Incentive Regulation and Investment Decisions Of European Energy Utilities Carlo Cambini♣ (Politecnico di Torino) Laura Rondi♣ (Politecnico di Torino) This draft: March 23th , 2009
Abstract This paper investigates whether the investment and financing decisions of a sample of EU energy utilities are affected by the regulatory regimes and whether this influence is sensitive to the firm’s private vs. state ownership. We find that the investment rate is higher under the price cap regime. However, ownership does matter, as both investment and leverage, but also profitability, tend to be higher when the firm is privately controlled. When we account for the effect of specific regulatory measures, we find that investment decisions at energy utilities under rate of return regulation are positively affected by the WACC rate, while investment at firms under incentive regulation (i.e. price/revenues cap) are negatively affected by the level of the X-factor. Moreover, the effect of a change in the X-factor is asymmetric, as investment rates decrease significantly when the X- factor is raised, but do not increase when the regulator adjusts it downward. This suggests that an increase in the X factor generates a decrease in the ex post returns from investment and additional financial constraints, leading utilities to curb investment plans. JEL: L51, L94, L95 Keywords: Incentive regulation; Investment; Capital structure; Ownership
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Politecnico di Torino, DISPEA, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy. Tel: + 39-0115647292, Email:
[email protected] ♣ Politecnico di Torino, DISPEA, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy. Tel: + 39-0115647232, Email:
[email protected]
1. Introduction Provision of utilities services in adequate quality and quantity and at fair prices requires large amounts of investment that is both irreversible and risky. Investment in infrastructure, and their modernization, is crucial to both prices and quantities in the long run, because delayed investment can have enormous costs from the social point of view. However, investments in regulated industries are highly influenced by the regulatory policy adopted by national regulators. One primary goal of regulation is to promote competition and to enhance social welfare (Armstrong and Sappington, 2006). A major drawback however is that a conflict between social and private interests may arise whenever pro-competitive and efficiency enhancing regulatory regimes undermine the firm’s incentives to invest. In their book, Laffont and Tirole (2000, p. 7) note that: “There is in general a trade-off between promoting competition to increase social welfare once the infrastructure is in place and encouraging ex ante the incumbent to invest and maintain the infrastructure”. This tension is typical of all regulated firms, and particularly so of energy utilities. The relationship between regulation and investment has received a lot of attention by economic theory in the last twenty years (see the survey by Guthrie, 2006), but the empirical evidence is scant and mostly focussed on US regulated utilities. Using a panel of European energy utilities from 2000 to 2007, we investigate the relationship between regulatory mechanisms, investment and financing decisions and how this relationship is influenced by regulated utilities’ ownership. We focus on ownership because in the EU, despite the recent privatisation wave, many energy utilities are still controlled by central or local governments,1 and because ownership issues appear to matter in the relationship between regulators and utilities’ financing and investment decisions (Bortolotti, Cambini, Rondi, Spiegel, 2008; BCRS hereafter). Our data include detailed and comprehensive information on regulatory regimes as well as regulators’ interventions such as WACC and X-factor levels for price cap regimes at various regulatory hearings. Therefore this paper also contributes to the understanding, at the country level, of the evolution of regulatory regimes for energy networks in various EU countries. Different regimes have been implemented in Europe from the mid Nineties, when liberalization started in western European Countries. In the energy industry, like in many 1
At the end of 2000 governments controlled 62,4% of privatized firms (Bortolotti and Faccio, 2008). From that year onwards no significant process of privatization has been carried on in all Europe.
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other utilities, the most significant change in the regulatory regimes was the shift from rate of return to incentive regulation (Crew and Kleindorfer, 1996 and 2002; Vogelsang, 2002; Joskow, 2008). The theoretical framework for our analysis thus draws on the literature that studies the implications of different regimes for capital investment decisions. The famous Averch-Johnson (1962) effect indicates that a monopoly firm under RoR has an incentive to overinvest to expand its asset base, but few incentives to raise its productive efficiency. Mainly to reduce managerial slack, regulators turned to alternative incentive mechanisms, like price- and revenue-cap or profit- and revenue-sharing, which are thought to provide powerful incentives to increase efficiency while leaving excess profits to the regulated operator.2 The literature suggests that regulatory policies affect utilities’ investment decisions depending on which type of investment – in infrastructure or cost reducing - the firm undertakes (Armstrong and Sappington, 2006). Typically, regulatory interventions that deliver no extra profit to the firm, even when its operating costs decline (like in a rate of return regulation), deprive the firm’s incentive to invest in cost reduction. In contrast, if firm’s allowed revenues do not depend on realized cost savings (like in a price- or revenuecap mechanism), the incentive to invest in cost reducing activities should be more pronounced3. A complicating factor, however, is that incentives work differently for infrastructure and for cost reducing investment. Rate of return regulation is thought to provide strong incentives for developing new infrastructure since the rate of return on the asset base is guaranteed and the risk faced by the firm considerably reduced. On the contrary, price cap mechanism may weaken the incentive to invest in infrastructure, especially when the regulatory lags are shorter than the life of the assets, due to regulatory opportunism.4 Therefore regulatory opportunism is expected to affect investment decisions in infrastructure in an important way. Guthrie (2006), surveying the literature on the implications of different regulatory regimes on capital investment in infrastructure industries where irreversibility and uncertainty are key factors, notes that the analysis of the relationship between investment and regulation has still to be understood and strengthened, especially from the empirical point of view. 2
Sappington (2002) is a comprehensive survey of incentive regulation mechanisms and instruments. Joskow (2008) surveys incentive regulation schemes as adopted in the energy industry. 3 Cabral and Riordan (1989) theoretically show that investment in cost reduction is higher under price cap than under rate of return. 4 Vogelsang (2006), focusing on electricity transmission regulated pricing, arrives at similar conclusions.
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Specifically, there is little systematic analysis of the effects of the application of incentive regulation mechanisms for public utilities’ investment, and for energy firms in particular. Greenstein et al. (1995) and Ai and Sappington (2002) investigate the impact of incentive regulation for U.S. telecoms and show that price cap regulation accelerates network modernization, i.e. encouraging investment mainly for cost reducing purposes. Newbery and Pollitt (1997) and Domah and Pollitt (2004) show that UK regional electricity distribution companies increase their productivity and service quality after the introduction of incentive regulation. Estache and Rodriguez-Pardina (1998), Rudnik and Zolezzi (2001), and Pollitt (2004) focus on the impact of incentive regulation on labour productivity of electric distribution companies operating in developing countries. However, none of these studies on energy utilities focus on the interaction between investment and regulatory regimes. To our knowledge, this paper is the first attempt to investigate the relationship between regulatory mechanisms and investment for European energy utilities. We present empirical evidence for a small, but representative panel of electric and gas utilities in France, Germany, Italy, Spain and UK, using detailed information on the regulatory schemes implemented by the National Regulatory Agencies (NRAs) in the period 2000-2007. The first contribution of the paper is therefore to document the main features and changes in the regulatory regimes for the five countries, focussing on how firm-level investment decisions changed over time due to changes in the regulatory framework. Our empirical analyses investigate the impact of alternative regulatory regimes on the investment policy and also highlight if and how different regulatory instruments - the WACC level and the X factor in the cap regulation - affect the utilities’ capital expenditures decisions. During the last twenty years, spurred by the European “Single Market Programme”, a privatisation wave transformed the ownership structure of many public utilities in many EU member states. However, among energy utilities, private ownership and control is still the exception rather than the rule. Another contribution of this paper is to investigate whether the impact of regulatory regimes on investment activities is constant across private and public ownership. The third contribution of this paper is to investigate the effect of regulatory intervention on the financing decisions of regulated energy firms. Regulation is often viewed as the utility’s financial safety net due to greater earnings stability and lower bankruptcy risk. This translates into a greater debt capacity for the regulated utility compared to non-
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regulated firms, thus confirming that regulation per se has an influence on utilities’ financial decisions.5 Recent economic literature (Spiegel and Spulber, 1994 and Spiegel, 1994) shows that regulated firms may increase debt leverage to strategically influence the regulator’s tariff decisions, and that high debt and the related bankruptcy threat mitigate the regulator’ incentive to engage in ex-post opportunism. By this view, leverage may discipline regulatory opportunism while providing the regulated firm with a higher incentive to invest. BCRS (2008) offer empirical support for the interaction between capital structure decision, regulation and ownership for a panel of EU utilities from 1994 to 2005. Our findings suggest that the regulatory regime affects the investment decisions of energy utilities, as firms under incentive regulation tend to invest more than firms under RoR. However, we also find that ownership does matter, as privately controlled firms are found to invest more than state controlled firms, especially if they are under incentive regulation. We find that the allowed cost of capital – the WACC level positively affects the investment decisions of European energy utilities, particularly so those of firms under rate of return. Our results also show that firms under incentive regulation invest more than firms subject to rate of return regulation, but whenever the regulatory environment becomes tougher (the X factor increases) they tend to reduce their investment plans. Focussing on capital structure decisions, we find that private firms rely more heavily on debt finance and that utilities under incentive regulation appear less leveraged, though our results also show that the difference between regulatory regimes disappear when we account for the competitive environment. The paper is organized as follows. Section 2 illustrates the regulatory framework and the institutional context, Section 3 describes the data and presents summary statistics. Section 4 presents the results of the econometric analysis and the robustness checks. Section 5 concludes. 2. Regulatory and Institutional framework in Europe In the energy industry, like in other utilities, two forms of regulatory mechanism are typically implemented – rate-of-return and incentive regulation (for a recent survey and an application to the energy sectors in the US, see Joskow, 2006 and 2008). RoR regulation is a 5
For empirical evidence on U.S. energy (and not only) utilities, see Bradley, Jarell and Kim (1984), Taggart (1985), Daspupta and Nanda (1993) and Bulan and Sanyal (2005). Recently, Ofgem (2008), the UK energy regulator, presented a position paper discussing the arrangements for responding in the event that a network energy company experiences deteriorating financial health.
