measures are upwardly biased.

Because the number of hired farm workers is not observed, the farm level count of farm workers is under-estimated, as it only includes household members who work on the farm. Underestimation of the number of farm workers leads to upward bias in estimates of annual farm output per worker and downward bias in the productivity gaps between household farms and other activities (i.e., wage labor and NFEs in various sectors) that are depicted in Figure 8a and Figure 9a. Hiring in of some farm labor, though, is a common practice. I use the average hours worked on the farm per household farm worker to convert the hours worked by hired farm workers into a predicted number of hired farm workers. I then predict a new estimate of farm output per farm worker, and calculate new unconditional and conditional productivity gaps between household farms, NFEs and wage labor. These can be found in the Figure 12. These alternative measures of farm labor productivity gaps are slightly larger, but the effect is small, and the overall story remains the same.

# **6. Conclusion**

Micro level cross-sector labor productivity gaps are smaller than those generated using national accounts data and vanish almost completely when computed on a per-hour basis. The ratios of consumption levels per capita between households that primarily participate in agriculture vs. other sectors are also small, confirming small cross-sector gaps in returns to sector participation. Inter-sectoral differences in annual earnings per worker arise from differences in employment volume (hours per worker of labor supplied) rather than wages or productivity per hour of labor supplied. The tendency is for individuals participating in agriculture to supply fewer hours to agriculture, on average, than individuals participating in other sectors.

The findings underscore agriculture’s importance to structural transformation in Sub-Saharan Africa. Agriculture, and specifically the operation of household farms, remains a dominant economic activity in rural areas of Sub-Saharan Africa. And, furthermore, much of the labor supplied to industry and services sectors involves the processing and trading of agricultural and other primary goods for consumers whose incomes are dominated by agriculture-sector activities. Furthermore, the non-farm activities in which rural households are involved, across countries, are incredibly closely linked with agriculture. These strong links highlight additional benefits to achieving agricultural productivity growth, which can increase the supply of raw materials for manufacturing and increase the demand for non-tradable goods and services. These linkages are also sobering as, apart from agriculture, no engine for rural economic growth is apparent.

Generally, the micro evidence seems consistent with the idea that there is scope for releasing labor from agriculture should it be demanded by other sectors. Households expect industry and service sector wage workers to earn higher returns per year than farm workers. Non-farm enterprises can bring higher annual returns to participants than farm enterprises in some countries, and much lower in others. This remains true when comparing within-household returns only for households participating in multiple activities, though, in this case, off-farm returns look much closer to farm returns.

16