public primary school expansion may result in crowding out of girls at the post primary levels.

For our empirical analysis, we use the full count census data from census 2000 (BPS, Government of Indonesia) and follow closely the dierence-in-dierence (DiD) strategy developed by Duo (2001, 2004).<sup>10</sup> The large data set is especially important for our analysis because we follow Duo (2001) to dene the treatment (Inpres) and comparison (pre-Inpres) groups of children in a narrow window of age cohorts (5 years). Even with this cohorts restriction, our estimation sample consists of 2,048,164 father-child pairs. We provide evidence that the estimates from the census data do not suer from any substantial bias relative to a widely used data set for estimating intergenerational eects in Indonesia: the Indonesia Family Life Survey (IFLS). IFLS includes information on nonresident household members, and thus is not subject to sample truncation.<sup>11</sup> We also report estimates using an inverse probability weighting (IPW) scheme that corrects for biases due to possible nonrandom sample truncation in census data even though such truncation bias seems small in our context.<sup>12</sup> The evidence suggests ve key conclusions. First, the conditional expectation function (henceforth CEF) for both the Inpres and pre-Inpres cohorts is concave when we estimate the inuence of a father's education (years of schooling) on the probability of primary completion by children. The Inpres schools had a positive eect on the intercept, a negative eect on the linear term, and a positive eect on the quadratic term, and this sign pattern holds across gender. The primary completion estimates thus suggest that the children from the most disadvantaged background (fathers with no schooling) enjoyed higher relative mobility (negative eect on the linear term) and absolute mobility (positive eect on the intercept).<sup>13</sup> In our application, the standard linear model underestimates substantially the improvements in 10The inuential contribution by Chetty et al. (2014) highlighted the advantages of big data in understanding intergenerational economic mobility. Card et al. (2022) use 1940 census data to analyze the eects of teacher quality on intergenerational educational mobility of Black children in the United States at the beginning of the 20th century. A number of recent papers on intergenerational educational mobility in Africa use the 10 percent sample of the census available through IPUMS; see, for example, Alesina et al. (2021) and Azomahou and Yitbarek (2021).

> 11We do not use IFLS for our analysis because the sample size becomes too small once we restrict to the relevant cohorts. There are fewer than 250 household level observations available for some of our analysis where some districts have only 2 households in the sample.

> 12Nicoletti and Francesconi (2006) provide evidence that IPW performs better than Heckman selection correction in correcting coresidency bias in intergenerational mobility analysis.

> 13The intercept term represents the conditional expected years of schooling, a measure of absolute mobility, for children of fathers with no schooling. The linear term is the slope of the mobility CEF evaluated at father's zero schooling, and the slope of the mobility CEF is a measure of relative mobility.

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