DP2012-34

Impa cts of A gricultural
Extension on C rop Productivity,
Pov erty and Vulnerability:
Evidence from Uganda*
Md. Faruq HASAN
Katsushi S. IMAI
Takahiro SATO
Revised February 21, 2013

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Impacts of Agricultural Extension on Crop Productivity, Poverty and
Vulnerability: Evidence from Uganda
Md. Faruq Hasan
Department of Agricultural Extension, Hajee Mohammad Danesh Science andTechnology University, Bangladesh
Katsushi S. Imai*
School of Social Sciences, the University of Manchester, UK and Research Institute for
Economics & Business Administration, Kobe University, Japan
Takahiro Sato
Research Institute for Economics & Business Administration, Kobe University JapanFirst Draft: 30th November 2012
This Draft: 21st February 2013
Abstract
The present study examines whether agricultural extension improves household crop
productivity and reduces poverty and vulnerability in rural Uganda drawing upon
Uganda National Panel Survey data in 2009-10. We first estimate household crop
productivity using stochastic frontier analysis that can allow for stochastic shocks in the
production function. Then, the effect of different types of agricultural extension
programmes - namely NAADS or government, NGO, cooperatives, large farmer, input
supplier and other types extension service providers - on the crop productivity is
estimated by treatment effects model which controls for the sample selection bias
associated with household participation in the agricultural extension. In this model, thedistance to agricultural extension service centre is used as an instrument for participation
equation. It is found that participation in agricultural extension programs significantly
raised crop productivity and household expenditure per capita in most cases with a few
exceptions. This is consistent with the central objectives of agricultural extension to
improve productivity and reduce poverty. Further evidence has been provided on the roleof most types of agricultural extension in reducing vulnerability as expected poverty.

Key Words: Agricultural Extension, Poverty, Vulnerability, Treatment Effects Model, Uganda
JEL Codes: C21, C31, I32, N57, O13, O16
Contact Address
Katsushi S. Imai (Dr) Economics, School of Social Sciences, University of Manchester, Arthur Lewis
Building, Oxford Road, Manchester M13 9PL, UK; Telephone: +44-(0)161-275-4827, Fax:
+44-(0)161-275-4812 Email: Katsushi.Imai@manchester.ac.uk.

1Impacts of Agricultural Extension on Crop Productivity, Poverty and
Vulnerability: Evidence from Uganda
1. Introduction
Agricultural extension has long been grounded in the technology diffusion model that
accounts for the transfer of improved agricultural technologies and information at the farmlevels (Swanson et al., 1998). The model includes a range of functions, consisting pluralisticrural knowledge and information systems of agricultural production about, for instance,service delivery, problem-solving, and financing (Alext, et al., 2004). Agricultural extension
operates in diverse social settings and it involves a range of actors and organizations, such as,
central and local governments, NGOs and private providers (Anderson and Crowder 2000).
Among these various providers, central government is the dominant stakeholder in formal
agricultural extension which is designed to achieve primary functions of the technology
diffusion model.
In recent years, most of the agricultural sectors in less developed countries haveundergone a series of reforms that aim at positioning the role of the market as a leading force
to promote optimal resource allocation among the various economic activities (Diaz, 2004),
which often led the private sector to take over some of the functions which were formerly
performed by governments. The developing countries which are dependent on agricultural
sector are more “liberalised” in reforming the public sector to adopt non-government
extension systems, especially in some African countries that face budget constraints (Ndegwa,
2002).
Many countries in sub-Saharan Africa including Uganda still rely on agriculture as aleading force of economic growth and poverty reduction. About 31.1 percent of Ugandans
were estimated to be poor, corresponding to nearly 8.4 million people (UBOS, 2009). About
85 percent of Uganda’s population live in rural areas, while 80 percent of the poor living in2

rural areas are mainly dependent on subsistence agriculture as a main earning source for theirsurvival. Agriculture is the third largest sector contributing about 21.8% to Uganda’s GDP,
next to services and industry sector. However, despite the importance of agricultural sector in
the economy and its large share, agriculture has not been used to its full potential fordevelopment in Uganda due to farmers’ lack of access to farm production technology andinformation that would be necessary for increasing agricultural productivity (World Bank,
2008).
In Uganda, reformation of public sector extension was accomplished in 2001 to
improve the efficiency and quality of agricultural extension services through a decentralized,
demand-driven and client oriented and farmer-led approach and those extension services
operated under a new statutory semi-autonomous body called NAADS (World Bank, 2001).
The NAADS was planned to ensure (i) shifting from large national bureaucratic structures,such as privatization of funding, delivery of extension, and decentralization of authority tolower levels of the government; (ii) involving the private sector in a way of delegation toNGOs, farmer organizations, and other grassroots control; and (iii) focusing on the issues,
such as equity, empowerment, and sustainability as a wider agenda in addition to technology
transfer (Bashaasha et al., 2011). Resources of central government were allocated at the
sub-national or regional level, so were managed advisory services. This framework took theform of the Poverty Eradication Action Plan (PEAP) under which a multi-sectoral Plan forModernization of Agriculture (PMA) was performed with the ultimate goal of poverty
eradication (MAAIF, 2000). The NAADS approach was an attempt to respond to current
pressures on agricultural extension to be more responsive to the needs of small scale farmers
and the rural poor for the goal of reducing rural poverty.
Under the NAADS statute enacted by parliament in 2001, the public extension system
in Uganda was gradually phased out and replaced by the private provider (or NGO sector)
3advisory system (Bashasha, et al., 2011, p. 14). In operational terms, this means that theprivate extension service providers were invited to take up information dissemination roles
and investigate (i) whether there would be enough demand for the services among farmers inorder to attract private providers and (ii) what factors would influence farmers’ demand forextension services. The shift from the public to private extension delivery under the NAADS
approach was faced with a number of challenges, including whether and how effectively
agricultural productivity would be raised, poverty would be reduced, and ultimately,
livelihood would be improved. The system calls for new relationships, knowledge and skills
among the key stakeholders from the private sector, farmers, farmers’ cooperatives, input
suppliers, and government as they strive to realize the ultimate goal of poverty eradication.
Bearing in mind these backgrounds, the present study will examine whether thetransformation of agricultural extension policy has been successful to improve farmhouseholds’ livelihood in terms of productivity improvement as well as poverty and/or
vulnerability reduction. We will also compare the impact of participation in NAADS or
government extension programmes, NGO extension programmes, and other different types ofagricultural extension supported, for instance, by cooperatives, large farmers, or input
suppliers to identify the effect of the policy reform on effectiveness of agricultural extension.
The present study thus aims at (i) estimating the current level of crop productivity andpotentials for further improvement, (ii) comparing the impacts of participation in different
extension programmes on crop productivity provided by different actors, and (iii) examining
the impacts of participation in different extension programmes on mean per capita
consumption expenditure and vulnerability.
The rest of the paper is organised as follows. The next section briefly explains the data
we have used for the econometric analysis. Section 3 provides detailed description of oureconometric models. Section 4 summarises the results. The final section offers concluding4remarks.

