Presentation Agriculture and Rural Transport in Ethiopia (2)

By Naod Mekonnen Anega

The Effect of Accessibility and Mobility in Rural Road Transport on Agricultural Efficiency and Commercialization of Smallholder Farmers in

Ethiopia

ADDIS ABABA UNIVERSITYCOLLEGE OF DEVELOPMENT STUDIES

CENTRE FOR RURAL LIVELOOOD AND DEVELOPMENT

1.Introduction 1.1 Background to the study 1.2 Statement of the problem 1.3 Research questions 1.4 Research objectives 1.5 Scope and limitation of the study2.Literature review 2.1 Summary of the empirical literature and gap3. Conceptual Framework4.Materials and Methods 4.1 Data source and type, 4.2 Sampling frame 4.3 Sampling design 4.4 Measuring Efficiency, Total Factor Productivity, Commercialization and Pro Poor Growth 5. Data Analysis 5.1 Descriptive Statistics 5.2 Econometric Strategy

Outline

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1.1 Background• Rural isolation is an impendent to rural development. • Rural people are poor mainly due their isolation and

lack of access to transport is also one of the factors explaining low agricultural growth ( (Carney, 1999).

• The impacts of rural isolation are more real in developing countries in general and Sub Saharan Africa (SSA) in particular(Faiz, 2012)

1 Introduction

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Rural road transport In SSA Africa • Accounts 80 % of freight and 90 % of passenger traffic

Background…….Cont’d

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So, what are the challenges ?

The average Rural Access Index for SSA was just 34 % (RAI for middle-income countries is 94 %).The road-to-population ratio of Africa was just 26 km per 10,000 inhabitantsLess than one-fourth of total SSA road network is pavedOnly 14 per cent of rural households have access to a paved road

African countries invested 15 % of GDP in transport infrastructure over the period 2005–2012, on average ( India and China invested about 32 percent and 42 % of GDP, respectively

Low utilization of motor vehicle

Farmers spent significant time, energy and effort in moving small loads over relatively short distances.

Low Investment Limited access Low use of IMT

Background…….cont’d

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Road density per 1000 sq.km is 49.1 km

The total road network of the country is 63083 kms (9875 kms of Asphalt (15 percent of the road network), 14675 kms of Gravel road; 31550 kms of rural roads and 6983km of Woreda roads

Road density per per 1000 population is 0.66

Account for 90 to 95 % of motorized inter-urban freight and passenger movements

Road Transport Profile of Ethiopia

• Globally, there are still 1 billion people in rural areas without adequate access to all-weather roads ( IBRD, 2014).

• Massive investment in rural roads in Asia and Latin America helped to transform smallholder agriculture through;

• lowering transport cost, decreasing transaction cost, and increasing access to agricultural inputs markets.

1.2 Statement of the problem

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• Nothing less is expected from rural road transport in Ethiopia.

Statement …..Cont’d

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Okay but Why ?

About 83 percent of the population lives in rural area

Agriculture is the dominant sector (employing 80 % of the labour force)

Rural road transport accounts 90 % of rural transport

Makes rural road important


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• Aching goals of (ADLI),• Aching the goals (PASDEP)• Other sectoral programs

How ?

• By improving accessibility, • Reducing transport cost, • Minimizing travel time and • Alleviating transport/mobility

burden to the rural population

• RTTP • WIDP• RSDP (1997/98)

Their Objectives was

Ethiopia has been implanting different transport programs

• The recent five-year Growth and Transformation Plan (GTP) has also underscored the role of rural roads (e.g URRAP, 2010)


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URRAP Visions • to connect all kebeles to the nearby all-weather roads• the construction of 11,212 kms of new rural roads • the construction of 71523 kms of Woreda roads

However, in spite of such efforts, rural road transport has still

remained low both in terms of;

• Accessibility• Mobility


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What are the evidence ? For low accessibility ?• Proportion of area further than 5 km

from all-weather roads is 40.5percent• The average distance to all-weather

roads is 6 kms • Close to 70 percent of the rural

population in Ethiopia still need to travel about six hours to reach all weather roads

• Most of these roads are dry weather roads that cannot be passable by any formal transport modes during the wet season

• The average rural accessibility index (RAI) for the country is around 50 percent

• The proportion of number of rural population within 2km access is only 28 percent

What are the evidence ? For low Mobility ?• Mainly rely upon pack animals • Majority carrying loads on their own

heads • Low utilization of Intermediate mode of

transport for agricultural purpose


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This poor access and Mobility has been a…

• Major impendent to rural development• Major impendent to agricultural

development • Major constraint to the overall efforts to

improve agricultural growth and reduce poverty … (World Bank (2004); Wondemu (2015).


