The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables with Moments from Two Samples
Abstract
We present a model in which compulsory school attendance laws, which typically require school attendance until a specified birthday, induce a relationship between years of schooling and age at school entry. Variation in school starting age created by children's dates of birth provides a natural experiment for estimating the effect of age at school entry. Because no large data set contains information on both age at school entry and educational attainment, we use an instrumental variables (IV) estimator with data derived from the 1960 and 1980 Censuses to estimate and test the age-at-entry/compulsory schooling model. In most IV applications, the two covariance matrices that form the estimator are constructed from the same sample. We use a method-of-moments framework to discuss IV estimators that combine moments from different data sets. In our application, quarter of birth dummies are the instrumental variables used to link the 1960 Census, from which age at school entry can be derived for one cohort of students, to the 1980 Census, which contains educational attainment for the same cohort of students. The results suggest that compulsory attendance laws constrain roughly 10% of students to stay in school.
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If an auxiliary sample containing {p, y, b} is available for first stage, it can be combined with the reduced form to form a two-sample instrumental variables estimator.
“If an auxiliary sample containing {p, y, b} is available for estimation of the first stage (11), the two can be combined to form a two-sample instrumental variables estimator (Angrist and Krueger, 1992).”
From Permanent Income and the Black-White Test Score Gap (2013) · cites this paper as Angrist and Krueger, 1992
Cites
A Note on Regression When There is Extraneous Information About One of the Coefficients
Durbin, J. (1953) · Journal of the American Statistical Association · doi:10.1080/01621459.1953.10501201 · Cited by 143 · see citing works
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Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230 · see citing works
Self-citationBias due to participant overlap in two‐sample Mendelian randomization
Burgess, S., Davies, N. M., & Thompson, S. G. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21998 · Cited by 1,624 · see citing works
Hartwig, F. P., Davies, N. M., Hemani, G., & Davey Smith, G. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyx028 · Cited by 860 · see citing works
Pre-Event Trends in the Panel Event-Study Design
Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019) · American Economic Review · doi:10.1257/aer.20180609 · Cited by 427 · see citing works
Inverse probability tilting for moment condition models with missing data
Graham, B. S., De Xavier Pinto, C. C., & Egel, D. (2012) · The Review of Economic Studies · doi:10.1093/restud/rdr047 · Cited by 174 · see citing works
Quasi-experimental designs for causal inference
Kim, Y., & Steiner, P. (2016) · Educational Psychologist · doi:10.1080/00461520.2016.1207177 · Cited by 135 · see citing works
“Angrist and Krueger (1992) is an example of a creative application of the design with birthday as the IV.”
BackgroundMendelian Randomization analysis of the causal effect of adiposity on hospital costs
Dixon, P., Hollingworth, W., Harrison, S., Davies, N. M., & Davey Smith, G. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2020.102300 · Cited by 48 · see citing works
Permanent Income and the Black-White Test Score Gap
Rothstein, J., & Wozny, N. (2013) · Journal of Human Resources · doi:10.3368/jhr.48.3.510 · Cited by 41 · see citing works
Constrained instruments and their application to Mendelian randomization with pleiotropy
Jiang, L., Oualkacha, K., Didelez, V., Ciampi, A., Rosa‐Neto, P., Benedet, A. L., Mathotaarachchi, S., Richards, J. B., Greenwood, C. M. T., & Initiative, A. F. T. A. D. N. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22184 · Cited by 23 · see citing works
- Authors
- Angrist, J. D., & Krueger, A. B.
- Affiliations
- National Bureau of Economic Research (US)
- ORCID
- Angrist, Joshua D.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1992-06
- Volume
- 87
- Issue
- 418
- Pages
- 328-336
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2015046142
- Cited by
- 463
- References
- 1
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 25 · 2023: 30 · 2022: 30 · 2021: 24 · 2020: 24 · 2019: 26 · 2018: 18 · 2017: 23 · 2016: 27 · 2015: 28 · 2014: 23 · 2013: 22 · 2012: 16
- FWCI
- 10.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Social Sciences › Education › School Choice and Performance
- Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
- Social Sciences › Education › Early Childhood Education and Development
- Keywords
- school entry age, educational attainment, birth date, instrumental variables, method of moments, years of schooling
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