (GSO 2008).
15
0%
10%
20%
30%
40%
50%
RRD NE NW NCC SCC CH NES MRD
regions
buffalo beef
Figure 2-3: Proportion of Beef and Buffalo Animals in the Total Herd in Different
Regions of Vietnam in 2007
Source: GSO 2008
Other trends in livestock development relate to increases in some animals such as goat
and sheep. Total goats and sheep in 2006 reached 1.52 million individuals, up from 0.57
million in 2001. Between 2001 and 2006 the average growth rate was 21.6 percent per
annum (MARD 2007).
2.2. Issues in Development Process of the Livestock Sector in Vietnam
Over the last 20 years, the livestock sector has made significant progress in terms of fast
growth rates, increased diversification in production, better responsiveness to market
requirements both in quantity and quality, etc. Hence, it contributed to improving
income of household producers and nutrition for people, especially in rural areas.
However, during its development, there has been concern about some emerging issues
such as the small-scale of livestock production, low productivity, low quality of
livestock products, high price and low quality of livestock feed, low level and poor
focus on genetic improvement, poor state of animal health and spread of disease, etc.,
which have implications for the sustainable development of the sector. In this section,
some main issues are presented.
16
2.2.1. Scale of Production of Livestock Husbandry
As in other developing countries, Vietnam livestock production has evolved into three
types, namely subsistence, semi-commercial, and commercial industrial.
The subsistence type is largely based on crop production activity of the farm, with
animals subsisting on by-products from crop production. This system has been the basis
of household livestock production in the North and Central regions for many years. This
system still exists but at present its importance and popularity is decreasing.
The semi-commercial type is between the subsistence and commercial types. The
majority of households with this production system are likely to be more specialised in
livestock production than the subsistence type. The household is more likely to make
production decisions based on market price information. However, there is diversity in
management across households within this type of production system. Some households
use mainly feeds produced at home, while other households buy feed from the market.
This type of household often considers livestock husbandry as a secondary activity in
agricultural production, and its capability to link the production operations closely with
the market is also still limited. These households predominate in many rural areas, but
in varying proportions depending on the region.
The commercial/industrial type differs significantly from the household-based system.
The production is on a larger scale, often employing more hired labour, and using more
capital and industrial feed. Many farms of this type produce mainly for export and
under contract with a livestock processing company3. This commercial production type
is often found in the South, such as MRD and NES, where people have larger land areas
for husbandry. However, the number of commercial/industrial types is still quite
limited. In some regions, due to constraints on land, households find it hard to expand
production. In RRD, for instance, on average, a household often owns an area of only
about 500m2, which makes it difficult to increase pig numbers per year to hundreds of
animals.
Regarding scale, commercial pig farms often raise more than 30 sows and 100 fattening
pigs. The smaller farms that are considered as semi-commercial raise about 3 to 10
3 Under the form of contract farming.
17
sows and 15 to 50 fattening pigs. Production from these two types consists of about 20
percent of total pig production. Farms with 1 to 3 sows, not more than 15 fattening pigs
per year or both sows and pigs are often considered small producers. This is a popular
system throughout Vietnam, with pig production accounting for as much as 80 percent
of total production (Dinh et al. 2006).
0%
20%
40%
60%
80%
100%
1994 2001 2006
1 head 2 heads 3-5 heads 6-10 heads > 11heads
Figure 2-4: Production Scale of Household Pig Raising
Source: calculated from Agricultural Census 1994, 2001, 2006
The proportion of households according to pig numbers raised is presented in Figure 2-
5. The data are extracted from three Agricultural Censuses in 1994, 2001, and 2006.
The figure shows that the number of large scale producers increased over time, however
the proportion is still low. In 2006, the number of households raising more than 11 pigs
per year accounted for only 8.33 percent of the total.
This situation is similar for small-scale production of poultry. The small-scale producer,
both semi-commercial and subsistent poultry farm, with less than 200 birds, raises about
80 percent of the total poultry in Vietnam (Figure 2-6). Meanwhile, households who
raise more than 200 birds and considered to commercial farms, account for only 0.5
percent of the total.
18
27.39
39.25
28.05
4.8 0.51
<10 heads 10- 19 heads 20 - 49 heads 49-200 heads >200 heads
Figure 2-5: Production Scale of Household Poultry Raising in 2006
Source: calculated from Agricultural Census 2006
Small-scale production with traditional methods of poultry husbandry has existed for a
long time throughout Vietnam. This production type is characterised by a scavenging or
extensive system. Chicken, for instance, are allowed to scavenge in the garden without
fences, and are fed by scavenging and with supplementary home-produced grain like
paddy rice, maize, kitchen waste, etc. Due to the scavenging system, management is
poor, rearing time is longer, and chicken are more prone to disease, heat and cold stress,
low survival rates and low economic efficiency. The advantage of this system is the low
initial investment and use of local breeds. Even poor farmers can manage some dozens
of chicken. Although the system is not very productive or economically efficient, it is
adoptable by almost any household. The meat of chickens raised in this system is
preferred by many consumers for its taste and receives a premium price. This system
produces about 65 percent of total broiler chickens in Vietnam, or about 70 million
birds per annum (Lapar et al. 2003).
The commercial livestock production system of both pig and poultry has many
advantages such as high productivity, short rearing time, greater survival rates, lower
incidence of diseases, and ease of management. However, the proportion of the
households applying this production type is still quite modest since it requires a large
area and sufficient funds for husbandry.
At present, livestock are still predominantly raised in small-scale households, and their
production supplies the majority of meat in the market. However, according to
19
specialists, this type of livestock husbandry does not have many opportunities for future
development, as they are likely to be pushed out of business in a more market-
orientated environment (Akter et al. 2003, Vu 2003).
2.2.2. Quality and Productivity of Meat Production
Small-scale production often uses kitchen waste and agricultural by-products for
livestock feed, uses local breeds, and applies traditional methods of husbandry.
Therefore its productivity and quality of meat is often low.
In comparison with other countries, the average weight of a pig in Vietnam stands at 70-
80 kg for live hog (feed over 8 months) compared to an average of 100-120 kg
elsewhere in the world fed for only 6.5 months. The average slaughter weight of beef
cattle in Vietnam is around 300 kg fed over 27 months compared with about 500 kg
over 15 months in other countries. Using local breeds is a major reason for lower
productivity. The average carcass weight of a local pig is about 68.5 percent of the live
weight, while the world average (with the exotic breeds) is about 80 percent. The
average rate of local pig lean meat accounts for only 35 percent, compared with 55
percent of the world level (Dinh et al. 2006).
Table 2-4: Average Weight of Lean and De-boned Meat by Region (percent)
Region
North Central South
Average
Local pig 33.6 36 34 34.5
Cross-breeding pig 40.6 41.4 46 42.6
Beef 35.7 37.5 36.7 36.6
Source: IFPRI 2001
2.2.3. Price and Quality of Livestock Feed
Animal feed has an important role in livestock production and accounts for about 70
percent of total production cost (Akter et al. 2003, Nguyen & Tran 2005). Its quality is
a key determinant of animal growth and health. Recently, industrial feed factories have
developed and expanded. However, the price and quality of livestock feed has been a
20
concern for producers, especially small ones. Industrial feed prices in Vietnam are
higher in comparison with those in other countries, even ASEAN countries in the same
region. For example, complete feed for pig fattening in Vietnam was about 28 percent
higher than that in Malaysia in 2000 (IFPRI 2001). One reason for this is the relatively
higher price of key materials used in feed production including maize, soybean, and
others.
In Vietnam, maize is the second largest food crop after rice. Recently, the area planted
to maize and yield has increased sharply, with average growth reaching 6.2 and 6.1
percent per year, respectively. Soybean also has developed, with annual average growth
in area and output of 4.2 percent and 8.3 percent, respectively (GSO 2008). However,
yield of maize and soybean in Vietnam is very low compared to other countries.
Currently, maize yield in Vietnam is 56 percent of Chinese maize yield and only one
third of USA maize yield. Similarly, yield of soybean in Vietnam is about 60 percent of
average world yield, only two third of Chinese yield and 40 percent of USA soybean
yield.
0
1
2
3
4
5
6
7
8
9
10
Maize Soybean
Vietnam
China
Thailand
United States
Figure 2-6: Yield of Maize and Soybean (tons/ha) By Country in 2004
Source: FAO 2005
In 2005, Vietnam used about 11 million tons of animal feed, not including green forage
and other vegetables. According to an estimate by MARD, with the development of
livestock husbandry, the requirements for industrial feed will increase by about 10 to 15
percent annually (Nguyen & Tran 2005). Based on the domestic capacity and
requirement, it is estimated that each year Vietnam needs to import about 60 percent of
raw materials including maize, soybean, dry soybean cake, milled bone and the other
raw materials to produce industrial feed.
21
However, in recent years, the import tax on raw materials has ranged from 5 to 7.5
percent, compared with other countries in the region, which have no tax on these
products (IFPRI 2001). This is one of the reasons for the high cost of feed in Vietnam,
however normally, feed prices in Vietnam (maize, soybean) are 30–40 percent higher
than the world market (Figures 2-8, 2-9), so these import taxes are not the only reason
for the divergence.
0
40
80
120
160
200
1998 1999 2000 2001 2002 2003 2004 2005 2006
pri
ce (
US
D/ t
on
)
retail price in VN world price
0
100
200
300
400
500
600
1998 1999 2000 2001 2002 2003 2004 2005 2006
pri
ce (
US
D/ t
on
)
retail price in VN world price
Figure 2-7: Price of Maize in Vietnam and
the World, 1998–2006 (USD/ton)
Figure 2-8: Price of Soybean in Vietnam
and the World, 1998–2006 (USD/ton)
Source: Domestic prices for maize and soybean summarised from Review 'Today index price' published
weekly by Price and Trade Department, GSO. World price extracted from Indexmuni of the IMF 2008
(http://indexmundi.com/commodities)
Not only do prices differ, so too does quality between industrial feed produced by
domestic companies with imported feed or produced by foreign-owned companies in
Vietnam. Good quality animal feeds have relatively high prices compared to inferior
quality feeds. Animal feeds produced locally by private local feed companies are
generally perceived to be of lower quality than those produced by foreign-owned feed
milling companies (IFPRI 2001). However, with loose regulations for labelling and
product quality certification, it is difficult to differentiate between low and high quality
feeds on the market. Consequently, farmers’ decisions to purchase feed are largely
driven by price. Small producers, in an attempt to reduce cost due to limited finances,
choose lower priced feeds, without considering the effects on the quality of animals
produced.
22
2.2.4. Livestock Diseases and Animal Breeds
Livestock producers also face the risk of livestock disease. In a normal year, mortality
can be up to 3.69, 2.28 and 2.48 percent of total herd of exotic pigs, crossbred pigs and
local pigs, respectively. For chicken this rate is even higher with 14.67 percent of local
breeds and 5.31 percent of exotic chickens (IFPRI 2001). Due to limited prevention and
treatment, especially in small households, animal diseases have spread widely in recent
years. Loss of production due to diseases in the livestock sector is very serious and
pushes up production costs. There are many reasons for this, mainly a veterinary system
with inadequately trained staff, poor data collection, storage, and retrieval systems, and
lack of monitoring which limits the effectiveness of veterinary extension services.
Since winter 2004, the avian influenza outbreak resulted in large losses for poultry
husbandry in Vietnam. As each winter season approaches, the risk of another larger
outbreak concerns people. According to an estimate by the World Bank, the outbreak in
winter 2004 resulted in the output value of the poultry sector in Vietnam declining by
15 percent. The net impact of avian influenza was estimated, from the combined effect
on poultry and egg production and subsequent livestock production, as 0.12 percent of
total GDP (World Bank 2005). This estimate did not include economic losses in general
due to the outbreak such as reduced tourism, vaccination costs and compensation for
producers. Due to the large population of small producers in Vietnam, losses from the
outbreak were felt most by small-scale producers. Many found themselves in debt when
their birds died or were culled, especially if they borrowed from banks for their
production (Leod et al. 2005).
The adoption and accession to improved animal breeds is an increasing issue in
livestock husbandry. There is a difference between small and large scale producers in
the adoption of exotic breeds. According to an IFPRI survey, choosing exotic breeds is
heavily dependent on the farm size. For pig husbandry, around 10 percent of small
farms have exotic breeds in their inventories, whilst over 55 percent of large farms have
some exotic pigs and 45 percent consist entirely of exotic pigs. Adoption of exotic
breeds by poultry farms is higher than by pig farms. However, the numbers are only 22
percent for small chicken farms and 70 percent for large farms (IFPRI 2001).
23
One factor that influences low levels of exotic breed adoption by small producers is the
lack of access to exotic genetics. Small farms often purchase breeding stocks from other
farmers or from traders. Only 8 percent of pig producers and 18.6 percent of chicken
producers indicate that they purchase breeds from government agencies, who often
supply exotic and high quality breeds (IFPRI 2001).
This situation occurs for reasons: on one hand, government breeding centres generally
operate inefficiently and with high transaction costs they find it difficult to supply small
numbers of livestock to scattered producers; on the other hand, management systems of
small households, which use low cost feed and salvage kitchen waste are more suited to
local breeds rather than the exotic ones. This livestock raising method helps small
producers save costs.
2.2.5. Other Trade and Marketing Issues
With a population of about 85 million people (2007), Vietnam has a large market for
animal products. With income increases, the demand for livestock products has risen
dramatically in recent years. Figure 2-10 shows the increasing trend in meat
consumption per capita when people have more income available.
0
2
4
6
8
10
12
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
year
GD
P p
er c
apita
(m
ill.
VN
D)
0
5
10
15
20
25
30
35
40
mea
t co
nsum
ptio
n pe
r ca
pita
(kg
)
GDP per capita (in the 1st axis) total meat per capita beef and buffalo pork poultry
Figure 2-9: Changes in Population, Income and Meat Consumption per capita
Source: GSO 2008. Meat per capita consumption is live weight meat per capita (kg)
24
Among livestock products, pork is consumed the most, with over 75 percent of total
meat. One reason is its cheap price and it is customary to eat pork compared with other
meat types. Poultry is the second most popular meat to consume. Figure 2-11 presents
pig and chicken consumption per capita over the regions.
In terms of quantity, pig consumption in urban areas is much higher than in rural areas,
but there is little difference for chicken. However, in terms of quality, consumer
requirements in urban areas are rather different to rural areas. About 36 percent of
households in urban areas prefer to buy high quality meat despite relatively higher
prices (Lapar et al. 2003). They are willing to pay a premium of about 6 to 16 percent
above the regular price if they can be sure of receiving high quality and safe products.
0
4
8
12
16
20
RRD NE NW NCC SCC CH NES MRD
region
cons
umpt
ion
per
capi
ta (
kg)
Pork Urban Pork Rural Chicken Urban Chicken Rural
Figure 2-10: Pig and Chicken Consumption per capita over Regions (kg)
Source: calculated from VHLSS 2006
Regarding consumption habits, Vietnamese consumers prefer fresh meat rather than
frozen or processed meat4, especially in rural areas. Processed meat consumption in
households is less than 5 percent of total meat volume5. For daily consumption,
consumers often buy fresh meat products from wet markets, where almost all livestock
products produced in Vietnam are sold.
Even though consumption per capita has increased over recent years, Vietnam’s
consumption is quite low compared with other countries. The most recent data shows
4 The processed meat is popularly in the form of canned meat such as slices, ham, and sausage. 5 Calculated from VHLSS 2006
25
that in 2003, when meat consumption per capita of Vietnam was about 26 kg, the
numbers for China, Malaysia, USA and Europe were 54 kg, 48 kg, 123 kg, and 91 kg,
respectively (FAOStat 2008). This gap suggests that the potential domestic market for
meat in Vietnam is quite high. Thus, focusing on the domestic market is still an efficient
strategy, especially since Vietnam does not have a high comparative advantage in
livestock production for export.
However, in order to meet the requirements of markets and have opportunities to
expand livestock production, livestock households need to overcome some difficulties
in trade and marketing.
Most trading activity takes place locally, with most small farmers selling at the farm
gate, as there is little access to organised markets and auctioning systems. Information
about markets, prices and other supporting information is limited. The lack of a
widespread system of organised live animal markets in Vietnam means that the majority
of marketing and distribution of live animals and animal products is undertaken through
a network of marketers operating in informal groupings and often undertaking
exchanges on a face-to-face basis. Table 2-4 reports the proportion of pig and poultry
sold to different stakeholders in the marketing channel, rather than to final consumers or
assemblers and distribution companies of livestock products.
Table 2-5: Share of Pig and Chicken for Sale by Household (percent)
Assemblers Retailers Slaughterhouses Wholesalers
Pig producers
Small-scale 23 12 60 5
Large scale 30 30 40 0
Chicken producers
Small-scale 50 25 0 25
Large scale 40 50 0 10
Source: IFPRI 2001
26
The marketing channels of livestock husbandry are complicated and involve many
stages. Stakeholders usually involved in the channel include producers, assemblers,
wholesalers, processors, retailers and consumers. There are often four middlemen:
traders, wholesalers, slaughterhouses/meat processors, and retailers. Live pigs and
piglets are primarily sold to traders, wholesalers and slaughterhouses while pig
carcasses and other meat products are usually sold to retailers or directly to consumers
in wet markets. Marketing channels in the chicken marketing system are similar to those
of the pig marketing system. Live chickens are usually sold to wholesalers and retailers,
while carcasses are sold to wholesalers, retailers or directly to customers. Through
marketing channels, profits are shared in many segments and final prices of the products
are also affected6.
Moreover, due to the predominantly small-scale operations and wide dispersion of
production units, costs of collection, selection, and classification of assemblers also
increase. In addition, the high risk of getting poor quality animals from various sources
with uncertain product quality, add to the costs in terms of moral hazard. Specifically,
when buying from uncertified sources, the uncertainty about product quality imposes a
tax on the value of the good, so that the buyer will not be motivated to pay the real
market price, but rather would offer to pay a lower price, thus generating a loss in
potential income to the producer.
2.3. Policy Environment for Livestock Husbandry Development
In the last sections, a review of livestock sector development and issues in development
were presented. For a clearer picture about the livestock husbandry situation in
Vietnam, the policy environment and strategy for the development of the sector in
recent years and the near future are now presented.
Since implementing economic reforms, a set of policies that support the development of
the agricultural sector in general was promulgated. In these, policies such as admitting
households as one unit in agricultural production, allocating production factors and land
6 According to research of IFPRI, the marketing margin (difference between farm gate price and retail price) of pig meat in 2001 varied from 40 percent of farm gate price in the North East to 76 percent in the Central Highlands
27
to each individual household, and opening the economy to external markets contributed
to remarkable growth of Vietnam’s agricultural sector.
For sustainable development of the agricultural sector, a diversification strategy was
adopted in which the livestock sector was considered a major component. However,
unlike the rice or sugar sectors, there was no specific set of government policies in place
for the livestock sector (Lapar et al. 2003) but the policies supporting the development
of the sector often form part of the framework in the general policy for agriculture.
For example, with resolution No.10 issued in 1988, households are recognised as
independent economic units and have the right to use their land over the long term (30
to 50 years), they are their own bosses and can decide how and what to produce and sell
and where to get their inputs7. This and other policies on lending to families and
agricultural extensions, connect two paramount resources, labour and land, in order to
develop agriculture to the utmost. This was an impetus for households to help their
productivity and sell products for more income. With limited prospects for high benefit
through rice production alone, and rapid demand increases for meat due to population
growth and urbanisation, livestock husbandry was chosen by households to develop in
response to market demand.
There were some other policies and programs such as supported programs from the
government for crop varieties and livestock breeds, hunger alleviation and poverty
reduction programs, etc., which also supported the development of the livestock sector.
These policies are often designed and combined into one general supporting policy. At
the national level, program 125 provides 10 to 13 billion VND every year for
supporting the cost of breeding of pigs, cows, and poultry for producers; Program 225
provides about 100 billion VND to upgrade research institutes which develop crop
seeds, and animal breeds, and to subsidize seed imports and promote seed
multiplication; the program for agricultural extension provides a subsidy of about 30 to
50 billion VND each year, to support the transfer of new technologies into agricultural
production. In addition to that, many programs are conducted at the provincial level to
7 Before then, under the collectivisation period, household members took part in production as workers and received salaries. The farmer-workers did not have any decision about cost-benefit in production. Since their salary was fixed and not affected by their works, they contributed as little as they could, this led to an unavoidable collapse of the collective system.
28
support agricultural production in general, therefore it is difficult to define the full
extent of support for the livestock sector (Barker et al. 2001).
The Vietnamese government continuously values livestock husbandry as an important
economic sector in agricultural production, contributing to the agricultural growth rate.
As an orientation for development of the livestock sector in the future, a Livestock
Sector Development Strategy to 2020 was developed at the end of 2007. The general
objectives were three-fold, that by 2020: (i) increase the share of livestock in
agricultural sector GDP to 42 percent (from an estimate of 32 percent in 2010); (ii)
increase the use of commercial feeds in total feeds to 71 percent; and (iii) monitor and
control a number of dangerous animal diseases such as Foot and Mouth Diseases
(FMD) and avian influenza (MARD 2007).
These objectives are being pursued through more rigorous planning from feed,
production, slaughtering, processing to marketing; using advanced science and
technology to improve productivity and quality of livestock products; supporting
livestock production through commercial farms and industrial farms at the same time
reorganising small household producers; and supporting other services in the livestock
sectors such as extension, market information, breeds and feed supply, etc.
2.4. Summary
Recently, the livestock sector has developed considerably, both in quantity and quality.
This development has been considered a major component in agriculture, contributing
to the agricultural diversification program not only in response to increased demand but
also as a source of higher, more stable incomes and better nutrition for people.
However, the development level of the Vietnam livestock sector is still low, in
comparison with other similar economies. In the development process, the sector also
faces many issues such as low productivity, low quality of livestock products, high
price and low quality of livestock feed, slow improvement, poor genetics, animal
diseases, etc. The complexity of these issues represents a challenge for the sustainable
development of the sector.
Livestock production in Vietnam is undertaken predominately at the household level, by
producers who are non-specialised. These households generally undertake cropping in
29
addition to raising livestock and they usually keep more than one type of livestock in a
mixed raising system, which often combines pigs and chickens. The traditional method
of raising livestock with local breeds and low cost feed products does not produce good
quality and high standard meat products with competitive prices.
In the future, confronted with opportunities to expand the livestock sector, the question
of how to develop the livestock production system effectively is important. The strategy
of the Vietnamese government is to develop the modern livestock system to large-scale,
intensive commercial farms to meet rising market requirements. In that context, how the
small livestock producers with traditional systems, which are the largest sector in the
rural areas, can cope with change is an important question. In particular, how they can
adjust to the challenges presented by trade liberalisation, is the question that this thesis
aims to address.
30
CHAPTER 3 : THEORETICAL FRAMEWORK OF
AGRICULTURAL HOUSEHOLD MODEL
The analysis of the household is an area of concern in all the social sciences:
anthropology, economics, sociology, etc. However, each discipline approaches the
household on the discipline's own terms. From the point of view of the economist, they
come to an analysis of the household from their earlier focus on the individual (as
consumer) and the firm (as producer) to the neglect of the family and have been
concerned primarily with defining the household in relation to production and
consumption.
A basic model with a conventional approach of maximising a utility function is useful
for understanding decision making of an agricultural household, since it can be used to
integrate information on their production and consumption activities. Such models have
been widely applied to households in developing countries (Braverman & Hammer
1986; Barnum & Squire 1979a; Strauss 1984; Ellis 1988; Singh et al. 1986; Janvry &
Sadoulet 1995). The main difference with this kind of model compared to applications
of consumer choice theory is that it provides a methodology for integrating an
agricultural household’s production and consumption decisions into a unified
theoretical and econometric framework. It means that, when consumers maximise their
utility, they are confronted with not only a limited budget, but also time and technology
constraints of the household.
The first model to integrate production and consumption decisions in the analysis of the
peasant household was proposed in the 1920s by Chayanov, a Russian agricultural
economist (Thorner et al. 1966), and later developed by Mellor in 1963, Sen in 1966,
and Nakajima in 1986 and others.
In the Chayanov model, the household seeks to maximize its utility, where utility is
derived from the consumption of goods produced on the farm, purchased goods, and
leisure. By combining utility maximization from consumption theory with the
production function from production theory, the Chayanov model provides a foundation
31
for the integrated models of household decision making. The conclusions of the
Chayanov model are linked to key assumptions regarding the interaction between the
household and markets. Most notable among these assumptions are that the household
lacks access to a wage labour market and that the household has unlimited access to
land. These assumptions lead to a prominent feature of the Chayanov model, namely,
the demographic cycle (or "life-cycle") hypothesis. Chayanov proposed a positive link
between the household's level of work effort and the consumer to worker ratio (c/w)
within the household. (Thorner et al. 1966).
The new Home-economics model is a refinement of the neoclassical theory of
consumption. In it, Becker provides an alternative model of time allocation within the
household (Becker 1965, 1991). Utility is redefined in the “new home economics
model” so that, rather than being based on purchased goods and services, the utility of
the household is based on home produced commodities. The household members
combine their time and human capital with purchased goods and services to create these
home produced commodities, otherwise known as “Z-goods”8. In general, most items
purchased by the household must be combined with household labour in order to
contribute to utility levels. In the model, the household, rather than the individual, is
clearly established as the unit of analysis. The model also has one advantage in
comparison with the previous one, as it provides a logical structure for exploring the
links between utility maximization and the allocation of time to productive activities.
More specifically, the model postulates that the time (or labour) of household members
is allocated according to the opportunity cost of each member's time. Finally, given that
the new home economics model is based on the assumption of full access to wage
labour markets, it means that the value of wage work, home production, and leisure are
all assigned an opportunity cost equal to the market wage rate.
In this chapter, a combined model of an agricultural household as both producer and
consumer is presented. The model was proposed by Barnum and Squire and further
developed in Singh, Squire, and Strauss (Barnum & Squire 1979a; Strauss 1984; Singh
et al. 1986). In the model, the demand side is determined by prices and income as
normal, however, income is partly determined by household production activities.
8 “Z-good” is produced for consumption by the household only. An example of a Z-good would be meals prepared at home, or higher-order goods such as “nutrition”, or “shelter”.
32
Therefore consumption behaviour is not independent of production behaviour. Under
some key assumptions this establishes a recursive property of the model: production
decisions are assumed to be made independently of consumption, but production profits
influence consumption. The framework of the model and relation between household
labour and labour market are presented in the next section, and detailed analytical
framework of the property is in Appendix A3.1.
3.1. The Basic Model
In general, an agricultural household is assumed to maximise its utility function. This is
specified as a function of market purchased goods, home produced goods and leisure
time9, and is written succinctly as:
),,,( iaMCLUU i =1, …., (3-1)
where
L = leisure
C = own-consumption of agricultural output
M = consumption of market purchased goods
ai = household characteristics (for example, number of dependents)
Clearly, L, C, and M could be vectors of commodities or leisure consumption for
different members of the household. However, in this particular application there is no
concern for intra-household distribution, so the household is assumed to maximise its
joint utility function as a whole.
This optimisation is subject to certain constraints. In the household model, the objective
function is constrained by three restrictions on household actions.
The first one is the technology constraint(s):
),( , AdDFF j j = 1,…,m, (3-2)
Where
9 The model is presented in this part is developed by Barnum, Squire, and Singh (Barnum & Squire 1979a, Singh et al. 1986)
33
F = total agricultural output
D = total labour inputs (both of household and hired) used in production of F
dj = other variable inputs, range of j from 1 to m
A = area of land used in F production
The production function of the household is assumed to be quasi-convex and increasing
in inputs, but marginal product is decreasing in inputs. The household can produce more
than one output, and hence have more than one technology constraint. However, total
land for cultivation activity is (here) assumed to be fixed.
The household has the opportunity of utilising its total endowment of time either
working on- or off-farm, or for leisure:
offf HHLT (3-3)
As mentioned above, total working time for on-farm work, D, includes both household
working labour and labour hired from outside (if needed).
hiredf HHD (3-4)
So if combining (3-3) and (3-4) together, we can rewrite the time constraint of the
household as follows:
hiredoff HHDLT (3-5)
where
T = total household time available for labour
L = leisure
D = total labour inputs (both of household and hired) used in production of F
Hf = level of household labour working on-farm
Hoff = level of household labour working off-farm
Hhired = level of hired labour working on-farm
34
The household maximises its utility subject to a budget constraint, which states that
total expenditure for physical commodities can not exceed the total money that the
household gets from work plus exogenous income. Assume that household labour and
hired labour are perfect substitutes and face the same wage rate:
jjhiredoff dwpFRHHwpCqM )( (3-6)
where
R = non-wage, non-farm net other income
q = price of M
p = price of C
w = wage-rate
Hoff = time working off- farm of household labour
Hhired = working time of labour hired in for farm
wj = prices of other variable factors
From this constraint, it can be seen that the role of the labour market is especially
emphasised in the model. In order to simplify the problem, those three constraints are
collapsed into a single constraint, namely the “full income” constraint as follows:
wTRwLpCqM (3-7)
where jj dwwDDpF )( is net profit from household’s agricultural
production. The left-hand side of the equation (3-7) is total expenditures of the
household and includes “expenditure” on leisure valued at the wage rate and the right-
hand side is an augmented version of Becker’s concept of “full income”, which is the
sum of any non-wage, non-farm net other income (R), a measure of the farm’s profits
(∏), and the value of the household’s stock of time (wT) (Becker 1965). Since land is
treated as a fixed factor, the rent payments or receipts, if any, are captured in the
definition of R.
This 'full income' constraint in particular distinguishes agricultural household models
from other approaches and highlights the interdependency between consumption and
35
production decisions made at the household level. Farm technology, quantities of fixed
inputs, and prices of variable inputs and outputs affect household consumption
decisions since they determine the size of the farm profit portion of the full income
constraint. Thus, this approach permits identification of linkages between farm
household production and consumption decisions.
3.2. Solving the General Model
By rearranging the full income constraint, now the problem of the household is
maximising its utility (3-1) with the constraint (3-7). The household can choose
quantities of the consumption for commodities and labour input for agricultural
production. Forming the Lagrangian, the household problem takes the following form:
)(),,( * wLpCqMYMCLU (3-8)
Where λ is the Lagrangian multiplier and Y* is the value of the full income that results
from profit maximising behaviour:
**** ),,( wDdwAdDpFRwTRwTY jjj (3-9)
where D* is labour input that the household chose for the farm’s agricultural production
to get maximum profit Π*, with land cultivation fixed A . Maximizing utility subject to
the full income constraint yields the following first-order conditions:
0
wL
U
L (3-10a)
0
qM
U
M (3-10b)
0
pC
U
C (3-10c)
0*
wLpCqMY
(3-10d)
The marginal conditions of the equations (3-10) can be solved to yield three demand
equations for choice variables C, M, L as follows:
36
),,,,( *iaYwpqCC (3-11a)
),,,,( *iaYwpqMM (3-11b)
),,,,( *iaYwpqLL (3-11c)
where ai are household characteristics. The demand system follows neoclassical theory,
where demand depends upon prices income and possibly household characteristics.
However, in the household model, full income, Y*, is determined by technological
production in the equation (3-9). Therefore changes in the factors that influence
production, profit and hence change in Y* will lead to changes in consumption
behaviour.
The model is also set up under some simplified assumptions, which help derive
consumer demand, output supply and variable input demand equations by modelling the
farm household decision-making process recursively as two separate stages, despite
their simultaneity in time. These assumptions briefly include: the household is a price-
taker in all markets and all markets exist; commodities are homogeneous, including the
labour market; decisions relating to the total stock of land and labour are treated as
given; intertemporal allocation and risk are omitted (Barnum & Squire 1979b).
3.3. Role of Labour Market
As mentioned in the last section, the assumption of the existence of a labour market,
and homogeneity in labour are particularly important in solving a recursive agricultural
household model. The dual role of an agricultural household as both producer and
consumer lets it make decisions at two disaggregated levels; that is one of an individual
farmer determining labour, and as the individual household deciding upon leisure or
supplying labour.
37
Figure 3-1: Separation of Household Decisions
The important implication of an active labour market is that household decisions about
the level of production and labour use can be made independently of household
decisions about consumption. In Figure 3-1 the relationship between household profit
and the amount of labour employed is represented by the curve π, which in turn is a
function of the physical production function and exogenous prices (P), at a particular
level of agricultural production technology. Given the objective of maximising profit,
and subject to prices of inputs, including labour cost, the household chooses the optimal
amount of labour to use on the farm at Hf = D* (where the marginal return to labour is
equal to the wage rate). The decision does not depend on household preference of
leisure, rather, it depends only on the production technology, and inputs and output
prices. Hence the difference between labour supply availability (DS) and labour demand
for farm (D*) is labour worked off-farm, Hoff. Since the household has access to a labour
market, and there is no restriction on the mix of household and hired labour, the
separation property allows separate estimation of consumer and producer sides of the
model.
However, this separation is very sensitive to violations of the assumptions of the
household model, of which the most important is an incomplete labour market10. Figures
10 There are a number of reasons why the household model could not be solved separately, such as preferences of farmers for working (on- or off-farm), family and hired labour are not perfect substitute, and the markets are incomplete. However, in the scope of this section, only the case of an imperfect labour market is considered to see the important role of taking part in the labour market
w
Hoff
U
DDS
$
0 Hf =D*
π {F(D,diA);P}
38
3-2 and 3-3 illustrate the role of a competitive labour market in decision making of an
agricultural household. Maintaining the assumption of perfect substitution of household
and hired labour, there are two cases which are most relevant in the “real world”:
“surplus labour” where off-farm opportunities are very limited, especially in a slack
season or after harvest time, and “labour shortage” in the peak season.
Figure 3-2: An Agricultural Household in Slack Season
Figure 3-2 shows a case of an agricultural household with limited opportunities to work
outside the farm11. Firstly, with the market wage w, the household would work on-farm
up to Hf, then supply the rest of the household labour Hoff12
to the labour market to get
the utility at U. Assume that there is a constraint on the amount of labour that can be
supplied off-farm H*off, which is less than Hoff. In that case, the income/labour frontier is
given by F’, which is production curve F(D,dj,A)+wH*off in the figure. Then, the
household will sell H*off of family labour to the market to have wH*
off off-farm income.
If applying the market wage for their farm, they only supply labour to farm work up to
the D* level, hence there is still some surplus labour in the household. In this case, the
11 The figure is drawn by the author, based on the discussion of Benjamin and Souficas (Benjamin 1992; Skoufias 1994), 12 Labour worked off-farm, Hoff, is the difference between total labour supply and labour working on farm, Hf.
U*
F(D,dj,A)
0
$
H*off
w
w*
U
F’=
F(D,dj,A)
+ wH*
off
wH*
off
D*D Hf Hoff Hf
*
39
household can increase utility by employing more labour on the farm till H*f, where the
opportunity cost of each day working is not the market wage w anymore, but w*<w
defined as the shadow wage. By supplying labour to H*f, the utility of the household
now is U*, lower than the utility U that the household could achieve if there was no
constraint on the labour market.
Figure 3-3: An Agricultural Household in Peak Season
Figure 3-3 shows the situation of an agricultural household in peak season, when there
is a shortage in labour. The figure includes two parts. Part A shows the standard
outcome where labour is hired in, without limit. The figure assumes that the labour
market is perfect, at the market wage rate w, as a producer, the household maximises
U
U*
w*
$
H*hired
w
F(D,dj,A)
- wH*
hired
D Hf Hf* D*
Hhired
F(D,dj,A)
- wHhired
U
w
$
D Hhired
0 Hf DD
F(D,dj,A)
- wHhired
0
Part A
Part B
40
their farm profit by producing at DD, using Hf household labour and Hhired13
labour
bought from the market, and get utility at U.
Part B shows the outcome when there is a limit on the amount of labour that can be
hired in. It is assumed that labour will be hired to the limit. The farm production
function is moved down a line with slope equal to the wage, so that the horizontal
displacement is equal to the limit. The intersection of this function with the vertical axis
indicates the profit that can be enjoyed from hired labour alone. The function then
indicates that increase in income earned as additional household labour is applied.
Utility maximisation occurs at the normal tangency between this function and the
highest indifference curve. In the first case, where the limit is exactly equal to the
amount of hired labour required - the constraint is not binding effectively. The
household with production function F, indicated by F(D,dj,A)- wHhired, chooses to
supply household labour at Hf, and pay wHhired to hire labour from market. By
construction, it can be seen that the optimal utility achieved is the same as if the market
were “unconstrained” at utility U. However, if the limit is genuinely constraining, only
H*hired labour can be hired from market, at the wage rate w* > w, the household supplies
the household labour to the farm till H*f, then a lower level of utility can be achieved, at
U*.
Both of the cases above illustrate the non-separation property of the household model if
the labour market is not perfect. Then, the decision of taking leisure and demanding
labour for the farm is a convolution, and depends not only on production technology,
prices of inputs, but also characteristics and composition of the household.