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• Production

• Productivity

• Market Integration

Moreover, the agriculture sector

didn’t witnessed significant change in terms of ……

• In this regard, in addition to other factors, lack of rural infrastructure in general and road transport in particular has been mentioned as a major underline reason for low agricultural growth.

• But questions like how important is accessibility and mobility in contributing to and raising agricultural productivity, commercialization, and efficiency in smallholder farming system has not been adequately addressed in the existing literatures.


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• Empirical studies on the impact of rural road transport have shown that rural roads can play a meaningful role in ;

• fostering consumption; • improving rural income; • and reducing poverty (e.g Worku (2011); (Decron, 2009);

(Kiflet et al 2012); (Lulit, 2012) etc. • However, less has been studied on the effect of rural

road transport on agricultural efficiency and commercialization of smallholder farmers


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• Although there are few empirical studies on the effect of rural road on agricultural productivity (for example, (Kassali et al, 2012); (Wondemu, 2015)); (Tunde & Adeniyi, 2012); (Lee, 2010)),

• These studies didn’t explicitly show and capture the effect of rural road transport in relation to total factor productivity, efficiency and commercialization.


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• Although (Wondemu ,2015) has tried to link road quality with allocative efficiency, he has not addressed the effect on total factor productivity and effect of rural mobility on economic efficiency and commercialization.

• Moreover, there remains much to be done in rural accessibility and mobility (Porter, 2013)


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• What is the level of total factor productivity among smallholder farmers in Ethiopia? And what factors determine the level of total factor productivity?

• What is the level of technical, allocative, economic efficiency scores in smallholder farming in Ethiopia? And what factors explain the sources of variations in efficiencies among smallholder’s farmers?

1.3 Research Question

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• What factors explain smallholder’s market participation and variation in the level of smallholder’s commercialization in rural Ethiopia?

• Which socio economic factors are important to understand consumption or poverty variations across different income quantities in rural Ethiopia?

Research Question..Cont’d

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General Objective

• The general objective of the study is to examine the factors that explain agricultural efficiency and level of commercialization in smallholder farming in Ethiopia; so as to identify policy relevant issues.

1.4 Objective

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The specific objectives• Investigate the effect of rural accessibility and mobility

on total factor productivity • Estimate the effects of rural accessibility and mobility on

technical, allocative and economic efficiency of smallholder farmers

• Analyze the contribution of rural accessibility and mobility to market participation and level of commercialization in smallholder farmers in Ethiopia

• Analyze whether rural accessibility and mobility is pro-poor

• Draw possible policy implication based on findings

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Objective…..Cont’d

• Secondary level data which included almost all administrative regions of the country.

• The study will focus only on rural road transport

• Rural mobility is narrowly defined

1.5 Scope and Limitation of the Study

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Rural Road Transport and Agriculture• Fan et al., (2002) indicated that governments spending on rural

roads and irrigation have high contributions to increase agricultural productivity and poverty reduction.

• Gollin & Rogerson (2010) show that agriculture is highly sensitive to transportation costs.

• Egret, (2009) found that infrastructure (including road) contribute to long-run productivity and income growth as compared to investment on other capital.

• Wondemu (2015) found that households that have all weather road access are 16% technically and two times allocatively more efficient.

2. Literature Review

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Rural Road Transport and Markets• Manohar (1989) found that the development of road

network has made farmers to enjoy faster and equitable distribution of inputs and marketing of products.

• Renkow & Hallstrom (2015) found that transaction costs in rural Kenya constrain farmers’ market participation.

