Ameet Morjaria NSF-AERC-IGC Workshop Mombasa, 4 th Dec 2010 - PowerPoint PPT Presentation

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Ameet Morjaria NSF-AERC-IGC Workshop Mombasa, 4 th Dec 2010

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  1. Comments on: “Adoption and Impact of Conservation Agriculture in Central Ethiopia: Application of IV and Control Function Approaches”byKassie et al. Ameet Morjaria NSF-AERC-IGC Workshop Mombasa, 4th Dec 2010

  2. Overview • Motivation • Natural resource degradation is a serious and worsening issue for rural livelihoods in developing countries. • Amplified when agriculture is operated by smallholder farmers (plough based agriculture) especially in Africa (ILRI 2009). • Conservation agriculture (CA) aims at mitigating and making better use of agricultural resources. [CA≡ interventions to deterioration of soil + water resources] • What does the paper do? • Empirical investigation into … 1. Adoption of CA = f( ?) 2. Adoption of CA impact on land + labor productivity

  3. Overview • Data • 2/9 Ethiopian districts where SG 2000 was promoting CA. • Within district choose kebeles– criteria (CA + farmers cooperative) • Within kebeles choose households (random sampling) • Cross-sectional data collected in 2007/8 of HH level characteristics. • Methodology • Adoption: Multivariate probit model to estimate different/all adoption of the components of CA estimation using GHK simulator • Impact: estimating structural models of outcomes (crop yield + labor productivity) taking into account endogeneity and heterogeneity concernsby using control functions.

  4. Overview • Findings • Adoption of CA = f( location, family size, access to extension, formal education) • Herbicide application (one component of CA) land productivity • Land productivity influenced by location, gender of hh head, livestock wealth, human labor endowment. • None of the above impact labor productivity.

  5. Concerns + Suggestions • The CA implementation how was it done? Phased in? All districts at once? Non-random program placement • Explain institutional set up of SG 2000 • Explain district selection – why 2/9 districts? Are other districts similar? Kebeles selection? Sample selection concern • Show district characteristics to see if similar • IV use suggested – but what endogenous variables are the concern in the context? Discussion is abstract and would be useful to be context-specific.

  6. Concerns + Suggestions • IV solves the endogeneity problem but concern when unobservable factors interact non-linearly with the exogenous regressors. • But why is this a concern in this context - theory? Examples? • The above concern is mitigated by estimating control functions (CF) which are generalizations of IV estimation. Discussion of CF missing. • Relies on same identification assumptions as IV methods (2SLS/GMM) but is based on conditional mean rather than linear projection. • If assumptions hold likely to more efficient but less robust than IV approach.

  7. Concerns + Suggestions • Is the sample size enough to run multivariate probit? Any benchmarks on what is reasonable to have in each choice? • Run probit unpooled regressions i.e. sub-sample as less restrictive • Compare estimation from general IV and CF – to give indication of the bias. • Account for social networks in adoption? e.g. BandieraRasul (2006)…. • No findings of CA on labor productivity. Is that surprising? Labor productivity + learning might take longer and cannot be captured in X-sectional data… • Minor point:Woreda dummy significant soaks up anything that is time invariant at district level including the type of crop (page 9).

  8. Conclusion • Contribution • Important question • Add to the limited empirical literature on CA • Go beyond adoption of CA and look at outcomes (productivity) • Data contribution as Africa data scarcity