respect to labor (Pardey 2014). This intensification has taken the form of higher cropping intensity and a shift to higher value crops (Binswanger-Mkhize and Savastano 2014; Nin-Pratt and McBride 2014). Addressing agricultural labor exits in the context of population pressure in rural areas over the past forty years, Headey and Jayne (2014) find that population pressure has a strong effect on agricultural intensification, but a mixed effect on households diversifying their income sources to include non-agricultural activities.

The process of structural transformation necessarily involves some occupational as well as geographic mobility, with an ultimate egress of people from the agricultural sector and from rural areas (Collier and Dercon 2014). Migration out of rural areas and occupational shifts out of agriculture have been associated with poverty reduction in long term panels of rural households (Beegle, Weerdt, and Dercon 2011). Household income diversification decisions are influenced by growth in the industry and services sectors (Foster and Rosenzweig 2003; Steven Haggblade, Hazell, and Reardon 2010), as well as by increased population density and heightened access to population centers (Steven Haggblade, Hazell, and Reardon 2007). However, households may face important barriers in diversifying their occupations or migrating (Barrett, Reardon, and Webb 2001). In many cases, non-farm work requires quite different skill sets than farm work, and so individuals with farming skill sets must find ways to retool (Rodrik 2014). By influencing the ability of individuals to move between sectors, such barriers shape the overall opportunity to achieve labor productivity growth through structural change. Recent micro evidence suggests that, while non-agricultural sources of income bring the highest returns across the welfare distribution, the majority of households in rural areas remain specialized in agricultural income earning activities (Davis, Di Giuseppe, and Zezza 2014).

# _1.2 Research Gaps_

The premise of higher returns to labor outside of agriculture is quite central to the structural change mantra. So are these productivity differentials really as high as national accounts data suggest? After examining many of the assumptions used to calculate agricultural labor productivity gaps from national accounts data, Gollin et al. (2014b) find a number of biases that lead to over-estimation of gaps. These biases arise from the methods used to classify workers as agricultural or non-agricultural, the assumption that workers in each sector work an equal number of hours, and the assumption that workers from each sector have the same levels of human capital<sup>3</sup> . Even after correcting for these biases, the authors find that large productivity gaps remain, with an average corrected productivity gap of 3.3 in Africa.

If productivity gaps are indeed as large as African macro statistics suggest, then one must wonder why so much labor remains in rural areas and why rural income diversification remains so low (McMillan and Headey 2014). One explanation is that, though households may face large productivity gaps, they are not able to diversify because of limited human capital, experience, or financial capital. Alternatively, the cross-sector gaps that households face actually may smaller than those suggested by national accounts, should the differential returns to non-agriculture sector activities accrue to owners of capital rather than labor. It is also possible that differences in expected returns between sectors are offset by different levels of risk. Understanding micro

> 3 Typically, input quality controls, including human capital, are used in productivity measurement.

3