2. Data
The present study is based on the data set of Uganda National Panel Survey (UNPS)
conducted by the Uganda Bureau of Statistics (UBOS) in 2009-2010. The survey was
financially and technically supported by the Government of Netherlands, and the World Bank
Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)
project. The first round of UNPS was carried out over a twelve-month period on a nationally
representative sample of households, which is a subsample of the 2005 Uganda National
Household Survey (UNHS). To select the sample for the UNPS, the UNHS sample wasdivided into five strata (Kampala, Central, Eastern, Northern and Western). Within eachstratum, enumeration areas (EAs) were selected using simple random sampling, but theprobabilities of selection varied between strata. In Kampala, all UNHS EAs were selected toensure sufficient sample size in that stratum. In the other four strata, the probability of
selection ranged between 36 and 43 percent.
The basic objective of the survey is to provide the Government of Uganda with (i) the
information required for monitoring the National Development Strategy and for facilitating
monitoring poverty and service outcomes using the nationally representative household data,and (ii) a framework for conducting policy oriented analysis and for building relevant
capacity. The survey is a multi-topic household survey covering a wide range of issues,
including household composition and characteristics; item-wise crop production inputs andoutputs; expenditure for both food and non-food items, health and education; andparticipation of households in agricultural extension programs. The survey also includes
community level data on local infrastructure, public services, such as education, extension,
health facilities, etc.
5

3. Methodology
3.1 Household crop productivity estimation
The effectiveness of agricultural extension programs on household crop productivity is
estimated by adopting the two step procedure. In the first step, crop productivity aggregatedat household level is estimated to examine whether there is any room for further productivity
improvement. For productivity estimation, Stochastic Frontier Analysis (Aigner et al., 1977
and Meeusen and Broeck, 1977) has been applied to allow for stochastic shocks in the
production function. In the second step, treatment effects model has been used to examine if
participation in agricultural extension influences outcome variables (namely, productivity
estimated in the first stage, per capita consumption, and vulnerability) after taking account ofsample selection associated with participation in the extension programmes. The stochastic
frontier model allows for partitioning the stochastic error term into two components:
systematic random error to account for statistical noise and an inefficiency component(Battese and Coelli, 1992). In case of cross sectional data, the inefficiency component is
supposed to represent the productivity.
For estimation, we consider Cobb-Douglas without restriction and with restriction(wherein homogeneity conditions are imposed on the parameters). The conventional
production inputs are used in both specifications. The production function with
Cobb-Douglas specification is specified asln Outputi = 0 + 1 (ln Landi ) + 2 (ln Labori ) + 3 (ln Inputi ) + i  ui ,(1)

where lnOutput is the natural log of total receipts obtained from output and by-products;
lnLand is the natural log of the total land under cultivation; lnLabor is the natural log of total
wage expenditures for labour; lnInput is the natural log of total expenditures for different
inputs (seed, fertilizer, pesticides, water); and i is the individual farmer.
6

The error term is decomposed into two components such as  i  vi  ui . The mainidea of this decomposition is to derive, one pure random term (vi) accounting formeasurement errors and the effects which cannot be influenced by the firm such as weather,trade issues and access to materials. This component is assumed to be an identicallysymmetric and independently distributed error with a mean zero and variance  v2 . The othercomponent is a non-negative one (ui), measuring the technical inefficiency, i.e. the stochastic
shortfall of output from the most efficient production (systematic departures from thefrontier) (Battese and Coelli, 1995).
As the error term E( i )  E(ui )  0 ,  i  vi  ui is not symmetric, thus theestimation by OLS will provide consistent estimates of all parameters except the intercept
term. But, OLS cannot isolate technical efficiency from the residual term. As the efficiency
estimates fall between 0 and 1, normal distribution problem arises. The maximum likelihood(ML) estimation is a unified approach in such case of the normal distribution problem. The
ML estimation selects values of the model parameters that produce the distribution most
likely to have resulted in the observed data (i.e. the parameters that maximize the likelihood
function). In ML estimation the half-normal distribution works reasonably well and is mostoften used because the standard deviation of the distribution is able to concentrate efficiencies
near zero or spread them out (truncated at zero) (Greene, 1990). Other empirical studies using
different distributional assumptions for comparison showed that both rankings and efficiency
scores are generally similar across distributions (Fujii, 2001; Street, 2003). Thus, we adopt
the half-normal distributional assumption of the inefficiency component. Now, the technicalefficiency of production for the ith farmer can be computed asTEi  exp  ui   Yi / Yi * ,(2)where Yi is its observed output and Yi * is its maximum possible output given the available
7inputs.

3.2 Impact of extension programs on crop productivity
In the second step, the effect of agricultural extension programs on household level crop
productivity is estimated. In non-experimental data, the sample households were notgenerally comparable between “treated” (participants) and “untreated” households(non-participants in the programmes) because of the non-random placement of the
programmes where policy-makers select the households according to household or regional
characteristics (e.g. poor households/ regions), or of self-section where households selectthemselves depending on their inherent characteristics. The literature regarding program
evaluation has addressed these problems through randomization that makes individuals in atreatment group and a comparison group comparable. Rosenbaum and Rubin (1983)
introduced matching methods by randomizing the data through a propensity score. A
propensity score is a probability that a subject would be assigned to a specific group, andmatching subjects on propensity scores produces comparison groups of subjects who would
be equally likely to have been assigned to the study’s group or condition. Thus, propensity
score matching provides an estimate of the effect of a “treatment” variable on an outcome
variable that is largely free of bias arising from an association between treatment status andobservable variables. However, matching methods are not robust against “hidden bias”
arising from unobserved variables (such as farmers’ motivation, management and production
skills) that simultaneously affect assignment to treatment (participation) and the outcome
variable (productivity) due to violation of mean conditional independence (see, for example,
Cameron and Trivedi, 2005).
On the other hand, the control function approach with endogenous treatment variableaccommodates the self-selection of participating farmers. The approach explicitly corrects for8the potential bias in the fashion of Heckman’s (1979) selection model. Further, randomizationis conducted through the selection equation in the control function methods, which isequivalent to that in matching methods using a propensity score (Otsuki, 2011). Our study
thus adopts the control function approach because of the need for randomization and theendogenous nature of participation. Specifically, we use the treatment-effects model which
considers the effect of an endogenously chosen binary treatment on another endogenous
continuous variable, conditional on two sets of independent variables. The merit of treatment
effects model is that sample selection bias is explicitly estimated by using the results of probit
model. However, the weak aspects include (i) strong assumptions are imposed on
distributions of the error terms in the first and the second stages, (ii) the results are sensitive
to choice of the explanatory variables and instruments, and (iii) valid instruments are rarely
found in the non-experimental data and if the instruments are invalid, the results will dependon the distributional assumptions.
The treatment effects model estimates the effect of an endogenous binary treatment, Ti
on a continuous, fully observed outcome variable Yi , conditional on the independent
variables X i and Z i . The selection into the treatment Ti is a function of  i , which iscorrelated with vi , the error term in the equation of the outcome Yi . The binary decision to
obtain the treatment Ti

is modelled as the outcome of an unobserved latent variable, Ti * . It

is assumed that Ti * is a linear function of the exogenous covariates Z i and a random
component  i . Specifically,
Ti*   Zi'   i ,(3)

The latent variable is unobservable and its relationship with Ti is specified by*1 if Ti > 0 Ti  
.
*
0
if
T

