IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v39y2014i5p333-367.html
   My bibliography  Save this article

Composition, Context, and Endogeneity in School and Teacher Comparisons

Author

Listed:
  • Katherine E. Castellano
  • Sophia Rabe-Hesketh
  • Anders Skrondal

Abstract

Investigations of the effects of schools (or teachers) on student achievement focus on either (1) individual school effects, such as value-added analyses, or (2) school-type effects, such as comparisons of charter and public schools. Controlling for school composition by including student covariates is critical for valid estimation of either kind of school effect. Student covariates often have different effects between schools than within schools. Econometricians typically attribute such differences to a form of endogeneity , specifically, “Level-2 endogeneity,†or the confounding of student covariates with unobserved school characteristics, whereas education researchers primarily interpret the differences as contextual effects or the effects of collective peer attributes on individual student achievement. This article considers both and makes connections between the econometric and education research literatures. We show that the Hausman and Taylor approach from panel data econometrics can be used for valid estimation of individual school or school-type effects when there is only Level-2 endogeneity but can lead to bias when there are also contextual or peer effects. In contrast, contextual effects are typically estimated by including school means of student covariates in addition to the student-level covariates (equivalent to the Mundlak device), but this leads to biased school comparisons in the presence of Level-2 endogeneity. We interpret the estimates from these two competing estimators in terms of the “Type A†and “Type B†school effects defined by Raudenbush and Willms and show that both estimators are preferable to the common group-mean-centering approach.

Suggested Citation

  • Katherine E. Castellano & Sophia Rabe-Hesketh & Anders Skrondal, 2014. "Composition, Context, and Endogeneity in School and Teacher Comparisons," Journal of Educational and Behavioral Statistics, , vol. 39(5), pages 333-367, October.
  • Handle: RePEc:sae:jedbes:v:39:y:2014:i:5:p:333-367
    DOI: 10.3102/1076998614547576
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998614547576
    Download Restriction: no

    File URL: https://libkey.io/10.3102/1076998614547576?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
    ---><---

