The bars labeled “Hours” in Figure 2 show the net effect of these two, in this case opposing, sources of bias on labor share estimates. In all countries, agriculture’s share in labor is lower when an hour-based measure is used than when the LSMS-ISA participation-based measure is used. These results suggest that agricultural productivity may be underestimated relative to other sectors when participation-based labor shares are used. When hour-based labor shares are used, estimates of agricultural productivity are likely to be higher, and estimates of gaps are likely to be smaller. This bias proves extremely important to any discussion about structural change in rural Africa.

# _3.2 Sector Productivity Gaps_

Cross-sector productivity gaps calculated from the LSMS-ISA dataset are indicative of the productivity differentials that households face when allocating their labor. Figure 5a depicts sector-level productivity measures with 95% confidence intervals across the LSMS-ISA countries, based on output per person per year. Output per worker per year is highest in the industry and service sectors in Tanzania and Uganda. Agricultural output per worker is lower than $1,000 (USD ppp) per worker per year everywhere but Ethiopia. Ethiopia is also the only country where industry and services bring lower output per worker per year than agriculture.

Figure 5b shows the same based on output per hour of labor input. After adjusting for labor inputs (hours worked), all sectors are between $1 and $8 (USD ppp) per hour labor supplied. Ranking of sectors stays the same in Tanzania and Uganda after the correction. In Malawi, the superiority of industry and services is reversed. In Ethiopia, agriculture’s superiority is exaggerated.

These productivity measures are not meant to replicate productivity measures from national accounts, which use different sampling approaches. Corporations are not sampled in the LSMSISA surveys, so their activities are only detected through wages paid to workers that they hire. Industry and service sectors still could be more productive should the returns accrue to the owners of capital.

Figure 6 depicts these micro-level productivity gaps (simple ratios between each sector’s productivity and productivity in the agricultural sector) along with a few other data points gathered for the purpose of comparison. Gaps in output per worker per year are close to national accounts gaps in Tanzania and Uganda, and smaller than national accounts gaps in Ethiopia and Malawi (Figure 6a). When considering time inputs in each sector, however, productivity gap measures almost disappear completely (Figure 6b). The hours-based gap measures are smaller than the per-person-per-year gap measures in all countries. Large gaps persist only in Tanzania, where an hour worked in the industry or services sector generates between six and eight times the returns of an hour worked in agriculture. In Uganda, an hour in industry or services is about twice as productive as an hour in agriculture. Much of the productivity differences observed in national accounts statistics could be attributable to differences in hours supplied by workers in each sector rather than differences in output produced per hour worked in each sector.

The persistence of some cross-sector gaps in output per worker per year suggests there are some forces enticing agricultural workers into other activities. These activities may allow for fuller levels of employment, that is, for participants to supply more hours per year to their economic 9