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Modeling and Policy Impact Analysis(MPIA) Network
A paper presented during the 4th PEP Research Network General Meeting,June 13-17, 2005, Colombo, Sri Lanka.
Modeling and Policy Impact Analysis(MPIA) Network
Ganga Manjari TilakaratnaSri Lanka
Trade Liberalisationand Poverty in Sri Lanka:
An Analysis with a Poverty FocusedComputable General Equilibrium
(CGE) Model
Ganga Manjari TilakaratnaSri Lanka
Trade Liberalisationand Poverty in Sri Lanka:
An Analysis with a Poverty FocusedComputable General Equilibrium
(CGE) Model
1
Proposal 10467
Trade Liberalisation and Poverty in Sri Lanka:
An Analysis with a Poverty-Focused Computable
General Equilibrium (CGE) Model
A Research Proposal submitted to
The Modelling and Policy Impact Assessment (MPIA) Sub-network of
Poverty and Economic Policy (PEP) Network.
By
Ganga Tilakaratna (Team Leader) Institute of Policy Studies of Sri Lanka P.H. Thusitha Kumara Institute of Policy Studies of Sri Lanka Athula Naranpanawe Sri Lankan PhD Student, Griffith University J.S. Bandara (External Resource Person) Griffith University, Australia
15 February 2005
2
RESEARCH PRPOSAL
Trade Liberalisation and Poverty in Sri Lanka: An Analysis with a Poverty
Focused Computable General Equilibrium (CGE) model
1. Abstract: Many trade and development economists, policy makers and policy
analysts around the world believe that trade liberalisation plays a vital role in
reducing poverty in developing countries. However, understanding the links between
trade and poverty is not simple. These links are complex. There exists a large body of
theoretical and empirical literature on how trade liberalisation helps to promote
growth and reduce poverty. The critics of globalisation, however, argue that
developing countries’ integration into the world economy makes the poor poorer and
the rich richer in developing countries. The most common criticism of globalisation is
that it increases poverty and inequality. Much of the research related to the links
between openness, growth and poverty has been based on cross-country regressions.
In recent years, many critics have pointed out the limitations of these studies. These
limitations of cross-country studies have given rise to systematic case studies using
poverty-focused computable general equilibrium (CGE) models and the results of
these case studies at least complement the results of cross-country studies. Sri Lanka
provides another good case study as a first country in South Asia to open the
economy. It has also been the second hard-hit country by the recent tsunami. This
recent tragic event created an urgent need for a poverty-focused CGE model. The
purpose of this study, therefore, is to develop a poverty-focused CGE model for Sri
Lanka. It is expected that the results of this study will provide a better understanding
of the links between trade liberalisation and poverty that can make trade regime an
effective mechanism for poverty reduction in Sri Lanka.
3
2. Main Research Questions and Core Research Objectives
In recent years, there have been serious concerns over the links between trade and
poverty. Dollar and Kraay (2000; 2001), using regression analysis, argue that growth
is pro-poor. Moreover, the study suggests that growth does not affect distribution and
poor as well as rich could benefit from it. Later, they demonstrated that openness to
international trade stimulates rapid growth; thus, linking trade liberalisation with
improvements in well-being of the poor. Several other cross-country studies have
demonstrated a positive relationship between trade openness and economic growth
(see for example Dollar, 1992; Sach and Warner, 1995 and Edward, 1998). In
contrast, Rodriguez and Rodrick (2001) questioned the measurements related to trade
openness in economic models, and suggested that generalisations cannot be made
regarding the relationship between trade openness and growth. Several other studies
have also criticised the pro-poor growth argument based upon the claim of weak
econometrics and have placed more focus on distributional aspect (see, for example,
Rodrick, 2000). Ultimately, openness and growth have therefore become an empirical
matter, and so has the relationship between trade and poverty.
These weaknesses of cross-country studies have led to a need for providing further
evidence from case studies. Various researchers have adopted different empirical
approaches to identify the complex link between globalisation and poverty. In
addition to Dollar and Kraay, several other studies have found pro poor effects on
trade reforms using partial equilibrium data-based analysis (see Case, 2000; Minot
and Goletti, 2000 and Dercon, 2001) and general equilibrium analysis (see Bautista
and Thomas,1997 and Ianchovichina et al 2001). In contrast, empirical evidence on
negative impacts on the poor has been observed by several other researchers using
partial equilibrium data-based analysis (see Ravallion and Walle, 1991) and general
equilibrium analysis (see Lofgren, 1999 and Harrison et al, 2000). Several others have
found mixed results (see Cockburn, 2001; Devarajan and Mensbrugghe, 2000; and
Cogneau and Robilliard, 2000 for general equilibrium treatments).
Recently many policy analysts have focused on case studies since systematic case
studies related to individual countries will at least complement cross-country studies
such as the study by Dollar and Kraay. As Chen and Ravallion (2004) argue,
4
aggregate inequality or poverty may not change with trade reform even though there
are gainers and losers at all levels of living. They further argue that policy analysis
that simply averages across diversities may miss important matters, which are critical
to the policy debate.
In this study we use Sri Lanka as a case study and seek to apply a poverty-focused
CGE model in our analysis. Sri Lanka is selected as an interesting case in point to
investigate this linkage for the following reasons:
• Although Sri Lanka was the first country in the South Asian region to
liberalise its trade substantially in the late seventies, it still has experienced an
incidence of poverty of a sizeable proportion that cannot totally be attributed
to the long standing civil conflict.
• Moreover, the trade-poverty linkage within the Sri Lankan context has hardly
received any attention.
• Multi-sectoral general equilibrium poverty analysis within Social Accounting
Matrix (SAM) based CGE model has never been attempted.
• It has also been one of the hard-hit countries by the Indian Ocean Tsunami and
the severe damage to its economy has caused increased vulnerability to
poverty.
The key questions that the study seeks to answer are:
• What is the relationship between trade and poverty in Sri Lanka?
• What is the potential role of trade liberalisation in poverty reduction in Sri
Lanka?
• Does trade liberalisation reduce or increase inequality?
• What lessons can we learn from the Sri Lankan experience?
• What are the economic effects of tsunami and the post-tsunami recovery
package?
In order to address the above questions, this study attempts to achieve the following
objectives more specifically:
• To develop a poverty-focussed CGE Model for the Sri Lankan economy to
examine the links between trade and poverty;
5
• To estimate empirically income distribution functional forms for different
household groups and link to the CGE model in "top down" mode to compute
a wide range of household level poverty and inequality measurements;
• To conduct a set of simulation experiments to identify the impacts of trade
liberalisation in manufacturing and agricultural industries on absolute and
relative poverty at household level.
• To conduct two sets of simulations to examine the economy-wide effects of
tsunami and the effects post-tsunami recovery package on the exchange rate,
the balance of payments and trade, productivity and growth, poverty, labour
supply etc.
3. Scientific Contribution of the Research
Computable General Equilibrium (CGE) models have widely been used to analyse a
wide variety of policy issues ranging from trade liberalisation to environmental
degradation both in developed and developing countries over the last three decades or
so. The use of these models has even become more popular among policy analysts in
developing countries, particularly in countries where adjustment policies have been
implemented in recent years. Recently, International Development Research Centre
(IRDC) initiated a project to analyse the micro impact of macro-economic and
adjustment policies (known as the MIMAP project). The Institute of Policy Studies
(IPS) has become the Sri Lankan partner of this project and carried out significant
research related to the impact of adjustment policies in Sri Lanka. Under this project,
however, CGE models have been developed for many countries of the project (such as
India, Pakistan, Bangladesh, Philippines, Vietnam and Nepal), except for Sri Lanka.
