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Value-added taxation and consumption

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Abstract

One of the main rationales for taxing consumption rather than income is that it is believed that consumption taxes discourage consumption, encourage savings, and thus generate higher economic growth. However, empirical evidence on the actual effectiveness of consumption taxes in stimulating savings is very limited. In this paper, we estimate the impact of a broad-based consumption tax, the value-added tax (VAT), on the aggregate consumption of fifteen European Union countries over the period 1961–2005. Our empirical results indicate, across a variety of estimation methods and specifications, that a one percentage point increase in the VAT rate leads to roughly a one percent reduction in the level of aggregate consumption in the short run and to a somewhat larger reduction in the long run.

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Notes

  1. Much of the VAT literature has been concerned with its design, implementation, administration, efficiency, and revenue performance. See Ebrill et al. (2001) for a detailed discussion of these issues.

  2. Note that there is also a large literature on the distributional effects of consumption taxes. For a recent and comprehensive review, see Warren (2008).

  3. There is also a large theoretical literature that examines the impact of taxes (e.g., income versus consumption taxes, progressive versus flat rate taxes) on endogenously determined economic growth. For example, see King and Rebelo (1990), Rebelo (1991), Barro and Sala-i-Martin (1992), Saint-Paul (1992), Jones et al. (1993), Stokey and Rebelo (1995), Turnovsky (1996), and Milesi-Ferretti and Roubini (1998).

  4. In a similar vein, several empirical papers have tested for the effects of fiscal policy in general, and taxes in particular, on country-level economic growth. See Levine and Renelt (1992), Miller and Russek (1997), Kneller et al. (1999), Myles (2000), and Padovano and Galli (2001), among others.

  5. Also, Poterba (1988), Hubbard et al. (1986), and Steindel (2001) examine how changes in income taxes affect consumer spending. However, these studies do not look at how expenditures are affected by changes in consumption taxes.

  6. Note that there is some work that uses numerical methods to assess the impact on consumption of different types of taxes (e.g., taxes on consumption, capital income, wages, corporate income, individual income). For example, see Summers (1981), Fullerton et al. (1983), Rege (2002), and Narayan (2003).

  7. These fifteen countries (with their entry into the EU in parentheses) are: Austria (1995), Belgium (founding member), Denmark (1973), Finland (1995), France (founding member), Germany (founding member), Greece (1981), Ireland (1973), Italy (founding member), the Netherlands (founding member), Portugal (1986), Spain (1986), Sweden (1995), and the United Kingdom (1973). Another ten countries joined the EU in 2004 (Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia), and two countries joined in 2007 (Bulgaria and Romania).

  8. See Beck et al. (2000) for the initial estimates of wealth. Beck et al. (2010) have recently updated these estimates.

  9. Total trade taxes are the sum of tax revenues collected from import and export taxes.

  10. The minimum value is zero because we include observations in years in which the VAT was not yet introduced in some countries. However, if we drop the zeros from the sample, then the number of observations drops from 138 to 105 in the averaged dataset; in this case the minimum effective VAT rate is 0.19 percent. If the zeros are dropped from the annual dataset, the number of observations falls from 665 to 493, and the minimum effective VAT rate is 0.43 percent.

  11. This section borrows heavily from Arellano and Bond (1991), Loayza et al. (2000), and Bond et al. (2001).

  12. The dynamic model is a regression in which the lagged value of the dependent variable is one of the explanatory variables. As pointed out by Bond (2002), even if we are not directly interested in the coefficient on the lagged dependent variable, allowing for dynamics in the underlying process may be crucial for recovering consistent estimates of other parameters.

  13. We test for panel-level heteroskedasticity using the likelihood ratio test. The significant p-value reveals that the null hypothesis of homoskedastic errors can be rejected.

  14. We are indebted to the Editor, Dhammika Dharmapala, and to an anonymous referee for this insight and suggestion.

  15. We have tested for panel-level autocorrelation using the Wooldridge (2002) test. The test-statistics are significant, and indicate the presence of autocorrelation.