    References listed on IDEAS

    as
    1. Jacob M. Markman & Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 2003. "Does peer ability affect student achievement?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(5), pages 527-544.
    2. 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.
    3. Sacerdote, Bruce, 2011. "Peer Effects in Education: How Might They Work, How Big Are They and How Much Do We Know Thus Far?," 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 4, pages 249-277, Elsevier.
    4. Buddin, Richard, 2011. "Measuring teacher and school effectiveness at improving student achievement in Los Angeles elementary schools," MPRA Paper 31963, University Library of Munich, Germany.
    5. Vincent Boucher & Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2014. "Do Peers Affect Student Achievement? Evidence From Canada Using Group Size Variation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 91-109, January.
    6. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    7. Greene, William, 2011. "Fixed Effects Vector Decomposition: A Magical Solution to the Problem of Time-Invariant Variables in Fixed Effects Models?," Political Analysis, Cambridge University Press, vol. 19(2), pages 135-146, April.
    8. Peter Ebbes & Ulf Böckenholt & Michel Wedel, 2004. "Regressor and random‐effects dependencies in multilevel models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 161-178, May.
    9. Buddin, Richard & Zamarro, Gema, 2009. "Teacher qualifications and student achievement in urban elementary schools," Journal of Urban Economics, Elsevier, vol. 66(2), pages 103-115, September.
    10. Anders Skrondal & Sophia Rabe‐Hesketh, 2009. "Prediction in multilevel generalized linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 659-687, June.
    11. Dan D. Goldhaber & Dominic J. Brewer, 1997. "Why Don't Schools and Teachers Seem to Matter? Assessing the Impact of Unobservables on Educational Productivity," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 505-523.
    12. Breusch, Trevor & Ward, Michael B. & Nguyen, Hoa Thi Minh & Kompas, Tom, 2011. "On the Fixed-Effects Vector Decomposition," Political Analysis, Cambridge University Press, vol. 19(2), pages 123-134, April.
    13. Saïd Hanchane & Tarek Mostafa, 2012. "Solving endogeneity problems in multilevel estimation: an example using education production functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1101-1114, November.
    14. Plümper, Thomas & Troeger, Vera E., 2007. "Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects," Political Analysis, Cambridge University Press, vol. 15(2), pages 124-139, April.
    15. Cory Koedel & Julian Betts, 2010. "Value Added to What? How a Ceiling in the Testing Instrument Influences Value-Added Estimation," Education Finance and Policy, MIT Press, vol. 5(1), pages 54-81, January.
    16. Jee-Seon Kim & Edward Frees, 2006. "Omitted Variables in Multilevel Models," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 659-690, December.
    17. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    18. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    19. Richard Buddin & Gema Zamarro, 2009. "Teacher Qualifications and Middle School Student Achievement," Working Papers 671, RAND Corporation.
    20. Sophia Rabe-Hesketh & Anders Skrondal, 2012. "Multilevel and Longitudinal Modeling Using Stata, 3rd Edition," Stata Press books, StataCorp LP, edition 3, number mimus2, March.
    21. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521405515.
    22. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    23. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    24. Jee-Seon Kim & Edward Frees, 2007. "Multilevel Modeling with Correlated Effects," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 505-533, December.
    25. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    26. Jonah E. Rockoff & Brian A. Jacob & Thomas J. Kane & Douglas O. Staiger, 2011. "Can You Recognize an Effective Teacher When You Recruit One?," Education Finance and Policy, MIT Press, vol. 6(1), pages 43-74, January.
    27. Yongyun Shin & Stephen W. Raudenbush, 2010. "A Latent Cluster-Mean Approach to the Contextual Effects Model With Missing Data," Journal of Educational and Behavioral Statistics, , vol. 35(1), pages 26-53, February.
    28. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models 2 volume set," Cambridge Books, Cambridge University Press, number 9780521478373, July.
    29. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-358, March.
    30. Richard Buddin & Gema Zamarro, 2009. "Teacher Qualifications and Middle School Student Achievement," Working Papers WR-671-IES, RAND Corporation.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lucy Prior & John Jerrim & Dave Thomson & George Leckie, 2021. "A review and evaluation of secondary school accountability in England: Statistical strengths, weaknesses, and challenges for 'Progress 8' raised by COVID-19," CEPEO Working Paper Series 21-04, UCL Centre for Education Policy and Equalising Opportunities, revised Apr 2021.
    2. Ceron, Francisco I. & Bol, Thijs & van de Werfhorst, Herman G., 2022. "The dynamics of achievement inequality: The role of performance and choice in Chile," International Journal of Educational Development, Elsevier, vol. 92(C).
    3. Daniela Rohrbach-Schmidt, 2019. "Putting Tasks to the Test: The Case of Germany," Social Inclusion, Cogitatio Press, vol. 7(3), pages 122-135.
    4. Chatoro, Marian & Mitra, Sovan & Pantelous, Athanasios A. & Shao, Jia, 2023. "Catastrophe bond pricing in the primary market: The issuer effect and pricing factors," International Review of Financial Analysis, Elsevier, vol. 85(C).
    5. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.
    6. Dave Thomson, 2016. "The Short Run Impact of the Building Schools for the Future Programme on Attainment at Key Stage 4," DoQSS Working Papers 16-07, Quantitative Social Science - UCL Social Research Institute, University College London.
    7. Gebremeskel Berhane Tesfay & Babatunde Abidoye, 2019. "Shocks in food availability and intra-household resources allocation: evidence on children nutrition outcomes in Ethiopia," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-21, December.
    8. de Assis, Maíra Macário & Pessoa, Milene Cristine & Gratão, Lucia Helena Almeida & do Carmo, Ariene Silva & Jardim, Mariana Zogbi & Cunha, Cristiane de Freitas & de Oliveira, Tatiana Resende Prado Ran, 2023. "Are the laws restricting the sale of food and beverages in school cafeterias associated with obesity in adolescents in Brazilian state capitals?," Food Policy, Elsevier, vol. 114(C).
    9. Burger, Kaspar, 2019. "The socio-spatial dimension of educational inequality: A comparative European analysis," MPRA Paper 95309, University Library of Munich, Germany, revised 2019.
    10. Lucy Prior & John Jerrim & Dave Thomson & George Leckie, 2021. "A review and evaluation of secondary school accountability in England: Statistical strengths, weaknesses, and challenges for ‘Progress 8’ raised by COVID-19," DoQSS Working Papers 21-12, Quantitative Social Science - UCL Social Research Institute, University College London.