0


i



(4)
9

We denote household productivity (technical efficiency) by stochastic frontier analysis as
outcome variable and the outcome equation is
Yi   Ti   X i'  i ,(5)  , v  ~ bivariate normal 0, 0,1,  v ,   .
where  is the average net effect (ANE) of participation in agricultural extension programs.
It is not necessarily true to argue that the positive estimate for  implies that participatingin agricultural extension programs increases productivity because, as we will see below, thisshows only the net effect without taking account of sample selection term. Here X i' is avector of determinants of Yi .
If unobserved factors in (3) are correlated with  i of (5), the correlation coefficient
between  i and  i (denoted by  ) is non-zero, and thus, the OLS estimate is inconsistent(Greene, 2008). Then, the expected outcome assuming normal distribution for T becomes
E[Yi Ti , X i , Z i ]  X i'    Ti  E[ i Ti , X i , Z i ](6)

 X i'    Ti   1 v1 { (Z i' ) /  (Z i' )} P (Ti  1 X )  0 v0 { (Z i' ) /1   (Z i' )} [1  P (Ti  1 X )],where the expected outcome for the participants is
E[Yi Ti , X i , Zi ]  X i'    Ti   1 v1 { (Zi' ) / (Zi' )} ,(7)

and the expected outcome for the non-participants is
E[Yi Ti , X i , Zi ]  X i'    0 v0 { (Zi' ) /1  (Zi' )} .

(8)

Here 1 v equals the covariance between  i and i for participants, 0 v equals the10

covariance between  0 and 0 for non-participants,  ( Zi' ) is the marginal probability ofstandard normal distribution at Zi' and ( Zi' ) is the cumulative distribution function of
the standard normal distribution at Zi' . The third term of (7) and second term of (8) include
10

the inverse Mills ratio to control for possible sample selection bias. The difference in
expected outcome between participants and non-participants then becomes
E[Yi Ti  1, X i , Zi ]  E[Yi Ti  0, X i , Zi ]    selection term.

(9)

The positive (negative) sign of the selection term implies that OLS overestimates(underestimates)  and the sign of the selection term depends on that of  . The maximum
likelihood estimation is utilized because it produces consistent estimators (Maddala, 1983,
p.122). Greene (2000, p.180) discusses the standard method of reducing a bivariate normal toa function of a univariate normal and the correlation  , which jointly estimates theparticipation andproductivity equations and allows the testing of the significance ofcross-equation correlation. The following is the log likelihood for observation i,2 Z i  (Yi  X i    )  /  
 1  Yi  X i    ln   
  ln 2 , Z i =1
2
2




1







ln Li 2
  Z i  (Yi  X i  )  /  
 1  Yi  X i  
Z i =0 
ln    ln 2 ,
2
2



1

















(10)

In the maximum likelihood estimation,  and  are not directly estimated. Rather ln 
and atanh  are directly estimated, where
atanh 1  1  ln 
2  1  (11)The standard error of    is approximated through the delta method, which is given by
Var( )  D Var (atanh ln  ) D'(12)

where D is the Jacobian of  with respect to atanh  and ln  (Cong and Drukker, 2000).
The predicted values of (7) and (8) are derived and compared by the standard t test to
examine whether the average treatment effect on treated (ATT) or productivity improvement
effect is significant. We considered distance from village centre to extension centre as
instrument for the participation equation. To construct the distance variable community levelraw distances are used. The missing observations without distances are replaced by the values
predicted by Tobit estimation.

11

3.3 Estimation of the impact of agricultural extension on household consumption andvulnerability
We considered mean per capita consumption expenditure (MPCE) as a measure of householdpresent poverty status. We also derived a measure of ‘Vulnerability as Expected Poverty’(VEP), an ex-ante measure based on Chaudhuri (2003) and Chaudhuri, Jalan and Suryahadi(2002) who applied it to a large cross-section of households in Indonesia and definedvulnerability as the probability that a household will fall into poverty in the future after
controlling for the observable household characteristics. It takes the value from 0 to 1 and thehigher the value of vulnerability measure, the higher is the probability of a household falling
into poverty in the next period. Impact of agricultural extension programs participation on
household vulnerability and mean per capita consumption expenditure will also be examinedby using the treatment effects model as described in the sub-section (3.2).
3.3.1 Deriving Vulnerability Measure
Vulnerability measure as an expected poverty is specified as:
VEPit  V it  Prc i,t 1  z (13)where vulnerability of household i at time t, Vit , is the probability that the i-th household’s
level of consumption at time t+1, c i , t 1 , will be below the poverty line, z.
Three limitations, amongst others, should be noted in our measure of vulnerability.
First, the present analysis is confined to a consumption (used synonymously with income)threshold of poverty. Second, our measure of vulnerability in terms of the probability of ahousehold’s consumption falling below the poverty threshold in the future is subject to the
choice of a threshold. Third, while income/consumption volatility underlies vulnerability, theresilience in mitigating welfare losses depends on assets defined broadly-including human,
physical and social capital. A household with inadequate physical or financial asset or savings,
12

for example, may find it hard to overcome loss of income. This may translate into lower
nutritional intake and rationing out of its members from the labor market (Dasgupta, 1997;
Foster, 1995). Lack of physical assets may also impede accumulation of profitable portfolios
under risk and generate poverty traps.
The consumption function is estimated by the equation (14).1

ln ci  X i   ei

(14)

where ci is mean per capita consumption (MPCE) (i.e. food and non-food consumption
expenditure) for the household and X is a vector of observable household characteristics andother determinants of consumption. It is further assumed that the structure of the economy isrelatively stable over time and, hence, future consumption stems solely from the uncertainty
about the idiosyncratic shocks, ei . It is also assumed that the variance of the disturbanceterm depends on: e2,i  X i (15)

The estimates of  and  are obtained using a three-step feasible generalized least squares(FGLS) 2. Using the estimates ˆ and ˆ , we can compute the expected log consumption andthe variance of log consumption for each household as follows.

E[ln Ci X i ]  X iˆ

(16)

V[ln Ci X i ]  X i ˆ

(17)

By assuming ln ci as normally distributed and letting   denote the cumulative density
function of the standard normal distribution, the estimated probability that a household will

1
2

We have used White-Huber sandwich estimator to overcome heteroscedasticity in the sample.
See Chaudhuri (2003), Chaudhuri et al. (2002), and Hoddinott and Quisumbing (2003) for technicaldetails.

13

be poor in the future (say, at time t+1) is given by: ln z  X ˆ i VÊP i  v̂ i  P̂r ln c i  ln z X i   
X i ˆ (18)This is an ex ante vulnerability measure that can be estimated with cross-sectional data. Notethat this expression also yields the probability of a household at time t becoming poor at t+1
given the distribution of consumption at t.
A merit of this vulnerability measure is that it can be estimated with cross-sectional
data (Imai 2011; Imai et al., 2011; Imai et al., 2010; Gaiha and Imai, 2009). However, itcorrectly reflects a household’s vulnerability only if the distribution of consumption acrosshouseholds, given the household characteristics at time t, represents time-series variation ofhousehold consumption. Hence this measure requires a large sample in which somehouseholds experience positive shocks while others suffer from negative shocks. Also, themeasure is unlikely to reflect unexpected large negative shocks (e.g., Asian financial crisis),
if we use the cross-section data for a normal year.