This research gap has created a demand for developing a CGE model for Sri Lanka in
order to analyse the micro impact of macroeconomic policies and impact of reforms
on poverty.
6
4. Policy Relevance
Sri Lankan Economy and Trade liberalization
Sri Lanka is an open economy with its total trade equivalent to over 65 per cent of the
country’s GDP. It had an average GDP growth rate of over 5 per cent during the last
decade. The service sector is the dominant sector in the economy, accounting for
about 55 per cent of the GDP and 43 per cent of employment (2003). With a growth
rate of about 7.7 per cent, it has contributed about 70 per cent to the economic growth
in 2003.1 Moreover, the Industrial sector2 accounted for approximately 26 per cent of
the GDP and 22 per cent of the employment. However, the relative importance of the
agricultural sector has declined during the last decade with a lower contribution to
GDP (19 per cent in 2003) and to the economic growth (only 5 per cent in 2003).
Nevertheless, the share of employment in the agricultural sector is still high –
accounting for nearly one third of the total employment in country. 3
Trade liberalization was an important component of the economic liberalization
policy package introduced in 1977. Since then, the tariff structure in the country has
undergone a major reduction of levels and compression of tariff bands, indicating the
country’s commitment to trade liberalization. In 1977, quantitative restrictions on
imports, which were near universal, were replaced by a revised tariff system, retaining
only 280 items under license. Moreover, under the ‘second wave of liberalization
initiated in late 1980s, import tariffs were further reduced with the aim of moving
towards a three-band tariff system (with rates of 10, 20 and 35 per cent). The
government further reduced the maximum tariff rate to 30 per cent in 1998, and then
again to 25 per cent (in 1999) except for few agricultural products. By 1998r, over 50
per cent of the tariff lines in Sri Lanka’s tariff schedule carried tariff rates of 5 per
cent or below and about 73 per cent of the tariff lines contained rates of 10 per cent or
below. In addition, approximately one fifth of the tariff lines carried zero rates.
1 The high growth momentum in trade, transport, telecommunication, tourism and financial sub-sectors
has helped to achieve this high growth in the service sector. 2 The industrial sector is defined to include mining and quarrying, Manufacturing, Construction and
electricity, gas, water and sanitary services.
3 Figures taken from the Central Bank of Sri Lanka, Annual Report 2003
7
However, since 2001, there were some setbacks in the trade liberalization process due
to the economic down turn in 20014 and the fiscal constraints resulting from the civil
war of the country. A temporary surcharge of 40 per cent (subsequently reduced to 20
per cent) was introduced in 2001 across the board, except for very few items. The
tariff system took a backward step when the government introduced new tariff bands
of 2, 15 and 20 per cent (in addition to the bands of 5, 10, and 25 per cent that were
already in place) in 2002. Moreover, specific duties were re-introduced in place of ad
valerom rates for some selected agricultural and industrial goods. With the 2004
budget, the nominal level of protection has increased due to changes in the tariff rates
and the calculation of the total tax base. The budget brought down the import
surcharge to 10 per cent and introduced other taxes like Port and Aviation Levy
(PAL) and Education tax (Para taxes) which increased the level of protection.
Despite some policy reversals in the area of trade liberalization in recent years, Sri
Lanka has achieved considerable progress in liberalizing its trade regime during the
last decades. The figure 1 below clearly depicts how, the tariff lines have moved
towards lower tariff bands over the last decade.
Free
2%5%10
%15
%20
%25
%30
%35
%45
%50
%O
ther
19942000
0%5%10%15%20%25%30%35%40%45%50%
% of traiff lines
Tariff rate bands
Year
Figure1 : Changes in the Tariff Structure
1994 1998
2000 2002
Source: Various government gazettes and tariff advisory council.
4 Year 2001 was considered a “bad’ year for Sri Lanka with a negative economic growth for the first
time in the history after its independence in 1948.The budget deficit (as a % of GDP) rose to a double-digit number (10.8%).
8
The total tax revenue (as percentage of GDP) declined from 17.2 per cent in 1994 to
about 13.2 per cent in 2003. One of the major contributions to this decline was the
import duty, which reduced from 3.9 per cent of GDP in 1994 to 1.9 per cent in
2003.5 Moreover, as a share of total tax revenue, import duty has fallen to about 14.7
per cent by 2003 from approximately 22.7 per cent in 1994.
In the case of economic cooperation in the area of trade with the rest of the world, Sri
Lanka is a signatory to the SAARC Preferential Trading Arrangement (SAPTA),6
Framework Agreement on SAARC Free Trade Arrangement (SAFTA), Bangkok
Agreement, Generalized Scheme of Preferences (GSP), and Global System of Trade
Preferences (GSTP). The progress of these trading arrangements has been less than
satisfactory. The Indo-Sri Lanka Free Trade Agreement (ISFTA) was signed in
December 1998, and came into effect from 1st of March 2000. In order to include
service sector as well, the framework agreement - Comprehensive Economic
Partnership Agreement (CEPA) was signed between India and Sri Lanka in 2003. In
addition, Sri Lanka, signed a Free Trade Agreement with Pakistan in February 2005,
which will come in to effect in near future.
To conclude this section it is important to note that the devastating effects of the
tsunami that hit the country on 26 December 2004. This has been the worst natural
disaster Sri Lanka experienced in its more than 2500 years of history. Around 39, 000
people were killed and approximately 443,000 people have been displaced. Of those
killed, 27,000 belonged to fishing families and 65 percent of the country’s fishing
fleet has been completely destroyed or damaged. The tourism sector has been badly
affected. In addition to these sectors, physical and social infrastructure has been
severely damaged. According to estimates the overall damage to the economy is
around $1 billion (or 4.5 percent of GDP) and country needs around $1.5 to 1.6 billion
to rebuild the affected sectors and infrastructure. The tsunami had an impact on a
large poor and vulnerable people in the country. The economic growth rate in 2005 is
expected to be 1 percent slower than the expected rate of 6 percent. As noted before,
5 Sri Lanka has eliminated export duties, but cesses on tea, coconuts and some other items still apply.
6 Sri Lanka is a member of the South Asian Association for Regional Cooperation (SAARC)
9
it is important to examine the effects of tsunami and the post-tsunami recover package
in this study.
Poverty and inequality in Sri Lanka
There is a growing concern among policy makers of Sri Lanka on the distributional
and poverty implications of trade reform process. As per the Official Poverty Line for
Sri Lanka,7 using the Household Income and Expenditure Survey (HIES) of the
Department of Census & Statistics (DCS), approximately 22.7 per cent of the
population is identified as poor (in 2002). Moreover, the figures show a decline in the
aggregate poverty levels during the 1990-2002 period. The fall in poverty is
significant both in the urban and rural sectors. Particularly in the urban sector, the
percentage of poor has more than halved during the last decade. On the contrary, the
estate sector has recorded an increase in poverty levels from 20.5 per cent in 1990/91
to 30 per cent in 2002 (in 1995/96, incidence of poverty in this sector even increased
to 38.4 per cent). Furthermore, it should be noted that despite the declining trend in
poverty in the rural sector, poverty in Sri Lanka is predominantly a rural phenomenon.
(See Gunewardene, 2000 and Kelegama, 2001).