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typical cost plus mechanism whereby regulatory agencies fix the rate of return that a utility can earn on its assets. They set the price the utility can charge so as to cover all main operating costs and to allow it to earn a specified rate of return. The regulated price can then be adjusted upward if the firm starts making a lower rate of return, and it will be adjusted downward if the utility makes a higher rate. Incentive regulation is typically implemented as price- or revenue-cap mechanisms, originally developed by Littlechild (1983), through the application of fixed-price contracts (Laffont and Tirole, 1993; Armstrong and Sappington, 2007; Joskow, 2007). More specifically, price (or revenue) caps are defined by a (weighted average) price (or average revenue) of a single (or a set of) regulated service(s), adjusted annually by (1) a retail price index that takes care of the economy-wide price level or of the level of input prices, (2) an X-factor that reflects efficiency improvements of the firms6 and (3) a Y-factor that allows for pass-through of specific cost items outside the firm’s control7. The index is further adjusted in the regulatory proceedings over longer term fixed intervals of 3-5 years (regulatory lag). These fixed-price contracts tend to be more successful in reducing managerial slack and operating costs then in extracting the benefits (rents) associated with efficiency increases. Despite the theoretical implications of the different regulatory mechanisms, quoting Joskow (2008, p. 554) “the implementation of any form of incentive regulation is much more complicated and their efficiency properties more difficult to evaluate than it is often implied”. Similarly to RoR regulation, when an incentive mechanism applies, the regulator has to evaluate the capital related costs, the return on this capital, and the expected operating efficiency, it has to forecast the future capital additions which are required to provide service quality targets, and so forth. Therefore, at least in practice, the application of incentive regulation consists in the adoption of elements of traditional cost of service regulation and high-powered “fixed price” incentives.8 Over the past decade the price cap approach has become increasingly successful if compared to RoR regulation, both in the U.S and in Europe, especially after the introduction of liberalization reforms.
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See Bernstein and Sappington (1999) for a theoretical analysis on how the X factor in a price cap plan should be set. 7 See Lyon (1996) and Holzleitner (2001) for to an analysis on how to adjust a cap mechanism in order to provide efficient incentives to effort supply 8 See also Liston (1993) for an earlier review of the differences and the similarities of the two regulatory mechanism.
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Differently from the U.S. where liberalization and regulation of energy sectors dates back to the beginning of last century, in Europe the liberalization of the energy sector only started in the early Nineties. The milestone legislation introducing market liberalization is European Commission Directive 96/92 for the electricity, followed by EC Directive 98/30 for the gas market. The purpose of these directives was to introduce competition in generation/production and distribution, as well as to vertically separate the different segments in the energy value chain. With respect to ownership issues, however, the Commission left the decision about whether privatise or not entirely in the hands of national governments. In most EU countries, privatisations started in the early Nineties with the sale of non-majority stakes and by the end of the decade the process was suspended and was not resumed in the years 2000s (Bortolotti and Faccio, 2008). As a result, central and local governments still hold major ownership stakes in many European energy utilities. The EC Directives established National Regulatory Agencies (NRAs) with the purpose to ensure non-discrimination, effective competition and to regulate wholesale and retail charges in order to implement some forms of incentive mechanism needed to enhance productive efficiency, foster investment and increase service quality. However, the Directives did not impose any mandatory rule on the form of regulatory intervention and delegated to each NRA the definition of the most appropriate regulatory mechanism to apply. To date, most of the NRAs implemented, and actually adopted, fixed-price mechanisms, such as price or revenues cap, while only a few implemented RoR regulation. 3. The Data and descriptive statistics 3.1 The sample firms The main purpose of our empirical analysis is to shed light on the impact of alternative regulatory policies on energy utilities’ investment and financing decisions by matching firm level variables and regulatory data. To this end, we construct panel data for 23 energy companies operating in five EU member states - France, Germany, Italy, Spain, and UK from 2000 to 2007. The sample firms operate in electric transmission and distribution sectors and in the gas transmission and distribution sectors: 13 utilities are active only in one of the regulated services, namely transmission (TSO, Transmission Service Operator) and distribution, 5 are vertically integrated firms operating in both transmission and distribution,
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5 are horizontally integrated, i.e. operate in both electric and gas distribution.9 If we break down the sample
by ownership, 10 firm are privately controlled and 13 are publicly
controlled.10 Following BCRS (2008), we define firms as “privately-controlled” if the state holds less than 30% of the control rights (otherwise the firm is “state-controlled”). Table 1 reports the list of firms by the country and sector. Although we have just 23 firms in our dataset, our sample is largely representative of the firms operating in these sectors for each country. For Italy, Terna and Snam Rete Gas are TSOs for electricity and gas transmission, respectively; in electric and gas distribution the firms in our sample cover approximately the 90% of the market. In France, EDF and Gaz de France are, in practice, the (near) monopolists in electricity transmission and distribution and gas transmission and distribution, respectively. In Germany, E.On and RWE cover approximately 60-70% of the electricity and gas markets.11 In Spain, Red Electrica Espana and Enagas are TSO for electricity and gas transmission, respectively. Gas Natural is the incumbent in gas distribution, with 80% of market share, while in electricity distribution Endesa, Union Ferosa and Iberdrola cover almost all retail market. In UK, National Grid is the TSO for both gas and electricity transmission and has a more than 50% market share in gas distribution, while Scottish Power, Scottish and Southern Energy and CE Electric cover approximately 35-40% market share of the retail electric sector. For this sample of firms, we collected data on accounting and financial data as well as detailed information on regulatory measures for each country (i.e., type of regulation – RoR or incentive regulation – and their changes over time, the regulatory lags, WACC levels, level of X-factor for price cap mechanism). The information collected on the regulatory instruments enables us both to analyse how the regulated energy firms react to the implementation of incentive regulation, and to investigate the direct impact of each single regulatory ingredients - WACC level, X factor, regulatory changes - on firm’s investment and financing decisions.
3.2 Changes in regulatory variables
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We used the largest fraction of sales revenues as a criterion to assign horizontally integrated firms to either the electricity or the gas industry. 10 The firms that are privately owned (mainly the UK and Spanish utilities) were privatized in the mid of Nineties. 11 It is worth noting that the rest of German energy utilities are not listed and it is therefore very difficult to find reliable and comparable data on financial and economic variables.