4. Econometric results
4.1 Household crop productivity estimation
Table 1 presents the results of household crop productivity estimated by stochastic frontier(SF) models with two specifications, that is, Cobb-Douglas without homogeneity conditions
imposed on the parameter (or without ‘constant returns to scale (CRS)’ constraints) (Column
1) and Cobb-Douglas with CRS constraints (Column 2). Both models show that land andlabour are primary inputs, with coefficient estimates (or elasticity estimates) ranged from
0.44 to 0.45 for land, 0.42 to 0.49 for labour and 0.14 to 0.15 for other inputs.
(Table 1 to be inserted around here)

14

Table 2 presents the descriptive statistics of technical efficiencies (TE) estimated fromthe SF models. The descriptive statistics of technical efficiency are defined by the equation(3) for each model. It is noted that technical efficiency indicates how the observed output iscompared with the maximum output which could be achieved if all the available inputs wereused most efficiently. The results indicate that household level crop productivity, or TE isonly around 22 percent, which is generally very low. Hence, there is further prospect forhousehold crop productivity improvement in Uganda. The level of technical efficiency issurprisingly similar between these models.
(Table 2 to be inserted around here)

4.2 Impact of extension program participation on crop productivity, consumption andvulnerability
This subsection summarises the results of treatment effects model which has been applied toestimate the effects of participation in agricultural extension programs. We have used theestimates of technical efficiency derived by Cobb-Douglas model without CRS as a measureof productivity because this model is simpler than that with CRS. We also considered (i)mean per capita consumption expenditure (MPCE) as a proxy for household poverty as wellas (ii) vulnerability derived as expected poverty. We present the results of extension program
participation impact on productivity in Table 3, those on log MPCE in Table 4 and those on
vulnerability in Table 5 respectively. Table 6 summarises the average treatment effect on
treated (ATT) derived by treatment effects model (the results of which are shown in Tables 3,
4 and 5) as well as whether extension program improves productivity and log MPCE andreduces vulnerability.
(Tables 3, 4, 5 and 6 to be inserted around here)15

In Tables 3, 4 and 5, six different types of agricultural extension services have been
considered for impact estimation, namely, (i) NAADS (Government), (ii) NGO, (iii)
Cooperatives, (iv) large farmers, (v) input suppliers and (vi) other types of extension.
Treatment effects model is applied for these six cases separately. The NAADS extension
services are government extension services provided under the new extension approach
introduced in 2001, while the NGO extension services are provided by non-governmentorganizations.
The results of participation equations based on probit model are shown in the second
panel of Tables 3, 4 and 5.3 Our instrument for the participation equation, the distance toextension service centre - shown in bold in these tables - is negative and significant for
NAADS, NGO extension programme participation (NGO extension programme participation
is significant at exactly 10% significance level for Table 5 - vulnerability model), while
positive and significant for cooperatives extension program participation. This is expected asa longer distance to the formal extension service centre deters the NAADS and NGO
programme participation, validating our specification. Again, a longer distance to the formal
extension service centre emboldens the cooperatives program participation, which validates
our specification. However, the distance is not significant for other types of extension
services, implying that the availability of extension service centre mainly influences theparticipation in government, non-government and cooperative extension programmes, but notothers. Though statistically not significant, the coefficient estimate of the distance toextension service centre is positive for (v) Input suppliers in Tables 3 and 5. This implies that
farmers tend to seek different providers if the village is located far away from the extension

3

The results of probit model in the first stage are similar for Table 3, Table 4 and Table 5, but not
identical as (i) the number of observations is different depending on the availability of outcome
variables and (ii) we have adopted the full maximum likelihood estimation in which unlike two step
estimation the first stage results are allowed to be influenced by the second regressions in iteration
processes.
16

service centre. The distance to extension services is unlikely to have a direct effect on
agricultural productivity, expenditure or vulnerability, which is supported by relatively low
coefficient of correlation between the instrument and outcome variables (see Table A2 in
Appendix).4
To summarise the coefficient estimates of selected explanatory variables in Tables 3,
4 and 5, the participants on NAADS following the second panels of these tables are likely tobe older, more educated, from a larger household and households that has more memberswith training. Similarly, better educated household heads tend to participate in NGO
extension programmes more likely than less educated heads. That is, education is afundamental determinant for participation in NAADS and NGO programmes. Larger
households are more likely to be supported by NAADS, NGO and Cooperatives than smaller
households. Geographical location appears to be a main determinant of extension services
from large farmers, input suppliers and “others” because they tend to concentrate in specificregions ((iv), (v) and (vi) in Table 3).
The first panel of Tables 3, 4, and 5 reports the results of the second stage regressions.
It is generally observed in Table 3 that crop productivity is positively affected by household
head’s education ((ii), (iii), (v) and (vi)), male headedness (all cases), household size (except(i)), and female share ((ii)-(v)), whilst negatively affected by ‘belonging to tribe 1’ ((ii)-(v))
and a higher dependency rate for female members ((ii)-(v)). The results are mostly expected
except a positive and significant coefficient estimate of the share of female members. This
may imply an important role played by women in working age in increasing agricultural
productivity.

4

However, when distance variable is not statistically significant, the final results of impact estimation
should be interpreted with caution as they are dependent on the distributional assumptions for thetreatment effects model.
17

The first panel of Table 4 shows the results of the second stage regressions for log
MPCE. Expenditure is positively affected by the household having more members who
received training (except (i) NAADS), and a higher share of female members. On the other
hand, it is negatively influenced by household size and by a higher burden share of female
members. In Table 5, we find that a household with a younger and/or less educated household
head tends to be more vulnerable. Having more household members is likely to make thehousehold more vulnerable. As expected, a household headed by a woman is more likely to
be vulnerable to future poverty. Having more members with training is positively associatedwith higher vulnerability, but this is presumably because training programmes have targetedpoor households. Households under tribe 1 are significantly vulnerable to poverty in future
time. Regional variation in vulnerability is observed across different extension models.
Table 6 summarises ATT for six different types of extension services. The results
indicate that participation in government agricultural extension service (NAADS) improves
productivity about 3.42 percent. The percentage improvement varies among different types ofproviders: +9.94 percent for Cooperatives, +1.96 percent for Large Farmers and +6.89
percent for Input Suppliers. The extension service received from “Other” sources ofextension services showed negative impact on productivity. It is noted that extension service
from cooperatives has the largest impact on productivity improvement. NAADS programs are
more effective compared to NGO programs for productivity improvement. Given the lowlevel of agricultural productivity, the average improvement of productivity by 3.42% issubstantial and supports the hypothesis that government extension service was effective in
improving household’s crop productivity.
However, the productivity improvement may not necessarily lead to poverty orvulnerability reduction directly and so we have estimated the treatment effects of agriculturalextension programmes on MPCE and vulnerability. On log MPCE, access to all types of18extension service reception increased household expenditure significantly (except extension
service received from NGO). The percentage increase of the effect of participation inextension programmes on MPCE (after taking account of sample selection) is substantial –ranging from 9.75% to 112.32%. However, it should be noted that calculations of ATT arebased on cross-sectional data for the two samples with or without access to extension
programmes and the results do not necessarily suggest that access to extension programmes
suddenly increases MPCE. However, the results at least imply that there are likely to besignificant consumption-increasing (or poverty-reducing) effects expected from participationin extension programmes.
The last row of Table 6 shows that participation in all types of program participation
significantly reduces household vulnerability (except input suppliers). However, the absoluteeffects are small and vulnerability as an expected poverty is reduced ranged from 0.38% to3.79%. Our results serve as another piece of evidence to show the effectiveness of NAADS in
reducing vulnerability.