Table 1 :Incidence of Poverty (head count ratio) by Sector 1990 – 2002
Survey period Sector
1990-91 (%) 1995-96 (%) 2002 (%)
Sri Lanka8 26.1 28.8 22.7
Urban 16.3 14.0 7.9
Rural 29.4 30.9 24.7
Estate 20.5 38.4 30.0
Source: Department of Census & Statistics (DCS); estimates based on HIES 1990/91, 1995/96 and 2002.
7 The Official Poverty Line for Sri Lanka was introduced in June 2004. According to the Official Poverty Line, the
persons living in the households whose real per capita monthly total consumption expenditure is below Rs 1423 in the year 2002 in Sri Lanka are considered poor. It takes into account both the food and non-food consumption expenditure. The poverty line for 2002 has been adjusted by using the Colombo Consumer Price Index (CCPI) to obtain poverty lines for 1995/96 and 1990/91.
8 Household Income and Expenditure Surveys based on which the poverty levels have been estimated, have excluded the North and East (conflict areas) from their surveys. However, it is expected that exclusion of these areas have led to underestimation of the level of poverty in the country, considering that over 800,000 people have been internally displaced, and that the majority of the inhabitants in these areas and their properties have been affected by the civil war that lasted for nearly 20 years.
10
In spite of the long standing welfare policies adopted in Sri Lanka, a massive
government poverty reduction program (the Janasaviya Program) was initiated in
1989 and another version of it (the Samurdhi Program) is in operation since 1995.
However, these poverty alleviation programs have attracted wide criticisms for their
poor targeting and mismanagement due to political bias (World Bank, 2000).
A careful observation of the trend in income inequality measured by the income-based
Gini coefficient, reveals a gradual increase in inequality from 0.41 in 1973 (under the
protectionist policy regime) to 0.52 in 1986/87 (towards the end of the first wave of
liberalisation). This trend has however, declined slightly towards 1996/97. Figure 29
presents the trends in Gini coefficients based on both income receivers and spending
units. This movement indicates that the gap between rich and poor in the society has
widened towards late 1980's under the liberalised policy regime, resulting in an
increase in relative poverty. The marginal decline in the inequality observed towards
1996/97 may be attributed to the long run positive distributional effects emanating
from the trade liberalisation process or other factors which may have influenced the
income transfers to rural areas. For instance, Dunham and Edwards (1997) identified
migrant remittances, particularly coming from Middle East migrant workers and
income coming from armed force personnel engaged in the North and East conflict
zone of Sri Lanka, as two other vital factors that contribute to alleviating poverty
among rural households.
9 Source: Central Bank of Sri Lanka, Consumer Finances and Socio-Economic Surveys, various issues, Colombo.
00.10.20.30.40.50.6
Gin
i coe
ffic
ient
1973 1978/79 1981/82 1986/87 1996/97
Year
Gini C o e ffic ie nt (Inc o m e R e c e ive rs ) Gini C o e ffic ie nt (S pe nding Units )
Figure 2: Trends in income distribution in Sri Lanka
11
5. Methodology
Literature Survey on Methodology
The majority of empirical studies attempting to investigate the linkage between trade
and poverty rely on partial equilibrium analytical framework. Partial equilibrium
framework, however, ignores the mutual relationships between prices, outputs of
various goods and factors. Trade impulse transmission mechanism that operates
within many channels, together with the difficulty in isolating the impacts of
numerous other policy-induced or natural shocks that causes poverty, demand a better
experimental design to trace this linkage.
Counterfactual analysis within a general equilibrium framework, therefore, provides
an ideal experimental environment to investigate this relationship. The general
equilibrium framework not only allows analysts to capture the direct, as well as the
indirect interactions among different agents and markets, but also provides a
convenient framework in carrying out controlled policy experiments where the impact
of trade reforms could be isolated from other shocks by fixing their impacts. These
models have long been used to analyse poverty and income distributional issues.
Savard (2003) provides a review of the methods used in these studies.
The traditional CGE models that focussed on the distributional consequences of
policy induced or other shocks were mainly based on representative households
(RHs). Two main approaches in specifying representative households can be
identified in the CGE literature. In the first approach, the household sector was
disaggregated into different representative household groups, assuming that
representative agent from each group could well be representing the economic
behavior of the whole group. These representative agents, in other words, correspond
to the mean values of variables such as income and expenditure of different household
groups in a household income or consumption survey10.
The second approach attempts to partially endogenise the within group income
distribution using more realistic distribution functions such as lognormal, pareto and
more flexible beta distribution. These models further assume that the mean of the
10 See Coxhead, et al, 1995; Horridge, et al, 1995; Bautista, et al, 1997; Maio, et al, 1999; Lofgren, 1999 and Storm, 2001.
12
income distribution could well be determined endogenously while its variance
remains fixed or determined exogenously. The main advantage of this approach over
the previous approach is the specification of income distribution using more
disaggregated household survey data. These representative household distributions are
estimated using the base year household survey data. Once a comparative static
simulation has been carried out, a new mean income for each representative
household would be generated within the CGE model. However, the variation of the
distribution has been assumed to be constant (or determined exogenously). Therefore,
the household income distribution would shift to right or left depending on the change
in mean income while maintaining the same shape of the distribution as the variance
is fixed. Unlike the first approach, the latter approach partially captures the intra-
group income distribution as well as the inter group income distribution. Thus, they
could be used effectively in capturing absolute poverty. Pioneering attempts to
capture the intra-group income distribution goes back to the work of Adelman and
Robinson (1979) in which they used the lognormal distribution function in specifying
income distribution of various representative household groups in Korea. Later, de
Jenvry et al (1991) and Chia et al (1991) used Pareto and lognormal distribution
functions, respectively, in specifying the income distribution of representative
household groups.
Despite the simplicity, the second approach noted above suffers from various
limitations. As Decaluwe et al (2001) points out, non-of those studies satisfactorily
justify why distribution functions such as lognormal and Pareto were preferred to
other more flexible functional forms, such as the beta function. As the beta function
could adopt fairly well under asymmetric situations by generating skewed
distributions, it is more flexible in terms of utilizing socio-economic information in
forming household income distributions. Furthermore, in those studies, the poverty
line, which is used as a reference point in measuring poverty, was determined
exogenously. As prices of different commodities are generated from the model,
treating poverty line (which is determined based on the prices of a distinctive basket
of commodities) as exogenous is somewhat misleading.
13
In recent years, a number of innovative approaches have been used to analyse poverty
issues using CGE models. An innovative development in specifying intra-group
income distribution has been proposed by Decaluwe et al (1998) for an archetype
African country. In this model, the income distributions of different household groups
have been estimated using the beta functional form. Moreover, the poverty line has
been determined endogenously. Hence, once a simulation has been conducted, the
model would generate a new set of mean incomes for each household group and also
generate a new value for the poverty line based on the changes in commodity prices.
However, this model also assumes that the within group variation is fixed, thus
depending on the direction and the magnitude of the income change, the mean of the
distribution remains constant or shifts to the right or the left while keeping the shape
of the distribution unaffected. Additionally, the poverty line too shifts to either
direction depending on the change. This process, therefore, would capture the changes
in the poverty line as well as the income in computing poverty measurements.
