Corporatization and the Behavior of Public Firms: How Shifting Control Rights Affects Political Interference in Water Prices

Abstract

As an alternative to privatization, corporatization implies shifting control rights from politicians to managers—through the creation of a separate legal entity—while ownership remains with the government. Even though corporatized firms are fairly common, little empirical work has tried to quantify the effects of corporatizations. This paper tries to fill this gap by analyzing the effect of corporatization on the price-setting behavior of public firms. The theoretical prediction that corporatization decreases political interference in price setting is tested using a dataset of Austrian water providers. The empirical evidence largely corroborates this hypothesis. Specifically, the results show that the impact of electoral cycles and intense political competition on price setting is significantly restrained in corporatized firms.

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Notes

  1. 1.

    As noted in Bilodeau et al. (2007, p. 120) corporatized entities are “variously labeled as: “executive agencies”, “special operating agencies”, “government corporations”, “contract agencies”, and “nondepartmental public bodies”.

  2. 2.

    A more informal approach arriving, however, at similar conclusions can be found in Frant (1996).

  3. 3.

    The idea of a separation of control and cash-flow rights has received attention not only from a privatization perspective (e.g. Halonen-Akatwijuka and Propper 2008) but also the corporate governance literature (e.g. Edwards and Weichenrieder 2009).

  4. 4.

    It is not a coincidence that corporatized firms share many features with another type of organization with soft incentives that is typically located between state and market: non-profit enterprises. In the spirit of Glaeser and Shleifer (2001), non-profit enterprises are a commitment to soft economic incentives because control and cash-flow rights are separated. Similarly, corporatized firms are a commitment to soft incentives, both economic but also political. The separation of control and cash-flow rights is therefore not only expected to weaken incentives in the case of profit versus non-profit but also for political versus non-political firms. From the point of view of Frant (1996), corporatized public firms are the public sector equivalent to non-profit enterprises in the private sector: Both characterized by soft incentives.

  5. 5.

    See Rechnungshof (2011a).

  6. 6.

    As shown in Shleifer and Vishny (1994), these theoretical results remain valid even if a limited amount of subsidies or bribes between the manager and the politician are introduced. This possibility will be further discussed along with the empirical results.

  7. 7.

    For the related literature that analyzes the role of politics as a determinant of organizational decisions in the water sector, see Bel and Miralles (2003) or Bel and Fageda (2009).

  8. 8.

    Solutions involving private partners are being discussed but have only been implemented in a few cases. Interestingly, the ’private’ partners in these public private partnerships are mostly subsidiaries of public or publicly-owned companies. See Schönbäck et al. (2004).

  9. 9.

    See Finanzausgleichsgesetz 2008, Art. 1 §15.

  10. 10.

    The most important sources of finance to an Austrian municipality are shares from the fiscal equalization scheme (33 %), local taxes, e.g., on business and property (16.7 %), and tariffs for public services (17.4 %). See Statistik Austria (2008).

  11. 11.

    For example the largest Austrian newspaper Krone published an article on 20 August 2011:“33 Percent more: Protests against the water usury”.

  12. 12.

    In Austria only public law entities are allowed to charge ‘Gebühren’ (tariffs).

  13. 13.

    The survey population comprises all Austrian cities above 5000 inhabitants and is therefore comparable to the sample used in the ensuing empirical analysis, which covers all Austrian cities with a population above 10000.

  14. 14.

    Phone interviews were used to follow up on cities that did not complete the questionnaire previously sent by email.

  15. 15.

    Observations were eliminated when unrealistic, e.g. negative values for water losses.

  16. 16.

    A representative household is presumed to consume 150 m\(^{3}\) per year on average.

  17. 17.

    It is therefore important to control for factors like quality and access to water services, which may justify price increases or even be welfare improving.

  18. 18.

    In section 4.5 the related variable \(majoritydi\!f\), indicating the percentage difference of the strongest party from 50 %, will be used as a sensitivity test.

  19. 19.

    See BMLFUW (2009) for details.

  20. 20.

    External providers are typically neighboring municipalities. The percentage rate of ground water is excluded because together with spring water and external provision, it sums to 1 and the coefficient would therefore not be identified.

  21. 21.

    In addition to being restrictive, such an approach can produce misleading or plainly wrong results based on a mis-specified model (see Masten 1993 or Ohlsson 2003).

  22. 22.

    Potentially, all right-hand-side variables except those related to election dates are endogenous. Election dates are fixed on the provincial level and cannot be altered by the municipalities. Lagging election cycle variables would also be inconsistent with theory because it would assume that water prices are affected one year after elections. Thus, the election cycle variable is not lagged.

  23. 23.

    See Vella and Verbeek (1999) for an excellent discussion of control function approaches and their close relation to IV.

  24. 24.

    The two-step procedure first estimates the governance choice by probit using all covariates \(X\) and the instrument \(Z\). To adapt the procedure to panel data, the first-stage probit is estimated using a Mundlak-type transformation to account for the time invariant component in the first stage. Two selection terms, one for selection into treatment \(h\_sel\) and one for selection into non-treatment \(h\_mills\), are subsequently calculated from the first-stage residuals and added to the outcome equation.

  25. 25.

    A similar interpretation arises regarding the interaction effect of \(spring\). While spring water decreases prices for inhouse production, the effect is not significantly different from zero when a service was corporatized. It is unclear from the obtained results if corporatized firms extract the cost savings from spring water as rents or reinvest them into the company.

  26. 26.

    For the model in (2), there is also one first stage for each interaction term. For representation purposes and also because the first stage regarding \(corp\) only is the most interesting one, these additional estimations are not presented here. Test statistics regarding over-identification and weak instruments are, however, given in the respective tables.

  27. 27.

    See Baum et al. (2010) for more details on these test statistics and the associated Stata command.

  28. 28.

    Using more efficient IV-type estimators like LIML or GMM does not solve this problem.

  29. 29.

    The procedure was implemented by using the Stata command ’suest’, which estimates a seemingly unrelated model that allows separate equations for each subgroup while at the same time producing cluster and heteroskedasticity robust standard errors by generating a stacked (common) covariance-matrix. The procedure has the additional advantage that it allows to test for cross-equation coefficient differences. Despite the fact that estimating the equations separately transforms the underlying endogeneity issue to a sample selection problem, controlling for the self-selection using a Heckman selection model (using the same exclusion restrictions as for IV and the control function estimator) yields very similar results to the OLS estimations that are presented here. The results are available on request.

  30. 30.

    Alternative indicators like the win-margin of the leading party lead to similar results.

  31. 31.

    The source for the difference between the interaction and separate equation model is related to the fixed effects. Specifically, when estimating the price-setting regressions separately, municipalities that change their status—e.g., from inhouse to a corporatized firm—are allowed to have different fixed effects in either estimation. Without switchers, the coefficient estimates would be exactly the same as in the interaction model.

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Acknowledgments

The author is grateful to Eshien Chong, Markus Leibrecht, Gabriel Obermann, and seminar participants at Chaire EPPP and ISNIE 2011, as well as two anonymous referees and the editor for helpful comments.

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Correspondence to Michael Klien.

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Klien, M. Corporatization and the Behavior of Public Firms: How Shifting Control Rights Affects Political Interference in Water Prices. Rev Ind Organ 44, 393–422 (2014). https://doi.org/10.1007/s11151-013-9401-0

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Keywords

  • Corporatization
  • Control rights
  • Political interference

JEL Classification

  • D22
  • D72
  • L33