The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
The Nonequivalence of High School Equivalents
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This paper analyzes the causes and consequences of the growing proportion of high-school-certified persons who achieve that status by exam certification rather than through high school graduation.Exam-certified high school equivalents are statistically indistinguishable from high school dropouts.Both dropouts and exam-certified equivalents have comparably poor wages, earnings, hours of work, unemployment experiences and job tenure.This is so whether or not ability measures are used to control for differences.Whatever differences are found among exam-certified equivalents, high school dropouts and high school graduates are accounted for by their years of schooling completed.There is no cheap substitute for schooling.The only payoff to exam certification arises from its value in opening post-secondary schooling and training opportunities.However, exam-certified equivalents receive lower returns to most forms of post-secondary education and training.We also discuss the political economy of the recent rapid growth of exam certification.There has been growth in direct government subsidies to adult basic education programs that feature exam certification as an output.In addition, there has been growth in government subsidies to post-secondary schooling programs that require certification in order to qualify for benefits.These sources account for the rapid growth in the use of exam certification in the face of the low economic returns to it.
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Cites
Sample Selection Bias as a Specification Error
Heckman, J. J. (1979) · Econometrica · doi:10.2307/1912352 · Cited by 28,779
Self-citationThe Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Schooling, Experience, and Earnings
Mincer, J. (1974) · RePEc: Research Papers in Economics · Cited by 6,840
Changes in Relative Wages, 1963-1987: Supply and Demand Factors
Katz, L. F., & Murphy, K. M. (1992) · The Quarterly Journal of Economics · doi:10.2307/2118323 · Cited by 4,793
Mathematical Statistics: Basic Ideas and Selected Topics.
Wachter, K. W., Bickel, P. J., & Doksum, K. A. (1979) · Journal of the American Statistical Association · doi:10.2307/2286373 · Cited by 1,886
MacKinnon, J. G., & White, H. (1985) · Journal of Econometrics · doi:10.1016/0304-4076(85)90158-7 · Cited by 1,879
Politics, markets, and America's schools
(1991) · Choice Reviews Online · doi:10.5860/choice.28-3405 · Cited by 1,837
Heckman, J. J., & Walker, J. R. (1990) · Econometrica · doi:10.2307/2938322 · Cited by 423
Self-citationThe Moment Generating Function of the Truncated Multi-Normal Distribution
Tallis, G. M. (1961) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1961.tb00408.x · Cited by 352
(1922) · Journal of Education · doi:10.1177/002205742209602009 · Cited by 273
Cited by
The importance of noncognitive skills: lessons from the GED testing program
Heckman, J. J., & Rubinstein, Y. (2001) · American Economic Review · doi:10.1257/aer.91.2.145 · Cited by 1,488
Self-citationDropping Out of Middle School: A Multilevel Analysis of Students and Schools
Rumberger, R. W. (1995) · American Educational Research Journal · doi:10.3102/00028312032003583 · Cited by 1,251
HECKMAN, J. J. (2008) · Economic Inquiry · doi:10.1111/j.1465-7295.2008.00163.x · Cited by 956
Self-citationCognitive Skills and Noncognitive Traits and Behaviors in Stratification Processes
Farkas, G. (2003) · Annual Review of Sociology · doi:10.1146/annurev.soc.29.010202.100023 · Cited by 620
The American High School Graduation Rate: Trends and Levels
Heckman, J. J., & LaFontaine, P. A. (2010) · Review of Economics and Statistics · doi:10.1162/rest.2010.12366 · Cited by 568
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Metadata
- Authors
- Cameron, S., & Heckman, J.
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 1991-08
- OpenAlex
- W3121749185
- Cited by
- 422
- References
- 15
- Type
- report
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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