up together, going to the same neighborhood school, socializing with the same cohorts of peers. Thus, we would like to exclude the siblings who grew up far apart in dierent times (or dierent places like a sibling who left home for a boarding school), and might have experienced dierent family, neighborhood, and school environment when growing up.<sup>11</sup> This includes, for example, much older children of the household head who are not coresident because they are in college or working in a dierent location after graduating from college. We restrict the age of children to 16-28 years in the survey year of a wave for our analysis.<sup>12</sup> This also ensures that possible sample truncation because of coresidency is small.<sup>13</sup> We provide estimates of sibling correlation in educational attainment for 53 developing countries and trace out the evolution of intergenerational educational mobility for three decade-wise birth cohorts (1970s to 1990s).<sup>14</sup> We use 230 waves of Demographic and Health Surveys (DHS) to build a comparable data base across countries. This ensures that the estimates are not dierent because of dierences in survey instruments or measurement of schooling (years of schooling vs. categorical). Another important advantage is that the information on parental education is not based on recall of the children, and thus are likely to contain much less measurement error. The advantages of the Demographic and Health Surveys for cross-country comparison studies have been well appreciated in the recent literature (see, for example, Bhalotra and Rawlings (2013), and Lleras-Muney et al. (2022)).

Another important contribution of this paper is that we take advantage of recent methodological advances to provide a credible answer to a long-standing policy-relevant question:

> 11This is supported by our cohort-wise estimates that show signicant changes in sibling correlation over time. Nicoletti and Rabe (2013) provide evidence that sibling correlation in academic attainment declines sharply as more distantly spaced siblings are considered.

> 12Thus, the maximum age dierence between siblings in our sample can be 12 years. Bingley and Cappellari (2019) also use a maximum of 12 years age gap to dene their sample. This implies that the cohort variations in our analysis primarily come from dierent survey years of dierent waves of DHS for a given country. We note here that the main conclusions are robust to alternative age ranges. These alternative results are available from the authors.

> 13It is well understood in the literature that the degree of sample truncation due to coresidency declines as we focus on younger children. See, for example, the recent inuential analysis on intergenerational educational mobility in Africa by Alesina et al. (2021) where they use census data (coresident samples) from IPUMS and restrict age of children to 18-24.

14The only other paper we are aware of that provides estimates of sibling correlation for multiple developing countries is Dahan and Gaviria (2001) who report estimates for 16 Latin American countries. But their estimates are not comparable to the other estimates available in the literature because they use a dierent measure. They focus on the educational failure (lack of grade progression) of a child rather than educational attainment.

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