2000).<sup>17</sup> It is difficult to tell whether those households who had high incomes or engaged in a particular activity in one month would also have high incomes or would they have been engaged in similar activities in different months. Measurement errors also occur with occupational choice, since the individual level 7 day recall status can deviates from the 12 month recall status. These measurement errors will result in inconsistent estimates in regressions using earnings at both the individual and household level as well as analysis involving individual employment variables.

In sum, while the recall period has a negligible impact on aggregate indicators of primary employment, it matters a lot for indicators capturing multiple activities both at aggregate and household/individual level analysis. A short recall period biases any kind of individual and household level analysis because it understates participation in secondary activities, and by implication gives a misleading picture of livelihoods and overstates underemployment. It also makes it difficult, if not impossible, to measure annual earnings for important segments of the labor force – likely the poor. It follows then that indicators based on the 12 month recall period are the more reliable, especially for earnings and livelihood level analysis<sup>18</sup> .

# **4. Suggestions on minimizing measurement error when calculating LFPR in low-income countries**

The Uganda analysis, as well as other analysis cited above, showed that there is significant variation in the LFPR depending on the questionnaire design. There are categories of respondents – specifically women and those in school - whose labor force participation is systematically understated. The key question is how to construct a meaningful indicator for LFP in a given country, in the presence of non-sampling measurement error traced to the type of questionnaire or the implementation. The first step is to recognize that the bias of the error is to underestimate the labor force. But then, what can the analyst do after the data have been collected?

Guarcello et al (2010) offer one option. Using data from 24 countries, they constructed a model which would predict LFPR for an individual or group based on characteristics observed consistently in all surveys for that country, as well as dummies for type of questionnaire. They used this model to estimate the LFPR for children aged 10-14 in each country. While their predicted (“harmonized”) LFPR based on individual/household characteristics showed a more consistent pattern than the one derived from the different types of surveys, they noted that a large share of the variation in estimates across surveys is explained by _the type of survey_ , not individual characteristics. They therefore found these results less than satisfactory, since type of survey (CWIQ, DHS, LSMS) is only a weak proxy for the extent of non-sampling error.

> 17 The measurement of earnings encounters many difficulties beyond; recall period is only one of these.

> 18 See annex 1 of World Bank, 2012 which among other things, discusses the analytical implications of the absence of a 12 month recall secondary activity question in the UNHS 2009/10.

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