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Our information on regulatory schemes reveals that RoR is in place in France and in Germany in both electric and gas sectors, while incentive regulation applies in UK, Spain and Italy mostly in the form of revenue caps. Table 2 – breaks down the sample assigning each firm to the regulatory mechanisms in place in its country (Panel A) and compares the mean values of the WACC and X Factor by ownership and regulatory scheme, testing the statistical significance of mean differences. More specifically, in France and Germany, the regulators implement RoR regulation for electricity and gas in both transmission and distribution. In France, the Commission de Régulation de l’Energie, CRE, set the rate of return on capital for the regulatory asset base (RAB) at 7.25% for both gas and electric transmission in the year 2000 and has not changed thereafter. Within electric distribution, the rate of return was raised from 6.50% in the period 2001-2002 to 7.25% in the most recent regulatory periods. The rate of return in gas distribution followed an opposite trend, decreasing from 7.25% to 6.75%. In Germany, the mechanism currently in place is a cost-plus structure, where the rate-or-return is the same in all segments of the energy market and is generally negotiated between the Government and the regulated companies. The rate of return has been stable at 6.50% until the end of 2006 and then increased to 7.91% at the end of 2007. In both countries, incentive regulation is expected to be introduced starting from 2009.12 In Spain, the CNE, Comisiòn Nacional de Energìa, switched from RoR to incentive regulation in 2001-2002; RoR applied only to gas distribution and transmission before 2001 and 2002, respectively. Transport regulation both of electricity and gas are regulated with a revenues cap mechanism. In gas distribution, the rate for the WACC originally set at 8.25% remained unchanged from 2001 to 2007. Finally, electricity distribution has experienced a revenue cap mechanism, which has been recently revised. The UK regulator, OFGEM, (Office of the Gas and Electricity Markets), has been applying incentive regulation since the mid-Nineties. After 2000, the level of WACC real pre-tax applied in each regulatory period to network services has not gone through relevant changes, while the WACC for the gas networks has remained broadly stable over three regulatory periods. In gas transmission and distribution, the WACC remained equal to 6.25% and the X factor to 2% in the whole period 2000-2007. In electric transmission, the WACC increased from 6.25% in 2001 till 6.90% in 2007, while the X factor reduced from 3% to
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In Germany, the evolution from RoR regulation to an incentive mechanism, due to start in 2009, follows the recent establishment of the Energy Regulatory Authority, named Bundesnetzagentur
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2%. In electric distribution, the WACC increased from 6.5% to 6.9%, while the X factor dropped from 3% to zero after 2005. In Italy, the national authority, Autorità per l’Energia Elettrica e il Gas (AEEG) has been applying an incentive regulation since 2000. This mechanism consists of a mix of price cap and revenue cap, respectively applying to the commodity and to the capacity components.
More specifically, in the electricity the WACC of the transport operator
increased from 5.6% in the period 2000-2003 to 6.66% in the following period, while the X factor declined from 4% to 2.4% over the same periods. In distribution, the WACC was decreased from 7.4% to 6.85%, while the cap became less tough passing from 4% to 3.5%. The gas industry reports higher but declining values of the average cost of capital. The WACC went from 9.1% in 2000 to 6.7% in 2006 for gas transmission, and dropped from 8.8% to 7.5% in gas distribution.
3.3 Descriptive statistics Table 3 reports descriptive statistics of the main variables used in the empirical analysis. On the regulatory side, we observe that the average level of the WACC is 7%, ranging from 4% till 9.7%. Within the sample of firms regulated by an incentive mechanism, the average level of the X factor is equal to 2.5%. Table 2, Panel B provides useful information to discuss the main differences across regulatory regimes and ownership. Looking at the statistics for the average cost of capital allowed by the regulator, we find that WACC is not significantly different across incentive and rate of return regulation, but it is significantly different across ownership, as privately controlled firms appear to be allowed a significantly lower rate of return than state-controlled firms. The pattern is similar, but the implications are opposite, when we compare the mean X-factor for private and state controlled utilities under incentive regulation. The average level of the X factor is in fact significantly higher for statecontrolled than for private firms. Table 3 also reports information about some relevant financial and economic variables. Panel A reveals that the average investment rate as measured by the ratio between capital expenditure and total asset, is 6.6%, while the ratio of total investment over total asset is equal to 8.3%. Gearing, i.e. the ratio between net financial position and equity, with an average value of 1.26, is quite impressive. The differences across regulatory regimes appear remarkable (Panel B): firms under incentive regulation report investment rates higher than
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firms subject to RoR (7% versus 4.8% in terms of capital expenditure over total asset), a higher gearing (1.35 versus 0.94), and higher profitability as measured by the EBIT to total assets ratio. Panel C suggests that also ownership seems to matter. The investment rate is higher at privately controlled firms than at state-controlled utilities (7.1% versus 6.1%) while the gearing ratio is twice as high that of state owned firms. Interestingly, this might explain why we actually found a higher average WACC for state-controlled firms, as their capital structure appears to rely more on equity than on debt. The difference in WACC should be discounted for the difference in capital structure. Once we account for this difference, we may interpret the higher X factor for state-controlled utilities as a regulatory strategy to reduce managerial slack. In order to provide some additional insights, we provide graphical evidence on the evolution of the investment and the gearing ratios. Figures 1a and 1b describe the evolution of the investment ratio. We note that investment in firms under RoR follows a descending trend up to 2004 and then resurges with a U-shaped evolution. Investment at capped firms is more stable, except for a spike in the year 2001. The spike is common across state and private utilities, but after 2001 the former follow an increasing trend from 2004 whereas publiclycontrolled utilities continue their descent and only revert it in 2007. In Figures 2a and 2b, we note that the gearing ratio has an inverted-U shaped trend which is determined by firms under incentive mechanisms, as firms under RoR constantly exhibit a lower gearing after 2002. Leverage seems to decline after 2004, but there appears to be a reversal of trend in 2007. 4. Empirical Analysis Our goal is to examine the effects of alternative regulation regimes on the investment and financing behaviour of a sample of European energy utilities, while also controlling for potential differences due to private or public ownership. We begin in Table 4 by dividing our firm-year observations on capital investment and total investment ratios, leverage and profitability into four groups depending on whether they are regulated by incentive mechanisms (price or revenue cap) or by rate of return regulation and depending on whether they are privately- or state-controlled. Panel A presents mean values of the ratio between capital expenditure and total asset while Panel B reports the mean values for the total investment (including acquisitions, i.e. external growths) to total
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assets rate. Panel A shows that, irrespective of whether the ownership is private or public, the average capital investment ratio is higher for firms regulated by cap regulation and, irrespective of regulatory regimes, privately-controlled firms invest significantly more than state controlled firms. However, among firms under incentive regulation, privatelycontrolled firms report, on average, higher investment rates, although the difference is only significant at the 10.9% level. In panel B, where we consider both capital expenditures and investment in merger and acquisitions, we find that privately-controlled firms invest significantly more than state-controlled firms, and that this occurs both under incentive and RoR regulation. We find an interesting difference across ownership, though, because for privately controlled firms only the mean value is not significantly different across regulatory regime. This suggests that when investment for acquisition purposes is included, private firms under RoR do not invest significantly less than firms under incentive regulation. In contrast, the difference is significant for state-controlled firms, where companies under price or revenue cap tend to invest significantly more than companies under Rate of Return. In Panel C, we turn to leverage, as defined by net financial position divided by the book value of equity, or gearing ratio. Our findings suggest that both ownership and regulatory regimes matter for financing decisions, although in a quite intricate way. Firms under incentive regulation appear significantly more leveraged than firms under RoR, but only if control is with private investors. For state controlled energy utilities it is just the opposite, as firms under cap regulation are significantly less leveraged than firms under RoR. The pattern is similar if we examine the gearing ratio across ownership by regulatory regime, since private firms are more leveraged than state firms under incentive regulation, but less leveraged than state firms under RoR (all differences being statistically significant). In panel D, we compare utilities’ profitability across ownership and regulatory regimes using the EBIT to total asset ratio (the ROA, Return on Asset ratio). Not surprisingly, we find that incentive regulated firms overperform firms under Rate of Return, regardless of ownership. We also find that private firms under incentive regulation are more profitable than state firms under RoR. Less obvious is the comparison between state and private firms under incentive regulation, as we find that the difference in mean profitability is no longer significant, suggesting that also state firms may be profitable, provided regulators introduce proper incentive schemes. We then turn to regression analysis, and report the results for investment equations in Tables 5, 6, and 7, and for leverage equations in Table 8.