3.4. Profit Effects
As mentioned above, one of the major results of the agricultural model is the linkage
between production and consumption through profit. This section presents how the
consumption of a commodity changes when its price changes, if the household both
produces and consumes that commodity. In accordance with the previous section, the
commodity is C, with price p.
13 Labour hired from market is to cover the shortage between required labour DD and supply of household labour, Hf.
41
From the demand equation of C, the total change in quantity demanded, dC, where the
change in its price, dp, is determined as:
p
Y
Y
C
p
C
dp
dC
*
* (3-12)
The first term on the right hand side of the equation (3-12) is the standard substitution
effect. For normal goods, the change in the quantity consumed, given a change in its
own price, is negative. The second term captures the profit effect. A change in the price
of agricultural goods changes profit and hence full income through equation (3-9).
Then, in turn, full income changes the quantity demanded for C in equation (3-11a).
The second term can be expressed as:
dpDFp
dpp
Ydp
p
Y
Y
C)(**
**
*
(3-13)
Therefore equation (3-12) can be rewritten:
dpFp
C
dp
dC)(
As F(D) (the quantity of agricultural output) is always positive, the second term is
unambiguously positive, while, according to neoclassical demand theory, the first term
is negative for a normal good. Thus the total change in quantity demanded is the net
effect of two opposing substitution and profit effects.
Therefore, the “profit effect” is considered as a distinctive feature of the household
model. In applying the model, there are some policy implications different from those
that would be arrived at by traditional methods. Previous research results show the
advantages of the household model. Table 3-1 presents various studies in the literature
where notable differences in price elasticities with and without incorporation of the
“profit effect”.
42
Table 3-1: Selected Response Agricultural Price Elasticity With and Without
Profit Effect
Agricultural
commodity
Non-agricultural
commodity
Labour supply
Country/economy
A B A B A B
Taiwan -0.72 0.22 0.13 1.18 0.21 -1.59
Malaysia -0.04 0.38 -0.27 1.94 0.08 -0.57
Korea, Rep. of -0.18 0.01 -0.19 0.81 0.03 -0.13
Japan -0.87 -0.35 0.08 0.61 0.16 -1.00
Thailand -0.82 -0.37 0.06 0.51 0.18 -0.62
Sierra Leone -0.74 -0.66 -0.03 0.14 0.01 -0.09
Northern Nigeria -0.05 0.19 -0.14 0.57 0.03 -0.06
Source: Singh, Squire, and Strauss, 1986, Table 1-2, p.26.
A: Agricultural price elasticity without profit effect, B: Agricultural price elasticity with profit effect
However, the possible outcome of the total effect of price change on consumption is
highly dependent on what dominates: the substitution or profit effect, and varies across
each empirical study.
3.5. Summary
This chapter briefly presents an agricultural household model. As producers, the
household tries to maximise its net income with respect to levels of outputs and inputs,
subject to constraints determined by market prices, fixed factors and technology. As
consumers, the household maximises utility with respect to the quantity of goods
consumed, and leisure, subject to the constraints determined by the market prices
(includes wage), income, total time available, etc. However, in the household model,
additional flexibility regarding the consumption response to income change is allowed.
Through the “profit effect”, the two sides of production and consumption are linked
together in a recursive decision making.
43
The model of household behaviour presented above is a semi-commercial household
farm with a competitive labour market. As in other LDC countries, this type of
agricultural household is common in Vietnam, and lies on a continuum between wholly
commercialised farms only employing hired labour and marketing all output and a pure
subsistence farm using household labour and producing solely for home consumption.
This study applied this theoretical framework to build a representative household model
for Vietnamese small-scale livestock households. The results from econometric
estimation of the production and consumption sides of the model are presented in the
next chapter. Then the integration of these two sides allows for the construction of a full
simulation model of the household. The impacts on the household from trade policy
change can be simulated by integrating the trade and household model.
44
CHAPTER 4 : THE ECONOMETRIC MODELS
In this chapter, econometric models of production and consumption behaviour of the
agricultural household are presented. Developed as a recursive household model, the
production segment of the model is analysed employing a Cobb-Douglas (CD)
production function to estimate output, and hence profit from the farm. The
consumption side is specified using two stages: the Linear Expenditure System (LES)
for a broad grouping of goods and expenditures in the first stage, with the integration
between demand for commodities and the allocation of time for leisure and labour
supply. In the second stage, expenditure for each individual commodity in the main
food group is allocated using the Linear Approximation Almost Ideal Demand System
(LA-AIDS).
In this empirical study, econometric models are estimated from primary data of the
Vietnam Household Living Standard Survey 2004, a multi-purpose household survey.
The total survey sample was 45 900 households, in which 36 720 households report
income only, with 9180 households reporting both income and expenditure. The sample
covers and represents 3063 communes, over eight ecological regions14 and for almost
all provinces of the country. The survey included detailed questions on a household’s
characteristics and composition; income, expenditure, and saving; labour activities of
adults and children; health care and education access; housing and durable goods;
electronics; land holding and non-agricultural activities. The survey was carried out in
May and September 2004 (GSO 2004) with all questions relating to production and
consumption being framed for the previous 12 months. Therefore, even though the
avian influenza outbreak occurred in the winter of 2004, data collected from the survey
represents a normal year of the household.
Since this study pays specific attention to small-scale households in the livestock sector,
around 7000 of the 9180 households were chosen for research purposes15. In this
14 The ecological regions are Red River Delta, North East, North West, North Central Coast, South Central Coast, Central Highland, North East South, and Mekong River Delta 15 The classification of a household in the livestock sector is based on the criteria of gross value of production, includes value from raising livestock, cultivation production and other activities. This methodology is adopted from the research on Vietnam livestock sector of International Food Policy
45
research, the country is divided into four regions16: Red River Delta, the Northern
upland (includes North East and North West, in short NE+NW), the Central region
(includes North Central Coast, South Central Coast and Central Highland), and the
South (includes Mekong River Delta and North East South). Each region represents
ecological areas where agro-ecological and economy conditions are similar, and there is
a degree of homogeneity in farming system or activities of the household in production.
4.1. Production of Agricultural Household
All characterisations of the theoretical model seem to be suitable for the present study.
In Vietnam, most agricultural households take part in both livestock and other
agricultural crop production (mainly rice cultivation). Small-scale livestock production
accounts for as much as 80 percent of total livestock product in the country. Each
agricultural household often keeps 1–2 sows or less than 10 fatteners and/or some
dozens of chicken per year. For the purpose of the research, it was assumed that a
representative household produces only three commodities: paddy rice, pigs, and
chickens.
For the production segment of the household model, a direct estimate of the production
functions is employed, and the Cobb-Douglas functional form is chosen because of
analytical tractability.
Production functions take specific forms as follows:
rrrrrice VDAF 321
0 (4-1a)
ii
ii
iiii VDGF 321
0 i = pig, chicken (4-1b)
where A is land for rice production and is fixed, D is labour requirement, V are variable
inputs and G is feed for pigs or chickens. It is assumed that these production functions
can be estimated independently. It is also possible that there will be spatial
Research Institute (IFPRI 2001). Other criteria such as economic and farming systems are also considered for the purpose of household stratification. 16 In general, the researchers often divide Vietnam into 3 regions, the North, the Center, and the South based on the ecological conditions. However, in the realization that, RRD is in the delta, near by Ha Noi, the capital of the country, so the people in that area seem to have more opportunities to access to market and the other services rather than the people in the mountainous areas (NE and NW), therefore dividing the North into RRD and Up north mountainous areas (includes NW and NE) is necessary.
46
autocorrelation, given specific local conditions may affect spatially close farms.
However, detailed spatial information is not available on households and hence
consideration of any spatial correlation is limited to the fixed effects associated with
estimating regional models.
In the survey, only information on the total cost for each kind of input variable for
production, not the quantities, were available. In order to estimate input quantities for
production for the purpose of production function regression, the price of inputs are
required. Therefore price information was collected from the database of the
Vietnamese Institute for Market and Prices. This covers almost all agricultural and
consumption commodities’ retail prices over districts and provinces, weekly. The local
market price for each kind of variable input from the database was used as the price that
the household has to pay. According to Justino and Litchfield (2002), commune or local
prices often reflect accurately the prices faced by the households, since those
households as a commune usually have a single market where they purchase similar
goods at the same price.
Table 4-1 (next page) presents basic information on mean inputs and output levels of
rice, pig and chicken production of agricultural households in the four regions.
The results from ordinary least squares estimation of the CD production functions for
Vietnamese agricultural households are reported in Appendix A4.1. Here, the estimated
production functions are summarised as:
058.0223.0048.0059.061.00 pesticidefertilizerseedricerrice VVVDAF
095.0171.0584.00 pigpigpigppig VDGF
137.021.046.00 chickenchickenchickencchicken VDGF
47
Table 4-1: Arithmetic Means of Inputs and Outputs for four Regions
Arithmetic means (whole year) RRD NE+NW Central South
Rice production
No. of households 1367 1280 1402 984
Area (ha) 0.38 0.44 0.47 1.82
Yield (ton/ha) 6.48 5.35 4.99 4.58
Labour (man-day) 92.73 167.79 113.03 44.58
Seed (kg/ha) 188.68 283.61 316.24 309.94
Chemical fertiliser (kg/ha) 430.04 321.47 459.88 474.79
Pesticide (bottles/ha) 47.95 30.71 38.61 72.20
Pig production
No. of households 1037 1176 1212 597
Pig output (kg) 333.97 262.40 246.85 766.58
Labour (man-day) 115.09 129.43 86.15 150.93
Feeding cost (mil. VND) 2.62 1.53 1.51 5.99
Veterinary cost ('000 VND) 62.93 40.35 55.06 316.27
Other costs ('000 VND) 253.70 283.99 253.64 584.37
Chicken production
No. of households 596 719 573 71
Chicken output (kg) 31.63 43.64 30.09 34.03
Labour (man-day) 15.65 39.63 19.27 10.00
Feeding cost ('000 VND) 365.85 426.87 215.30 233.13
Veterinary cost ('000 VND) 15.81 22.21 18.54 22.46
Other costs ('000 VND) 14.68 14.08 18.59 12.00
48
The differences in technological productivity among regions are represented in the
differences in the constant terms:
Table 4-2: Neutral Technological Efficiency Parameters in Different Regions
Production function of RRD NE+NW Central South
Rice (α0r) 751.48 666.82 561.54 564.27
Pig (α0p) 0.98 0.98 1.08 1.25
Chicken (α0c) 0.78 0.82 0.91 1.21
All coefficients are significant at the 1 percent level, except for the technology
efficiency parameter for pigs (See Appendix A4.1 for more detail).
As mentioned in the previous chapter, it was assumed that the household maximises
profits with respect to labour, and other variable inputs in each production function.
Note that output supply elasticity can be obtained directly from the parameter estimates
of the linearly transformed CD production function. For instance, if 3210
VDAF ,
then elasticity of output supply to labour is:
2ln
ln
D
F
F
D
D
F (4-2)
Moreover, the profit maximising condition for the allocation of labour defines that the
household employs labour until the value of the marginal product equals the wage cost,
or:
wD
Fp
D
(4-3)
Accordingly, we can derive demand functions for labour from the production equation
and the profit maximisation condition, by multiplying both sides of equation (4-3) with
FD / then FwpD )/(2
Therefore demand functions for all factors can be generalised as FppX iii / ,
where Xi is demand of factor i (i = 2,…n), αi is parameter of the factor in CD production
function, pi is price of that factor, p and F are price and output of the agricultural
49
product, respectively. Once all demand functions of variable inputs are given, we can
re-write the production function and profit function in terms of fixed factors and relative
prices as follows:
321/13
3
3
2
2
210
p
p
p
pAF (4-4)
pFXppFi
ii
ii )1( (i = 2,….n) (4-5)
Applying the general formula to production functions of pig and chicken, we derive
profit and production functions for rice, pig and chicken for RRD as follows17:
01.0086.0007.001.0446.0 )/()/()/()/(*13262 pesticidericefertilizerriceseedricericerice wpwpwpwpAF
ricericerice Fp*612.0
702.0767.0922.0 )/()/()/(*878.0 VpigpigpigGpigpigpig wpwpwpF
pigpigpig Fp*15.0
681.0739.0861.0 )/()/()/(*792.0 VchickenchickenchickenGchickenchickenchicken wpwpwpF
chickenchickenchicken Fp*193.0
4.1.1. Production Elasticity
This section outlines the derivation and calculation of the production elasticity relevant
to the agricultural household model. Using the parameters estimates above, a table of
labour demand, output supply, and profit elasticity to price of output and price of labour
for each agricultural activity was constructed (Table 4-3). These elasticities are
interpreted for their significance in production decisions and are used later to determine
the direction and magnitude of the profit effect and its impact on household output price
responsiveness.
17 Production functions for other regions are calculated in similar ways; however, different neutral technological efficient parameters lead to shifting of production functions along output value.
50
Table 4-3: Elasticity of Output, Labour Demand and Profit with Respect to
Selected Variables in the Production Functions
Elasticity Variables
Output (Fi) Labour demand (Di) Profit (П)
Rice production function
Rice price (price) 0.634 1.634 1.634
Labour wage (w) -0.096 -1.096 -0.096
Technical efficiency (α0rice) 1.634 1.634 1.634
Pig production function
Pig price (ppig) 5.667 6.667 6.667
Labour wage (w) -1.140 -2.140 -1.140
Technical efficiency (α0pig) 6.667 6.667 6.667
Chicken production function
Chicken price (pchicken) 4.181 5.181 5.181
Labour wage (w) -1.088 -2.088 -1.088
Technical efficiency (α0chicken) 5.181 5.181 5.181
From the first two diagonal elements in each block of the above table, the own-price
elasticities of output and labour can be interpreted. Pig and chicken output produced are
very responsive to changes in price of that output (live pig and chicken prices,
respectively). The high responsiveness of demand for labour in livestock production to
changes in the labour wage is also clear, with elasticities of -2.140 and -2.088. These
values are similar to the value of -2.57 found by Haughton using data for Malaysian
agricultural households and also a CD production function (Haughton 1985).
The production elasticities are compared with results from other countries with similar
conditions to Vietnam. Results of elasticities of rice production function are, in general,
consistent with earlier studies about paddy farms in Muda River Valley, Malaysia
(Table 4-3). Responses of the household in paddy output and labour demand to price
changes of paddy and labour in this study are 0.634 and -1.096, which are quite close to
51
0.61 and -1.47 that were found by Barnum and Squire for Malaysia (Barnum & Squire
1979a).
Table 4-3 also shows positive elasticities of labour demand for changes in price of
outputs, indicating that the household has an incentive to produce more when output
price increases, to get more profits, as expected. One important factor needed from the
production side for the agricultural household model is the impact of change in output
price and labour wage on profits, which will be used later to consider the reaction of the
household as both producer and consumer. Note that these elasticities of livestock
profits are quite large, hence promising very clear impacts of exogenous price changes
to changes in demand for commodities through profit effects.
4.2. Consumption
4.2.1. Linear Expenditure System (LES) Model
The first stage of demand analysis operates at an aggregate level, and identifies demand
functions for food commodities, other expenditure, and at the same time, the household
labour supply function was also obtained. The wage rate of labour plays an important
role not only in determining income, but also in the allocation of non-market time and
the demand for commodities (Becker 1965, Mincer 1963, Abbott & Ashenfelter 1976).
An assertion of the classical theory of consumer demand is that the consumer-worker
acts as if maximising the own-utility function. In this section, a direct utility function is
used, based on the LES (Stone 1954), which is extremely useful because it assumes
consumption is a linear function of prices and disposable income. Since the intra-
household distribution can not be considered in detail, it is assumed that the household
maximises its joint utility function, and the utility function for each individual member
is identical and additive over the number of household members. For an individual
member of the family the utility function is written as:
)ln( iii xu i = 1,….,n, (4-6)
where xi indicates per capita quantity consumption of the ith commodity, and γi are
committed quantity of ith commodity for consumption, n is the total members of the
52
household, and i includes leisure as a consumption good, with 1i , and
0 iix
It is assumed that the household in this study consumes three broad groups of
commodities: main food, other food and other expenditure (including the industrial
commodity group and other daily expenditure), and leisure. Dependents are assumed to
consume all their available time in the form of leisure and to consume the same
quantities of other goods as do working family members. The household has n1 working
members and n2 dependents, and the total number of members is n = n1 + n2. For the
present application, the household utility is defined as:
)ln()ln()ln()ln()ln( 443322112111 mncncntnlnU ofdfd
(4-7a)
subject to
EqMCpCpwL ofdofdfdfd (4-8)
where cfd is per capita consumption of main food group of commodity Cfd, cofd is per
capita consumption of commodity group of other foods Cofd, m is per capita
consumption of industrial goods and other expenditure M, l is leisure for working
family members, and L is total leisure time; w, pfd, pofd, and q are wage of labour, price
indices of main food group, other food group, and industrial goods and other
expenditure group, respectively. E is full income as defined in the previous chapter.
Substituting l=t-s to the equation (4-7a), where t is the total time available per
individual, s is the quantity of time supplied to work activities, the utility function now
is:
)ln()ln()ln()ln()ln( 443322112111 mncncntnstnU ofdfd
(4-7b)
Dividing equally the household utility function for n, the problem now is maximising an
individual member’s utility function:
)ln()ln()ln()ln()1()ln( 4433221111 mcctkstku ofdfd
(4-7c)
53
subject to nEqmcpcpstkw ofdofdfdfd /)( (4-8b)
where nnk /1 . Let 11 k and kww ' . It is apparent that the problem is that of the
standard linear expenditure system, for which the expenditure equations are
)/()( 432111 qppwnEwstw ofdfd (4-9a)
)/( 432122 qppwnEpcp ofdfdfdfdfd (4-9b)
)/( 432133 qppwnEpcp ofdfdnfdnfdnfd (4-9c)
)/( 432144 qppwnEqqm ofdfd (4-9d)
In this case, the system of equations (4-9) has the intuitively appealing interpretation
that each member of the household firstly sets aside subsistence expenditures on the
commodities and leisure, then allocates the difference between full income (per capita)
and the minimum subsistence expenditures, among leisure time and the various
commodities in the fixed proportions βi.
However, one of the problems in estimating the model is that the measurement of
leisure as a residual after deducting working time from total available time may
introduce a specification error (Abbott & Ashenfelter 1976). Following their approach,
the system of equations is modified by substituting (t - ) for 1 in the equation (4-9a).
This yields:
432111 ))(/1( qppwqMCpCpwLnwwswt ofdfdofdofdfdfd
4321111 )/(' qppwqmcpcpnLww ofdfdofdofdfdfd
Substituting L/n1 = st and st 1 we obtain
4321 ''' qppwtwqmcpcpswtwwwtwswt ofdfdsofdofdfdfds
which can be rearranged to form equation (4-10a).
54
)'( 4321 qppwbwws ofdfd (4-10a)
Similar derivations can be used to obtain equations (4-10b), (4-10c), (4-10d)
)'( 43222 qppwbpcp ofdfdfdfdfd (4-10b)
)'( 43233 qppwbpcp ofdfdnfdofdofd (4-10c)
)'( 43244 qppwbqqm ofdfd (4-10d)
where qmcpcpkwsqmcpcpswb ofdofdfdfdofdofdfdfd '
The variables appearing in the demand system functions, such as expenditures of the
household on the broad group of commodities, and average working days, are simply
drawn from the database. The prices of consumed commodities are also needed for
estimation. However, in the VHLSS, the price of each individual commodity is not
collected. So we rely on computing the unit value, which is a “price” derived by
dividing expenditure by quantities bought or sold.
Since demands of broad groups of commodities are considered, the prices here are not
the price of each individual commodity, but a price index, formed from the formula:
n
iii qpp
1
(4-11)
where pi is the price of each individual commodity in the group, qi is quantity share of
each commodity in the total quantity consumed of the group. Each group includes n
commodities. Table 4-4 presents the mean and standard deviation of the variables as
used in LES.
55
Table 4-4: Description, Means, and Standard Deviation of Variables of Quantity
Consumption and Price Indices in LES
Variables Description ('000 VND) Mean Std.Dev.
pfd Price index of food group 9.79 2.87
pofd Price index of other food group 3.81 1.44
cfd.pfd Expenditure on food group per capita/year 1098.34 383.22
cofd.pofd Expenditure on other foods per capita/year 538.18 380.19
m.q Expenditure on industrial and others per
capita/year
576.75 365.69
s Average working day of each labour/year (days) 193.06 70.36
k Ratio of labour/ number of people in the household 0.65 0.22
w Average market wage of agricultural labour 26.19 11.76
w* Adjusted wage of agricultural labour 12.02 10.80
In the demand system of equations (4-10), parameters of γi and βi need to be estimated.
The parameters 432 ,,, appear in each of the three expenditure and labour supply
equations, and thus the estimation procedure chosen that constrains the estimates of the
s' to be consistent across equations. Note that, for marginal budget shares to sum to 1,
4321 k must equal unity: that is an estimate of β1 can be obtained from
estimates of β2, β3, β4. In order to estimate appropriate parameters, identifying prices of
each commodity group and the opportunity cost for each day of labour is important.
In the initial method of LES estimation, labour wage, or in other words, opportunity
cost of each day of labour, is based on market wages. However, some households in the
dataset do not take part in the labour market in either selling or buying labour; they only
work on their farm. The main reason may be that those households face constraints in
seeking off-farm jobs, due to seasonal features of the agricultural sector, or the
households live in isolated areas. For them, using market wages as the opportunity cost
of labour may overstate, or undervalue the cost of family labour, and lead to an
inaccurate estimation of their reaction in demand. This raises the need to account for the
implicit value of family labour. That is presented in the following section.
56
4.2.1.1. Adjusting the Wage of Agricultural Labour
The methodology developed here estimated a “shadow wage” for household labour,
who may be confronted with an imperfect labour market. This approach was applied to
estimate labour supply of the household when off-farm employment is limited
(Benjamin 1992), household and hired labour are imperfectly substituted (Deolalikar &
Vijverberg 1987, Jacoby 1992), or there is differences between female and male labour,
or between adult and child labour (Jacoby 1993, Menon et al. 2005), or farmers have
preferences towards working on- or off-farm (Lopez 1986, Skoufias 1994, Seshan
2005).
If the household does not take part in the labour market, the cost of family labour
cannot be evaluated at the market wage. And as their internal wages are unobserved, an
estimation of marginal productivity of family labour from production behaviour is
needed. Assume that there is no difference between the labour of men and women in the
household. The production function for the composite agricultural commodity produced
by the household is specified as
),,( iVADYY (4-12)
where Y is a concave function, D is total labour work for their farm in rice cultivation,
and livestock raising, A is land area, Vi is a vector of all variable inputs. Table 4-5
presents the OLS estimates of the production function.
57
Table 4-5: Household’s Production Function for Composite Agricultural
Commodity
Variables Coefficient Std. error
Log family labour days D (rice+pig+chicken production) 0.005* 0.006
Log rice cultivation cost 0.162* 0.015
Log livestock cost for feeding and veterinary 0.289* 0.005
Log other fees for farm works 0.071* 0.013
Log total areas of rice cultivation 0.314* 0.010
NE 0.031** 0.013
NW 0.100* 0.019
NCC -0.088* 0.014
SCC -0.104* 0.018
CH 0.092* 0.022
NES -0.029 0.033
MRD 0.025 0.023
Constant 5.189* 0.077
Adjusted R-squared 0.845
Number of observations 3314
*: indicates significance at 1% level, ** at 5% level of confidence
Dependent variable Y is total farm income from rice cultivation, pig and chicken raising ('000 VND)
Based on the results of Table 4-5, the shadow wage rate or agricultural marginal
product of family labour *w is derived using the following expression:
DD
Yw
ˆˆ * (4-13)
where Y denotes the fitted value of farm income derived based on the estimated
coefficients ( j ). D is total family labour and D is the estimated coefficient for
labour. In principle, it would then be possible to use this shadow wage for those
households who do not enter the labour market in the LES demand system. However, as
58
noted in Chapter 3, if there is an imperfect labour market, production and consumption
decisions of the household are determined simultaneously, and hence the opportunity
cost of labour cannot be considered as exogenous to consumption decisions.
Since the shadow wage is derived from farm income, there is simultaneity among
shadow wage and agricultural production decisions. To control for that, an instrumental
variable set is used in estimation, to break the simultaneity of the shadow wage and
consumption decisions, as follows:
Table 4-6: Instrumental Variables Estimates of Shadow Wage
Variables Description Coefficient Std. error
Log(head edu) Log of head education in levels 0.045*** 0.018
Log(head age) Log of head household age in years 0.005** 0.002
Log(no mem) Log of number of member of the HH 0.035** 0.015
Log(no labour) Log of number of labour of the HH -0.127* 0.019
Exo income Household’s exogenous income (‘000VND) 0.014 0.011
Log(rice price) Log of rice price 0.154*** 0.089
Log(pig price) Log of pig price 0.173* 0.042
Log(chic price) Log of chicken price -0.152 0.122
NE + NW = 1 for North mountainous region -0.538* 0.046
Central + CH = 1 for Central region + central highland -0.352* 0.050
NES +MRD = 1 for the South 0.611* 0.176
Constant 0.767*** 0.410
Number of observations 1022
Adjusted R-squared 0.221
*: indicates significance at 1% level, ** at 5% level, *** at 10% of confidence.
Dependant variable is log of estimated shadow wage
Comparing shadow wages calculated above with those from other research gives us
similar results about estimation of wage determination in households in Vietnam.
59
Table 4-7: Comparison of Ratio of Instrumental Estimated Shadow Wages to
(Local) Market Wages over Years
Regions 1993* 1998* 2004**
Northern Uplands (NE+NW) 0.20 0.15 0.12
Red River Delta 0.25 0.19 0.16
North Central Coast 0.17 0.15
South Central Coast 0.16 0.19
Central highland 0.13 0.24
0.14
South East 0.20 0.27
Mekong River Delta 0.23 0.29
0.34
*: Calculated by Seshan for dataset VLSS 1993 and VLSS 1998 (Seshan 2005)
**: Calculated from the above estimation for dataset VHLSS 2004
The low ratio of the shadow wage compared with the market wage confirms that there
are limited off-farm job employment opportunities in those households. The results also
show that in the North upland and Central regions, there is a higher labour intensity on-
farm, lower labour productivity, hence lower shadow wages in comparison with the rest
of the country.
4.2.1.2. Estimation of LES Model
Estimation of the LES provided estimates of parameters γi and βi in the demand system
of equations (4-10). As mentioned above, identifying accurate prices for each
commodity group and opportunity cost for each day of labour worked is important for
the estimation of the parameters.
Data about price, or unit value of a commodity, is derived by dividing expenditure by
quantities bought or sold. Price or opportunity cost of each labour day is defined as the
weighted average of the market wage and shadow wage dependent on the amount of
time worked on-farm and off-farm. The price of each day working off-farm is the
labour market wage that households are facing, and the price of on-farm work is defined
by shadow wages above.
Estimation of the LES proceeds under the assumption that the disturbance terms in each
equation are independent and have zero means and uniform variances. The equation of
60
labour supply was omitted from the system in the estimation to avoid singularity of the
variance-covariance matrix, hence its parameter, β1, was obtained from the restriction
that the marginal budget shares add up to 1.
The estimation of the LES is difficult due to non-linearity in the coefficients γi and βi
which enter in a multiplicative form. Therefore, the technique of Seemingly Unrelated
Regression with an iterative approach is applied to overcome this difficulty. Given
initial estimates of the βi, the remaining parameters were estimated, and then the βi, re-
estimated given these results. This was continued, iteratively, until parameter estimates
converged. Table 4-8 presents parameters of the linear expenditure system for
households in four regions.
Table 4-8: Estimated Parameters of LES of Household
Commodity
group
Coefficient RRD NE+NW Central South
β1* 0.223 0.172 0.133 0.126
Labour supply 206.35
(67.70)
254.35
(93.15)
244.25
(87.09)
252.66
(25.11)
β2 0.308
(36.93)
0.321
(46.46)
0.289
(40.96)
0.294
(17.09)
Main foods
2 61.363
(42.26)
57.799
(41.37)
51.020
(40.69)
48.057
(12.81)
β3 0.334
(34.09)
0.245
(54.91)
0.268
(47.93)
0.252
(21.42)
Other foods
3 -9.07E-14
(-3.84)
3.46E-13
(8.31)
-9.38E-14
(-2.64)
-5.64E-15a
(-0.14)
β4 0.136
(21.02)
0.262
(48.14)
0.310
(48.68)
0.327
(20.75)
Industrial and
Others 4 4.024
(23.10)
-0.404
(-2.00)
-1.009
(-4.75)
-0.254a
(-0.42)
* derived from the restriction that kβ1+β2+β3+β4=1. In calculating β1, k was set at its mean value of
0.682 for RRD, 0.62 for Northern Upland, 0.608 for Central, and 0.665 for the South, with the
implication that the parameters presented in the table are for a representative household in each region.
Numbers in parentheses are t values; a: not statistical significant
61
All coefficients γi and βi are statistically significant at 1 percent level, except subsistence
quantity γ4 for industrial goods and other expenditures, in the Northern upland is
significant at 5 percent level, and γ3 and γ4 in the South regions are not statistically
significant. See Appendix A4.4 for more detail. The results in Table 4-8 show all βj>0,
meaning that all broad groups of goods and expenditure are considered normal goods.
Some previous studies suggest a negative subsistence quantity indicates that the
household has no commitment for that item, or they do not consuming that item until
they reach a certain level of income (Berges & Casellas 2002, Raper et al. 2002). The
negative subsistence quantities γ4 in the Northern upland and Central region imply,
strictly, that the household has a negative consumption of these items at very low
income. The threshold level of income is estimated as 1.4 million VND/year per capita
in the Northern upland and Central regions, and only 2.59 and 5.55 percent of the
samples, respectively, have income less than this.
Table 4-9 (next page) presents the elasticities of the LES with respect to selected
variables such as prices of commodity groups, wage rate and total income, based on the
assumption that total expenditure for broad groups of commodities and leisure of the
household remains constant as prices change.
All elasticities were calculated at the arithmetic means of independent variables. This
makes the elasticities suitable for applying to a representative household in later
chapters, since all the information and characteristic of the household were built from
average numbers of households in the database.
All own-price elasticities for food, industrial commodities and other expenditures are
similar for all regions and range from -0.56 to -0.73. Due to the zero subsistence
quantity for “other food”, the own price elasticity in all regions for this group is unity.
Elasticities of other food with respect to total expenditure are greater than 1 in all
regions, therefore the other food group can be classified as a luxury. Since the LES
admits a direct estimation of the labour supply function, the elasticity of demand for
leisure was omitted, and replaced by labour supply elasticities. As expected, when the
wage rate increases all households tend to increase labour supplied.
62
Table 4-9: Elasticities for LES with Respect to Selected Variables, with Total
Expenditure Assumed Exogenous
With respect to
Elasticities Food
price
Other food
price
Industrial
goods
price
Labour
wage
Expenditure
Red River Delta
Food -0.616 0.000 -0.061 -0.470 1.185
Other Food -0.412 -1.000 -0.151 -1.046 2.674
Industrial and Others -0.188 0.000 -0.567 -0.478 1.236
Labour Supply 0.147 0.000 0.050 0.575 -0.864
Northern Upland (NE+NW)
Food -0.601 0.000 -0.061 -0.260 0.871
Other Food -0.602 -1.000 -0.213 -0.789 2.761
Industrial and Others -0.194 0.000 -0.570 -0.251 0.887
Labour Supply 0.313 0.000 0.103 0.406 -1.355
Central
Food -0.575 0.000 -0.069 -0.371 1.079
Other Food -0.508 -1.000 -0.196 -0.935 2.751
Industrial and Others -0.168 0.000 -0.593 -0.305 0.907
Labour Supply 0.226 0.000 0.081 0.559 -1.102
South
Food -0.622 0.000 -0.055 -0.461 1.303
Other Food -0.425 -1.000 -0.146 -1.182 3.281
Industrial and Others -0.123 0.000 -0.735 -0.334 0.929
Labour Supply 0.179 0.000 0.054 0.973 -1.229
4.2.2. Linear Approximately - Almost Ideal Demand System (LA-AIDS) Model
In the second step of estimating the demand function and assessing the effect of
expenditure and price on demand for commodities in the main food group, initially the
AIDS model, proposed by Deaton and Muellbauer (1980) is used.
63
In the AIDS model, demand is represented by the budget share of each commodity,
while prices and income are expressed in logarithms.
The functional form of the AIDS model can be expressed as follows:
iij
jijii P
Mpw )ln()ln( (4-14)
where
wi is the budget share of a given food commodity
pi is the price of commodity i
i = rice, pork, chicken, fish and prawn, vegetable, and other meats
M is a measure of household welfare, typically per capita income or per capita
expenditure for the main food group
μi is random disturbances with zero mean and constant variance
P is a translog price index, and defined by
lkk l
klk
kk pppP lnln2
1lnln *
0 (4-15)
where k is = 1, …6, l = 1,…,6, and the γij parameters are defined under symmetry as
follows:
jijiijij )(2
1 ** (4-16)
Moschini pointed out that the AIDS model automatically satisfies the aggregation
restriction, and with simple parametric restrictions, homogeneity and symmetry can be
imposed (Moschini 1998). However, the AIDS model may be difficult to estimate
because the price index is not linear in the parameters. In addition, the theory of the
household does not provide any empirically plausible value for α0. Therefore, due to its
simplicity, the Linear Approximate Almost Ideal Demand System (LA-AIDS) with the
Stone index is widely used (Asche & Wessells 1997). The Stone’s price index (P*) is
calculated as follows:
i
ii pwP )ln()ln( * (4-17)
64
where wi is the budget share among the commodities, and pi is the price of each
individual commodity. The Stone index is an approximation proportional to the
translog, i.e. P=φP* where E(ln(φ))=α0. The LA-AIDS model with the Stone index is
denoted as follows:
**
* )ln()ln( iij
jijii P
Mpw (4-18)
where iiii * and )))(ln()(ln(* Eiii
Since prices are never perfectly collinear, it is widely cited that applying the Stone
index will introduce some measurement error (Moschini 1995). The Stone index does
not satisfy the fundamental property of index numbers because it varies with changes in
the units of measurement for prices. One solution is to ensure that prices are scaled by
their sample mean. Following Moschini’s suggestion, a Laspeyres price index can be
used to overcome measurement error. Specifically, the log-linear analogue of the
Laspeyres price index is obtained by replacing wi with iw , which is a mean budget
share. Hence the Laspeyres price index becomes a geometrically weighted average of
prices:
i
iiL PwP )ln()ln(
Substitution PL into the equation wi with αi *, yields a LA-AIDS model with the
Laspeyers price index as follows:
**** ))ln()(ln()ln( ijjij
jijii pwMpw (4-19)
where j
jjiii pw ))ln(( 0**
Data needed for estimating system (4-19), includes prices and expenditure share of each
individual food in the main food group, were drawn from the VHLSS 2004. With
households that do not purchase food from the market, the price or unit value is derived
by dividing total value of food consumption to consumed quantity. For households that
do not consume one particular commodity, the price of the commodity that household
may have to face, if they want to consume it, is derived from the average price at the
65
commune or local level. In estimation of LA-AIDS, one equation has to be dropped
(here other meats), and the Seemingly Unrelated Regression technique was used. The
other demand equations were estimated with homogeneity and symmetry restrictions
imposed. Estimated parameters of the LA-AIDS for households with six commodities in
the main food group for four regions are in Appendix A4.3, and the estimated
elasticities derived from the LA-AIDS model are presented in Table 4-10 (next page).
The results show that all goods in the main food group are inelastic in demand, except
pork and other meat in the South region, since their own price elasticity is negative and
less than 1. These commodities are also indicated as necessary goods. In all regions,
other meat is the most sensitive to expenditure change, followed by pork, fish and
chicken, whilst the least sensitive to income are rice and vegetables18, which is
consistent with prior expectations.
