these pseudo panels allows us to compare relevant outcomes for generations born more than 50 years apart.

Consider, for example, Table 2, which presents data on women’s employment rate for the pseudo panel created using eight cross-section surveys for Bangladesh. The first cross-section is from the DHS of 1993, and then seven more surveys provide data every three or four years, covering the period up to 2017.<sup>9</sup> Each cell of Table 2 contains two pieces of information: a) the size of a group, i.e., how many people were born in a certain period and observed at a certain age range, and b) the share of women employed in that group. Reading from top to bottom along the columns, the table shows how the employment rate for a specific cohort changes as that cohort ages, thus providing an estimate of the age effect for each cohort. Reading from left to right along the rows, the table shows the employment rates for different cohorts observed at the same age, thus revealing the cohort effect. The bottom row of the table is the weighted average of the ‘lifetime’ employment rate for all the cohorts of Bangladeshi women that we observe in the pseudo-panel. And the rightmost column illustrates the weighted average employment rate at each age across all cohorts.

There are two important issues to be noted. The first is that the size ‘N’ for some cells in the table is relatively small. This means that the employment shares (or any other relevant outcome variable) for these cells are estimated on a restricted number of observations, and they will be less precise (Verbeek and Nijman, 1992). The second issue is that the full lifecycle – or at least the period from age 15 to 49 – is observed only for some cohorts, those born between the 1960s and 1980s, in the middle of the table. This means that the lifetime employment rate (the bottom row of the table) is biased at the extreme ends of the table. It overrepresents older people at the left end (for the 1940s cohorts) and overrepresents younger people at the right end (for the 1990s cohorts). This is because the earliest surveys for the DHS are in the 1990s, and so we can observe people who were born in the 1940s only when they are about 50 years old, but we have no information about them when they were younger because we do not have surveys fielded before the 1990s. Conversely, since the most recent DHS surveys were collecting data around 2015, we can observe cohorts who were born in the 1990s only when they are about 25 years old or younger, but not when they become older. A similar bias affects the weighted average for the rightmost column of the table. The employment rates for the young ages are overrepresenting the cohorts born more recently, while those for the older ages overrepresent cohorts born in earlier years.

# _Two types of variables: With and without age profiles_

The first issue means that the estimates for the oldest and youngest cohorts (and ages) should be taken with a grain of salt. In fact, when summarizing the long-term evolution of women’s economic engagement

> 9 See table A2 in the annex for details about the availability of DHS and WVS for the countries covered in this study.

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