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4.1. Analysis of Company Investment We begin our analysis by investigating the impact of price regulation and ownership structure on investment decisions in Table 5. For an investment model we follow the microeconometric literature on company investment (see, for example, Hubbard, 1998 for a comprehensive survey of company investment models estimated with panel data, and Fazzari, Hubbard and Petersen, 1988, for a seminal contribution) which suggests to include the log difference of sales to account for accelerator effects and the lagged cash flow to total assets to account for capital markets imperfections. We thus apply a fixed effects approach to the following specification:
yit =α0 +α1∆LogSalesit +α2CFKit-1+α3 PrivateControlit+α4IncentiveRegulationit+βXit+µi+δt+εit [1]
where the yit variable is the investment rate, defined as the capital expenditure to total assets ratio in columns (1)-(4) and as the total investment to total assets ratio in columns (5)-(8), where total investment includes capital investment additions due to mergers and acquisitions; Private Control is a dummy equal to 1 when the utility is privately controlled (that is where the government holds an equity share lower than 30%), Incentive Regulation is a dummy which is equal to 1 when the utility is under the price cap or revenue cap regime and Xit is a vector of other explanatory variables controlling for the competitive and regulatory environment. The µi and δt variables denote the firm specific and time-specific dummies and εit is the error term. Our estimates in columns (1)-(4) show that the control variables are significant, indicating that the model is well specified. In columns (1) and (2), we explore the impact of the regulatory and ownership regimes. We find that firms under Incentive Regulation tend to invest significantly more than firms under rate of regulation, and that this result holds even when we add the dummy controlling for firm ownership. More specifically, when we look at the Private Control dummy we find that the coefficient is positive and significant, which indicates that the investment rate is higher at privately controlled firms. In column (3), we include two time variant industry- and country-specific controls for the regulatory and competitive environment: the OECD indexes constructed by Conway and Nicoletti et al. (2006), to measure the extent of Entry Regulation (reflecting the terms and conditions of
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third party access and the degree of market openness at wholesale and retail level) for the Electricity and the Gas sectors and the degree of Vertical Integration, which reflects the separation between the transmission and generation segments of the electricity industry, and between production, distribution and supply in the gas industry. We find, perhaps quite surprisingly, that neither Entry Regulation nor Vertical Integration enter the investment equation estimated in column (3). Finally in column (4) we estimate the same specification for the sub-sample of private firms to further disentangle the relationship between form of regulation and form of ownership, and we find that the coefficient on the incentive mechanism dummy keeps its sign and significance. Although the result that investment is higher under incentive regulation was anticipated by the t-tests on the mean differences in table 4, this is somewhat at odds with the conventional wisdom that firms under rate of return regulation, and not firms under price or revenue caps, tend to display higher investment rates, and often overinvestment. This is a key result of our analysis and we will return soon on this issue. The four remaining columns re-estimate the same specifications with Total Investment, i.e. including investment for mergers and acquisitions, to total assets ratio as dependent variable. The results are very similar except for the lack of significance of the cash-flow variables. As a positive coefficient on liquidity variables usually denotes excess sensitivity of investment projects to cash flow availability due to capital market imperfections, our interpretation is that companies decide upon, and carry on, mergers and acquisitions regardless of financing constraints, possibly because they are more successful in obtaining outside finance when they pursue external growth strategies.13 So far, our regression results are consistent with the mean difference tests in Table 4. Firms under private control and firms under price-cap and tend to invest more than firms under state control and firms under rate of return regulation. This evidence suggests that to sort out the impact of alternative regulatory regimes on investment activity we may have to consider which type of investment the utilities undertake. As proposed by Armstrong and Sappington (2006), the impact of incentive regulation and RoR may differ depending on the specific types of investment delivered in the industry in the time span considered – and incentive regulation is viewed as more appropriate to spur cost-reducing investment while
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Within the EU, in many cases, and especially in recent years, these external growth strategies involve in fact cross-border mergers.
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RoR regulation is expected to be more conducive to infrastructure investment. 14 Although our data do not allow us to distinguish of the typology of investment delivered by the firms, the empirical results lead us to speculate that European energy utilities did not typically undertake physical infrastructure, network expanding investment in the years from 2000 to 2007, but more likely invested in activities that serve primarily to reduce operating costs and to enhance efficiency and profitability. Consistent with this, we found in Table 4, Panel D, that profitability is significantly (both statistically and economically) higher at firms under incentive regulation than at Firms under RoR, regardless of ownership. In Table 6, we investigate the impact of alternative regimes more in detail by introducing two regulatory decision variables, the Weighted Average Cost of Capital (WACC) and the X-factor that the NRAs applied in the period 2000-2007 in their own country.
These are the factual regulatory rates, varying across countries and across
regulatory lags, as two to three revisions were held in the sample period, and also differing for gas and electricity operators. Column (1) shows that the WACC enters with a positive and significant coefficient in our investment equation, suggesting that firms tend to increase their investment the higher is the allowed rate of return. This result, however, does not hold when we introduce the regulation dummy in column (2) and the private control in column (3). While both the private control and the incentive regulation dummies enter with (expected) positive and significant coefficients, the coefficient on the WACC becomes insignificant. As this is thought to be a key variable for companies operating under rate of return regulation, we dig into it a bit further by estimating a specification where the cost of capital variable is interacted with a dummy which is equal to one for firms under the RoR regime. We report the results in column (4). They show, as expected, that firms under RoR invest less than firms under incentive regulation, and, more importantly, that investment decisions of firms under ROR respond positively and significantly to an increase in the WACC.15 This result is perhaps not surprising if one considers that the WACC is the main direct regulatory tool that the regulator sets for utilities under the RoR regime.
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This view is consistent with Vogelsang (2006) which considers the relationship between the length of regulatory cycles and investment when performance-based regulation is applied to electric transmission. Vogelsang (2006) concludes that in the short run productivity-enhancing objectives could be achieved by using specific types of cap mechanisms, such as for example profit sharing schemes. On the contrary, infrastructure investment incentives should be based on rate-of-return regulation with a "used and useful" criterion because they imply long-term adjustments. 15 In an unreported regression we estimated the model for the (small) sub-sample of firms in the RoR regime and found that the WACC enters with a positive and significant coefficient (p-value=2.4%). The results are available on request.
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The remaining columns focus on firms under incentive regulation. Our estimates in columns (5) and (6) confirm that the coefficient on the WACC, although positively signed, is not statistically significant and that privately controlled utilities tend to exhibit higher investment rates than state-controlled firms. In column (7), we focus on the impact of the Xfactor. The results show that the coefficient on the X-factor is negative and significant, indicating that, under more stringent regulatory schemes, European energy utilities tend to curb their investment. Our interpretation is that a tighter cap on current and future prices or revenues leads to tighter liquidity constraints and to underinvestment: an increase in the X factor reduces the ex post expected return from investment from cost savings and the regulated firm’s cash flow; these factors, in turn, have a negative effect on the firm’s investment decision. We decide to further investigate the impact of the X-factor by estimating a model which has the main purpose to analyse the utilities’ investment behaviour following a change in the X-factor or, in other words, to analyse the impact of a change in the X-factor on the investment rate at the time when the X is raised (reduced) as well as one and two years after the upward (or downward) change.16 2
yit = α + ∑ β j ∆XFactort − j +µi + λt + ε it
[2]
j =0
Table 7 reports the results from estimating a model in which the dependent variable is the investment rate (the “Capex” to total assets ratio) and the explanatory variables are a set of contemporaneous and lagged dummies equal to one when there is an increase (Panel A) or a decrease (Panel B) in the X-factor. Lags of ∆XFactor then control for persistence in the effects of the regulatory change and help describe the ex post investment decisions of the energy utilities in our sample. In Table 7, the results are to be read by rows and not by columns as usual. So, for example, reading the coefficients by rows, we see whether the investment rate increases or decreases in the year when the X is raised (Year 0), one year after the X has been raised (Year+1) and two years after the X was raised (Year+2). We then report the F-test that indicates the joint significance of the coefficients on the Year 0, Year 16
From the theoretical point of view, Biglaiser and Riordan (2000) study the dynamics of regulation and, focussing on the capacity investment and replacement cycles, show that under price cap, investment is likely to occur in the early years not in the later years of the regulatory cycle. Moreover, price-cap regulation leads to more efficient capital replacement decisions than RoR regulation.
