Skip to main content
SpringerLink
Log in

The Use of Panel Data Models in Higher Education Policy Studies

  • Chapter
  • First Online:
Higher Education: Handbook of Theory and Research

Part of the book series: Higher Education: Handbook of Theory and Research ((HATR,volume 25))

Abstract

Panel data consist of multiple units observed at multiple time periods. Due to the availability of panel data and recent development in econometrics, panel data analysis has become an increasingly popular and important analytical tool in social and behavioral sciences; however, the use of panel data models in higher education research is a fairly recent phenomenon. This handbook chapter provides a discussion on panel data, focusing on different models of panel analysis (including for example, fixed and random effects models). Variations of panel data models such as pooled cross sections, difference-in-differences, and random coefficient models are also briefly discussed. In addition, two empirical examples are presented to illustrate the use of panel data models in higher education policy studies. The chapter concludes with a brief discussion of popular statistical software and procedures capable of performing panel analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Allison, P. D. (2009). Fixed Effects Regression Models. London: Sage Publications.

    Google Scholar 

  • Angrist, J. D., & Pischke, J. (2009). Most Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Archibald, R. B., & Feldman, D. H. (2008). Explaining increases in higher education costs. The Journal of Higher Education, 79(3), 268–295.

    Article  Google Scholar 

  • Arellano, M. (2003). Panel Data Econometrics. Oxford, UK: Oxford University Press.

    Book  Google Scholar 

  • Ashenfelter, O. A., & Card, D. (1985). Using the longitudinal structure of earnings to estimate the effect of training programs. The Review of Economics and Statistics, 67, 648–660.

    Article  Google Scholar 

  • Baltagi, B. H. (1988). An alternative heteroscedastic error components model. Econometric Theory, 4, 349–350.

    Article  Google Scholar 

  • Baltagi, B. H. (1989). The equivalence of the Boothe-MacKinnon and the Hausman specification tests in the context of panel data. Econometric Theory, 5, 454.

    Article  Google Scholar 

  • Baltagi, B. H. (2008). Econometric analysis of panel data (4th ed.). Chichester, UK: Wiley.

    Google Scholar 

  • Baltagi, B. H., & Griffin, J. M. (1988). A generalized error component model with heteroscedastic disturbances. International Economic Review, 29, 745–753.

    Article  Google Scholar 

  • Baumol, W. J. (1967). Macroeconomics of unbalanced growth: The anatomy of urban crisis. The American Economic Review, 57, 415–426.

    Google Scholar 

  • Baumol, W. J., & Bowen, W. G. (1966). Performing arts: The economic dilemma. New York: Twentieth Century Fund.

    Google Scholar 

  • Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1), 249–275.

    Article  Google Scholar 

  • Bowen, H. R. (1980). The costs of higher education: How much do colleges and universities spend per student and how much should they spend? San Francisco: Jossey-Bass

    Google Scholar 

  • Berry, F. S., & Berry, W. D. (2007). Innovation and diffusion models in policy research. In P. Sabatier (Ed.), Theories of the policy process (2nd ed.). Boulder, CO: Westview Press.

    Google Scholar 

  • Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47, 239–253.

    Article  Google Scholar 

  • Bryk, A., & Raudenbush, S. (1992). Hierarchical linear models. Newbury Park, CA: Sage.

    Google Scholar 

  • Campbell, R., & Siegel, B. N. (1967). The demand for higher education in the United State1919–1964. American Economic Review, 57(3), 482–494.

    Google Scholar 

  • Card, D. (2001). Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica, 69(5), 1127–1160.

    Article  Google Scholar 

  • Chamberlain, G. (1984). Panel data. In Z. Griliches & M. Intriligator (Eds.), Handbook of econometrics (Vol. II, pp. 1247–1318). Amsterdam: North-Holland.

    Google Scholar 

  • Chen, R., & DesJardins, S. (2008). Exploring the effects of financial aid on the gap in student dropout risks by income level. Research in Higher Education, 49, 1–18.

    Article  Google Scholar 

  • Cheslock, J. J., & Gianneschi, M. (2008). Replacing state appropriations with alternative revenue sources: The case of Voluntary Support. The Journal of Higher Education, 79(2), 208–229.

    Article  Google Scholar 

  • Chow, G. (1960). Tests equality between sets of coefficients in two linear regressions. Econometrica, 28, 591–605.

    Article  Google Scholar 

  • Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528.

    Article  Google Scholar 

  • Collins, L. M., & Horn J. L. (Eds.). (1991). Best methods for the analysis of change: Recent advances, unanswered questions, future directions. Washington, DC: American Psychological Association.

    Google Scholar 

  • Cornwell, C., Mustard, D. B., & Sridhar, D. J. (2006). The enrollment effects of merit-based financial aid. Journal of Labor Economics, 24, 761–786.

