Strategic Cost shifting in the Swedish District Heating and Electricity Markets.

AuthorSoderberg, Magnus
  1. INTRODUCTION

    The design of the Swedish district heating (DH) market has been subject to extensive discussions over the last 15 years, with many of these discussions focusing on whether prices should be regulated or not. Historically, the monopolistic Swedish DH firms were subject to a cost-of-service regulation, but in 1996 this regulation was removed since it was argued that the level of competition was high in the network connection stage, i.e. in the market where consumers decide which heating technology to invest in.

    More recently, it has been argued that once consumers have invested in connections to the DH network, they are locked in to the DH technology and to their DH service provider (Biggar et al., 2018; Ryden et al., 2013). The important implication of this is that, in the market where consumers decide how much energy to purchase, DH customers run the risk of being exploited. (1) The incentives to exploit customers are particularly strong if the DH firm operates a combined heat and power (CHP) plant, i.e. if it sells both DH and electricity. These incentives occur because DH firms can increase their profits by strategically shifting costs from the electricity market, where price is determined in a highly competitive spot market, to the protected DH market. Firms' incentives to engage in such cost manipulation were identified in an investigation by the national government already in 2003 (SOU, 2003), and the same investigation also emphasized that the joint production process in CHP plants could lead to reduced cost transparency and uncertainty about how firms allocate shared costs between the two services. The purpose of this paper is to empirically investigate whether there are any signs of DH customers in Sweden being exploited through such strategic cost shifting.

    To this end, we divide the firms into two groups: those that have one or several CHP plants and those that do not. (2) A simple way to determine the effect of CHP on cost is to specify a cost function and add a dummy variable that takes the value one for all firms that belong to the first group. However, this approach is only valid if firms in the two groups have the same cost structure, i.e. if group membership is unrelated to any unobserved factor that is correlated with the firm's cost. This assumption is likely to be violated since firms endogenously determine which group they belong to. It is plausible, for example, that the choice to adopt a CHP plant is related to firm size since the high cost of building a CHP plant has been highlighted as a barrier to CHP adoption and large firms can have access to more and/or cheaper capital. Thus, a larger firm could be in a favorable position when evaluating a CHP investment. It might also be that firms located in warmer areas have more fluctuating demand with higher variable cost per produced kWh, leading to less favorable conditions for CHP. A third reason why CHP adoption can be endogenous is that CHP is viewed more favorably by local parties that have a green profile since CHP allows for reduced dependence on fossil fuel technologies. Regardless of the precise nature of the group selection mechanism, it is clear there is a risk that group selection is endogenous, which would result in a biased estimate of the CHP effect. One way to circumvent this problem is by instrumenting the CHP dummy, but as pointed out by Berthelemy el al. (2019), finding a strong instrument when the treatment is highly persistent is difficult. To get a baseline estimate of the CHP effect, we begin by ignoring the endogeneity and estimate this dummy variable model using OLS.

    An easier way to relax the exogeneity assumption is to use matching algorithms, which build on the idea that missing values are imputed from other, similar firms. The degree of 'similarity' can be determined in several different was. Two common approaches are: 1) designing a weighted function of covariates, called the nearest neighbor matching, and 2) using estimated treatment probabilities, i.e. propensity score matching. Both of these approaches are used in this study. With reported cost data for the non-CHP firms and reported and imputed cost data for the CHP firms, it is possible to investigate 1) how going from non-CHP to CHP affects cost, and 2) conditioned on strategic cost shifting, how much of that shifted cost is passed on to consumers.

    Some attempts have previously been made to better understand the underlying cost structure of Swedish DH operations. For example, Reidhav and Werner (2008) investigated the cost of providing networks in low density areas, Persson and Werner (2011) scrutinized the distribution capital cost under different supply and demand conditions and Westin and Lagergren (2002) and the Swedish government (SOU, 2004) tried to determine the size of the price-cost margins. These studies focus on specific cost aspects and there are very limited insights about the total cost characteristics in DH markets. Moreover, no study, in Sweden or elsewhere, has used econometrics to evaluate the effect of CHP on DH cost.

    The results presented in Section 5 reveal that CHP-firms inflate their average cost by 20-25% and this increase is fully passed on to their customers. The CHP firms' apparent price-cost margin is 8%, which is similar to the margin of non-CHP firms, but by strategically shifting costs they enjoy an actual margin of 30-35%.

    The rest of the paper is structured as follows. Section 2 gives further information about the Swedish DH market and the cost of providing DH. Section 3 describes the data and Section 4 outlines the empirical strategy. Results are displayed in Section 5, and Section 6 concludes the paper.

  2. THE SWEDISH DISTRICT HEATING FIRMS

    DH builds on the principle that heat is generated in centralized thermal power plants by burning different types of fuel, e.g. natural gas, oil, household waste, coal and bio-materials. In some industrial processes, heat is produced as a by-product and sometimes fed into the DH network in the form of waste heat, providing an additional source of heat.

    A necessary condition for strategic cost shifting to occur is that it must increase firms' total profits. Cost shifting increases profits if two conditions are met: 1) once connected to the DH network, consumers are locked into their DH providers, and 2) demand among existing DH customers is price inelastic. While consumers can, in principle, switch to other technologies before the end of the lifetime of their devices, the high upfront cost and excessively high cost of investing in two parallel technologies vastly reduce such incentives. Qualitative and anecdotal evidence supports this conclusion. For example, Ryden et al. (2013) conduct interviews with representatives from district heating firms and find that only a negligible share of customers has switched prematurely from district heating to other technologies. In fact, many district heating firms report that no single customer has switched from district heating in recent decades. (3) Some municipalities have even decided that property owners are not allowed to connect to any other heating source than district heating. This clearly eliminates all competition--in both the investment stage and the energy purchasing stage. (4) The second condition is also supported empirically since the short-run price elasticity of space heating has been found to be very low in Scandinavia. Brannlund et al. (2007) find that this elasticity is -0.05 in Sweden based on quarterly national data for the period 1980-1997. Leth-Petersen and Togeby (2001) restrict their analysis to existing customers (i.e. exclude the effect of customer entry). Based on annual panel data for residential customers in Denmark, they find a price elasticity even closer to zero. These conditions imply that sold quantity of heat would not decline if prices increased.

    2.1 The cost of providing DH

    Several factors influence the cost of DH. The input side consists of prices for heat fuels (described above), labor and capital. Prices for labor and capital are included in the subsequent estimations, but fuel prices are not available. This is not...

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