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The impact of school entry laws on female education and teenage fertility


The literature on school entry laws in the USA suggests that school entry laws affect educational success in offsetting ways, where students born after the entry cutoff date tend to achieve higher test scores yet complete fewer years of schooling. However, the laws have little impact on a number of other outcomes, including fertility, wages, and employment. This paper has two goals. First, using a North Carolina dataset which individually links birth certificate data to school administrative records, it more fully explores the opposite impacts on educational success than previous papers and investigates why students born after the cutoff date have lower educational attainment despite doing better in school. Second, it investigates the impact of school entry laws on teenage fertility and provides some evidence that test scores and years of education have negative impacts, but that these impacts offset each other in the case of school entry laws.

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  1. Black et al. (2011) find no impact of Norwegian school entry laws on completed schooling at age 27 or older. There are two possible explanations for their findings: (a) the effects of school entry age are weaker in Norway than in the USA, and (b) while individuals born after the cutoff date are more likely to drop out of high school, they are also more likely to have more years of higher education (Bedard and Dhuey 2006), potentially due to better performance in school.

  2. Both Dobkin and Ferreira (2010) and McCrary and Royer (2011) use data from California and Texas. However, Dobkin and Ferreira’s sample is drawn from males and females who completed the 2000 Decennial Census Long Form (around 15 % of the population), while McCrary and Royer’s sample is based on females who gave birth before age 25.

  3. While McCrary and Royer (2011) examine the impact of school entry on female fertility up to age 23, I focus on teenage motherhood for two reasons. First, the dataset used in this paper does not contain information on fertility outcomes after age 19 for most cohorts. Second, fertility outcomes are less reliably measured at older ages in this dataset, since there is less evidence that the individuals have not moved out of state.

  4. An important advantage of using school entry laws rather than compulsory schooling laws is that the “treatment” is targeted at individuals rather than at entire cohorts, so that there are no changes in labor market conditions which could account for the observed differences in outcomes (Black et al. 2011; Cook and Kang 2013).

  5. While Cook and Kang (2013) show using a similar dataset that the optimal bandwidth for the regression discontinuity analysis is around 10 according to the “rule of thumb” method proposed by Fan and Gijbels (1992), they also show that the results are qualitatively robust to bandwidth choice and present their results using the 50-day bandwidth.

  6. Unfortunately, this dataset does not contain information on high school graduation or GED certification. Instead, grade attendance indicated by student registration is used as a proxy for educational attainment.

  7. While most of the individuals in this sample attended third to eighth grade between 1996 and 2007, estimates of school poverty rates, proportions of school students who passed end-of-grade tests, and the number of crimes per 100 school students are based on 2005–2010, 2001–2010, and 2004–2010 data, respectively; estimates of school district poverty rates, proportions of school district students who passed end-of-grade tests, and proportions of school district students in single-parent homes are based on 2004, 2002–2010, and 2004 data, respectively. Around 0.4 % of observations have missing values for school or school district characteristics; for these observations, missing values are imputed using other school and school district characteristics.

  8. Since test scores are represented by their Z scores relative to the scores of all students who took the test, the average test score should be zero if the sample is representative of North Carolina public school students. For this dataset, reading scores are slightly higher than zero because only non-Hispanic white and black girls born in North Carolina, who are unlikely to be taking English as a second language, are included.

  9. The smaller test score gaps in eighth grade may be partly due to the fact that girls born before the cutoff date are more likely to be retained between ages 11 and 15 (Cook and Kang 2013), reducing the “intention-to-treat” effects.

  10. Cook and Kang (2013) find that school entry age reduces youth criminality among males at younger ages, but increases it at older ages, and argue that the effect of higher test scores may dominate at younger ages while the effect of fewer years of education may dominate past the minimum dropout age. Unlike the authors, I find no differences between outcomes at younger and older ages, possibly due to the low incidence of childbearing at younger ages.

  11. For the IV regression analysis, student poverty rates at the school and school district levels are represented by binary rather than continuous variables, which take the value of 1 if the poverty rate is above the median level (around 55 and 16 % at the school and school district levels, respectively), since this specification yields the largest Cragg–Donald statistic and, hence, lowest risk of instrument weakness. The IV regression results are similar whether binary or continuous variables are used.

  12. For the preferred specification (which uses 12th grade attendance and 3rd grade test scores for the endogenous variables), the Cragg–Donald statistics are 4.291 for the 45-day bandwidth, 5.922 for the 60-day bandwidth, and 8.358 for the 75-day bandwidth. Although all three statistics exceed the critical value at the 10 % significance level (3.78 for the LIML specification), the risk of weak instruments is higher for the smaller bandwidths, which may bias the results downward toward OLS estimates of the impact of test scores and educational attainment (−0.023 and −0.178, and −0.023 and −0.175 for the 45- and 60-day bandwidths, respectively).

  13. For instance, using data from the 1979 National Longitudinal Survey of Youth, Upchurch (1993) finds that 56.8 % of women who dropped out of high school and had a teenage birth did so at least 8 months after dropping out (n = 468). Using the same dataset, Mott and Marsiglio (1985) find that among women aged 20–26, only 6 % gave birth prior to dropping out or graduating from high school (another 3 % were pregnant) and that among those who dropped out of school, 29 % were pregnant or had given birth before dropping out, and 44 % gave birth only after dropping out.