  16. In fact, the presence of a lagged dependent variable among the regressors implies that it is by construction correlated with the country-specific component of the error term.

  17. As pointed out by Arellano and Bond (1991), the moment conditions of the GMM-Difference estimator imply the use of lagged levels of the explanatory variables dated (t−2) and earlier as instruments for the equation in first-differences.

  18. Note that we conduct several specification tests, also designed to examine the robustness of our results. Consistency of the GMM-System estimator depends on the assumptions that the error term (ε i,t ) is serially uncorrelated and that the instruments are valid ones. Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998) suggest two specification tests. The first test considers the assumption of serially uncorrelated errors, and is applied to the first-difference equation residuals in order to purge the unobserved and perfectly autocorrelated (η i ). This test examines whether the differenced error term is second-order serially correlated. Under the null hypothesis of no second-order correlation, the test statistic has a standard normal distribution. If we do not fail to reject the null of no second-order correlation, it indicates that some lags of the dependent variable, which might be used as instruments, are in fact endogenous, and thus considered “bad” instruments. The second test is the Hansen test of over-identifying restrictions, which examines the overall validity of the instruments (i.e., the instruments are uncorrelated with the residuals). Under the null hypothesis of the validity of the instruments, the Hansen J-statistic has a χ 2 distribution with (J–K) degrees of freedom, where J is the number of instruments and K is the number of regressors. This test is robust to heteroskedasticity and autocorrelation. Failure to reject the null hypotheses of both tests provides support to our model.

  19. These test statistics are based on the first stage F-statistic, which equals 12.36 and 11.83 for the one-step and two-step GMM-System estimators of Table 3, columns 4 and 5, respectively. See Stock and Yogo (2005), who provide critical values of the F-statistic based on alternative definitions of “weak” instruments. See also Stock et al. (2002), who focus on weak instruments in the GMM framework.

  20. We are indebted to an anonymous referee for emphasizing the difference between the short-run and the long-run impacts of the tax.

  21. This result follows from 5.09−3.62=1.47, and (1.47)×(1.11)=1.63, where 1.11 is the point estimate on EffectiveVATRate in column 5 of Table 3.

  22. One way to interpret the coefficient of lagged consumption is in terms of the “speed of adjustment” in consumer behavior. For example, with the point estimate of lagged consumption equal to 0.37 in column 5 of Table 3, the adjustment rate is equal to 0.63, which means that 63 percent of the difference between desired and actual consumption is eliminated in five years.

  23. For recent surveys of consumption behavior, see Browning and Crossley (2001) and Jappelli and Pistaferri (2010).

  24. Again, see Browning and Crossley (2001) and Jappelli and Pistaferri (2010).

  25. Recall that consistency of GMM estimates requires that T is small and that N is large. However, we have 45 annual observations for 15 countries, which means that T>N, and this invalidates the main assumption. Put differently, the large time dimension in the annual regressions means that there are many more moment conditions than parameters to estimate.

  26. All results are available upon request.

  27. For example, the coefficient on EffectiveVATRate in the GMM-System Two-step estimation with five-year averaged data is now −0.0076, significant at 5 percent. We are grateful to an anonymous referee for this suggestion. Again, all results are available upon request.

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Acknowledgements

The views expressed here are those of the authors, and should not be interpreted as those of Tulane University or of the International Monetary Fund and its Board. We are grateful to the Editor and to an anonymous referee for helpful comments.

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  1. Department of Economics, Tulane University, 6823 St. Charles Avenue, 208 Tilton Hall, New Orleans, LA, 70118-5698, USA

    James Alm

  2. International Monetary Fund, 700 19th Street, N.W., Washington, DC, 204321, USA

    Asmaa El-Ganainy

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Correspondence to James Alm.

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Alm, J., El-Ganainy, A. Value-added taxation and consumption. Int Tax Public Finance 20, 105–128 (2013). https://doi.org/10.1007/s10797-012-9217-0

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