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Elite interests and public spending: Evidence from Prussian cities

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Abstract

When do economic and political elites demand investment in public goods and services? The prevailing view is that non-democratic governments engage in low levels of government spending and taxation, because elites have interests in low taxation. Non-democracies exhibit significant variation in levels of government spending; the causes of these discrepancies have thus far not been thoroughly examined. I argue that where elites own capital that is conducive to government spending, regimes make higher investments. I test this argument using newly collected data on government spending as well as political and economic characteristics of 110 cities in 19th century Prussia. Using both standard regression models and instrumental variable analysis, I show that the economic needs of the local elites drove local government decisions on public spending.

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Notes

  1. But see recent work by Paglayan (2017, 2018).

  2. Gift and Wibbels (2014) make a similar point with regards to research on investments in education.

  3. Lastly, in the Online Appendix I present a bounding exercise concerning selection on unobservables, which lends additional credence to these findings (Oster 2017).

  4. See Mulligan et al. (2004) and personal calculation based on population data by the World Bank (World Bank Group 2018) and regime type coded by Boix et al. (2013).

  5. Data on regime type is based on Boix et al. (2013), expenditure data are taken from Mauro et al. (2015). The plot for years 1800-1900 excludes one data point for Greece in 1898 (Expenditure as % of GDP of 51.4%) to make the figure more readable.

  6. Mares and Queralt (2015) show that a similar development is true for the introduction of the income tax.

  7. As Hollenbach (2018) argues, this demand for spending also provides incentives to increase the capacity of the state to collect taxes.

  8. The county-level data is originally based on Prussian censuses and statistical yearbooks but is generally taken from Becker et al. (2014) and Galloway (2007). Electoral results for 1893 are based on Ziblatt (2009) and the electoral district map comes from Ziblatt and Blossom (2011).

  9. Specifically I divide the number of students in the Volksschule by the number of 5 to 15-year-olds, which approximates the number of children that are eligible to attend school. Unfortunately, the number of students for other schools is not available for the same year as the number of 5 to 15-year-olds, and thus cannot be included in the enrollment ratio. For years in which the number of Bürger- and Middleschool students is also available, the correlation between the number of Volksschul students and the total number of students in Volks-, Bürger-, and Middleschools is 0.997. This should alleviate concerns that the results are due to the reliance on Volksschulstudents.

  10. To create this variable I use data from the occupational census in 1882, which is taken from Becker et al. (2014). Specifically, I code all self-employed and employed workers in mining and steel mill operations, ground or treated mineral and earth manufacturing, metal processing, engineering, and the chemical industry as industrial workers. Data on employment is not available at the city level. I therefore use county-level data on employment and divide it by the total number of workers in the county. This is not a perfect measure, but I believe it is reasonable to assume that the share of industrial workers at the county level would be highly correlated with that in cities and that most industrial workers lived in cities. As an alternative, I use the logged absolute number of industrial workers in the county. The results are shown in Table C.3 in the Appendix.

  11. To calculate the Gini coefficient, some assumptions have to be made. First, I assume that for a given category of income, all persons in that group have the average income of that bin. For example, for incomes between 900 and 3000 marks, I assume all people in this bin earn 1,950 marks. For the last bin, e.g., incomes above 100,000 marks, I assume all persons in the bin earn the lower limit, i.e., 100,000 marks. This is more likely to underestimate inequality, assuming that most persons in the last category earn more than the lower limit. The data source provides seven income categories.

  12. As Hallerberg (2002) shows, the composition of the Prussian parliament stays mostly constant between 1880 and 1913, speaking to the strength of the Prussian electoral system in suppressing progressive votes.

  13. In addition to the large set of included control variables, I undertake a bounding exercise concerning selection on unobservables (Oster 2017). For space reasons, the results are discussed and presented in the Online Appendix but they generally lend support to the results presented below.

  14. Table C.6 in the Appendix shows the results when school expenditure is measured in 1895. Table B.1 in the Appendix shows the summary statistics for all variables, their source, and the year for which they are measured.

  15. The full results with all covariates are presented in Table C.1 in the Appendix.

  16. Table C.4 in the Appendix shows the full regression results in detail.

  17. As further discussed in the conclusion, it is likely that the theoretical mechanism outlined above also works through the development of taxation and tax capacity, as spending provides the underlying motivation for elites to increase taxation (Hollenbach 2018).

  18. For the IV models with enrollment as the dependent variable, I drop the logged rainfall covariate. Otherwise, the model suffers from singularity. Instead of dropping logged rainfall any of the other additional controls in the full model can be dropped with very similar results.

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Acknowledgments

I thank Joshua Alley for excellent research assistance. The paper has been presented at the Meeting of the European Political Science Association, Milan, Italy, June 2017. I thank Franziska Keller, the editor, three excellent reviewers, and participants at the Texas A&M Conference on Taxation, Revenue, and Fiscal Capacity for helpful comments on this project. All remaining errors are my own.

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  1. Department of Political Science, Texas A&M University, College Station, TX, 77843-4348, USA

    Florian M. Hollenbach

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Hollenbach, F.M. Elite interests and public spending: Evidence from Prussian cities. Rev Int Organ 16, 189–211 (2021). https://doi.org/10.1007/s11558-019-09347-z

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