15
+1, Year+2 dummies and, finally, the usual R-squared statistics for the goodness of fit. We test three specifications to account for potential differences in the timing of the investment decisions’ reactions to a change in the X. Notably, in the first row we allow for a contemporaneous effect and one-year lag; in the second row, we take also the second lag to allow for a longer time response, while in the third row, we hypothesise that the investment reacts one and two years after the change and not in the contemporaneous year. The estimates in Table 7 are not only consistent with the results in Table 6, but also provide an important qualification about the direction of the impact of a regulatory change. In Panel A, regardless of the timing of the response, the results consistently indicate that an increase in the X is followed by a decrease in the investment rate and that the slowdown may last up to two years after the regulatory change. The null hypothesis of joint insignificance of the coefficients is rejected in all estimations. In contrast, in Panel B, when we examine the impact of a decrease in the X-factor we find no effect whatsoever on the investment decisions. These findings are interesting for two reasons. Firstly, they indicate that an upward or downward revision of the X-factor generate quite an asymmetric response from energy utilities’ investment decisions whereby an increase in the X causes a slowdown in the investment programs, but a decrease in the X appears to be useless if the purpose is to foster investment. Secondly, they throw some light on the nature of the investment decisions of the sub-group of utilities operating under incentive regulation in the first decade of the 2000s. An increase in the X may in fact be viewed as leading to either an efficiency-seeking strategy – possibly involving more investment – or anticipated liquidity constraints due to reduced expected revenues and cash flows, which in turn implies under-investment. In practice, anticipating that the regulator might expropriate the returns from investment through a tighter cap, firms would revise their investment decisions downward. Although it is true that a spur to efficiency might involve new investments aimed at reducing costs, our results suggest that the liquidity constraint effects seem to prevail.17 4.2. Robustness analysis To account for potential misspecifications of the investment model, we performed a comprehensive robustness analysis to account for following issues: i) an omitted variables problem represented by heterogeneous time invariant country- and sector-specific effect that 17
We estimated the same models using the WACC, but as we did not find any informative results we do not report them. Our interpretation is that, after accounting for all the lags the specification requires, there remains too few observations to obtain meaningful results.
16
cannot be estimated with our fixed-effect model; which we deal with by using the random effects approach to panel data analysis; ii) an additional omitted variable bias for not controlling for growth opportunities as estimated by the stock market which we address by including the Market-to-Book value of the firm (see Hubbard 1998, for example); iii) the inclusion of dynamics in the investment model by estimating a model which adds the lagged dependent variable among the regressors, and therefore requires instrumental variable techniques and, in particular, the Generalised Method of Moments proposed by Arellano and Bond (1991) and Blundell and Bond (1998).; iv) the potential endogeneity of the adopted regulatory regime, which we deal with the two-stage least squares instrumental variable method, choosing our instruments among a set of institutional, sectoral and political variables (see for example, Ai and Sappington, 2002). For all these analyses, we briefly describe the main results in this section, and report the results in the Appendix. In Appendix 1 Panels A and B, we address the problem of omitted variables. Panel A reports the random effect estimates that allows us to account for industry and country heterogeneity that may influence the behaviour of energy utilities differently as their country of origin may have followed (slightly) different liberalization and privatisation reforms, have different approaches to regulation and differing ability to enforce regulation itself. When we look at the results in Panel A, however, we find that our main result, that investment is higher at firms under incentive mechanism, is confirmed. The other findings, such as that firms under RoR positively respond to an increase in the WACC (column 2), and that firms under price cap invest less the higher is the X-factor (column 3) are also robust to this change in estimation method. The only exception is the coefficient on the private control dummy that is positive, but not significant for the full sample, although the coefficient becomes significant again when we use the sample of utilities under price cap. Panel B adds the market-to-book ratio, which is included to control for future investment opportunities. The results support the models estimated in Table 6, and the fitness is even improved, as the market to book is highly significant. The main reason why we did not apply this specification from the start is that we would loose several observations and a few firms. As our sample is not very large, we opted for showing the market to book regressions as part of the robustness check. In Panels C and D we address the problem of endogeneity. In Panel C, we estimate the dynamic model using the Arellano-Bond GMM estimator, which employs lags of the dependent variable (and of non-strictly exogenous regressors, such as the cash flow to total
17
asset and sales growth) as instruments. GMM estimators are especially designed for situations where independent variables, including the lagged dependent variable, are not strictly exogenous, and allow for heteroskedasticity and autocorrelation within individuals. The estimated results show that incentive regulation and private control positively affect the investment decisions of EU energy utilities (columns 1 and 2). When we focus on firms under cap regulation, we note that the coefficient on the private control dummy looses significance, although the X-factor remains negatively and strongly significant. Finally, in panel D we address the potential endogeneity of the regulatory regime by instrumenting the incentive regulation dummy with a set of instruments, which include entry regulation, a sector specific dummy, investor protection, and political orientation.18 These are environmental, institutional and political factors, which we think may have influenced the decision to introduce either incentive or rate of return regulation. Our IV estimation supports the results in Table 6, as we find that the both the Incentive regulation and the Private control dummy enter the equations with positive and significant coefficients. We do not estimate the model with the X-factor, as this model does not include the regulatory regime decision variable.19 4.3. Analysis of Leverage Equations Over the last two decades, many utilities in Europe have substantially increased their financial leverage. This “dash for debt” is viewed as potentially dangerous by UK Department of Trade and Industry (DTI-HM, 2004) mainly because high leverage could imply greater risks of financial distress on utilities side, transferring risk to consumers and taxpayers and threatening the future financeability of investment requirements. More recently, the UK energy regulator promoted a public consultation to define regulatory procedures to address a case of financial distress by UK energy utilities OFGEM (2008). In particular, together with specific financial requirements and constraints on the licensees (ring fencing provisions, financial monitoring arrangements, cash lock ups, use of credit ratings 18
The OECD Entry regulation index, as described by Conway and Nicoletti (2006), reflects the terms and conditions of third party access in each country and the degree of market openness at wholesale and retail level; the Investor protection index by Pagano and Volpin (2005) measures the legal protection of and is an updated, time varying version of the anti-director rights of La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998); the Political orientation index measures the political orientation of the government using the scores of the parties forming the executive branch of government (Bortolotti and Faccio, 2008). 19 Lastly, in order to control for the potential bias from a relatively larger number of Italian firms in the sample, we re-estimated the specification in Table 5 after excluding the energy utilities from Italy. The estimated results confirm that both privately controlled firms and incentive regulated utilities display higher investment levels. We do not report the results for reason of space, but they are available on request.
18
etc.), the UK regulator has established the case for a re-opening of price controls in case of potential risk of financial distress. The relationship between regulation and capital structure decisions has been the object of recent empirical studies, such as BCRS (2008), which showed that privately controlled utilities in fourteen EU countries display significantly higher levels of indebtedness than state-controlled firms, the more so when they operate under an independent NRAs. In this paper, we specifically address if and how alternative regulatory schemes may influence the capital structure decisions of a utility firm. To the best of our knowledge, this is the first attempt to investigate the impact of Incentive versus Rate of Return regulation for EU energy utilities’ financial structure. Conventional wisdom, as well as our evidence in Table 5, suggests that firms under RoR are more sensitive to the average cost of capital than firms under incentive mechanisms. Should regulators use the firm’s weighted average cost of capital as a basis for computing the appropriate rate of return which the firm should earn, one would expect that – because the cost of equity is generally higher than the cost of debt firms under Ror will prefer equity over debt. However, this argument may not be sufficient, or appropriate, to explain the financial decisions of firms under incentive mechanisms where also the X factor, and not only the WACC, plays the key role in regulating prices and revenues. Our evidence in Tables 6 and 7 showed that the investment at “capped” utilities is highly responsive to the X-factor in a way that suggests that an increase in the X may act as a liquidity constraint. The interplay of investment and financing decisions motivates the empirical analysis of leverage we present in this section. In Table 8, we examine the financing behaviour of our sample of EU energy utilities. The dependent variable is the Gearing ratio defined as net financial position – total financial debt less cash and equivalents - divided by the book value of equity in columns (1) to (3) and the ratio between the net financial position and total assets in columns from (4) to (6). Similarly to the investment analysis, the key variables are the Incentive Regulation dummy and the Private Control dummy. Our model includes, as explanatory variables, the firm level controls that the corporate finance literature suggests (see for example, Rajan and Zingales, 1995), the log of total assets to proxy for Size effects, the non current asset to total assets to proxy for Tangibility (tangibility of assets is viewed as a guarantee for lenders), the EBIT to total assets to control for Profitability and “efficiency” (more efficient firms are likely to make higher earnings
19
with the same assets) and the ratio of cash flow to capital expenditure to account for Financial Flexibility (companies with greater cash flows have less need to raise external funds to finance investment projects).20 We also control for the competitive environment andfor the extent of market liberalization because competitive pressure may affect the ability of the firm to generate cash flow. We thus include the industry- and country-specific OECD sub-indicators of “Entry Regulation” and “Vertical Integration”. Our baseline regression is the following: yit =α0 +α1Sizeit + α2Tangibilityit + α3FinancialFlexibilityit+ α4Profitabilityit+
α5PrivateControlit + α6IncentiveRegulationit+β1Entryjt+β2VerticalIntegrationjt +µi+δt +εit
(3)
The results of our leverage regressions indicate that ownership affects the capital structure in our sample of European energy utilities. The Private Control dummy enters with a positive and significant coefficient, even when we control for regulatory regime and for the competitive environment, and when we use net debt to total assets as a dependent variable (columns 4 to 6). When we look at the effect of the regulatory regime, our results are less clear-cut. In column (1), the estimated coefficient on the incentive regulation dummy is negative and significant at the 10% level, suggesting that, contrary to the conventional wisdom, firms under incentive regulation are significantly less leveraged than firms under ROR. However, when we include the entry regulation and vertical integration dummies to account for the competitive environment in columns (2) and (3), and when we use the net debt to total asset ratio as an alternative dependent variable, the coefficient on the incentive regulation dummy becomes insignificant. So, differently from the analysis of company investment, where the evidence supported the key role of regulation regimes, and not of the OECD market liberalization variables, we find here that injections of competition and liberalization eventually make firms under the two regulatory regimes more similar from the capital structure, and financing decisions point of view. Notably, when we use the alternative dependent variable (net debt divided by total assets), we find in columns (5) and (6) that the industry-specific degree of Vertical Integration is also significant. The negative coefficient again supports the interpretation that the more competitive and liberalized is the market – where generation and transmission are vertically separated - the stronger is the need to tap 20
Following the literature, in unreported regressions we also included the Depreciation to Total Assets ratio to control for non-debt tax shields, as tax deductions for depreciations are substitutes for the tax benefits of debt financing. Because the coefficient was always insignificant, we eliminated this variable after controlling that the omission did not affect the estimation of the remaining coefficient.