18 Except vegetables in the South.
66
Table 4-10: Uncompensated Elasticity of LA-AIDS Model for Main Food
Commodities
Regions With
respect
to
Rice
quantity
Pork
quantity
Chicken
quantity
Fish
quantity
Vege
quantity
Other
meat
quantity
Price -0.539 -0.565 -0.428 -0.512 -0.138 -0.591
Ppork -0.118 -0.812 -0.006 0.265 0.079 -0.154
Pchicken -0.044 -0.023 -0.550 0.058 -0.120 -0.074
Pfish -0.070 0.097 0.075 -0.879 0.002 -0.023
Pvegetable -0.019 -0.010 -0.125 -0.016 -0.666 0.000
Pother meats -0.020 -0.026 -0.024 0.014 0.041 -0.744
Red
River
Delta
Incomereal 0.810 1.339 1.059 1.069 0.801 1.586
Price -0.833 -0.078 -0.349 -0.389 -0.058 -0.166
Ppork 0.012 -0.778 -0.171 -0.033 -0.126 -0.414
Pchicken -0.054 -0.101 -0.563 0.070 0.168 -0.309
Pfish -0.018 0.007 0.077 -0.925 -0.044 -0.023
Pvegetable -0.029 -0.091 0.106 -0.107 -0.684 0.099
Pother meats 0.011 -0.083 -0.119 -0.011 0.091 -0.660
Northern
upland
(NE+NW)
Incomereal 0.913 1.124 1.019 1.395 0.652 1.473
Price -0.667 -0.232 -0.008 -0.443 -0.077 -1.204
Ppork -0.003 -0.806 -0.314 0.023 -0.139 -0.067
Pchicken 0.020 -0.131 -0.990 0.083 -0.015 -0.053
Pfish -0.074 0.017 0.217 -0.923 -0.019 0.222
Pvegetable 0.003 -0.080 -0.028 -0.023 -0.895 0.186
Pother meats -0.082 -0.009 -0.025 0.083 0.153 -0.572
Central
Incomereal 0.802 1.241 1.147 1.199 0.992 1.489
Price -0.454 -0.409 -0.186 -0.417 -0.447 -0.522
Ppork -0.059 -1.137 -0.216 0.187 0.025 0.407
Pchicken -0.007 -0.115 -0.804 -0.008 0.061 0.086
Pfish -0.144 0.184 -0.028 -0.860 0.155 -0.182
Pvegetable -0.059 -0.009 0.070 0.062 -0.882 -0.030
Pother meats -0.033 0.126 0.073 -0.030 -0.007 -1.093
South
Incomereal 0.756 1.361 1.092 1.065 1.095 1.334
67
4.3. Summary
This chapter presents results of econometric models for both aspects of household
behaviour: consumption and production. The Cobb-Douglas production function form
was chosen for estimation because of its simple structure and ease of manipulation. The
derivations of inputs demand, profit function and profit-maximised output supply
equations are also presented. Results of OLS estimation for three household production
functions (rice, pig, and chicken) are presented. Hence production elasticities including
elasticities of output, labour demand and profit with respects to output and labour price
are derived.
On the consumption side, a two stage LES-LA-AIDS demand model was chosen
because it is easily derived from a direct utility function, which is needed to represent
the first objective of the household. LES is also very helpful since it assumes
consumption of a broad group of commodities, including leisure, is a linear function of
prices and disposable income. The complexity of a household’s problem in allocating
time between labour working time and leisure can also be solved by LES. With this
demand system, the quantity of commodities consumed are functions of wages, prices
of goods, household characteristics (e.g. number of labourers and dependants) and also
conditions of production, represented by total income. Appling the Seemingly Unrelated
Regression (SUREG) technique, parameters of LES were estimated, hence elasticities
were calculated with the assumption that full income is exogenous, which means the
demand side was not affected by production decisions.
In the second stage of the demand side estimation, a LA-AIDS model was applied to see
the effects of price on household consumption of each individual food in the main food
group, including pork and chicken.
In the next chapter, integrated household models for a representative household of each
region are presented. By linking the two sides (production and consumption) of the
model, the reaction of the household to a signal of price change is presented. Household
behaviour represents both production and consumption elements, including leisure and
labour supply, as part of maximising utility.
68
CHAPTER 5 : VIETNAMESE HOUSEHOLD MODEL -
INTERACTION OF PRODUCTION AND CONSUMPTION
DECISIONS
In the previous chapter, econometric models were used to estimate parameters of the
functions of consumption, production and utility functions of the household. In this
chapter, a household model representing each region is constructed and the process of
calibrating the model is presented. In this process, the model's elements and
relationships are determined based on the econometric results. Since the representative
household simulation model is built based on integration of three models, some
parameter values needed to be adjusted so that the predicted values for the optimal
solution for the household model match the average observed values of the household
for the base year. Moreover, each region has its own characteristic farming system
activities, so the calibration process allows for additional information, such as the ratio
of raw and industrial feed used in raising livestock, to be incorporated into the model.
Once the model is accepted by validation, it is optimised for each policy scenario. The
whole process is calibrated in Excel, and the utility maximising response to changes in
conditions for the household is determined using Excel solver.
An important feature of the agricultural household model is the household’s responses
to policy changes in both consumption and production. By constructing the integrated
household model, this chapter illustrates changes in household behaviours to a change
in an exogenous variable consisting of both restructuring consumption patterns
attributed to expenditure and consumption substitution effects, and making production
decisions. This gives different results compared with responses of the household to
changes if the household acted only as consumers.
5.1. Calibration of the Model
Calibration and simulation of a model that represents a real world situation is difficult
given the complexity of factors involved. In this section, based on the results of the
econometric model from Chapter 4 and additional information, parameters and data in
69
the base year are determined. This is an important process since it influences not only
how well the model represents a real situation, but also how useful it is in predicting
policy changes in the future.
5.1.1. The Production Model
In Chapter 4, functional form of the agricultural production function was specified with
a Cobb Douglas (CD) form.
rrrrrice VDAF 321
0
ii
ii
iiii VDGF 321
0 i = pig, chicken
In the econometric models, parameters of the production functions of rice, pig and
chicken were determined. In the calibration process, all elasticity parameters associated
with the input variables were kept, with only technology constants (α0) being adjusted
to make the observed outputs of the average household correspond to fitted output
values from the CD functions. Table 5-1 presents the adjustment of the technological
efficiency parameters in the production functions.
Table 5-1: Calibration of Neutral Technological Efficiency Parameters
In production function of RRD NE+NW Central South
Econometric model 751.48 666.82 561.54 564.27 Rice (α0r)
Calibration 738.98 751.16 566.33 622.35
Econometric model 0.98 0.98 1.08 1.25 Pig (α0p)
Calibration 1.31 1.56 1.57 1.92
Econometric model 0.78 0.82 0.91 1.21 Chicken (α0c)
Calibration 1.23 1.21 1.33 1.86
Since the prices of raw and industrial feed for pig and chicken in each household were
not available, the dependent variable for feed used in the CD is the total value rather
than quantity of feed used. This implies that prices are equal across all farms. In the
representative household model, average price indices of raw feed and industrial feed
70
from the Vietnamese Institute for Market and Prices database19 are used as the prices
that households face, and the ratio of raw feed and mill feed in raising pig and chicken
from research in IFPRI are used to calculate the quantity of each kind of feed used for
livestock in the model.
Table 5-2: Ratio of Using Raw Feed/Total Feed for Pig and Chicken Raising
(percent)
Ratio of using raw
feed/total feed in
RRD NE+NW Central South
Pig production 0.94 0.95 0.92 0.36
Chicken production 0.64 0.85 0.58 0.26
Source: calculated from database of IFPRI 2001 assuming that the characteristics of the small household
in raising livestock have not changed for years.
Applying the ratio of feed used, production functions of pig and chicken can now be
derived as follows:
095.0171.0584.00 )( pigpigIpigIpigRpigRpigppig VDGpGpF
137.021.046.00 )( chickenchickenIchickenIchickenRchickenRchickencchicken VDGpGpF
where, D is labour requirement, V are variable inputs, and GR, GI are quantities of raw
and industrial feeds, and pR and pI are average price indices of raw and industrial feeds,
respectively. In the simulations, the proportion of raw and industrial feed in total feed is
constant.
5.1.2. The Consumption Model
On the consumption side, all parameters from the estimated econometric demand
functions are used in the household model. To estimate the demand of each food in the
19 In this study, retail prices of some main commodities are obtained from a weekly database representing markets in provinces of Vietnam constructed by the Institute for Market and Prices (under the Ministry of Finance) .
71
main food group using the LA-AIDS model, the functional form20 outlined in Chapter 4
is used:
**
* )ln()ln( iij
jijii P
Mpw
The constants (αi*) need to be adjusted to let the modelled demand shares equal the
observed ones of the household in the base year. Table 5-3 presents adjustments in
constants of the LA-AIDS model for the purpose of the models’ calibrations.
Table 5-3: Calibration of Constants in LA-AIDS
Regions α*rice α*pork α*chicken α*fish α*vegetable α*othermeat
Original 1.441 -0.369 -0.043 -0.028 0.174 -0.002 RRD
Calibration 1.723 -0.368 -0.034 -0.024 0.184 -0.361
Original 0.899 -0.002 0.042 -0.145 0.276 0.206 NE+NW
Calibration 0.903 -0.006 0.041 -0.134 0.277 0.123
Original 1.331 -0.139 0.017 -0.138 0.081 -0.070 Central
Calibration 1.322 -0.134 0.016 -0.137 0.084 -0.137
Original 1.346 -0.262 0.022 0.005 -0.004 -0.112 South
Calibration 1.339 -0.254 0.025 0.000 -0.006 -0.192
5.1.3. Opportunity Cost of Each Working Day and Exogenous Income in Base
Year
As mentioned in Chapter 4, in estimation of the LES model, defining proper prices for
consumption commodities and opportunity cost of each labour day is necessary to get
appropriate parameters. For a representative household, the opportunity cost of each
labour day in the base year is defined as the weighted average of wages dependent on
time working on-farm and off-farm, and is used as base year’s labour price.
20 Where wi is the budget share of a given food; pi is the price of commodity i; M is a measure of household welfare, typically per capita income or per capita expenditure for main food group; P is the price index, μi is random disturbances.
72
Note that the standard household model applies the recursive property, where the model
assumes factor markets are complete and the household is a price taker. In the model
simulations in Chapter 7, all prices including labour are considered as exogenous
variables.
As mentioned in Chapter 3, the household maximises its utility with a “full income”
constraint: wTRwLpCqM . This constraint implies total expenditure of
the household (left hand side of the equation) is not greater than total income (right
hand side). Therefore, in calibrating the model in the base year, the sum of any non-
wage, non-farm net other income (R) of the household is calculated as a subtraction
from total expenditure and net farm profit () and the value of the household’s stock of
time (wT).
For the purpose of simulating, it is assumed that the parameters defined in the base
year’s calibration remain constant over time.
5.2. The Interaction of Production and Consumption Decisions
According to the theoretical framework of an agricultural household model, the
household reacts to a change in an exogenous variable, such as the output price of a
commodity the household produces, through price and income effects on consumption
plus a change in production decisions. The change in production decisions is captured
through its influence on on-farm profits and referred to as the profit effect. Since the
model is designed to capture both consumption and production, it yields more realistic
results than an examination of each side individually. To get an indication of the
quantitative significance of the integrated model, the partial household response
elasticity is first derived under the assumption that net farm profit, П, is exogenous,
thereby ignoring the production side of the model. Then the total response elasticity of
the full model is derived by allowing profits to be determined endogenously and
incorporating the production side of the model.
The complete household model consists, on the consumption side, of the demand
functions derived from the first order conditions and the expenditure constraint (3-7).
And on the production side, of the profit function derived from the production functions
and first order profit maximising conditions. Or in terms of the estimated system, the
73
complete model consists of the demand functions derived directly from the equation
system (4-10).
Totally differentiating the full system of household equations and rewriting allows the
derivation of the set of total response elasticities, giving the proportional change in any
endogenous variable, Z, in response to a proportional change in any exogenous variable,
X. The total response elasticities are written in the form of a sum of component partial
elasticities:
X
XE
E
Z
E
E
Z
Z
X
X
Z
Z
X
X
Z**)constant()variable( (5-1)
or in other notation:
XEZEZXZX ..* (5-2)
where ZX represents the elasticity obtained if farm profits are allowed to change and
*ZX is the elasticity obtained if farm profits are fixed (Barnum & Squire 1979b). This is
the price elasticity of demand. The remaining three terms on the right hand side are
expenditure elasticity of demand, profit elasticity of expenditures and the exogenous
variable elasticity of profits. Note that the profit elasticity of expenditures
E simplifies to П/E from the 'full income' constraint (3-7)
Using the elasticities calculated for production and consumption in Chapter 4, Table 5-4
presents the elasticities of the RRD household with and without the profit effect.
Table 5-4 indicates the significance of integrating production and consumption
decisions by comparing ZX and *ZX . For some commodities, when the price changes it
affects both consumption and production (rice, pig, chicken, and wage of labour). When
farm profits are assumed to remain constant, the own-price effects reduce consumption
of all commodities. For example, when farm profit is assumed to remain constant,
increases in the price of rice, pork and chicken reduce consumption of the main food
group. But when farm profit is allowed to change, the response to price is reduced. This
may be explained by the fact that farm profits increase as these prices rise, and hence
households have more profit and income to cover expenditure.
74
Table 5-4: Household Response Elasticities with Farm Profit Alternatively
Exogenous and Endogenous in RRD
Elasticities with the consumption quantities/labour supply Variables
Main foods Other foods Industry and others Labour supply
P other foods 9E-17 -1 1E-16 -2.8E-17
P industrial goods -0.053 -0.109 -0.616 0.016
P main foods -0.645 -0.323 -0.180 0.049
P rice *ZX -0.656 -0.329 -0.183 0.049
ZX -0.264 0.476 0.264 -0.072
P pork *ZX -0.675 -0.338 -0.188 0.051
ZX -0.502 0.016 0.009 -0.002
P chicken *ZX -0.657 -0.329 -0.183 0.049
ZX -0.568 -0.147 -0.082 0.022
Labour wage *ZX -0.606 -1.243 -0.691 0.337
ZX 0.832 1.706 0.948 -0.106
Note: *ZX elasticity with exogenous farm profit, ZX elasticity with endogenous farm profit
At the same time, labour supply responds positively to increases in exogenous prices of
commodities. This may be due to increased prices reducing household purchasing
power and forces the household to sell more labour to cover expenditure. The last row
of Table 5-4 shows that an increase in wage results in an increase in consumption of all
foods, industrial commodities and leisure. These changes can be interpreted as more
household income due to wage increases resulting in increased consumption.
For commodities where price changes affect both consumption and production such as
rice, pork, chicken and wage, of the 16 pairs of elasticities shown in Table 5-4, 10 pairs
differ with respect to sign. Once again, this emphasises the need for assessing the
effects of exogenous variables to behaviours of the household in a single model, with
the integration of production and consumption decisions for more accurate results. The
response elasticities of the households in other regions are reported in Appendices A5.1,
A5.2, A5.3.
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5.3. Summary
This chapter presents the process of constructing and calibrating representative
household models for each region. All parameters used in the model are taken from the
results of the econometric model in the previous chapter. In addition, some information
defined private characteristics in livestock production of households in each region are
used in the calibration.
By integrating two sides of the model - consumption and production - the response of
households to changes in exogenous variables through standard price and income
effects, and in production reactions, rather than changes in household behaviours of
consumption or production individually is also presented.
The models21 constructed here are considered representative of typical households for
each region, assuming that all households in the region have similar characteristics and
respond to a given change in the same way. A model for each region is constructed for
the base year. The model includes 2 elements: production of rice, pig, and chicken by
the household is represented by Cobb Douglas production functions; and consumption
by the household, represented by three categories:the main food group (which includes
rice, pork, chicken, beef, fish and shrimp, and vegetable), other food group, and
industrial group. the two sides of the model are linked together by a utility function.
Following any impact from the outside, such as price fluctuations, the household would
react by changing both production and consumption activities. On the production side:
the household will choose production level of outputs, and labour supply so that the
household maximises benefit. The benefit from agricultural production plus exogenous
income, known as total income of the household will be used to maximise utility of the
household via consumption of commodities.
In the next chapter, trade liberalisation scenarios and a global trade model will be
examined. Linking the trade and household models allows for analysis of how
households will be affected by changes in trade scenarios.
21 The structure of the model and detail of the model in equation form with the coefficients for each regions are presented in the Appendix A5.4
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CHAPTER 6 : VIETNAM’S TRADE LIBERALISATION
AND SIMULATION MODEL
As part of the Doi Moi (renovation) restructuring process, Vietnam has started to
integrate into the world economy. Since then, a series of trade reforms in various forms
of unilateral, bilateral, regional and multilateral liberalisation have occurred. In its
integration process, Vietnam negotiated and signed with more than 100 trade partners.
Among them, a trade agreement with the European Union (EU) was signed in 1992, an
agreement to become an official member of the Association of South East Asia Nations
(ASEAN) in 1995 and a joint ASEAN Free Trade Area (AFTA) in 1996. In 2000
Vietnam entered into a bilateral trade agreement (BTA) with the USA. Becoming the
150th member of the World Trade Organisation (WTO) on 11th January 2007 is an
important milestone in the long process of efforts of Vietnam to integrate into
international markets (Nguyen 2004, Abbott et al. 2007).
Each time a major agreement was reached, Vietnam’s trade with that region expanded,
and these trade agreements were clearly an impetus to ongoing domestic economic
reforms in Vietnam to become a more open economy in the process of integration into
the global economy. Implementation of multilateral and bilateral trade agreements is
likely to provide benefits for the economy and increase welfare for society.
In order to investigate the implications of trade liberalisation on Vietnam, this chapter
applies a multi-country general equilibrium trade model using the Global Trade
Analysis Project (GTAP). Since the final objective of this study is to examine impacts
of trade liberalisation on small livestock households raising pig and chicken, separating
live pig and live chicken from the aggregate livestock sectors in GTAP database is
needed. This is achieved using SplitCom software, a necessary tool for splitting GTAP
commodities into homogeneous and differentiated sub-groups. Scenario simulations of
trade liberalisations are also presented.
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6.1. Brief of Vietnam’s Trade Liberalisation Process and Commitments
In 1992 the ASEAN Free Trade Area (AFTA) was established in Singapore with
common targets of improving regional trade of ASEAN, absorbing foreign investment
through setting up one single market, and facilitating ASEAN countries to adapt to
increasingly changing international conditions, especially enhancing regional and
international trade negotiations. The major legal framework for the establishment of
AFTA is the Agreement on Common Effective Preferential Tariff (CEPT) which has
objectives of reducing tariffs and removing all non-tariff barriers. Officially
participating in ASEAN in 1995, Vietnam committed to reduce tariffs according to
CEPT, with the main commitment to reduce import tariffs to 0–5 percent for a wide
range of product groups (Vo 2005):
(a) The Inclusion List (IL) comprises products subject to immediate tariff reduction
ranging from 0–5 percent by 2006 (by 2003 for other ASEAN countries); those
products are neither sensitive nor require high levels of protection, and account for
about 96 percent of total tariff lines in Vietnam;
(b) The Temporary Exclusion List (TEL) comprises those items temporarily not subject
to immediate tariff reduction at the beginning, transferred gradually to the IL; and
(c) The Sensitive List (SL) comprises 89 unprocessed items of agricultural products,
such as eggs, vegetables, rice, prepared meat, sugar, etc. Tariff reduction for these
items, with the final rates ranging from 0–5 percent, started on 1 January 2004, to be
completed by 1 January 2013, except for sugar by 2010.
It also required the elimination of non-tariff barriers22 for most products originating
from other ASEAN members by 2006. However, AFTA allows member countries to
exclude some commodities from tariff reduction under CEPT in order to protect
national security, social morality, human lives and health. The commodities are put in a
group named General Exclusion List (GEL), with a requirement that these items are
also categorised as an exception under the General Agreement on Tariffs and Trade
22 According to the CEPT Agreement, non-tariff barriers that create quantitative restrictions on imports need to be removed after the tariff imposed on these imported products are reduced to less than 20 percent. Other non-tariff barriers applied to those products need to be removed within the next 5 years, but in any case not later than 2006.
78
(GATT) and other international commitments. Other items are excluded for different
reasons, such as guiding the consumers (tobacco and alcoholic drink), market control
(petroleum) or protection (un-manufactured tobacco), etc.
Since 2003 when CEPT/AFTA entered its final implementation phase. ASEAN leaders
decided to prioritise 11 sectors to speed up regional integration. Vietnam is actively
involved in some sectors including agriculture, fishery, textile, electronics, wood based
products and tourism (Vu & Nguyen 2005). In accordance with the program, in 2010 all
these sectors shall be liberalised for free flow of trade and investment throughout the
region. Beyond that target, these industries will become fully integrated both in terms of
trade, investment and technical aspects.
ASEAN agreed to expand the regional trade border by negotiating with other trade
partners. In November 2002, a new stage of cooperation between China in the post-
WTO accession era and dynamic ASEAN economies started by signing a framework
agreement on comprehensive economic cooperation between ASEAN and China. The
major goal of the agreement is to create an ASEAN-China Free Trade Area (ACFTA) in
2010, with the exception of Cambodia, Lao, Myanmar and Vietnam (CLMV) who will
join in 2015. The main content of the tariff negotiations under ACFTA covers: Early
Harvest Program (EHP), General Exclusion List (GEL), Sensitive List (SL) and Normal
List (NL). In particular, the EHP concerns all products in Chapters from 1 to 8 at the 8/9
digit level (Harmonize System - HS Code) which mainly covers agricultural products
and fish. Tariff reduction is scheduled for Vietnam from 2004 to 2008 with tariff rates
needing to be gradually reduced to 0 percent by 2008. Non-tariff barriers and safeguard
measures must comply with WTO disciplines (Vu & Nguyen 2005).
ASEAN also started negotiations with India and Japan in 2003, with Korea, Australia
and New Zealand in 2004 to establish free trade areas with these partners. ASEAN and
Japan agreed on the general pattern for Closer Economic Partnership (CEP) in 2012,
and 2015 for CLMV. However, unlike the ASEAN-China model of negotiation and
liberalisation commitment, this agreement is based on a bilateral Economic Partnership
Agreement (EPA) between Japan and each individual ASEAN country. Since then,
Japan has had five official talks with five ASEAN dialogue partners on EPA including
Thailand, the Philippines, Malaysia, Indonesia and Singapore. For the rest of ASEAN,
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Japan has had limited consultations. ASEAN also started trade talks with Korea
covering three areas for liberalisation, namely trade in goods, trade in services and
investment. In 2004, the parties agreed on the objective to liberalise 80 percent of tariff
lines by 2009 (Vu & Nguyen 2005). Although these free trade agreements are still in the
process of being negotiated, and the scope of commitments have not yet been
determined fully, dialogues between ASEAN with other trade partners in the Pacific
Asia region are moving forward to the target of full liberalisation.
Simultaneously while taking part in the regional free trade agreements, Vietnam
negotiated and signed bilateral trade agreements (BTA) with other partners. The textile
trade agreement with the European Community in 1992 constituted one of its first trade
deals with a Western partner. The cooperation was followed by a broader cooperation
agreement in 1995, which granted Vietnam most favoured nation treatment in its trade
relations with the EU. The negotiation continued in 2004, when Vietnam was
negotiating to join the World Trade Organisation (WTO). With an agreement on market
access signed in December 2004, the European Union lifted all quantitative restrictions
for Vietnamese textiles. In return, Vietnam agreed to open its market further in a
number of areas of interest to the EU (Vo 2005).
After many rounds of negotiation, on 13 July 2000, Vietnam and the USA officially
signed a bilateral trade agreement, towards trade normalisation. The agreement was the
most comprehensive agreement ever signed by either Vietnam or the USA. The
agreement was prepared on the basis of WTO principles and regulations, especially
those relating to intellectual property and services trade23. In terms of trading goods,
USA committed to reduce the average rate of import tariffs on Vietnamese goods from
40 percent to 3 percent. In turn, Vietnam would also reduce import tariff rates on
agricultural and industrial products from the United States within two to seven years,
gradually removing all non-tariff measures, according to WTO standards. Clauses on
intellectual property and service trade were negotiated in BTA, based on the principles
and regulations under the WTO legal framework (Vo 2005).
23 The Agreement contains 63 articles and 14 appendixes with seven chapters, namely, (I) Trade in Goods; (II) Intellectual Property Rights; (III) Trade in Services; (IV) Development of Investment Relation; (V) Business Facilitation; (VI) Transparency-Related Provisions and Right to Appeal; and (VII) General Articles.
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At the time of joining ASEAN in 1995, Vietnam formally began its WTO accession
talks. In 12 years of negotiation, Vietnam’s accession was undertaken through both
multilateral and bilateral channels. On the multilateral side, the working party
composed of 63 WTO member countries, organised dozens of meeting sessions to deal
with multilateral issues. Vietnam responded to more than 2000 questions from working
party members for transparency purposes relating to import licensing procedures,
sanitary and phytosanitary, technical trade barriers, intellectual property rights, etc
(Nguyen 2004). On the bilateral side, Vietnam negotiated with 20 countries/regions
including the USA, EU, China, Japan, India, Korea and Australia. The bilateral
negotiations mostly dealt with market access concession for goods and commitments in
the service sectors.
To be a member of WTO, Vietnam needs to conform to various WTO rules and
obligations, such as the Agreement on Agriculture24 (AoA), Sanitary and Phytosanitary
(SPS), Agreement on Technical Barriers to Trade (TBT), Agreement on Trade-Related
Aspects of Intellectual Property Rights (TRIPs), and other regulations related to state
trading enterprises (STEs), etc. The purpose of implementing these agreements is to
avoid distortion of trade and production.
Becoming the 150th member of WTO in January 2007, Vietnam has committed to
comply with WTO requirements since acceding. Particularly, Vietnam accepts as
binding its ceiling import tariff at an average rate of 17.4 percent for 10 600 tariff lines,
which decrease gradually to an average rate of 13.4 percent within 5 to 7 years. The
average of the current applied rate for agricultural products of 23.5 percent would
reduce to 20.9 percent within 5 to 7 years25. Regarding non-tariff measures, Vietnam
also commits to abolish export subsidies and domestic supports, except subsidies that
do not create obstacles to trade and is allowed by WTO. At the same time, all non-tariff
measures are transformed into tariffs or, in other words, tariffication. Other
commitments on service trading, STEs, TRIPs will also be implemented (MARD 2005).
24 The purpose of the agreement is to curb the policies that have, on a global level, created distortion in agriculture production and trade. This includes three categories namely market access restrictions, domestic support and export subsidies. The main contents of AoA are converting all non-tariff-barriers (NTBs) to trade into tariff equivalents, lowering import tariffs, gradually decreasing domestic support and export subsidy. 25 While the average committed agricultural tariffs of recently acceding members is only 19.8 percent, and of China, a recent WTO member, is 15 percent.
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6.2. Trade Liberalisation in GTAP Model
6.2.1. Previous studies on Vietnam trade liberalisation
The previous section dealt with the process of trade liberalisation of Vietnam. It was
believed that joining multilateral and bilateral trade agreements would bring substantial
benefits to the Vietnam economy, by increasing efficient investment, technology
progress, and the ability to access cheaper and better quality goods and services.
However, it may affect the sensitive and infant sectors of the economy, since they have
to face fierce competition from foreign firms. To quantify the total effects to the
economy and stake holders in trade liberalisation, people often apply computable
general equilibrium (CGE) models.
There are number of such studies that assess the economy wide effects of Vietnam’s
trade liberalisation. Nguyen Chan and Tran Kim Dung (2001) used a CGE model to
evaluate the impact of trade liberalization for Vietnam as a small open/ price-taking
economy. By categorizing 97 sectors identified in the 1996 Vietnam I/O table into 33
production sectors (17 for domestic sale and 16 for export), the authors analysed the
effects of trade and tariff reforms on export.
Pham Lan Huong (2000) used a CGE model to study the effect of trade liberalization on
Vietnam’s economy. She found that removing tariffs eventually lead to a depreciation
of VND, which means an increase in competitiveness. Improvement in the
competitiveness will translate into an expansion in exports and consequently increased
output in the economy. She also estimated that under the context of tariff removal and
trade reforms, aggregate employment is expanded. And because of expansion of
employment, i.e. an increase in the demand for labour, household nominal income
increases although it may differ among household groups (Pham 2000).
Using a multi-sector, dynamic applied general equilibrium model and the Initial Offer
by Vietnam, Roland-Holst et al (2002) presented a set of assessments of the long-term
economic effects of Vietnam’s accession to the WTO. The study indicated that, while
WTO accession is essential to economic development and fuller participation in the
global economy, it is only a partial step towards realizing Vietnam’s economic
potential. They calibrated five different scenarios for Vietnam’s economy during the
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period 2000-2020. The first scenario calibrates the Vietnam’s economy for this period
based upon the Business as Usual trends for productivity growth. This scenario is
viewed as the baseline for the dynamic counterfactuals of five different types. The other
four scenarios include the country joining the WTO and conforming to its commitments
but the domestic economy is not reformed; the country joins the WTO and implements
the agreed offers; the country signs the BTA with the US; and the capital market is
liberalized (Holst et al. 2002).
Another CGE model of trade, GTAP, which involves basic accounting relations that
track the value flows through the global data base, has been used with some previous
applications to Vietnam.
In 2001, Le Quoc Phuong used the GTAP to assess the economic impacts of the process
of Vietnam’s integration. Based on the arguments that Vietnam is integrating into the
world economy at different level, Phuong proposes four scenarios for his assessments.
These scenarios include: Vietnam’s unilateral integration; Vietnam joining AFTA;
Vietnam’s following the requirements of APEC; and global liberal trade. He found that
economic integration does have positive impacts on the Vietnam’s economy (Le 2001).
Nguyen Tien Dung and Misuo Ezaki (2005) link a global CGE model with a GTAP
2001 I-O database with a focus on international relations. The model includes 10
industries and 11 countries or regions. They found that regional economic integration
generally has positive economic impacts. The integration is not only welfare enhancing,
but also leads to a less unequal income-distribution. The research also predict that
household consumption and income rise significantly as a consequence of liberalization,
and the poor and rural groups benefit more than the rich. Moreover, the removal of
tariffs in trading partners provides Vietnam with greater market access, and exports rise
in all simulations (Nguyen & Ezaki 2005).
In 2005, Dimaranan et al. used GTAP to analyse liberalisation of tariffs and textile
export quotas. Dimaranan used the same GTAP I-O data as Nguyen and Ezaki (2005),
but aggregated into 22 sectors and 12 regions, and the research focused on industries
rather than households. (Dimaranan et al. 2005).
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Recently, David Vanzetti and Pham Lan Huong (2006) provide the most recent CGE
impact assessment of the WTO accession on the Vietnamese economy. Six scenarios
are simulated in the GTAP 6 model: Unilateral, bilateral, harmonized, bilateral,
regional, multilateral liberalization, and free trade. Similar to Dimaranan et al. (2005),
they predict only limited gains in the agriculture and resource sectors, but large effects
on the textiles and apparel sectors. All scenarios, with the exception of harmonization,
lead to an increase in exports. The largest sectoral effects are in textiles and apparel. In
other sectors, chemicals show large percentage gains from a relatively low base. Growth
in these sectors pulls resources out of agriculture, and exports fall in several agricultural
sectors. The model also predicts that Vietnam can obtain most of the potential gains
from trade reform from unilateral liberalization (Vanzetti & Pham 2006).
To assess the impact of trade liberalisation on the livestock sector in Vietnam, Nin et al.
(2003) use a micro/macro approach that combines the GTAP general equilibrium model
with a simple micro model to measure expected impacts of trade liberalization on a
representative sample of Vietnam’s livestock producers. The results show that the
impact of trade liberalization on Vietnam’s livestock production tends to be small but in
general a more open Vietnamese economy would result in a deterioration of the trade
balance of livestock products. In spite of this, trade liberalization would benefit poor
livestock producers by increasing livestock prices relative to production costs, in
particular feed costs, and by increasing non-agricultural income (Nin et al. 2003).
Apart from using a CGE model, which provides a framework for economy-wide
analyses, and taking into account existing relations among the different sectors, factor
markets, households and the government, some researchers take a partial approach in
analysing impacts of trade liberalisation to specific sectors. It ignores feedback links
among markets and activities and relies instead on in-depth knowledge of specific
sectors and the economic actors who participate in them. There have been a number of
studies following this approach, including research on the impact of trade liberalisation
on some sectors such as rice (Oxfam 2001) or livestock in Vietnam (AusAid 2004).
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6.2.2. GTAP database
In this research, GTAP, with its focus on worldwide trade policy is used, as it takes
advantage of the CGE model structure, in that it offers a consistent economy-wide
framework for analyse trade policies. The purpose is to analyse trade liberalisations,
both bilateral and multilateral trade agreements between Vietnam and other countries.
Since the latest version and most recent database include data for Vietnam, the
Vietnamese economy with all its factor and activity flows is represented in the model.
GTAP was initially developed in 1992 at Purdue University in the USA. It is a standard
Computable General Equilibrium (CGE) model based on the neoclassical theory of firm
and household behaviour assuming perfect competition, rational and utility optimising
behaviour. It is designed to be a multi-region, general equilibrium model with bilateral
trade flows between all regions and linkages between economies and between sectors
within an economy. The model uses the Armington approach where products are
differentiated by origin and assumed to substitute imperfectly for one another forming a
composite import aggregate that substitutes imperfectly for domestically produced
goods. Primary factors (land, unskilled labour, skilled labour, capital and natural
resources) are substitutable but are used as a composite in fixed proportions to
intermediate inputs (Hertel & Tsigas 1997).
Simulations are undertaken using the GTAP version 6.2 database, released in November
2003. The database has 96 countries and regions, 57 sectors, and includes tariffs, export
subsidies and taxes, subsidies on outputs and inputs such as capital, labour and land that
represented the world economy in 2001 (Dimaranan 2006). It is possible within the
GTAP model to define the required level of aggregation. For this study, in the
region/country aggregation, the objective is to split the ASEAN countries as much as
possible to distinguish the economic effects of trade liberalisation and highlight
important regional economic relations. Other important trade partners of Vietnam, such
as the USA, Japan, China, Korea, and Australia are detailed. Meanwhile, countries with
similar economic conditions such as European countries, or some developed countries
are aggregated together. African and Latin American countries are also grouped
together, since Vietnam has quite limited trade with them. Table 6-1 presents the final
aggregation for 20 countries and regions from the GTAP database for this study.
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Table 6-1: GTAP Regional Concordance
No New region Old countries/regions
1 USA United States of America
2 EU25 (European Union 25)
Austria, Belgium, Denmark, Finland, France, Germany, United Kingdom, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, Cyprus, Czech Republic, Hungary, Malta, Poland, Slovakia, Slovenia, Estonia, Latvia, Lithuania
3 JPN Japan
4 CHN China, Hong Kong
5 VNM Vietnam
6 IDN Indonesia
7 MYS Malaysia
8 PHL Philippines
9 THA Thailand
10 KOR Korea
11 IND India
12 XEA (Rest of East Asia)
Taiwan, Rest of East Asia
13 XSE (Rest of South East Asia)
Cambodia, Singapore, Rest of Southeast Asia
14 XSA (Rest of South Asia)
Bangladesh, Pakistan, Sri Lanka, Rest of South Asia
15 AUS Australia
16 ODV (Other developed countries)
New Zealand, Canada, Rest of North America, Switzerland, Rest of EFTA
17 LAM (Latin America)
Mexico, Bolivia, Colombia, Ecuador, Peru, Venezuela, Argentina, Brazil, Chile, Paraguay, Uruguay, Rest of South America, Central America, Rest of Free Trade Area of Americas, Rest of the Caribbean
18 AFR (Africa) Egypt, Morocco, Tunisia, Rest of North Africa, Botswana, South Africa, Rest of South African Customs, Malawi, Mauritius, Mozambique, Tanzania, Zambia, Zimbabwe, Rest of Southern African Development Community, Madagascar, Nigeria, Senegal, Uganda, Rest of Sub-Saharan Africa
19 CEE (Central and East Europe)
Rest of Europe, Albania, Bulgaria, Croatia, Romania
20 ROW (Rest of the world)
Rest of Oceania, Russian Federation, Rest of Former Soviet Union, Turkey, Iran, Islamic Republic of, Rest of Middle East
Source: Aggregate from GTAP v 6.2
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GTAP includes 57 sectors, covering all industries and commodities of the economy.
This can be classified into three broad sectors, namely, food and agriculture,
manufactures, and services. Food and agriculture includes 20 sectors, such as rice,
vegetables, fruit, cereal, livestock and meat products, etc., which together comprise 8
percent of world sales value. The 22 manufacturing sectors contribute 31 percent, and
the high-value services sectors comprise 61 percent of world sales value (Dimaranan
2006). For the purpose of this research, these 57 sectors are firstly aggregated into 18
commodity groups, with 7 in agricultural and food commodities and 11 in
manufacturing and services.
Since the study is interested in the impacts of trade liberalisation on small households
who raise pig and chicken as the main source of income, an examination of how trade
liberalisation affects the individual pig and chicken sectors at a national level is
necessary, and the impact of price changes of these two commodities to the household
production at the micro level is considered. Disaggregation of pork and poultry in the
meat products group is also needed, however separating pork and poultry is too
complicated since the required data on bilateral trade is not available in some countries,
so they remain aggregated in one group. The software SplitCom is used to separate live
pig and poultry sectors from the livestock group used in the standard GTAP framework,
the method of which is described below.
6.2.3. Using SplitCom and Introducing New Sectors into GTAP Database
Generating new sectors for live pig and live chicken to introduce into GTAP database is
based on extracting data from the sources of UN Comtrade, International Statistics,
WITS, FAOStat, and SAMs of countries, and using the SplitCom program, developed
by Mark Horridge, Centre of Policy Studies (Monash University) in 2005. The process
takes the following steps:
Step 1: Aggregating 18 sectors and 20 regions/countries from 57 individual sectors and
96 countries of GTAP version 6.2. The sectoral aggregation attempts to split sectors
with significant protection, such as textiles and apparel, manufactures, and electronics,
while grouping some sectors with similar characteristics in production and approximate
protection level together. In the initial aggregation, of the 11 sectors belonging to
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manufacturing and services, four are process sectors that provide inputs from
agriculture.
