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Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools

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  • Battistin, Erich
  • De Nadai, Michele
  • Vuri, Daniela

Abstract

We derive bounds on the distribution of math and language scores of elementary school students in Italy correcting for pervasive manipulation. A natural experiment that randomly assigns external monitors to schools is used to deal with endogeneity of manipulation as well as possible misclassification of the manipulation status. Bounds are obtained from properties of the statistical model used to detect classes with manipulated scores, and from restrictions on the relationship between manipulation and true scores. Our results show that score distributions are heavily affected by manipulating behavior, with regional rankings by academic performance being reversed once manipulation is taken into account.

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  • Battistin, Erich & De Nadai, Michele & Vuri, Daniela, 2016. "Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools," CEPR Discussion Papers 11667, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:11667
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    1. Anders Böhlmark & Mikael Lindahl, 2015. "Independent Schools and Long-run Educational Outcomes: Evidence from Sweden's Large-scale Voucher Reform," Economica, London School of Economics and Political Science, vol. 82(327), pages 508-551, July.
    2. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    3. Andrea Ichino & Giovanni Maggi, 2000. "Work Environment and Individual Background: Explaining Regional Shirking Differentials in a Large Italian Firm," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(3), pages 1057-1090.
    4. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 4, number 4, June.
    5. Aigner, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," Journal of Econometrics, Elsevier, vol. 1(1), pages 49-59, March.
    6. Joshua D. Angrist & Parag A. Pathak & Christopher R. Walters, 2013. "Explaining Charter School Effectiveness," American Economic Journal: Applied Economics, American Economic Association, vol. 5(4), pages 1-27, October.
    7. Joshua D. Angrist & Erich Battistin & Daniela Vuri, 2014. "In a Small Moment: Class Size and Moral Hazard in the Mezzogiorno," FBK-IRVAPP Working Papers 2014-04, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.
    8. Hanushek, Eric A. & Woessmann, Ludger, 2011. "The Economics of International Differences in Educational Achievement," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 2, pages 89-200, Elsevier.
    9. Kreider, Brent & Pepper, John V., 2011. "Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 49-60.
    10. Rebecca Diamond & Petra Persson, 2016. "The Long-term Consequences of Teacher Discretion in Grading of High-stakes Tests," NBER Working Papers 22207, National Bureau of Economic Research, Inc.
    11. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    12. Kreider, Brent & Pepper, John V., 2007. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 432-441, June.
    13. Brian A. Jacob & Steven D. Levitt, 2003. "Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 843-877.
    14. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    15. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.
    16. Battistin, Erich & De Nadai, Michele & Sianesi, Barbara, 2014. "Misreported schooling, multiple measures and returns to educational qualifications," Journal of Econometrics, Elsevier, vol. 181(2), pages 136-150.
    17. Battistin, Erich & Meroni, Elena Claudia, 2016. "Should we increase instruction time in low achieving schools? Evidence from Southern Italy," Economics of Education Review, Elsevier, vol. 55(C), pages 39-56.
    18. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, March.
    19. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    20. Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc.
    21. Bratti, Massimiliano & Checchi, Daniele & Filippin, Antonio, 2007. "Territorial Differences in Italian Students’ Mathematical Competencies: Evidence from PISA 2003," IZA Discussion Papers 2603, Institute of Labor Economics (IZA).
    22. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    23. Hahn, Jinyong, 1996. "A Note on Bootstrapping Generalized Method of Moments Estimators," Econometric Theory, Cambridge University Press, vol. 12(1), pages 187-197, March.
    24. Luigi Guiso & Paola Sapienza & Luigi Zingales, 2004. "The Role of Social Capital in Financial Development," American Economic Review, American Economic Association, vol. 94(3), pages 526-556, June.
    25. Brent Kreider & John V. Pepper & Craig Gundersen & Dean Jolliffe, 2012. "Identifying the Effects of SNAP (Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 958-975, September.
    26. Tommaso Nannicini & Andrea Stella & Guido Tabellini & Ugo Troiano, 2013. "Social Capital and Political Accountability," American Economic Journal: Economic Policy, American Economic Association, vol. 5(2), pages 222-250, May.
    27. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    28. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    29. Xiaohong Chen & Han Hong & Denis Nekipelov, 2011. "Nonlinear Models of Measurement Errors," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 901-937, December.
    30. Thomas S. Dee & Will Dobbie & Brian A. Jacob & Jonah Rockoff, 2019. "The Causes and Consequences of Test Score Manipulation: Evidence from the New York Regents Examinations," American Economic Journal: Applied Economics, American Economic Association, vol. 11(3), pages 382-423, July.
    31. Bertoni, Marco & Brunello, Giorgio & Rocco, Lorenzo, 2013. "When the cat is near, the mice won't play: The effect of external examiners in Italian schools," Journal of Public Economics, Elsevier, vol. 104(C), pages 65-77.
    32. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    33. Joshua D. Angrist & Erich Battistin & Daniela Vuri, 2017. "In a Small Moment: Class Size and Moral Hazard in the Italian Mezzogiorno," American Economic Journal: Applied Economics, American Economic Association, vol. 9(4), pages 216-249, October.
    34. Luigi Cannari & Francesco Nucci & Paolo Sestito, 2000. "Geographic labour mobility and the cost of housing: evidence from Italy," Applied Economics, Taylor & Francis Journals, vol. 32(14), pages 1899-1906.
    35. AIGNER, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," LIDAM Reprints CORE 130, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    36. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 3, number 3, June.
    37. Aprajit Mahajan, 2006. "Identification and Estimation of Regression Models with Misclassification," Econometrica, Econometric Society, vol. 74(3), pages 631-665, May.
    38. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    39. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    40. Robert P. Sherman & Jeff Dominitz, 2006. "Identification and estimation of bounds on school performance measures: a nonparametric analysis of a mixture model with verification," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1295-1326.
    41. Di Addario, Sabrina & Patacchini, Eleonora, 2008. "Wages and the City. Evidence from Italy," Labour Economics, Elsevier, vol. 15(5), pages 1040-1061, October.
    42. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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    Cited by:

