Small per-worker-per-year micro productivity gaps suggest that workers, who are the owners of labor, may not stand to reap the large benefits of labor exiting agriculture that are expected in the economy as a whole (should national accounts data indeed reflect true economy-wide productivity gaps). Small per-worker-per-year micro gaps also suggest that agriculture-sector workers do not feel as strong a “pull” from industry and services as one might expect based on national accounts data.

However, the evidence also suggests that individuals and households may face barriers to participation in non-agriculture activities. Workers who primarily participate in the industry and service sectors are able to participate in agriculture, while the reverse is not true of workers who are primarily agricultural. Service sector participants, in particular, tend to have higher education levels than workers in other sectors. Some households may face structural barriers to labor productivity growth that span across multiple sectors. The small size of conditional gaps (withinhousehold gaps faced by participants in multiple sectors) relative to unconditional gaps (pooled, cross-sector gaps) suggests that selection effects into non-agriculture activities contribute to cross-sector productivity differentials. Households who are unable to diversify might face even smaller productivity gains outside of agriculture than those who are, further eroding the benefits of structural reallocation of labor.

<mark>Nonexistent per-hour micro gaps suggest that differences in the annual output of</mark> agricultural workers vs. industry and service sector workers, whether observed in micro or macro data, might be largely explained by differences in hours worked in agriculture vs. in industry and services. Small per-hour gaps do not undermine agriculture’s role in structural transformation. Despite low per-hour gaps in agriculture, it appears that workers have an excess of labor that could be absorbed productively in other sectors. This requires growth in demand for labor outside of agriculture.

Why these employment gaps exist is not well understood. They raise the question of what limits the supply of hours in the agricultural sector and what role technology, infrastructure and policies might play in closing the cross-sector employment gaps. Is it the time-sensitivity of agricultural labor demand, or, more generally, seasonality? If so, could demand be smoothed with water control technologies and infrastructure? Is it barriers to participate in non-farm self or wage employment? Is it financial constraints that prevent farmers from procuring non labor complements? The results suggest that the upside to “saving” agricultural labor may not be very big, placing the onus on numerator (i.e. output value) driven productivity growth.

Overall, the analysis emphasizes agriculture’s key role in Sub-Saharan African economies, while also raising questions about agricultural employment gaps, their determinants, and how they shape the opportunity to achieve economy-wide labor productivity growth. A between-sector gradient in annual output per worker remains to be exploited. Improving annual output per worker within agriculture, the highest participation sector by far, requires a better understanding of labor demand by smallholder farmers.

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