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Cost disease in defense and public administration: Baumol and politics

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Abstract

William Baumol’s model predicts a steady increase in relative public sector prices (or costs) because of the combination of slow productivity growth and wage growth similar to sectors wherein productivity is growing more quickly. In this paper, we extend the Baumol model with political variables and analyze price growth in defense and public administration using Norwegian data. We find strong support for the mechanism of the Baumol model since manufacturing productivity is the most important determinant of relative public-sector prices. Greater political fragmentation has also contributed to the price growth, but its quantitative effect is smaller than that of manufacturing productivity. An analysis of a labor-intensive private service (restaurants and cafes) supports the broader relevance of the Baumol mechanism and the validity of the estimated effect of political fragmentation on the two sectors considered herein.

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Notes

  1. Many private service industries may also have limited scope for productivity growth. See the analysis of restaurant and cafes in Sect. 6.

  2. The view that the cost disease is a threat to the welfare state is challenged in theoretical contributions by Andersen (2016) and Andersen and Kreiner (2017).

  3. Our formulation of the production function is a bit more general than the formulation used by Baumol (1967) because we also allow for productivity growth in the non-progressive sector. A similar formulation is used by Colombier (2012).

  4. Equation (4) implies that the wage level is the same in the two sectors. None of the following results would be affected if we instead made the weaker assumption of proportionality.

  5. We do not allow for a time trend in any of the tests since that would make the interpretation of the test less clear. Trend-stationarity would, for instance, be consistent with a cost disease if the time trend is found to be a significant explanatory variable.

  6. The coefficient of correlation between HHI and Left is 0.61.

  7. The test results are reported in Table 6 in the Appendix.

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Acknowledgements

We appreciate comments from participants at LAGV (Aix-en-Provence), IIPF (Lugano) and EPCS (Groningen), and in particular from Anna Laura Mancini, Shintaro Nakagawa and Atin Basuchoudhary. We are also grateful to Gunnar Bårdsen for advice on specification and estimation, and to Steinar Todsen at Statistics Norway for supplying the data. Finally, we thank two referees for comments and suggestions that have improved the paper substantially.

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Authors and Affiliations

  1. Department of Economics, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Lars-Erik Borge & Per Tovmo

  2. Norwegian Defence Research Establishment (FFI), P.O. Box 25, 2027, Kjeller, Norway

    Kjetil Hatlebakk Hove & Tobias Lillekvelland

Authors
  1. Lars-Erik Borge
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  2. Kjetil Hatlebakk Hove
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  3. Tobias Lillekvelland
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  4. Per Tovmo
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Correspondence to Lars-Erik Borge.

Appendix: Variable definitions, descriptive statistics and tests of time series properties

Appendix: Variable definitions, descriptive statistics and tests of time series properties

See Tables 4, 5 and 6.

Table 4 Variable definitions and sources
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Table 5 Descriptive statistics
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Table 6 Time series properties tests
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Borge, LE., Hove, K.H., Lillekvelland, T. et al. Cost disease in defense and public administration: Baumol and politics. Public Choice 175, 1–18 (2018). https://doi.org/10.1007/s11127-018-0510-z

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