• Dercon and Hoddinott (2005) found that an increase of 10 km in the distance from the rural village to the closest market town has a effect on the likelihood that the household purchases inputs, controlling for the effect of other factors.

Empirical …Cont’d

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• Dercon et al., (2008) found that roads in these localities make it easier for households to access local market towns which in turn are linked to larger urban centers.

• Dercon and Hoddinott (2005) found that improvement in rural road quality increase the likelihood of purchasing crop inputs (by 29 to 34 per cent, depending on the season) and, for women, selling artisanal products (by 39 per cent)

Empirical …Cont’d

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Factors Influencing Productivity, Efficiency, Commercialization and Poverty

• Empirical studies show that there are different possible factors that can influence the level of ;

• Total factor productivity;• Efficiency (technical, allocative and

economic);• Commercialization; • and Poverty in smallholder farming system.

Empirical ……Cont’d

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Empirical …..( Factors Influencing Productivity)Parameters Definitions Type of data Expected sing Source B1 Age Years + Ukoha et al(2009);B2 Gender ( 1 male =0 Female) - Ukoha et al(2009)B3 Education Years in school + or - Hayami and Ruttan( 1970)B4 Household size

Number - 0r + Ukoha et al(2009)

B5 Farm size Hectare (Ha) + or - Frisrold and Ingram (1994)B6 Fertilizer K.G + Render (2004); Ukoha et

al(2009)B7 Labour Man day days + Hayami and Ruttan( 1970)B8 No of oxen No used for ploughing + Pender (2004)B9 Irrigation use 1 =users 0 =non users + Hayami and Ruttan( 1970) etcB10 Access to

credit 1 yes 0=No Ukoha et al(2009)

B11 Access to ext. 1=yes 0= no + Pender(2004)B12 Distance to

market Km + Weibe (2001)

B13 Mobility 1=foot 2=IMT 3= Animal drawn cart

+ or - To be tested

B14 Agro-climate zone

Categorical variable + Pender (2004)

B15 Farm income ETB + Akpan et al (2011)

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Empirical .. ( Factors Influencing efficiency scores) Variable Measurement Expected sign Sources

Age In completed years + or - Lockhead et al. (1980)

Education Years in school Coelli & Battese, 1996)

Farm size Hectare (Ha) + or - Coelli and Battese (1996)

Fertilizer Kg +

Soil fertility Index + Haile (2015)

Farm income ETB + Haile (2015)

Labour Mandays + Khail and Yabe(2007)

Access to extension 1 =yes 0 =no + Sibiko (2013)

Value of asset ETB + Sibiko (2013)

Access to credit 1 =yes 0 =no Khan & Saeed (2011)

Accessibility Distance to market (km) + or - To be tested but Sibiko (2013) found + result for technical efficiency

Mobility 1=Foot 2= IMT 3=Animal drawn cart

+ or - To be tested

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Parameters definitions Type of data Expected sing Source B1 Age Years + Ukoha et al(2009);B2 Gender ( 1 male =0 Female) - Ukoha et al(2009)B3 Education Years in school + or - Hayami and Ruttan( 1970)B4 Household size Number - 0r + Ukoha et al(2009)B5 Farm size Hectare (Ha) + or - Frisrold and Ingram (1994)B6 Fertilizer K.G + Render (2004); Ukoha et

al(2009)B7 Labour Man day days + Hayami and Ruttan( 1970)B8 No of oxen No used for ploughing + Pender (2004)B9 Irrigation use 1 =users 0 =non users + Hayami and Ruttan( 1970) etcB10 Access to credit 1 yes 0=No Ukoha et al(2009)B11 Access to ext. 1=yes 0= no + Pender(2004)B12 Distance to

market Km + Weibe (2001)

B13 Mobility 1=foot 2=IMT 3= Animal drawn cart

+ or - To be tested

B14 Agro-climate zone

Categorical variable + Pender (2004)

B15 Farm income ETB + Akpan et al (2011)

Empirical… (Factors Influencing Market Participation and Commercialization )

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Variable Measurement Expected sign Sources

Age Completed years + Bogale, et al (2005)

