The value of political connections in the post-transition period: evidence from Czechia

Abstract

This paper studies the relationship between political connections and reported profits using a newly compiled dataset on all corporate donations to political parties in Czechia during its post-transition period (between 1995 and 2014). I develop a dynamic matching approach to identify non-connected firms that are similar to their connected peers on a range of observable characteristics, including profitability prior to becoming connected. I find that being politically connected is associated with superior reported profits: I estimate conservatively that the connected firms outperform their non-connected but otherwise similar competitors by 8–12% following the establishment of the connection, which is a larger effect than found previously for more developed economies. What is more important, however, I find that the effect virtually vanishes for non-connected firms aligned closely with the public sector. That evidence suggests that other forms of connections, such as personal ties and those established at subnational levels of government, such as regional and municipal governance tiers, are likely to have played a significant role in Czechia during its post-transition period.

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Fig. 1

Source: Author based on data from PolitickeFinance.cz

Fig. 2

Source: Author

Notes

  1. 1.

    The few papers that do rely on contributions to parties include the works that I reference above (Titl and Geys 2019; Boas et al. 2014; Baltrunaite 2020).

  2. 2.

    For a thorough review of political financing in Czechia, see, for example, Simral (2015) and Skuhrovec et al. (2015).

  3. 3.

    The laws changed in January 2017 when donations were capped at CZK 3 million per person (natural or legal) per year and limits on campaign spending were introduced.

  4. 4.

    For example, likely the most widely used measures of institutional quality, the Worldwide Governance Indicators (see Kaufmann et al. 2011 for methodology), include an indicator of “Control of corruption”, which consistently places Czechia in the bottom ten of OECD countries.

  5. 5.

    Cíl, akciová společ nost v Praze is a company owned entirely by ČSSD and its reported main aim is to print and publish or sell advertising materials. That company alone donated more than CZK 930 million to ČSSD over the examined time period.

  6. 6.

    A different but related approach was taken by Boubakri et al. (2012a), who employ a two-stage regression model to, first, construct an instrumental variable estimating the probability of political connectedness of firms based on their locations, sizes and other firm-specific characteristics. In the second stage, they rely on that variable to estimate the effects of political connectedness. However, at least two reasons can be found for why that methodology is not suitable for the present case study of Czech data. First, the longitudinal character of our dataset does not permit the estimation of political connectedness based on firm characteristics because, for some firms, those characteristics vary significantly over time. Second, especially for an individual country study, that approach is not likely to resolve the endogeneity issue, since a brief look at the data reveals that better—performing firms are more likely to be larger in size, operate in relatively more capital—intensive industries, work closely with the public sector, and so on.

  7. 7.

    That approach follows also from the notion that firms may view political campaign contributions as a form of short-term investment, as outlined, for example, by Hersch et al. (2008). As an illustration, let us suppose that a political donation made during 2010 is paid off by a public procurement contract signed in 2011 and completed in 2012. The full effect of that donation on profits is then reflected in the 2012 financial year. For the results presented herein, I rely on different time horizons as robustness checks (see below).

  8. 8.

    The classification is somewhat tricky because the composition of the government changes following parliamentary elections (not on January 1). I consider a party as in power in year t if it was in the government for at least 6 months of year t.

  9. 9.

    Although I do not explore the implications of that result here, one possible explanation consistent with economic theory is that firms operating in larger cities generally face more vigorous competition.

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Acknowledgements

Support from the Grant Agency of Charles University under Project GA UK No. 328215 gratefully is acknowledged. The present work has been supported by Charles University Research Centre Program No. UNCE/HUM/035 and has benefited from the H2020-MSCA-RISE project GEMCLIME-2020 GA No. 681228. I am grateful to William Shughart, Petr Janský, Evžen Kočenda, Jiří Skuhrovec, Vítězslav Titl, Vít Šimral, Petr Gongala, Jan Outlý, Michal Šoltés, Tereza Palanská, Martin Gregor, Felipe Gonzalez, Jan Mareš, anonymous referees, and seminar participants at Charles University and the University of Wroclav for their excellent comments and suggestions. Any remaining errors are mine. Code and data are available at http://miroslavpalansky.cz/research/connections-2020/.

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Appendix

Appendix

See Tables 9, 10, 11, 12, 13, 14, 15, 16 and Fig. 3.

Table 9 Balancing properties of the samples of connected and non-connected firms, before and after matching
Table 10 Results of a t-test, clustered at the party network level, of equal means of financial performance indicators for connected and non-connected (but otherwise similar) firms
Table 11 Results of the pooled model with return on equity as the dependent variable, using different trimming and winsorization procedures, while using the preffered smoothing period t \(\rightarrow\) t + 2; OLS
Table 12 Results of the pooled model with return on equity as the dependent variable, using different smoothing periods, while using the preffered trimming procedure at \(\langle -\,1,1\rangle\); OLS
Table 13 Sensitivity analysis for the pooled model with return on equity as the dependent variable; OLS
Table 14 Sensitivity analysis for the pooled model with return on assets as the dependent variable; OLS
Table 15 Results of the pooled model which divides the sample into 5 quintiles based on the volume of public procurement supplied by firms within the same sector between 2006 and 2014
Table 16 Results of the pooled models in the matched sample, OLS
Fig. 3
figure3

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Histogram of the number of successful matches of non-connected firms to connected firms.

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Palanský, M. The value of political connections in the post-transition period: evidence from Czechia. Public Choice (2020). https://doi.org/10.1007/s11127-020-00816-3

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Keywords

  • Political connections
  • Political donations
  • Firm performance
  • Rent-seeking
  • Corruption

JEL Classification

  • D22
  • D72
  • H7