sensitive employment outcome variables are to the design and implementation of a questionnaire. For example, if an analyst chooses to use a 7-day recall period employment variable for analysis, erroneous conclusions can be drawn with respect to key outcomes for specific types of individuals and households. There is also the possibility that different analysts, using variables constructed in different ways, could easily come to different conclusions on level indictors using the same data. Trends over time are even more difficult to compute and interpret. We show that measurement bias can affect economic analysis relating these employment outcomes to other key variables at the household, sectoral or macro level. By implication, these results also apply to country comparisons when different countries use different instruments.

Inadequate questionnaires mean that analysis of labor force participation and employment outcomes is not a simple prospect in low-income countries. In Uganda, older surveys used questionnaires which clearly led to an underestimation of LFP at the household and aggregate level. Many countries still use these formats today. This complicates even the simplest aggregate trend analyses, such as whether the share of the labor force in agriculture is going up or down. Even data from the most recent Ugandan surveys, which include formats specifically designed to measure LFPR more accurately, have to be used carefully owing to multiple economic activities. Although the 7-day recall period is an international standard, and has worked just fine for OECD countries employment statistics, using it in a low-income setting will underestimate, and possibly distort, analyses of individual outcomes such as underemployment and household livelihoods.

Some have argued that the concepts of labor force participation and employment embedded within the SNA are not suited to a low-income setting. For example, it has been suggested that economic activities which produce output included in the SNA (such as growing food for home use) should not be considered employment as no income is earned.<sup>20</sup> The problem with this argument is that it goes against decades of revisions of the SNA, all designed to include more economic activities, not less. As SSA countries such as Ghana, Nigeria, and Uganda make progress on implementing the 1993 SNA recommendations on including non-market activities such as household production, their employment data needs to catch up too. Calculating national accounts under the 1993 SNA revision, including the Social Accounting Matrix (SAM) used in checking consistency and balance, requires more extensive data on non-wage activities from household surveys. Mismeasurement at the micro level will pose problem for the macro statistics. The argument here is that it is possible to measure employment in low-income countries in a manner consistent with the SNA and internationally accepted definitions if the questionnaire design takes into account the reality of how economic activities are performed in the country context. The problems occur when this does not happen. Statistical offices need to recognize this, and raise their game.

The flagship household survey of most SSA countries is their 12 month multi-purpose one (including household consumption), which is used to monitor household welfare outcomes, including poverty. Over time, as these surveys were used for policy analysis, it was recognized that employment outcome variables (including earnings) need to be included in order to discern the growth-poverty linkages and the reasons why growth was or was not shared. As a result, more surveys are adding questions on the economic activities of household members. To be useful, these surveys need to use formats that are adapted to a low-income country

> 20 This argument was recently made in ILO (2012), which was a draft discussion document.

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