4.3 Poverty and vulnerability incidence in Uganda
This section considers categorised incidence of poverty and vulnerability in Uganda. We
consider the poverty line of $1.25 per day as a basis of classification of households underpoor and non-poor categories. For classification of households according to vulnerability
incidence we used mean ± standard deviation (0.3724 ± 0.0568) of overall (whole sample)
vulnerability. The results are presented in Table 7.
(Table 7 to be inserted around here)

The most important point to note in Table 7 is that, although 76.70 percent of thesample households surveyed by the UNPS in Uganda in 2009-10 were poor, about 16.82
19

percent were highly vulnerable to poverty and the other 66.44 percent moderately vulnerable.
In other words, vulnerability is more widespread than poverty in Uganda. This result implies
that a substantial share in the population has a risk of falling into poverty, even if they are notcurrently poor. Again, the poor constitute 77 percent of the sample, and about 14 percent (143out of 994) of those are highly vulnerable. On the other hand, about 25 percent (75 out of302) of non-poor are highly vulnerable and have a risk of falling into poverty in future time.
As we found the negative impact of agricultural extension participation on vulnerability, theextension programs need to be more widely introduced not only for the poor, but also for thevulnerable non-poor to prevent them from slipping into poverty in the future. Education isfound to be negative and significant in explaining vulnerability, but it is not significant forpoverty (or log MPCE) as shown in Table A3 in Appendix. It is implied by these results that
educational programs might need to be strengthened in Uganda to reduce vulnerability topoverty. Table A3 also indicates that the household headed by a female member tends to be
more vulnerable. In allocating agricultural extension programmes, prioritising female-headedhouseholds would be necessary as one of the policy measures to help them avoid falling intopoverty.

5. Concluding Observations
The present study has examined whether participation in different agricultural extension
programs has any effect on household crop productivity, poverty, or vulnerability in Uganda.
To take account of sample selection bias associated with household participation in extension
programmes, we have applied treatment-effects model, a variant of Heckman sample
selection model.
It is found that household crop productivity was significantly improved byparticipation in government extension programmes called the National Agricultural Advisory
20

Services (NAADS), cooperatives, large farmers and input suppliers extension sources. Our
results show that the NAADS programs are more effective in productivity improvementcompared to NGO programs. This would serve as indirect evidence to show that the reformof agricultural extension which government has undertaken using a decentralized,
demand-driven, client oriented and farmer-led approach was successful in increasing theefficiency and quality of the agricultural extension services. However, given that ourestimations of agricultural productivity by stochastic frontier analysis suggest that there
remains inefficiency in agricultural productions, it would be important for the policymakers
of government to allocate enough budgets for NAADS programmes so that poor households
can have access to these programmes for the duration to exist from poverty.
Log mean per capita expenditure (MPCE) - our proxy for consumption poverty –was significantly increased by extension program participation. This is consistent with thepoverty reducing role of different extension programs. It is also found that vulnerability thathas been derived as a probability of the household falling into poverty in the future was
reduced by the participation in extension programs. The share of vulnerable households ismuch higher than the poverty incidence in rural Uganda and education was found to be thekey to reducing the former.
Because the results are mixed, we cannot derive a single conclusion, but some of theresults on poverty and vulnerability are consistent with the recent observation that
agricultural extension programs play a central role in helping poor agricultural households
improve crop productivity and escape from poverty. The present study implies that the policy
interventions to improve agricultural household’ livelihood through agricultural extension
services would potentially raise crop productivity and reduce not only poverty but also
vulnerability.
One of the distinct contributions of the present study is that we disaggregated the21effects of agricultural extension programmes by the type of providers and showed theeffectiveness of governmental programmes of agricultural extension in improving crop
productivity and reducing poverty and vulnerability. However, our results are based on the
cross-sectional data and are subject to the caveat that we have not analysed the welfare or
productivity changes of households before or after the participation in extension services. As
inefficiency in agricultural production as well as poverty still persists, the future study should
examine the role of agricultural extension with panel data.

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25

Table 1: Results of stochastic frontier analysis
Cobb-Douglas
Cobb-Douglas –CRS
Coeff.
SE
Coeff.
SE
Constant
13.2103***0.444913.7120***0.2523lnLand
0.4542***
0.05300.4393***0.0516lnLabor
0.4915***0.07420.4229***0.0544lnInput
0.1462***0.02820.1378***0.0274Sigma_v
0.84140.05090.82730.0493Sigma_u3.53850.09073.55740.0895Sigma2
13.2288
0.612013.3392
0.6105Lambda4.20560.12064.30010.1176
Wald Chi2
299.65***1001.37***
Obs. No.
1454
1454
LR statistics
2.8e+02***
Note: The symbol ** and *** indicate 5% and 1% significance levels, respectively.
Variables

Table 2: Descriptive statistics of technical efficiency of different models
Models
Cobb-Douglas
Cobb-Douglas -CRS

Obs
1454
1454

Mean

Std. Dev.

Min

Max

0.21990.21900.19980.20080.00004
0.00004

0.82760.824726

Table 3. The Results of Treatment Effects Model on the Effects of Agricultural Extension Programs Participation on Household Crop
Productivity in Uganda
Variables
Impact equation
Head age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female share
Extension
Constant
Participation equation
Distance to extension
centreHead age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female share
Extension
athrholnsigmaN
Rho
Sigma
LambdaLR statistics
Wald Chi2(i)NAADS(Government)
Coef.
Z value(ii) NGO
Coef.
Z value

Coef.

Z value

Coef.

Z value

Coef.

Z value

Coef.

Z value-0.00250.00001-0.00004-0.00002
0.00090.0417-0.0169-0.0009
0.0112-0.0595-0.1304
0.2641
0.0412-0.0489-0.0652
0.06800.28020.1701

-0.79
0.45
-0.01
-0.04
0.38
2.12**
-0.83
-0.04
0.47
-2.82***-5.19***
2.03**
1.65-1.93*-1.06
1.20
6.13***
2.05**

0.0013-0.00002
0.0120-0.0003
0.00530.0399-0.0399-0.0322-0.0160-0.0522-0.07880.08190.0279-0.0057-0.11540.1161-0.18200.10140.47-0.73
2.21**-0.80
2.45**
2.28**-2.21**-1.56-0.76-2.76***-3.30***
0.72
1.25
-0.26
-2.1**
2.29**-3.05***
1.38

0.0014-0.00001
0.0096-0.00020.00450.0410-0.0319-0.0188-0.0265-0.0609-0.1070
0.09580.0297-0.0141-0.09640.09890.01310.1022

0.54
-0.75
1.82*
-0.54
2.10**
2.39**-1.78*-0.93-1.28-3.28***-4.86***
0.87
1.36
-0.66
-1.81*
2.01**
0.17
1.43

0.0005-9.53e-06
0.0084-0.0001
0.00450.0388-0.0333-0.0197-0.0468-0.0711-0.1067
0.10180.0428-0.0040-0.0956
0.08690.45900.1149

0.18
-0.36
1.53
-0.30
2.06**
2.16**-1.81*-0.95-2.20**-3.69***-4.71***
0.88
1.88*
-0.18
-1.71*
1.68*
12.84***
1.53