Furthermore, Deacaluwe et al (1998) used the Foster, Greer and Thorbecke (FGT)
poverty measure to compute the headcount ratio and income poverty gap (Foster et al,
1984). Although, this approach maintains the assumption of RHs, it turned out to be a
better framework in capturing poverty compared to previous models. More
explanations on the methodology and archetype applications of this approach could be
found in Decaluwe et al (1999a, 1999b, 2001); Thorbecke, 2001; and Boccanfuso et al
(2003). Several other researches have adopted this approach in their applications
within the LDC context (Colatei and Round, 2000; Pradhan and Sahoo, 2000 and
Croser, 2002).
Another major development in specifying households within poverty-focussed CGE
models is the integration of individual households into a CGE model in the form of a
linked micro-simulation (MS) model. As individual households are taken into
account, these models are more reliable in capturing the individual heterogeneity.
Moreover, these models allow researchers to completely endogenise the within group
income distribution together with the within group variation (see Cogneau and
Robillard 2000, Cockburn 2001 and Cororaton and Cockburn 2004)11. This is a two
steps (or open loop) approach. A CGE model with a single representative agent is
11 Pioneering work on incorporating micro simulation models into CGE framework was carried out by Meagher, (1993). Since then few others have adopted micro simulation approach by attempting to fully integrate individual households into a CGE model (see Cogneau and Robillard, 2000 and Cockburn, 2001).
14
implemented to obtain the estimated price changes from a policy shock as the first
step and these price changes are then fed into a micro-simulation model as the second
step. In this approach, however, the causality usually runs from the CGE model to the
MS model, not the other way around. To avoid this limitation, more recently new
approaches have been adopted by several researchers.
Firstly, Savard (2003) raised the above limitation and linked both models by running
them in a repeated sequence of CGE-MS model runs. Following similar approach
Ferreira-Fiho, et al, (2004) have used a CGE model linked to a MS model with bi-
directional linkages to analyse the effects of economic integration on poverty and
regional inequality in Brazil. Secondly, Rutherford, et al (2004) have linked 54,000
plus households as agents in their CGE model of the Russian economy to examine the
households and poverty effects from Russia’s accession to the WTO. These authors
claim that this is a methodological breakthrough in this area of research.
Proposed Method in This Study
Sri Lanka has a long history of applying CGE models in analysing various issues
related to the economy. The availability of quality data has been one of the main
reasons for this trend. In fact, Sri Lanka is the first developing country for which a
Social Accounting Matrix (SAM) was developed in the early 1970’s (Pyatt and Roe,
1977). This has influenced the wide adoption of CGE framework in economic policy
analysis of the country. De Melo (1978) developed the pioneering CGE model for Sri
Lanka. Since then, several studies have developed CGE models for the Sri Lankan
economy12. Bandara (1989) developed the first CGE model of the Johansen class
(with linearized system of equations) following the ORANI model (Dixon et al, 1982)
of the Australian economy. This model contained a limited income distribution
component. However, no study has attempted to build poverty-focussed, multi-
sectoral, SAM based CGE model to analyse absolute poverty within the general
equilibrium framework for Sri Lanka. One of the most surprising facts is that Sri
Lanka is the only country for which a CGE model has not been developed so far
12 See Blitzer & Eckaus (1986); Jayawardena et al, (1987); Bandara (1989); CIE (1992); Herath (1994); Somaratne (1998); Bandara & Coxhead (1999); Kandiah (1999). For a comprehensive survey of CGE applications for the Sri Lankan economy see Bandara (1990).
15
under a MIMAP or PEP project. This is itself a justification for our attempt to develop
a poverty-focused CGE model under this proposal.
In this study a comparative static multi-sectoral SAM based poverty focused CGE
model will be developed by adopting the approach proposed by Decaluwe et al (1998)
to capture the link between trade reforms and absolute poverty within the Sri Lankan
context. Income distribution functions are empirically estimated and are linked to the
SAM based CGE model using the ‘top down’ approach to estimate absolute and
relative poverty. This is the initial stage of our project to begin the poverty-focused
CGE modelling approach in Sri Lanka. It is expected to develop a fully integrated
CGE and MS modelling framework depending on the outcome of this initial stage.
As noted in earlier, economy-wide CGE models have been used to investigate the
trade and poverty link in recent years. This first attempt of incorporating poverty into
a CGE framework in Sri Lankan is an extension of previous Sri Lankan CGE
modelling work led by one of the team members (for example, Bandara, 1989;
Bandara and Coxhead, 1999 and Bandara, et al, 2001). The core component of the
proposed Sri Lankan CGE model will follow the previous models, which were based
on the Australian ORANI model (Dixon, et al, 1982). Similar to its predecessors,
most of the behavioural equations of the core model of the proposed Sri Lankan
model are derived on the basis of neo-classical utility maximisation and profit
maximisation assumptions. They:
• Describe household and other final demands for commodities;
• Describe industry demand for primary factors and for intermediate inputs
from domestic and imported sources;
• Ensure zero-pure profit conditions, that is, the prices of commodities reflect
costs of production;
• Ensure market clearance;
• Relate producer prices paid by purchasers;
• Describe income distribution of households;
• Describe government income and expenditure sides and
• Define key macroeconomic identities.
16
All equations in the model can be grouped into a number of blocks as shown in Table
2.
Table 2 Main blocks of Equations in the Model
Block Equations Block 1:
Demands Industry Inputs
Intermediate inputs (domestic and imported) Primary factors Labour by occupation Production subsidies Block 2:
Final Demands for Commodities
Demand for capital creation Household demands Exports Government demand Block 3:
Demand for Margins
Block 4:
Zero Pure Profits Conditions
Production Capital creation Importing Exporting Distribution Block 5:
Investment Allocation
Distribution of investment Investment budget constraint Block 6:
Market-Clearing Equations
Domestically produced commodities Imported commodities Primary factors Block 7:
Balance of Trade
Imports Exports Balance of trade Block 8:
Income Distribution
Firm’s income Household income Government income Block 9:
Miscellaneous Equations
17
The equation system of the model closely follows the Australian ORANI model
which belongs to the well-known Johansen class (Johansen, 1960). The full system of
equations, variables and exogenous variables are shown in Tables 1, 2, and 3 of the
appendix (NOTE: this system of equations and variables may be modified to
accommodate the trade-poverty linkage). Following the Johansen method, all
variables in the model are shown in percentage change forms.
6. Data Requirements and Sources
Two types of data sets are required to implement the proposed model in this study.
They are;
• A recently compile SAM with a detailed input-output database; and
• Household survey data on expenditure and income.
The main problem in developing a CGE model for the Sri Lankan economy has been
the lack of recently compiled SAM (see Bandara and Kelegama, forthcoming). This
must probably explain why a CGE model has not been developed for Sri Lanka under
MIMAP projects, although there are CGE models for many other developing
countries under the MIMAP projects. Fortunately, two of the team members of this
proposal (Bandara and Naranpanawa) are currently undertaking a research project in
developing a Sri Lankan SAM for a recent year under an IPS funded project. This
database can easily be used in the proposed project. Moreover, Sri Lanka has two
recent household survey data sets: (i.) Household Income and Expenditure Survey
(HIES)-2002 of the Department of Census and Statistics and, (ii.) Consumer Finance
and Socio-Economic Survey- 2003/2004 of the Central Bank of Sri Lanka, that can be
used in this study.
7. Dissemination of Results of the Study:
The results of this study will be disseminated in many ways:
1. The findings of the study will be presented at a workshop organised in
Colombo to a broad group of policy makers including officials from the
Ministry of Samurdhi and Poverty Alleviation (Government arm for poverty
alleviation), Ministry of Finance, Ministry of Trade, Commerce and Consumer
Affairs and various NGOs engaged in poverty alleviation projects at local
levels, Researchers and Academics.
