activities. This analysis cannot disentangle whether differences in hours worked across sectors are determined by sector level labor supply or demand characteristics. It could be that agricultural labor demand is not well smoothed throughout the year. Because of biophysical and agricultural constraints, it might not be possible for individuals to increase their agricultural sector returns by supplying more hours to their farms. Presumably, because employment is so limited across Sub-Saharan Africa, low demand for labor by agriculture is a key constraint. Understanding what limits supply and/or demand of labor in the agricultural sector is an important topic that is left for future research.

# **4. Exploring the Non-Farm Economy**

This section contains a close examination of the non-farm activities in which households are involved, with a view towards understanding their implications for structural change patterns and prospects.

# 4.1 _Household and worker characteristics_

It is important to explore any systematic differences in characteristics of sector participants, so that they can be taken into account when using national accounts data on sector labor shares. Indeed, the macro-economic literature is concerned with systematic differences in human capital across sectors and the implications for bias in productivity measures (Vollrath 2014). Table 2 includes basic summary statistics according to individuals’ primary sectors of participation.

There are no major differences in the average age of each sector’s work force in any country. In Malawi, Tanzania, and Uganda, the agricultural work forces are slightly more female, while the industry and services work forces are strongly male. On the other hand, in Ethiopia, the agricultural work force is slightly more male than female, while the industry and services work forces are slightly more female. Individuals who do not supply hours to any sector are younger and more female, on average, than individuals who do. The average years of education completed tend to be highest in the services sector and lowest in the agricultural sector. The educational differences point to possible systematic skill differences across sector work forces. However, individuals who do not participate in any sector, on average, have education levels similar to those of industry workers, and higher than agricultural workers.

Figure 7 depicts the changing primary sector of workers across all major age cohorts. Youth (ages 15-24) generally have lower participation in economic activities than do young adults (ages 25-34). Economically active youth have similar labor shares to economically active young adults, though they have higher rates of participation in agriculture and lower rates in industry and services. Despite these differences, labor shares are robust to the specification of the “adulthood” threshold at age 25 rather than age 15.<sup>8</sup> There is always concern that differences in productivity observed between different activities reflect differences in the households participating in the activities rather than inherent differences in the economic productivity of the activities themselves. Productivity-determining characteristics may or may not be observed in survey datasets. I explore the possible contribution

> 8 Results can be shared upon request.

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