Step 2: Applying SplitCom to disaggregate the live animals sector into three new
sectors: live pig, live poultry and live other. The program works with three sub-folders:
Input, Work, and Output. The original GTAP database and its associated files
(basedata.har, default.prn, and sets.har) are copied into an input folder. The files
SplitSec.har and UserWgt.har are created automatically by SplitCom, once the
commands of creating new split and new database are set up. However, in the primary
action, SplitCom weights the three new commodities equally, and the user needs to
supply the desired weights by adding new headers26 TWGT, RWGT, CWGT, and
XWGT to the userwgt.har file. To do this, data on bilateral trade between
countries/regions in 2001 are taken from UN Comtrade, International Statistics, and
WITS. Data on production and consumption of pig, chicken and other animals comes
from FAOStat, and Social Accounting Matrices (SAMs) of various countries27; and
assumptions are made that some countries with similar economic conditions have
similar production activity. Hence, SplitCom is used with updated userwgt.har file to
get the final split. Finally, the new expanded GTAP database is stored in the Output
folder of the SplitCom, ready for loading to run the GTAP model.
The database is now disaggregated into 20 commodity groups (Table 6-2).
26 These headers are found in the file named nuweght.har in the work folder of the SplitCom. These headers are originally designed to introduce user weights for sales, costs, self uses and trade data into SplitCom. 27 SAMs of countries are used from source of International Food Policy Research Institute (IFPRI)
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Table 6-2: GTAP Sectoral Concordance
No New sector Old sectors
1 RIC (Paddy and processed rice) Paddy rice; Processed rice
2 VF (Vegetable and fruit) Vegetables, fruit, nuts
3 OCR (Other crops) Wheat; Cereal grains nec; Sugar cane, sugar beet; Plant-based fibres; Crops nec; Sugar
4 LivePig
5 LivePoultry
6 LiveOther
Live pig, poultry, cattle, sheep, goats, horses; Animal products nec; Wool, silk-worm cocoons
7 OMT (Pork, poultry, other meats) Meat products nec
8 CMT (Beef and sheep meats) Meat: cattle, sheep, goats, horses
9 FSH (Fishing) Fishing
10 OSO (Oilseed and vegetable oil) Oil seeds; Vegetable oils and fats
11 OFD (Processed food) Food products nec
12 B_T (Beverages and tobacco) Beverages and tobacco products
13 MLK (Milk and dairy products) Raw milk; Dairy products
14 RES (Natural res, petroleum product)
Forestry; Coal; Oil; Gas; Minerals nec; Petroleum, coal products
15 CRP (Chemicals, rubber, plastic) Chemicals, rubber and plastic products
16 TXT (Textile and apparel) Textiles; Wearing apparel; Leather products
17 MAN (Manufactures) Wood products; Paper products, publishing; Mineral products nec; Ferrous metals; Metals nec; Metal products; Motor vehicles and parts; Transport equipment nec; Machinery and equipment nec; Manufactures nec
18 ELE (Electronic) Electronic equipment
19 TCN (Transport, communication) Transport nec; Sea transport; Air transport; Communication
20 SVC (Services) Electricity; Gas manufacture, distribution; Water; Construction; Trade; Financial services nec; Insurance; Business services nec; Recreation and other services; PubAdmin/Defence/Health/Educat; Dwellings
Source: aggregate from GTAP v 6.2 and disaggregate new sectors using SplitCom
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6.2.4. Trade Scenarios of Trade Liberalisation Simulation
In this study, several scenarios are explored using the GTAP model:
(1) Unilateral Vietnam trade liberalisation; Vietnam completely removes all of its trade
taxes.
(2) Vietnam and all other ASEAN countries fully eliminate all tariff and subsidies, and
apply a free trade area in ASEAN. The trade barriers among other countries stay the
same.
(3) Extension of AFTA by expanding free trade area to include Japan, Korea and China.
In this scenario, China is a competitor of many ASEAN economies, with its large, low-
cost labour force, and it may have some impacts for adjustment in the economies of
ASEAN in general and Vietnam in particular.
Bilateral trade agreements are relatively easy to negotiate but are of limited value if the
two economies are similar. For developing countries, agreements with large developed
countries were generally considered the most beneficial. Two agreement options were
investigated:
(4) Between Vietnam and the USA
(5) Between Vietnam and EU.
The reasons for choosing USA and EU is that both are large economies; the USA has
the potential to export maize and soybean to Vietnam which may affect the livestock
sector, and both USA and EU are large trade partners of Vietnam in apparel and textile
trading, which can absorb more labour from rural areas once they have opportunity to
develop.
Multilateral liberalisation refers to a potential WTO agreement. To simplify the analysis
the sixth scenario is:
(6) A 50 percent reduction in tariffs, exports subsidies and domestic support for all
regions.
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(7) Full global liberalisation, without any trade barriers among countries that indicate
the potential gains from trade liberalisation and the opportunity cost of not liberalising
fully.
Even though the simulations in these scenarios do not fully describe real trade
liberalisation, they help to provide a closer representation of prospects that Vietnam has
to face in the near future, when all regulations of the agreements are confirmed.
Table 6-3: Alternative Trade Scenarios28
Scenarios Title Change in tariffs
(1) Unilateral Vietnam unilateral trade
liberalisation
VNM exempts 100% of import tax
for all countries
(2) AFTA Free trade area in ASEAN ASEAN countries exempt 100%
import tax to each others
(3) AFTA+3 Free trade area in ASEAN
plus China, Japan and Korea
ASEAN countries and JPN, KOR,
CHN exempt 100% import tax to
each others
(4) VNM-USA Bilateral trade between VNM
and USA
VNM and USA exempt 100% on
trade between two countries
(5) VNM-EU Bilateral trade between VNM
and EU
VNM and EU25 exempt 100% on
trade between two regions
(6) Multilateral Multilateral trade
liberalisation
All countries exempt 50% of import
tax to the others
(7) Global Free trade over the world No import tax over the world
6.3. Simulations with Different Modifications of GTAP Closures
The simulation model is based on the GTAP database version 6.2, aggregated into 20
regions and 20 commodities, with all macroeconomic, trade and protection data
referring to the common reference year of 2001, which is the most updated version at
the time of this study. The standard model is a comparative static model. Therefore, if
28 Implementing the detail and complexity of actual trade policy within a model requires a number of simplifying assumptions to be made i.e. rules of origin introduce considerable complexity, and representation of details of tariff schedules. However, the representation of trade policy used here is deemed sufficient to give an insight into the overall impact of liberalization.
91
all markets in the multi-region model are balanced (market-clearing condition), all firms
earn zero profit (zero-profit condition) and all households are on their budget constraint
line, then global savings must equal global investment and with this Walras’ Law is
satisfied. It implies that equilibrium is obtained for a system of n equations if n-1
equations are solved (Hertel & Tsigas 1997). When an exogenous shock is introduced,
the model works out a new equilibrium in all markets and determines new values for the
endogenous variables (Francois et al. 2003). The default solution method for the GTAP
model is Gragg’s method where the model is solved several times with an increasingly
fine grid until convergence is achieved.
Due to the introduction of new sectors of live pig, live poultry and other live animals to
the GTAP database, import tariffs for these commodities need to be adjusted to align
with the real situation. The changes to the tariffs contained in the database were
implemented as a 'pre-experiment' using 'Alter tax'29. Then, the updated post-simulation
database is used as a starting point for our subsequent policy experiments. Table 6-4
presents outputs and trade data of sectors of Vietnam in base year.
One of the questions that often arises when using a comparative static model is how to
“close” the model. Since the study tries to link a trade GTAP model at macro level with
a household model at a micro level, where labour allocation and wages are important
factors affecting welfare and behaviour of the household, the labour issue is especially
considered.
In the GTAP model, relating to labour market closure, the two most common
alternatives involve either assuming flexible wages and full employment or fixed real
wages and unemployment. However, GTAP also gives users a wide range of closure
options. In this study, the GTAP model is applied with some different closures for the
labour market.
29 Altertax runs a simulation that shocks tax rates to their desired value. It uses a special closure and a special parameter file to ensure that the rate-changing simulation leaves other cost and sales shares as little changed as possible.
92
Table 6-4: Vietnam’s Output and Trade Flows, 2001 (mill. USD)
Sector Output Export Import
Paddy and processed rice 6467 374 17
Vegetable and fruit 1902 257 71
Other crops 1541 810 225
Live Pig 881 2 5
LivePoultry 434 0 7
LiveOther 545 62 29
Pork, poultry, and other meats 168 33 20
Beef and sheep meats 22 0 7
Fishing 1541 49 6
Oilseed and vegetable oil 93 45 90
Processed food 2895 1365 374
Beverages and tobacco 1222 22 395
Milk and dairy products 241 2 239
Natural res, petroleum product 3703 2346 1692
Chemical, rubber, plastic 2938 495 2796
Textile and apparel 7994 4746 1848
Manufactures 10203 2313 6780
Electronic 528 446 1002
Transport, communication 2143 534 2546
Services 26763 1552 6997
Total 72223 15453 25145
Source: GTAP v.6.2
The first implication of the factor market clearing conditions (Closure A), an
assumption of an archetypal free market model is specified, with full employment and
93
full factor mobility in all factor markets. This is the standard closure, which is often
applied in the GTAP model.
Since there is an acknowledgement of unemployment in the world, the presumption of
full employment in all economies is questionable. The second alternative assumption on
the labour market (Closure B), in which excess supplies of unskilled labour in
developing countries exist, but other factors are still fully mobilised, is made. When
there is unemployment, the real wage is held constant and the supply of unskilled labour
adjusts following a policy shock.
The different assumptions on the employment situation will result in different values for
employment, wages, and prices, and thus different final welfare results, an assumption
on the level of unemployment needs to be considered if possible. The Closure C is made
based on an assumption that there is an unemployment situation that exists in Vietnam,
but the unskilled labour supply is not unlimited, and at some point real wages rise.
In the next section, the implications of alternative labour market clearing conditions are
investigated and the results of trade liberalisation scenarios presented.
6.3.1 Closure A - Standard GTAP Closure
Closure A is often used in simulations of the GTAP model. In the standard process,
prices, quantities of all non-endowment commodities, and regional incomes are
endogenous variables, conversely, policy variables, technical change variables, and
population are exogenous variables to the model.
In this closure, labour supply in all markets is fixed exogenously and, according to the
profit-maximising behaviour of the productive sector, labour demand is determined.
Since the fundamental property of a neoclassical general equilibrium model is the
concept of flow equilibrium in product and factor markets, labour demand is derived
endogenously as a function of output price and the wage, and is equalised to labour
supply. This is a fundamental requirement for market clearing. In the labour market the
variable responsible for equilibrating is the average wage rate which varies in order to
achieve economy-wide full employment. In that context, the labour market in Vietnam
can be illustrated as in Figure 6-1. The economy-wide supply of labour is fixed at L*.
Assume that there is a policy shock, which makes the demand for labour increase from
94
D1 to D2, hence the equilibrium in the labour market will move from L1 to L2, and the
price of labour will increase from P1 to P2.
Figure 6-1: Labour Market under GTAP Standard Closure - Closure A
The results of the GTAP simulations are presented in broad categories, such as
production, price, trade, and welfare effects, etc. Table 6-5 provides an overview of the
output effects of the various scenarios with simulations under Closure A.
Significant adjustments in production are observed following trade liberalisation. In
most scenarios, rice, pig and poultry outputs increase, or at least stay the same30.
Textile, electronic, and service sectors experience positive production effects31.
Meanwhile, manufacturing, meats and processed food sectors reduce production. Of
interest is the difference in the impacts of Unilateral and Regional or Multilateral
scenarios on production. In the Unilateral scenario there is no expansion in export
markets, as countries other than Vietnam do not reduce their tariffs. Most sectors
contract. With liberalisation in AFTA there is an increase in Vietnamese production of
oilseeds (OSO) whereas EU liberalisation led to an increase in Vietnamese production
of livestock. This contributes to a limited flow of labour into electronics and services.
30 Except paddy sector in Unilateral scenario, and live pig and live poultry in AFTA. 31 Except case of Electronic under the VNM-EU scenario.
D2
D1
L1
L2
S PL
QL
P2
P1
0 L*
95
Table 6-5: Initial Values (mill.USD) and Percentage Changes in Vietnamese Outputs under Alternative GTAP Scenarios* with Closure A
Sector Initial output
Unilateral
(1)
AFTA
(2)
AFTA+3 (3)
VNM-USA (4)
VNM-EU (5)
Multilateral (6)
Global
(7)
Paddy and processed rice 6467 -2 6 6 0 0 1 4
Vegetable and fruit 1902 -3 -2 -3 -1 -1 -1 -3
Other crops 1541 -5 -6 -14 -1 -6 -9 -18
Live Pig 881 0 -1 1 0 2 2 3
Live Poultry 434 0 -1 0 0 2 1 2
Live Other 545 -3 -3 -6 0 -2 -3 -6
Pork, poultry, other meats 168 -13 -10 -21 -1 -9 -10 -27
Beef and sheep meats 22 -6 -1 -6 -2 0 -3 -10
Fishing 1541 -2 -1 -2 0 -1 -1 -4
Oilseed and vegetable oil 93 -17 37 27 -2 -9 -7 -6
Processed food 2895 -6 -1 -10 -1 -5 -8 -18
Beverages and tobacco 1222 -22 -18 -20 0 0 -9 -21
Milk and dairy products 241 -26 -5 -6 -1 -4 -12 -24
Natural res, petrol product 3703 -5 -1 -8 -1 -4 -4 -10
Chemical, rubber, plastic 2938 -10 -4 28 -1 -7 0 14
Textile and apparel 7994 32 1 12 6 27 19 42
Manufactures 10203 -17 0 -18 -2 -8 -10 -24
Electronic 528 41 19 22 0 -6 11 19
Transport, communication 2143 -1 -1 -5 -1 -5 -2 -6
Services 26763 4 1 3 0 1 2 3
Source: GTAP simulations; * Definition of trade liberalisation scenarios in all following tables and figures are as stated in Table 6-3 above.
96
A more obvious effect on Vietnam of trade liberalisation is the change in trade flows.
Two sectors with a positive change in production, textiles and electronics, also show an
increase in exports in all scenarios. These sectors are export oriented. Textile exports
are 60 percent of production and electronics 85 percent. As with output, the increase in
trade is greatest with unilateral liberalisation. Trade increases are driven by domestic
reforms rather than improved market access. In the livestock sector, initial trade in pigs
and poultry is minimal. Unilateral liberalisation generates increases in exports of
livestock but the other scenarios do not, even though livestock production increases in
all scenarios. This implies that other countries source their supplies from elsewhere as a
result of lower costs of production in response to tariff changes (see Appendix A6.1 for
more detailed information about changes in exports across scenarios in Closure A).
In the model closure used here there is no requirement for an individual country that
import value must equate to export value. Any increase in trade deficit would be
accommodated by capital inflows. The removal of tariffs leads, as expected, to
significant increases in imports (see Appendix A6.2). The most notable exception is
livestock, where the initial tariffs are quite low, 5 percent for pigs and poultry. In this
sector, imports exceed exports. There is a big increase in processed meat consumption,
but much of this includes the “LiveOther” category. There is significant variation across
the scenarios, with the AFTA+3 and the globalisation scenario most important. This
shows the importance of China, on Vietnam’s doorstep.
Due to taxes and subsidies, there are different prices on different market levels, which
are called “price wedges” (Daude 2004). These tax instruments drive a wedge between
prices of producer, trader and consumer. When policy scenarios are implemented in
GTAP, new prices and quantities are calculated, through the inter-relationship between
supply and demand, to ensure the model is in equilibrium. For the purpose of analysing
the impacts of these policies on the households, the resulting price changes for
commodities and production factors are used in the simulation analysis of the household
model in the next chapter. Table 6-6 presents price changes of some commodities and
factors in Vietnam’s market through scenarios with Closure A. Details of price changes
for consumption commodities are in Appendix A6.3.
97
Table 6-6: Price Changes in the Vietnamese Market under Alternative GTAP
Scenarios with Closure A (percentage)
Factors/
Commodities
Uni-lateral
(1)
AFTA
(2)
AFTA +3
(3)
VNM-USA (4)
VNM-EU (5)
Multi-lateral
(6)
Global
(7)
Land -5.59 5.23 11.48 -0.39 1.26 1.6 6.67
Unskilled labour 9.15 3.21 13.56 1.3 6.52 7.17 17.66
Skilled labour 11.73 3.49 15.29 1.45 7.14 8.44 20.17
Capital 9.64 3.03 13 1.38 6.75 7.25 17.67
Natural Resources -14.1 -3.7 -18.95 -1.9 -9.07 -11.21 -26.88
Paddy and processed rice
1.78 3.65 9.82 0.54 3.58 3.71 9.99
Vegetable and fruit 0.41 2.4 9.39 0.22 3.22 3.37 9.18
Other crops 0.44 1.01 4.82 0.28 2.19 1.12 4.23
Live Pig -0.17 2.03 7.97 0.36 3.64 2.89 7.63
Live Poultry -3.31 2.09 7.64 0.04 2.98 2.01 5.73
Live Other 0.5 0.92 5.61 0.26 3.36 2.08 5.71
Pork, poultry, other meats
1.1 1.52 6.59 0.45 3.41 2.77 7.09
Beef and sheep meats
-2.62 0.85 3.78 -0.51 3.32 1.07 2.39
Fishing -1.71 0.01 0.79 0.53 2.65 0.03 -0.43
Oilseed and vegetable oil
-0.32 6.78 12.56 0.44 2.77 2.55 8.52
Processed food -0.36 0.66 4.11 0.42 2.95 1.47 3.81
Beverages and tobacco
-2.72 -2 0.74 0.49 2.86 0.37 1.57
Milk and dairy products
-3.56 -0.16 2.36 0.23 2.49 -0.11 0.56
Natural res, petrol product
-0.34 0.25 -0.04 0.06 0.42 0.03 0.41
Chemical, rubber, plastic
1.29 0.65 4.09 0.58 2.99 1.97 5.18
Textile and apparel -6.77 -0.38 -3.15 -0.11 1.45 -2.12 -4.2
Manufactures -1.61 -0.05 0.67 0.48 2.52 0.6 1.8
Electronic -4.61 -2.39 -2.31 0.02 0.71 -1.45 -2.56
Transport, communication
0.61 0.63 2.79 0.55 3.18 1.64 4.66
Services 2.19 0.97 4.91 0.74 3.78 2.78 7
Source: GTAP simulations
98
Since the closure fixed the supply of labour as exogenous, in all scenario simulations,
wages of both unskilled and skilled labour increase, with the biggest changes at 17.66
and 20.17 percent respectively, if full globalisation happens.
6.3.2 Closure B - Non Standard Closure in Labour Market
This closure is made based on an assumption that there is significant unemployment in
developing and less developed countries such as Asia, Africa, and some of Latin
America where the unemployed account for approximately one-third of the world’s
unemployment rate (Kurzweil 2002) and poor people are always willing to work, even
at a wage level lower than the current market price. Therefore, in this closure, real
wages for unskilled labour in all those countries are fixed, and labour supply is taken to
be endogenous. Then the labour market of a developing country can be illustrated
through the following figure.
Figure 6-2: Labour Market of a Developing Country under GTAP Closure B
With this non-standard closure of labour, the real price of labour is fixed at P* and at
that wage there is unlimited labour supply. Assume that economic integration of one
country increases trade in goods or services with others. It may lead to stimulation of
production through increased exports and thus at the same time increase demand for
workers in the country from D1 to D2, hence the equilibrium in labour market moves
from L1 to L2, and labour supply increases from Q1 to Q2. This allows the unemployed
or underemployed to sell additional labour should there be demand for unskilled labour
in intensive goods and services sectors. Table 6-7 presents changes in outputs and
unskilled labour through scenario simulations under Closure B.
S
D1
D2
P*
PL
QL 0 Q1 Q2
L1 L2
99
Table 6-7: Changes in the Vietnamese Outputs and Unskilled Labour under
Alternative GTAP Scenarios with Closure B (percentage)
Sector
Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM
-EU
(5)
Multi-
lateral
(6)
Global
(7)
Unskilled labour (%) 17.97 5.84 18.27 1.43 6.06 10.13 24.7
Paddy and processed rice 1 7 10 0 1 3 8
Vegetable and fruit -1 -2 0 0 0 0 0
Other crops -2 -5 -11 -1 -5 -7 -14
Live Pig 7 2 8 1 5 6 12
Live Poultry 6 1 6 1 4 5 10
Live Other 5 0 2 0 0 2 5
Pork, poultry, other meats -8 -8 -16 -1 -7 -7 -22
Beef and sheep meats -1 0 -2 -1 1 -1 -5
Fishing 2 1 2 0 1 1 2
Oilseed and vegetable oil -12 39 34 -1 -7 -4 1
Processed food -5 -1 -8 -1 -5 -7 -16
Beverages and tobacco -17 -16 -16 0 2 -6 -15
Milk and dairy products -17 -2 6 0 0 -5 -11
Natural res, petrol product -2 0 -5 -1 -3 -2 -6
Chemical, rubber, plastic 1 0 45 0 -3 7 35
Textile and apparel 46 4 24 7 32 26 61
Manufactures -8 3 -10 -1 -5 -5 -13
Electronic 54 23 35 0 -3 17 37
Transport, communication 6 1 2 0 -3 2 3
Services 11 3 11 1 4 6 14
Source: GTAP simulations
100
The tendency for production adjustments in all simulations of trade liberalisation under
Closure B are similar to that under Closure A. Rice and livestock sectors increase in
most simulations. Two export-oriented sectors - textiles and electronics - also increase
production under alternative scenarios. However, absolute change values of simulations
under Closure B are larger in comparison with those under Closure A. The changes in
trade balance are detailed in the Appendix A6.4. Trade surplus only happens for rice,
textile and electronic sectors; meanwhile, due to the reduction of taxes, the trade deficit
happens in almost all other sectors. Changes in market prices are reported in Table 6-8.
Table 6-8: Price Changes in the Vietnamese Market under the Alternative GTAP
Scenarios with Closure B (percentage)
Factors/ Commodities Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM
-EU
(5)
Multi-
lateral
(6)
Global
(7)
Land 6 9.36 24.97 0.61 5.62 8.81 24.6
Unskilled labour -3.5 -1.03 0.29 0.24 2 -0.23 -0.01
Skilled labour 14.32 4.5 18.17 1.71 8.21 10.07 23.9
Capital 13.01 4.25 16.7 1.68 8.04 9.27 22.6
Natural Resources 2.64 2.25 -1.15 -0.47 -3 -0.47 -2.99
Paddy and processed
rice
1 3.43 9.2 0.49 3.37 3.34 9.2
Vegetable and fruit 0.81 2.62 10.19 0.27 3.47 3.79 10.3
Other crops 0.16 0.94 4.6 0.27 2.13 1 3.9
Live Pig 1.57 2.71 10.34 0.52 4.39 4.12 10.8
Live Poultry 4.35 4.83 16.72 0.7 5.92 6.85 17.9
Live Other -4.02 -0.57 0.94 -0.11 1.78 -0.52 -0.48
Pork, poultry, other
meats
0.7 1.48 6.53 0.44 3.37 2.7 7.01
Beef and sheep meats -1.58 1.27 5.21 -0.41 3.78 1.82 4.31
Fishing 2.56 1.51 5.54 0.9 4.29 2.67 5.56
101
Table 6-8: Price Changes in the Vietnamese Market under the Alternative GTAP
Scenarios with Closure B (percentage) - continous
Factors/ Commodities Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM
-EU
(5)
Multi-
lateral
(6)
Global
(7)
Oilseed and vegetable
oil
-1.38 6.5 11.66 0.37 2.44 2.02 7.27
Processed food -0.06 0.83 4.59 0.47 3.14 1.74 4.35
Beverages and tobacco -4.4 -2.51 -0.94 0.36 2.3 -0.57 -0.72
Milk and dairy products -5.16 -0.68 0.69 0.1 1.93 -1.03 -1.63
Natural res, petrol
product
-0.39 0.32 0.01 0.06 0.43 0.26 0.8
Chemical, rubber,
plastic
-0.67 0.04 2.09 0.43 2.34 0.89 2.49
Textile and apparel -8 -0.8 -4.42 -0.21 1.02 -2.84 -5.9
Manufactures -2.98 -0.47 -0.72 0.38 2.07 -0.16 -0.08
Electronic -5.62 -2.74 -3.4 -0.06 0.38 -2.05 -4.05
Transport,
communication
-0.81 0.25 1.42 0.46 2.75 0.95 2.81
Services 0.16 0.36 2.86 0.59 3.14 1.67 4.18
Source: GTAP simulations
In all scenarios, live pig and live poultry show increased market prices and, as a result
of splitting the sectors, the increases are different between pig and poultry. Comparing
changes in wages of unskilled labour under alternative scenarios with application to
Closures A and B, the simulation results are significantly different. Labour wage
increases fastest under Closure A, where labour supply is fixed, and is almost
unchanged, or slightly fluctuates around zero, under Closure B. These changes in wages
may seem counterintuitive, since the closure fixes real wage as exogenous and makes
labour supply endogenous. However, note that this applies to real prices and wages. In
the simulation, real wages are maintained, but nominal wages may change as other
102
prices in the system alter, and this is reflected in the above results. Changes in prices of
consumption commodities are shown in Table 6-9.
Table 6-9: Price Changes of Consumption Commodities under Alternative GTAP
Scenarios with Closure B (percentage)
Commodities Uni-lateral
(1)
AFTA
(2)
AFTA +3 (3)
VNM-USA (4)
VNM-EU (5)
Multi-lateral
(6)
Global
(7)
Paddy and processed rice 0.98 3.43 9.17 0.49 3.37 3.33 9.17
Vegetable and fruit -0.19 2.35 9.15 -0.01 3.35 3.24 8.96
Other crops -2.78 -0.52 2.04 0.07 1.5 -0.31 0.5
Live Pig 1.56 2.7 10.29 0.52 4.37 4.09 10.8
Live Poultry 4.26 4.75 16.55 0.69 5.84 6.76 17.7
Live Other -3.89 -0.63 0.57 -0.11 1.6 -0.67 -0.91
Pork, poultry, other meats -2.12 -0.41 3.64 0.1 2.3 1.24 2.23
Beef and sheep meats -1.6 1.26 5.19 -0.42 3.78 1.81 4.27
Fishing 2.5 1.5 5.5 0.87 4.27 2.64 5.48
Oilseed and vegetable oil -14.9 -13.42 -12.72 -0.04 0.57 -6.3 -14.0
Processed food -6.17 -0.92 0.81 -0.14 1.35 -1.68 -3.73
Beverages and tobacco -23.26 -18.25 -20.27 0.04 0.74 -9.63 -21.2
Milk and dairy products -11.33 -1.68 -1.09 -0.4 0.7 -4.07 -8.34
Natural res, petrol product -7.19 -1.09 -6.79 -0.24 -0.15 -3.26 -6.8
Chemical, rubber, plastic -3.15 -0.83 -0.71 0.13 0.72 -1.04 -1.88
Textile and apparel -15.13 -1.8 -10.82 -0.84 -0.55 -6.62 -14.5
Manufactures -8.56 -1.9 -6.66 0.13 0.81 -3.1 -7.04
Electronic -8.53 -4.65 -6.58 -0.47 -1.29 -4.03 -8.46
Transport, communication -0.25 0.1 0.32 0.14 0.82 0.18 0.59
Services 0.11 0.27 2.01 0.43 2.26 1.15 2.85
Source: GTAP simulations
103
6.3.3 Closure C - Modified Non-Standard Closure
In Closure C the wage of unskilled labour in all developing countries, except Vietnam,
is fixed. It is assumed that there is a limit on the maximum increase in unskilled labour
in Vietnam of R percent. If a trade liberalisation scenario results in a simulated increase
in labour of R percent, then the increase in labour is fixed at R percent and wages rise to
get the market in equilibrium. The reason for doing so is based on an assumption that
there is an unemployment situation in Vietnam and the unemployed are willing to work
at the current wage level, however, the number cannot increase over the total number of
unemployed of the society. Hence, the market for unskilled labour in Vietnam is
illustrated in Figure 6-3.
Figure 6-3: Unskilled Labour Market in Vietnam under Closure C
When labour demand is lower than Q1(1+R%), equilibrium of the labour market is at L1,
where Q1 unskilled labour is willing to work at wage level P*, as is the case for Closure
B. Labour would agree to work at the current market price until labour supply reaches
Q1(1+R%), after which, if demand for labour increases the new equilibrium on the
market would be L2, where the new wage is P2, higher than the current wage.
6.3.3.1. Estimation of R
Applying Closure B, fixing wages of unskilled labour in all developing countries, and
allowing labour demand in Vietnam to increase in all scenario simulations led to a
maximum increase in demand of approximately 25 percent, if global liberalisation
QL
D2
D1
L1
L2
P*
P2
PL
Q1(1+R%) 0
S
Q1
104
happens (Table 6-7). However, the issue is whether or not society can supply that much
labour at the current wage.
Statistics from Vietnam’s General Statistics Office show that in 200132, the
unemployment rate in Vietnam’s urban areas was 6.28 percent, and under-employment
in the rural areas was 25.74 percent (GSO 2008). However, to move to complete full
employment is impossible. “Full employment” has come to mean an unemployment rate
close to 6 percent; it would be difficult to reduce unemployment below this rate, even in
a highly developed economy (Baumol & Blinder 1988). Based on that argument, with
about 64 percent of the population working in rural areas (2001), increases in the labour
force supplied to the economy is about 12 percent33 at the current wage. Assuming that
the ideal solution is that about 12 percent of the population can find a job (limitations of
information accession, transportation, skill, etc., are no obstacle). With that assumption,
Closure C now fixes the maximum increase in labour supply of Vietnam at 12 percent.
When the demand for labour increases over that level, wages would increase since the
elasticity of labour supply is perfectly inelastic (Figure 6-3).
In this study, a rate of 12 percent more than the baseline labour supply is chosen as the
upper limit of the change in unskilled labour supply (R) in Vietnam at the current wage.
This means scenarios (1) (3) and (7) above need to be resolved using Closure C, as all
have increases in labour exceeding 12 percent (see Table 6-7). With closure C, fixing
the potential increase in unskilled labour supply in Vietnam at 112 percent compared to
the baseline, and allowing the wage of labour to vary34, changes in market prices under
trade scenario simulations are found in Table 6-10.
Scenarios (1), (3) and (7) have market wage increases of 0.31, 4.44, and 8.23 percent
respectively, corresponding with increases in labour demand over 12 percent in the
three scenarios with Closure B (see Table 6-7).
32 The study is based on reports of unemployment and underemployment in year 2001 to match with the economy situation of the base year in the GTAP model. 33 Assume that unemployment rate cannot go lower than 6 percent and unemployed people are prepared to work at the current wage level, the maximum labour can absorb into labour market = 100% - %labour in work - 6% limited = 0.64* (25.74% - 6%) + 0.36*(6.28% - 6%) = 12.73%. 34 For other developing countries and LDCs, wages of unskilled labour are fixed, as in Closure B
105
Table 6-10: Percentage Changes in Vietnamese Market Prices under Alternative
GTAP Scenarios with Closure C (R=12%)
Factors/ Commodities Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM-
EU
(5)
Multi-
lateral
(6)
Global
(7)
Land 2.26 9.36 20.5 0.61 5.62 8.81 15.7
Unskilled labour 0.31 -1.03 4.44 0.24 2 -0.23 8.23
Skilled labour 13.54 4.5 17.3 1.71 8.21 10.07 22.2
Capital 11.97 4.25 15.54 1.68 8.04 9.27 20.2
Natural Resources -2.88 2.25 -7.09 -0.47 -3 -0.47 -14.3
Paddy and processed rice 1.23 3.43 9.39 0.49 3.37 3.34 9.54
Vegetable and fruit 0.63 2.62 9.89 0.27 3.47 3.79 9.69
Other crops 0.22 0.94 4.65 0.27 2.13 1 4.01
Live Pig 0.95 2.71 9.5 0.52 4.39 4.12 9.14
Live Poultry 1.84 4.83 13.66 0.7 5.92 6.85 11.8
Live Other -2.67 -0.57 2.4 -0.11 1.78 -0.52 2.41
Pork, poultry, other meats 0.78 1.48 6.51 0.44 3.37 2.7 6.96
Beef and sheep meats -1.94 1.27 4.72 -0.41 3.78 1.82 3.31
Fishing 1.06 1.51 3.84 0.9 4.29 2.67 2.36
Oilseed and vegetable oil -1.06 6.5 11.97 0.37 2.44 2.02 7.9
Processed food -0.19 0.83 4.4 0.47 3.14 1.74 4.02
Beverages and tobacco -3.87 -2.51 -0.39 0.36 2.3 -0.57 0.38
Milk and dairy products -4.66 -0.68 1.23 0.1 1.93 -1.03 -0.59
Natural res, petrol product -0.38 0.32 0.02 0.06 0.43 0.26 0.84
Chemical, rubber, plastic -0.04 0.04 2.75 0.43 2.34 0.89 3.82
Textile and apparel -7.61 -0.8 -4 -0.21 1.02 -2.84 -5.07
Manufactures -2.54 -0.47 -0.26 0.38 2.07 -0.16 0.86
Electronic -5.3 -2.74 -3.06 -0.06 0.38 -2.05 -3.36
Transport, communication -0.34 0.25 1.91 0.46 2.75 0.95 3.81
Services 0.81 0.36 3.56 0.59 3.14 1.67 5.58
Source: GTAP simulations
106
Market prices of agricultural products under alternative scenarios have similar change
tendencies between application of Closures B and C. Prices of rice, live pig and live
chicken all increase under scenario simulations with both Closure B and C. This may be
an incentive for households to produce more. However, there is little difference in
absolute change values between the two closures. The price of rice increases more, but
live pig and chicken increase less in Closure C under scenarios (1), (3), and (7)
compared with the change levels in Closure B. This is attributed to changes in labour
supply in the economy. Market price changes in scenarios (2), (4), (5), and (6) under
Closure C have the same results as with Closure B, due to the constraints on increases in
unskilled labour demand is not being binding, with predicted increases lower than 12
percent.
Table 6-11 presents changes in the price of consumption commodities that have a direct
impact on consumption behaviour of the household. Compared with Closure B (Table
6-9), Closure C has similar changes in consumption prices (Table 6-11). However,
under scenarios (1), (3), and (7), consumers face slightly higher prices for rice
consumption, but less change for pork, chicken meat and beef.
Other results for changes in the output and trade of Vietnam at the macro level with
Closure C are reported in Appendices A6.6 - A6.8.
107
Table 6-11: Percentage Change in Consumption Commodity Price under
Alternative GTAP Scenarios with Closure C (R=12%)
Commodities Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM
-EU
(5)
Multi-
lateral
(6)
Global
(7)
Paddy and processed rice 1.21 3.43 9.36 0.49 3.37 3.33 9.51
Vegetable and fruit -0.36 2.35 8.86 -0.01 3.35 3.24 8.35
Other crops -2.74 -0.52 2.08 0.07 1.5 -0.31 0.57
Live Pig 0.94 2.7 9.46 0.52 4.37 4.09 9.09
Live Poultry 1.78 4.75 13.54 0.69 5.84 6.76 11.6
Live Other -2.67 -0.63 1.88 -0.11 1.6 -0.67 1.66
Pork, poultry, other meats -2.06 -0.41 3.62 0.1 2.3 1.24 2.19
Beef and sheep meats -1.96 1.26 4.7 -0.42 3.78 1.81 3.28
Fishing 1 1.5 3.81 0.87 4.27 2.64 2.31
Oilseed and vegetable oil -14.85 -13.42 -12.68 -0.04 0.57 -6.3 -13.9
Processed food -6.26 -0.92 0.68 -0.14 1.35 -1.68 -3.95
Beverages and tobacco -23.01 -18.25 -20 0.04 0.74 -9.63 -20.7
Milk and dairy products -11.15 -1.68 -0.86 -0.4 0.7 -4.07 -7.98
Natural res, petrol product -7.2 -1.09 -6.79 -0.24 -0.15 -3.26 -6.8
Chemical, rubber, plastic -2.89 -0.83 -0.43 0.13 0.72 -1.04 -1.37
Textile and apparel -14.95 -1.8 -10.63 -0.84 -0.55 -6.62 -14.1
Manufactures -8.34 -1.9 -6.43 0.13 0.81 -3.1 -6.59
Electronic -8.51 -4.65 -6.56 -0.47 -1.29 -4.03 -8.43
Transport, communication -0.11 0.1 0.47 0.14 0.82 0.18 0.88
Services 0.59 0.27 2.5 0.43 2.26 1.15 3.84
Source: GTAP simulations
108
6.3.4 Alternative Labour Assumptions and Real Wages
An interesting finding is the difference in the simulated changes in real wages for the
seven trade scenarios, using different assumptions about the possible expansion in
labour supply. In order to graphically present these differences, the GTAP model was
run with different closures based on the different levels of unskilled labour supply in
Vietnam (with the value of R% varying over 0, 8, 12 percent, to unlimited supply in
Closure B). Note that these are real wages, that is, the market wage deflated by the
consumer price index (CPI), and hence changes at a 12 percent change in unskilled
labour are different to changes in market wage reported in Table 6-10. The assumption
within GTAP under Closure C is that it is the real wage that remains constant.
