IDEAS home Printed from https://ideas.repec.org/a/bla/ecorec/v86y2010is1p18-21.html
   My bibliography  Save this article

Administrative Data and Economic Policy Evaluation

Author

Listed:
  • LORRAINE DEARDEN

Abstract

This article looks at the strengths and weaknesses of using administrative data for economic policy evaluation. It does this by looking at how school administrative data have been used to assess school effectiveness and the impact of month of birth on educational outcomes with varying degrees of success. It concludes that if there is some natural experiment in the way that education is delivered or an education initiative is introduced, then schools’ administrative data offer the opportunity of answering questions of extreme policy interest in a robust way – even without rich background information on the students and their families.

Suggested Citation

  • Lorraine Dearden, 2010. "Administrative Data and Economic Policy Evaluation," The Economic Record, The Economic Society of Australia, vol. 86(s1), pages 18-21, September.
  • Handle: RePEc:bla:ecorec:v:86:y:2010:i:s1:p:18-21
    DOI: 10.1111/j.1475-4932.2010.00666.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1475-4932.2010.00666.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1475-4932.2010.00666.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Haroon Chowdry & Claire Crawford & Lorraine Dearden & Alissa Goodman & Anna Vignoles, 2013. "Widening participation in higher education: analysis using linked administrative data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 431-457, February.
    2. Claire Crawford & Lorraine Dearden & Costas Meghir, 2010. "When you are born matters: the impact of date of birth on educational outcomes in England," IFS Working Papers W10/06, Institute for Fiscal Studies.
    3. George Leckie & Harvey Goldstein, 2009. "The limitations of using school league tables to inform school choice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(4), pages 835-851, October.
    4. Ladd, Helen F. & Walsh, Randall P., 2002. "Implementing value-added measures of school effectiveness: getting the incentives right," Economics of Education Review, Elsevier, vol. 21(1), pages 1-17, February.
    5. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arpino, Bruno & Varriale, Roberta, 2009. "Assessing the quality of institutions’ rankings obtained through multilevel linear regression models," MPRA Paper 19873, University Library of Munich, Germany.
    2. Bruno ARPINO & Roberta VARRIALE, 2010. "Assessing The Quality Of Institutions’ Rankings Obtained Through Multilevel Linear Regression Models," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(1(11)_Spr), pages 7-22.
    3. Sulis, Isabella & Giambona, Francesca & Porcu, Mariano, 2020. "Adjusted indicators of quality and equity for monitoring the education systems over time. Insights on EU15 countries from PISA surveys," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    4. Francesca Giambona & Mariano Porcu & Isabella Sulis, 2017. "Students Mobility: Assessing the Determinants of Attractiveness Across Competing Territorial Areas," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 1105-1132, September.
    5. Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.
    6. Devereux, Paul J. & Fan, Wen, 2011. "Earnings returns to the British education expansion," Economics of Education Review, Elsevier, vol. 30(6), pages 1153-1166.
    7. Isabella Sulis & Mariano Porcu, 2015. "Assessing Divergences in Mathematics and Reading Achievement in Italian Primary Schools: A Proposal of Adjusted Indicators of School Effectiveness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(2), pages 607-634, June.
    8. Isabella Sulis & Mariano Porcu & Vincenza Capursi, 2019. "On the Use of Student Evaluation of Teaching: A Longitudinal Analysis Combining Measurement Issues and Implications of the Exercise," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 1305-1331, April.
    9. George Leckie, 2022. "A celebration of Harvey Goldstein’s lifetime contributions: Memories of working with Harvey Goldstein on educational research and statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 758-762, July.
    10. Nicholas Tibor Longford, 2016. "Decision Theory Applied to Selecting the Winners, Ranking, and Classification," Journal of Educational and Behavioral Statistics, , vol. 41(4), pages 420-442, August.
    11. Rosenthal, Leslie, 2004. "Do school inspections improve school quality? Ofsted inspections and school examination results in the UK," Economics of Education Review, Elsevier, vol. 23(2), pages 143-151, April.
    12. Columbu, Silvia & Porcu, Mariano & Sulis, Isabella, 2021. "University choice and the attractiveness of the study area: Insights on the differences amongst degree programmes in Italy based on generalised mixed-effect models," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    13. Michele La Rocca & Maria Lucia Parrella & Ilaria Primerano & Isabella Sulis & Maria Prosperina Vitale, 2017. "An integrated strategy for the analysis of student evaluation of teaching: from descriptive measures to explanatory models," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 675-691, March.
    14. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    15. Arnaud Chevalier & Colm Harmon & Vincent O’ Sullivan & Ian Walker, 2013. "The impact of parental income and education on the schooling of their children," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-22, December.
    16. Tahir Andrabi & Jishnu Das & Asim Ijaz Khwaja & Tristan Zajonc, 2011. "Do Value-Added Estimates Add Value? Accounting for Learning Dynamics," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 29-54, July.
    17. Ron Diris, 2017. "Don't Hold Back? The Effect of Grade Retention on Student Achievement," Education Finance and Policy, MIT Press, vol. 12(3), pages 312-341, Summer.
    18. Peter M. White & David M. Lee, 2020. "Geographic Inequalities and Access to Higher Education: Is the Proximity to Higher Education Institution Associated with the Probability of Attendance in England?," Research in Higher Education, Springer;Association for Institutional Research, vol. 61(7), pages 825-848, November.
    19. Claire Crawford & Lorraine Dearden & Ellen Greaves, 2013. "Identifying the drivers of month of birth differences in educational attainment," DoQSS Working Papers 13-07, Quantitative Social Science - UCL Social Research Institute, University College London.
    20. George M. Holmes, "undated". "Teaching To The Test: Triage in the Classroom," Working Papers 0110, East Carolina University, Department of Economics.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I20 - Health, Education, and Welfare - - Education - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:ecorec:v:86:y:2010:i:s1:p:18-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/esausea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.