Amount of credit/Access

Number or ; 1=yes 0=No + Bogale & Genene, (2012)

Education Years of schooling + Hagos & Holden, (2008)

Size of land/Adult Equivalent Ha/AE + Bogale & Genene, (2012)

Extension visit 1=yes 0=No + Dercon et al (2008)

Livestock holding Number + Hagos & Holden, (2008)

Dependency ratio adults(13-60)/ 20+elderly above 60

- Ojimba (2013)

Household size Number - Ojimba (2013)

Access to Market Km + Dercon et al (2008)

Mobility + or - To be tested

Irrigation use 1=yes 0=No Runsinarith (2011)

Summary of Factors Influencing Poverty

Gaps in the empirical literature • The effect of Accessibility and mobility has not been

addressed in TFP analysis • The effect of mobility has not been adequately

studied in relation Allocative efficiency • The effect of accessibility and mobility ahs not been

studied in relation to Economic efficiency • Even if few studies tried to see the effect of

Accessibility on technical efficiency, the effect of mobility has not been adequately addressed

Empirical Gaps …Cont’d

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Conceptual Framework

Rural accessibility and mobility

Direct effect of accessibility and mobility

Indirect effect of rural accessibility and mobility

• Agricultural Commercialization

• Agricultural TFP • Agricultural efficiency etc)• Poverty

· Lower input costs

·High output price

·reduce production cost

·better technologies,

· lower input costs,

· Lower transaction cost

·reduce vehicle operating cost

·reduce travel distance

·reduced ravel time

Economic factors

Income Value of asset Farm income

Demographic

Age EducationGender Family size Dependency ration

Farm factors

Land ownershipSoil fertility Labour Seed Fertilizerland size

Institutional factors

Access to extensionAccess to credit

Agro climate factors Agro ecology

Own conceptual Framowrk

4.1 Data source and type • Ethiopian Rural Socioeconomic survey (ERSS)

prepared by the CSA and the World Bank. • The first survey was conducted in 2011/12 and the

second round was conducted after two years later in 2013/14.

• Contains quantitative information on agriculture production, consumption and household’s socio economic characteristics.

• In addition, a unique geospatial data was also included in the data set.

4.Materials and Methods

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4.2 Sampling Frame• The sampling frame for both rounds surveys of the

Ethiopia Socio Economic Survey (ESS) is constructed based on CSA survey sampling frame containing EAs.

• Enumerations Area (EA’s) are the area mainly found within a kebele administrative

Materials …. Cont’d

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4.3 Sampling Design • The Ethiopia Socio Economic Survey (ESS) survey

applied a stratified sample design technique and in combination with a two-stage design probability sample design.

• The nine regions of Ethiopia served as the strata (Central Statistical Agency and World Bank, 2015).

• Following the stratification, quotas were set for the number of EAs in each region to ensure a minimum number of EAs are drawn from each (EA).

Materials cont’d

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• The first stage of sampling involved selecting primary sampling units, using simple random sampling (SRS) from the sample of the CSA enumeration areas (EAs).

• The agricultural sample survey enumeration areas were selected based on probability proportional to size of the total EAs in each region.

• A total of 290 rural sample enumeration area (EAs) were selected from the Annual Agricultural Sample Survey.

Materials cont’d

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Total EAs Rural EAs Small town EAs Mid and Large Town EAs

National 433 290 43 100

Tigray 49 30 4 15

Afar 13 10 2 1

Amhara 86 61 10 15

Oromiya 85 55 10 20

Somali 26 20 3 3

Benishangul-Gumuz 11 10 1 0

SNNP 99 74 10 15

Gambela 12 10 1 1

Harari 14 10 1 3

Dire Dawa 18 10 1 7

Addis Ababa 20 NA NA 20

Materials………. cont’d Sampling of Enumeration Areas

Source: Extracted from CSA: Ethiopian Source Economic Survey Report

• The second stage of sampling was the selection of households units to be interviewed in the selected Enumerations Area (EAs).

• For rural EAs, a total of 12 households are sampled in each EA. Of these, 10 households were randomly selected from the sample of 30 Agricultural Sample Survey households (AgSS).