  14. Household incomes are likely to be higher not only due to higher own earnings but also to higher spousal earnings since education is associated with more stable marriages (Isen and Stevenson 2010) with better educated partners (Schwartz and Mare 2005).

  15. Black et al. (2008) find that compulsory schooling laws continue to affect childbearing at older ages when the women are not legally required to be in school, suggesting that the impact of educational attainment does not operate solely through the “incarceration effect,” whereas DeCicca and Krashinsky (2015) find the opposite.

  16. Absenteeism data are available only up to age 11 for the cohorts born in 1987–1989 since the data are only collected up to and including 2000. The variable has four categories (absent for 7 days or less, absent for 8–14 days, absent for 15–21 days, and absent for more than 21 days during the school year). For the 75-day bandwidth sample, 126 observations (0.38 %) have missing data on absenteeism.

  17. These estimates are based only on data from the first two cohorts (born in 1987–1988) since data on fertility at age 20 are not available for the third cohort (born in 1989). The similarity of the results for teenage motherhood and childbearing at age 20 provides some preliminary evidence for the argument that the negative impact of educational attainment on teenage motherhood is due to increased human capital accumulation rather than an “incarceration effect,” consistent with Black et al. (2008).


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This project is supported by funding from the Lee Kuan Yew School of Public Policy, National University of Singapore. I thank Philip J. Cook, M. Giovanna Merli, S. Philip Morgan, and Seth G. Sanders for their helpful comments. I also thank the anonymous reviewers for the Journal of Population Economics for their helpful comments and suggestions.

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Correspondence to Poh Lin Tan.

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Table 8 Maternal characteristics in North Carolina, 1987–1992

A2: Advantages of the dataset for the paper’s purposes

In addition to containing information on each individual’s performance in end-of-grade tests in the third and eighth grades and years of education completed by age 20, this dataset has a number of useful features for studying the impact of school entry laws on educational outcomes and teenage fertility.

First, unlike previous studies, this dataset uses individuals’ administrative school records up to age 20 rather than self-reported age and education data from birth certificates, which are not only higher quality but are also more representative of individuals’ final educational attainment since the majority of girls who give birth at school ages eventually return and graduate from high school (Mott and Marsiglio 1985; Upchurch and MacCarthy 1990). Comparing data from administrative school records and data from birth certificates for mothers aged 18 or below in this dataset, I find that while actual age and self-reported age on birth certificates are almost always identical (99.2 %), there is substantial disagreement between administrative records of grades attended and self-reported years of education. In particular, administrative records of number of grades attended exceed self-reported years of education for 40.3 % of observations (and fall below self-reported years of education for another 19.7 %, possibly due to private schooling or over-reporting).

Second, this dataset excludes girls who did not attend in-state public school and are hence more likely to have moved out of state. Since births to these girls are more likely to be unobserved, excluding these observations from the sample reduces potential downward bias on birth probabilities. Moreover, data on teenage childbearing outcomes are available at the individual rather than the cohort level. Hence, this dataset allows for more unbiased and precise estimation of the impact of school entry age on early childbearing.

Third, this dataset includes all women from six birth cohorts who attended public school in North Carolina between third grade and age 15 rather than only women who have had a live birth. Hence, the analysis of the impact of school entry age on educational success in this paper applies to a relatively broad socioeconomic spectrum.

Fourth, this dataset contains detailed information about characteristics at the individual and school and school district levels, allowing for a more effective search for heterogeneous treatment effects.

A3: Lower compliance rates among women born before the cutoff date

Among women born up to 60 days before the cutoff date, 77.5 % start third grade at age 8 together with the majority of their birth cohort, with 21.7 and 0.8 % starting at ages 9 and 10, respectively. There are two plausible reasons for why so many women born just before the cutoff date start third grade at age 9 rather than at age 8. First, parents may feel that their children are still too young to start kindergarten. Second, women born just before the cutoff date tend to have much weaker school performance and are hence more likely to be held back a year in kindergarten, first, or second grade.

Using nationally representative data, Bedard and Dhuey (2006) find that the first reason accounts for 57.4 % of students born up to 30 days before state cutoff dates who enter third grade at ages 9 or older, while the second reason accounts for 42.6 % (in their sample, 41.4 % of students born before the cutoff date enter third grade at higher-than-expected ages, which is substantially higher than in this paper, possibly due to their narrower window and the inclusion of boys, who are more likely to be held back).

On the other hand, statistics on retention rates between kindergarten and third grade in North Carolina public schools suggest that the second reason is likely to be more important. According to the Kindergarten Readiness Issue Group (2003), the probability of being retained between kindergarten and third grade rose from around 9 % in 1991–1992 to around 17 % in 2001–2002, possibly reflecting the large increase in the proportion of students from Hispanic immigrant families during this period. Since only 11.6 % of women in this dataset start third grade at higher-than-expected ages, a majority of them are likely to have been retained at an early age.

Table 9 Impact of test scores and years of education on teenage childbearing, with excluded instruments
Table 10 IV regression first-stage and diagnostic test results
Table 11 IV regression balancing test results
Table 12 Maternal characteristics at time of birth by birth month

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Tan, P.L. The impact of school entry laws on female education and teenage fertility. J Popul Econ 30, 503–536 (2017).

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  • Education
  • Teenage fertility
  • Quasi-experimental
  • Season of birth

JEL classification

  • J13
  • J18
  • J24