0.0014-0.00002
0.0096-0.00020.00440.0425-0.0344-0.0192-0.0213-0.0594-0.10250.11850.0296-0.0111-0.0903
0.0959-0.07190.1026

0.54
-0.77
1.81*
-0.52
2.11**
2.47**-1.94*-0.97-1.02-3.22***-4.64***
1.04
1.35
-0.51
-1.68*
1.93*
-0.90
1.43

0.0006-0.00001
0.0100-0.00040.00430.0380-0.0234-0.0114-0.0460-0.0681-0.1103
0.09340.0276-0.0014-0.07140.07420.38610.1272

0.23
-0.45
1.82*
-0.80
2.00**
2.13**-1.28-0.55-2.17**-3.56***-4.90***
0.81
1.22
-0.06
-1.28
1.44
9.76***
1.7*-0.0001
0.0491-0.0004
0.0867-0.00140.03840.0018-0.1227-0.1606-0.4579-0.12630.1819-5.71670.00020.3371-0.4650
0.4356-2.2771-0.8330-1.5537924-0.68210.2115-0.14427.78
122.80-2.83***
2.38**-2.16**
2.25**-0.47
2.46**
0.01
-0.93
-1.10
-2.96***
-0.93
1.16
-0.00
0.00
2.26**-1.20
1.21-4.17***-4.88***-29.16***-0.00020.0013-0.00010.1353-0.0069
0.0445-0.1716-0.5368-0.97830.8226
0.57881.7114-3.07700.06150.3172-1.0727
0.9575-2.53440.4586-1.6711924
0.42890.18800.0807
3.81
117.06-1.79*
0.04-0.31
2.12**
-1.45
1.94*
-0.85
-2.55**-3.57***
3.09***
2.40**
5.45***
-0.00
0.27
1.65*
-1.79*
1.77*-2.91***
2.61***-58.75***0.00010.0151-0.0002-0.0005
0.00130.05910.3705-0.6236-0.97140.7366
0.59711.0841-9.98460.14450.23340.8106-0.2892-3.48110.1898-1.6934924
0.18760.18390.03450.86
112.70

1.91*
0.39
-0.4
-0.01
0.21
2.21**
1.17-2.59**-2.81***
2.61***
2.18**
2.79***
0
0.43
0.94
0.93
-0.35
-3.17***
1.04-70.89***-0.0001
0.0157-0.00020.0116-0.00130.0065-0.09430.0797-0.0704
0.74920.73510.5700-2.4671-0.0793-3.8096
0.3836
0.0583-2.4241-1.3885-1.6457924-0.8828
0.1929-0.1703
20.67
267.26

-1.12
0.36
-0.50
0.17
-0.25
0.24
-0.45
0.38
-0.30
2.51**
2.46**
1.58
.
-0.29
.
0.54
0.08
-2.20**-7.33***-66.04***0.00003
0.0591-0.0008
0.0688-0.0024-0.0425
0.2169-0.6549-0.07291.66860.73891.41422.5384-0.1900
0.4687
1.7863-1.0435-3.91530.2358-1.6892924
0.23150.18470.04281.01
112.54

0.32
1.03
-1.24
0.69
-0.35
-1.08
0.66
-2.06**-0.21
3.34***
1.46
2.62***
2.21**-0.57
1.89*
1.98**-1.29-2.68***
1.18-70.30***-0.00006
0.0306-0.0002
0.0350-0.00130.01250.1024-0.0888-0.1633
0.5786
0.19910.2686-2.74700.0257-6.4296-0.8879
0.5932-3.1821-1.2515-1.6513924-0.84870.1918-0.162810.67
198.85

-0.80
0.74
-0.60
0.48
-0.25
0.49
0.40
-0.41
-0.63
2.36**
0.77
0.84
.
0.09
.
-1.20
0.81
-2.84***-6.65***-66.45*********(iii) Cooperatives

*
***

***

Note: The symbols *, ** and *** indicate 10%, 5% and 1% significance levels, respectively.
27(iv) Large farmer

***
***

(v) Input supplier***(vi) Others

***
***

Table 4. The Results of Treatment Effects Model on the Effects of Agricultural Extension Programs Participation on log MPCE in
UgandaVariables
Impact equation
Head age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female share
Extension
Constant
Participation equation
Distance to extension
centreHead age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female share
Extension
athrholnsigmaN
Rho
Sigma
LambdaLR statistics
Wald Chi2(i)NAADS (Government)
Coef.
Z value(ii) NGO
Coef.
Z value(iii) Cooperatives
Coef.
Z value(iv) Large farmer
Coef.
Z value(v) Input supplier
Coef.
Z value(vi) Others
Coef.
Z value0.00132.51e-06-0.01240.0020-0.0482-0.0704-0.3430-0.2260
0.63650.12700.07040.83670.14010.1571-0.8343
0.41290.55696.4229

0.11
0.02
-0.48
1.09
-4.80***-0.92-4.29***-2.50**
6.47***
1.55
0.71
1.59
1.43
1.53
-3.48***
1.86*
1.86*
19.59***

0.0094-0.0001-0.0001
0.0020-0.0430-0.0673-0.3538-0.22900.53610.10270.04280.54140.12330.2039-0.84220.42680.49956.2876

0.81
-0.55
-0.01
1.06
-4.66***-0.89-4.53***-2.57**
5.95***
1.26
0.41
1.11
1.29
2.14**-3.56***
1.95*
1.86*
19.91***

0.0090-0.0001
0.00670.0016-0.0446-0.0844-0.3406-0.22280.52460.10010.08040.49200.10520.2155-0.9150
0.47580.65806.3150

0.78
-0.56
0.29
0.87
-4.80***-1.12-4.33***-2.50**
5.79***
1.23
0.83
1.01
1.10
2.28**-3.91***
2.20**
2.02**
20.06***

0.0114-0.0001
0.00970.0014-0.0409-0.0646-0.3720-0.2632
0.61830.15330.11930.48730.08360.2006-0.8965
0.5056-1.2084
6.2519

0.95
-0.75
0.40
0.74
-4.33***-0.83-4.67***-2.93***
6.71***
1.84*
1.22
0.97
0.85
2.06**-3.70***
2.26**-6.08***
19.21***

0.0094-0.0001
0.00690.0018-0.0432-0.0475-0.4108-0.25530.65010.14510.19190.94120.11040.2812-0.78450.4167-1.40236.3057

0.78
-0.66
0.29
0.92
-4.53***-0.61-5.11***-2.81***
6.99***
1.72*
1.93*
1.85*
1.11
2.85***-3.21***
1.84*-10.04***19.21***

0.0122-0.0001
0.00500.0022-0.0402-0.0577-0.4118-0.2974
0.64650.15700.13500.51320.12710.1750-0.9968
0.5759-1.59316.1816