Materials………. cont’d

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• Another 2 households were randomly selected from all other households in the rural EA (those not involved in agriculture or livestock), thus adding up to 12 households from both.

• In case where there is only one or no such households in EAs, less than two non-agricultural households were surveyed and more agricultural households were interviewed so that the number of households per EA could remains the same (CSA and World Bank, 2015).


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Total Sample Finally covered

EAs HHs EAs HHs

Amhara 61 610 58 519

Afar 10 100 5 4

Tigray 30 300 30 240

Oromia 55 550 55 446

Somalie 20 200 20 95

Benshangul_Gumz 10 100 10 96

SNNP 74 740 70 572

Gambella 10 100 10 72

Harari 10 100 10 92

Diredawa 10 100 10 97

Country level 290 2900 278 2236


Source: Extracted from CSA: Ethiopian Source Economic Survey Report

4.4 Measuring Efficiency, TFP, Commercialization and Pro Poor Growth

Analytical Framework to Measure Efficiency • There are different approaches and methods (data

envelopment analysis, deterministic frontier, stochastic frontier approach, and thick frontier approach).

• The difference line in the assumptions made on the functional form of the frontier, whether a random error is included if there is random error, and the type of probability distribution assumed for the efficiency scores (Thabethe et al.,2014).

Materials and methods ….Cont’d

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• In order to estimate the effect accessibility and mobility and other variables on technical , allocative and economic efficiency the study will make use of the stochastic frontier model.

• The stochastic frontier model is appropriate for Developing(Coelli, (1995); Bravo-Ureta & Rieger, (1991); (Thabethe et al, 2014)) .

• The general form of the panel data version of the production technology function of a given farmer can be represented by a panel stochastic production frontier model as :

Material and method …….Cont’d

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( , is the maximum potential output for a particular input vector ;

thi measures the value of agricultural output of the farmer in year t,;

is a vector of the input quantities of the ith farmer in year t; i

is a vector of parameters or the unknown parameter to be estimated ;

is the composite error tem;

a two sided normally distributed stochastic variable or random error that represent measurement error and other uncontrolled random shocks in production(e.g. weather change etc) and is the one sided efficiency component with a half normal distribution or are non-negative random variables, associated with technical inefficiency in production

=

Material and method …….Cont’d (

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the Cobb-Douglas production function is the best production functions as it is easy to interpret results and provides constant elasticity of substitution production function.

Following (Aboki et al., (2013); Thabethe et al., (2014)), the following stochastic Cobb Douglas production function that consider the panel nature of the data will be used to estimate the technical efficiency scores of smallholder farmers


• In order to estimate the allocative efficiency and hence the economic efficiency of smallholders the cost function is proposed.

• Thus, by considering the assumptions that production function would follow a Cobb-Douglas production function, the corresponding dual cost frontier can be derived and formulated as follows:

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From production theory : Cost function is the inverse of production function.


Short run marginal product curve or production with Q output and L unit of labour

Short run marginal cost curve with C unit of cost and L unit of input or labor

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the minimum cost of the ith farmer in period t associated with output

is a vector input price of the ith farmer in period t

is a vector of parameters which are function of the parameter in the production function

Input oriented adjusted output level of the ith farmer in period t


• From the Cobb Douglas production function the technical efficiency of an individual farmer can be defined in terms of the ratio of the observed output to the corresponding frontier output given the available technology, conditional on the levels of input used by the firm.

• Thus, the specification of the technical efficiency (TE) will have the following from:


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Given the available technology, conditional on the levels of input used by the firm. Thus, the specification of the technical efficiency (TE) will have the following from:

TE : =

)

=

Or TE =

Measuring …….Cont’d

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• According to (Khan and Saeed , 2011) we can define the farmer specific economic efficiency as the ratio of minimum observed total production cost (C*) to actual total production cost (C) using the from

• the corresponding cost frontier of Cobb- Douglass functional form was used as the basis of estimating the allocative efficiencies of the farmers. The implicit form of the cost frontier production form is specified as follows:

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• Furthermore, a measure of farm specific allocation efficiency can be obtained from the estimated values of technical and economic efficiencies as follows: AE=EE/TE and ( 0 ≤ AE ≤ 1)


Estimating Factors Influencing TE, AE and EE


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• To determine the relationship between hypothesized factors and the computed indices of technical, allocative and economic efficiency, a two - limit Tobit procedure model will be used.

