Where a survey does not contain this extra information to rely on, we suggest using a rule of thumb: _code as active people who report doing domestic work but live in agricultural households_ . Table 10 below uses the IHS 1992/93 to show that while not a perfect adjustment, this rule of thumb on aggregate considerably adjusts for underreporting for participation when compared to a finer adjustment based on the use of information from other sections as stated above. Nonetheless, the rule of thumb is not a panacea, as it overstates men’s participation when compared to a detailed adjustment, while restricting it to agriculture household still excludes those in urban areas who carry out household chores that should be counted as economic activities.

**Table10: Comparison of unadjusted and adjusted labor force participation rates by gender, 1992/93**

|**Labor forceparticipation rate**<sup>**a**</sup>|**Male**|**Gender**<br>**Female**|**Uganda**|
|---|---|---|---|
|Unadjusted LFPR|81.8%|75.2%|78.3%|
|<br>Rule of thumb adjustment|90.9%|82.1%|86.3%|
|<br>Detailed adjustment(from table3)|86.7%|90.4%|88.6%|

**_Source:_** _Authors’ calculations based on the IHS 1992/93._ **_Note:_** _The unadjusted LFPR is based only on the main employment status questions, rule of thumb simply codes as active people who reported domestic work but resides in agricultural households where similarly aged people of their gender report agriculture work, and detailed adjustment makes adjustments using information from other questions and modules of the questionnaire (secondary activities, and time use and household enterprise module)._

When the information content differs across surveys, a trade-off between comparability and adequately capturing the concept of economic participation is implicitly involved. As an example, the UNHS 2005/06 and UNHS 2009/10 both have screening questions that form a better base for measuring economic participation (LFPR), but indicators constructed from these will not be comparable with previous surveys. If the trend is to stretch back to 1992/93, it will be even more difficult to create comparable indicators, even if we use both primary and secondary employment status responses for all three years. The suggestions above are simply ways to approach data comparability and minimize non-sampling bias, but obviously user discretion is advised where questionnaires are simply too different.

# **5. Conclusions:  A framework for capturing activities, not dreams**

While much has been made of the poor quality of national accounts or poverty data in Sub-Saharan Africa, less has been written on the quality of labor force and employment outcome data. Yet the shortcomings in employment data are at least as important, not only because they inform the national accounts data. The main problem has been the use of poorly worded questionnaires. These questionnaires – the “bed” in the title of the paper – are applied without first clarifying the reality they seek to measure. So they end up measuring what is in people’s heads as they hear the words of the interviewer - their “dreams”. This makes the data difficult to interpret or to use in cross-country or trend analysis.

The analysis in this paper showed the sensitivity of labor outcome estimates to type of questions, their framing, recall periods and computation methods. Using multiple Ugandan cross section household survey data sets and exploiting the variations across surveys, and sometimes within surveys, we illustrate how 23