Malawi (2010 and 2013), Nigeria (2013 and 2016), and Tanzania (2011 and 2013); three survey rounds from Ethiopia (2012, 2014 and 2016); and four survey rounds from Uganda (2010, 2011, 2012 and 2014).<sup>4</sup> Significant effort has been devoted to make the LSMS-ISA-supported surveys comparable across countries but some questions are adapted to the specific local context. Particularly important for our analysis are that the data are collected at the individual-level; that the sampled households and individuals are tracked over time, and that the multi-topic questionnaire instruments include extensive modules on labor in off-farm wage employment as well as household non-farm enterprises. The questionnaire package also includes a community questionnaire administered at the enumeration area (EA) level, collecting data on services, infrastructure, and institutions from a focus group of community leaders.

We use two different samples for our analysis. In the first part we focus on cross-sectional participation rates in off-farm employment. For each survey round the sample consists of household members<sup>5</sup> aged 15-64, which is in line with the conventional working-age range. We exclude persons who are currently enrolled in school. In the second part we focus on the dynamic nature of off-farm employment and construct panel data sets that consist of household members aged 15-64 throughout the panel rounds. We retain individuals for which we have at least two observations over time to be able to investigate dynamics.<sup>6</sup> Integrated Household Panel Survey (IHPS), the Nigeria General Household Survey Panel (GHS Panel), the Tanzania National Panel Survey (TZNPS), and the Uganda National Panel Survey (UNPS).

> 4 The 2010 survey round in Nigeria, and the survey rounds in Niger were not considered because the distinction between wage and self-employment cannot be made. In the case of Tanzania, the 2008 survey round was not considered because the definition of wage employment was not comparable to the definition used in the later rounds. 5 Household membership is defined as having spent at least six months (three in case of Tanzania) in the household during the past 12 months.

> 6 To correct for potential attrition bias, we run a logit model with a binary dependent variable taking the value of one if the individual appears in the sample at least twice. We control for variables at individual-level (age, gender, education level, current school enrollment, marital status, relation to household head and employment status) and at the household-level (household size, dependency ratio, wealth index (a factor analysis-based index of household dwelling attributes and durable asset ownership) and participation in farm activities). Baseline enumeration area fixed effects are also included in the specification. We predict the probability of non-attrition for each individual and calculate the attrition weight as the inverse of this probability. To make the sample nationally representative, we use the sampling weights calculated by the national statistical offices and multiply it with the attrition weight. We winsorize the weights at the top 1 percent level to minimize the effects of potential outliers.

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