province and has a larger share of the sample in that province, it is not surprising that imputation using the LFS understates poverty in urban areas nationally.

What is causing these differences in imputed poverty rates within strata? In fact, the HIES and LFS have important differences in the wording of the questionnaire. In particular, for questions regarding employment the reference period of the LFS is one week, while the HIES asks about the respondent’s current activity. In addition, the LFS classifies people as employed if they typically engage in economic activity, even if they did not in the past week. How questions are worded and the reference period used can have major effects on responses to labor-related questions.

In this case the different reference periods and questions affect trends in employment in addition to levels (Figure 1). Employment rates are consistently higher in the LFS than the HIES for the total population, because the LFS definition includes people that engaged in a few hours of work. For heads, who tend to work longer hours, these differences in wording do not appear to lead to a systematic bias. The differences in employment rates due to the different wording of the questionnaires is only moderately large, however, and never exceeds four percentage points.

For the purposes of survey-to-survey imputation, however, what is critical is the discrepancy in the two surveys in employment trends. Between 2006 and 2009, the employment rate for the HIES declined substantially for both heads and adults, by about 2 percentage points. On the other hand, the LFS employment rate changed little, declining slightly for heads and increasingly slightly overall. Because of differences in the wording of the questionnaire, in conjunction with differences in sampling strategy, employment changes in the LFS do not reflect those in the HIES.

One way to see how using the HIES rather than the LFS affects the results is to examine how mean predicted consumption is affected. Table 10 shows that using the LFS instead of the HIES raises predicted mean household expenditure per capita by 11 percent. For rural areas and estates, however, the results are identical whether the HIES or the LFS is used to predict consumption. This is consistent with the above results.

Two variables explain a large portion of this discrepancy in imputed per capita consumption in urban areas: Whether a person is employed, and whether the household head has an advanced level of education (see Table 11).<sup>11</sup> The differences in these two variables across the surveys explains 5.5 percent of the difference in imputed household expenditure, which is about half of the 11 percentage point difference in imputed household expenditure.<sup>12</sup> In rural areas, the employment ratio is also higher in the LFS, but the impact on the imputation is negligible since the magnitude of the coefficient is half of that in urban areas. The differences in screening questions and the reference period used by each survey likely account for the observed discrepancy in employment rates. With respect to education, in contrast, the two surveys use identical questions. Therefore, the inconsistent reported rates of education between the two surveys likely stems from the different sampling strategy employed by each survey. Further analysis will be necessary to fully understand the source of the differences in estimated means between HIES and LFS.

> 11 In this paper, we grouped completed education levels into four categories: (1) Passed Grade 5 or less, (2) Passed Grade 6 to 10, (3) Passed Grade 12 or General Certificate of Education (GCE) Ordinary Level (hereafter OL), (4) Passed GCE Advanced Level or above (hereafter AL).

> 12 These numbers are not strictly comparable, because the 11 percent difference reported in table 9 is based on the ratio of averages while the percentage difference reported in table 10 are based on differences in logs, but they are indicative of the important role of these two variables.

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