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An attempt to restore Wagner’s law of increasing state activity

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Abstract

According to Adolph Wagner’s “Law of Increasing State Activity” economic development is accompanied by an increasing role of state activities and functions. Unfortunately, this view has never been stated explicitly by Wagner and the lack of a precise mathematical formulation led researchers to use various and sometimes incomparable specifications. Moreover, in previous empirical studies state activity was almost always replaced by public spending, and hence Wagner’s Law was effectively narrowed down to the “Law of Increasing State Spending”. This paper is an attempt to restore Wagner’s Law. Contrary to previous studies, it examines whether Wagner’s Law in its original form held in Australia in the 1901–2008 period by studying the relationship between real per capita income and a composite variable of state activity that takes both financial and legislative activities of the federal government into account. Although this composite variable still falls short of capturing all levels and sorts of state or government activities, it is a far more comprehensive measure than any of its components used individually in earlier studies. The results based on this composite measure provide no empirical evidence in favour of Wagner’s Law in Australia.

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Notes

  1. An interesting account of this subject is provided by Tarschys (1975).

  2. As regards cross-sectional studies, they have been rightfully criticised by Bird (1971) on the ground that Wagner’s Law asserts that state activities expand by economic development in any particular country, but not necessarily across countries.

  3. Even recent studies which make reference to Biehl (1998), like, e.g. Mohammadi et al. (2008), Durevall and Henrekson (2011) and Richter and Dimitrios (2012), appear to miss or disregard his warning.

  4. The most recent study that offers a thorough review of both the theoretical considerations underlying Wagner’s Law and the related empirical evidence is Peacock and Scott (2000).

  5. As mentioned above, Kirchner (2012) tries to establish the order of integration by performing the ADF tests on the levels and first differences of the variables. Apart from the fact that this test has relatively low power, and hence it is better to supplement it by some alternative unit-root/stationarity tests, it is also questionable why to run the ADF test in two versions, with a constant only and with a constant and linear time trend, on both the levels and first differences of trending variables. Since the level variables appear to be trending, some of these specifications are clearly unreasonable and contradictory and this indecisiveness unnecessarily blurs the picture.

  6. An alternative approach would be to consider each measure of state activity individually but simultaneously. Although this approach has some merit compared to our approach based on principal components analysis, it would make the VAR system unnecessarily complicated since Wagner’s Law is not about particular state activities, but about overall state activity.

  7. We use each variable in per capita terms, in accordance with equation (1).

  8. The consistency of data between Butlin (1977) and ABS (2011) was cross-checked for the 1960–1974 period, and all series reported in Butlin have been adjusted by a constant factor difference revealed by this comparison.

  9. All calculations were performed with EViews 7.2 and WinRATS Pro 7.30.

  10. We have also considered the weighted average of all four principal components (using the proportions of the total variance accounted for by these principal components as weights), but since it follows a similar time path to the first principal component, we do not report the details.

  11. LNPRC is a linear combination of the logarithms of the original four variables, so it is equal to the logarithm of the product of the variables raised to the power of their loadings.

  12. About unit-root tests see e.g. Maddala and Kim (1998, Part II) and Glynn et al. (2007).

  13. Since both variables appear to be stationary and hence, by definition, they are not cointegrated, it is not necessary to estimate a vector error correction (VEC) model.

  14. We have also estimated a VAR model with one more pair of dummy variables for 1977, but they were insignificant. In addition, similarly to Kirchner (2012), in order to account for the possible disruptive effect of general elections on federal legislative activity, we have also experimented with a dummy variable defined as being one in calendar years that include federal elections. This variable too proved to be insignificant, so we have dropped it.

  15. The moduli of the autoregressive characteristic roots are 0.7376, 0.7376, 0.2472 and 0.1744.

  16. Non-normality is not a major issue at our large effective sample size (\(T = 106\)), and the approximate 95% confidence bands shown in Fig. 3 are based on Monte Carlo standard errors.

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Acknowledgements

We are grateful for the helpful comments of the editor and two anonymous referees of this journal.

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Authors and Affiliations

  1. The School of Economics, Finance and Marketing, RMIT University, Melbourne, Australia

    László Kónya

  2. The Australian APEC Study Centre, RMIT University, Melbourne, Australia

    Bekzod Abdullaev

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  1. László Kónya
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Correspondence to László Kónya.

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Kónya, L., Abdullaev, B. An attempt to restore Wagner’s law of increasing state activity. Empir Econ 55, 1569–1583 (2018). https://doi.org/10.1007/s00181-017-1339-x

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