    Article  Google Scholar 

  • Curs, B., & Singell, L. D., Jr. (2002). An analysis of the application and enrollment processes for in-state and out-of-state students at a large public university. Economics of Education Review, 21(2), 111–124.

    Article  Google Scholar 

  • Deaton, A. (1985). Panel data from time series of cross sections. Journal of Econometrics, 30, 109–126.

    Article  Google Scholar 

  • Doyle, W. R. (2006). Adoption of merit-based student grant programs: An event history analysis. Educational Evaluation and Policy Analysis, 28(3), 259–285.

    Article  Google Scholar 

  • Doyle, W. R. (2009). The effect of community college enrollment on bachelor’s degree completion. Economics of Education Review, 28(2), 199–206.

    Article  Google Scholar 

  • Dynarski, S. (2000). Hope for whom? Financial aid for the middle class and its impact on college attendance. National Tax Journal, 53(3), 629–661.

    Google Scholar 

  • Ehrenberg, R. G., & Sherman, D. R. (1984). Optimal financial aid policies for a selective university. Journal of Human Resources, 19(2), 202–230.

    Article  Google Scholar 

  • Fuller, W. A., & Battese, G. E. (1973). Transformations for estimation of linear models with nested error structure. Journal of the American Statistical Association, 68, 626–632.

    Article  Google Scholar 

  • Fuller, W. A., & Battese, G. E. (1974). Estimation of linear models with cross-error structure. Journal of Econometrics, 2, 67–78.

    Article  Google Scholar 

  • Greene, W. H. (2003). Econometric analysis (5th ed.). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Griliches, Z., & Hausman, J. (1986). Errors in variables in panel data. Journal of Econometrics, 31(1), 93–118.

    Article  Google Scholar 

  • Halaby, C. N. (2004) Panel models in sociological research: Theory into practice. Annual Review of Sociology, 30, 507–544.

    Article  Google Scholar 

  • Hansen, C. (2007). Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects. Journal of Econometrics, 140(2), 670–694.

    Article  Google Scholar 

  • Harris, C. W. (1986). Problems in measuring change. Madison, WI: University of Wisconsin Press.

    Google Scholar 

  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46, 1251–1371.

    Article  Google Scholar 

  • Hausman, J. A., & Taylor, W. E. (1981). Panel data and unobserved individual effects. Econometrica, 49, 1377–1398.

    Article  Google Scholar 

  • Hearn, J. C., & Griswold, C. P. (1994). State-level centralization and policy innovation in U.S. postsecondary education. Educational Evaluation and Policy Analysis, 16(2), 161–190.

    Google Scholar 

  • Hearn, J. C., McLendon, M. K., & Mokher, C. G. (2008). Accounting for student success: An empirical analysis of the origins and spread of state student unit-record systems. Research in Higher Education, 49, 665–683.

    Article  Google Scholar 

  • Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.

    Article  Google Scholar 

  • Heckman, J. J., & Robb, R. (1985). Alternative methods for evaluating the impact of interventions. In J. Heckman & B. Singer (Eds.), Longitudinal analysis of labor market data. Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Heller, D. (1997). Student price response in higher education: An update to Leslie and Brinkman. Journal of Higher Education, 68(6), 624–659.

    Article  Google Scholar 

  • Hsiao, C. (2003). Analysis of panel data. New York: Cambridge University Press.

    Book  Google Scholar 

  • Ishitani, T. T. (2008). How do transfers survive after “transfer shock”? A longitudinal study of transfer student departure at a four-year institution. Research in Higher Education, 49, 403–419.

    Article  Google Scholar 

  • Johnson, I. (2008). Enrollment, persistence and graduation of in-state students at a public research universities: Does high school matter? Research in Higher Education, 49(8), 776–793.

    Article  Google Scholar 

  • Leslie, L. L., & Brinkman, P. T. (1987). Student price response in higher education: The student demand studies. Journal of Higher Education, 58(2), 181–204.

    Article  Google Scholar 

  • Long, B. T. (2004). How do financial aid policies affect colleges? The institutional impact of the Georgia HOPE scholarship. Journal of Human Resources, 39(3), 1045–1066.

    Article  Google Scholar 

  • McHugh, R., & Morgan, J. N. (1984). The determinants of interstate student migration: A place-to-place analysis. Economics of Education Review, 3(4), 269–278.

    Article  Google Scholar 

  • McLendon, M. K., Deaton, R., & Hearn, J. C. (2007). The enactment of reforms in state governance of higher education: Testing the political instability hypothesis. The Journal of Higher Education, 78(6), 645–675.

    Google Scholar 

  • Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business & Economic Statistics, 13(2), 151–161.

    Article  Google Scholar 

  • Mazodier, P., & Trognon, A. (1978). Heteroscedasticity and stratification in error components models. Annales de l’INSEE, 30–31, 451–482.

