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 UNIVERSITY
COLLEGE 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 study
2. Literature review
2.1 Summary of the empirical literature and gap
3. Conceptual Framework
4. 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
2

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
3

Rural road transport In SSA Africa
• Accounts 80 % of freight and 90 % of passenger traffic
Background…….Cont’d
4
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 inhabitants
Less than one-fourth of total SSA road
network is paved
Only 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 accessLow use of IMT

Background…….cont’d
5
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
6

• Nothing less is expected from rural road transport in
Ethiopia.
Statement …..Cont’d
7
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

8
•Aching goals of (ADLI),
•Aching the goals (PASDEP)
•Other sectoral programs
Ho
w ?
• 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)
9
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

•Accessibility
•Mobility
However, in spite of such
efforts, rural road transport
has still remained low both
in terms of;
10

11
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

12
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).

13
•Production
•Productivity
•Market Integration

• 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.
14

• 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
15

• 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.
16

• 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)
17

• 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
18

• 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
19

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
20

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
21
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
22

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
23

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
24

• 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
25

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
26

27
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) etc
B10 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)

28
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

29
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) etc
B10 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 )

30
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
31

32
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
Education
Gender
Family size
Dependency ration
Farm factors
Land ownership
Soil fertility
Labour
Seed
Fertilizer
land size
Institutional factors
Access to extension
Access 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
33

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
34

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
35

• 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
36

37
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).
38

• 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).
39

40
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
41

• 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
42

43
( , is the maximum potential output for a particular input vector;
th
imeasures 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
=
(

44
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:
45

46
• 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

47
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:
48

49
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 : =
)= =
Measuring …….Cont’d

50
• 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:

51
• 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
52
• 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
53
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
54
Measuring …..Cont’d

55
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
56

57
• 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

58
• 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:
59

60
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)
61

• The commercialization index of smallholder’s famers
can be constructed using the following simple
formula for data with panel structure:
62

• 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,
63

• considering the fact that the data is a two wave
panel data the following model is formulated:
64

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 :
65

66
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.
67

• Total expenditure per capita in real terms how?
1. Family size will be converted to AEU
2. Then each years expenditure will be converted into real
terms using price index
3. The total expenditure in real terms in each household will
be divided by AEU to get per capita expenditure
68

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
69

• 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
70

71
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
Allocative efficiency comparison by type of mode of transport One way ANOVA
Economic efficiency and
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

72
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

73
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 )

Presentation Agriculture and Rural Transport in Ethiopia (2)

Recommended

Recommended

More Related Content

What's hot

What's hot (12)

Similar to Presentation Agriculture and Rural Transport in Ethiopia (2)

Similar to Presentation Agriculture and Rural Transport in Ethiopia (2) (20)

Presentation Agriculture and Rural Transport in Ethiopia (2)

Editor's Notes