Micro-Level Welfare Impacts of Agricultural Productivity: Evidence from Rural Malawi

Abstract

This article analyses the micro-level welfare impacts of agricultural productivity using a two-wave nationally representative, panel data from rural Malawi. Welfare is measured by various dimensions of poverty and food insecurity; and agricultural productivity is measured by maize yield and value of crop output per hectare. The poverty measures included per capita consumption expenditure, relative deprivation in terms of per capita consumption expenditure, poverty gap and severity of poverty; and the measures of food insecurity included caloric intake and relative deprivation in terms of caloric intake. Depending on the measure of welfare, the impact of agricultural productivity was estimated with a household fixed effects estimator, a two-part estimator or a correlated-random effect ordered probit estimator. The results indicate that growth in agricultural productivity has the expected welfare-improving effect. In terms of economic magnitude, however, both the direct effect and economy-wide spillover effect (in the non-farm sector) of a percentage increase in agricultural productivity on the poverty and food security measures are small. Efforts to effectively improve the welfare of rural agricultural households should therefore go beyond merely increasing agricultural (land) productivity.

Acknowledgements

Funding for this research was provided by the Bill and Melinda Gates Foundation under the Guiding Investments in Sustainable Agricultural Intensification in Africa (GISAIA) project. The authors are grateful to the two anonymous referees who reviewed the paper, as well as Thomas Jayne (guest editor) and Richard Palmer-Jones (managing editor) for their useful comments and suggestions. All remaining errors are our own. The data and stata codes used in the analyses are available upon request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary Materials

Supplementary Materials are available for this article which can be accessed via the online version of this journal available at https://doi.org/10.1080/00220388.2018.1430771

Notes

1. The two-part estimator is implemented using the twopm command in stata (Belotti et al., 2015). Twopm has a variety of estimators that can be used for the first and second parts depending on research interest. More importantly, marginal effects for the combined model can be easily recovered using the margins command.

2. Attrition bias in the data could not be tested for in our data because there are no regression-based tests for attrition when fixed effects or MC devise models are used with a panel of only two waves. A panel of more than two-waves are required for such tests (Mason & Smale, 2013; Wooldridge, 2010). That notwithstanding, the study is confident that attrition bias is not likely to be a concern because as indicated earlier, the attrition rate is only 3.78 per cent at the household level.

3. Estimates of yield gap reported by Global Yield Gap Atlas (www.yieldgap.org), indicate that maize yield (and yield of cereals in general) in countries such as Zambia, Tanzania, Uganda, Kenya and Ethiopia that are in the same geographical area as Malawi can be increased by over 300 per cent. Hence the range of the incremental changes in agricultural productivity (0–100%) used in the simulation analysis is reasonable.

4. The simulations assume that there are no general equilibrium effects in the sense that changes in the determinants do not affect the partial regression parameters or other exogenous variables. This assumption is (highly) likely to be valid because the simulated changes are incremental (0%, 5%, 15%, …, 100%). The results should therefore be interpreted with this caveat in mind.