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To assess the conventional view that assets uniformly improve childhood development through wealth effects, this paper tests whether different types of assets have different effects on child education. The analysis indicates that household durables and housing quality have the expected positive effects, but agricultural assets have adverse effects on highest grade completed and no effects on exam performance. Extending the standard agricultural-household model by explicitly including child labor, the study uses three waves of panel data from Tanzania to estimate the effects of household assets on child education. The analysis corrects for the endogeneity of assets and uses a Hausman-Taylor instrumental variable panel data estimator to identify the effects of time-invariant observables and more efficiently control for time-invariant unobservables. The negative effect of agricultural assets is more pronounced among rural children and children from farming households, presumably due to the higher opportunity cost of their schooling.

To assess the conventional view that assets uniformly improve childhood development through wealth effects, this paper tests whether different types of assets have different effects on child education. The analysis indicates that household durables and housing quality have the expected positive effects, but agricultural assets have adverse effects on highest grade completed and no effects on exam performance. Extending the standard agricultural-household model by explicitly including child labor, the study uses three waves of panel data from Tanzania to estimate the effects of household assets on child education. The analysis corrects for the endogeneity of assets and uses a Hausman-Taylor instrumental variable panel data estimator to identify the effects of time-invariant observables and more efficiently control for time-invariant unobservables. The negative effect of agricultural assets is more pronounced among rural children and children from farming households, presumably due to the higher opportunity cost of their schooling.

This paper estimates differentiated effects of household asset ownership on educational outcomes of children ages 6 and above in Tanzania. The paper contributes to the literature by providing a theoretical framework that portrays a mechanism for different assets to have differential effects on child education. We use data from Living Standard Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) in Tanzania which provides panel data on both household wellbeing and agricultural practices and resources. Use of the LSMS-ISA data allows us to disentangle the complicated relationship between child education and agricultural assets in ways which would not be possible using traditional cross-sectional surveys of either household wellbeing or farm practices. We use the Hausman-Taylor instrumental variable (HTIV) panel-data estimator to efficiently control for time-invariant variables omitted from our specifications while allowing us to identify the effects of fixed controls while correcting for the endogeneity of assets. We find that, controlling for household income, different asset types have opposing effects on child educational outcomes. Household durables and housing quality characteristics have positive effects but agricultural assets have adverse effects on highest grade completed and test scores. We demonstrate that the negative effect of agricultural assets emerges from higher opportunity cost of schooling and that the effect is more pronounced among boys and children from poor households, grain crop farmers, and rural residents.

Over the years, the sugar sector has experienced tremendous growth which has been driven by increased Foreign Direct Investment (FDI). According to the United Nation’s Food and Agricultural Organisation (FAO 2013), raw sugar production rose from 135,000 tons in 1990 to 430,500 tons by 2012, while exports grew from less than a thousand tons to 273,000 tons over the same period FAO 2013). While increased investments have generated growth, they have also tended to create market structures in which a single multinational is dominant. This concentration raises concerns about the functioning of these increasingly important markets and the efficiency and equity implications.

Effective poverty reduction programs require careful measurement of poverty status. Several studies have shown conceptually that assets reflecting productive capacity form a more robust basis for identifying the poor than do flow variables such as expenditures or income. Nonetheless, little work has empirically compared poverty measurements based on assets and expenditures. This paper uses panel data from Ethiopia to generate an asset-based poverty classification scheme. Regression results are used to estimate an asset index and classify households into categories of structural poverty. Asset index dynamics are also explored to test for the existence of multiple asset index equilibria; evidence of potential poverty traps. Results provide evidence of multiple equilibria in the study sample as a whole as well as convergence at different levels over space, depending on commercialization opportunities and agroecological factors. The asset-based poverty classifications consistently predict future poverty status more accurately than do income-based measures, confirming that the asset-based measure could be used to more carefully target poverty interventions in rural areas and to more accurately assess the impact of those interventions.