0
4
8
12
16
20
0 4 8 12 16 20 24 28Maximum possible increase in labour supply (%)
perc
enta
ge c
hang
e of
rea
l wag
e co
mpa
re
with
bas
elin
e
Uni AFTA AFTA+3 VNM-USA VNM-EU Multi Glob
Figure 6-4: Changes in Real Wages under Scenario Simulations with Different Possible
Maximum Labour Supplies
Source: GTAP simulations
If there is zero expansion in possible labour (equivalent to Closure A) then Figure 6-4
shows increases in real wages ranging from 1.02 to 16.89 percent. If there are potential
increases in labour supply then the change in real wages fall but remains positive as
long as the labour constraint is binding (Closure C). Once the maximum possible labour
increase exceeds the increase in labour demand induced by a liberalisation scenario,
then the constraint is not binding, and real wages do not change (Closure B). The figure
shows the maximum change in labour supply at the point of transition from Closure C
to B for each scenario. At the maximum level of labour change used in this study (12
109
percent) only scenarios (1), (3) and (7) hit the constraint and have increased real wages.
One advantage of the figure is that it identifies the simulated increase in real wages for
any level of labour constraint, for each scenario.
6.3.5 Changes of Vietnam’s Welfare under Alternative Closures
The results of the GTAP simulations are presented in some broad categories. The
previous section presented changes in production, price, and trade of Vietnam through
scenario simulations under alternative closures. Another important indicator is the
welfare effect of trade liberalisation on the country at the macro level. In the GTAP
model, the welfare indicator summarises policy changes by incorporating changes in
consumption, production, price and trade flows, and is measured using equivalent
variation35 (EV) in income.
Figure 6-5 presents the changes of Vietnam’s welfare in trade simulation scenarios
under different closures. It shows significant improvement in welfare under different
closures, but with some variations.
0
1000
2000
3000
4000
Unilateral AFTA AFTA+3 VNM-USA VNM-EU Multilateral Global
Scenarios
mil.
US
D
closure A closure B closure C
Figure 6-5: Changes in Welfare under Alternative Trade Scenarios with Different
Closures
Source: GTAP simulations
With Closures B and C, creating more jobs and improving the employment situation or
unemployment threats in specific sectors under trade liberalisation scenarios enhance 35 EV represents the money-metric equivalent to the utility change brought about by a change in prices. It measures the amount of money needed to be taken away from the consumer before the price change to leave her as well off as she would be after the change in prices.
110
welfare gains of Vietnam. Different welfare changes under scenarios (1), (3) and (7)
between Closure B and C are due to the binding upper limit of unskilled labour supply.
Under scenarios (2), (4), (5), and (6), there are no differences in welfare, since demands
for unskilled labour do not reach the limiting 12 percent increase. This improvement in
welfare is attributed to a significant contribution of job creation for unskilled labourers
(Table 6-12). Further analysis of the basis for the differences in welfare is conducted in
the following chapter.
Table 6-12: Number of Jobs Created for Unskilled Labour and its Contribution to
Total Social Welfare in Vietnam under Trade Scenarios (percentage)
Number of jobs for unskilled labour
created compare with baseline
Contribution to total social
welfare
Scenarios
Closure B Closure C Closure B Closure C
Unilateral 17.97 12 20.37 13.79
AFTA 5.84 5.84 6.63 6.63
AFTA+3 18.27 12 20.76 13.78
VNM-USA 1.43 1.43 1.63 1.63
VNM-EU 6.06 6.06 6.89 6.89
Multilateral 10.13 10.13 11.5 11.5
Global 24.71 12 28.05 14.05
Source: GTAP simulations
Choosing the closure of the GTAP model has a significant effect on defining the scope
and changes of economies in general and of commodity/factor prices in particular.
However, it should be kept in mind that simulations are based on a database that
incorporates pre-existing distortions and these interact with policy shocks and produce
second-best effects. Another important point is to interpret numbers with an appropriate
attitude as certain numbers should not be taken literally, but they suggest certain
directions e.g. big returns to simulated policies (Daude 2004).
111
6.4. Summary
This chapter briefly presents progress in Vietnam and its main commitments in its
integration into the world economy. Even though some negotiations are still in progress,
prospects of near future trade liberalisation and its impacts on the economy are needed.
The computable general equilibrium model has been used widely to investigate the
implications of trade liberalisation in the world, and in some sectors in Vietnam,
previously. The current research uses the multi-country trade model GTAP to assess the
impacts of trade liberalisation at the national level and to household level. Trade
agreements between Vietnam and trade partners are very complicated, with variation
between fields. Presenting all agreements in a model simulation is not possible;
however, simulation scenarios are designed to represent real situations closely.
Since the study is interested in impacts of trade liberalisation on households raising pigs
and poultry, three new sectors of live pig, poultry and other animals were introduced to
the GTAP database using SplitCom software. This means price signals of these
commodities are better captured, hence reactions in household behaviours to the
changes are expected to be measured more accurately when linking trade and household
models together.
The labour market issue in the GTAP model is also considered. Acknowledgment of the
employment and underemployment situation in the process of liberalisation has an
important role in policy simulation, especially in the case of developing countries in
general and Vietnam in particular. This helps the model to be more useful in analysing
economic impacts of policy changes, since it presents a more accurate situation of
resource allocation and use of endowments. In this study, Closure C is chosen based on
the unemployment and under employment situation in Vietnam. With this closure, the
Vietnamese labour force can increase labour supply to the economy by 1.12 times in
comparison with the baseline, without causing any wage increase. After that point,
when the demand for labour increases, wages would increase according to the normal
rules of closure.
Regarding impacts of trade liberalisation on Vietnam at the macro level, the largest
benefit would be if full liberalisation happens. The voluntary trade liberalisation of
112
Vietnam in a unilateral liberalisation would benefit itself without negotiating with
others. However, market access benefits are limited because other countries do not open
their markets.
For the purpose of assessing impacts of trade liberalisation on the household, the next
chapter links GTAP results with household models to examine how small livestock
households react to price changes induced by trade liberalisation.
113
CHAPTER 7 : IMPACTS OF TRADE LIBERALISATION
ON SMALL LIVESTOCK HOUSEHOLDS - LINKAGES
BETWEEN TRADE AND HOUSEHOLD MODELS
The previous chapter presented the trade liberalisation process of Vietnam and
simulated impacts at the national level using the GTAP model. In this chapter, to
examine impacts of trade liberalisation on Vietnam’s small household livestock sector,
price changes of trade scenarios from simulations of the GTAP model are linked with
the household model from Chapter 5. Changes in household welfare and responses to
price signals in terms of substitution between commodities in consumption and
production, and labour allocation in the household, are reported. However, the analysis
only examines one-way effects of trade liberalisation on households, and not their
influence on trade. Therefore, feedback from households to the international system is
not considered. Policy conclusions on opportunities and threats from trade liberalisation
for small livestock producers under alternative assumed liberalisation scenarios and
labour market conditions are also presented.
7.1. Price Changes in the Trade Scenario Simulations
The impacts of trade liberalisation on Vietnam’s welfare, production, and trade at a
national level, were reported in the previous chapter. This chapter uses price changes for
both consumption and production as a result of trade liberalisation scenarios of the
GTAP macro model to assess impacts at the household level. To determine
liberalisation effects on consumption and production, as well as reactions in labour
allocation, certain assumptions are made to align different sectors or commodities of
GTAP with those in the household model.
Changes in consumption prices (pp) from trade scenario simulations in the GTAP
model are matched with the consumption side in the household model. Consumption
commodities at the household level are more detailed than aggregated sectors in the
GTAP macro level, however commodities in both models are aggregated into groups
which can be matched as best as possible. In the main food group, rice consumption
114
corresponds to the GTAP categories of processed rice and paddy rice (RIC),
consumption of fruits and vegetables are directly comparable with the fruit and
vegetable sector (VF), fish and shrimp consumption at the household level match the
fishing group (FSH), pork and chicken meat connect to OMT (pork and poultry meats)
and other meats correspond to CMT (group of beef, sheep, and other meats),
respectively in the GTAP. All other foods in the household model are summarised as
one common commodity namely “other food”, which matches various processed foods
in the GTAP model under oilseed and vegetable oil (OSO), processed food (OFD),
beverages and tobacco (B_T), and milk and dairy products (MLK). Household
consumption of all industrial commodities such as clothing, electronic equipment or
other expenditures on services, for example eating out, are grouped together to match
categories of textile and apparel (TXT), manufacture goods (MAN), electronic (ELE),
transport and communication (TCN) and services (SVC). Price changes in broad groups
such as “other food” or “industrial and other expenditures” in the household model are
therefore calculated as weighted changes in prices of corresponding commodities in the
GTAP model.
Similarly, inputs and outputs of a household’s production are suitably related to
subsumed sectors in the GTAP model. Changes in market price (pm) of commodities
from trade scenario simulations in the GTAP model are used in the household model.
For example, price changes for chemical fertiliser and pesticides for rice cultivation are
limited to average price changes of the chemical, rubber, and plastic (CRP) group in the
GTAP.
One of the main inputs of livestock production is feed. According to statistics, in
Vietnam feed often accounts for 65 to 70 percent of the total cost of raising livestock in
small households (IFPRI 2001, Lapar et al. 2003, Nguyen & Tran 2005). To
disaggregate industrial feed as an individual sector in the GTAP model is not possible,
so to estimate price changes in livestock feed due to trade liberalisation, an assumption
on feed contents is needed36. Hence, changes in the price of raw and industrial feeds for
36 Industrial feed for chicken often contains 60 percent maize, 20 percent soybean, 5 percent fish, and 15 percent other ingredients. Industrial feed for pig often includes 40 percent maize, 15 percent soybean, 7 percent meat and milled bone, 5 percent fish, 10 percent rice bran, and 23 percent other ingredients. Different kinds of feed have different ingredients; these numbers are an estimate based on author’s interview with feed livestock companies in Vietnam, in August 2007, during a CARD project (Collaboration for Agricultural and Rural Development Program) namely 'Developing a Strategy for
115
raising livestock are calculated based on changes in prices of feed ingredients such as
maize, soybean, milled bone, fish and others, all subgroups in GTAP. Table 7-1
presents the aggregation and/or splitting sector/commodities available in GTAP to be
matched with those of the household model.
Table 7-1: Matching between GTAP Sectors and Endowments in this Study and
their Concordance with Commodities and Goods in Vietnam’s Household Models
In household model Matched GTAP sectors and factors
Rice, Paddy, and Seeding RIC: Paddy and processed rice
Live pig Live Pig
Live chicken Live Poultry
Chemical fertiliser and
Pesticide
CRP: Chemical, rubber, plastic
Pork and chicken meat OMT: Pork and poultry meats
Fish FSH: Fishing
Vegetable and fruit VF: Vegetable and fruit
Other meats CMT: Beef, sheep, and other meats
Other foods OSO: Oilseed and vegetable oil, OFD: Processed food,
B_T: Beverages and tobacco, MLK: Milk and dairy products
Industrial commodities
and other expenditures
TXT: Textile and apparel, MAN: Manufactures, ELE:
Electronic, TCN: Transport, communication, SVC:Services
Labour UnSkLab: Unskilled Labour
In the household model, as mentioned in Chapter 3, total land for production is assumed
to be fixed, therefore changes in land prices due to trade liberalisation are assumed to
have no effect on production behaviour of the household. Other production factors such
as capital and natural resources are also assumed to have no impact on decision making
of the household in production. In order to simplify the model, labour in the household
is categorised as unskilled and assumed to be equivalent to unskilled labour in the Enhancing the Competitiveness of Rural Small and Medium Enterprises in the Agro-Food Chain: the Case of Animal Feed' 030/06VIE. Raw feed is assumed to include 50 percent rice bran and 50 percent vegetables.
116
GTAP model. In fact, this assumption is probably close to the truth: most agricultural
labour receives a low wage irrespective of education level.
7.2. Price and Wage Transmission
Four household models are constructed in this study to represent households in four
different regions: (1) Red River Delta, one of two main important deltas in the country
with favourable conditions for rice cultivation, however land cultivation is limited and
population density is the highest; (2) Northern Uplands (includes North East (NE) and
North West (NW)), where agricultural production is limited by ecological and economic
conditions; (3) the Centre, including North Central Coast, South Central Coast and
Central Highland; and (4) the South, including Mekong River Delta and North East
South, is similar to the Red River Delta, where urbanisation and industrialisation have
recently expanded very quickly. The South also has the highest commercialisation of
rice and livestock production. Different agro-ecological areas with different economic
conditions may affect the farming system and activities of the household in production
in different ways due to trade liberalisation.
Simulation results of trade liberalisation in the GTAP model only generate average
price changes of consumption commodities and production inputs/outputs at the
national level. However, it is possible that these national changes will translate into
differences in price changes of those commodities/inputs in different regions. This
section analyses differences in price transmission of some commodities/inputs between
each region from the simple average price at the national level.
Data used in this study include monthly prices of main commodities from 24 provinces
and cities in Vietnam, such as rice, paddy rice, live pig, live chicken, beef, maize,
soybean, cassava, orange, tomato and fertiliser. Data were collected by the Vietnamese
Institute for Market and Prices from 1997 to 2006 (120 observations) for all
commodities except rice, pig, beef and fertiliser which were collected from 2001 to
2004 (48 observations). The average price in the Red River Delta is calculated from five
provinces: Ninh Binh, Ha Tay, Hung Yen, Hai Duong and Ha Noi; Northern Uplands
(NE+NW) from six markets: Bac Giang, Thai Nguyen, Yen Bai, Tuyen Quang, Lai
Chau, and Son La; the Central region from six provinces: Thanh Hoa, Nghe An, Hue,
Da Nang, Quang Ngai, Dac Lac; and the South from seven markets including Dong Nai,
117
Ba Ria, Binh Duong, Tien Giang, Ben Tre, Can Tho, and Ho Chi Minh city. The
average price at national level is calculated from all 24 provinces.
A simple econometric method for measuring price integration is used. The functions
take the functional forms as follows:
iiiri Pp loglog
where
Pi is average price of ith commodity at national level
rip is price of ith commodity at region r, which includes RRD, NE+NW, the Central,
and the South
i include rice, paddy rice, live pig, live chicken, beef, maize, soybean, cassava, orange,
tomato and fertiliser.
This is the simplest way to measure spatial price relationships between two markets and
the transmission coefficients, βi are interpreted as a measure of how closely price
movements of a commodity at different markets are linked (Luu 2003).
The regression results show that the coefficients βi of most commodities in the four
regions fluctuate around 1 (range 0.712 to 1.33). However, testing the result shows 28
of the 44 coefficients are not significantly different from 1 at 95 percent confidence
level. Another five coefficients are different within 10 percent between regional and
national price (see Appendix A7.1). The results also show that there is no consistent
pattern of transmission of price for any region. In order to simplify the calculation, the
study assumes that if the price of a commodity changes by 1 percent at national level,
the price at regional market level also changes approximately by 1 percent.37
A similar question arises for changes in labour wages at the regional and national levels.
Since there is no annual labour and salary survey, time series data of labour wages in
Vietnam are not available. The main information sources are surveys on Vietnam living
standards - one in 1998 (VLSS 1998) and two other recent household living standard 37 The impacts of trade liberalization at the domestic level will obviously be affected by assumptions about the degree of price transmission. However, given the relatively weak statistical evidence for deviations from unity, that is applied here. Further research on identifying more precise estimates of price transmission in Vietnam are warranted.
118
surveys in 2002 and 2004 (VHLSS 2002 and 2004). Table 7-2 presents average wages
and annual growth rate of wages in the four regions.
Table 7-2: Average Wage of Labour at Current Price and Annual Growth Rate
Average wage ('000 VND) Annual growth rate of wage (%)Region
1998 2002 2004 1998–2002 2002–2004
RRD 411.58 652.96 773.70 1.122 1.089
NE+NW 367.82 642.90 728.37 1.150 1.064
Central 366.14 515.99 661.07 1.090 1.132
South 561.77 729.46 872.00 1.067 1.093
Whole country 481.07 666.97 791.79 1.085 1.090
Source: calculation based on data of VLSS1998, VHLSS 2002, and VHLSS2004
Table 7-2 shows similarities in annual growth rates of wages between each region and
national level over time. Assuming from 1998 to 2004 there were some macro policies
as well as changes in the economy that impacted on labour wage levels across the whole
country then it can be concluded that a change in labour wages will be approximately
the same in all regions of the country.
Therefore, changes in commodity prices and labour wages due to trade liberalisation
would be applied to all regional household models at the same percentage changes as
predicted by the GTAP national model.
7.3. Impacts of Trade Liberalisation on Households
Having generated price changes for trade liberalisation scenarios from the GTAP model,
and linking them to the household model, this section analyses how a household
responds to trade scenarios by changes in behaviour. Changes in commodity and factor
markets affect household welfare because adjustments are transmitted through changes
in production, consumption, and labour allocation, hence changes in utility.
Changes in prices are drawn from simulations of the GTAP model, using Closure C,
where the maximum increase in unskilled labour supply in Vietnam is fixed at 12
119
percent. Hence, a trade scenario can absorb labour up to a level equal to 112 percent of
the baseline without causing a wage increase, but wage increases occur when labour
demand goes beyond that point.
In calculating welfare impacts on the household using the household model, a measure
of compensating variation (CV) in income is applied. That is, the amount of money
which, when taken away from the household after price and income changes, leaves the
household with the same utility as before the change (Varian 1996):
000101 ,, upeupeYYCV
where: Y1 is income after the price change from p0 to p1, Y0 is baseline income and
expenditure function e(p,u) is the minimum income necessary to reach the level of
utility u at given price p.
Table 7-3 summarises the change in utility (measured by compensating variation) for
each region for each scenario. The results show that the biggest changes in welfare in
three of the regions occur under scenario 7, global liberalisation, although the NE+NW
are similarly affected by both this and scenario 3, an expanded AFTA arrangement.
Table 7-3: Welfare Changes in Households in Different Regions under Alternative
Liberalisations Compare with Baseline (percentage)
Welfare
change of
household
Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM-
EU
(5)
Multi-
lateral
(6)
Global
(7)
RRD 6.41 4.63 13.82 0.3 -0.12 3.26 19.13
NE+NW 5.90 -0.03 12.18 -- 4.76 5.62 11.56
Central 6.11 4.22 11.86 -- 3.36 3.50 15.80
South 6.85 7.29 19.56 -- 5.06 6.78 22.31
Source: Household model simulation
-- Negligible price changes due to trade liberalisation predicted, welfare change can be considered equal
to zero
120
The change in welfare of the household in the South in each scenario is always the
biggest, in (7), its welfare increases by 22 percent compared with the baseline, with a
CV of about 3 million VND.
Underlying each simulation is a large number of changes in specific commodity
production and demand. It is not possible to review, in detail, this information for all
regions, for all trade scenarios. Therefore, the South model is used as an example for a
more detailed analysis of the changes that occur in production, consumption and labour
as a result of the global liberalization. Later in the chapter, more general overviews of
the changes that occur in all regions are presented.
7.3.1. Impacts of Global Trade Liberalisation on Households in the South
On the production side, in response to increases in market output prices, the household
in the South increases agricultural production under the global trade scenario. Figure 7-
1 shows increases of about 7 and 10 percent in pig and chicken production, respectively,
compared with the base year, when live pig and chicken prices increase by 9 and 11.8
percent, respectively.
0
10
20
30
Rice Pig Chicken
quan
tity
(kg)
0%
4%
8%
12%
16%
Change in production quantity
Change in production compare w ith base year (%)
Change of output price compare w ith base year (%)
Figure 7-1: Changes in Agricultural Production of Household in the South under
Global Trade Scenario
Source: Household model simulation
Rice production, with the highest increase in quantity, has a very small increase in
percentage terms compared with the base year: 0.43 percent. This can be explained by
121
the limited possibility for expansion in land for rice cultivation, and productivity of rice
production may have already reached its upper bound.
0.5
1.0
1.5
2.0
2.5
3.0
Rice Pig Chicken
Rat
io o
f ch
ange
s in
out
put
pric
es
and
inpu
t pr
ices
-4%
0%
4%
8%
12%
Out
put
chan
ges
com
pare
with
ba
selin
e
Ratio o f changes o f P output/P raw feed Ratio o f changes of P output/P industrial feed
Ratio o f changes o f P output/wage Ratio o f changes of P output/P chemical inputs
Output change compare with baseline(%)
Figure 7-2: Changes in Relative Prices of Outputs and Inputs and Changes in
Agricultural Production of Household in the South under Global Trade Scenario
Source: Household model simulation
As stated in Chapter 6, the global trade scenario creates changes in market prices of all
commodities and factors. The Figure 7-2 illustrates the reaction of household
agricultural production activities to price changes. Since an increase in output price is
faster than that of input prices, using inputs are relatively cheaper compared with the
baseline. In livestock production, inputs such as feed are cheaper in comparison with
pre-liberalisation. For rice cultivation, using both chemical fertiliser and pesticides are
also less costly in the global scenario. This indicates why the household has incentives
to expand production of rice and livestock products under the simulation.
Thanks to the expansion in agricultural production as well as increases in output prices,
under the scenario, the household in the South increases their profit for rice and
livestock production by 14 and 30 percent, respectively (Figure 7-3).
122
Figure 7-3: Changes in Profit of Agricultural Production of Household in the South
under Global Trade Scenario
Source: Household model simulation
The increase in prices of all outputs and relative decrease in prices of inputs help the
household to expand agricultural production, hence increase farm profit. At the same
time, increased labour wages under the global scenario enrich the household in terms of
full income, and help cover the bigger cash expenditure they incur, and longer leisure
time.
-100
0
100
200
300
400
Food qty Other food qty Industry and others Leisure day
kg/u
nit/
day
-15%
-10%
-5%
0%
5%
10%
Ow n price effect Cross price effect
Income effect Total change in consumption
Price change compare w ith baseline (%)
Figure 7-4: Disaggregate Effects of Price and Income to Changes in Consumption of
Household in the South under Global Trade Scenario Compared with Baseline
(percentage)
Source: Household model simulation and calculation based on LES elasticities in Chapter 6
123
In the simulation, prices of groups of “main food” and “industrial commodities”
increase at 5.6 and 1.4 percent, and labour wage increases by 8.23 percent compared
with the pre-simulation period. As expected, own price effects make consumption of
these groups decrease. However, due to the availability of more income from the
expansion of agricultural production and the value of farm labour, income effects
dominate and help increase consumption of all broad groups of commodity under the
scenario.
Changes in consumption due to own-prices and income effects in the “main food” group
including rice, pig, chicken meat, fish, vegetable and other meat are shown in Table 7-4.
Conforming to the theory of demand, when prices of all individual commodities
increase under the simulation, consumption of each commodity decrease by 1.43, 0.82,
0.58, 0.66, 2.44 and 1.19 kg for rice, pork, chicken, fish, vegetable and other meats,
respectively. Nevertheless, increased cash income of the household allows allocation of
more funds for “main food” group consumption. This large income effect not only
prevents reduced consumption of each commodity, but also increases total quantity of
individual commodities. This explains why the household consumes more, even though
the commodities become more expensive in the trade liberalisation scenario.
Opportunities for consuming all commodities as well as leisure, at the same time
expanding agricultural production of the farm under the scenario of global trade,
significantly increase welfare of the household by 22 percent compared with the
baseline.
124
Table 7-4: Disaggregated Changes in Main Food Consumption Quantity of Household in the South due to Price and Expenditure Effects
under Global Trade Scenario (kg)
Effects of changes in price of Change in
Consumption
Quantity of
Rice
Pig
Chicken
Fish
Vegetable
Other meat
Due to
effects of
expenditure
Total change
(kg)
Rice -1.43 -0.04 -0.01 -0.11 -0.16 -0.04 6.68 4.89
Pig -1.29 -0.82 -0.08 0.14 -0.02 0.14 12.02 10.09
Chicken -0.58 -0.16 -0.58 -0.02 0.19 0.08 9.64 8.57
Fish -1.31 0.14 -0.01 -0.66 0.17 -0.03 9.40 7.7
Vegetable -1.41 0.02 0.04 0.12 -2.44 -0.01 9.67 5.99
Other meat -1.64 0.29 0.06 -0.14 -0.08 -1.19 11.79 9.09
Expenditure and price changes compared to baseline (%)
9.51
2.19
2.19
2.31
8.35
3.28
26.70
Source: calculation based on household model simulation and LA-AIDS elasticities in Chapter 6
125
7.3.2. Impacts of Global Trade Liberalisation on Households in Different Regions
The following section analyses the effects of each trade scenario on households in the
regions to compare changes in welfare and different reactions of households in
production and consumption within each trade liberalisation context.
With changes in prices of agricultural inputs and outputs under the global trade
scenario, households in the four regions increase farm profit. The household in the RRD
has the largest increase of more than 30 percent followed by the Central and the South.
The household in NE+NW has a limited change of 10 percent.
-20%
-10%
0%
10%
20%
30%
40%
RRD NE+NW the Central the South
Region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken Farm profit
Figure 7-5: Changes in Output Production and Farm Profit of Households in Different
Regions under Global Trade Scenario
Source: Household model simulation
Rice outputs only increase at limited rates in the Central and South regions while in the
other regions, households tend to reduce rice production. Almost all households expand
livestock production by about 5 to 10 percent in pig and chicken output compared with
the baseline. In the NE+NW, chicken production reduces by 12 percent compared with
the baseline, even though the household in this area is confronted with the same price
changes as households in other regions. This may be due to the different method of
chicken production, manifested by the high proportion of raw feed in total chicken feed
(85 percent). Hence, with a price change in raw feed under the simulation of about 10
percent and industrial feed of 4.7 percent, obviously the household in NE+NW faces
higher prices for chicken feed, the main input for production.
126
Table 7-5: Ratio of Raw Feed and Price Change of Feed in Different Households
(percent)
RRD NE+NW Central South
Ratio of raw feed in total chicken feed 64 85 57.9 25.6
Price change of chicken feed* under
global trade scenario
6.79
8.05
6.39
5.47
Source: Household model simulation
* change in price of combination feed (based on ratio of raw and industrial feeds) in each region
With pig production, the household in the North mountainous area (NE+NW) uses a
similar technique to other households in other regions, in terms of raw feed in total
feed38. Therefore, with the same impacts of price change in all regions, households have
similar changes in production. However, since the household in the NE+NW area
restricts its rice and chicken production under the simulation, some resources are now
used to produce more pig, leading to higher total farm income. It should be noted that,
compared with other areas, the percentage increase in pig production of the NE+NW
household is quite large; however with modest output in the baseline, the absolute
change in pig output is only 46 kg. A similar situation occurs for chicken production,
with a 2.3 kg reduction in chicken production equating to approximately 12 percent of
total chicken output of the household.
On the consumption side, households in the RRD, NE+NW and the Central region have
similar reactions to the household in the South, with increased consumption in all broad
group of commodities and expenditures, even though prices of the “main food” group
and “industrial goods” are higher than the baseline. This is because of the strength of
income effects in the household. Under the simulation, the price of the “other food”
group decreases by nearly 13 percent, hence its own-price effect combined with the
income effect drives big changes in consumption of this group of commodities in all
regions.
38 The ratio of using raw feed in total feed for pig in the regions are around 92-95 percent, except the household in the South, which is more commercially oriented which, uses only 36.4 percent raw feed for pig production.
127
0%
10%
20%
30%
40%
RRD NE+NW the Central the South
regionch
ange
com
pare
with
bas
elin
e
Main foods Other foods Industrial goods & others
Figure 7-6: Changes in Consumption Quantities of Households in Different Regions
under Global Trade Scenario
Source: Household model simulation
In all regions, when more funds are available to allocate for “main food” consumption,
households increase consumption particularly pork, chicken, other meats, fish and
prawn. This sounds quite sensible since small household producers are often not rich
consumers.
-4%
0%
4%
8%
12%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pork Chicken Fish-shrimp Vegetable Other meat
Figure 7-7: Changes in Main Food Consumption of Households in Different Regions
under Global Trade Scenario
Source: Household model simulation
Inside the “main food” group, meat is still considered a luxury food compared with
other foods such as rice and vegetables. The expenditure elasticities of all meats range
128
from 1.1 to 1.5 in all regions, meanwhile the elasticities of rice and vegetables vary
between 0.6 to 0.8. Therefore, when income increases, expansion of rice and vegetable
consumption is quite limited compared with meat consumption. For households in
NE+NW, consumption of rice and vegetables do not increase as per the other regions
but decrease, as the income effect could not cover the negative effects of price
increases.
One factor contributing to increased welfare of households under the global trade
scenario is changes in time allocation of the household. Households in all regions chose
more leisure and less work than in the pre-simulation period.
Table 7-6: Changes in Labour Allocation of Households in Different Regions under
Global Trade Scenario Compared with Baseline (percentage)
RRD NE+NW Central South
Leisure 1.57 0.45 1.05 2.40
Labour supply -1.64 -0.35 -0.75 -1.69
Days working on-farm 3.03 84.42 3.87 3.55
Days working off-farm -2.16 -10.83 -1.25 -2.83
Source: Household model simulation
Relatively, the number of leisure days in households increase by about 1 to 3 percent.
These changes in leisure allow households to increase their utility by about 2 to 4.5
percent compared with the baseline. In order to have more leisure, households chose to
work less off-farm, while at the same time increasing work on-farm. The latter is driven
by the increased demand for farm labour, due to expansion of livestock production, as
mentioned above. For the household in the north mountainous area (NE+NW), as pig
production expanded by 27 percent, the number of days working on-farm increases by 2
man months in comparison with the baseline.
7.3.3. Impacts of Unilateral Trade Liberalisation on Households in Different
Regions
With the unilateral trade scenario, Vietnam reduces taxes for all imported commodities.
This allows Vietnam some benefit without negotiating with others, by reallocating
129
resources more efficiently. Unilaterally decreasing taxes has a marginal impact on many
prices of commodities in agricultural sectors, prices of outputs and production inputs:
about 1 percent compared with the baseline. Inside households, the reallocation of
resources for a more efficient production combination also happens. The household re-
structures agricultural production on-farm to gain more farm profit. Pig and chicken
production increases in almost all regions under this scenario. In RRD, rice production
reduces slightly and livestock production increase. Expansion of all pig, chicken and
rice production occurs in households in NE+NW and the South. In the Central region,
the household slightly reduces pig production, while increasing production of rice and
chicken.
-3%
-1%
1%
3%
5%
RRD NE+NW the Central the South
region
change c
om
pare
with
base
line
Rice Pig Chicken Farm profit
Figure 7-8: Changes in Agricultural Production of Households in Different Regions
under the Unilateral Trade Scenario
Source: Household model simulation
Restructuring farm production under the scenario helps households increase farm profit,
hence have more cash income for expenditure. Table 7-7 presents changes in
consumption of the households under the simulation. Price and income effects are
negligible in household consumption of “main food” and “industrial goods”.
Meanwhile, due to the impact of the trade scenario, the price of “other food” decreases
by 15 percent compared with the baseline: this price effect, along with the income effect
helps households in all regions to increase “other food” consumption by about 20
percent.
130
Table 7-7: Changes in Price and Consumption Quantity of Households in Different
Regions under the Unilateral Trade Scenario (percentage)
RRD NE+NW Central South
Change in “main food” price -0.32 0.02 0.63 1.26
Change in ”main food” quantity 1.71 1.60 1.38 1.95
Change in “other food” price -15.22 -15.22 -15.22 -15.22
Change in “other food” quantity 19.69 19.83 19.62 20.28
Change in price of “industrial and
other expenditures” group
-1.40
-1.40
-1.40
-1.40
Change in quantity of “industrial
goods and others”
1.74
3.13
3.07
3.46
Source: Household model simulation
In the main food group, rice and fish have higher prices, with all other individual
commodities having slightly lower prices, about 1 to 2 percent cheaper in comparison
with pre-simulation. Figure 7-9 shows that in all regions, households consume cheaper
commodities more and less rice and fish.
-3
-2
-1
0
1
2
3
4
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
(%)
Rice Pork Chicken Fish-shrimp Vegetable Other meats
Figure 7-9: Changes in Food Consumption of Households in Different Regions under
Unilateral Trade Scenario (percentage)
Source: Household model simulation
In general, by changing production and consumption behaviours, households in all
regions increase welfare under the unilateral trade scenario. Welfare of households
131
increases by 6 - 7 percent due to impacts of trade liberalisation, with the value of
welfare change in terms of money ranging from 600 000 to 900 000 VND per
household per year, depending on agro-ecological areas (see Appendix A7.2a).
Apart from changes in behaviour in production and consumption, time and labour
allocation inside the households also affects changes in welfare. The NE+NW and the
South are two regions which expand farm work, at about 1 percent compared with the
baseline. In comparison, households in RRD and the Central regions reduce the number
of working days on-farm, and work more off-farm. However, the deciding factor that
makes the household better off in terms of utility is the reduction in number of days of
labour supply and increased leisure days in all regions. It should be noted that behaviour
change of the household in commodity consumption and labour supply is decided by the
assumption made in the household model. With the application of the LES, whenever
households become richer and have more income available, they consume more of both
commodities and leisure.
Table 7-8: Changes in Time Allocation of Households in Different Regions under
Unilateral Trade Scenario Compare with Baseline (percentage)
RRD NE+NW Central South
Leisure 0.20 0.41 0.26 0.46
Labour supply -0.21 -0.31 -0.19 -0.32
Days working on-farm -3.08 1.25 -2.73 1.08
Days working off-farm 0.10 -0.51 0.09 -0.63
Source: Household model simulation
7.3.4 Impacts of Regional AFTA to Households in Different Regions
Under the simulation of free trade in the ASEAN region, the price of agricultural
outputs of pig, chicken and rice all increase by 3 to 5 percent. That leads to a household
response to increase farm production in most regions. Households in RRD, the Central,
and the South show similar reactions to price changes by expanding farm production of
rice, pig and chicken. As a result, farm profit in these regions increases by 8 to 14
percent in comparison with pre-simulation.
132
Table 7-9: Changes in Production Outputs and Total Farm Profit of Households in
Different Regions under AFTA Compare with Baseline (kg)
RRD NE+NW Central South
Rice output change 4.05 -107.94 16.39 35.32
Pig output change 4.99 41.02 4.24 13.33
Chicken output change 1.83 -3.95 1.82 4.47
Total farm profit change (%) 14.10 -3.48 11.66 8.25
Source: Household model simulation
Response of the household in NE+NW is similar with that under global trade
liberalisation, since the price of chicken feed becomes relatively more expensive than in
other regions, and the household reduces chicken production. The ecological conditions
for rice cultivation are not competitive in the mountainous area, which drives the
household to concentrate more resources on pig production.
-4%
2%
8%
14%
20%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Main foods Other foods Industrial goods & others
Figure 7-10: Changes in Quantity Consumption of Households in Different Regions
under AFTA in Comparison with Baseline (percentage)
Source: Household model simulation
On the consumption side, prices of the broad group of “other food” commodities, and
“industrial goods” reduce by 9.85 and 0.09 percent, respectively, under the simulation.
This leads to increased consumption of those groups in all regions. The increased level
of consumption of the “other food” group in the NE+NW is lower than other regions,
133
and at the same time consumption of industrial commodities reduces by 2 percent. This
is due to decreased farm profits in the household. Under the scenario, prices of the
“main food” group (weighted prices for individual foods (rice, pork, chicken, etc.) in
each region) increase by 1.1 to 2.8 percent. However, since households in the RRD, the
Central and the South have higher profits from farm production, they spend more,
irrespective of the slight increase in prices.
Inside the “main food” group, price increases of rice, fish, vegetable and other meats
lead to a decrease in consumption quantities of those individual commodities in all
regions. However, allocation of more funds for the “main food” group expenditure of
the household in the South compared with those in other regions cause negligible
changes in consumption of rice, fish and other meats in the household. As a result of
trade liberalisation, consumption prices of both pork and chicken are reduced by 0.41
percent. These price effects, combined with increased expenditure, increase pork and
chicken consumption in the South in the simulation.
