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

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Abstract

We derive bounds for the average of math and language scores of elementary school students in Italy correcting for pervasive score manipulation. Information on the fraction of manipulated data is retrieved from a natural experiment that randomly assigns external monitors to schools. We show how bounds can be tightened imposing restrictions on the measurement properties of the manipulation indicator developed by the government agency charged with test administration and data collection. We additionally assume that manipulation is more likely in those classes at the lower end of the distribution of true scores. Our results show that regional rankings by academic performance are reversed once manipulation is properly taken into account.

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  • Erich Battistin & Michele De Nadai & Daniela Vuri, 2014. "Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools," CEIS Research Paper 329, Tor Vergata University, CEIS, revised 08 Sep 2014.
    • Handle: RePEc:rtv:ceisrp:329
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    Cited by:

    1. Bertoni, Marco & Brunello, Giorgio & De Benedetto, Marco Alberto & De Paola, Maria, 2019. "External Monitors and Score Manipulation in Italian Schools: Symptomatic Treatment or Cure?," IZA Discussion Papers 12591, Institute of Labor Economics (IZA).
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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. 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.

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    More about this item

    Keywords

    Corrupt sampling; Measurement error; Nonparametric bounds; Partial identification.;
    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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