Fox and Gaal (2008) proposed another alternative, arguing that knowledge of the biases by module design (e.g. exclusion of screening questions for economic activity) can be used to make an adjustment in the data and bring better (but not complete) comparability. This method involves recognizing that a satisfactory measure of economic participation should be able to capture participation in any form of economic activity, irrespective of whether the respondent deems such an activity primary or secondary. In the absence of screening questions that capture participation in all economic activities over a 12 month period for example, information from various questions in the labor module (primary and secondary employment status) and other modules where economic activities are separately captured (e.g. a household enterprise module, agriculture module and time use or non-labor market activities module), can be used to get a complete picture of LFP. This search of the questionnaire and the data should also include looking at time use data to pick up some domestic duties technically classified as work such as fetching wood and water.<sup>19</sup> This method was applied in Table 10 to estimate the extent of the non-sampling bias in the Uganda data. The steps are as follows.

- a. _Use secondary activity information_ . In the absence of screening, users can identify those who are reportedly inactive, or are missing the status variable in the main employment status question. Then, the user can search for a secondary activity response. If they report a secondary economic activity, the person would be recoded as active, and the secondary activity would be recoded as the primary. For example in 1992/93 a woman might report domestic activity (i.e. economically inactive) in the main employment status question but reports helping without pay in a household enterprise or farm in the secondary employment status question. She should be coded as active. This is the same adjustment shown for Uganda in Table 3 which compared LFPR based on primary activity only to the LFPR including secondary activity.

- b. _Use additional information from the survey to verify and recode those who still remain inactive_ . Where there is a household enterprise module, identify household members working in the enterprise. If they have been coded as inactive, recode them as active, and their primary activity as a contributing family worker in the nonfarm enterprise sector.

- c. Use the relationship to head information to code all domestic servants as _economically active, and as wage workers_ .

- d. For those who are still inactive, look for _time use or household chores modules_ . Code as active those who reported economic activities in either the detailed household chores or time use modules (e.g. activities such as agriculture, or fetching water or wood) _._

- e. Identify agriculture households (i.e. where at least one household member reports agriculture work) and code all “inactive” women who reported ‘domestic duties’ as reason for inactivity as _active_ , in the agriculture sector. Mostly likely, they did participate, but were doing unpaid and unrecognized work, and this should be captured.

- f. For those still coded inactive or missing use the education, health, and age information to verify reasons for inactivity.

> 19 Many surveys check for participation in household chores unpaid but considered “economic” (such as fetching water) in a separate section on household chores, not in the screening section.  This approach works as long as the analyst is careful to include this information when constructing indicators of LFP.

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