18
2. The results will also be presented at the Institute of Policy Studies (IPS)
seminar series in Colombo and make them available for Sri Lankan policy
makers and academics through IPS Working Paper series.
3. The results will be presented at international workshops and conferences
organised by the PEP and other modelling conferences such as Annual Global
Economic Analysis conferences.
4. Revised papers on the basis of the comments made by policy analysts and
policy makers and participants of workshops and conferences will be
submitted to international refereed journals.
8. List of Team Members and prior training and experience in the issues and
techniques involved.
The research team consists of one senior researcher (“external resource person”) and
three young Sri Lankan researchers. The team is led by Ms Ganga Tilakaratna
(Female, 28) is a Research Economist at the Institute of Policy Studies of Sri Lanka
(IPS). As the researcher in-charge of the Poverty and Social Welfare Policy Unit, she
has been actively involved in research, policy analysis and formulation in the areas of
poverty, social policy and microfinance. Currently she is the Project Leader of the
MIMAP- Sri Lanka (Phase II) funded by the IDRC and has been actively participating
in the MIMAP/PEP meetings and trainings since 2002. Among several other projects,
she has carried out a research on trade and poverty linkage in Sri Lanka (with special
reference to the rice sector) using the partial equilibrium analysis (a study funded by
the North-South Institute, Canada). This proposed project will undoubtedly help her
to strengthen her knowledge and skills on poverty analysis within a general
equilibrium framework.
Dr Bandara (as an “external resource person” in the team) has extensive research
experience in CGE modelling in general and Sri Lanka in particular. He is the first Sri
Lankan national to develop a CGE model for the Sri Lankan economy in the late
1980s. Since then he has been working in CGE modelling projects related to the Sri
Lankan economy although he is an academic at Griffith University in Australia.
Currently he is working at the Institute of Policy Studies of Sri Lanka, to complete a
project on developing a SAM database for Sri Lanka and a general purpose CGE
model for the Sri Lankan. The proposed project will provide an excellent opportunity
19
for him to interact with young Sri Lankan policy analysts and train them in CGE
modelling area where there is a lack of local expertise. He has also actively involved
in Global Trade Analysis Project (GTAP) at Purdue University and he contributed the
Sri Lankan, Vietnamese and South African input-output tables to the version 4 GTAP
database in 1997 when he was on sabbatical leave at Purdue University. He has
presented papers at international conferences and published articles in international
journals such as the Journal of Policy Modelling and the World Economy (please see
his CV for details).
Mr Naranpanawa is a young Sri Lankan PhD candidate who is very close to complete
his PhD thesis at Griffith University in Australia. In recent years he has been working
with Dr Bandara as a PhD candidate in CGE modelling. The proposed project will
provide him with an excellent opportunity to continue his modelling work related to
the Sri Lankan economy.
Mr Thusitha Kumara (Male, 27) is working as a Research Assistant at the Institute of
Policy Studies of Sri Lanka. He is also currently pursuing an MPhil degree in
Economics at the University of Peradeniya (Sri Lanka). He has been involved in
number of research projects in the areas of poverty and microfinance.
9. Description of Research Capacities that Team Members and Their Institutions
are Expected to Build
Although there is a long history of CGE modelling in Sri Lanka there is significant
research gap in the area at present (as noted previously). Several attempts were made
by one of the team members of this proposed study (see Bandera’s CV). However,
there is no continuity of CGE modelling, particularly by local policy analysts.
Therefore, it is important to have a detailed research plan in capacity building. In this
proposal we propose a detailed research plan to build research capacities of IPS
researchers and other local interested researchers. We treat this research proposal as a
component of a detailed integrated IPS research agenda in the area of economy-wide
modelling.
20
The proposed research agenda consists of several stages, of increasing length and
weight as outlined below. Some initial stages have already been completed or
undertaken under different research projects:
1. To update our knowledge of the CGE modelling experience and economy-
wide data bases such as input-output (IO) tables and Social Accounting
Matrices (SAMs) in Sri Lanka (this has already been done);
2. To compile a Sri Lankan SAM for a recent year to lay the foundation for the
proposed CGE model on the basis of unpublished input-output tables, National
Accounts and other primary data (this is currently undertaken by two members
of this team);
3. To develop a poverty-focused CGE model using a Sri Lankan SAM and other
relevant data such as household surveys;
4. To undertake some preliminary policy experiments with the model;
5. To document the model properly to demonstrate the basic functions of the
model and its usefulness in policy analysis; and
6. To train local policy analysts at Ministries and government and non-
governmental organisations that are involved in poverty alleviation programs,
in running policy experiments with the model and interpreting results with a
view of capacity building through the project.
10. Any Ethical, Social, gender or environmental issues or risks which should be noted - None
11. List of Past and Current and Pending Projects in Related Areas Involving Team Members
• Project Title: Promoting Empowerment through Microfinance Programs
(on-going project) Funding Institution: South Asia Centre for Policy Studies (SACEPS) Team Members: Ganga Tilakaratna (team leader), Thusitha Kumara, Ayodya Galappatige of IPS
• Project Title: Impact of Trade Liberalization on Poverty in Sri Lanka (2004)
Funding Institute: North-South Institute (NSI), Canada Project Team: Ganga Tilakaratna, Sanath Jayanetti (IPS)
21
• Project Title: Microfinance for Poverty Alleviation in Sri Lanka: Current Status and Future Options ( to be completed by February 2005) Funding Institution: MIMAP- IDRC, Canada Team Members: Ganga Tilakaratna (IPS) Upali Wickremasignhe (University of Jayawardanapura), Thusitha Kumara (IPS)
• Project Title: Upgrading Educational Opportunities for the Poor (on-going project)
Funding Institution: South Asia Centre for Policy Studies (SACEPS) Team Members: Ganga Tilakaratna (team leader), Ruwan Jayatileka, Ayodya Galappatige of IPS
• Project Title: Community-based Poverty Monitoring Systems (to be completed by February 2005) Funding Institution: MIMAP- IDRC, Canada Team Members: S.T. Hettige, Markus Mayer ( University of Colombo), Ganga Tilakaratna (IPS)
• Project Title: Country Paper for the preparation of SAARC Development
Goals (2004) Funding Institution: South Asian Association for Regional Cooperation (SAARC) Team Members: Ganga Tilakaratna
• Project Title: Complementarities and Competition Between the Australian and South African Economies ( 2001)
Funding Institution: Griffith University Research Grant (GURG) Team Members: J.S. Bandara (team leader), D.T. Nguyen
• Project Title: A Survey on GTAP Applications ( 1999-2000) Funding Institution: Global Trade Analysis Project (GTAP)Grant.
Team Members: J.S. Bandara
• Project Title: South Asia and its Integration into the World Economy: Implications for Australia. (1999)
Funding Institution: GURG Team Members: J.S. Bandara (team leader), C. Smith
• Project Title: Optimal Land Use in Sri Lanka with Particular Application to Land Degradation and the Plantation Industries (1996-2000)
Funding Institution:Australian Centre for International Agricultural Research (ACIAR)
Team Members: J.S. Bandara and a team from La Trobe University and the Ministry of Plantation Industries in Sri Lanka
• Project Title: Economic Reforms in Vietnam and Implications for Australia ( 1995-96)
Funding Institution: Large Australian Research Council grant
22
Team Members: J.S. Bandara (team leader), D.T. Nguyen
• Project Title: Construction of a Computable General Equilibrium (CGE) Model for the Sri Lankan Economy to Analyse Policy Issues (2003-2004)
Funding Institution: IPS Team Members: J.S. Bandara and A. Naranpanawa.
