Farm households and nonfarm activities in Ethiopia: does clustering influence entry and exit?

Abstract

This article examines how clustering affects the entry and exit decisions of farm households into and from nonfarm enterprises in rural Ethiopia. We find that the existence of clusters of microenterprises in the same district increases the likelihood of a rural household to start a nonfarm enterprise. Similarly, clustering of big manufacturing firms in the same zone is found to increase the likelihood of farm households to start a nonfarm enterprise. Nonfarm enterprises operating in clusters are also found to have a lower probability of exit than those operating outside of clusters. The study further investigates the impact of entry and exit into and from nonfarm enterprises on farm household's well‐being using as indicators total household income, the food security status of a household, and the household's ability to raise enough money in case of emergency. Using propensity score matching to account for selection bias, we find that entry into nonfarm enterprises significantly increases household's income and food security status. Exit from nonfarm enterprises, on the other hand, is found to significantly reduce household's income.

A Propensity Score Matching Analysis of the Impact of Participation in Non-farm Enterprise Activities on Household Wellbeing in Rural Nigeria

“…Similarly the average treatment effect on the treated (ATT) suggests that enterprise households are more food secured than non-enterprise households. This result is in line with the finding of Ali and Peerlings (2012) who used a similar approach to investigate the effect of participation in non-farm enterprise activities on farm household's wellbeing in Ethiopia Exchange rate as at 2011 is US $1= ₦150 *, **, *** represents 10%, 5% and 1% significance levels respectively Sensitivity analysis is performed by using Nearest Neighbor and Kernel Gaussian matching techniques to check if our radius matching results are robust to other matching methods. The results of the two methods presented in Table 3 confirm that our radius matching result method are quite robust and are not sensitive to other matching techniques.…”

“…In the same vein Owusu, et al (2011) used propensity score matching approach to assess the impact of non-farm work on food security status and households' income in northern Ghana; they revealed that non-farm work has a positive impact on income and food security status of the households. Similarly, Ali and Peerlings (2012) also used propensity score matching approach to examine the impact of non-farm enterprise diversification on households' wellbeing in Ethiopia using income and food security status of the household as indicators of their wellbeing, the result shows that non-farm enterprise diversification has a positive significant impact on all the measures.…”

Non-Farm Enterprises and Poverty Reduction amongst Households in Rural Nigeria: A Propensity Score Matching Approach

“…Specifically, the estimates of the average treatment effect show that households that engage in non-farm enterprises have on average, more annual consumption expenditure of ₦78,716 ($524) than those who have not engage into nonfarm enterprise activities, depending on only farm activity.. This result is in line with the finding of [12] who used a similar approach to investigate the effect of participation in non-farm enterprise activities on farm household's wellbeing in Ethiopia. Exchange rate as at 2011 is US $1= ₦150 *, **, *** represents 10%, 5% and 1% significance levels respectively\ Sensitivity analysis is performed by using Nearest Neighbour and Kernel Gaussian matching techniques to check if our radius matching results are robust to other matching methods.…”

“…In the same vein [8] used propensity score matching approach to assess the impact of non-farm work on the food security status and household income in northern Ghana; they revealed that non-farm work has a positive impact on income and food security status of the households. Similarly, [12] also used propensity score matching approach to examine the impact of non-farm enterprise diversification on household wellbeing in Ethiopia using income and food security status of the household as indicators of their wellbeing, the result shows that non-farm enterprise diversification has a positive significant impact on household wellbeing by all measures.…”

The Effects of Non-Farm Enterprises on Farm Households’ Income and Consumption Expenditure in Rural India

“…Ellis (2000) argues that non-farm diversification is often a strategy used by farm households to moderate seasonal income variability and minimize the risks associated with agriculture due to hostile agroecological factors. Under this situation, non-farm activities play a key role in improving the well-being of rural farm households and providing them with income diversification opportunities that help to curb rural-urban migration, reduce poverty and improve food security (Haggblade et al, 2007;Lanjouw, 2007;Ali & Peerlings, 2012).…”