shares are generally close to and slightly larger than the national-accounts measures in all countries except Malawi, where they are slightly smaller. Fourth, the DHS-based measures of agricultural participation shares are quite a bit lower than the LSMS-based individual participation shares except in Uganda, where they are almost identical. This implies that DHSbased labor share estimates might under-estimate agricultural labor shares and therefore overestimate labor productivity in agriculture relative to other sectors. Since individuals selfidentify their primary sector in DHS surveys, it is possible that respondents involved in multiple sectors are more likely to identify the non-agriculture occupation even though it accounts for a lower share of labor supplied.

Per-person productivity measures based on categorizing individuals by their primary sector of occupation implicitly ignore individuals’ contributions to secondary sectors. They also assume that participants in different sectors supply equal hours of labor. Such assumptions can be problematic when individuals supply labor to secondary sectors, or when there are systematic cross-sector differences in hours supplied.

Figure 3 examines the one sector assumption, categorizing individuals by their primary sectors and depicting the average hours supplied to individuals’ primary as well as secondary sectors. The data imply that both the equal-hours and primary-sector assumptions are problematic. While those who are primarily categorized as agricultural laborers do not supply much labor to other sectors, workers who are primarily in industry or services sectors do supply labor to agriculture. Because secondary sectors are an important part of individuals’ labor supply, we likely underestimate labor supplied to agriculture by ignoring the labor supplied by individuals who participate in agriculture as a secondary activity, thus leading to an overestimation of labor productivity in agriculture relative to other sectors. Gollin et al.’s (2014b) analysis on secondary sector bias suggests that labor supply to non-agriculture by agriculture workers is greater than labor supply to agriculture by non-agriculture workers. These data indicate bias in the opposite direction.

Violation of the equal hours assumption, on the other hand, leads to overestimation of agricultural labor shares. Figure 4 depicts the average hours worked in a sector by those who participate in it. Generally, those working in the non-farm sector supply significantly more hours than those working in the farm sector. Gollin et al. (2014b) address the differences in hours supplied by agriculture and non-agriculture workers, using rural and urban distinctions where sector distinctions are not available. They find that, in poor countries, non-agricultural workers supply 1.3 times as many hours as agricultural workers to their respective sectors. This analysis confirms higher supply of labor to non-agriculture by non-agriculture workers than supply of labor to agriculture by agriculture workers, though our cross-sector differences in labor supply are large (5.4 in Malawi vs. Gollin’s 1.45, between 2.7 and 3 in Ethiopia, and between 3.6 and 3.8 in Tanzania). Our Uganda estimates, however, are smaller (between 1.29 and 1.76 vs. Gollin’s 2.32).

Overall, the LSMS-ISA datasets suggest large ratios between hours supplied to agriculture and hours to secondary sectors. By calculating labor productivity based on sector participation rather than hours worked, one under-estimates labor productivity in agriculture compared with other sectors.

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