Abstract

Previous literature, ignoring regional heterogeneity, has mainly explored the interrelationship among growth, inequality, and poverty. Exploring the incidence of poverty and growth, we classify rural Ethiopia into three regions based on the difference of production technologies and climates. We find evidence that regional heterogeneity exists across the three regions. To find sources of the heterogeneity, we estimate a pseudo-fixed effect probit model controlling for household fixed effects within a random effect probit model across regions. We find that poverty is determined by different sources across the three regions each with different farming systems. Moreover, poor households can escape poverty only when their expected level of well-being is improved by increases in asset holdings and/or returns to assets. Hence, we propose, using counterfactual decomposition, that pro-poor growth can be decomposed into two components: changes in the amount of attributes such as observable household assets or capital, and changes in ‘aggregate marginal product’ of the attributes. We find that the impacts of the changes in the aggregate marginal product on pro-poor growth are significant in hoe area, but the changes in attributes do not significantly affect growth in this region. The aspects of growth are determined heterogeneously across regions: in the highland area, both components works together; in the hoe area, the changes in aggregate marginal product mainly determine the growth; and in the enset area, it is changes in attributes that mainly determine the positive growth. We find evidence that pro-poor growth in a relative sense appear in the hoe area, where the impact of changes in the aggregate marginal product on growth is heterogeneous along the income distribution; the larger impact appears in the lower tails, while smaller impact can be seen in the upper tails. However, we find no evidence of pro-poor growth in the highland and the enset areas, where the impacts of the marginal product on growth are anti-poor and insignificant, respectively. Therefore, since the impact of changes in productivity on growth differs across regions, disseminated technology to increase household productivity should be tested whether it could generate pro-poor growth based on the recipient’s environment. For example, in providing agricultural technologies through the Korea Project on International Agriculture (KOPIA) or new knowledge via the Knowledge Sharing Program (KSP), we have to deliberate carefully on whether the knowledge and technology do indeed have pro-poor aspects.

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