• Project Title: Estimation of Multipliers for the Northern Territory Economy (2000)
Funding Institution: KPMG Consultants Team Members: J.S. Bandara
• Project Title: Economic Structural Changes and International Migration Pressures in Vietnam (1995)
Funding Institution: International Labour Organisation Team Members: J.S. Bandara, D.T. Nguyen
• Project Title: An Assessment of the Economic and Social Impact of Defence Activity on the Darwin Region for the Darwin Committee as part of Wran Committee of Inquiry into Future of Darwin Region,Economic Structural Changes and International Migration Pressures in Vietnam Economy (1994)
Funding Institution: Federal Government of Australia Team Members: J.S. Bandara, Ciaran O’Faircheallaigh, Christine Smith
23
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27
Table A1: Equations of the Model (in percentage changes)
No. Equation Subscripts Range
Number Description
1.
Industry Inputs (Block 1)
[ ]x (is)j
(1) = −
−=∑
x
p S p
j ij
is j iw j iw jw
( )( ) ( )
( )( ) ( )
( )( )( )
10 1
1 1 11
2
σ
i=1,...,n
j=1...,n
s=1,2
2n2 Demands for intermediate inputsof good i from source s to industry j for current production.
2. xn+2,j(1) = x j( )
( )1
0 j=1,...,n n Demands for production subsidies
3. p((n+1,1)j(1) = + +=∑ S pn q j n q jq ( , , )
( )( , , )( )
111
1 11
13 j=1,...,n n Price of labour in
general
4.
[ ]x j x
p S p
n q n j n j
n q j n m j n m jm
( , , )( )
( , )( )
( , )( )
( , , )( )
( , , )( )
( , , )( )
+ + +
+ + +=
= −
−∑1 1
11 1
11 1
1
1 11
1 11
111
13
σ
j=1,...,n
q=1,2,3
3n Demands for labourby occupational groups
5.
[ ]x j x
p S p
n v j n j
n v j n w j iw jw j
( , )( )
( )( )
,( )
( , )( )
( , )( )
( )( )
( )
+ +
+ +=
= −
−∑1
11
01
1
11
11 1
12
σ
j=1,...,n
v=1,2
2n Demands for primary factors.
Final Demands (Block 2)
6. [ ]x y p
S p
is r r ir is r
iw r iw rw
( )( ) ( )
( )
( )( )
( )( )
2 2 2
2 21
2
= −
−=∑
σ
i=1,...,n
s=1,2
r=1,2,3,4
8n Demands for inputsto capital creation
7. y S yr j jjer= ∑ ( )2 r=1,2,3,4 4 Aggregation of industry wise capital creation intosectors
8. [ ]x x p S pis h i h ih is h iw h iw hw( )( )
( )( )
( ) ( )( )
( )( )3 3 3 3 3 3
12= − −=∑σ
i=1,...,n
s=1,2
h=1,2,3
6n Households demands for commodities by different sources.
9. p S pi h is h is hs( )( )
( )( )
( )( )3 3 3
12−=∑ i=1,...,n
h=1,2,3
3n Price of ‘effective commodities’
28
10 x q p
c qi h h ik k hk
n
i h h h
h( )( )
( )( )
( ) ( )
3 31− =
+ −=∑ η
ε
i=1,...,n 3n Households demands for ‘effective commodities’
11. p x fie
i ieei( ) ( )
( )( )1 1
41= − +γ i=1,...,n n Export demands
12. x h c fis is R is( )( )
( )( )
( )5 5 5= + i=1,...,n
s=1,2
2n Government demands for commodities by different sources
13. c cR = − ξ( )3 1 Real household expenditure
Demands for Margins (Block 3)
14. x xis jis j( , )
( ) ( )( )( )
23 11 1= i=1,...,n
j=1,...,n
s=1,2
23=margin
2n2 Demands for margins current production
15. x xis ris r( , )
( ) ( )( )( )
23 12 2= i=1,...,n
s=1,2
r=1,2,3,4
23=margin
8n Demands for margins - capital creation
16. x xis his h( , )
( ) ( )( )( )
23 13 3= i=1,...,n
s=1,2
h=1,2,3
23=margin
6n Demands for margins - household consumption
17. x xii( , )
( )( )( )( )
23 11 4
14= i=1,...,n
23=margin
n Demands for margins - exports
18. x xisis( , )
( )( )( )( )
23 15 5= i=1,...,n
s=1,2
23=margins
2n Demands for margins - goverment consumption
Zero Pure Profits (Block 4)
29
19. p H p
H p
H p
H p
i in
s is j is j
q n q j n q j
n j n j
n j n j
( )( )
( )( )
( )( )
( , , )( )
( , , )( )
( , )( )
( , )( )
,( )
,( )
10
1 12 1 1
13
1 11
1 11
1 21
1 21
21
21
=
+
+
= =
= + +
+ +
+ +
∑ ∑∑
I = 1,...,n n Zero pure profits in production
20. π r in
s is r is rH p== =∑ ∑1 1
2 2 2( )( )
( )( ) r=1,2,3,4 4 Zero pure profits in
capital creation
21. p p ti im
i( )( )
( ) ( , )20
2 2 0= + +φ i=1,...,n n Zero pure profits in importing
22. p t Q p
Q p
ie
i i i
i
( )( )
( , ) ( , ) ( )( )
( , ) ( , )( )
1 1 4 1 1 4 10
2 1 4 23 10
+ + =
+
φ
i=1,...,n
23=margin
n Zero pure profits in exporting
23. ( )p Q Q p
Q t Q p
is j is j is j is
is j is j is j
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( , )( )
111
21 0
11 1
31
23 10
= +
+
i=1,...,n
j=1,...,n
s=1,2
23=margin
2n2 Zero pure profits in distribution of goods to users of current production.