-5%
-3%
-1%
1%
3%
5%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pork Chicken Fish-shrimp Vegetable Other meats
Figure 7-11: Changes in Food Consumption of Households in Different Regions under
AFTA in Comparison with Baseline (percentage)
Source: Household model simulation
Under the AFTA simulation, the household in the South has the biggest welfare
increase of more than 7 percent compared with the baseline. The households in RRD
and the Central increase welfare by more than 4 percent, while the household in the
northern upland (NE+NW) seems unaffected by trade liberalisation in terms of total
welfare (see Appendix A.7.2b). Changes in time allocation of households’ members are
134
shown in Table 7-10, with number of days of leisure increasing in all regions except
NE+NW. More labour is allocated to on-farm work, and less to off-farm work to meet
requirements from increasing agricultural production of households under the scenario.
Table 7-10: Changes in Time Allocation of Households in Different Regions under
AFTA Trade Scenario Compare with Baseline (days)
RRD NE+NW Central South
Leisure 2.45 -1.73 2.63 6.68
Days working on-farm 3.87 71.32 3.41 6.88
Days working off-farm -6.32 -69.59 -6.04 -13.56
Source: Household model simulation
7.3.5 Impacts of Expansion of the Regional AFTA (ASEAN plus China, Korea and
Japan) to Households in Different Regions
In the last chapter, results of the GTAP model showed that expansion of the regional
free trade area, ASEAN, to include China, Korea and Japan creates the second largest
increase in welfare for Vietnam in general. In the household model, the simulation also
shows that households in all regions of the country under the scenario are better off in
terms of welfare, with the biggest change in the South, where welfare of the household
is improved by 20 percent compared with the baseline. In the other three regions,
welfare increases by around 10 percent (see Appendix A7.2c).
Increases in output prices in the market of 10 to 13 percent are good incentives for
households to expand farm production. In all regions, livestock production and rice
cultivation increase, improving total farm profits of households by 20 to nearly 40
percent compared with the baseline.
With improvements in farm profits, households have more cash income available for
consumption. Consumption of all goods and commodities increases in terms of quantity
over all regions. Inside the “main food” group, due to more funds being allocated,
consumption of almost all individual foods increase under the simulation, even though
the increase in prices lead to negative effects on consumption (see Appendix A7.3a and
b for more detail).
135
-10%
0%
10%
20%
30%
40%
50%
RRD NE+NW the Central the South
regionch
ange
com
pare
with
bas
elin
e
Rice Pig Chicken Farm profit
Figure 7-12: Changes in Production of Households in Different Regions under
AFTA+3
Source: Household model simulation
Expansion of farm production requires more labour on-farm. As a consequence all
households increase labour on-farm by approximately 10 percent, and decrease off-farm
days by 2 to 4 percent compared with the base, depending on region (see summary
Table 7-15).
7.3.6 Impacts of Bilateral Trade Liberalisation with the United State on
Households
Bilateral trade liberalisation between Vietnam and the United States has almost no
impact on consumption prices or output and input prices in Vietnam. Table 7-11
presents price changes under the simulation used in the optimisation of the household
model.
Facing negligible changes in prices, households in all regions except the RRD, do not
respond with changes in production and consumption. Welfare of the household in RRD
changes by only 0.3 percent (see Appendix A7.5a for more detail). As a result, further
details are not discussed here.
136
Table 7-11: Changes in Prices of Consumption Commodities and Production
Inputs and Outputs in Vietnam under Bilateral Trade Liberalisation with USA
(percentage)
Production side Price
change
Consumption side Price
change
Rice and Paddy seedling 0.49 Rice 0.49
Live Pig 0.52 Pork meat 0.10
Live Chicken 0.70 Chicken meat 0.10
Raw feed for pig 0.38 Fish and prawn 0.87
Industrial feed for pig 0.40 Vegetable and fruit -0.01
Raw feed for chicken 0.38 Other meats -0.42
Industrial feed for chicken 0.35 Industrial goods and others 0.28
Chemical fertiliser and pesticides 0.43 Other food group -0.07
Unskilled labour 0.24
Source: GTAP simulation
7.3.7 Impacts of Bilateral Trade with EU and Multilateral Trade Liberalisation on
Households in Different Regions
The simulation results of these two trade liberalisations in the GTAP model show
similar trends in changes in prices of consumption commodities as well as prices of
outputs and inputs of agricultural production. Therefore, reactions of households to the
changes are similar. With higher prices of agricultural outputs under the simulations,
households have incentives to increase farm production (Figures 7-13 and 7-14).
In both optimisations, livestock production is improved in terms of quantity in all
regions. Rice production is not expanded in the household in the South under the
bilateral trade simulation, and decreases slightly in the RRD household under the
multilateral one.
137
0%
5%
10%
15%
20%
RRD NE+NW the Central the Southregion
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken Farm profit
-5%
0%
5%
10%
15%
20%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken Farm profit
Figure 7-13: Changes in Production of
Households in Different Regions under
Scenario of Bilateral Trade Liberalisation
with EU
Source: Household model simulation
Figure 7-14: Changes in Production of
Households in Different Regions under
Multilateral Trade Scenario
The same trends in consumption of households under the scenarios are also presented.
The “other food” group has the biggest increase in terms of consumption quantity. The
increase under the bilateral trade scenario is due to the positive effect of income, which
dominates over the negative own price effect39, and consumption increase under the
multilateral simulation due to a combination of income and own-price effects (Figures
7-15 and 7-16)40.
-1%
1%
3%
5%
7%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Main foods Other foods Industrial goods and others
Figure 7-15: Changes in Consumption of
Households in Different Regions under
Scenario of Bilateral Trade Liberalisation
with EU
Source: Household model simulation
0%
4%
8%
12%
16%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Main foods Other foods Industrial goods and others
Figure 7-16: Changes in Consumption of
Households in Different Regions under
Multilateral Trade Scenario
39 Under the bilateral scenario, the price of the “other food” group increase by 0.97 percent in comparison with baseline. 40 Price of the “other food” group decreases by 5.95 percent in the multilateral trade liberalisation.
138
Funds allocated for the “main food” group in both scenarios increase by about 1-2
percent compared with the baseline. That is why consumption of each individual
commodity in the “main food” group increases by a maximum of 3 percent. In both
cases of liberalisation, the consumption quantity of each commodity in the “main food”
of the household in RRD reduces, since the price of each food group expenditures
increase insignificantly. This is also the case for the household in the Central region
under the multilateral scenario (Figures 7-17 and 7-18).
-2%
-1%
0%
1%
2%
3%
4%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pork Chicken Fish-shrimp Vegetable Other meats
-2%
-1%
0%
1%
2%
3%
4%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pork Chicken Fish-shrimp Vegetable Other meats
Figure 7-17: Changes in Main Food
Consumption of Households in Different
Regions under Scenario of Bilateral Trade
Liberalisation with EU
Source: Household model simulation
Figure 7-18: Changes in Main Food
Consumption of Households in Different
Regions under Multilateral Trade Scenario
The combination of reactions in production and consumption of households under the
two simulations make them better off in terms of welfare in all cases, apart from RRD
household under bilateral trade liberalisation with the European Union where there is a
small decline (see Appendices A7.2d and e)
In both scenarios, households have similar tendencies in using their time and in
allocation to work. Table 7-12 shows slight increases in leisure time. Due to the
requirements for an expansion in farm production, on-farm work of household members
increases in all regions, especially in RRD under the bilateral trade agreement, at the
same time a reduction in days worked off-farm is reported.
139
Table 7-12: Changes in Time Allocation of Households in Different Regions under
Alternative Liberalisations (percentage)
RRD NE+NW Central South Changes in
compare with
baseline VNM-
EU
Multi-
lateral
VNM-
EU
Multi-
lateral
VNM-
EU
Multi-
lateral
VNM-
EU
Multi-
lateral
Leisure 0.09 0.37 1.20 1.45 0.61 0.58 1.03 1.42
Labour supply -0.09 -0.39 -0.92 -1.11 -0.43 -0.41 -0.72 -1.00
On-farm work 52.87 5.98 5.11 7.87 4.02 6.46 3.87 5.99
Off-farm work -5.96 -1.10 -1.67 -2.22 -0.92 -1.16 -1.72 -2.52
Source: Household model simulation
7.4. Impacts of Trade Scenarios, Opportunities and Threats in Each
Region
The section above analyses in detail reactions of households to price and wage changes
due to trade liberalisations. In this section, the benefits and losses indicated in each of
the scenarios simulated are presented. Opportunities and threats to households from
changes in production and consumption are mentioned. Alternative specifications of
assumptions relating to the labour markets in the global general equilibrium model,
which lead to different simulation results for welfares levels and reactions of
households under the trade liberalisations, are also discussed.
In general, the results from alternative liberalisation scenarios in all regions show that
Vietnam’s small households in the livestock sector would benefit from trade
liberalisation. Table 7-13 presents the summary of welfare changes in households under
alternative scenarios.
The largest benefit that households have is when full trade liberalisation occurs over the
world and the second largest is when ASEAN expands its regional free trade agreement
to include Japan, China and Korea. The results are slightly different in the NE+NW,
where the biggest change in welfare occurs with the AFTA+3 scenario followed by
global trade liberalisation.
140
Table 7-13: Welfare Changes of Households in Different Regions under
Alternative Liberalisations Compared with Baseline (percentage)
Welfare change
of the household
Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
EU
(5)
Multi-
lateral
(6)
Global
(7)
RRD 6.41 4.63 13.82 -0.12 3.26 19.13
NE+NW 5.90 -0.03 12.18 4.76 5.62 11.56
Central 6.11 4.22 11.86 3.36 3.50 15.80
South 6.85 7.29 19.56 5.06 6.78 22.31
Source: Household model simulation
Welfare changes under VNM-USA (4) simulation were omitted since the scenario only made a negligible
change in RDD (0.3%) and had no impacts on the other regions.
The households in almost all regions do not show any response to bilateral trade
liberalisation with the United States, as it leads to very small price changes. Bilateral
trade liberalisation with the European Union also had an insignificant impact on
households in the RRD. Meanwhile liberalisation increases welfare of the other three
regions by 4.76, 3.36, and 5.06 percent in NE+NW, the Central, and the South,
respectively.
Unilateral trade liberalisation of Vietnam increases welfare in households in all regions
equally, by around 6 percent compared with the baseline. All other trade scenarios help
households in Vietnam.
On the production side, trade liberalisation scenarios increase prices of agricultural
outputs, with the biggest increases under AFTA+3 and global liberalisations. As a
result, households in all regions have a tendency to increase livestock production.
Chicken production increases in almost all scenarios with rates ranging from 5 to 10
percent over the regions. Unilateral trade liberalisation in Vietnam seems to have little
impact on changes in behaviour of households in raising chickens. Meanwhile the
expanded AFTA increases chicken output over the country. Pig production also expands
under alternative scenarios, with the rate up to 10 percent higher than the baseline. In
the NE+NW area, pig output in particular increases in the scenarios of AFTA and global
141
trade liberalisation, meanwhile production of chickens and rice are narrowed (see Table
7-14).
Table 7-14: Production Changes of the Households in Different Regions under
Alternative Liberalisations Compared with Baseline (percentage)
Production Changes in
the Households
Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
EU
(5)
Multi-
lateral
(6)
Global
(7)
Rice -2.25 0.15 -0.89 2.57 -1.23 -1.71
Pig 2.11 2.96 4.27 5.44 5.42 7.19
RRD
Chicken 1.34 9.79 42.83 11.77 16.93 5.39
Rice 0.34 -3.82 1.93 1.47 2.00 -4.28
Pig 0.67 24.31 7.73 2.80 3.21 26.95
NE+NW
Chicken 1.41 -15.85 11.06 4.51 6.99 -12.80
Rice 1.93 0.76 1.19 0.72 0.54 0.87
Pig -2.26 2.73 7.00 2.71 3.17 5.51
Central
Chicken 1.84 5.92 14.95 5.39 7.81 10.11
Rice 0.18 0.59 0.73 0.03 0.38 0.43
Pig 0.64 3.84 9.57 3.96 4.36 7.00
South
Chicken 1.91 7.64 16.36 4.39 9.45 10.57
Source: Household model simulation
Benefits from expanding farm production as well as an increase in the value of working
time of households gives more cash income to purchase more goods and commodities
under all trade scenarios (see Appendix A7.6). “Other food” consumption increases
significantly every time households have more available income. Price decreases in this
group of commodities under the scenarios, especially the 10 percent reduction in
AFTA+3 and the global trade scenario, also cause increased consumption. Consumption
of “main foods” and “industrial goods” increase under trade liberalisations, albeit at
modest levels.
142
For the “main foods” group, households consume more under AFTA+3 and global trade
scenarios in all regions. Pork, chicken and other meats are consumed more as soon as
households have available income. The household in the South consume more
individual foods in all scenarios, since their income effects are large enough to dominate
the opposite own-price effects. On the other hand, the RRD household reduces food
consumption in most simulations, except under scenarios of expanded AFTA and global
liberalisation (see Appendix A7.7).
Table 7-15: Changes of Time Allocation of Households in Different Regions under
Alternative Liberalisations Compared with Baseline (percentage)
Changes in Time
Allocation of
Households
Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
EU
(5)
Multi-
lateral
(6)
Global
(7)
leisure 0.11 0.09 0.75 0.05 0.20 0.82
days on-farm -3.08 0.14 10.05 52.87 5.98 3.03
RRD
days off-farm 0.10 -0.24 -2.77 -5.96 -1.10 -2.16
leisure 0.17 -0.06 0.79 0.50 0.60 0.19
days on-farm 1.25 2.52 10.12 5.11 7.87 84.42
NE+NW
days off-farm -0.51 -2.46 -2.89 -1.67 -2.22 -10.83
leisure 0.10 0.12 0.47 0.23 0.22 0.39
days on-farm -2.73 0.16 9.89 4.02 6.46 3.87
Central
days off-farm 0.09 -0.28 -2.06 -0.92 -1.16 -1.25
leisure 0.20 0.62 1.31 0.45 0.62 1.05
days on-farm 1.08 5.73 8.77 3.87 5.99 3.55
South
days off-farm -0.63 -2.47 -4.48 -1.72 -2.52 -2.83
Source: Household model simulation
One of the most important impacts of trade liberalisations on households is the way they
reallocate time between leisure, working on-farm and off-farm. With the application of
Closure C in all regions, households have a tendency to increase working days on-farm,
diminish off-farm days under trade scenarios, except the unilateral liberalisation. Labour
143
allocation between on-farm and off-farm is driven by demand by of the household for
increased farm production, when market prices of agricultural outputs become higher
under the impact of trade liberalisations.
In bilateral trade between Vietnam and the European Union for the RRD, and global
trade liberalisation for NE+NW, the expansion of farm production increases days
worked on-farm by more than 50 and 80 percent, respectively, compared with the
baseline, which limits the time that household labour could be devoted to off-farm jobs.
In all trade scenarios, households chose to increase leisure time. This is attributed to
increased household income. Since the household model applies LES in defining the
household’s utility, the household would choose less work and more leisure every time
they have more available income. The change in time allocation under trade
liberalisation helps households to improve welfare, with the increase in leisure
contributing about 20 to 25 percent of total welfare gained under the scenarios (see
Appendix A7.5).
In the previous chapter, at the national level under scenario simulations, the number of
jobs for unskilled labour increased (Table 6-12). In Appendix A7.11, the change in
unskilled labour jobs in sectors at the national level is reported, showing that labour in
live pig and live poultry sectors increase under simulations compared with baseline.
However, labour in the aggregate OMT sector, which produces pork and poultry meat
decreases in all trade liberalisation scenarios. It is therefore difficult to conclude at the
national level, whether jobs created in the livestock sector increase or not. Meanwhile,
the household model reports a decrease in labour supply from small household livestock
producers, therefore the increase in labour at national level can be attributed to increases
from labour in other sectors such as rice production, textile, electronic or service sectors
(see Appendix A7.11). The increase in labour in the livestock sector at national level (if
there is any) may come from commercial households and bigger scale producers or by
joining of landless labourers.
In the GTAP model simulations, the restructure of the economy due to effects of trade
liberalisation lead to changes in supply, demand and wages in the labour market.
However, the assumption about possible labour market expansion, or in other words, the
binding labour constraint, makes the change in wages more significant in some
144
scenarios. This leads to different impacts on household’s welfare under the context of
trade liberalisation. This is illustrated by the following comparison of household welfare
and behaviour in labour allocation, when households are faced with alternative labour
markets.
Table 7-16 shows the changes in wages of unskilled labour under trade scenarios with
two alternative assumptions of the labour market. In Closure A, the supply of labour is
fixed as exogenous, therefore labour wages increase when the demand for labour
increase. The assumption in Closure C is that Vietnam’s unskilled labour market has
potential to supply labour up to 112 percent compared to the baseline under trade
scenarios, without causing increased labour wages, then allowing labour wages to vary
when demand increases over that limit.
Table 7-16: Change Unskilled Labour Wage under Alternative GTAP Scenarios
with Different Assumptions of Labour Market (percentage)
Changes of
Unskilled Labour
Wages
Uni-
lateral
(1)
AFTA
(2)
AFTA
+3
(3)
VNM-
USA
(4)
VNM-
EU
(5)
Multi-
lateral
(6)
Global
(7)
Closure A 9.15 3.21 13.56 1.3 6.52 7.17 17.66
Closure C (R=12%) 0.31 -1.03 4.44 0.24 2.00 -0.23 8.23
Source: GTAP simulations
Clearly Closure A leads to higher wage levels. The differences in the changes of wages
as well as other input and output prices give different welfare changes under the trade
simulations.
Figure 7-19 presents changes in welfare of households in the South if alternative
assumptions on the labour markets are applied. Similar presentations for other regions
are in Appendix A7.10.
145
0
1000
2000
3000
4000
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenarioch
ange
com
pare
with
bas
elin
e (0
00V
ND
)Welfare change w ith Closure C Welfare change w ith Closure A
Figure 7-19: Welfare Changes of the South Household under Alternative Scenarios
with Different Assumptions of Labour Market
Source: Household model simulation
Fixing labour supply in the economy (Closure A) lead to increased wage in all scenarios
(Table 7-16) which is partly attributed to increased welfare of the household under
Closure A compared with Closure C. An obvious source of the differences is changes in
the household’s labour allocation (Figure 7-20)
Figure 7-20: Changes in Labour Allocation of Household in the South under
Alternative Scenarios with Different Assumptions on Labour Market
Source: Household model simulations
With Closure A, household labour can earn higher wages compared with Closure C.
Since agricultural output prices are not increasing as fast, the high wage pulls labour
146
from their farm. The household chooses to reduce agricultural production, and work off-
farm more for more cash income. Similar results in other regions are presented in
Appendix A7.9.
7.5. Summary
By linking the simulation results of the GTAP model with a household model, this
chapter examines how small livestock households react to changes in economic
policies, especially in the context of trade liberalisation. Analytical results allow one to
see how household behaviour changes when they are both consumers and producers,
taking into account how income changes (from profit, via production) influence
consumption to give a more accurate assessment.
Since four household models are constructed to represent four different agro-ecology
regions over the country, question remains whether or not there are differences in price
transmission between regional markets. A simple method of bivariate correlation
coefficients to measure price integration is applied, concluding that there are
approximately equal changes of prices and wages in all regions.
Regarding impacts of trade liberalisation on the household, the results from different
liberalisation scenarios run with Closure C, show that Vietnam’s small households in
the livestock sector would benefit from most trade liberalisations. The largest benefit for
households is if full trade liberalisation occurs over the world. Households do not seem
to have significant gains from bilateral trade liberalisation with the USA. Meanwhile
unilateral trade can help households increase their welfare.
On the production side, households tend to increase livestock production under trade
liberalisation scenarios, while rice production does not change much in output. Since
land expansion for cultivation is limited, productivity in rice is limited to increases in
variable inputs (given the assumption within the model that rice is the only extensive
land use).
On the consumption side, since more cash income is available under the trade scenarios,
households purchase more goods and commodities, and in particular increase
consumption of the “other food” group.
147
Welfare of the household is dominated by the effect of the household’s labour decision
(working or taking leisure), rather than increases in production profit and consumption
only. Under all trade scenarios, households increase their leisure, at the same time
working more on-farm to meet requirements of labour increase due to the expansion of
agricultural production, despite increases in off-farm wages.
The comparative illustration at the end of the chapter shows the importance of the
assumption made with respect to the national labour market in the GTAP model.
Closure C in the GTAP model is chosen in this study based on the unemployment and
under employment situation in many developing countries, and Vietnam. Even though
Closure A is often used in trade liberalisation simulations, applying that closure in this
study may lead to imprecise results, since the higher wage increase under trade
liberalisations would pull them out of on-farm work, instead of expanding the
production.
148
CHAPTER 8 : CONCLUSIONS
This study analysed the impacts of trade liberalisation on small livestock households in
Vietnam, using a multi-country general equilibrium model (GTAP) at the macro level,
linked with a household model at the micro level. The modelling process was
implemented in three steps:
1. Build a representative household model for each of four regions: RRD, North
mountainous area (NE+NW), Central, and South.
2. Simulate the effects of trade liberalisation scenarios on commodity and input prices,
using the GTAP model, after implementing SplitCom to disaggregate live pig and live
poultry from the general livestock group.
3. Take the simulated prices from the trade model and use them in the household model,
and optimise the household model to investigate household changes in welfare,
production and consumption, as well as in time allocation behaviours under the price
changes that the trade liberalisation scenarios imply.
8.1. Summary, Conclusions, and Policy Implications
The following summary, conclusions, and policy implications are drawn from the study:
Households involved in livestock production in Vietnam are mainly characterised by
small-scale production, not specialised in livestock activities, but raising livestock is
often considered a secondary activity combined with others such as cultivation. Small-
scale production, based on the traditional livestock husbandry method, is very popular
throughout the country. Though constrained by poor access to markets, very low scale
operations, poor access to improved genetics and high-quality forage and concentrated
feeds, and poor animal husbandry and animal nutrition, the smallholder sector supplies
the majority of meat in the market, and raising livestock is an important source of
household cash income.
149
In building the model at the micro level, a recursive household model with
characteristics of a semi-commercial family farm was constructed. In undertaking this,
some assumptions were made: the household is a price-taker in all markets and all
markets exist; commodities are assumed homogeneous, including labour; the total stock
of land and labour are treated as given; issues relating to intertemporal allocation and
risk are omitted. In the model, the role of the labour market is of particular importance.
The labour market was assumed to be an active market, where family and hired labour
are perfect substitutes and face the same wage rates regardless of whether they work on-
or off-farm.
The household was assumed to produce only three agricultural commodities: rice, pigs
and chicken. They consume rice, pork, chicken in the main food group, “other foods” as
well as “industrial goods” bought through the market. For each output produced by the
household, a CD production function was estimated. Consumption decisions were
specified using the LES for demand estimation of broad groups (including leisure), and
LA-AIDS for individual commodity demand in the “main food” group. Two aspects of
production and consumption were then linked together through farm profit.
Household behaviour in consumption and production are driven by elasticities, which
are taken from estimated econometric models. The results showed livestock raising
activities are very responsive to changes in output prices. The labour demand for
livestock production was also very responsive to changes in labour wages. The results
are broadly in agreement with those from previous studies of other countries in similar
conditions. On the consumption side, elasticities from the LES showed that the “other
foods” group are classified as a luxury.
Building the household model for each representative small livestock household was the
main contribution of the study. A recursive model with both consumption and
production linked together allows a more accurate assessment of the determinants of
household consumption, since the household’s response to a change in an exogenous
variable such as a commodity price consists of both restructuring of consumption
patterns attributed to expenditure and consumption substitution effects, and making
production decisions which will influence income. Choosing the LES functional form,
which is easily derived from a direct utility function, is very helpful since it allows one
150
to assess consumption demand of broad groups of commodities, including leisure, and
also help solving the complexity of problems in households allocating time between
work and leisure.
At the macro level, several scenarios for trade liberalisation in Vietnam including
unilateral, bilateral, regional, multilateral and fully global liberalisations were explored
in the GTAP model. All simulation scenarios were constructed so as to describe as
closely as possible the rules and regulations required by trade agreements for all trade
partners, including Vietnam.
One important step in applying the GTAP model was the disaggregation of livestock
into new sectors for live pig, poultry and other animals using SplitCom software. By
disaggregating the group into its different components, price signals of the separate
commodities were better captured, particularly since changes in prices of live pig and
poultry were different in all trade liberalisation scenarios, and the study was interested
in impacts of trade liberalisation on households who raise pigs and poultry.
In implementing the GTAP model, the labour market issue was also considered since it
has a very significant influence on simulated changes of social welfare and household
behaviour. In the GTAP, three different closures relating to the labour market were
explored: (1) a standard closure (Closure A) with the assumption of full employment
utilisation and full factor mobility in all factor markets; (2) in Closure B, where
unemployment exists, real wages were held constant and the supply of unskilled labour
adjusted following the policy change; and (3) in Closure C, unemployment also exists,
but the supply of unskilled labour is not unlimited. With trade liberalisations, Vietnam
can supply unskilled labour up to a maximum 112 percent compared with the baseline,
without causing any increase in wages, but if demand for labour increases beyond that
point, wages must increase to clear the market. In this study, Closure C was chosen as
the base situation, as it is believed to be a better representation of the Vietnamese
situation in resource allocation and usage of endowments. There were some sensitivity
results reported for Closures A and B, since implementing different closures in the
GTAP model showed significantly different effects in defining the scope and changes of
commodity/factor prices.
151
At the national level, the results of the trade liberalisation simulations in the GTAP
model show that Vietnam can get the largest benefit if full liberalisation occurs. The
expansion of the ASEAN free trade area to include China, Korea and Japan brings the
second largest welfare increase to the society. The voluntary trade liberalisation of
Vietnam in a unilateral liberalisation would generate some benefit without the need for
negotiating with others. However, the market access benefits are limited because other
countries do not open their markets. A bilateral trade agreement with USA does not
bring much benefit to the country as a whole, but has a positive impact on the textiles
sector41
All trade scenario simulations increased production in rice and livestock sectors. Some
export-oriented sectors, such as textiles and electronics also increased production under
most of the alternative scenarios. And since most of the output of these sectors is
exported, the sectors also increased exports in all scenarios. Trade liberalisation also led
to significant increases in imports for all commodities.
By linking simulated prices of the GTAP model with the household model, the results
of the simulations showed that Vietnam’s small households in the livestock sector
would benefit from almost all trade liberalisations. The largest benefit was generated by
full liberalisation - by comparison there was almost no gain from bilateral trade
liberalisation with the USA. Meanwhile, in line with the macro-economic level results
from GTAP, unilateral trade liberalisation led to increases in household welfare.
For the small households of the livestock sector, output prices of rice, pig and chicken
increased under trade scenarios - an incentive for households to produce more on-farm
thereby increasing farm profit and cash flow. At the same time, by removing import
taxes, inputs for production and consumption goods were relatively cheaper compared
with the baseline. This effect was particularly noticeable for the “'other food”
consumption, with the increase in consumption ranging from 10 to 30 percent.
Producing more agricultural products also required households to devote more labour
41 However, it should be noticed that since the base year of the simulation in GTAP is 2001, these conclusions may be affected by trade situation in that period of time. For example, the possibility of trade between Vietnam with the United States in agricultural goods was limited because of diplomatic relationships between two countries were only just normalised after a long period of time. If database of the model is upgraded, says to GTAP version 7 (with base year data of 2004), the conclusion may be different. This is one of the study limitations, which will be returned to later in the chapter.
152
time for on-farm work in almost all trade scenarios. There are also some benefits to
households due to trade liberalisation, which are not captured by the model but which
should be recognised. Households who are producers can exploit opportunities of
productivity-enhancement by easier access to improved production technologies.
One of the important issues contributing to increases in household welfare is the
decision of the household on time allocation. In almost all scenarios, the household
decided to take more leisure. This contributed about one quarter of the household’s total
welfare increases in the trade liberalisations.
The decision of the household in time allocation was driven by the type of utility
function chosen. By applying the LES, the household chose to work less and take more
leisure every time they had more income available. Since full income is defined by the
sum of non-farm income, net other income, a farm’s profits, and the value of the
household’s stock of time (wT), increased wage rates under trade scenarios made
households feel more wealthy, hence they spent more time for leisure than work.
Choosing an appropriate assumption about possible labour market expansion in the
general equilibrium trade model is very important. Assumptions about whether wages
or labour supply changes to clear markets have important implications for wages
simulated by the model. The study compared decisions of the household on labour
allocation under alternative trade scenarios with Closure A and Closure C. Assuming
full utilisation of labour at the national level, labour wages under Closure A were much
higher than that under Closure C, where the economy supplied up to 12 percent more
unemployment to the labour force. Higher wages under Closure A made the household
wealthier, in terms of full income, resulting in the household choosing more leisure time
by working less compared with that under Closure C. At the same time, since
agricultural output prices are not high enough, farm production does not promise
increased benefits, so higher wages pull out more household labour from their farm
under Closure A. The household chooses to reduce agricultural production, and work
more off-farm. This illustrates that choosing Closure C is appropriate, since it represents
Vietnam’s economy more precisely with wage increases due to trade liberalisations at a
more suitable level, that wouldn’t draw household labour away from on-farm work to
an excessive degree.
153
In summary, the research shows that Vietnam’s small households in the livestock sector
would benefit from almost all trade liberalisation simulation scenarios. What
implications do these results have for policy? We can envision that in the long run
Vietnam will keep moving towards a more open economy. The household seem
continuously get benefits, which are derived from an increase in livestock output prices
relative to production costs. The availability of improved production technologies for
production could contribute to increase competitiveness and income of the household in
a globalised economy. As experienced in other countries, which follow liberalisation,
increased specialization in production and trade with income growth and increasing
urbanization is happened. Higher labour costs, including for unskilled labour and
opportunities in the manufacture sector will act as pull factors attracting workers from
agricultural household, which promises a possible increase of non-agricultural income
of the household. Certainly, as the past experience showed, this transition is not
necessarily smooth.
In that context, developing enabling policies to help small households to better exploit
the opportunities that globalisation presents is not an easy exercise for the government.
Given Vietnam’s current policy is oriented to increasing market orientation in livestock
production, with a focus toward the development of industrial livestock production
system, how can the government facilitate and support the small household? Since
liberalisation leads to a tightening of the link between domestic and world markets, it is
desirable for government to have a policy to strength other sectors such as industry or
service, or the export sector, to generate jobs and absorb labour from the small
household/ agricultural sector, before exposing small households to a fiercer
competition with large-scale producers in the domestic economy and others in the
world. The alternative option of facilitating more productive smallholder production
systems through adoption of productivity-enhancing technologies may be more
beneficial, equitable, and sustainable in the long run.
8.2. Limitations of the Study and Potentials for Further Study
Constructing a household model was a major contribution to this study. With the
principle of maximising household utility in response to changes in external factors,
reactions of the household to production, consumption and time allocation are easily
154
captured. This capability is the power of the household model in assessing impacts of a
number of policy issues on the household, such as government intervention to input and
output prices, restrictions on exports or imports, decisions about subsidisation, etc. In
the context of urbanisation and industrialisation, migration or capacity of the industrial
zones in absorbing labour may impact households in terms of welfare which can be
assessed using the model.
The household model was constructed using a recursive form, in which the household
decision making process is divided into two separate stages. The benefit of the model is
through its treatment of the profit effect on consumption: the two sides of production
and consumption are linked, and then the reaction of the household to an external factor
change is captured more precisely. However, the model has some disadvantages itself.
Some assumptions made in the model, such as the household is a price-taker in all
markets and all markets exist and are perfect and commodities are homogeneous,
including labour, are not always suitable. In some cases, these assumptions may be
violated, for example in the situation of under- or unemployment in the agricultural
sector in the slack season. Therefore an extension to the model allowing for incomplete
markets may be useful. In particular, in the labour market, where there may be different
wages for on- and off-farm work, or limits to obtaining work may be good options for
future research.
The household model in the study is often used for assessment in the short run, since
some factors are fixed under the model. It was assumed that cultivation land area was
fixed, technology of agricultural production did not change, savings and exogenous
income of the household were also unchanged, but in the long term the above factors
may vary and be endogenous. Therefore relaxing these assumptions may be good
applications for future modelling. In this study, the impact of risk was not included.
However, the issue of risk, which may vary under alternative liberalisation scenarios,
may also influence production decisions, so extending the model to include risk issues
is another potential area of study.
In the study, LES and LA-AIDS models were used for the demand side, and Cobb
Douglas models were applied for production functions. These choices were based on
suitability for inclusion in the household model, and their ability to be solved in that
155
context. However, choice of functional forms for the econometric models may change
policy conclusions. For example, an assumption of the LES model implies the
household will consume more leisure as soon as they become wealthy, which is partly
defined by the value of total stock of time. This may not be strictly true, especially for
very poor households. Therefore a study that allows different values for labour wages
and 'cost' for leisure would be helpful. The choice of functional forms applied in the
model may be a useful area to explore further in development and application of the
household model.
To assess possible impacts of trade liberalisation on the household, a trade model at the
macro level was linked with the micro household model. Matching perfectly an
aggregate model such as GTAP with a household model is always difficult. In this
study, in order to match sectors, commodities as well as inputs and factors between the
models, some simplifying assumptions were made. Disaggregating the live pig and
poultry sectors as separate activities within the GTAP database was implemented using
SplitCom. Splitting commodities requires massive supporting data sets. If possible,
further work on the appropriate level of aggregation of commodities in GTAP for the
purpose of this research would be useful. The “SplitCom technology” is also a potential
application for trade liberalisation research using the GTAP model.
Choosing the closure rule in GTAP is required, and has significant implications for
simulated changes in the economy. In this study, several closures were used to compare
the sensitivity of results. Identifying a closure which is most suitable for the Vietnamese
situation is not straightforward. Assuming a maximum level of labour increase in
Vietnam of 12 percent was supported by some macro-economic data, however other
closures may also need to be explored in future research. Currently, the only variable
that was changed was labour, but there may be other variables, such as the treatment of
trade balances that could also be explored. Since the GTAP model applied in this
research is a static model, it cannot capture ongoing changes of the policy. The static
model only allows assessment at a point in time, , which does not allow a consideration
of the time path between equilibria to be considered.. Research with a dynamic GTAP,
which is available now, would be more ideal for future studies of this nature.
156
In conclusion, applying the GTAP model at the macro level and linking it with
household models at the micro level, the study met the objective of the study: to assess
impacts of globalisation and trade liberalisation on small households in the Vietnam
livestock sector. The study showed the welfare of the households is improved under the
trade scenarios. However, the results depend significantly upon assumptions made in
the models. The assumptions made about the operation of the labour market (both at the
aggregate level within GTAP, and on household decisions about work/leisure) are most
important. Acknowledgement of advantages as well as understanding of the limitations
of the models are necessary for further and deeper studies in the future.
157
APPENDICES
A3.1: Recursive Model Matrix
The annex is verbatim drawn from Singh, Squire and Strauss, 1986 to give more detail on how
the household model is separated between revenue and expenditure.
A household utility function is assumed to exist:
),.....,( 1 LXXUU i =1, …., (A3.1-1)
Where Xi is vector of commodity i consumption for different members of the household, XL
denotes leisure time.
Its utility is maximised subject to a budget constraint:
L
iii XpY
1
(A3.1-2)
Where Y is the household’s full income and pi’s are commodity prices (pL is wage rate):
RLpVqQqTpY L
N
iii
M
jjjL
11
(A3.1-3)
Where:
T= time endowment
Qj = output, j= 1,…,M
Vi = non-labour variable inputs, i=1,….,N
L = labour demand
qj = price of Qj
pi = price of Vi
R = exogenous income
It is assumed that L is labour demanded by household both family and hired, and they are
perfect substitutes.
An implicit function is applied:
0),.......,,,,........,,,.......,( 0111 KKLVVQQG NM (A3.1-4)
Where Ki’s are fixed inputs.
158
To see that the model is recursive, comparative statistics are examined. Let the household
consume three commodities: leisure, XL, industry goods purchased on the market, Xm, and an
agricultural good produced by the household, Xa. The household uses labour, L, other variable
inputs, V, and fixed input K to produce Qa to consume at home and Qc to sell on the market. The
Lagrangian is written as:
),,,,(]
)([),,(
KVLQQGXpXpXpR
VqLpQpQqTpXXXU
acmmLLaa
vLaaccLamL
(A3.1-5)
The first-order conditions are:
0
LLL
pUX
(A3.1-6)
0
mmm
pUX
0
aaa
pUX
0)()(
RXpVqXQpQqLXTp mmvaaaccLL
01
ccc
GqQ
01
aaa
GpQ
01
LL GpL
01
Vv GqV
0),,,,(
KVLQQG ac
Totally differentiating (A3.1-6), have (A3.1-7)
159
000000
0000
0000
0000
0000
000000
00000
00000
00000
v
L
a
c
a
m
L
a
c
a
m
L
vLac
vvvvLvavc
LLvLLLaLc
aavaLaaac
ccvcLcacc
amL
aaaamaL
mmammmL
LLaLmLL
dp
dp
dp
dq
dp
dp
dp
d
dV
dL
dQ
dQ
d
dX
dX
dX
GGGG
GGGGG
GGGGG
GGGGG
GGGGG
ppp
pUUU
pUUU
pUUU
(A3.1-7)
where dKGVdqdqQdRdpXQdpXdpLXT kvccaaammLL )()(
when differentiating the budget constraint we have substituted
)( dVGdLGdQGdQG vLaacc
for dVqdLpdQpdQq vLaacc
This equals dKGk
since G(.)=0.
