ICRISAT Archival Report 2010

However, by only controlling for the observable covariates the PSM only removes the part of the selection bias
called “overt bias” (Lee, 2005; Rosenbaum, 2002). PSM may not remove what is called “hidden bias” which is
caused by the unobservable covariates that may also affect the individual’s self-selection into the program and
the outcomes indicators (Rosenbaum, 2002). . This warrants the estimation of the Local Average Treatment
Effect (LATE) Abadie, 2003; Imbens and Angrist, 1994) as opposed to the Average Treatment Effect (ATE).
The local average treatment effect (LATE), introduced by Imbens and Angrist (1994) identifies the causal
effect of program participation on a restricted sub-sample of potential participants that can comply to the
assignment as program participants. Consequently in Malawi the estimated the LATE as opposed to the ATE
using parametric estimation procedures to assess the impact of improved groundnut adoption on market
integration.
Main findings/Results & Policy Implications:
In Malawi, adopters of improved groundnut technologies had higher consumption expenditure than nonadopters.
After PSM matching to identify equivalent adopter and non-adopter groups, the results showed that
groundnut adoption in Malawi positively and significantly increased household expenditure. The Incidence of
poverty, depth of poverty, and severity of poverty were significantly lower among adopters. The findings
suggest that by raising farm productivity improved groundnut technology can significantly contribute to poverty
reduction. However, the Impact of cultivating improved pigeonpea was not significant. In Ethiopia,
Partner Institutions:
National Agricultural Advisory Services (NAADS), National Agricultural Research Organisation (NARO),
National Smallholder Farmers Association of Malawi (NASFAM)
Intermediate target output in 2010: Environment for Development Initiative
Achievement of Output Target:
100%
Countries Involved:
Uganda
Objectives/Rationale:
Determine ex post impact of improved groundnut varieties to crop income poverty reduction.
Methodology/Approach:
The study was based on a random sample of 945 households from 7 districts, representing four farming systems,
surveyed in 2006. A weakness of many studies that evaluate the impact of improved technology is that they do
not control for differences between adopters and non-adopters. If these groups are significantly different, results
may over-estimate impacts on farm income. To overcome this problem, the study used the propensity score
matching (PSM) method to identify treatment and control groups with similar characteristics, except that one
group (adopters) adopted new technology while the other (non-adopters) did not. The Foster-Greer-Thorbecke
poverty indices were used to compare impact on poverty for the two groups.
Main findings/Results & Policy Implications:
Adopters of improved groundnut technology had higher incomes than non-adopters. After PSM matching to
identify equivalent adopter and non-adopter groups, the results showed that groundnut adoption positively and
significantly increased crop income. Incidence of poverty, depth of poverty, and severity of poverty were
significantly lower among adopters. Although the effect on poverty indices was not dramatic, by raising farm
productivity improved groundnut technology can significantly contribute to poverty reduction.
Partner Institutions:
National Agricultural Advisory Services (NAADS), National Agricultural Research Organisation (NARO)
Special Project Funding:
Swedish Cooperation Agency
MTP Output target 2011 1.3.3: Lessons learnt from analysis of impact pathways of representative ICRISAT
NARS technologies
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