    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. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.
    2. Aleksey Oshchepkov & Anna Shirokanova, 2020. "Multilevel Modeling For Economists: Why, When And How," HSE Working papers WP BRP 233/EC/2020, National Research University Higher School of Economics.
    3. Paul Kabaila & Rheanna Mainzer & Davide Farchione, 2017. "Conditional assessment of the impact of a Hausman pretest on confidence intervals," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(4), pages 240-262, November.
    4. Duncan McVicar & Julie Moschion & Chris Ryan, 2013. "Right Peer, Right Now? Endogenous Peer Effects and Achievement in Victorian Primary Schools," Melbourne Institute Working Paper Series wp2013n22, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Régis BRETON & Sébastien GALANTI & Christophe HURLIN & Anne-Gaël VAUBOURG, 2011. "Does the firm-analyst relationship matter in explaining analysts' earnings forecast errors?," LEO Working Papers / DR LEO 469, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    6. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 603-629, August.
    7. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    8. Bernhard C. Dannemann, 2020. "Peer Effects in Secondary Education: Evidence from the 2015 Trends in Mathematics and Science Study Based on Homophily," Working Papers V-428-20, University of Oldenburg, Department of Economics, revised Feb 2020.
    9. Tunçer, Coşkun, 2012. "Monetary sovereignty during the classical gold standard era: the Ottoman Empire and Europe, 1880-1913," Economic History Working Papers 44725, London School of Economics and Political Science, Department of Economic History.
    10. Chatelain, Jean-Bernard & Ralf, Kirsten, 2021. "Inference on time-invariant variables using panel data: A pretest estimator," Economic Modelling, Elsevier, vol. 97(C), pages 157-166.
    11. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    12. Angrist, Joshua D., 2014. "The perils of peer effects," Labour Economics, Elsevier, vol. 30(C), pages 98-108.
    13. Gershenson, Seth & Holt, Stephen B. & Papageorge, Nicholas W., 2015. "Who Believes in Me? The Effect of Student-Teacher Demographic Match on Teacher Expectations," IZA Discussion Papers 9202, Institute of Labor Economics (IZA).
    14. Wennberg, Karl & Norgren, Axel, 2021. "Models of Peer Effects in Education," Working Papers 21/3, Stockholm School of Economics, Center for Educational Leadership and Excellence.
    15. Tammuz Alraheb & Amine Tarazi, 2016. "Local Versus International Crises, Foreign Subsidiaries and Bank Stability: Evidence from the MENA Region," Working Papers hal-01270806, HAL.
    16. Thomas Cornelissen & Christian Dustmann & Uta Schönberg, 2017. "Peer Effects in the Workplace," American Economic Review, American Economic Association, vol. 107(2), pages 425-456, February.
    17. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    18. Pierre MANDON & Clément MATHONNAT, 2014. "Forms of Democracies and Financial Development," Working Papers 201421, CERDI.
    19. Cheti Nicoletti & Birgitta Rabe, 2019. "Sibling spillover effects in school achievement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 482-501, June.
    20. Arta Mulliqi & Nick Adnett & Mehtap Hisarciklilar & Artane Rizvanolli, 2018. "Human Capital and International Competitiveness in Europe, with Special Reference to Transition Economies," Eastern European Economics, Taylor & Francis Journals, vol. 56(6), pages 541-563, November.

    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:sae:jedbes:v:39:y:2014:i:5:p:333-367. 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: SAGE Publications (email available below). General contact details of provider: .

    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.