Ameet Morjaria

NSF-AERC-IGC Workshop

Mombasa, 4th Dec 2010

Comments on: “Adoption and Impact of Conservation Agriculture in

Central Ethiopia: Application of IV and Control Function Approaches”

byKassie et al.

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

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 concerns by using control functions.

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.

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.

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.

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. Bandiera Rasul (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).

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