poverty. The former were important as public social service delivery was where the largest share of donor funding went in the post-HIPC period. Human welfare outcomes such as school enrollment and infant mortality were regularly scrutinized as part of MDG monitoring, while employment outcomes were not. Indicators of poverty reduction (as measured by household consumption) were given attention as well, and still remain the ultimate goal of both SSA country development strategies and donor finance (see a recent story on IDA results, 2000-2010 for an example<sup>2</sup> ).

A second reason for the poor quality of data is that it is not easy to collect data on employment and earnings outcomes in low-income Sub-Saharan African countries (hereafter, SSA). Survey research on the measurement of employment outcomes tends to take place in OECD countries, where most of the experts on the measurement of employment and earnings work. But the employment pattern in SSA is not at all similar to that found in OECD countries. In OECD countries, almost all employment takes the form of wage and salary employment, where employed labor force participants work for an unrelated individual according to an agreement covering the terms of the task and the remuneration. Job search involves entering the labor market and trying to find someone to hire you on acceptable terms. In SSA, employment takes the form of self and/or household employment, where a task is performed for family profit or gain (including agriculture for home food consumption). Most labor force participants never even enter the labor market. Yet SSA statistics offices routinely adopt survey instruments or modules for collecting data on employment supplied by statistical agencies in OECD countries. They may modify the surveys to fit local context, but without the benefit of local survey research or knowledge on the best techniques to use when measuring household or self-employment. This results in employment and earnings data that lack relevance to the country setting, and mismeasure the key features of employment (non-sampling error). Not surprisingly, in their detailed analysis of surveys from 23 countries, Guarcello et al (2010) found that the most consistently measured employment variable was wage employment.

The purpose of this paper is to _highlight the sensitivity of employment outcomes to questionnaire methodology and show the analytical difficulties in trying to use survey data from SSA countries (where questionnaire methodologies are inconsistently applied and non-sampling error appears to be large) to understand how employment and earnings patterns have changed over time, let alone forming of policies based on such analysis._ While it uses Ugandan data as an example<sup>3</sup> , the objective of the paper is not analyzing recent trends in employment outcomes in Uganda or any other country. Rather, this paper is about why employment data from SSA countries are unreliable and what issues analysts face in using these data to try to identify employment outcomes and relate them to government policy or household behavior, for the purposes of diagnosing past problems or establishing a baseline to track progress.

The paper mainly relies on Uganda data simply because the labor module in its surveys has significantly evolved over time and contains variations both within and across surveys that can be exploited

> 2 <u>http://www.worldbank.org/ida/results.html, accessed on August 15, 2012.  Shining a spotlight on an indicator maybe necessary but clearly not</u> sufficient to improve quality. Despite the attention paid to poverty reduction in the past twenty years, it could be argued that the collection of the consumption data for needed for calculating absolute poverty is plagued by similarly high non-sampling errors. See for example, Jerven (2012). 3 This paper is not on how to interpret Uganda’s employment trends over time; interested readers on this topic may wish to consult Fox and Pimhidzai (2011).

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