Abstract
This study provides an empirical test of price mimicking among publicly owned water utilities. Using a fixed effects spatial Durbin model with data from Swedish municipalities during 2002–2012, I estimate the elasticity of the own relative to neighbors’ average price to 0.14. This behavior can be explained in terms of an informal yardstick competition: when consumers use neighboring municipalities’ prices as benchmarks for costs or as behaviorally based reference prices, policy makers will face the risk of consumer complaints and reduced voter support if deviating too much from neighboring municipalities’ prices. Further, I find some evidence that price mimicking is more pronounced in municipalities where voter support for the ruling coalition is weak.
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Notes
As discussed in the introduction, these markets are not subject to a c-o-s regulation. Still, it should be noted that in none of these markets utilities are completely free to set their own prices. The Austrian utilities are not allowed to set prices that exceed twice the total cost of production. However, Klien (2015) notes that “...price setting appears very ad-hoc and discretionary...” (p. 6) and that “...the Austrian water sector...is characterized by the absence of a regulator...” (p. 6) Similarly, the Swedish market for district heating is in theory regulated by a specific district heating law. But this regulation does not cover price setting per se, so the market may be characterized as unregulated, as argued by Konkurrensverket (2013).
In total there are 290 municipalities. Huddinge municipality has been excluded due to missing data on water prices, and Knivsta municipality has been excluded since it was formed in 2003 (Knivsta was earlier a part of Uppsala municipality).
Moran’s I is a measure of spatial autocorrelation, see Moran (1950) for a technical discussion.
The estimator has been implemented using Stata’s spreg command, which is described in detail by Drukker et al. (2013).
Since the SAR model is nested within the Durbin model, the first procedure is to test the null hypothesis \({\hat{\gamma }}=0\), i.e., that all coefficients on the spatially lagged independent variables in the Durbin model are zero. The p value is 0.035, so the null hypothesis is rejected. Further, even if the Mixed model provides no statistically significant results on the presence of autocorrelation, Wald tests indicate that there is a stronger case for spatial dependence in the dependent variable than in the error term: Since the mixed model nests both the SAR and the SEM models, the procedure is to first test the restriction \(\hat{\rho }=0\), which yields a p value of 0.16. When instead testing the restriction \(\hat{\lambda }=0\) the p value is 0.97.
Testing the null hypothesis \({{\hat{\upgamma }}}=0\) yields a p value of 0.24, so there is a relatively high probability that the SAR model is appropriate.
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Acknowledgements
I would like to thank Richard Friberg, Pär Holmberg, two anonymous referees, and participants at the 2014 ENTER-Jamboree for valuable comments. Financial support from the Jan Wallander and Tom Hedelius Foundation (Grant nr H13-0467) and the Swedish Competition Authority is greatly appreciated.
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Lundin, E. Price mimicking under cost-of-service regulation: the Swedish water sector. J Regul Econ 52, 313–332 (2017). https://doi.org/10.1007/s11149-017-9345-7
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DOI: https://doi.org/10.1007/s11149-017-9345-7