Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications

Barrett, Christopher B.; Reardon, Thomas; Webb, Patrick

doi:10.1016/s0306-9192(01)00014-8

Cited by 1,509 publications

(1,329 citation statements)

References 25 publications

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“…Yeboah and Jayne (2018) use the limited available time-use data to show how rural households are often engaged in both farm and non-farm work, some of which involves temporary or permanent migration within rural areas as well as to or from urban settlements. Barrett, Reardon, and Webb (2001) show how reallocation over time is driven by the rise of new nonfarm opportunities as well as change in available farmland and other agricultural resources per rural worker. Yeboah and Jayne (2018) find only five African countries with repeated surveys on time use by sector, so for a broader comparison, we use International Labour Organization (ILO) estimates of primary employment in agriculture from the most recent ILO (2015) World Employment and Social Outlook database.…”

Section: Methodsmentioning

confidence: 99%

Agricultural Transformation, Nutrition Transition and Food Policy in Africa: Preston Curves Reveal New Stylised Facts

Masters

Rosenblum²,

Alemu

2018

The Journal of Development Studies

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This paper uses a Preston Curve approach to test for changes over time in agriculture, nutrition and food policy, comparing national averages in Africa and elsewhere at each level of national income per capita from the 1990s to the 2010s. Our statistical tests and data visualisations reveal that, at each level of income, African countries have faster rural population growth, a larger share of workers in agriculture and lower agricultural labour productivity than countries elsewhere, with no significant shift in these patterns from the 1990s to the 2010s. In contrast, there have been structural shifts towards less child stunting everywhere, and towards more adult obesity in high-income countries. The overall pattern of African governments’ food policies and government expenditures have not shifted, however, as they continue price interventions and low investment levels characteristic of low-income countries around the world.

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Section: Methodsmentioning

confidence: 99%

Agricultural Transformation, Nutrition Transition and Food Policy in Africa: Preston Curves Reveal New Stylised Facts

Masters

Rosenblum²,

Alemu

2018

The Journal of Development Studies

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“…It is therefore not clear to what extent the conclusions derived from urban investment climate surveys of relatively large manufacturing firms generalize to rural firms, which tend to be smaller. Yet, diversification beyond agriculture is often considered a promising pathway out of poverty for impoverished rural economies and there is a widespread belief that small enterprises may play an important role in especially the early stages of diversifying beyond agriculture (see the discussions in Barret et al, 2001, Lanjouw and Lanjouw, 2001, Reardon et al, 2000. This paper attempts to fill this knowledge gap and compares and contrasts the performance of rural manufacturing enterprises in Ethiopia with their urban counterparts, in order to examine how location and certain investment climate characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Rural–Urban Comparison of Manufacturing Enterprise Performance in Ethiopia

2010

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Abstract:Manufacturing enterprises in rural and urban Ethiopia are compared to examine how location and investment climate characteristics affect performance. Urban firms are larger, more capital intensive and have higher labor productivity than rural firms. The rural-urban gap in labor productivity is due to differences in capital intensity and total factor productivity. There is no strong evidence of increasing returns to scale. The hypothesis that firms in rural towns have the same average total factor productivity as urban firms is not rejected, however firms in remote rural areas are less productive. Rural firms grow less quickly than urban firms. These results can partly be attributed to differences in the quality of infrastructure, access to credit and transportation costs across rural and urban areas. Since rural firms operate in a business environment that is very different from its urban counterpart, lessons derived from urban investment climate surveys cannot immediately be transferred to rural areas.

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“…The non-farm sector also plays an important role in providing an alternative source of income and employment for resource-poor households. The literature is replete with evidence that non-farm income could provide self-insurance against likely shocks, overcome farm credit constraints and enhance farm investment, absorb labor surplus, and ultimately improve the financial well-being of households through increased total income (Barrett, Reardon, and Webb 2001;Reardon, Berdegué, and Escobar 2001;Hoang, Pham, and Ulubasoglu 2014). The demographic variables include: the literacy status of the mother and of the father, the proportion of dependents in the household, and location (urban vs. rural).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

The determinants of household-level well-being in Northern Ghana

Zereyesus

Shanoyan

Ross

et al. 2016

Development Studies Research

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Empirical analysis of the physical well-being at the household level was conducted for a sample of households in Northern Ghana using a Multiple Indicators Multiple Causes model. Physical wellbeing was indicated by the number of stunted children, number of wasted children, and number of underweight women. Results suggest that well-being at the household level is indeed represented by the latent variable and can be conceptualized in much the same way as the wellbeing of the individuals who constitute the household. Results show that the literacy of father and the number of dependents are associated with the largest shift in the underlying household's physical well-being. Locale and the household assets were also significantly associated with the collective underlying latent variable. The variability in household physical well-being is explained more by the number of stunted children in the household than any of the other indicators of household well-being. ARTICLE HISTORY

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Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications

Cited by 1,509 publications

References 25 publications

Agricultural Transformation, Nutrition Transition and Food Policy in Africa: Preston Curves Reveal New Stylised Facts

Agricultural Transformation, Nutrition Transition and Food Policy in Africa: Preston Curves Reveal New Stylised Facts

A Rural–Urban Comparison of Manufacturing Enterprise Performance in Ethiopia

The determinants of household-level well-being in Northern Ghana

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