Abstract
This paper extends the empirical literature about the effects of fiscal decentralization on the growth of government along three dimensions. It distinguishes between the effects of the level of decentralization from the way local governments finance their expenditures (common pool versus own resources); it uses a panel cointegration approach to separate the long run effects of decentralization from the short run dynamics; and it extends and revises the datasets generally used in these empirical analyses. The results show that the amount of revenue raised by sub-national governments leads to a long-term fall in the size of government but grants have the opposite effect. In addition, a greater decentralization of expenditure leads to greater overall spending. When the short-term is considered these influences work slowly, as the speed of adjustment towards the desired government size is relatively sluggish. In addition, in the short run, there is also a clear effect from the role of local revenue raising powers that stimulates the growth of government. These results appear robust to changes in the composition of the variables, countries and periods included the sample.
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Notes
The countries considered in the present study are Argentina, Australia, Austria. Belgium, Bolivia, Brazil, Canada, Chile, Denmark, Finland, France, Germany, India, Ireland, Israel, Italy, Luxemburg, Mexico, Netherlands, Norway, Romania, South Africa, Spain, Sweden, Switzerland, Thailand, United Kingdom and United States. The availability of a balanced panel of data, characterized by series definitions and sources that remain consistent across time and countries, determines the choice of the sample.
An example is provided by the financing of health care services in Italy, which constitutes the main responsibility of regional governments. Until the early 1990s, these expenditures were mainly funded by central government grants, ensuing their rapid growth in all regions, as each region could partly export the additional costs of greater spending. In 1996, the introduction of a regional tax, the IRAP, initially earmarked to finance health care, and the simultaneous reduction of central government transfers, contributed to the reduction in these expenditures (Bordignon and Turati 2009). Eskeland et al. (2003) provide many more examples of this kind.
In particular this covariate is defined as younger than 15 years of age and older than 65 and thus provides a consistent definition of the variable, though it is clearly only an approximation to the reality in each country of the actual dependency ratio, given the different ages for compulsory education across the sample.
The correlation between veto players and proportional representation is 0.521; this is more severe than the correlation with the parliamentary variables, where the correlation is 0.337.
For some countries data are available up to 2006. Extending the time series, however, severely limits the cross section dimension of the sample and its economic, institutional and geographic variety. Estimates on this other sample, available upon request, are not qualitatively different from those reported in the paper. This is reassuring from an econometric point of view, since Örsal (2008) shows that, with the available time series and number of countries, cointegration is likely to be found. Clearly, the power of the tests depends on the span of the data and not only on the number of observations, so the robustness of the results when the sample is extended by six years is supportive of our findings.
There is a debate as to the precision of these tests (Harris and Sollis 2003), in particular about how cross section independence is treated. To accommodate this, we checked the results using the tests proposed by Hadri (2000). The Hadri test is a sensible check, as it assumes a null hypothesis of stationarity, whereas the others assume a null hypothesis of nonstationarity. No change in inference with respect to the levels of integration of the variables was observed, confirming the outcome of the Im et al. (2003) tests. The results of this alternative testing procedure are available upon request.
We entered (see the Appendix) the EXCHANGE dummy which must be I(0), just like the political dummies, but its omission in the cointegrating relationship does not destroy the relationship. We thus infer that the breaks are not crucial to the estimation.
Other researchers examine various cross product terms too. It is not obvious that these should be introduced in the first, baseline estimates. Moreover, when explored, we found that various combinations were all dominated by the results presented.
Evidence of cointegration means that there is a minimum of one long run relationship. The negative sign on the ECM indicates that expenditure is at the very least one of the endogenous variables, if not necessarily the only one and we are then following other researchers in treating the regressors as exogenous. As a check on the reasonableness of this approach, IV estimation of the preferred equation ECM using lags 2 and 3 of the variables as instruments was undertaken with the coefficients from such a regression little changed from those presented; the results of one of these estimations are presented in model 13 (column 4) of Table 3.
Dynamic OLS (DOLS) estimations also were performed. Given annual data, the lags on the DOLS were taken to be first order. The difference between the FMOLS and DOLS estimation techniques is in their approach to dealing with potential non-exogeneity of the regressors and serial correlation (Harris and Sollis 2003). The FMOLS results take a non-parametric approach, while the DOLS adopt a parametric approach where lagged first differences are estimated. The preference for either technique rests on judgments with respect to the data (see Harris and Sollis 2003, for more). In this case, given the differential sizes of the countries, the FMOLS is probably to be preferred. The results from both estimation techniques are, however, consistent, which indicates a degree of robustness. DOLS estimates are available upon request.
The DEMOCRACY variable is omitted from later columns using only OECD countries, where its variability is very slight, causing difficulties in the estimation. Table A.1 in the Appendix provides results if the SYSTEM of government (however defined) is omitted. It can be seen that DEMOCRACY picks up the effect of SYSTEM, showing the degree of collinearity between the two variables. It is also clear that the SYSTEM of government, even simply defined as PARLIAMENTARY (or other), is more consistently significant; hence it is preferred and reported in Table 2 of the main text.
Some indicative results are placed in Table A.2. The full set of results relative to the preferred model are available from the authors upon request.
The plots of the residuals are available upon request.
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Acknowledgements
Paper presented at the University of Florence, at the University of Roma Tre, at the INFER conference, at the EPCS conference and at the PEARLE seminar. We thank the participants to these conferences and particularly Giampaolo Arachi, Geoffrey Brennan, Arthur Charpentier, Wallace Oates, Federico Revelli and three anonymous referees for helpful comments on previous drafts of this paper. Ilaria Petrarca provided excellent research assistance. The usual caveat applies.
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Appendix: Alternative estimates
Appendix: Alternative estimates
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Ashworth, J., Galli, E. & Padovano, F. Decentralization as a constraint to Leviathan: a panel cointegration analysis. Public Choice 156, 491–516 (2013). https://doi.org/10.1007/s11127-012-9962-8
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DOI: https://doi.org/10.1007/s11127-012-9962-8