for the purpose of our analysis. By exploiting these variations, the paper shows how difficult it can be to obtain key labor market indicators and identify their trend over time using household survey data in SSA. We show that it is possible that different users of the same data set may pick up questions from different parts to construct their key variables, possibly coming up with different results from the same data on the basic employment outcomes even if they are only doing simple tabulations. By implication, these results also apply to country comparisons when different countries use different instruments. The analysis suggests that we may actually know very little about employment outcomes in SSA, and how relationship to economic policy or household welfare.

We start the paper by reviewing the definitions of the main employment outcomes, using the framework established by the UN SNA for defining employment and the conventions established by labor statisticians through the International Labor Organization (ILO) for the measurement of these concepts. We discuss the issues to be faced when applying these definitions in a low-income setting, and review the survey research that is available to guide the measurement of these concepts. The section summarizes recommendations from past literature on questions and techniques most appropriate for measuring employment outcomes in a low-income setting. In Section 3, we present the questionnaires for the Uganda National Household Survey (UNHS ) used by UBOS from 1992-2010, which contain both good and not-sogood practice techniques, and estimate the effects that different questionnaire designs have had on nonsampling error in the measurement of employment outcomes. We conclude the paper with suggestions for SSA statistical offices on collecting employment data, for researchers on possible approaches to adjust for known measurement biases in data collected in other SSA countries, and for future survey research on measuring employment outcomes in low-income countries.

# **2. Some basics on collecting data on employment and labor force participation**<sup>_4_</sup>

The concepts used by social scientists in the analysis of labor force status and employment outcomes were initially defined in microeconomic and specifically labor economic theory and practice. The main variables commonly tracked are:

- labor force participation rate (LFPR, which is the economically active population<sup>5</sup> divided by the working age population);

- inactivity (and reasons for lack of participation in the case of working age people);

- primary and secondary employment characteristics (e.g. job type – that is, wage or non-wage, sector, occupation, etc.);

- unemployment and underemployment;<sup>6</sup> and

> 4 This section draws from Beegle, Bardasi, Dillon and Serneels (2010), and Schaffner (2000).

> 5 The economically active population is thefraction of the population which is either employed or seeking employment. See <u>http://www.businessdictionary.com/definition/economically-active-population.html.</u>

> 6 Unemployment is recognized to be of limited use as an outcome variable in low-income SSA, but it is still commonly tracked.  See  Fox and Gaal

> (2008), for a discussion. The concept of underemployment - defined as unmet demand for remunerative work – has been growing in popularity as

4