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Group-Average Observables as Controls for Sorting on Unobservables When Estimating Group Treatment Effects: the Case of School and Neighborhood Effects

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  • Joseph G. Altonji
  • Richard K. Mansfield

Abstract

We consider the classic problem of estimating group treatment effects when individuals sort based on observed and unobserved characteristics that affect the outcome. Using a standard choice model, we show that controlling for group averages of observed individual characteristics potentially absorbs all the across-group variation in unobservable individual characteristics. We use this insight to bound the treatment effect variance of school systems and associated neighborhoods for various outcomes. Across four datasets, our most conservative estimates indicate that a 90th versus 10th percentile school system increases the high school graduation probability by between 0.047 and 0.085 and increases the college enrollment probability by between 0.11 and 0.13. We also find large effects on adult earnings. We discuss a number of other applications of our methodology, including measurement of teacher value-added.

Suggested Citation

  • Joseph G. Altonji & Richard K. Mansfield, 2014. "Group-Average Observables as Controls for Sorting on Unobservables When Estimating Group Treatment Effects: the Case of School and Neighborhood Effects," NBER Working Papers 20781, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:20781
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    References listed on IDEAS

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    1. Jesse Rothstein, 2009. "Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables," Education Finance and Policy, MIT Press, vol. 4(4), pages 537-571, October.
    2. Dionissi Aliprantis, 2011. "Assessing the evidence on neighborhood effects from moving to opportunity," Working Papers (Old Series) 1101, Federal Reserve Bank of Cleveland.
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    Cited by:

    1. Bryan S. Graham, 2018. "Identifying and Estimating Neighborhood Effects," Journal of Economic Literature, American Economic Association, vol. 56(2), pages 450-500, June.
    2. Dante Contreras & Jorge Rodríguez & Sergio Urzúa, 2019. "The Return to Private Education: Evidence from School-to-Work Transitions," Working Papers wp479, University of Chile, Department of Economics.
    3. Jean-William Laliberté, "undated". "Long-term Contextual Effects in Education: Schools and Neighborhoods," Working Papers 2019-01, Department of Economics, University of Calgary.
    4. Scott E. Carrell & Michal Kurlaender, 2016. "Estimating the Productivity of Community Colleges in Paving the Road to Four-Year Success," NBER Working Papers 22904, National Bureau of Economic Research, Inc.
    5. Díaz, Juan & Sánchez, Rafael & Villarroel, Gabriel & Villena, Mauricio G., 2021. "Effects of Measures of Teachers' Quality on Tertiary Education Attendance: Evaluation Tests versus Value Added," IZA Discussion Papers 14277, Institute of Labor Economics (IZA).
    6. Hellerstein, Judith K. & Kutzbach, Mark J. & Neumark, David, 2019. "Labor market networks and recovery from mass layoffs: Evidence from the Great Recession period," Journal of Urban Economics, Elsevier, vol. 113(C).
    7. Jonathan Smith & Kevin Stange, 2016. "A New Measure of College Quality to Study the Effects of College Sector and Peers on Degree Attainment," Education Finance and Policy, MIT Press, vol. 11(4), pages 369-403, Fall.
    8. David Slichter, 2023. "The employment effects of the minimum wage: A selection ratio approach to measuring treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 334-357, April.
    9. Cook, Jason B. & Mansfield, Richard K., 2016. "Task-specific experience and task-specific talent: Decomposing the productivity of high school teachers," Journal of Public Economics, Elsevier, vol. 140(C), pages 51-72.
    10. Eleanor Jawon Choi & Hyungsik Roger Moon & Geert Ridder, 2019. "Within-District School Lotteries, District Selection, and the Average Partial Effects of School Inputs," Korean Economic Review, Korean Economic Association, vol. 35, pages 275-306.
    11. Nidhiya Menon & Yana van der Meulen Rodgers, 2017. "The Impact of the Minimum Wage on Male and Female Employment and Earnings in India," Asian Development Review, MIT Press, vol. 34(1), pages 28-64, March.
    12. Scott E. Carrell & Michal Kurlaender, 2018. "Estimating the Productivity of Community Colleges in Paving the Road to Four-Year College Success," NBER Chapters, in: Productivity in Higher Education, pages 291-315, National Bureau of Economic Research, Inc.
    13. Dmitry Arkhangelsky & Guido Imbens, 2018. "Fixed Effects and the Generalized Mundlak Estimator," Papers 1807.02099, arXiv.org, revised Aug 2023.
    14. Jo Blanden & Kirstine Hansen & Sandra McNally, 2017. "Quality in early years settings and children’s school achievement," CEP Discussion Papers dp1468, Centre for Economic Performance, LSE.
    15. Evan Riehl & Juan E. Saavedra & Miguel Urquiola, 2018. "Learning and Earning: An Approximation to College Value Added in Two Dimensions," NBER Chapters, in: Productivity in Higher Education, pages 105-132, National Bureau of Economic Research, Inc.

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    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • I20 - Health, Education, and Welfare - - Education - - - General
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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