(2011-12), the Malawi _Integrated Household Survey_ (2010-11), the Tanzania _National Panel Survey_ (2010-11), and the Uganda _National Panel Survey_ (2010-11). LSMS-ISA surveys were implemented by each country’s national statistics office, with technical support from the World Bank Development Economics Research Group. These datasets are nationally representative, including urban and rural households regardless of occupation or sector of employment. Rural and urban areas are defined by each country’s statistics office. Unlike the other surveys, the Ethiopian dataset is representative of rural areas and small towns and excludes urban areas. Table 1 depicts the basic characteristics the datasets used in this study. In the paper, “small town” results for Ethiopia are represented as “urban”, “rural” as “rural”, and full sample results as “all”. It is worth emphasizing the novelty of LSMS-ISA datasets. Surveys of farming populations often collect detailed plot-level farm management information similar to the LSMSISA surveys, but they do not also include information on time use off the farm, and generally do not sample non-farming households. The multi-topic, multi-purpose LSMS-ISA questionnaire includes questions on labor market participation, labor inputs into in household farm and nonfarm enterprises, and returns to enterprises and labor market participation. They are also internationally comparable to some extent, allowing for cross-country comparisons.

Using the LSMS-ISA data, I construct individual level, annualized labor supply aggregates for three types of activities – household operated farm enterprises (farms), household operated nonfarm enterprises (NFEs), and wage labor market participation. Labor supply recall questions differ in the LSMS-ISA surveys by type of activity. Appendix A contains detailed information about the construction of all of the variables used in this analysis.

Wage labor supply variables are generated over a twelve month recall period from individuals’ reported number of months worked over the last year, typical number of weeks worked per month, and typical number of hours worked per week. In the agriculture modules of the surveys, labor inputs by individual household members are collected for each farm plot. These inputs are aggregated for each household member to generate the annual own farm labor supply variable. For non-farm enterprises, participation by household members is flagged at the firm level. Different approaches to collecting NFE labor supply are followed in different countries. These are detailed in Table A.3 in the appendix.

Systematic measurement error in construction of labor supply variables is particularly concerning, should respondents recall different types of activities with different errors. Differences in recall period (through questionnaire design or timing of interview) or differences in recall ability for different activities (e.g., rare, “salient” events vs. common ones) can lead to differences in household responses (Beegle, Carletto, and Himelein 2012; Bound, Brown, and Mathiowetz 2001). The possibility of measurement error in the constructed labor supply aggregates is addressed in section 5 of this paper.

Next, I construct aggregates of labor demand by household operated farms and NFEs, which include hired labor in addition to labor supplied by family members. Of interest are both the number of firm workers and the labor inputs supplied by workers to each firm. In the case of farm enterprises, we have a good measure of labor inputs and the number of household members who work on the farm and the total number of hours worked by household members and hired workers, but we do not observe the number of employees hired. In the case of NFEs, we 5