_The husband and wife share the same bed, but they have different dreams. (Chinese proverb)_

# **1. Introduction**

Employment and earnings statistics are among the most important variables used to identify the causes of poverty as well as the pathways out - for households and for an economy. Therefore, employment outcomes are especially important to monitor consistently because they are the key link between the size and structure of economic growth and the welfare of households - the ultimate goal of development policy. The quality of employment outcomes has become a key issue for governments around the world. Gallup World Poll recently reported that jobs and economic security are the number one issue for people all over the world, replacing other concerns such as war, crime, and security (Clifton, 2012).

Policy makers in Sub-Saharan African countries, facing a large youth bulge, are increasingly focusing their national development strategies on creating more productive employment. Yet data on the nature and intensity of economic activities undertaken by the labor force in low-income Sub-Saharan-African countries, and the returns to these activities are hard to find. For example, the 2011 edition of the World Bank’s _African Development Indicators_ (ADI) reports data on the structure of employment by sector - one of the most basic statistics - for only 14 of the 48 countries included. In the same volume, data on labor force participation rate (LFPR) is available for all 48 countries, but the values vary improbably between countries. For example, Sudan, a very poor country where most adults cannot afford to be out of the labor force, is reported to have a labor force participation rate of 52 percent in 2009, while a rate of 85 percent is reported for Ethiopia, Sudan’s neighbor, and a rate of 81 percent is reported for Angola, a relatively richer country.<sup>1</sup> Even for countries where micro data from household surveys are widely available, the methods of questionnaire design and implementation generate indicators which fail to capture what the survey intended to measure (non-sampling errors), and are not comparable over time. Guarcello et al (2010) found this in the case of child labor statistics in low and middle-income countries. They noted substantial differences between indicators of child LFPR across similar countries, and even within the same country over a short period of time. They contrasted the variance in the measurement of child LFPR with measures of school attendance, and found that the latter did not show such high variance depending on type of survey. They attributed the unusual variance in point estimates of child LFPR to differences in the framing of questions and the methods by which the employment data are collected. These differences also plague measures of adult employment outcomes. As a result, even trends within country are hard to identify, much less trends among countries.

One reason for the poor quality of data is that until recently, employment outcomes were not a primary focus of development policy, and therefore of the household surveys. The indicators most widely tracked at the household level were social services access and outcomes, and household assets, consumption and

> 1 Many of the LFPR numbers commonly reported are not data points collected recently, but projections based on old surveys. Although they are available for most countries for most years, they may not be based on recent data.

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