This system of equations is block diagonal, as seen from (A3.1-7). The first set of equations,
upper left block of the matrix, gives the solution for commodity demands and marginal utility of
full income. The second set, the lower right one, gives the solution for output supplies, variable
input demands, and the associated multiplier. The assumptions concerning utility and
production functions ensure that second-order conditions are met. Hence, the consumption
decisions are partially determined by the outcome of production decisions. However,
consumption decisions have no impact on production decisions (Singh et al. 1986)
160
A4.1. OLS Estimation of Production Functions
A4.1-1: OLS Estimation of Rice Production Function
Region RRD is omitted
Source SS df MS Number of obs. = 3995
Model 2529.5133 8 316.189163 F(8, 3986) = 2736.94
Residual 460.487984 3986 0.115526338 Prob > F = 0.0000
Total 2990.00129 3994 0.748623257 R-squared = 0.8460
Adj R-squared = 0.8457
Root MSE = 0.33989
Rice output Coefficient Std. error t P> | t | [95% Conf. Interval]
Area 0.609834 0.014437 42.24 0.000 0.58153 0.638138
Seed 0.047518 0.007981 5.95 0.000 0.031871 0.063166
Chemical fertiliser 0.22347 0.009149 24.42 0.000 0.205532 0.241408
Pesticide 0.054194 0.006030 8.99 0.000 0.042373 0.066016
Labor 0.058637 0.005828 10.06 0.000 0.047211 0.070064
NE + NW -0.11952 0.015533 -7.69 0.000 -0.14998 -0.08907
The Central -0.29136 0.014538 -20.04 0.000 -0.31986 -0.26285
The South -0.2865 0.024463 -11.71 0.000 -0.33447 -0.23854
Constant 6.62204 0.072516 91.32 0.000 6.479869 6.764211
Rice output = total output of rice cultivation raising/year (kg)
Area= total areas of rice cultivation/year (ha)
Seed = total rice used as seeding/year (kg)
Chemical fertiliser = total chemical fertiliser used/year (kg)
Pesticide = total pesticide and herbicide used/year (bottle)
Labour = Total day working for chicken raising/year (man-days)
Other costs = total other cost for production ('000 VND)
161
A4.1-2. OLS Estimation of Pig Production Function
Region RRD, NE+NW are omitted
Source SS df MS Number of obs. = 3191
Model 2197.96921 5 439.593841 F(5, 3185) = 2218.69
Residual 631.051269 3185 0.198132266 Prob. > F = 0.0000
Total 2829.02048 3190 0.886840275 R-squared = 0.7769
Adj. R-squared = 0.7766
Root MSE = 0.44512
Pig output Coefficient Std. error t P> | t | [95% Conf. Interval]
Feed 0.5844212 0.0106976 54.63 0.000 0.5634462 0.6053961
Labor 0.1714647 0.010323 16.61 0.000 0.1512243 0.1917052
Veterinary+ others 0.0946292 0.0106225 8.91 0.000 0.0738016 0.1154568
The Central 0.0967256 0.0172795 5.60 0.000 0.0628455 0.1306057
The South 0.246214 0.0326408 7.54 0.000 0.1822149 0.3102131
Constant -0.0242914 0.0545676 -0.45 0.656 -0.1312825 0.0826997
Pig output = total output of pig raising/year (kg)
Feed = total cost of feeding pig/year ('000 VND)
Labour = Total day working for pig raising/year (man-days)
Other costs = total other cost for production ('000 VND)
162
A4.1-3. OLS Estimation of Chicken Production Function
Region RRD is omitted
Source S df MS Number of obs. = 1959
Model 837.416308 6 139.569385 F(6, 1952) = 924.45
Residual 294.705857 1952 0.150976361 Prob. > F = 0.0000
Total 1132.12217 1958 0.578203353 R-squared = 0.7397
Adj. R-squared = 0.7389
Root MSE = 0.38856
Chicken output Coefficient Std. error t P> | t | [95% Conf. Interval]
Feed 0.460 0.012 38.700 0.000 0.436 0.483
Labor 0.210 0.010 20.820 0.000 0.190 0.229
Veterinary+ others 0.137 0.011 12.560 0.000 0.116 0.159
NE+NW 0.048 0.024 2.030 0.043 0.002 0.094
The Central 0.158 0.024 6.460 0.000 0.110 0.206
The South 0.440 0.049 8.930 0.000 0.344 0.537
Constant -0.251 0.055 -4.560 0.000 -0.359 -0.143
Chicken output = total output of chicken raising/year (kg)
Feed = total cost of feeding chicken/year ('000 VND)
Labour = Total day working for chicken raising/year (man-days)
Other costs = total other cost for production ('000 VND)
163
A4.2. Estimation Shadow Wage of Labour
A4.2-1. Calculate Marginal Product of Labour from Production Function
Source SS df MS Number of obs. = 3314
Model 1159.58383 12 96.6319862 F(12, 3301) = 1504.01
Residual 212.08832 3301 0.064249718 Prob > F = 0.0000
Total 1371.67215 3313 0.414027212 R-squared = 0.8454
Adj R-squared = 0.8448
Root MSE = 0.25348
Income Coefficient Std. error t P> | t | [95% Conf. Interval]
Labour 0.049815 0.006339 7.86 0.000 0.037386 0.062244
Rice cultivation cost 0.162491 0.014545 11.17 0.000 0.133974 0.191008
Livestock cost 0.289491 0.004632 62.50 0.000 0.280409 0.298573
Other fees 0.070952 0.013116 5.41 0.000 0.045235 0.096668
Area 0.313744 0.009501 33.02 0.000 0.295116 0.332373
NE 0.031253 0.013215 2.37 0.018 0.005343 0.057163
NW 0.100236 0.019494 5.14 0.000 0.062014 0.138457
NCC -0.08841 0.013999 -6.32 0.000 -0.11586 -0.06096
SCC -0.10361 0.017589 -5.89 0.000 -0.1381 -0.06913
CH 0.091724 0.022322 4.11 0.000 0.047958 0.13549
NES -0.0289 0.03318 -0.87 0.384 -0.09396 0.036156
MRD 0.024851 0.022931 1.08 0.279 -0.02011 0.06981
Constant 5.188956 0.077439 67.01 0.000 5.037123 5.34079
Income = total farm income from rice cultivation, pig and chicken raising ('000 VND)
Cost of rice = total cost for rice cultivation ('000 VND)
Livestock cost = total cost for feeding and veterinary ('000 VND)
Labour = total day working for rice cultivation + pig, chicken raising/year (man-days)
Other fees = total fees for farm works/year ('000 VND)
Area= total areas of rice cultivation/year (ha)
164
A4.2-2. Using Instrumental Variables for Estimation of Shadow Wage
Source SS df MS Number of obs. = 1022
Model 96.552277 11 8.77747973 F( 11, 1010) = 27.29
Residual 324.81811 1010 0.321602089 Prob > F = 0.0000
Total 421.370387 1021 0.412703611 R-squared = 0.2291
Adj R-squared = 0.2207
Root MSE = 0.5671
Shadow wage Coefficient Std. error t P> | t | [95% Conf. Interval]
Head edu 0.044846 0.018125 2.47 0.014 0.015007 0.074686
Head age 0.005095 0.001873 2.72 0.007 0.002011 0.008179
No mem 0.035177 0.015068 2.33 0.020 0.01037 0.059985
No labor -0.12694 0.018594 -6.83 0.000 -0.15756 -0.09633
Exo income 0.014297 0.010719 1.33 0.183 -0.00335 0.031943
Rice price 0.153528 0.08859 1.73 0.083 0.007677 0.29938
Pig price 0.173112 0.042222 4.10 0.000 0.1036 0.242625
Chic price -0.15215 0.122458 -1.24 0.214 -0.35376 0.049459
NE + NW -0.53810 0.045793 -11.75 0.000 -0.61349 -0.46271
The Central -0.35212 0.049609 -7.10 0.000 -0.43380 -0.27045
The South 0.610628 0.175814 3.47 0.001 0.321176 0.900081
Constant 0.766624 0.410344 1.87 0.062 0.091048 1.44220
Shadow wage = (Income-hat *_b[Labour])/Labour
Head edu = education level of head household
Head age = age of head household
No mem = no of member in the household
No labour = no of labour in the household
Exo income = total exogenous income of the household
Rice price = price of rice ('000 VND/kg)
Pig price = price of pork ('000 VND/kg)
Chic price = price of chicken ('000 VND/kg)
165
A4.3. Results of LA-AIDS Models
A4.3-1. LA - AIDS Model in RRD
Iteration 1: tolerance = .00616148
Iteration 2: tolerance = .00009929
Iteration 3: tolerance = 1.644e-06
Iteration 4: tolerance = 2.675e-08
Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Rice 913 6 0.119509 0.149 162.12 0.0000
Pork 913 6 0.08901 0.106 122.09 0.0000
Chicken 913 6 0.047949 0.0272 28.15 0.0001
Fish 913 6 0.063733 0.0192 29.09 0.0001
Vegetable 913 6 0.033437 0.0683 73.80 0.0000
Coefficient Std. error t P>| t| [95% Conf. Interval]
Rice qty
Rice price 0.185345 0.023026 8.05 0.000 0.140214 0.230475
Pork price -0.07996 0.014382 -5.56 0.000 -0.10815 -0.05177
Chic price -0.02951 0.009122 -3.24 0.001 -0.04739 -0.01163
Fish price -0.04475 0.009314 -4.80 0.000 -0.063 -0.02649
Vege price -0.01593 0.005687 -2.80 0.005 -0.02708 -0.00479
Othmeat price -0.01519 0.007147 -2.13 0.034 -0.0292 -0.00119
Real income -0.09708 0.010781 -9.00 0.000 -0.11821 -0.07595
Constant 1.404675 0.07929 17.72 0.000 1.249271 1.56008
Pork qty
Rice price -0.07996 0.014382 -5.56 0.000 -0.10815 -0.05177
Pork price 0.052484 0.013692 3.83 0.000 0.025649 0.079319
Chic price 0.000453 0.006735 0.07 0.946 -0.01275 0.013652
Fish price 0.026245 0.006913 3.80 0.000 0.012695 0.039794
Vege price 0.002591 0.004448 0.58 0.560 -0.00613 0.01131
Othmeat price -0.00181 0.005315 -0.34 0.733 -0.01223 0.008606
Real income 0.069049 0.008026 8.60 0.000 0.053319 0.084779
Constant -0.36941 0.059246 -6.24 0.000 -0.48553 -0.25329
166
Coefficient Std. error t P>| t| [95% Conf. Interval]
Chic qty
Rice price -0.02951 0.009122 -3.24 0.001 -0.04739 -0.01163
Pork price 0.000453 0.006735 0.07 0.946 -0.01275 0.013652
Chic price 0.033656 0.008017 4.20 0.000 0.017942 0.049369
Fish price 0.005932 0.004649 1.28 0.202 -0.00318 0.015044
Vege price -0.00896 0.003501 -2.56 0.011 -0.01582 -0.0021
Othmeat price -0.00157 0.004192 -0.37 0.708 -0.00979 0.006646
Real income 0.004358 0.004334 1.01 0.315 -0.00414 0.012852
Constant -0.03448 0.033142 -1.04 0.298 -0.09944 0.030477
Fish qty
Rice price -0.04475 0.009314 -4.80 0.000 -0.063 -0.02649
Pork price 0.026245 0.006913 3.80 0.000 0.012695 0.039794
Chic price 0.005932 0.004649 1.28 0.202 -0.00318 0.015044
Fish price 0.012005 0.006779 1.77 0.077 -0.00128 0.025292
Vege price -0.00109 0.003079 -0.35 0.724 -0.00712 0.004946
Othmeat price 0.001656 0.003693 0.45 0.654 -0.00558 0.008895
Real income 0.006473 0.005757 1.12 0.261 -0.00481 0.017757
Constant -0.0254 0.040694 -0.62 0.532 -0.10516 0.054357
Vege qty
Rice price -0.01593 0.005687 -2.80 0.005 -0.02708 -0.00479
Pork price 0.002591 0.004448 0.58 0.560 -0.00613 0.01131
Chic price -0.00896 0.003501 -2.56 0.011 -0.01582 -0.0021
Fish price -0.00109 0.003079 -0.35 0.724 -0.00712 0.004946
Vege price 0.021349 0.003117 6.85 0.000 0.015239 0.027459
Othmeat price 0.002041 0.002623 0.78 0.436 -0.0031 0.007182
Real income -0.01321 0.003019 -4.38 0.000 -0.01912 -0.00729
Constant 0.184329 0.022606 8.15 0.000 0.140022 0.228635
167
* Uncompensated Elasticities in RRD
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Real
income
Rice qty -0.53934 -0.11804 -0.04378 -0.06989 -0.0186 -0.01992 0.809563
Pork qty -0.56494 -0.81158 -0.02287 0.09693 -0.00981 -0.02647 1.338731
Chic qty -0.42839 -0.00588 -0.54998 0.074559 -0.12486 -0.02427 1.05883
Fish qty -0.51154 0.265362 0.058049 -0.87866 -0.01617 0.014048 1.068919
Vege qty -0.13841 0.079482 -0.12007 0.00228 -0.6656 0.041025 0.801302
Othmeat qty -0.59122 -0.15427 -0.07365 -0.02312 0.000388 -0.74381 1.585686
* Compensated Elasticties in RRD
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Rice qty -0.12664 0.04699 0.016182 0.006147 0.035213 0.022112
Pork qty 0.117512 -0.53869 0.076291 0.222674 0.079176 0.043032
Chic qty 0.111372 0.209956 -0.47156 0.174012 -0.05449 0.030702
Fish qty 0.033363 0.483257 0.137224 -0.77826 0.054873 0.069542
Vege qty 0.270067 0.242823 -0.06072 0.077544 -0.61234 0.082626
Othmeat qty 0.217121 0.168963 0.043803 0.125817 0.105783 -0.66149
A4.3-2. LA - AIDS Model in NE+NW
Iteration 1: tolerance = .05016562
Iteration 2: tolerance = .00269992
Iteration 3: tolerance = .0001371
Iteration 4: tolerance = 7.660e-06
Iteration 5: tolerance = 4.202e-07
Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Rice 996 6 0.132031 0.0247 40.41 0.0000
Pork 996 6 0.087304 0.0369 35.10 0.0000
Chicken 996 6 0.056136 0.0327 37.63 0.0000
Fish 996 6 0.056747 0.0421 43.99 0.0000
Vegetable 996 6 0.048459 0.0645 94.29 0.0000
168
Coefficient Std. error t P>| t| [95% Conf. Interval]
Rice qty
Rice price 0.063509 0.02129 2.98 0.003 0.021782 0.105236
Pork price -0.00237 0.013293 -0.18 0.858 -0.02842 0.023682
Chic price -0.03304 0.009416 -3.51 0.000 -0.0515 -0.01459
Fish price -0.01281 0.007595 -1.69 0.092 -0.02769 0.002077
Vege price -0.01891 0.006687 -2.83 0.005 -0.03201 -0.0058
Othmeat price 0.00362 0.006229 0.58 0.561 -0.00859 0.015828
Real income -0.04585 0.011078 -4.14 0.000 -0.06756 -0.02414
Constant 0.899446 0.082064 10.96 0.000 0.738605 1.060288
Pork qty
Rice price -0.00237 0.013293 -0.18 0.858 -0.02842 0.023682
Pork price 0.045083 0.013429 3.36 0.001 0.018763 0.071403
Chic price -0.01631 0.007787 -2.09 0.036 -0.03157 -0.00105
Fish price 0.002802 0.006103 0.46 0.646 -0.00916 0.014764
Vege price -0.01491 0.005225 -2.85 0.004 -0.02515 -0.00467
Othmeat price -0.01429 0.005219 -2.74 0.006 -0.02452 -0.00406
Real income 0.022747 0.007329 3.10 0.002 0.008383 0.03711
Constant -0.00232 0.05522 -0.04 0.966 -0.11055 0.105907
Chic qty
Rice price -0.03304 0.009416 -3.51 0.000 -0.05150 -0.01459
Pork price -0.01631 0.007787 -2.09 0.036 -0.03157 -0.00105
Chic price 0.042696 0.009766 4.37 0.000 0.023556 0.061836
Fish price 0.007656 0.004806 1.59 0.111 -0.00176 0.017076
Vege price 0.010509 0.004117 2.55 0.011 0.002439 0.018578
Othmeat price -0.01151 0.004642 -2.48 0.013 -0.02061 -0.00241
Real income 0.001838 0.004715 0.39 0.697 -0.0074 0.011079
Constant 0.042374 0.036591 1.16 0.247 -0.02934 0.114091
Fish qty
Rice price -0.01281 0.007595 -1.69 0.092 -0.02769 0.002077
Pork price 0.002802 0.006103 0.46 0.646 -0.00916 0.014764
Chic price 0.007656 0.004806 1.59 0.111 -0.00176 0.017076
Fish price 0.007229 0.005339 1.35 0.176 -0.00324 0.017694
169
Coefficient Std. error t P>| t| [95% Conf. Interval]
Vege price -0.00535 0.003335 -1.60 0.109 -0.01189 0.001188
Othmeat price 0.000469 0.003328 0.14 0.888 -0.00605 0.006993
Real income 0.027894 0.004763 5.86 0.000 0.018559 0.037228
Constant -0.15087 0.034723 -4.34 0.000 -0.21892 -0.08281
Vege qty
Rice price -0.01891 0.006687 -2.83 0.005 -0.03201 -0.0058
Pork price -0.01491 0.005225 -2.85 0.004 -0.02515 -0.00467
Chic price 0.010509 0.004117 2.55 0.011 0.002439 0.018578
Fish price -0.00535 0.003335 -1.60 0.109 -0.01189 0.001188
Vege price 0.022695 0.004033 5.63 0.000 0.014791 0.030599
Othmeat price 0.005965 0.002816 2.12 0.034 0.000446 0.011483
Real income -0.0273 0.004068 -6.71 0.000 -0.03527 -0.01933
Constant 0.276218 0.029883 9.24 0.000 0.217648 0.334788
* Uncompensated Elasticities in NE+NW
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Real
income
Rice qty -0.83341 0.011517 -0.05433 -0.0182 -0.0291 0.010691 0.91283
Pork qty -0.07799 -0.77751 -0.10077 0.006509 -0.09082 -0.08315 1.123735
Chic qty -0.34942 -0.17107 -0.56314 0.077335 0.106491 -0.11907 1.018885
Fish qty -0.38931 -0.03295 0.070006 -0.92548 -0.10682 -0.01062 1.395164
Vege qty -0.05792 -0.12594 0.16762 -0.04357 -0.68376 0.091128 0.652442
Othmeat qty -0.16602 -0.41408 -0.30943 -0.02265 0.099349 -0.66028 1.473126
* Compensated Elasticties in NE+NW
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Rice qty -0.35325 0.179327 0.034512 0.046238 0.042596 0.050576
Pork qty 0.513117 -0.57093 0.008594 0.085831 -0.00256 -0.03405
Chic qty 0.186528 0.016233 -0.46398 0.149255 0.186518 -0.07455
Fish qty 0.344566 0.223534 0.20579 -0.82700 0.002763 0.050345
Vege qty 0.28527 -0.00600 0.231118 0.002483 -0.63251 0.119636
Othmeat qty 0.608864 -0.14327 -0.16606 0.081331 0.215052 -0.59592
170
A4.3-3. LA - AIDS Model in the Central
Iteration 1: tolerance = .0275376
Iteration 2: tolerance = .00080431
Iteration 3: tolerance = .00003048
Iteration 4: tolerance = 1.043e-06
Iteration 5: tolerance = 3.363e-08
Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Rice 1005 6 0.1246 0.1098 170.68 0.0000
Pork 1005 6 0.077306 0.0426 65.22 0.0000
Chicken 1005 6 0.04528 0.0173 27.84 0.0001
Fish 1005 6 0.082862 0.0448 67.94 0.0000
Vegetable 1005 6 0.037377 0.0187 38.32 0.0000
Coefficient Std. error t P>| t| [95% Conf. Interval]
Rice qty
Rice price 0.118058 0.018912 6.24 0.000 0.080992 0.155125
Pork price -0.01699 0.010928 -1.55 0.120 -0.03841 0.00443
Chic price 0.004175 0.007077 0.59 0.555 -0.0097 0.018045
Fish price -0.05309 0.008832 -6.01 0.000 -0.0704 -0.03578
Vege price -0.00569 0.004754 -1.20 0.232 -0.015 0.003631
Othmeat price -0.04647 0.00665 -6.99 0.000 -0.0595 -0.03344
Real income -0.10077 0.010307 -9.78 0.000 -0.12097 -0.08056
Constant 1.330537 0.076552 17.38 0.000 1.180499 1.480575
Pork qty
Rice price -0.01699 0.010928 -1.55 0.120 -0.03841 0.00443
Pork price 0.036052 0.010067 3.58 0.000 0.01632 0.055784
Chic price -0.01805 0.005291 -3.41 0.001 -0.02842 -0.00768
Fish price 0.008412 0.005775 1.46 0.145 -0.00291 0.019732
Vege price -0.00984 0.003513 -2.80 0.005 -0.01673 -0.00296
Othmeat price 0.000416 0.004884 0.09 0.932 -0.00916 0.009988
Real income 0.037492 0.006398 5.86 0.000 0.024953 0.050031
Constant -0.13892 0.047294 -2.94 0.003 -0.23161 -0.04623
171
Coefficient Std. error t P>| t| [95% Conf. Interval]
Chic qty
Rice price 0.004175 0.007077 0.59 0.555 -0.0097 0.018045
Pork price -0.01805 0.005291 -3.41 0.001 -0.02842 -0.00768
Chic price 0.001201 0.006431 0.19 0.852 -0.0114 0.013805
Fish price 0.014858 0.003799 3.91 0.000 0.007413 0.022304
Vege price -0.00108 0.002543 -0.43 0.671 -0.00607 0.003903
Othmeat price -0.00111 0.004007 -0.28 0.782 -0.00896 0.006747
Real income 0.009131 0.003751 2.43 0.015 0.00178 0.016482
Constant 0.016924 0.028846 0.59 0.557 -0.03961 0.07346
Fish qty
Rice price -0.05309 0.008832 -6.01 0.000 -0.0704 -0.03578
Pork price 0.008412 0.005775 1.46 0.145 -0.00291 0.019732
Chic price 0.014858 0.003799 3.91 0.000 0.007413 0.022304
Fish price 0.016749 0.007235 2.31 0.021 0.002568 0.030929
Vege price -0.0014 0.002808 -0.50 0.618 -0.0069 0.004103
Othmeat price 0.014468 0.003634 3.98 0.000 0.007346 0.02159
Real income 0.030928 0.006863 4.51 0.000 0.017478 0.044378
Constant -0.13847 0.048212 -2.87 0.004 -0.23296 -0.04398
Vege qty
Rice price -0.00569 0.004754 -1.20 0.232 -0.01500 0.003631
Pork price -0.00984 0.003513 -2.80 0.005 -0.01673 -0.00296
Chic price -0.00108 0.002543 -0.43 0.671 -0.00607 0.003903
Fish price -0.0014 0.002808 -0.50 0.618 -0.00690 0.004103
Vege price 0.007306 0.00242 3.02 0.003 0.002562 0.012050
Othmeat price 0.010707 0.002382 4.50 0.000 0.006039 0.015376
Real income -0.00055 0.003093 -0.18 0.859 -0.00661 0.005512
Constant 0.080924 0.022458 3.60 0.000 0.036907 0.12494
172
* Uncompensated Elasticities in the Central
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Real
income
Rice qty -0.66714 -0.00261 0.020477 -0.0736 0.002692 -0.08173 0.801899
Pork qty -0.23198 -0.80556 -0.13104 0.016657 -0.08022 -0.00905 1.241195
Chic qty -0.00758 -0.31432 -0.98974 0.217022 -0.02779 -0.02504 1.147434
Fish qty -0.4431 0.023209 0.083336 -0.92309 -0.02296 0.083471 1.199135
Vege qty -0.07721 -0.13937 -0.01496 -0.01877 -0.89512 0.153289 0.992135
Othmeat qty -1.20426 -0.06742 -0.05302 0.221618 0.185968 -0.57163 1.488747
* Compensated Elasticties in the Central
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Rice qty -0.25924 0.122044 0.070139 0.050945 0.058846 -0.04273
Pork qty 0.39937 -0.61263 -0.05417 0.209429 0.006691 0.051304
Chic qty 0.576078 -0.13596 -0.91867 0.395231 0.052564 0.03076
Fish qty 0.166851 0.209606 0.157599 -0.73685 0.061012 0.141782
Vege qty 0.427451 0.014853 0.046488 0.135318 -0.82564 0.201534
Othmeat qty -0.44699 0.163998 0.039175 0.452837 0.290218 -0.49924
A4.3-4. LA - AIDS Model in the South
Iteration 1: tolerance = .04020703
Iteration 2: tolerance = .0019823
Iteration 3: tolerance = .00009177
Iteration 4: tolerance = 5.330e-06
Iteration 5: tolerance = 2.912e-07
Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Rice 192 6 0.117078 0.2077 48.89 0.0000
Pork 192 6 0.091636 0.1108 22.40 0.0010
Chicken 192 6 0.053138 0.0134 1.98 0.9219
Fish 192 6 0.101117 0.0138 27.95 0.0001
Vegetable 192 6 0.041957 0.0431 10.21 0.1161
173
Coefficient Std. error t P>| t| [95% Conf. Interval]
Rice qty
Rice price 0.187111 0.042787 4.37 0.000 0.103251 0.270971
Pork price -0.04138 0.028961 -1.43 0.153 -0.09814 0.015382
Chic price -0.01054 0.019748 -0.53 0.594 -0.04924 0.028169
Fish price -0.0828 0.018066 -4.58 0.000 -0.11821 -0.04739
Vege price -0.03323 0.01298 -2.56 0.010 -0.05867 -0.00779
Othmeat price -0.01917 0.015679 -1.22 0.222 -0.0499 0.011564
Real income -0.1031 0.022811 -4.52 0.000 -0.14781 -0.05839
Constant 1.346043 0.172254 7.81 0.000 1.008432 1.683655
Pork qty
Rice price -0.04138 0.028961 -1.43 0.153 -0.09814 0.015382
Pork price -0.01269 0.032205 -0.39 0.694 -0.07581 0.050435
Chic price -0.01444 0.018206 -0.79 0.428 -0.05012 0.021242
Fish price 0.042018 0.014651 2.87 0.004 0.013301 0.070734
Vege price 0.003302 0.011597 0.28 0.776 -0.01943 0.026032
Othmeat price 0.023188 0.014503 1.6 0.110 -0.00524 0.051614
Real income 0.058188 0.017891 3.25 0.001 0.023122 0.093253
Constant -0.26176 0.131823 -1.99 0.047 -0.52013 -0.00339
Chic qty
Rice price -0.01054 0.019748 -0.53 0.594 -0.04924 0.028169
Pork price -0.01444 0.018206 -0.79 0.428 -0.05012 0.021242
Chic price 0.014543 0.021251 0.68 0.494 -0.02711 0.056194
Fish price -0.00064 0.008867 -0.07 0.942 -0.01802 0.016737
Vege price 0.005548 0.008817 0.63 0.529 -0.01173 0.022829
Othmeat price 0.005526 0.011759 0.47 0.638 -0.01752 0.028573
Real income 0.006575 0.010445 0.63 0.529 -0.0139 0.027047
Constant 0.021993 0.079054 0.28 0.781 -0.13295 0.176935
Fish qty
Rice price -0.0828 0.018066 -4.58 0.000 -0.11821 -0.04739
Pork price 0.042018 0.014651 2.87 0.004 0.013301 0.070734
Chic price -0.00064 0.008867 -0.07 0.942 -0.01802 0.016737
Fish price 0.032738 0.017202 1.90 0.057 -0.00098 0.066453
174
Coefficient Std. error t P>| t| [95% Conf. Interval]
Vege price 0.014273 0.006723 2.12 0.034 0.001095 0.027451
Othmeat price -0.00559 0.007545 -0.74 0.459 -0.02037 0.009201
Real income 0.013734 0.019659 0.70 0.485 -0.0248 0.052265
Constant 0.005239 0.135223 0.04 0.969 -0.25979 0.27027
Vege qty
Rice price -0.03323 0.01298 -2.56 0.010 -0.05867 -0.00779
Pork price 0.003302 0.011597 0.28 0.776 -0.01943 0.026032
Chic price 0.005548 0.008817 0.63 0.529 -0.01173 0.022829
Fish price 0.014273 0.006723 2.12 0.034 0.001095 0.027451
Vege price 0.010253 0.008116 1.26 0.206 -0.00565 0.02616
Othmeat price -0.00015 0.007204 -0.02 0.983 -0.01427 0.013969
Real income 0.007769 0.008269 0.94 0.347 -0.00844 0.023976
Constant -0.00395 0.059786 -0.07 0.947 -0.12113 0.113225
* Uncompensated Elasticities in the South
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Real
income
Rice qty -0.45413 -0.05856 -0.00745 -0.1441 -0.05871 -0.03307 0.756024
Pork qty -0.40894 -1.13682 -0.11536 0.183828 -0.00898 0.125583 1.360699
Chic qty -0.18583 -0.21635 -0.8036 -0.02845 0.06994 0.072508 1.091769
Fish qty -0.41704 0.187339 -0.00765 -0.85964 0.061902 -0.02955 1.064642
Vege qty -0.44721 0.025089 0.061142 0.154617 -0.88217 -0.00663 1.095165
Othmeat qty -0.52205 0.406654 0.085809 -0.18201 -0.03028 -1.09255 1.334426
* Compensated Elasticties in the South
Rice
price
Pork
price
Chic
price
Fish
price
Vege
price
Othmeat
price
Rice qty -0.13464 0.063397 0.046719 0.016523 0.003012 0.004988
Pork qty 0.166073 -0.91732 -0.01787 0.472922 0.102102 0.194086
Chic qty 0.275546 -0.04022 -0.72537 0.20351 0.159069 0.127471
Fish qty 0.032865 0.359087 0.068632 -0.63345 0.148816 0.02405
Vege qty 0.015591 0.20176 0.139611 0.387296 -0.79277 0.048507
Othmeat qty 0.041867 0.621923 0.181421 0.101498 0.078659 -1.02537
175
A4.4. Results of LES Models
A4.4-1. LES Model in RRD
Iteration 1: tolerance = .0166451 Iteration 2: tolerance = .00343 Iteration 3: tolerance = .00052913 Iteration 4: tolerance = .0000844 Iteration 5: tolerance = .00001439 Iteration 6: tolerance = 3.337e-06 Iteration 7: tolerance = 7.574e-07 Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Main food 1028 4 364.2532 0.9046 10606.20 0.000
Other food 1028 4 448.8232 0.6768 9701.01 0.000
Industry 1028 4 303.4447 0.7621 9745.03 0.000
Coefficient Std. error t P>| t| [95% Conf. Interval]
Main food
Gamma food 60.468 1.445 41.830 0.000 57.634 63.301
Beta food 0.310 0.008 37.650 0.000 0.294 0.327
Gamma bar 204.535 2.958 69.140 0.000 198.737 210.333
Gamma othfood 0.000 0.000 -2.840 0.005 0.000 0.000
Gamma indust 3.537 0.174 20.300 0.000 3.195 3.878
Other food
Gamma othfood 0.000 0.000 -4.830 0.000 0.000 0.000
Beta othfood 0.332 0.010 34.620 0.000 0.313 0.350
Gamma bar 204.535 2.958 69.140 0.000 198.737 210.333
Gamma food 60.468 1.445 41.830 0.000 57.634 63.301
Gamma indust 3.537 0.174 20.300 0.000 3.195 3.878
Indust
Gamma indust 3.537 0.174 20.300 0.000 3.195 3.878
Beta indust 0.149 0.007 22.820 0.000 0.136 0.161
Gamma bar 204.535 2.958 69.140 0.000 198.737 210.333
Gamma food 60.468 1.445 41.830 0.000 57.634 63.301
Gamma othfood (dropped)
176
A4.4-2. LES Model in NE+NW
Iteration 1: tolerance = .442507 Iteration 2: tolerance = .02771702 Iteration 3: tolerance = .00203137 Iteration 4: tolerance = .00016556 Iteration 5: tolerance = .00001456 Iteration 6: tolerance = 1.354e-06 Iteration 7: tolerance = 1.305e-07 Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Main food 1222 4 304.5923 0.9374 23255.78 0.000
Other food 1222 4 238.3234 0.8226 16699.72 0.000
Industry 1222 4 254.1319 0.8479 17236.36 0.000
Coefficient Std. error t P>| t| [95% Conf. Interval]
Main food
Gamma food 57.799 1.397 41.370 0.000 55.061 60.537
Beta food 0.321 0.007 46.460 0.000 0.307 0.334
Gamma bar 254.350 2.730 93.150 0.000 248.999 259.702
Gamma othfood 0.000 0.000 -2.800 0.005 0.000 0.000
Gamma indust -0.404 0.202 -2.000 0.046 -0.799 -0.008
Other food
Gamma othfood 0.000 0.000 8.310 0.000 0.000 0.000
Beta othfood 0.245 0.004 54.910 0.000 0.236 0.254
Gamma bar 254.350 2.730 93.150 0.000 248.999 259.702
Gamma food 57.799 1.397 41.370 0.000 55.061 60.537
Gamma indust -0.404 0.202 -2.000 0.046 -0.799 -0.008
Indust
Gamma indust -0.404 0.202 -2.000 0.046 -0.799 -0.008
Beta indust 0.262 0.005 48.140 0.000 0.251 0.273
Gamma bar 254.350 2.730 93.150 0.000 248.999 259.702
Gamma food 57.799 1.397 41.370 0.000 55.061 60.537
Gamma othfood (dropped)
177
A4.4-3. LES Model in the Central
Iteration 1: tolerance = .6732174 Iteration 2: tolerance = .04393632 Iteration 3: tolerance = .00471102 Iteration 4: tolerance = .00053876 Iteration 5: tolerance = .00006323 Iteration 6: tolerance = 7.536e-06 Iteration 7: tolerance = 9.066e-07 Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Main food 1252 4 289.8065 0.9258 18920.63 0.000
Other food 1252 4 263.4267 0.8166 14426.39 0.000
Industry 1252 4 272.7707 0.8479 15118.56 0.000
Coefficient Std. error t P>| t| [95% Conf. Interval]
Main food
Gamma food 51.020 1.254 40.690 0.000 48.562 53.477
Beta food 0.289 0.007 40.960 0.000 0.275 0.303
Gamma bar 244.249 2.805 87.090 0.000 238.752 249.745
Gamma othfood 0.000 0.000 1.900 0.058 0.000 0.000
Gamma indust -1.009 0.213 -4.750 0.000 -1.425 -0.592
Other food
Gamma othfood 0.000 0.000 -2.640 0.008 0.000 0.000
Beta othfood 0.268 0.006 47.930 0.000 0.257 0.279
Gamma bar 244.249 2.805 87.090 0.000 238.752 249.745
Gamma food 51.020 1.254 40.690 0.000 48.562 53.477
Gamma indust -1.009 0.213 -4.750 0.000 -1.425 -0.592
Indust
Gamma indust -1.009 0.213 -4.750 0.000 -1.425 -0.592
Beta indust 0.310 0.006 48.680 0.000 0.298 0.323
Gamma bar 244.249 2.805 87.090 0.000 238.752 249.745
Gamma food 51.020 1.254 40.690 0.000 48.562 53.477
Gamma othfood (dropped)
178
A4.4-4. LES Model in the South
Iteration 1: tolerance = .6650812 Iteration 2: tolerance = .3609569 Iteration 3: tolerance = .08167922 Iteration 4: tolerance = .01609599 Iteration 5: tolerance = .00372692 Iteration 6: tolerance = .00092618 Iteration 7: tolerance = .00023766 Iteration 8: tolerance = .00006185 Iteration 9: tolerance = .0000162 Iteration 10: tolerance = 4.253e-06 Iteration 11: tolerance = 1.118e-06 Iteration 12: tolerance = 2.940e-07 Seemingly unrelated regression, iterated
Equation Obs Parms RMSE 'R-sq' chi2 P
Main food 224 4 377.4217 0.9223 2795.28 0.000
Other food 224 4 324.3866 0.8076 1860.86 0.000
Industry 224 4 403.193 0.8347 1985.05 0.000
Coefficient Std. error t P>| t| [95% Conf. Interval]
Main food
Gamma food 48.057 3.751 12.810 0.000 40.705 55.408
Beta food 0.294 0.017 17.090 0.000 0.261 0.328
Gamma bar 252.658 10.061 25.110 0.000 232.939 272.377
Gamma othfood 0.000 0.000 1.170 0.240 0.000 0.000
Gamma indust -0.254 0.601 -0.420 0.672 -1.432 0.924
Other food
Gamma othfood 0.000 0.000 -0.140 0.887 0.000 0.000
Beta othfood 0.252 0.012 21.420 0.000 0.229 0.276
Gamma bar 252.658 10.061 25.110 0.000 232.939 272.377