20
external – non-equity - sources of finance. The impact of competition and liberalization appears to be that of reducing the free cash flow of firms on the one side and of increasing the financial requirements on the other side. When we look at the coefficients on the firm level controls we find that they enter with theory consistent signs throughout the estimated models. The positive coefficients of size and tangibility indicate that debt finance flows more easily to large firms and to firms with higher tangibility of assets. The cash flow to CAPEX ratio has the predicted negative sign and, interestingly, becomes insignificant when we add the competitive environment variables, thus confirming the relation between competitive pressure and liquidity constraints. Profitability enters with a positive coefficient (only when we use net debt to total asset in columns (4)-(6)), suggesting that once we control for liquidity, more profitable firms have easier access to debt funds.21 5. Conclusions The relationship between regulation and investment has received a lot of attention by economic theory in the last twenty years, but the empirical evidence is scant and mostly focussed on US regulated utilities. This paper contributes to the field by investigating whether the investment and, in turn, the financing decisions of a small but representative sample of EU energy (electricity and gas) utilities are affected by the regulatory regimes – incentive or rate of return regulation - and whether this influence is sensitive to the firm’s private vs. state ownership. To deal with this issue, we constructed an original panel data of 23 European energy utilities operating in five countries (France, Germany, Italy, Spain and UK), collecting detailed information on the regulatory policies adopted in each countries, their incentive schemes (RoR vs. price-, or revenues- caps), levels of the WACC, levels of the X Factor and at various regulatory revisions from 2000 to 2007. Our results can be summarized as follows.
21
Also for the analysis of leverage we performed robustness tests. In particular, we re-estimated our specification in column (1) using a random effects model to account for omitted country and sectoral time invariant effects, but the Hausman test rejected the random effects approach indicating that our fixed effects results are consistent and unbiased; further, we excluded UK energy utilities from the estimating sample in order to control whether our results might be affected by unusually high levels of gearing at UK firms, but we found that the results were unchanged. All the results are available on request.
21
We find that the investment rate is higher for energy utilities under incentive regime while capital structure, after accounting for firm-, industry- and country-specific controls, does not appear to change with alternative regulatory policies. In contrast, the influence of ownership appears more pervasive, as both investment and leverage tend to be higher when the firm is privately controlled. The empirical finding that firms under incentive regulation tend to invest more than firms under RoR leads to some reflections as to the expected impact of regulatory schemes on investment activity. In fact, our results suggest that incentive regulation may be more effective in inducing especially efficiency-enhancing investment, for example such as investments that make energy transportation and distribution more efficient, reduce energy losses along the network, or increase security. On the contrary, if infrastructure investment is needed, such as expanding networks or extending distribution in rural areas, then rate of return regulation or an additional premium on the WACC for specific types of investment, might provide better incentive for utilities to undertake largely sunk capital expenditures. When we examine the impact of regulatory instruments, namely the WACC and the Xfactor, we find that the allowed WACC positively affects investment decisions, although the influence is statistically significant only for utilities under RoR regulation. In contrast, the results show that investment rates at firms under incentive regulation are negatively and significantly affected by the X-factor and, more importantly, that they respond asymmetrically to regulatory changes of the X, as capital expenditures shrink when the X is raised, but do not augment when the X is reduced. Overall our results point at indicating that incentive regulation and, in particular, exclusive reliance on the X-factor, is highly effective in enhancing cost reducing, profitability boosting operating strategies, but less so at promoting investment. Regulated utilities are found to be highly responsive to increases in the X-factor. But an increase in the X factor may generate a decrease in the ex post returns from investment and additional financial constraints, leading utilities to curb investment plans. Whenever the regulator aims at balancing cost efficiency and investment incentives, any increase in the X should be compensated for by a revision of the WACC that includes a component of reward (premium) for investment programmes.
22
References
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25
Figure 1 – Capital expenditures to total asset ratio: Total, incentive mechanism and RoR 0,09
0,08
0,07
0,06
0,05
0,04
0,03 2000
2001
2002
2003 Total
2004
2005
Incentive mechanism
2006
2007
RoR
Figure 1bis – Capital expenditures to total asset ratio: Total, Private Control and State-control 0.085
0.08
0.075
0.07
0.065
0.06
0.055
0.05 2000
2001
2002
2003 Total
2004
Private contol
26
2005 State-control
2006
2007
Figure 2 – Gearing: Total, incentive mechanism and RoR 2
1,8
1,6
1,4
1,2
1
0,8
0,6
0,4 2000
2001
2002
2003 Total
2004
2005
Incentive mechanism
2006
2007
RoR
Figure 2bis – Gearing: Total, Private Control and State-control 2.9
2.4
1.9
1.4
0.9
0.4 2000
2001
2002
2003 Total
2004
Private contol
27
2005 State-control
2006
2007
Table 1 - Firms’ sample (2000-2007) ELECTRICITY
GAS
TRANSMISSION
DISTRIBUTION
Italy
Terna (TSO)
Spain France Germany
Red Electrica (TSO) EDF E.On, RWE
UK
National Grid (TSO)
Enel, AEM Milano ASM Brescia, Iride, Hera, ACEA Endesa, Iberdrola, Union Ferosa EDF E.On, RWE Scottish Power, CE Electric, Scottish and Southern Energy
Italy
Snam Rete Gas (TSO)
Spain France Germany UK
Enagas Gaz de France E.On (Ruhrgas), RWE National Grid
AEM Milano, ASM Brescia Italgas, Hera Gas Natural Gaz de France E.On (Ruhrgas), RWE National Grid
Table 2 – Panel A: Regulation for EU countries and for companies in the period 2000-2007 RoR regulation
Incentive regulation
France
EDF, GDF
-
Germany
E.On, RWE
-
Italy
-
Spain
Gas Natural (up to 2002) Enagas (up to 2001)
Terna, Snam Rete Gas, Enel Distr., AEM Mi, ASM Brescia, Hera, Iride, Acea, ENI (Italgas) Red Electrica Espana, Iberdrola, Endesa, Union Ferosa Gas Natural (from 2003 onwards) Enagas (from 2002 onwards) National Grid, Scottish Power, CE Electric, Scottish and Southern Energy
-
UK
Table 2 – Panel B: WACC and X Factor differences by regulatory schemes and ownership WACC (2000-2007) Total observations N = 160
Incentive Regulation N = 121
RoR N = 39
0.072 (0.001) N = 55 0.068 (0.000) N = 105
0.071 (0.000) 0.069 (0.001) N = 46 0.072 (0.001) N = 75
0.069 (0.000) 0.067 (0.001) N=9 0.070 (0.000) N = 30
0.004** p = 0.014
- 0.003** p = 0.035
- 0.003** p = 0.050
Total observations Privately-controlled State-controlled Ownership difference p-value
28
Regulation difference p-value
X Factor (2000-2007) Total observations N = 134
0.002 p = 0.395 0.002 p=0.531 0.002 p=0.3062
0.013 (0.001) N = 61 0.035 (0.001) N = 73 - 0.023*** p = 0.000
Table 3 - Descriptive statistics Panel A - Full sample Variable
Mean
Financial variables Capex to total asset Total investment to total asset Gearing Debt to total asset Market to book EBIT-to-Total Asset Cash Flow to total asset Regulatory variables WACC X Factor
Std. Dev.
