Sage Journals: Discover world-class research

Abstract

Despite the increasing spread of standardized assessments of student learning, longitudinal data on achievement data are still lacking in many countries. This article raises the following question: Can we exploit cross-sectional assessments held at different schooling stages to evaluate how achievement inequalities related to individual-ascribed characteristics develop over time? This is a highly policy relevant issue, as achievement inequalities may develop in substantially different ways across educational systems. We discuss the issues involved in estimating dynamic models from repeated cross-sectional surveys in this context; consistently with a simple learning accumulation model, we propose an imputed regression strategy that allows to “link” two surveys and deliver consistent estimates of the parameters of interest. We then apply the method to Italian achievement data of fifth and sixth graders and investigate how inequalities develop between primary and lower secondary school.

Keywords

achievement inequalities dynamic models pseudo-panel estimation repeated cross sections standardized assessments

Get full access to this article

View all access options for this article.

References

Ammermueller

2007. “PISA: What Makes the Difference? Explaining the Gap in Test Scores between Finland and Germany.” Empirical Economics 33:263–87.

Argentin

Martini

Romano

2012. “Giocare a scacchi aiuta a imparare la matematica? Evidenze da una sperimentazione controllata.” In Gli scacchi un gioco per crescere, edited by Trinchero

. Franco Angeli, Milano.

Barbieri

Rossetti

Sestito

. 2010. “The Determinants of Teacher Mobility. Evidence from a Panel of Italian Teachers.” Banca d’Italia Working Paper 761, Milan, Italy.

Betebenner

D. W.

Linn

. 2010. “Growth in Student Achievement: Issues of Measurement, Longitudinal Data Analysis, and Accountability.” Working Paper of the Centre for K-12 Assessment & Performance Management. Retrieved September 30, 2015, http://www.k12center.com/rsc/pdf/BetebennerandLinnPresenterSession1.pdf.

Card

Lemieux

. 1996. “Wage Dispersion, Returns to Skill, and Black-white Wage Differentials.” Journal of Econometrics 74:319–61.

Contini

(2014). “Cross-sectional Learning Assessments: Comparability of Regression Coefficients and Validity of Difference-in-difference Estimation to Evaluate Institutional Effects.” Working Paper 31/14, Department of Economics and Statistics, University of Turin, Turin, Italy.

Crowford

Dearden

Greaves

. 2013. “When You Are Born Matters. Evidence from England.” The Institute of Fiscal Studies, Report R80. Retrieved October 15, 2015 (http://www.ifs.org.uk/comms/r80.pdf).

Cunha

Heckman

J. J.

Lochner

Masterov

D. V.

. 2006. “Interpreting the Evidence on Life Cycle Skill Formation.” in Handbook of the Economics of Education, Chapter 12, edited by Hanushek

Welch

. Amsterdam, the Netherlands: North Holland.

Deaton

1985. “Panel Data from Time Series of Cross-sections.” Journal of Econometrics 30:109–26.

10.

De Simone

2013. “Render into Primary the Things Which Are Primary’s. Inherited and Fresh Learning Divides in Italian Lower Secondary Education.” Economics of Education Review 35:12–23.

11.

Fuchs

Woessmann

. 2007. “What Accounts for International Differences in Student Performance? A Re-examination Using PISA Data.” Empirical Economics 32:433–64.

12.

Fuller

W. A.

Hidiroglou

M. A.

. 1978. “Regression Estimation after Correcting for Attenuation.” Journal of the American Statistical Association 73:99–104.

13.

Goldstein

2010. Multilevel Statistical Models. 4th ed. West Sussex, UK: John Wiley and Sons.

14.

Goodman

Sibieta

Washbrook

. 2009. “Inequalities in Educational Outcomes among Children Aged 3 to 16.” Final report for the National Equality Panel, London, UK.

15.

Hanushek

E. A.

Woessmann

. 2006. “Does Educational Tracking Affect Performance and Equality? Differences-in-differences across Countries.” Economic Journal 116:C63–76.

16.

INVALSI (National Institute for the Evaluation of the Educational System). 2012. Le prove INVALSI 2011. Rapporto tecnico sulle caratteristiche delle prove INVALSI 2011. Rome, Italy: INVALSI. Retrieved October 15, 2015 (http://www.invalsi.it/areadati/SNV/10-11/Rapporto_tecnico_prove_invalsi_2011.pdf).

17.

Jerrim

Choi

. 2014. “The Mathematics Skills of School Children: How Does England Compare to the High Performing East Asian Jurisdictions?” Working Paper of the Barcelona Institute of Economics 2013/12.

18.

Lawlor

D. A.

Clark

Ronalds

Leon

D. A.

. 2006. “Season of Birth and Childhood Intelligence: Findings from the Aberdeen Children of the 1950s Cohort Study.” British Journal of Educational Psychology 76:481–99.

19.

Moffitt

1993. “Identification and Estimation of Dynamic Models with a Time Series of Repeated Cross-sections.” Journal of Econometrics 59:99–23.

20.

Mullis

I. V. S.

Martin

M. O.

Foy

Arora

. 2012a. TIMSS 2011 International Results in Mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

21.

Mullis

I. V. S.

Martin

M. O.

Foy

Drucker

K. T.

. 2012b. PIRLS 2011 International Results in reading. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

22.

OECD (Organization for Economic Cooperation and Development). 2010a. “PISA 2009 Results: What Students Know and Can Do. Student Performance in Reading, Mathematics and Science, Volume I.” Retrieved October 15, 2015 (http://dx.doi.org/10.1787/9789264091450-en).

23.

OECD (Organization for Economic Cooperation and Development). 2010b. “PISA 2009 Results: Overcoming Social Background. Equity in Learning Opportunities and Outcomes. Volume II.” Retrieved October 15, 2015 (http://dx.doi.org/10.1787/9789264091504-en).

24.

Quintano

Castellano

Longobardi

. 2009. “A Fuzzy Clustering Approach to Improve the Accuracy of Italian Student Data. An Experimental Procedure to Correct the Impact of Outliers on Assessment Test Scores.” Statistica & Applicazioni 7:149–71.

25.

Schuetz

Ursprung

H. W.

Woessman

. 2008. “Education Policy and Equality of Opportunity.” Kyklos 61:279–308.

26.

Singer

J. D.

Willett

J. B.

. 2003. Applied Longitudinal Data Analysis. Modelling Change and Event Occurrence. New York: Oxford University Press.

27.

Verbeek

Vella

. 2005. “Estimating Dynamic Models from Repeated Cross-sections.” Journal of Econometrics 127:83–102.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.54 MB