Structural Transformation in Sub-Saharan Africa: Agricultural productivity, labor supply, and occupational choice
McCullough_cornellgrad_0058F_10049.pdf (736.0Kb)
https://doi.org/10.7298/X44F1NP6
In the first analytical chapter of this dissertation, I draw on a new set of nationally representative, internationally comparable household surveys, in order to provide an overview of key features of structural transformation --- labor allocation and labor productivity --- in four African economies. New, micro-based measures of sector labor allocation and cross-sector productivity differentials describe the incentives households face when allocating their labor. These measures are similar to national accounts-based measures that are typically used to characterize structural change. However, because agricultural workers supply far fewer hours of labor per year than do workers in other sectors in all of the countries analyzed, productivity gaps shrink by half, on average, when expressed on a per-hour basis. Underlying the productivity gaps that are prominently reflected in national accounts data are large employment gaps, which call into question the productivity gains that laborers can achieve through structural transformation. Furthermore, agriculture's continued relevance to structural change in Sub-Saharan Africa is highlighted by the strong linkages observed between rural non-farm activities and primary agricultural production. The process of economic development is characterized by rising output per agricultural worker and the exit of labor from agriculture to other sectors, which together result in rising incomes and falling incidence of poverty. In my second analytical chapter, I explore the relationship between labor productivity and the occupational choice that underlies the structural transformation process. I model households' decisions to participate in different activities -- farming, wage employment, and self employment -- through operation of a household non-farm enterprise. I estimate a structural, polytomous model of occupational choice using nationally representative household survey datasets from Tanzania, matched geospatially to several other relevant datasets. Then, I simulate the response of occupational choice to stylized productivity shocks to farming, wage employment, and self employment. I find that participation in farming is not responsive to productivity shocks of any sort. This is most likely because farming participation rates are already quite high. Wage and self employment participation do respond to wage and self employment productivity shocks, respectively. These results highlight the importance of investing in improved smallholder farmer productivity, especially along the intensive margins of farming participation and especially in places with low population density and poor market access, where farming productivity gains are the only ones to impact households. Investing in productivity-enhancing inputs is complicated by variability in rainfall, temperature, infrastructure, soils, and market access, which condition the economic returns to input use over space and time. Newly available, spatially explicit data in Sub-Saharan Africa allow decision makers to better understand how agricultural production and prices change with this variation in climate and growing conditions. In my third analytical paper, I, along with coauthors, develop an innovative, ex ante, spatially explicit profitability assessment tool in order to inform large scale operational decisions in the presence of risk and uncertainty. This tool allows decision makers to visualize the probability of achieving profitability objectives when climate conditions and prices are unknown. We develop this decision tool in Ethiopia, a country characterized by its high levels of spatial heterogeneity, rainfall risk, and price risk, as well as its strong commitment to investment in agricultural growth and transformation. We use a large scale experimental dataset to cleanly estimate the production response to fertilizer application conditional on climate and soil conditions. Using these model parameters, we simulate the profitability of fertilizer use conditional on market conditions at the time of fertilizer purchase. We explore the implications for decision makers who are designing and targeting soil health interventions. Though this decision tool is developed for nitrogen management on maize in Ethiopia, the novel approach can be expanded to other crops, nutrients, and management practices.
Agricultural labor productivity; Climate smart agriculture; Occupational Choice; Structural transformation; Sub-Saharan Africa; Economics; Agriculture economics
Prowse, Victoria L; Just, David R.