Min
Max
No. Obs.
0.066 0.083 1.257 0.297
0.032 0.056 1.421 0.148
0.013 0.003 -0.873 -0.144
0.210 0.442 11.214 0.605
181 181 189 181
0.069 0.084
0.029 0.039
-0.012 0.001
0.141 0.236
189 188
0.070 0.025
0.008 0.016
0.040 0.000
0.097 0.050
160 134
Panel B – Regulation – Incentive and RoR Variable Financial variables Capex to total asset Total investment to total asset Gearing Debt to total asset Market to book EBIT-to-Total Asset Cash Flow to total asset Regulatory variables WACC X Factor
Incentive Regulation Mean
Std. Dev.
RoR
No. Obs.
Mean
Std. Dev.
No. Obs.
0.070 0.096 1.354 0.337
0.033 0.057 1.548 0.124
144 136 144 144
0.048 0.065 0.945 0.142
0.021 0.047 0.839 0.132
37 45 45 37
0.076 0.093
0.025 0.035
144 144
0.046 0.053
0.027 0.033
45 44
0.071 0.025
0.009 0.016
121 134
0.070 -
0.005 -
39 -
Panel C – Ownership Private and Public
Variable Financial variables Capex to total asset Total investment to total asset Gearing Debt to total asset Market to book EBIT-to-Total Asset Cash Flow to total asset Regulatory variables WACC X Factor
Private Mean
Std. Dev.
Public No. Obs.
Mean
Std. Dev.
No. Obs.
0.071 0.098 1.758 0.353
0.031 0.055 1.931 0.144
81 81 81 81
0.061 0.072 0.881 0.251
0.033 0.054 0.652 0.135
100 100 108 100
0.076 0.096
0.025 0.039
81 81
0.063 0.075
0.030 0.036
108 107
0.068 0.013
0.008 0.011
55 61
0.072 0.035
0.008 0.010
105 73
29
Table 4 –Investment, Gearing and Profitability by regulatory scheme and ownership type Firms are “state-controlled” if the state’s UCR exceed 30%. Gearing is Net Financial Position divided by the value of equity. The p-values are based on two-sided test of the Null hypothesis that the difference in the average leverage between two different groups is equal to 0. (Standard errors are in parenthesis). Panel A: Capital Expenditure to Total Asset (2000-2007) Incentive RoR Total observations Regulation N = 37 N = 181 N = 144 0.070 0.048 Total observations (0.003) (0.003) 0.071 (0.003) 0.074 (0.003) 0.052 (0.008) Privately-controlled N = 81 N = 68 N = 13 0.061 (0.003) 0.066 (0.004) 0.045 (0.002) State-controlled N = 100 N = 76 N = 24 0.010** 0.008 0.007 Ownership difference p = 0.034 p = 0.109 p = 0.335 p-value
Total observations Privately-controlled State-controlled Ownership difference p-value
Total observations Privately-controlled State-controlled Ownership difference p-value
Total observations Privately-controlled State-controlled Ownership difference p-value
Panel B: Total Investment to Total Asset (2000-2007) Incentive RoR Total observations Regulation N = 45 N = 181 N = 136 0.089 0.065 (0.005) (0.006) 0.098 (0.006) 0.100 (0.007) 0.090 (0.013) N = 81 N = 68 N = 13 0.071 (0.005) 0.079 (0.006) 0.055 (0.007) N = 100 N = 68 N = 32 0.027*** p = 0.001
0.021** p = 0.036
Panel C: Gearing (2000-2007) Incentive Total observations Regulation N = 189 N = 144 1.353 (0.129) 1.758 (0.214) 1.981 (0.244) N = 81 N = 68 0.880 (0.062) 0.792 (0.057) N = 108 N = 76 0.878*** p = 0.000
1.189*** p = 0.000
0.006 p = 0.128
30
Regulation difference p-value 0.024*** p = 0.010 0.010 p = 0.564 0.024** p = 0.037
0.035** p = 0.022
RoR N = 45 0.945 (0.125) 0.591 (0.157) N = 13 1.089 (0.158) N = 32
Regulation difference p-value 0.408* p = 0.092 1.390** p = 0.016 - 0.297** p = 0.030
- 0.498* p = 0.070
Panel D: EBIT to Total Asset (2000-2007) Incentive RoR Total observations Regulation N = 45 N = 189 N = 144 0.075 0.046 (0.020) (0.040) 0.076 (0.003) 0.079 (0.003) 0.061 (0.006) N = 81 N = 68 N = 13 0.063 (0.029) 0.073 (0.003) 0.040 (0.004) N = 108 N = 76 N = 32 0.013*** p = 0.002
Regulation difference p-value 0.022*** p = 0.000 0.022** p = 0.017 0.021*** p = 0.007
0.021** p = 0.022
Regulation difference p-value 0.029*** p = 0.000 0.018** p = 0.013 0.033*** p = 0.000
Table 5 – Impact of ownership and incentive regulation on investment decisions Fixed-effects estimates. Columns (4) and (8) estimated on the sub-sample of privately-controlled utilities. All regressions include year dummies. Standard errors in parentheses are robust to heteroschedasticity and to within group serial correlation. ***, **, * denote significance at 1%, 5% and 10%. Investment Rate
∆Log of Salest Cash Flow to Total Asset t-1 Private Control Dummyt Incentive Regulationt Entry Regulationt Vertical Integrationt
R squared (within) F-test (p value) N. Firms [N. Obs.]
Capital Expenditure to Total Asset
Total Investment to Total Asset
(1)
(2)
(3)
(4) Private
(5)
(6)
(7)
0.027*** (0.009) 0.179** (0.079) 0.045*** (0.016) -
0.028*** (0.009) 0.176** (0.079) 0.032*** (0.004) 0.046*** (0.016) -
0.029*** (0.010) 0.176** (0.081) 0.032)*** (0.020 0.046*** (0.004) -0.001 (0.003) 0.002 (0.004)
0.031** (0.011) 0.015 (0.068) 0.079*** (0.013) 0.017** (0.006) -0.007 (0.006)
0.082** (0.036) 0.037 (0.143) 0.075*** (0.018) -
0.084** (0.036) 0.031 (0.143) 0.045*** (0.008) 0.077*** (0.018) -
0.086** (0.036) 0.043 (0.143) 0.043*** (0.008) 0.089*** (0.024) 0.005 (0.006) 0.003 (0.005)
0.099*** (0.032) 0.016 (0.011) -0.002 (0.005)
0.227
0.252
0.253
0.389
0.221
0.231
0.235
0.308
4.51 (0.00) 4.83 (0.00) 5.82 (0.00) 1327.7(0.0) 9.65 (0.00) 158 [23]
158 [23]
158 [23]
71[11]
31
157 [23]
10.21 (0.00) 157[23]
(8) Private 0.084 (0.051) -0.189 (0.217)
9.23 (0.00) 13.31(0.00) 157 [23]
71[11]
Table 6 – Investment and Regulatory Regimes Columns (5)-(7) include only firms under Incentive Mechanisms. Fixed-effects estimates. All regressions include year dummies. Standard errors in parentheses are robust to heteroschedasticity and to within group serial correlation. ***, **, * denote significance at 1%, 5% and 10%.