24. p Q p Q pis r is r is is r( )( )
( )( )
( )( )
( )( )
( , )( )2
12 0
22
23 10= + i=1,...,n
s=1,2
r=1,2,3,4
23=margin
8n Zero pure profits in distribution of goods to users of capital creation
25. ( )p Q Q p Q
t Q p
is h is h is h is is h
is h is h
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( , )( )
313
23 0
23
333
23 10
= + +
+
i=1,...,n
s=1,2
h=1,2,3
23=margin
6n Zero pure profits in distribution of goods to government
26 p Q p Q pis is is is( )( )
( )( )
( )( )
( )( )
( , )( )5
15 0
25
23 10= + i=1,...,n
s=1,2
23=margin
2n Zero pure profits in distribution of goods to government
Investment Allocations (Block 5)
27. [ ]r Q pj j n j j( ) ( , )( )0 1 22= −+ π j=1,...,n
jεr
n Sectoral rates of return
28. ( )y k r wj j j j= + −( ) ( )*0 0β j=1,...,n n Sectoral Investment
30
29. i yjn
r j j= +=∑ 1 ( )π γ jεr 1 Aggregate
investment
30. i iR = − ξ( )2 1 Real Investment
31. f c iR R R= − 1 Ratio of real consumption expenditure to investment
32. r r fj jR( )0 = + j=1,...,n n Relationship
between rates of return in individual industries and the average annual rate of return
33. r w f w= + 1 Average rate of change of the net rate of return
Market-Clearing (Block 6)
34. x B x
B x
B x
B x
B x
k jn
k j k j
r k r k r
h k h k
k k
k k
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
10
1 11
11
14
12
12
13
13
13
14
14
15
15
=
+
+
+
+
=
=
=
∑
∑∑
+
+
+
+
+
= = =
= = =
= = =
=
= =
∑ ∑ ∑
∑ ∑ ∑∑ ∑ ∑∑∑ ∑
δk in
jn
s kis j
kis j
in
r s kis r
kis r
in
h s kis h
kis h
in
ki
ki
in
s kis
kis
B x
B x
B x
B x
B x
[
]
( ) ( ) ( ) ( )
( )( ) ( )
( )( ) ( )
( )( ) ( )
( )( ) ( )
( )( )( )
( )( )( )
( )( )( )
( )( )( )
1 1 12
11
11
1 14
12
12
12
1 13
12
13
13
1 11 4
11 4
1 12
15
15
δ
δk
k
if kif k
= =
= ≠
1 230 23
k=1,...,
n
n Demand equals supply for dometically produced commodities
35. l xm jn
n m j n m j== + +∑ 1 11
11 1
1B( , , )( )
( , , )( ) m=1,2,
3
3 Demand equals supply for labour of type m
31
36. k xj n j( ) ( , )( )0 1 21= + j=1,...,n n Demand equals
supply of capital
Balance of trade (Block 7)
37. x B x
B x B x
B x
k jn
k k j
r k r k r h k h k h
k k
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
20
1 21
21
14
22
22
13
23
23
25
25
=
+ +
+
=
= =
∑
∑ ∑
k=1,...,
n
n Import volumes
38. [ ]m M p xkn
k km
k= +=∑ 1 2 2 2
0( ) ( ) ( )
( ) 1 Foreign currency value of imports
39. [ ]e E p xkn
k ke
k= +=∑ 1 1 1 1
4( ) ( ) ( )
( ) 1 Foreign currency value of exports
40. 100∆B Ee Mm= − 1 Balance of trade
Income Distribution (Block 8)
41. yYY
yFjn j
F
F jF=
=∑ 1 1 Total firms’ income
42. ( )
( )
y S p x
TT
S S t S d
jF
jF
n j n j
jF
jF j
Fj
FjF
jF
j
= + +
−
−
+
+
+ +1 1 2 1 2
1 2 21
, ( , ) , )
, , ,
j=1,...,n n Firms’ sectoral
income
43. y Q d Q nf
Q u
jF
m j hF
j hF
A jF
A jF
F jF
jF
= +
+
=∑ 13
1 2
3
( , ) , ( , ) ,
( , )
j=1,...,n n Undistributed
profits
44. d yj hF
jF
, = j=1,...,n 3n Household dividends
45. uf yjF
jF= j=1,...,n n Profits and
dividends remitted overseas
32
46. y S
x
p
S d s tr
TT
t Q tr Q
h jn
jH n h j
n h j
jn
jF
j hF
hH
hA
h
hh
HhG H
=+
+ +
+−−
+
=
+
+
=
∑
∑
[
]
,( , , )( )
( , , )( )
, , ,
1 11 1
1
1 11
1 2 3
1 21
h=1,2,3 3 Household income
47. y p xT
Tt G
p xT
Tt G
G G
G G p
Gin
im
ii
ii i
in
ie
ii
ii i
in
i i
in
s jn
is j h is h is
i
= + +−
+ +−
+ +
+ +
+
=
=
=
= = = =
=
∑
∑
∑∑ ∑ ∑ ∑
[ ]
[ ]
[ ]
( ) ( )( ) ( , )
( , )( , )
( ) ( )( ) ( , )
( , )( , ) ,
, ,
( ) ( ) ( )
( )
1 2 20 2 0
2 02 0 1
1 1 14 1 4
1 41 4 2
1 1 2
1 12
1 3 13
40
1
1
1
φ
( )ns i
nis j is j is jx t G∑ ∑ ∑= =
+12
11 1
3( )( )
( )( )
( )
( )( )( )[ ]{ }
( )
+ +
+ +
+ + − +
+ + +
= = =
= + +
= + +
= = + +
=
∑ ∑ ∑∑
∑
∑ ∑
∑
in
s h is h is h is h
jn
n j n j j
jn
n j n j jF
j jF
jF
h jn
n h j n h j j hH
jn
j hF
x t G
p x G
x p W d W t
p x W
d
1 12
13 3 3
4
1 21
21
5
1 1 21
1 21
1 2
13
1 1 11
1 11
1
1
( )( )
( )( )
( )
( , )( )
( ,( )
,
( , )( )
( , ) , ,
( , , )( )
( , , )( )
( , )
,
{[W tr W t G
tr G
j hF
hA
hH
h h
h hH
s h
2 3 7
13
( , ) , ,
,
] }+ +
−=∑
1 Government income
48. Miscellaneous Equations (Block 9)
ξ( )( )( )
( )( )3
1 12
13 3 3=
= = =∑ ∑ ∑in
s h is h is hW p
1 Consumer price
index
49. ξh in
s is h is hV p( )( )( )
( )( )3
1 12 3 3=
= =∑ ∑ h=1,2,3 3 Household consumer price indices
50. ξ γ π( )21
4==∑r r r 1 Capita good price
index
33
51. p h
f f
f f
n m j n m j
n n j
n j n m j
( , , )( )
( , , )( ) ( )
( , )( )
( , )( )
( , )( )
( , , )( )
+ +
+ +
+ +
=
+ +
+ +
111
1 11 3
1 11
1 11
1 11
1 11
ξ
j=1,...,n m=1,2,3
3n Wage indexatiion
52. p h fn j n j n j+ + += +21
21 3
21
,( )
,( ) ( ) ( )ξ j=1,...,n n Price of production
subsidies
53. l lm m m==∑ 1
3 ψ 1 Aggregate employment
54. k kjn
j j( ) ( )0 01==∑ χ 1 Aggregate capital
stock
55. c y fh h hC= + h=1,2,3 3 Household
expenditure
56. c c Wh h h==∑ 1
3 1 Aggregate household expenditure
57. f f fhC C
hC= + * h=1,2,3 3 Shift variable for
consumption
58. y yhR
h h= − ξ( )3 h=1,2,3 3 Real household income
Total 6n2+82n+41
34
Table A2: Variables of the Model (in percentage changes)
Variable Subscript Range Number Description
x is j( )( )1 i,j=1,...,n
s=1,2 2n2 Demands for inputs from
domestic and foreign sources for current production
x is r( )( )2 i=1,...,n
s=1,2 r=1,2,3,4
8n Demands for inputs from domestic and foreign sources for capital creation
x n q j( , , )( )+1 1
1 j=1,...,n q=1,2,3
3n Demands for labour by occupational type and industry
x n v j( , )( )+1
1 j=1,...,n v=1,2
2n Demands for labour in general and capital by industry
xn j+21
,( ) j=1,...n n Demands for production
subsidies
x i h( .)( )3 i=1,...,n 3n Demands for effective
commodities by different household groups
x is h( )( )3 i=1,...,n
s=1,2 h=1,2,3
6n Demands for commodities in different sources by different household groups
x i( )( )
14 i=1,...,n n Export volumes
x is( )( )5 i=1,...,n
s=1,2 2n Government demands for
commodities classified by sources
x is j( , )( ) ( )23 1
1 i,j=1,...,n s=1,2 23=margin
2n2 Demands for margins associated with commodity flows to current production
x is r( , )( ) ( )23 1
2 i=1,...,n s=1,2 r=1,2,3,4 23=margin
8n Demands for margins associated with commodity flows to capital creation
x is h( , )( ) ( )23 1
3 i=1,...,n s=1,2 r=1,2,3,4 23=margin
