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Do jurisdictions compete on taxes? A meta-regression analysis

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Abstract

A sizable empirical literature examines government fiscal interactions. However, the empirical evidence is very mixed. We apply meta-regression analysis to quantify the size of inter-jurisdictional fiscal interactions and to explain the heterogeneity in empirical estimates. Several robust results emerge. While there are significant country differences, tax interactions exist in all countries studied and they are strongest in terms of total tax and weakest in terms of income tax. Interactions differ according to level of government: compared to the municipal level, horizontal tax competition is stronger when the jurisdiction is a county or a nation. We show that tax competition has actually not grown over time and that econometric specifications and estimation strategies influence reported fiscal interactions.

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Notes

  1. They can also occur as a means of avoiding becoming welfare magnets by discouraging ‘undesirable’ welfare-seeking residents, or as a result of competing for ‘desirable’ citizens by offering better public goods.

  2. Interactions can arise also for other reasons. For example, Ostrom et al. (1961) argue that interactions between governments can arise as a response to externalities and scale economies in the provision of public goods.

  3. Tiebout competition is not necessarily a ‘race to the bottom’. Rather, it is a race to find the mix of public good quantity, quality and tax price that caters to diverse sets of median voters.

  4. Fixed region and time effects can also be included in Eq. 1.

  5. The vector X can also include vertical tax competition and characteristics of neighboring jurisdictions.

  6. The dataset can be downloaded from www.deakin.edu.au/meta-analysis, enabling replication and extensions to our analysis. An appendix referencing the studies used in our meta-analysis is also available from this website.

  7. See Doucouliagos (1995) and Djankov and Murrell (2002) for examples of the use of partial correlations for meta-analysis in economics.

  8. Note that our focus in this paper is purely on the direct effect of the neighboring governments’ choice variable. Hence, we abstract from the various spatial impacts (LeSage and Fischer 2008).

  9. The inverse of the standard error is the most common measure of precision used to construct funnel plots.

  10. Impact Factors can change over time. We are assuming that the 2011 Impact Factors are representative of the relative quality ranking of the journals over time.

  11. Publication selection is analogous to sample selection biases and produces the conventional ‘Heckman regression’. In essence, the MRA with selection bias adjustment model replaces the inverse Mills ratio term with \(\beta_{se} SE_{i}\), giving rise to Eq. (3). See Stanley and Doucouliagos (2012) for details.

  12. Average Year is normalized at the mean of the sample, 1991. In unreported regressions, we also included the data span (last year minus the initial year); this variable is not statistically significant.

  13. There is some collinearity amongst these variables: The zero-order correlation between Fixed effects and Time effects is 0.49, while the correlations between Panel and Fixed effects and Time effects are 0.45 and 0.28, respectively.

  14. This is often done to get around endogeneity between the own and neighboring jurisdiction’s tax.

  15. This MRA model passes the RESET test (p value = 0.60) and the linktest (p value = 0.27).

  16. With the exception of column 4, the constant in Table 3 is the baseline and measures the degree of tax competition (as measured by partial correlations) for studies using US data on total tax charged at the municipality level, using cross-sectional data for 1991, estimated by OLS, without any of the controls listed in the table, and using distance to weigh neighbors’ tax.

  17. Using authors’ names to cluster studies (estimates are clustered around authors) produces little difference in the results.

  18. A referee pointed out that one explanation for this finding might be the so-called “tax cut cum base broadening” strategy that involves reducing statutory tax rates on capital but broadening the associated tax base, e.g., by reducing allowances and deductions. On this issue see Devereux et al. (2002).

  19. Region here refers to the level of the jurisdiction that is analysed, e.g., region can be a municipality or a nation.

  20. The relationship between OLS and IV is complicated by the existence of spatial error correlation. The statistical insignificance of IV in Table 3 is consistent with: (a) the literature using correct instrumental variables (IVs) and no endogeneity; (b) there is endogeneity but the literature has not used correct IVs; and (c) bias from spatial error correlation offsets the bias from OLS.

  21. The MRA can also be used to derive similar estimates for maximum likelihood techniques by adjusting the MRA predictions reported in Table 4 by the associated MRA coefficient from Table 3. In this case, the MRA predicts weaker fiscal interactions.

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Authors and Affiliations

  1. London School of Economics and Political Science, London, UK

    Joan Costa-Font

  2. CESifo, Munich, Germany

    Joan Costa-Font

  3. Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

    Hristos Doucouliagos

  4. Department of Economics, Birkbeck, University of London, London, UK

    Filipe De-Albuquerque

Authors
  1. Joan Costa-Font
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  2. Filipe De-Albuquerque
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  3. Hristos Doucouliagos
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Corresponding author

Correspondence to Hristos Doucouliagos.

Appendix

Appendix

See Table 5.

Table 5 Meta-regression analysis of horizontal tax competition (dependent variable = partial correlations)
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Costa-Font, J., De-Albuquerque, F. & Doucouliagos, H. Do jurisdictions compete on taxes? A meta-regression analysis. Public Choice 161, 451–470 (2014). https://doi.org/10.1007/s11127-014-0170-6

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