    Google Scholar 

  • Mundlack, Y. (1978a). On the pooling of time series and cross section data. Econometrica, 46, 69–85.

    Article  Google Scholar 

  • Mundlack, Y. (1978b). Models with variable coefficients: Integration and extension. Annales de l’INNSEE, 30–31, 483–509.

    Google Scholar 

  • National Research Council. (2002). Scientific Research in Education. Committee on Scientific Principles for Education Research. Shavelson, R. J. & Towne, L. (Eds.). Center for Education, Division of Behavioral and Social Sciences and Education, Washington, DC: National Academy Press.

    Google Scholar 

  • National Research Council. (2005). Advancing Scientific Research in Education. Committee on Research in Education. L. Towne, L. L. Wise, & T. M. Winters (Eds.). Center for Education, Division of Behavioral and Social Sciences and Education, Washington, DC: The National Academies Press.

    Google Scholar 

  • Noorbakhsh, A., & Culp, D. (2000). The demand for higher education: Pennsylvania’s nonresident tuition experience. Economics of Education Review, 21(3), 277–286.

    Article  Google Scholar 

  • Ostheimer, R. H. (1953). Student charges and financing higher education. New York: Columbia University Press.

    Google Scholar 

  • Randolph, W. C. (1988). A transformation for heteroscedastic error components regression models. Economics Letters, 27, 349–354.

    Article  Google Scholar 

  • Raudenbush, S. W. (2000). Comparing personal trajectories and drawing causal inferences from longitudinal data. Annual Review of Psychology, 52, 501–525.

    Article  Google Scholar 

  • Reynolds, C. L., & DesJardins, S. L. (2009). The use of matching methods in higher education research: Answering whether attendance at a 2-year institution results in differences in educational attainment. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 24). New York: Springer.

    Google Scholar 

  • Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson, R. J. (2007). Estimating causal effects using experimental and observational designs (Report from the Governing Board of the American Educational Research Association Grants Program). Washington, DC: American Educational Research Association.

    Google Scholar 

  • Stata (2007). Longitudinal/panel data reference manual. College Station, TX: Stata Press.

    Google Scholar 

  • Stock, J. H., & Watson, M. W. (2006). Heteroscedasticity-robust standard errors for fixed effects panel data regression (NBER Technical Working Paper No. 323).

    Google Scholar 

  • van de Klaauw, W. (2002). Estimating the effect of financial aid offers on college enrolment: A regression-discontinuity approach. International Economic Review, 43(4), 124–1287.

    Google Scholar 

  • Volkwein, J. F., & Tandberg, D. A. (2008). Measuring up: Examining the connections among state structural characteristics, regulatory practices, and performance. Research in Higher Education, 49, 180–197.

    Article  Google Scholar 

  • Wansbeek, T. J. (1989). An alternative heteroscedastic error components model. Econometric Theory, 5, 326.

    Article  Google Scholar 

  • Wansbeek, T. J., & Kapteyn, A. (1982). A class of decompositions of the variance-covariance matrix of a generalized error components model. Econometrica, 50, 713–724.

    Article  Google Scholar 

  • Wansbeek, T. J., & Kapteyn, A. (1983). A note on spectral decomposition and maximum likelihood estimation of ANOVA models with balanced data. Statistics and Probability Letters, 1, 213–215.

    Article  Google Scholar 

  • Wooldridge, J. M. (2002). Econometric analysis of cross-section and panel data. Cambridge, MA: MIT Press.

    Google Scholar 

  • Wooldridge J. M. (2003). Introductory econometrics: A modern approach. Mason, OH: Thomson South-Western.

    Google Scholar 

  • Workman, W. G. (1978). Nonresident enrollment demand at Utah State University. Unpublished doctoral dissertation, Utah State University.

    Google Scholar 

  • Zellner, A. (1962). An efficient method of estimating seemingly unrelated regression and tests for aggregation bias. Journal of the American Statistical Association, 57, 348–368.

    Article  Google Scholar 

  • Zhang, L. (2007). Nonresident enrollment demand in public higher education: An analysis at national, state, and institutional levels. The Review of Higher Education, 31(1), 1–26.

    Article  Google Scholar 

  • Zhang, L., & Ness, E. (2009). Does state merit-based aid stem brain drain? Working paper.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Education Policy Studies, The Pennsylvania State University, University Park, PA, 16802, USA

    Liang Zhang

Authors
  1. Liang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Zhang .

Editor information

Editors and Affiliations

  1. Center for the Study of Higher Education, University of Memphis, Browning Hall 308, Memphis, 38152-3340, USA

    John C. Smart

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Zhang, L. (2010). The Use of Panel Data Models in Higher Education Policy Studies. In: Smart, J. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 25. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8598-6_8

Download citation

Publish with us

Policies and ethics

Search

Navigation