• The Tobit model will be adopted because the technical, allocative and economic efficiency indices lie within a double bounded range of 0 to 1 that is censored in both tails Obare et al., (2010).

• the total factor productivity (TFP) measure the net growth of output per unit of total inputs that is total factor productivity is the productivity when all factors are taken in the determination of productivity ( Kabwe, 2012))

• To measure the effect of accessibility and mobility on TFP, the first step will be to construct productivity index.

• There are two key indices used in empirical studies to estimate total factor productivity, namely the Malmquist and the Törnqvist index.

Measuring……Cont’d

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Measuring total factor productivity (TFP)

• However, the choice between these two indexes matters little and can thus be left to the individual researcher (OECD, 2001).

• Thus, this study will adopt the Malmquist index to measure the total factor productivity indices of each farmer and following ((Coelli et al, 2005; ;(Tadesse, 2007) and (Ayele et al, 2007).

• The Malmquist TFP index has two elements these are technical efficiency change index and technological change index

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Measuring …..Cont’d

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TE = =

=

( The technical efficiency change index is the ration of two technical efficiency distance functions for t+1 and t periods for ith household or farmer, and it can easily be obtained from the previous equation given by

Measuring………..Cont’d

• On the other hand, the technological change index (TCI) between two consecutive years t+1 and t , for household i , can be obtained from the estimated parameters of the stochastic production frontier

Measuring ….Cont’d

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• According to (Ayele et al, 2007), the average measure of technological change can be extracted from the first derivative of the estimated function with respect to time t at mean values of input used in each year. This will give:

• According to (Coelli, Rao and Battese ,1998 as sited (Ayele et al, 2007) by applying the geometric mean on the derivated equation above will give the technological change for the two adjacent periods


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• according to Coelli, Rao and Battese ,1998 as sited (Ayele et al, 2007) the product of total technical change and total technological change given by ;

• After estimating the total factor productivity for each household the next step will be to identify the effect of accessibility, mobility and other factors on total factor productivity.

• Total factor productivity can be approximated with a linear function of the explanatory variables or factors (key & McBride, 2003).

• These factors on the other hand can be fitted by the OLS method but using diverse econometric specifications given as follow:


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Cobb–Douglas form Ln( TFPit) =

Exponential form Ln(TFPit)

=

Log linear form

=

Liner from =


Measuring Commercialization • The concept of commercialization is used to assess

farmer’s participation mainly in output market.• Commercialization index will be used to assess the

level of market participation • commercialization index or household Crop

Commercialization Index (CCI) is the ratio of gross value of all crop sales over gross value of all crop production multiplied by hundred (Strasberg et al. (1999); as cited in Abera, (2009)


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• The commercialization index of smallholder’s famers can be constructed using the following simple formula for data with panel structure:


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• In order to identify variables influencing market participation and level of commercialization the double hurdle model will be employed.

• The double hurdle model is selected because it will help to identify factors influencing market participation and factors influencing the level of commercialization (amount sold) at the same time

• This model is appropriate when yi = 0 is a genuine zero; in that the zero is the result of a utility maximizing choice,


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• considering the fact that the data is a two wave panel data the following model is formulated:


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Measuring pro-poorness of rural roads • In order to address the 4th objective of the research

that is, to see accessibility and mobility pro poor, the quantile regression method will be employed.

• This approach is selected because it allows parameter variation across quantiles of the income or consumption distribution Pede et al., (2011). The functional form is given by :


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the Log of total expenditure per adult equivalent model at quantile ( )

of the distribution of the dependent variable conditional on the value of


vector of explanatory variables

• The quantile regression model will be estimated at the 10th, 25th, 50th, 75th and the 90th percentiles of the distribution of expenditure of the households

• Total Expenditure in real terms is chosen than the income approach for welfare analysis : (1) farm production fluctuate (2) Not all income is consumed and also not all consumption is financed from income.


