universally observe the number of hired workers but not the hours that they supply to the firm. It is quite common for farm households to hire in labor (between 30 % and 94 % of farms do it), but less so for non-farm enterprises (in all cases, fewer than 19% of households operating an enterprise hire in any workers).

Returns to labor market participation are comprised of the gross total wages received by wage workers, including in-kind payments (e.g., meals received) and gratuities. Costs of participating in wage labor markets are not measured so it is not possible to construct a net revenue measure. The returns to operating a farm enterprise are based on net farm revenue, which is analogous with the “value added” concept that underlies national accounts data. The net value of farm output is derived from the Rural Income Generating Activities (RIGA) calculations and includes the value of own-consumed farm output as measured through the consumption module (Davis et al. 2010). For non-farm enterprises, reported enterprise profit is considered a more reliable measure of net firm revenue than a constructed measure based on gross revenues minus costs (de Mel, McKenzie, and Woodruff 2009). Where available, I construct the annualized net firm revenue variable using reported profits. Otherwise, I use household estimates of gross NFE revenue and costs. To facilitate cross-country comparison, all measures of returns are converted to constant international dollars using the purchasing power parity conversion factor for private consumption from the World Bank’s World Development Indicators.

Using the labor supply variables and the returns variables, I construct average labor productivity variables. These are done separately for the three types of activities – wage labor, farms, and NFEs – as a simple ratio between returns to an activity and labor inputs into the activity. Two types of average labor productivity measures are constructed. The per-worker measure is based on output per worker per year. The per-hour measure is based on output per hour of labor supplied to each activity per year. Because we do not observe how many hours hired workers supply to NFEs, I am unable to generate a per-hour productivity measure for these firms. And because we do not observe how many workers are hired by farms hiring in labor, the per-person farm enterprise productivity measure leaves out hired workers and necessarily over-estimates farm productivity<sup>4</sup> .

Next, all activities are assigned to their respective sectors of the economy (i.e., agriculture, industry, or services). Following McMillan and Harttgen (2014), we group these into the general categories of agriculture (primary agricultural, livestock, and fishery and forestry production), industry (manufacturing, mining, construction, and public utilities), and services (wholesale and retail trade, transport and communication, finance and business services, and community, social, personal and government services). I generate sector level aggregates of labor supply and returns for each household. Farm activities are classified as agricultural. Wage labor and NFE activities are classified using the Industry Standard Industrial Classification (ISIC) codes provided with each activity’s description. An additional sector definition of “unknown” is used when individuals report jobs for which no description or sector code is available. These labor sources most likely occur in the agriculture sector, but I avoid assuming so. The agricultural labor supply aggregates, based on hours worked, do not include livestock and post-harvest labor. And, similarly, per-hour agricultural labor productivity measures do not include livestock revenue. In

> 4 As a robustness check, I predict the number of hired workers per farm using the total person-days of hired labor and the mean person-days worked per household member. These results can be found in Section 5.

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