    1. Santiago Pereda Fernández, 2016. "A new method for the correction of test scores manipulation," Temi di discussione (Economic working papers) 1047, Bank of Italy, Economic Research and International Relations Area.
    2. Marina Cavalieri & Massimo Finocchiaro Castro & Calogero Guccio, 2020. "Does the Fish Rot from the Head? Organised Crime and Educational Outcomes in Southern Italy," Working papers 97, Società Italiana di Economia Pubblica.
    3. Marco Bertoni & Giorgio Brunello & Marco Alberto De Benedetto & Maria De Paola, 2019. "External Monitors And Score Manipulation In Italian Schools: Symptomatic Treatment Or Cure?," Working Papers 201907, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    4. Veronica Minaya & Tommaso Agasisti, 2019. "Evaluating the Stability of School Performance Estimates over Time," Fiscal Studies, John Wiley & Sons, vol. 40(3), pages 401-425, September.
    5. Martin Gustafsson & Carol Nuga Deliwe, 2017. "Rotten apples or just apples and pears? Understanding patterns consistent with cheating in international test data," Working Papers 17/2017, Stellenbosch University, Department of Economics.
    6. Erich Battistin, 2016. "How manipulating test scores affects school accountability and student achievement," IZA World of Labor, Institute of Labor Economics (IZA), pages 295-295, September.
    7. Abhijeet Singh, 2024. "Improving Administrative Data at Scale: Experimental Evidence on Digital Testing in Indian Schools," The Economic Journal, Royal Economic Society, vol. 134(661), pages 2207-2223.
    8. Claudio Lucifora & Marco Tonello, 2020. "Monitoring and Sanctioning Cheating at School: What Works? Evidence from a National Evaluation Program," Journal of Human Capital, University of Chicago Press, vol. 14(4), pages 584-616.
    9. Cavalieri, Marina & Finocchiaro Castro, Massimo & Guccio, Calogero, 2023. "Organised crime and educational outcomes in Southern Italy: An empirical investigation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    10. Joshua D. Angrist & Erich Battistin & Daniela Vuri, 2017. "In a Small Moment: Class Size and Moral Hazard in the Italian Mezzogiorno," American Economic Journal: Applied Economics, American Economic Association, vol. 9(4), pages 216-249, October.
    11. Berkhout, Emilie & Pradhan, Menno & Rahmawati, & Suryadarma, Daniel & Swarnata, Arya, 2024. "Using technology to prevent fraud in high stakes national school examinations: Evidence from Indonesia," Journal of Development Economics, Elsevier, vol. 170(C).
    12. Carmen Aina & Massimiliano Bratti & Enrico Lippo, 2021. "Ranking high schools using university student performance in Italy," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(1), pages 293-321, April.

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    Keywords

    Measurement error; Non-parametric bounds; Partial identification; Score manipulation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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