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• Total expenditure per capita in real terms how?

1. Family size will be converted to AEU2. Then each years expenditure will be converted into real

terms using price index3. The total expenditure in real terms in each household will

be divided by AEU to get per capita expenditure


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Descriptive Statistics• Since the study mainly depends on quantitative data,

the data analysis approach will follow quantitative data analysis techniques.

• Thus, statistics tests like t test, chi square test and ANOVA test will be used to test mean differences and associations between rural accessibility and mobility and other key socio economic variables

Data Analysis

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• For the decretive analysis the following criterions will be used to classify HHs in to good access and poor access • 1) if distance to main market is <2km good access

and if distance to market is > 2km poor access or 2) or if roads are all weather roads good access and if roads are not all weather roads poor access

• To study mobility, HHs will be also classified depending on type of transport mode used

Data Analysis….Cont’d

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Data Analysis …..Cont’d Variables Objective Method of analysis

Transport use and poverty Association between Intermediate mode transport users and non users and poverty (poor and non poor)

chi square test

Transport use and fertilizer use

Fertilizer use comparison by Intermediate mode transport users and non users

T test

Transport use and seed use Seed use comparison by Intermediate mode transport users and non users

T test

Technical efficiency and type of mode of transport

Technical efficiency comparison by type of mode of transport One way ANOVA

Allocative efficiency and type of mode of transport

Allocative efficiency comparison by type of mode of transport One way ANOVA

Economic efficiency and type of mode of transport

Economic efficiency comparison by type of mode of transport One way ANOVA

Technical efficiency and type of access

Technical efficiency comparison by type of access Chi Square

Allocative efficiency and type of access

Allocative efficiency comparison by type of access Chi Square

Bamlak

the table should be presented in two slides

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Variables Objective Method of analysis Total income and transport use

Farm income comparison between different mode of transport users

One way ANOVA

Farm income and transport use

Farm income comparison between different mode of transport users

One way ANOVA

Accessibility and poverty Association between transport use and poverty chi square test

Accessibility and fertilizer Fertilizer use comparison between the level of accessibility T test

Accessibility and non farm income

Farm income comparison using accessibility category T test

Accessibility and total income

Mean fertilizer use comparison between the level of accessibility

T test

Accessibility and extension service

Association between accessibility and extension service use chi square test

Accessibility and market participation

Association between accessibility and market participation chi square test

Accessibility and output market participation

Association between accessibility and market participation chi square test

Data Analysis …..Cont’d

Data Analysis …..Cont’d

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Econometric Strategy Objectives Method and Model Explanation

Investigate the effect of accessibility, mobility other factors on total factor productivity of farmers

Estimate total factor productivity index using the Malmquist Index, then run OLS regression using various specification to investigate the effect of accessibility, mobility other socio economic variables

Examine whether accessibility and mobility have effect on technical efficiency (TE) of smallholder farmers

Using the Cobb-Douglas production functional form to calculate the TE, then estimate the effect of accessibility , mobility and other socio economic variables using the Tobit model

Estimate the effects of accessibility and mobility on allocative efficiency (AE) of smallholder farmers

The cost frontier of Cobb- Douglass functional form will be used as the basis to estimate the allocative efficiencies (AE) , then estimate the effect of accessibility, mobility and other variables using Tobit model

Estimate the effect of accessibility and mobility on economic efficiency (EE) of smallholder farmers

The cost frontier of Cobb- Douglass functional form will be used as the basis to estimate the allocative efficiencies (EE) or simply EE=AE*TE , then estimate the effect of accessibility, mobility and other variables using Tobit model

Analyze the contribution of accessibility and mobility to market participation and level of commercialization

Double hurdle model to estimate effect of accessibility and mobility and other variables on market participation and the level of commercialization

Analyze whether accessibility and mobility in rural roads transport are pro-poor

Quintile regression model will be used to analyze effect of accessibility and mobility across different groups (consumption expenditure per adult equivalent )

Bamlak

divide the table into two and present it in two slides

Thank You

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Documents

Presentation Agriculture and Rural Transport in Ethiopia (2)