“adoptive” family, actually increases the magnitude and the significance of these coefficients. It is probably due to the fact that ignoring the very strong link between type of relationship in the new family and schooling was obfuscating the actual mechanism of intergenerational transmission of human capital.

An additional way to control for non-random placement is to include the schooling level of the biological parents: if biological children from educated parents are placed in other families with high education levels, controlling for the biological parents’ education will correct the bias. Since the EICV contains this information for most children—see the reduction is the sample size and therefore in power—this strategy is followed in column 6. This approach also suggests a very intuitive comparison between the nature and nurture components of the intergenerational transmission of human capital.<sup>9</sup> In panel A, both coefficients on the schooling of “adoptive” parents remain significant and their magnitude is comparable with column 5. In panel B, however, significance is only achieved for the male head of the household. What is striking about both panels is the strongly positive and significant coefficient on the education of the biological mother. Also very surprising the coefficient on the biological father’s education; although positive, it is not significant<sup>10</sup> .

It might be argued that including children identified as domestic employees of the household in the samples was not appropriate since it is clear that the family has no particular incentives to improve their schooling outcomes. Columns 7 and 8 acknowledge this by repeating columns 5 and 6 with samples from which all children employed as domestics workers have been removed. There is no changes in the significance of the coefficient on the education of “adoptive” parents, except that the coefficient for the male with the highest education in panel A is now only significant at the 10 percent confidence level, but the magnitude of most coefficients increases quite substantially. This is a sign that including domestic employees in the regressions might indeed have obfuscated some relationships.

Table 4 provides a separate analysis for boys and girls in order to investigate whether the mechanisms of intergenerational transmission of human capital are different across genders. The structure of the table and the regressions are similar to table 3. The regressions of columns 6 and 8 (without domestic employees) have been run separately for boys and girls. The results are very interesting and different than in table 2 among biological children when nature and nurture effect where confounded. Once the nurture component is isolated, for boys, the only significant coefficient are those on the “adoptive” father’s education, while for girls, it is only the “adoptive” mother’s education that seems to matter, at least in panel A, where it is significant. A similar conclusion can be reached by comparing the magnitudes of the coefficients: the “adoptive” father’s education has a strong effect for boys, the “adoptive”

> 9 This intuitive comparison is not valid, however, if adoption did not take place immediately after birth, which is often likely in Rwanda.

> 10 One potential explanation would be that many of the “adoptions” occurred several years after birth. In that case, the coefficient for the biological parents includes both a genetic and an environmental effect (for the years prior to adoption). But, the years prior to “adoption” will tend to be the early childhood years, where the mother’s impact is arguably more important that the father’s impact. This would explain both the very strong effect for the biological mother and the non-significant effect for the biological father.

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