1.01
-0.77
0.21
1.12
-4.21***-0.74-5.11***-3.27***
6.94***
1.86*
1.36
1.01
1.27
1.77*
-4.07***
2.54**-10.14***18.78***-0.0001
0.0436-0.0004
0.1022-0.00270.04470.0119-0.1745-0.1961-0.4352-0.0967
0.2396-5.9067-0.0284
0.3562-0.3860
0.3993-2.2294-0.3079-0.1978924-0.29860.8205-0.24500.84
135.09-2.64***
2.09**-1.84*
2.58**-0.86
2.87***
0.09
-1.32
-1.31
-2.76***
-0.70
1.49
-0.01
-0.18
2.27**-0.97
1.08-4.01***-1.36-4.67***-0.00020.0062-0.00020.1272-0.00650.0375-0.0480-0.5048-0.93620.73700.56371.6291-3.1200
0.08650.2845-1.1705
1.0937-2.6937-0.2852-0.2099924-0.2777
0.8107-0.2251
1.24
138.32-1.78*
0.18
-0.43
1.96*
-1.34
1.55
-0.24
-2.42**-3.37***
2.79***
2.28**
5.14***
-0.01
0.38
1.45
-1.89*
1.95*-2.99***-1.70*-8.15***

0.00010.0010-0.00003-0.00300.00120.06730.4192-0.5494-0.9766
0.70390.62281.0875-4.2282
0.18850.24960.8693-0.4547-3.1878-0.2860-0.2139924-0.2784
0.8075-0.22480.98
140.00

1.67*
0.02
-0.07
-0.04
0.21
2.42**
1.30-2.41**-2.82***
2.56**
2.27**
2.82***
-0.00
0.56
1.01
0.97
-0.54
-2.89***-1.56-8.71***-0.00020.0040-0.0001
0.0669-0.0066
0.01400.1492-0.0745-0.1268
0.61470.19460.1839-1.7054-0.1809-4.01810.12360.4255-2.3051
1.0778-0.1797924
0.79240.8356
0.66219.88
163.88

-1.37
0.11
-0.23
0.85
-0.98
0.53
0.59
-0.32
-0.48
2.31**
0.75
0.56
-0.00
-0.68
-0.00
0.17
0.61
-2.25**
5.78***-7.04***-0.0001
0.0475-0.00070.1086-0.0040-0.0082
0.3861-0.7806-0.1325
1.48720.2872
1.08451.5166-0.18180.25581.9133-1.3097-3.44841.4084-0.1708924
0.88710.84290.747830.92
225.57

-0.67
1.11
-1.51
1.12
-0.63
-0.27
1.13
-2.96***-0.47
4.10***
0.83
2.94***
1.36
-0.72
1.16
2.50**-1.83*-3.15***
7.95***-6.88***-0.0002
0.0464-0.0005
0.0706-0.0034
0.00320.2654-0.7130-0.74390.7039-0.05510.1757-2.7718
0.1131-19.8657-0.6449
1.2790-3.7032
1.6439-0.1686924
0.92800.84490.784025.33
226.98

-1.20
1.07
-1.09
0.80
-0.55
0.14
1.04
-3.36***-2.22**
3.21***
-0.24
0.40
.
0.48
.
-1.01
1.85*
-3.42***
6.35***-6.79***

***

***

***

Note: The symbols *, ** and *** indicate 10%, 5% and 1% significance levels, respectively.

28

***
***

***
***

***
***

Table 5. The Results of Treatment Effects Model on the Effects of Agricultural Extension Programs Participation on Vulnerability in Uganda
Variables
Impact equation
Head age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female share
Extension
Constant
Participation equation
Distance to extension
centreHead age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female share
Extension
athrholnsigmaN
Rho
Sigma
LambdaLR statistics
Wald Chi2(i)NAADS (Government)
Coef.
Z value(ii) NGO
Coef.
Z value(iii) Cooperatives
Coef.
Z value(iv) Large farmer
Coef.
Z value(v) Input supplier
Coef.
Z value(vi) Others
Coef.
Z value-0.00540.0001-0.01390.00110.0020-0.03160.02280.00260.01150.00700.0174-0.0026-0.0504
0.05250.0182-0.0203
0.01110.5503-8.99***
10.36***-11.48***12.76***
4.31***-8.78***
6.09***
0.61
2.45**
1.83*
3.68***-0.11-10.94***10.68***
1.55-1.82*
0.74
35.35***-0.00520.0001-0.01390.00110.0021-0.03140.02330.00400.00860.00570.0138-0.0070-0.0504
0.05270.0204-0.02190.02860.5477-9.36***
10.72***-12.39***12.85***
4.62***-8.71***
6.31***
0.93
1.99**
1.46
2.76***-0.30-10.98***
11.61***
1.72*-1.96*
2.19**
36.27***-0.00520.0001-0.01350.00110.0018-0.03290.02510.00570.00650.00480.0148-0.0104-0.05200.05300.0152-0.01850.0596
0.5498-9.17***
10.46***-11.95***12.39***
4.01***-8.91***
6.66***
1.30
1.49
1.21
3.14***-0.44-11.07***
11.52***
1.27(-1.64)a5.30***
35.64***-0.00540.0001-0.01370.00110.0022-0.03210.02200.00210.00720.00550.0182-0.0090-0.0497
0.05520.0174-0.0206
0.06590.5505-9.36***
10.65***-11.98***12.50***
4.73***-8.64***
5.83***
0.48(1.63)a1.38
3.85***-0.38-10.47***
11.87***
1.43-1.81*
9.02***
35.33***-0.00520.0001-0.01350.00110.0021-0.0315
0.02190.00200.01060.00720.0188-0.0065-0.05090.05440.0183-0.0197-0.00930.5473-9.50***
10.81***-12.42***12.93***
4.90***-8.84***
6.01***
0.48
2.40**
1.89*
4.03***-0.27-11.27***12.04***
1.55-1.79*-0.41
36.88***-0.00540.0001-0.01340.00110.0021-0.03220.02420.00340.00540.00540.0177-0.0099-0.05120.05680.0236-0.02620.08330.5531-9.39***
10.66***-11.77***12.03***
4.71***-8.67***
6.42***
0.78
1.21
1.36
3.75***-0.42-10.83***12.23***
1.95*-2.29**
13.56***
35.56***-0.0001
0.0471-0.0004
0.0964-0.00210.0398-0.0202-0.1528-0.1889-0.4592-0.0755
0.2472-6.3523-0.0323
0.3865-0.33010.4608-2.3073-0.2313-3.2673902-0.2272
0.0381-0.00870.95
1096.57-2.48**
2.21**-1.96*
2.40**-0.66
2.43**
-0.16
-1.14
-1.23
-2.81***
-0.54
1.50
-0.00
-0.20
2.45**-0.77
1.11-4.10***-0.97-87.23***-0.00020.0010-0.0001
0.1362-0.0067
0.0487-0.1029-0.5012-1.1692
0.76450.66331.8744-3.2175
0.11430.2798-1.4276
1.5567-2.9490-0.4059-3.2632902-0.3850
0.0383-0.01473.47
1091.85(-1.64)a0.03-0.22
2.07**
-1.35
1.85*
-0.50
-2.37**-3.65***
2.76***
2.56**
5.27***
-0.00
0.48
1.38
-2.00**
2.23**-3.22***-2.18**-114.99***0.00010.0334-0.0004-0.10400.00690.10720.2705-0.4351-1.1626
0.92920.7693
1.2237-3.9073
0.28080.37811.8603-0.7645-4.0846-0.8797-3.2421902-0.70620.0391-0.027610.83
1070.212.04**
0.78
-0.93
-1.28
1.10
3.56***
0.84-1.90*-2.52**
3.50***
3.08***
2.60***
-0.00
0.81
1.68*
1.76*
-0.78
-3.52***-4.26***-117.28***-0.0001
0.0432-0.00030.0848-0.0068
0.02220.26300.0034-0.0048
1.14820.8967-0.4519-2.3743-0.4336-4.4996-0.2240
1.9281-4.6964-1.4322-3.2321902-0.89210.0395-0.035224.37
1102.79

