Comparing Time Series Cross-Section Model Specifications: The Case of Welfare State Development

Abstract

In recent years, an impressive number of pooled time series (TSCS) cross-section models have been estimated in order to test hypotheses on welfare state development. Although most of these models share several of the variables, they can often be distinguished by the model specification adopted. This begs the question: what is the appropriate specification for modeling welfare state development? In order to answer this question some leading specifications are evaluated with respect to their ability to meet the theoretical assumptions about the theory of welfare state evolution in addition to the econometric canons on panel analysis. The main conclusions of this paper are the following. First, all specifications in levels are econometrically unfounded because most of the variables typically used for analyzing this topic cannot be considered to be stationary. Second, although a first difference model performs better from an econometric point of view, it is unable to test the hypothesized long-term relationships underlying welfare state dynamics. Third, and more importantly, the single equation error correction model represents the best pooled TSCS specification for modeling welfare state development since it is able tocapture long-run effects even in the presence of nonstationary processes.

This is a preview of subscription content, access via your institution.

References

  1. A. Banerjee J. Dolado R. Mestre (1998) ArticleTitleError-correction mechanism tests for cointegration in a single-equation framework Journal of Time Series Analysis 19 267–283 Occurrence Handle10.1111/1467-9892.00091

    Article  Google Scholar 

  2. A. Banerjee J. Dolado J.W. Galbraith D.F. Hendry (1993) Cointegration, Error-Correction, and the Econometric Analysis of Nonstationary Data Oxford University Press Oxford

    Google Scholar 

  3. N. Beck (1993) ArticleTitleThe methodology of cointegration Political Analysis 4 237–247

    Google Scholar 

  4. N. Beck (1991) ArticleTitleComparing dynamic specification: the case of presidential approval Political Analysis 3 51–88

    Google Scholar 

  5. Beck, N. & Katz, J.N. (2004). Time–series–cross-section issues: dynamics, 2004. Presented at the 21st Annual Summer Meeting of the Society for the Political Methodology, 29–31 July 2004. Stanford. pp. 1–35.

  6. N. Beck J.N. Katz (1996) ArticleTitleNuisance vs. substance: specifying and estimating time-series-cross-section models Political Analysis 6 1–36

    Google Scholar 

  7. N. Beck J.N. Katz (1995) ArticleTitleWhat to do (and not to do) with time-series cross-section data American Political Journal Review 89 634–647 Occurrence Handle10.2307/2082979

    Article  Google Scholar 

  8. S. De Boef (2000) ArticleTitleModeling equilibrium relationships: error correction models with strongly autoregressive data Political Analysis 9 14–48

    Google Scholar 

  9. R. Durr (1993) ArticleTitleAn essay on cointegration and error correction models Political Analysis 4 185–228

    Google Scholar 

  10. Engle, R.F. (1984) Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. In: Z. Griliches & M.D. Intriligator (ed.), Handbook of Econometrics, Amsterdam: North-Holland, pp. 775–826

  11. R.F. Engle C.W.J. Granger (1987) ArticleTitleCointegration and error correction: representation, estimation and testing Econometrica 55 251–276 Occurrence Handle10.2307/1913236

    Article  Google Scholar 

  12. G. Garrett D. Mitchell (2001) ArticleTitleGlobalization, government spending and taxation in the OECD European Journal of Political Research 39 145–177

    Google Scholar 

  13. C.W.J. Granger P Newbold (1974) ArticleTitleSpurious regressions in econometrics Journal of Econometrics 2 111–120 Occurrence Handle10.1016/0304-4076(74)90034-7

    Article  Google Scholar 

  14. Greene, W. (2003). Econometric Analysis, 5th edn. Upper Saddle River, N. J.: Prentice Hall.

  15. A. Hicks (1994) Introduction to pooling T. Janoski A. Hicks (Eds) The Comparative Political Economy of the Welfare State Cambridge University Press Cambridge 169–188

    Google Scholar 

  16. C. Hsiao (1986) Analysis of Panel Data Cambridge University Press Cambridge

    Google Scholar 

  17. E. Huber J.D. Stephens (2001) Development and Crisis of the Welfare State: Parties and Policies in Global Markets University of Chicago Press Chicago, IL

    Google Scholar 

  18. Huber, E., Ragin, C., Stephens, J.D. (1998). Comparative Welfare States data Set, Northwestern University and University of North Carolina.

  19. E. Huber C. Ragin J.D. Stephens (1993) ArticleTitleSocial democracy, christian democracy, constitutional structure, and the welfare state American Journal of Sociology 99 711–749 Occurrence Handle10.1086/230321

    Article  Google Scholar 

  20. T. Iversen T. Cusack (2000) ArticleTitleThe causes of welfare state expansion. Deindustrialization or globalization? World Politics 52 313–349

    Google Scholar 

  21. T. Janoski L.W. Isaac (1994) Introduction to time-series analysis T. Janoski A. Hicks (Eds) The Comparative Political Economy of the Welfare State Cambridge University Press Cambridge 31–53

    Google Scholar 

  22. G.G. Judge W.E. Griffiths R.C. Hill H. Lutkepohl T.C. Lee (1985) The Theory and Practice of Econometrics 2nd Edn Wiley New York

    Google Scholar 

  23. C. Kao (1999) ArticleTitleSpurious Regression and residual-based tests for cointegration in panel data Journal of Econometrics 90 1–44 Occurrence Handle10.1016/S0304-4076(98)00023-2

    Article  Google Scholar 

  24. J. Kmenta (1990) Elements of Econometrics, 2nd Edn Collier Macmillan London

    Google Scholar 

  25. Kittel, B. & Winner, H. (2002). How reliable is pooled analysis in political economy? the globalization-welfare state nexus revisited. MPIfG Discussion Paper 02/3: Max Planck Institute for the Study of Societies of Cologne.

  26. Kittel, B. & Obinger, H. (2002). Political parties, institutions, and the dynamics of social expenditure in times of austerity. MPIfG Discussion Paper 02/1: Max Planck Institute for the Study of Societies of Cologne.

  27. OECD. (various year). OECD Statistical Compendium (on cd-rom), Paris: OECD.

  28. R.W. Parks (1966) ArticleTitleEfficient estimation of a system of regression equation when disturbance are both serially and contemporaneously correlated Journal of the American Statistical Association 62 500–509 Occurrence Handle10.2307/2283977

    Article  Google Scholar 

  29. P. Pedroni (2004) ArticleTitlePanel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the ppp hypothesis Econometric Theory 3 579–625

    Google Scholar 

  30. Plumper, T., Troger V. & Manow P. (2003). Pooled data analysis in the comparative political economy of the welfare state: A note on methodology and theory. Presented at the ECPR Conference, 18–21 September 2003, Marburg, pp. 1–45.

  31. Westerlund J. (2005). ‘Testing for panel error correction’ S-WoPEc Working Paper 11/05: Department of Economics, Lund University.

  32. Wilson S.E. & Butler D.M. (2004) A lot more to do: the promise and peril of panel data in political science. Presented at the POLISCI 353 – Workshop in Statistical Modeling: Stanford University. pp. 1–40.

  33. Wooldridge, J.M. (2002). Introductory Econometrics: A Modern Approach. 2nd Edn. South-Western College Publishing.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Federico Podestà.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Podestà, F. Comparing Time Series Cross-Section Model Specifications: The Case of Welfare State Development. Qual Quant 40, 539–559 (2006). https://doi.org/10.1007/s11135-005-2076-3

Download citation

Keywords

  • pooled time series cross-section analysis
  • model specification
  • welfare state development
  • long-run effects