Capital Expenditure to Total Asset
All firms
-
(2) 0.027** (0.010) 0.179** (0.081) 0.065*** (0.005) -
0.811* (0.455) -
0.752 (0.477) -
(3) 0.029*** (0.010) 0.173* (0.081) 0.067*** (0.004) 0.031*** (0.004) 0.731 (0.476) -
WACC *Rort
-
-
-
X Factort
-
-
-
0.028** (0.010) 0.162* (0.086) 0.033*** (0.005) -0.167*** (0.037) 1.432*** (0.502) -
0.237
0.294
0.320
0.307
0.247
0.277
0.354
1004.96 (0.00)
52.27 (0.00)
71.29 (0.00)
67.13 (0.00)
913.63 (0.00)
1346.26 (0.00)
780.11 (0.00)
135 [20]
135 [20]
135 [20]
135 [20]
106 [16]
106 [16]
105 [16]
∆Log of Salest Cash Flow to Total Asset t-1 Incentive Regulation Dummy t Private Control Dummy t WACCt Rort
R squared (within) F-test (p value)
N. Firms [N. Obs.]
(1)
Firms Under Incentive Mechanisms
0.025** (0.011) 0.195** (0.080) -
..
32
(4)
(5) 0.029** (0.011) 0.180* (0.091) 0.679 (0.533) -
(6) 0.030** (0.011) 0.169* (0.090) 0.032*** (0.005) 0.615 (0.525) -
(7) 0.029*** (0.010) 0.174** (0.074) 0.038*** (0.005) 0.298 (0.251) -0.761** (0.271)
Table 7 – Impact of a Change in the X Factor on Investment decisions of Energy utilities 2
yit = α + ∑ β j XFactor_ CHANGEt − j + f i + λt + ε it j =0
The dependent variable is Capital Expenditure/Total Assets. X-CHANGEt is a dummy variable that returns a value of one when there is an Increase (Decrease) in the X factor (in subsequent years, e.g., one year after the change, X-CHANGEt-1 = 1, and X-CHANGEt = 0, etc.). Lags of X-CHANGE then control for persistence in the effects of the regulatory change and help describe the ex post Investment decisions of the EU Energy utilities in our sample. All regressions include firm and year dummies. t-statistics in round brackets. Standard errors are robust to heteroskedasticity and within firm (cluster) correlation. ***, **, * denote significance at 1%, 5% and 10%.
Dependent Variable
Capex_totasst Capex_totasst Capex_totasst
Year 0 -0.014 (0.013) -0.017 (0.015) -
Dependent Variable
Year 0
Capex_totasst
0.004
Capex_totasst Capex_totasst
(0.006) 0.000 (0.006) -
Panel A – Impact of an Increase in X-Factor Year Year H0 F-Test +1 +2 (p-value) -0.036** 0.012** (0.013) -0.042*** -0.029*** 0.010*** (0.013) (0.010) -0.0356*** -0.022*** 0.005*** 0.010 0.007 Panel B – Impact of a Decrease in X-Factor Year Year H0 F-Test +1 +2 (p-value) 0.010 0.337 (0.007) 0.009 (0.007) 0.009 (0.007)
33
0.001 (0.006) 0.001 (0.006)
R2-within 0.232 0.152 0.124
R2-within 0.206
0.557
0.062
0.384
0.062
Table 8 – Impact of ownership and incentive regulation on LeverageFixed-effects estimates. All regressions include year dummies. Standard errors in parentheses are robust to heteroschedasticity and to within group serial correlation. ***, **, * denote significance at 1%, 5% and 10%.
Gearing Log of Assett Non Current Asset to Total Assetst Cash Flow to Capext EBIT to Total Assett Private Control Dummyt Incentive Regulation Dummyt Entry Regulationt Vertical Integrationt
R squared (within) F-test (p value) N. Firms [N. Obs.]
(1) Full sample
(2) Full sample
(3) Full sample
(4) Net Debt to Total Asset 0.213*** (0.028) 0.229*** (0.067) -0.013 (0.009) 1.139*** (0.369) 0.078*** (0.022) -0.047 (0.045) -
(5) Net Debt to Total Asset 0.199*** (0.037) 0.196*** (0.065) - 0.011 (0.009) 1.090** (0.422) - 0.032 (0.039) 0.025***
(6) Net Debt to Total Asset 0.194*** (0.035) 0.185*** (0.064) - 0.012 (0.008) 1.090** (0.427) 0.079*** (0.022) - 0.032 (0.039) 0.024***
1.831** (0.836) 1.310** (0.508) - 0.157* (0.089) 4.412 (4.763) 0.651** (0.247) -0.337* (0.168) -
1.838** (0.803) 1.320*** (0.465) -0.115 (0.088) 3.365 (4.518) - 0.035 (0.214) 0.145**
1.801** (0.818) 1.246** (0.483) -0.119 (0.087) 3.366 (4.567) 0.543** (0.263) - 0.035 (0.213) 0.141**
-
(0.061) 0.092
(0.060) 0.088
-
(0.006) - 0.030***
(0.006) - 0.030***
-
(0.139)
(0.143)
-
(0.006)
(0.006)
0.292
0.308
0.308
0.420
0.475
0.484
406.8(0.00)
672(0.00)
612(0.00)
8.68(0.00)
10.1(0.00)
10.2(0.00)
181 [23]
181 [23]
181 [23]
181 [23]
181 [23]
181 [23]
34
Appendix 1 – Robustness Analysis (1) Panel A Random Effects Estimates (1) (2) (3) All Firms All Firms Price Cap
Panel B Specification with Market-to-Book Ratio (1) (2) (3) All Firms All Firms Price Cap 0.033*** 0.033*** 0.034** (0.011) (0.009) (0.013) 0.182* 0.182** 0.118 (0.104) (0.084) (0.070) 0.026** 0.032** 0.031* (0.012) (0.015) (0.063) 0.031*** 0.032*** 0.037*** (0.008) (0.005) (0.006)
∆Log of Salest
0.026*** (0.009)
0.024** (0.010)
0.026*** (0.010)
Cash Flow t-1
0.238** (0.088)
0.190** (0.097)
0.192** (0.082)
0.011 (0.107)
0.019 (0.012)
0.032*** (0.009)
0.034*** (0.012)
-
-
0.021* (0.011)
-
WACCt
-
-
0.519 (0.432)
-
0.146 (0.234)
RoRt
-
-0.157*** (0.037) 1.431*** (0.510) -
-0.640*** (0.220)
-
-0.136*** (0.031)
0.350
0.405
0.432
0.497
110.8(0.00)
987 (0.00)
Market to bookt-1 Private Control Dummyt Incentive Regulation Dummyt
WACC *RoRt X Factort
R squared (within)
1.136** (0.419) -0.921* (0.518)
0.232
0.299
2
1017.6 (0.00)
2238.6 (0.00)
2
0.61 (0.99)
7.44(0.83)
1.98(0.99)
-
-
-
158 [23]
135 [20]
105 [16]
113 [19]
104 [17]
80 [13]
Wald-test χ (p value) Hausman χ (p value) N. Firms [N. Obs.]
35
110.0 (0.00) 976.41 (0.00)
Appendix – Robustness Analysis (2) Panel C - Dynamic model with GMM
Panel D - I.V.
(1)
(2)
(3)
(1)
(2)
0.126 (0.171) 0.029** (0.011) 0.172* (0.099) 0.085* (0.044) -
0.133 (0.163) 0.027** (0.012) 0.172* (0.095) 0.017** (0.008) 0.087* (0.044) -
0.192* (0.112) 0.059* (0.032) -0.502 (0.302) 0.383 (0.421) -2.703** (1.172)
0.027** (0.011) 0.179** (0.071) 0.045** (0.022) -
0.028** (0.011) 0.180** (0.070) 0.032*** (0.009) 0.039* (0.022) -
Arellano-Bond test for AR(1) (p-value)
0.249
0.163
0.021
-
-
Arellano-Bond test for AR(2) (p-value)
0.697
0.739
0.188
-
-
0.879
0.957
0.738
-
-
0.64
0.65
158[23]
158[23]
Capex to Total Assetst-1 ∆Log of Salest Cash Flow to Total Assets t-1 Private Control Dummyt Incentive Regulation Dummyt X Factort
2
Hansen χ test of overid. restrictions (p-value) Adjusted R-squared N. Firms [N. Obs.]
112 [23]
112 [23]
82 [19]
Notes: Robust standard errors in parentheses. All regressions include time dummies. In Panel C, Column (3) includes only firms under Incentive regulation. The instrument set for GMM estimations include, in col. (1), lags of Capex_to_Total Assets, Cash Flow_to_Total Assets, Incentive regulation and the log difference of sales, and time dummies; Col. (2) adds one lag of the Private control dummy; Col (3) adds one lag of the X-Factor. In Panel D, the Incentive regulation Dummy is instrumented with a set of institutional, sectoral and political variables: entry regulation, sector dummy (gas vs. electricity), investor protection and political orientation.
36