-0.88
1.12
-0.94
1.07
-1.15
0.69
0.95
0.02
-0.02
4.59***
3.70***
-1.10
.
-1.56
.
-0.23
1.85*
-4.39***-7.19***-128.39***0.00010.0852-0.00110.0551-0.0014-0.04900.2873-0.6025-0.0306
1.52950.74281.35392.2114-0.30180.45002.2156-1.4266-4.30490.0742-3.2805902
0.07410.03760.00280.07
1125.54

0.60
1.32
-1.46
0.54
-0.20
-1.08
0.81
-1.86*-0.08
3.24***
1.52
2.68***
1.85*
-0.89
1.77*
1.99**-1.46-2.61***
0.26-138.45***-0.0001
0.0607-0.0006
0.03360.00020.03350.2626-0.4727-0.3070
1.60961.33330.2491-2.7109
0.0635-4.2540-3.6196
4.4488-6.1158-1.7116-3.2340902-0.9369
0.0394-0.036937.60
1209.31

-1.09
1.51
-1.50
0.46
0.03
1.17
1.09
-2.21**-1.09
6.52***
5.65***
0.68
.
0.22
.
-4.41***
4.33***-5.84***-7.90***-131.52***

***

*
***

***
***

Note: The symbols *, ** and *** indicates 10%, 5%, and 1% significance levels, respectively.

29

***
***

***

***
***

Table 6: Summary results of impact estimation (ATT, Average Treatment Effect onTreated)Dependent
variables
Productivity
MPCE
Vulnerability

Impactvariables
ATT
Improve?
ATT
Improve?
ATT
Reduce?

NAADS
3.42%***
Yes
13.74%***
Yes-0.38%*
Yes

NGO
0.25%
-1.05%-0.52%***
Yes

Extension services
Coop
Large farmer
9.94%***
1.96%***
Yes
Yes
9.75%***
56.35%***
Yes
Yes-1.20%***-3.79%***Yes
Yes

Input supplier
4.71%***
Yes
57.39%***
Yes-0.15%-

Others
-6.89%***
No
112.32%***Yes-2.87%***
Yes

Notes: The symbols * and *** indicates 10% and 1% significance levels, respectively. The cases of MPCE for Large Farmer
and Others are not shown as the result showed an extraordinary large effect.

Table 7: Poverty and vulnerability incidence in Uganda, 2010-11
Vulnerability

Poor (<1.25 $)Non-poor (≥1.25 $)All

High (>0.4292)

143 (11.03)

75 (5.79)

218 (16.82)

Moderate (0.3156 – 0.4292)

674 (52.01)

187 (14.43)

861 (66.44)

Less vulnerable (<0.3156)

177 (13.66)

40 (3.09)

217 (16.74)

All994 (76.70)

302 (23.31)

1296 (100)Note: Figures in the parenthesis indicate respective percentage of each cell.

30

Appendix
Table A1. Descriptive statistics of different variables used for the estimation
Variable

Minimum

Mean

Production variables
Land
0
9.30
Labor
0
273.19Inputs035990.95
Outputs
0
2.26e+07
Dependent variables for impact estimation
te
0.00004
0.2199Maximum

Std.Dev.

Description

601.5
9631
2470000
2.15e+09

21.95
343.96109161.9
8.27e+07

Land cultivated by the household
Labor used for cultivation
Inputs used for cultivation
Output from products and by-products

0.8276

0.1998

Technical efficiency estimated by SFA with
Cobb-Douglas specification
Mean per capita consumption expenditurePredicted mean per capita consumption
expenditure
Vulnerability of household
Variance of pMPCE

MPCE
pMPCE

1.37
287.85875.10817.319527.132013.40

800.70262.39Vulnerability
Variance_pMPCEHousehold variables
Head age
Head education
Household size
Head sex

0.1945755.880.37243584.91

0.489145526.57

0.05684189.35

13
0
1
0
0

44.95
4.79
5.94
0.72
0.2703

100
15
23
1
1

15.24
4.11
3.19
0.45
0.444300.3631

1

0.481100.36661

0.4820Region-Central
KampalaRegion-East
Region-North
Region-Kampala
Region-West
Rural/Urban

00.19261

0.3945000000.24070.26200.00410.2992
0.74

1
1
1
1
1

0.42770.43990.06410.4581
0.44

Household training

0

0.11

4

0.37

Female burden share

0

0.24

1

0.20

Female share

0

0.51

1

0.23

Household tribe 1
Household tribe 2Household tribe 3

Age of household head
Educational level of household head
Number of family members of the household
Sex of household head 0=female, 1=maleHousehold identified under different tribes, up
to score 21 provided by UNPS
Household identified under different tribes,
score 22 to 36 provided by UNPS
Household identified under different tribes,
more than score 36 provided by UNPS
Dummy for central region except Kampala
Dummy for east region
Dummy for north region
Dummy for Kampala region
Dummy for west region
Dummy for rural area
0=urban, 1=rural
Number of household members received
training
Share of female members within age of below
15 and above 64 to the total household
members
Share of female members to the total
household members

Table A2. Correlation between Instrument (distance from village centre to extension service)with Outcome Variables and Variables on Extension
Variables
Outcome variables
Technical efficiency
MPCE
Vulnerability
Extension variables(i) NAADS(ii) NGO(iii) Cooperatives(iv) Large farmers(v) Input suppliers(iv) Others

Correlation coefficient-0.0149-0.0267
0.0723-0. 1182-0.0604
0.0725-0.03700.0518-0.0257

31

Table A3. The Results of OLS model of household characteristics on the poverty(lnMPCE) and vulnerability in Uganda
Variables
Head age
Head age square
Head education
Head education squareHousehold size
Head sex
Household tribe 1
Household tribe 2
Region-Central Kampala
Region-East
Region-North
Region-Kampala
Rural/Urban
Household training
Female burden share
Female shareConstantN
R squared
F statistics

Poverty (lnMPCE)
0.0174*1.77-0.0001-1.34-0.0086-0.44
0.0027*1.69-0.0557***-6.7
0.05080.78-0.2815***-4.11-0.0968-1.34
0.4390***5.77
0.1645**2.33-0.0339-0.42
0.7386**2.02
0.06320.8
0.3209***3.94-0.7082***-3.5
0.5141**
2.69
6.0455***
23.25
1339
0.115110.74***( 16, 1322)

Vulnerability-0.0055***-12.4
0.0001***13.61-0.0137***-15.5
0.0011***
15.98
0.0020***5.45-0.0308***-10.420.0205***6.74-0.0038-1.14
0.0130***3.79
0.0117***3.7
0.0259***6.97
0.01160.72-0.0502***-14.090.0513***14.15
0.00981.02-0.0106-1.15
0.5527***
47.03
1296
0.55770.5577***( 16, 1279)

Note: The symbols *, ** and *** indicates 10%, 5%, and 1% significance levels, respectively.

32