Gamma food 48.057 3.751 12.810 0.000 40.705 55.408
Gamma indust -0.254 0.601 -0.420 0.672 -1.432 0.924
Indust
Gamma indust -0.254 0.601 -0.420 0.672 -1.432 0.924
Beta indust 0.327 0.016 20.750 0.000 0.297 0.358
Gamma bar 252.658 10.061 25.110 0.000 232.939 272.377
Gamma food 48.057 3.751 12.810 0.000 40.705 55.408
Gamma othfood (dropped)
179
A5.1: Household Response Elasticities with Farm Profit Alternatively Exogenous
and Endogenous in NE+NW
Elasticities with consumption quantities/labour supply Variables
Main foods Other foods Industry and others Labour supply
P other foods -3E-16 -1 -7E-16 1.2E-16
P industrial goods 0.006 0.011 -1.033 -0.002
P main foods -0.661 -0.320 -0.335 0.059
P rice *ZX -0.672 -0.326 -0.341 0.060
ZX -0.355 0.307 0.321 -0.056
P pork *ZX -0.685 -0.332 -0.347 0.061
ZX -0.049 0.940 0.982 -0.173
P chicken *ZX -0.677 -0.328 -0.343 0.060
ZX -0.096 0.832 0.869 -0.153
Labour wage *ZX -0.231 -0.463 -0.483 0.269
ZX 0.555 1.108 1.158 -0.020
A5.2: Household Response Elasticities with Farm Profit Alternatively Exogenous
and Endogenous in the Central
Elasticities with consumption quantities/labour supply Variables
Main foods Other foods Industry and others Labour supply
P other foods 1E-16 -1 2E-16 -2.7E-17
P industrial goods 0.014 0.028 -1.068 -0.004
P main foods -0.658 -0.268 -0.294 0.035
P rice *ZX -0.669 -0.272 -0.299 0.036
ZX -0.622 -0.181 -0.199 0.024
P pork *ZX -0.683 -0.278 -0.305 0.037
ZX -0.748 -0.402 -0.442 0.053
P chicken *ZX -0.670 -0.272 -0.299 0.036
ZX -0.501 0.052 0.057 -0.007
Labour wage *ZX -0.275 -0.529 -0.581 0.202
ZX 0.591 1.137 1.249 -0.018
180
A5.3: Household Response Elasticities with Farm Profit Alternatively Exogenous
and Endogenous in the South
Elasticities with consumption quantities/labour supply Variables
Main foods Other foods Industry and others Labour supply
P other foods 6E-18 -1 1E-17 -1.4E-18
P industrial goods 0.003 0.005 -1.011 -0.001
P main foods -0.706 -0.2107 -0.2141 0.030
P rice *ZX -0.714 -0.213 -0.217 0.031
ZX 0.942 2.628 2.669 -0.376
P pork *ZX -0.734 -0.219 -0.223 0.031
ZX 1.542 3.684 3.742 -0.527
P chicken *ZX -0.717 -0.214 -0.217 0.031
ZX 0.994 2.722 2.764 -0.389
Labour wage *ZX -0.248 -0.426 -0.433 0.204
ZX 0.585 1.003 1.019 0.000
181
A5.4: Model structure and equation forms for each regions using in the household model Framework of the household model:
The detail of the model in equation form for each region: 1. Production functions:
058.0223.0048.0059.061.0)( 98.738 pesticidefertilizerseedRRDrice VVVDAF
058.0223.0048.0059.061.0
)( 16.751 pesticidefertilizerseedNWNErice VVVDAF
058.0223.0048.0059.061.0
)( 33.566 pesticidefertilizerseedCentralrice VVVDAF
058.0223.0048.0059.061.0
)( 35.622 pesticidefertilizerseedSouthrice VVVDAF
182
095.00171.584.0
)( 31.1 pigpigpigRRDpig VDGF
095.00171.584.0
)( 56.1 pigpigpigNWNEpig VDGF
095.00171.584.0
)( 57.1 pigpigpigCentralpig VDGF
095.00171.584.0
)( 92.1 pigpigpigSouthpig VDGF
137.021.0.46.0
)( 23.1 chickenchickenchickenRRDchicken VDGF
137.021.0.46.0
)( 21.1 chickenchickenchickenNWNEchicken VDGF
137.021.0.46.0
)( 33.1 chickenchickenchickenCentralchicken VDGF
137.021.0.46.0
)( 86.1 chickenchickenchickenSouthchicken VDGF
in which A: land cultivation for rice production D: labour requirement V: variable inputs G: feed for pig or chicken 2. Utility functions – LES (of each individual people in the household):
)024.4ln(136.0)ln(334.0)363.61ln(308.0)35.206ln(071.0)35.206ln(152.0 mccsu ofdfdRRD
)ln(262.0)ln(245.0)8.57ln(321.0)35.254ln(065.0)35.254ln(107.0 mccsu ofdfdNWNE
183
)ln(31.0)ln(268.0)020.51ln(289.0)25.244ln(052.0)25.244ln(081.0 mccsu ofdfdCentral
)ln(327.0)ln(252.0)057.48ln(294.0)66.252ln(042.0)66.252ln(084.0 mccsu ofdfdSouth
in which:
s is the quantity of time supplied to work activities
cfd is per capita consumption of main food group of commodity Cfd,
cofd is per capita consumption of commodity group of other foods Cofd,
m is per capita consumption of industrial goods and other expenditure M
3. Demand for main food group of the household:
)ln()ln(*
*
P
Mpw i
jjijii
)ln(097.0)ln(015.0)ln(016.0)ln(045.0)ln(029.0)ln(079.0)ln(185.0404.1*)(
RRD
RRDothmeatvegefishchicporkriceRRDrice
P
Mppppppw
)ln(069.0)ln(0018.0)ln(0026.0)ln(0026.0)ln(0005.0)ln(052.0)ln(08.0369.0*)(
RRD
RRDothmeatvegefishchicporkriceRRDpork
P
Mppppppw
)ln(0044.0)ln(0016.0)ln(009.0)ln(006.0)ln(034.0)ln(0005.0)ln(029.0034.0*)(
RRD
RRDothmeatvegefishchicporkriceRRDchicken
P
Mppppppw
)ln(0065.0)ln(0017.0)ln(0011.0)ln(012.0)ln(006.0)ln(026.0)ln(045.0025.0*)(
RRD
RRDothmeatvegefishchicporkriceRRDfish
P
Mppppppw
)ln(0132.0)ln(0020.0)ln(021.0)ln(0012.0)ln(009.0)ln(0026.0)ln(016.0184.0*)(
RRD
RRDothmeatvegefishchicporkriceRRDvege
P
Mppppppw
184
)ln(030.0)ln(015.0)ln(0020.0)ln(0017.0)ln(0016.0)ln(0018.0)ln(015.0159.0*)(
RRD
RRDothmeatvegefishchicporkriceRRDothmeat
P
Mppppppw
)ln(046.0)ln(0036.0)ln(019.0)ln(013.0)ln(033.0)ln(0024.0)ln(064.0899.0*)(
NWNE
NWNEothmeatvegefishchicporkriceNWNErice
P
Mppppppw
)ln(023.0)ln(014.0)ln(0149.0)ln(0028.0)ln(0163.0)ln(045.0)ln(024.0002.0*)(
NWNE
NWNEothmeatvegefishchicporkriceNWNEpork
P
Mppppppw
)ln(0018.0)ln(0115.0)ln(0105.0)ln(007.0)ln(043.0)ln(0163.0)ln(033.0042.0*)(
NWNE
NWNEothmeatvegefishchicporkriceNWNEchicken
P
Mppppppw
)ln(0279.0)ln(0005.0)ln(0053.0)ln(072.0)ln(0076.0)ln(0028.0)ln(013.0151.0*)(
NWNE
NWNEothmeatvegefishchicporkriceNWNEfish
P
Mppppppw
)ln(0273.0)ln(0059.0)ln(0227.0)ln(0053.0)ln(0105.0)ln(0149.0)ln(019.0276.0*)(
NWNE
NWNEothmeatvegefishchicporkriceNWNEvege
P
Mppppppw
)ln(020.0)ln(016.0)ln(0059.0)ln(0005.0)ln(0115.0)ln(0142.0)ln(0036.0065.0*)(
NWNE
NWNEothmeatvegefishchicporkriceNWNEothmeat
P
Mppppppw
)ln(1.0)ln(046.0)ln(0057.0)ln(053.0)ln(0042.0)ln(019.0)ln(1181.0331.1*)(
Central
CentralothmeatvegefishchicporkriceCentralrice
P
Mppppppw
)ln(037.0)ln(0004.0)ln(0098.0)ln(0084.0)ln(0181.0)ln(036.0)ln(017.0139.0*)(
Central
CentralothmeatvegefishchicporkriceCentralpork
P
Mppppppw
)ln(0091.0)ln(0011.0)ln(0011.0)ln(015.0)ln(0012.0)ln(018.0)ln(042.0017.0*)(
Central
CentralothmeatvegefishchicporkriceCentralchicken
P
Mppppppw
)ln(031.0)ln(0147.0)ln(0014.0)ln(017.0)ln(015.0)ln(0084.0)ln(053.0138.0*)(
Central
CentralothmeatvegefishchicporkriceCentralfish
P
Mppppppw
)ln(0006.0)ln(0107.0)ln(0073.0)ln(0014.0)ln(0019.0)ln(0098.0)ln(0057.0081.0*)(
Central
CentralothmeatvegefishchicporkriceCentralvege
P
Mppppppw
)ln(024.0)ln(022.0)ln(0107.0)ln(0145.0)ln(0011.0)ln(0004.0)ln(046.0151.0*)(
Central
CentralothmeatvegefishchicporkriceCentralothmeat
P
Mppppppw
)ln(103.0)ln(019.0)ln(033.0)ln(083.0)ln(0105.0)ln(041.0)ln(187.0346.1*)(
South
SouthothmeatvegefishchicporkriceSouthrice
P
Mppppppw
185
)ln(058.0)ln(023.0)ln(0033.0)ln(042.0)ln(0144.0)ln(017.0)ln(041.0262.0*)(
South
SouthothmeatvegefishchicporkriceSouthpork
P
Mppppppw
)ln(0065.0)ln(0055.0)ln(0056.0)ln(0006.0)ln(0145.0)ln(0144.0)ln(0105.0022.0*)(
South
SouthothmeatvegefishchicporkriceSouthchicken
P
Mppppppw
)ln(014.0)ln(0056.0)ln(014.0)ln(0033.0)ln(0006.0)ln(042.0)ln(083.00052.0*)(
South
SouthothmeatvegefishchicporkriceSouthfish
P
Mppppppw
)ln(0078.0)ln(0002.0)ln(0102.0)ln(0142.0)ln(0056.0)ln(0033.0)ln(033.00039.0*)(
South
SouthothmeatvegefishchicporkriceSouthvege
P
Mppppppw
)ln(017.0)ln(0038.0)ln(0002.0)ln(0056.0)ln(0055.0)ln(023.0)ln(019.01076.0*)(
South
SouthothmeatvegefishchicporkriceSouthothmeat
P
Mppppppw ii
in which
wi: budget share of a given food commodity pi: the price of commodity i i = rice, pork, chicken, fish and prawn, vegetable, and other meats M: household expenditure P: price index
186
A6.1: Initial Values and Percentage Changes in Vietnamese Exports from Alternative Scenarios with Closure A
Sector Initial exports
(mill.USD)
Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 374 -8 57 65 -2 -4 12 42
Vegetable and fruit 257 -1 -7 7 -1 -10 4 10
Other crops 810 -2 -5 -18 -2 -10 -13 -24
Live Pig 2 1 -8 -2 -1 -13 -5 -13
LivePoultry 0 15 1 -17 0 -11 -4 -10
LiveOther 62 -2 -5 -17 0 -10 -3 -7
Pork, poultry, other meats 33 -9 -12 -45 0 -22 -20 -45
Beef and sheep meats 0 22 -6 -28 4 -22 2 15
Fishing 49 3 1 2 0 -5 2 7
Oilseed and vegetable oil 45 2 115 102 -2 -13 7 34
Processed food 1365 1 2 -12 -1 -7 -8 -19
Beverages and tobacco 22 6 12 19 0 6 8 19
Milk and dairy products 2 29 -1 278 73 -16 37 222
Natural res, petrol product 2346 3 -1 -1 -1 -4 0 -3
Chemical, rubber, plastic 495 -8 -1 194 -3 -16 28 140
Textile and apparel 4746 63 4 33 10 38 35 81
Manufactures 2313 11 20 10 -3 -12 2 0
Electronic 446 49 23 28 0 -5 14 26
Transport, communication 534 -2 -2 -7 -1 -8 -3 -8
Services 1552 -8 -4 -17 -3 -13 -11 -24
Source: GTAP simulation
187
A6.2: Initial Values and Percentage Changes in Vietnamese Imports from Alternative Scenarios with Closure A
Sector Initial imports
(mill.USD)
Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 17 70 26 118 2 13 44 130
Vegetable and fruit 71 48 13 47 15 7 25 62
Other crops 225 17 6 16 2 6 8 21
Live Pig 5 2 1 11 1 5 6 13
LivePoultry 7 -2 3 3 0 5 3 4
LiveOther 29 3 2 12 1 6 6 16
Pork, poultry, other meats 20 69 47 66 9 27 33 104
Beef and sheep meats 7 9 2 4 4 -7 -1 5
Fishing 6 9 1 2 7 3 4 7
Oilseed and vegetable oil 90 14 23 28 1 3 10 25
Processed food 374 39 13 25 5 13 21 49
Beverages and tobacco 395 51 47 55 1 7 22 59
Milk and dairy products 239 19 4 11 3 7 12 26
Natural res, petrol product 1692 7 2 8 0 0 4 8
Chemical, rubber, plastic 2796 10 3 19 1 7 9 24
Textile and apparel 1848 78 11 57 11 37 41 101
Manufactures 6780 25 10 26 2 7 13 30
Electronic 1002 11 7 7 0 0 3 6
Transport, communication 2546 1 0 2 1 4 2 5
Services 6997 5 2 10 1 7 6 15
Source: GTAP simulation
188
A6.3: Price Changes of Consumption Commodities under Alternative GTAP Scenarios with Closure A (percentage)
Commodities Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 1.77 3.64 9.79 0.54 3.58 3.7 9.96
Vegetable and fruit -0.58 2.15 8.38 -0.05 3.11 2.84 7.87
Other crops -2.6 -0.47 2.19 0.08 1.54 -0.22 0.72
Live Pig -0.18 2.02 7.93 0.36 3.62 2.87 7.59
Live Poultry -3.3 2.05 7.59 0.03 2.94 1.98 5.68
Live Other 0.17 0.71 4.73 0.23 3.02 1.66 4.58
Pork, poultry, other meats -1.83 -0.38 3.67 0.11 2.33 1.3 2.25
Beef and sheep meats -2.64 0.84 3.77 -0.52 3.33 1.07 2.36
Fishing -1.75 0.01 0.78 0.51 2.64 0.01 -0.47
Oilseed and vegetable oil -14.74 -13.31 -12.49 -0.02 0.65 -6.14 -13.76
Processed food -6.37 -1.05 0.46 -0.17 1.21 -1.88 -4.1
Beverages and tobacco -22.46 -18 -19.47 0.12 1.08 -9.12 -20.15
Milk and dairy products -10.77 -1.45 -0.39 -0.34 0.94 -3.71 -7.62
Natural res, petrol product -7.2 -1.16 -6.92 -0.24 -0.16 -3.5 -7.27
Chemical, rubber, plastic -2.35 -0.56 0.12 0.19 0.99 -0.58 -0.85
Textile and apparel -14.59 -1.54 -10.21 -0.78 -0.31 -6.23 -13.71
Manufactures -7.88 -1.64 -5.95 0.2 1.08 -2.66 -6.07
Electronic -8.47 -4.56 -6.42 -0.47 -1.27 -3.95 -8.27
Transport, communication 0.18 0.21 0.73 0.17 0.95 0.37 1.1
Services 1.58 0.71 3.47 0.54 2.72 1.95 4.85
Source: GTAP simulation
189
A6.4: Changes of Trade Balance under Alternative GTAP Scenarios with Closure B (mill. USD)
Sectors Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice -25 232 297 -5 -2 57 199
Vegetable and fruit -45 -24 -2 -13 -25 -3 -10
Other crops -57 -50 -164 -15 -82 -115 -235
Live Pig -1 0 -1 0 -1 -1 -2
Live Poultry -1 -1 -2 0 -1 -1 -2
Live Other 7 -1 -3 0 -4 3 7
Pork, poultry, other meats -17 -13 -29 -2 -13 -13 -37
Beef and sheep meats -1 0 -1 0 0 0 -1
Fishing -2 -1 -2 0 -2 0 0
Oilseed and vegetable oil -15 37 30 -2 -9 -6 -4
Processed food -170 -23 -232 -25 -124 -187 -449
Beverages and tobacco -224 -193 -242 -7 -34 -96 -265
Milk and dairy products -61 -15 -34 -6 -31 -40 -89
Natural res, petrol product -189 -94 -287 -27 -115 -135 -328
Chemical, rubber, plastic -450 -123 428 -64 -311 -161 -13
Textile and apparel 1388 48 621 275 1337 948 2112
Manufactures -1719 -284 -1783 -182 -826 -965 -2358
Electronic 44 8 4 -7 -33 1 -2
Transport, communication -175 -66 -241 -39 -198 -156 -361
Services -617 -234 -1103 -154 -772 -683 -1598
Source: GTAP simulation
190
A6.5: Price Changes of Consumption Commodities under Alternative GTAP Scenarios with Closure B (percentage)
Commodities Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 0.98 3.43 9.17 0.49 3.37 3.33 9.17
Vegetable and fruit -0.19 2.35 9.15 -0.01 3.35 3.24 8.96
Other crops -2.78 -0.52 2.04 0.07 1.5 -0.31 0.5
Live Pig 1.56 2.7 10.29 0.52 4.37 4.09 10.8
Live Poultry 4.26 4.75 16.55 0.69 5.84 6.76 17.7
Live Other -3.89 -0.63 0.57 -0.11 1.6 -0.67 -0.91
Pork, poultry, other meats -2.12 -0.41 3.64 0.1 2.3 1.24 2.23
Beef and sheep meats -1.6 1.26 5.19 -0.42 3.78 1.81 4.27
Fishing 2.5 1.5 5.5 0.87 4.27 2.64 5.48
Oilseed and vegetable oil -14.9 -13.42 -12.72 -0.04 0.57 -6.3 -14.04
Processed food -6.17 -0.92 0.81 -0.14 1.35 -1.68 -3.73
Beverages and tobacco -23.26 -18.25 -20.27 0.04 0.74 -9.63 -21.22
Milk and dairy products -11.33 -1.68 -1.09 -0.4 0.7 -4.07 -8.34
Natural res, petrol product -7.19 -1.09 -6.79 -0.24 -0.15 -3.26 -6.8
Chemical, rubber, plastic -3.15 -0.83 -0.71 0.13 0.72 -1.04 -1.88
Textile and apparel -15.13 -1.8 -10.82 -0.84 -0.55 -6.62 -14.46
Manufactures -8.56 -1.9 -6.66 0.13 0.81 -3.1 -7.04
Electronic -8.53 -4.65 -6.58 -0.47 -1.29 -4.03 -8.46
Transport, communication -0.25 0.1 0.32 0.14 0.82 0.18 0.59
Services 0.11 0.27 2.01 0.43 2.26 1.15 2.85
Source: GTAP simulation
191
A6.6: Percentage Changes in Vietnamese Outputs under Alternative GTAP Scenarios with Closure C (R=12%)
Sector Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 0 7 9 0 1 3 6
Vegetable and fruit -2 -2 -1 0 0 0 -1
Other crops -3 -5 -12 -1 -5 -7 -16
Live Pig 5 2 6 1 5 6 8
Live Poultry 4 1 4 1 4 5 6
Live Other 2 0 -1 0 0 2 -1
Pork, poultry, other meats -9 -8 -18 -1 -7 -7 -24
Beef and sheep meats -3 0 -3 -1 1 -1 -7
Fishing 1 1 1 0 1 1 -1
Oilseed and vegetable oil -13 39 32 -1 -7 -4 -2
Processed food -5 -1 -9 -1 -5 -7 -17
Beverages and tobacco -19 -16 -17 0 2 -6 -18
Milk and dairy products -20 -2 2 0 0 -5 -17
Natural res, petrol product -3 0 -6 -1 -3 -2 -8
Chemical, rubber, plastic -3 0 39 0 -3 7 25
Textile and apparel 41 4 19 7 32 26 51
Manufactures -11 3 -13 -1 -5 -5 -18
Electronic 50 23 30 0 -3 17 28
Transport, communication 4 1 0 0 -3 2 -2
Services 9 3 8 1 4 6 9
Source: GTAP simulation
192
A6.7: Percentage Changes in Vietnamese Exports from Alternative Scenarios with Closure C (R=12%)
Sector Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice -6 58 68 -2 -3 13 44
Vegetable and fruit -2 -8 6 -1 -10 3 9
Other crops -1 -5 -17 -1 -10 -12 -23
Live Pig -4 -10 -7 -2 -15 -8 -15
LivePoultry -7 -9 -33 -3 -21 -20 -27
LiveOther 11 0 -6 1 -5 8 6
Pork, poultry, other meats -6 -11 -45 0 -22 -19 -43
Beef and sheep meats 16 -9 -33 3 -24 -3 8
Fishing -2 -2 -4 -1 -8 -2 3
Oilseed and vegetable oil 6 118 109 -2 -12 10 39
Processed food 1 1 -12 -1 -8 -9 -20
Beverages and tobacco 9 14 22 1 7 11 23
Milk and dairy products 40 3 311 75 -13 46 251
Natural res, petrol product 4 -1 0 -1 -4 0 -1
Chemical, rubber, plastic 0 3 219 -2 -12 38 164
Textile and apparel 74 7 41 10 42 41 90
Manufactures 19 23 17 -2 -10 7 7
Electronic 59 27 37 1 -3 21 36
Transport, communication 1 -1 -4 -1 -7 -1 -6
Services -3 -1 -13 -2 -11 -7 -20
Source: GTAP simulation
193
A6.8: Percentage Changes in Vietnamese Imports from Alternative Scenarios with Closure C (R=12%)
Sector Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 71 26 120 2 14 44 131
Vegetable and fruit 54 15 53 16 9 29 69
Other crops 21 8 20 2 8 11 25
Live Pig 8 4 18 1 9 12 21
LivePoultry 9 9 16 2 11 13 17
LiveOther 3 2 12 1 6 7 16
Pork, poultry, other meats 73 50 72 10 30 37 111
Beef and sheep meats 14 4 9 5 -5 3 10
Fishing 17 4 8 7 6 10 13
Oilseed and vegetable oil 18 25 33 1 5 13 29
Processed food 44 15 29 5 15 24 55
Beverages and tobacco 55 49 59 2 9 25 63
Milk and dairy products 24 6 15 3 9 16 31
Natural res, petrol product 13 5 13 1 2 8 14
Chemical, rubber, plastic 15 5 24 2 9 13 29
Textile and apparel 87 14 64 11 40 46 109
Manufactures 30 12 30 2 10 17 35
Electronic 17 10 12 1 2 8 12
Transport, communication 5 2 7 1 6 6 10
Services 7 3 13 2 9 9 18
Source: GTAP simulation
194
A7.1: Estimated Parameters on Price Interrelation between Regions and National
Level for Some Main Commodities
Commodity Red River Delta NE+NW The Central The South
Rice 1.083515
(-1.47)
1.206036
(-2.48)**
0.89411138
(0.96)
0.7123662
(5.69)*
Paddy 0.958295
(1.87)
0.99446
(0.29)
0.985169
(0.74)
1.093085
(-3.03)*
Pig 0.9678404
(0.66)
1.334515
(-7.95)*
0.9001949
(2.95)*
0.9679933
(0.52)
Chicken 1.060107
(-3.68)*
1.086309
(-5.54)*
0.849838
(6.16)*
1.029625
(-1.44)
Beef .9649945
(0.90)
1.04176
(-1.24)
0.9984677
(0.06)
1.079186
(-1.61)
Maize 0.958644
(0.78)
0.801897
(4.64)*
1.204399
(-6.06)*
1.02281
(-0.38)
Soybean 0.979604
(0.81)
0.987675
(0.53)
0.847164
(8.64)*
1.139757
(-7.22)*
Cassava 1.163563
(-2.63)*
1.094039
(-1.78)
0.731602
(8.48)*
0.997324
(0.06)
Orange 1.156382
(-2.95)*
1.018733
(-0.33)
0.928677
(1.47)
0.91281
(1.65)
Tomato 0.996818
(-0.04)
1.092844
(-1.55)
0.955616
(0.53)
1.031711
(-0.87)
Chemical fertiliser 1.027977
(-1.55)
1.036671
(-1.73)
1.005628
(-0.69)
0.9123972
(3.41)*
*: indicates significant different from 1 at 1% level, ** at 5% level of confidence
Numbers in parentheses are t values for testing of divergence of parameter from 1
195
A7.2: Welfare Changes of Households in Different Regions under Alternative
Trade Scenarios
A7.2a: Welfare Changes of Households in Different Regions under Unilateral Trade Scenario
0
200
400
600
800
1000
RRD NE+NW the Central the South
region
000V
ND
5.0%
6.0%
7.0%
chan
ge c
ompa
re w
ith
base
line
A7.2b: Welfare Changes of Households in Different Regions under AFTA Scenario
-200
0
200
400
600
800
1000
1200
RRD NE+NW the Central the South
region
000V
ND
-1%
1%
3%
5%
7%
chan
ge c
ompa
re w
ith
base
line
A7.2c: Welfare Changes of Households in Different Regions under AFTA+3 Scenario
0
500
1000
1500
2000
2500
3000
RRD NE+NW the Central the Southregion
000V
ND
0%
5%
10%
15%
20%
25%ch
ange
com
pare
with
ba
selin
e
A7.2d: Welfare Changes of Households in Different Regions under VNM-EU Scenario
-200
0
200
400
600
800
RRD NE+NW the Central the South
region
000V
ND
-2%
0%
2%
4%
6%
chan
ge c
ompa
re w
ith
base
line
A7.2e: Welfare Changes of Households in Different Regions under Multilateral Scenario
0
200
400
600
800
1000
RRD NE+NW the Central the Southregion
000V
ND
0%
2%
4%
6%
8%
chan
ge c
ompa
re w
ith
base
line
A7.2g: Welfare Changes of Households in Different Regions under Global Scenario
0
500
1000
1500
2000
2500
3000
3500
RRD NE+NW the Central the Southregion
000V
ND
0%
5%
10%
15%
20%
25%
chan
ge c
ompa
re w
ith
base
line
Source: Household model simulation
Note: Bar graph: change in welfare in terms of value compare with baseline ('000 VND)
Line graph: percentage change of welfare compare with baseline
196
A.7.3 Changes in Consumption of Households under AFTA+3 Scenario
A7.3a: Changes in Consumption of Households in Different Regions under AFTA+3
Scenario
0%
5%
10%
15%
20%
25%
30%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Main food Oth foods Ind & oth exp
A7.3b: Changes in Main Food Consumption Quantity of Households in Different Regions
under AFTA+3 Scenario
-2%
0%
2%
4%
6%
8%
10%
RRD NE+NW the Central the South
region
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pork Chicken Fish-shrimp Vegetable Other meats
197
A7.4: Production Changes of Households under Alternative Scenarios
Production Changes in RRD Households under Alternative Scenarios
-20%
-10%
0%
10%
20%
30%
40%
50%
Uni AFTA AFTA+3 VNM-USA VNM-EU Multi Glob
scenario
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken
Production Changes in NE+NW Household under Alternative Scenarios
-20%
-10%
0%
10%
20%
30%
uni AFTA AFTA+3 VNM-EU Multi Glob
scenario
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken
Production Changes in the Central Household under Alternative Scenarios
-4%
0%
4%
8%
12%
16%
uni AFTA AFTA+3 VNM-EU Multi Glob
scenario
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken
Production Changes in the South Household under Alternative Scenarios
0%
4%
8%
12%
16%
20%
uni AFTA AFTA+3 VNM-EU Multi Glob
scenario
chan
ge c
ompa
re w
ith b
asel
ine
Rice Pig Chicken
198
A7.5: Welfare Changes of Households under Alternative Scenarios
Welfare Change in RRD Household under Alternative Scenarios
-2%
2%
6%
10%
14%
18%
22%
Unilateral AFTA AFTA+3 VNM-USA VNM-EU Multilateral Global
scenario
cha
ng
e c
om
pa
re w
ith b
ase
line
Welfare change compare w ith baseline (%) Welfare change due to leisure change (%)
Welfare Change in NE+NW Household under Alternative Scenarios
-2%
2%
6%
10%
14%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith b
asel
ine
Welfare change compare w ith baseline (%) Welfare change due to leisure change (%)
Welfare Change in the Central Household under Alternative Scenarios
0%
4%
8%
12%
16%
20%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith b
asel
ine
Welfare change compare w ith baseline (%) Welfare change due to leisure change (%)
Welfare Change in the South Household under Alternative Scenarios
0%
5%
10%
15%
20%
25%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Welfare change compare w ith baseline (%) Welfare change due to leisure change (%)
199
A7.6: Changes in Consumption Quantities of Households under Alternative Scenarios
Consumption Change in RRD Household under Alternative Scenarios
-10%
0%
10%
20%
30%
40%
Unilateral AFTA AFTA+3 VNM-USA VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Main foods Other foods Industrial goods & others
Consumption Change in NE+NW Household under Alternative Scenarios
-10%
0%
10%
20%
30%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Main foods Other foods Industrial goods & others
Consumption Change in the Central Household under Alternative Scenarios
0%
10%
20%
30%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Globalscenario
chan
ge c
ompa
re w
ith
base
line
Main foods Other foods Industrial goods & others
Consumption Change in the South Household under Alternative Scenarios
0%
10%
20%
30%
40%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Main foods Other foods Industrial goods & others
200
A7.7: Changes in Main Food Consumptions of Households under Alternative Scenarios
Main Food Consumption Changes in RRD Household under Alternative Scenarios
-3%
1%
5%
9%
Unilateral AFTA AFTA+3 VNM-USA VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Rice Pork Chicken Fish-shrimp Vegetable Other meats
Main Food Consumption Changes in NE+NW Household under Alternative Scenarios
-6%
-4%
-2%
0%
2%
4%
6%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Rice Pork Chicken Fish-shrimp Vegetable Other meats
Main Food Consumption Changes in the Central Household under Alternative Scenarios
-4%
-2%
0%
2%
4%
6%
8%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Rice Pork Chicken Fish-shrimp Vegetable Other meats
Main Food Consumption Changes in the South Household under Alternative Scenarios
-4%
0%
4%
8%
12%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
Rice Pork Chicken Fish-shrimp Vegetable Other meats
201
A7.8: Changes in Time Allocation of Households under Alternative Scenarios
Time Allocation Changes in RRD Household under Alternative Scenrios
-20%
0%
20%
40%
60%
Uni AFTA AFTA+3 VNM-USA VNM-EU Multi Glob
scenario
chan
ge c
ompa
re w
ith
base
line
leisure day onfarm day off-farm
Time Allocation Changes in NE+NW Household under Alternative Scenarios
-20%
0%
20%
40%
60%
80%
100%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
leisure day onfarm day off-farm
Time Allocation in the Central Household under Alternative Scenarios
-4%
0%
4%
8%
12%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
leisure day onfarm day off-farm
Time Allocation Change in the South Household under Alternative Scenarios
-8%
-4%
0%
4%
8%
12%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
leisure day onfarm day off-farm
202
A7.9: Changes in Labour Allocation of Households under Alternative Scenarios with Different Assumptions on Labour Market
Labour Allocation Changes of RRD Household under Alternative Scenarios with Different Assumptions on Labour Market
-40%
-20%
0%
20%
40%
60%
Uni AFTA AFTA+3 VNM-EU Multi Glob
scenario
chan
ge c
ompa
re w
ith
base
line
day onfarm w ith Closure C day onfarm w ith Closure A
day off-farm w ith Closure C day off-farm w ith Closure A
Labour Allocation Changes in NE+NW Household under Alternative Scenarios with Different Assumptions on Labour
Market
-20%
0%
20%
40%
60%
80%
100%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
day onfarm w ith Closure C day onfarm w ith Closure A
day off-farm w ith Closure C day off-farm w ith Closure A
Labour Allocation Changes in the Central Household under Alternative Scenarios with Different Assumptions on Labour
Market
-15%
-10%
-5%
0%
5%
10%
15%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re
with
bas
elin
e
day onfarm w ith Closure C day onfarm w ith Closure A
day off-farm w ith Closure C day off-farm w ith Closure A
Labour Allocation Changes in the South Household under Alternative Scenarios with Different Assumptions on
Labour Market
-10%
-5%
0%
5%
10%
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
day onfarm w ith Closure C day onfarm w ith Closure A
day off-farm w ith Closure C day off-farm w ith Closure A
203
A7.10: Welfare Changes in Households under Alternative Scenarios with Different Assumptions on Labour Market
Welfare Change in RRD Household under Alternative Scenarios with Different Assumptions on Labour Market
0
1000
2000
3000
Uni AFTA AFTA+3 VNM-EU Multi Globscenario
chan
ge c
ompa
re w
ith
base
line
(000
VN
D)
Welfare change w ith Closure C Welfare change w ith Closure A
Welfare Change in NE+NW Household under Alternative Scenarios with Different Assumptions on Labour Market
0
1000
2000
3000
Unilateral AFTA AFTA+3 VNM-EU Multilateral Globalsecenario
chan
ge c
ompa
re w
ith b
asel
ine
(000
VN
D)
Welfare change w ith Closure C Welfare change w ith Closure A
Welfare Change in the Central Household under Alternative with Different Assumptions on Labour Market
0
500
1000
1500
2000
2500
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith
base
line
(000
VN
D)
Welfare change w ith Closure C Welfare change w ith Closure A
Welfare Change in the South Household under Alternative Scenarios with Different Assumptions on Labour Market
0
1000
2000
3000
4000
Unilateral AFTA AFTA+3 VNM-EU Multilateral Global
scenario
chan
ge c
ompa
re w
ith b
asel
ine
(000
VN
D)
Welfare change w ith Closure C Welfare change w ith Closure A
204
A7.11: Percentage Changes in Unskilled Labour Demand in Sectors from Alternative Scenarios with Closure C (R=12%) (percentage)
Sector Unilateral
(1)
AFTA
(2)
AFTA+3
(3)
VNM-USA
(4)
VNM-EU
(5)
Multilateral
(6)
Global
(7)
Paddy and processed rice 1.53 10.11 13.79 0.34 2.44 5.94 9.36
Vegetable and fruit -1.31 -0.6 0.7 -0.36 0.07 1.43 -0.56
Other crops -2.82 -4.1 -11.65 -0.66 -4.65 -6.67 -16.69
Live Pig 5.63 3.08 8.6 1.25 5.88 8 9.61
LivePoultry 5.34 2.93 8.01 1.2 5.52 7.81 8.53
LiveOther 2.37 -0.09 -0.5 0.48 0.46 2.23 -0.41
Pork, poultry, other meats -5.84 -6.33 -14.5 -0.46 -5.3 -4.45 -20.92
Beef and sheep meats 2.92 2.86 2.3 -0.6 4.29 3.8 -2.32
Fishing 2.06 1.79 1.83 0.48 1.77 2.35 -1.05
Oilseed and vegetable oil -12.95 49.14 42.21 -1.01 -6.85 -1.62 0.45
Processed food 3.25 3.16 -1.46 0.29 -0.35 -0.03 -9.63
Beverages and tobacco -13.1 -13.42 -11.9 1.22 5.33 -0.6 -12.33
Milk and dairy products -16.49 0.37 6.11 0.34 2.41 -1.84 -13.76
Natural res, petrol product -3.04 0.05 -6.64 -0.66 -3.59 -1.96 -9.11
Chemical, rubber, plastic 5.23 3.71 49.49 0.63 1.28 13.75 34.14
Textile and apparel 53.1 7.99 28.63 7.89 37.26 34.2 62.55
Manufactures -3.42 7.16 -5.55 0.19 -0.25 1.77 -11.37
Electronic 61.6 27.47 39.77 1.2 0.61 24.83 38.14
Transport, communication 16.12 6.37 10.53 1.13 3.27 11.69 9.51
Services 19.54 7.93 17.92 2.06 8.72 14.88 18.97
Source: GTAP simulation
205
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