6n Demands for margins associated with commodity flows to household consumption
x il( , )( )( )23 1
4 i=1,...,n 23=margin
n Demands for margins associated with commodity flows to domestic ports for exports
x is( , )( )( )23 1
5 i=1,...,n s=1,2 23=margin
2n Demands for margins associated with commodity flows to government consumption
35
x k( )( )
10 k=1,...,n n Supplies of domestic
commodities
x k( )( )
20 k=1,...,n n Supplies of imported
commodities
yr r=1,2,3,4 4 Capital creation by investing sectors
y j j=1,...,n n Capital creation by industries
p is j( )( )1 i,j=1,...,n
s=1,2 2n2 Purchasers’ prices of produced
inputs for current production
p n q j( , , )( )+1 1
1 j=1,...,n q=1,2,3
3n Prices of different occupational labour paid by industries
p n v j( , )( )+1
1 j=1,...,n v=1,2
2n Prices of labour in general and capital paid by industries
p is r( )( )2 i=1,...,n
s=1,2 r=1,2,3,4
8n Purchasers’ prices of produced inputs for capital creation
p i h( .)( )3 i=1,...,n
h=1,2,3 3n Purchasers’ prices of effective
commodities of household consumption
p is h( )( )3 i=1,...,n
s=1,2 h=1,2,3,4
6n Purchasers’ prices of commodities for household consumption from different sources
p ie
( )( )
1 i=1,...,n n Foreign currency prices of exports in f.o.b. term
p is( )( )5 i=1,...,n
s=1,2
2n Purchasers’ prices of commodities for government consumption from differenct sources
p is( )( )0 i=1,...,n
s=1,2 2n Basic prices of domestically
produced and imported goods
p im
( )( )
2 i=1,...,n n c.i.f. prices of imports in foreign currency
pn j+21
,( ) j=1,...,n n Price of production subsidies
πr r=1,2,3,4 4 Costs of units of capital
φ 1 The exchange rate
qh h=1,2,3 3 Number of households in each household group
kj j=1,...,n n Current capital stock in each industry
36
rj(0) j=1,...,n n Current rate of return on fixed capital
ω 1 Economy-wide expected rate of return
r 1 The average growth rate of return
iR 1 Aggregate real invetment
i 1 Aggregate investment
k(0) 1 Aggregate capital stoc
l 1 Aggregate employment
lm m=1,2,3 3 Employment by occupational group
m 1 Value of imports in foreign currency
3 1 Value of exports in foreign currency
∆B 1 The balance of trade
ch h=1,2,3 3 Household consumption
yh h=1,2,3 3 Household nominal income
y hR
( )( ) h=1,2,3 3 Household real income
cR 1 Real aggregate household expenditure
c 1 Aggregate household expenditure
ξ(3) 1 Aggregate consumer price index
ξh( )3 h=1,2,3 3 Consumer price indices for
different households
ξ(2) 1 Capital-good price index
fR 1 The ratio of real invetment expenditure to real household consumption expenditure
f ie( )1 i=1,...,n n Shifts in foreign export
demands
f is( )( )5 i=1,...,n
s=1,2
2n Shifts terms for government demand
fn j+21
,( ) j=1,...,n n Shift terms for prices of
production subsidies
f n( , )( )+11
1 1 General wage shift variable
37
f n j( , )( )+11
1 j=1,...,n n Variable which can be used to simulate the effects of changes in wages payable by an industry relative to other industries
f n m( , , )( )+1 1
1 m=1,2,3 3 Variables which can be used to simulate the effects of changes in occupational wage relativities
f n m j( , , )( )+1 1
1 i=1,...,m
m=1,2,3
3n Variables allowing changes in both occupational and industrial wage relativities
fω 1 Shift variable allowing divergencies between r and w
fjr j=1,...,n n Shift variable allowing the
introductin of changes in relative rates of return across industries
fC 1 Shift variable fr aggregate consumption
fhC h=1,2,3 3 Marginal propensity to consume
fhC* h=1,2,3 3 Shift variables for marginal
propensity to consume
t(i2,0) i=1,...,n n One plus the ad valorem tariff rates on imports
t(i1,4) i=1,...,n n One plus the ad valorem rates of export subsidies or taxes
t is j( )( )1 i,j=1,...,n
s=1,2
2n2 Sales tax rates on produced inpouts for current production
t is h( )( )3 i=1,...,n
s=1,2
h=1,2,3
6n Sales tax rates on household consumption
yF 1 Firms’ income
y jF j=1,...,n n Sectoral firms’ income
t jF j=1,...,n n Taxes on sectoral profits
d j j=1,...,n n Sectoral depreciation
d j hF, n=1,...,n
h=1,2,3
3n Household dividends
nfjF j=1,...,n n Profits and dividends remitted
38
overseas
u jF j=1,...,n n Undistributed profits
trhG h=1,2,3 3 Government transfers to
households
trhA h=1,2,3 3 Transfers to households from
abroad
thD h=1,2,3 3 Direct income taxes on
household income
yG 1 Government income
Total 8n2+102n+64
39
Table A3: A Possible List of Exogenous Variables
Variable Subscript Range Number Description
p i jm( )2 i=1,...,n n Foreign currency import prices
in c.i.f. terM
t(i2,0) i=1,...,n n One plus the ad valorum tariff rates
t(i1,4) iεZ n Choice of export tax (subsidy) terms
x il( )( )4 i∉Z Complementary selection of
export volumes
kj(0) j=1,...,n n Current specific capita stocks
t is j( )( )1 i,j=1,...,n
s=1,2
2n2 Sales tax rates on intermediate inputs
t is h( )( )3 i=1,...,n
s=1,2
h=1,2,3
6n Sales tax rates on consumer goods
t jF( ) j=1,...,n n Income tax rates on sectoral
profits
th h=1,2,3 3 Tax rates on household income
trhA h=1,2,3 3 Transfers to household from
abroad
trhg h=1,2,3 3 Transfers to household from
government
dj j=1,...,n n Sectoral depreciation
fn j+21
,( ) j=1,...,n n Shifts variables for the price of
production subsidies
f ie( )1 i=1,...,n n Shift variables for foreign
export demands
f is( )( )5 i=1,...,n
s=1,2
2n Shift variables in government demands
l 1 Aggregate employment
f n m( , , )( )+1 1
1 m=1,2,3 3 Shift variables which can be used to simulate the effects of change in occupational wage relativities
f n j( , )( )+11
1 j=1,...,n n Shift variables which can be used to simulate the effects of
40
changes in industry wage relativities
f n m j( , , )( )+1 1
1 j=1,...,n
m=1,2,3
3n Shift variables which can be used to simulate the effects of change in both occupational and industry wage relativities
qh h=1,2,3 3 Number of households in different household groups
r 1 Average growth rate of rates of return
fR 1 Ratio of real investment to real consumption expenditure
∆B 1 Balance of trade
fhC− h=1,2,3 3 Shift variables for marginal
propensity to consume
φ 1 Exchange rate
Total 2n2+20n+23