The Effect of Electricity Taxation on Firm Competitiveness: Evidence from the German Manufacturing Sector Andreas Gerster RWI, RGS Stefan Lamp TSE Version: January 2017 - Preliminary and incomplete Abstract Renewable energy (RES) support mechanisms such as feed-in tariffs are often financed through a levy on electricity prices. This levy can represent an important cost factor for firms and adversely affect their international competitiveness. This paper analysis the impact of electricity prices on firm competitiveness in the German manufacturing sector, taking advantage of an exogenous policy reform in 2012 that led to the exemption of a large group of firms from paying the levy. We employ a matching difference-in-difference strategy to obtain precise estimates of the causal impact of electricity prices on firm outcomes. This approach allows us to test for heterogeneous treatment effects and to pin down the precise channel through which firm outcomes are affected. Our preliminary results show that plants exempt from the levy increase their electricity consumption relative to the control group. In contrast, we cannot detect effects of the exemptions on sales or employment. Keywords: Environmental taxation, electricity prices, manufacturing, matching differencein-difference. JEL classification codes: D22, H23, L60, Q41, Q48. RWI and Ruhr Graduate School in Economics, e-mail: andreas.gerster@rwi-essen.de. Toulouse School of Economics, e-mail: stefan.lamp@tse-fr.eu.
1 Introduction Renewable energies (RES) are considered a key pillar in climate-change mitigation. To accelerate their deployment, many countries have implemented feed-in tariffs (FIT) that are typically financed through a levy on electricity that both private and business end users have to pay. For the manufacturing sector, this FIT levy imposes additional costs on intermediate inputs in their production processes. For this reason, potential threats to firm competitiveness have been at the center of a heated policy debate on RES financing mechanisms. In this paper, we exploit a recent policy reform in 2012 in the German FIT levy design to investigate the causal impact of the electricity levy exemptions on firm competitiveness and a broader set of firm outcomes. Our empirical strategy rests on a comparison of the subgroup of firms that became exempt from the levy in 2013 ( treated ) and contrast their outcomes to a group of matched control firms. Our main econometric specification combines matching and differencein-difference (DID) techniques. This combined estimator has gained increasing attention in the literature on ex post evaluation of emission markets (Calel and Dechezlepretre 2016, Petrick and Wagner 2014, Fowlie, Holland, and Mansur 2012). Our matching is based based on pre-treatment covariates that directly affect firm eligibility to participate in the program. Combining matching with the standard DID estimator allows us to exploit both the longitudinal structure of our dataset and the rich information on firm characteristics to recover the average treatment effect of the treated under weaker identifying conditions. We base our analysis on Germany as the increasing penetration of RES led to an important rise in the FIT levy from 0.19 cent per kilowatt hour (kwh) in 2000 to 6.24 cent per kwh in 2014. The levy hence imposes a non-negligible cost on the mostly export-oriented manufacturing industry and firms have strong incentives to apply for an exemption. The 2012 policy reform has considerably extended the number of firms eligible from 734 to 1,716. This reform was targeted at firms with an annual electricity consumption of 1 to 10 gigawatt hours (GWh) and a ratio of electricity expenditure to gross value added of at least 14%. 1 We employ administrative firm 1 Before 2012, firms were eligible to apply for an exemption if their annual electricity consumption was above 10 GWh and the ratio of electricity expenditure to gross value added of at least 15%. 1
level data (Amtliche Firmendaten in Deutschland, AfiD) from the German manufacturing sector that contains a large set of economic variables (employment, sales, exports, etc.), and provides additionally information on investments (breakdown by asset type), quarterly production, coststructure, and detailed energy-usage. We combine the AfiD data with the list of firms that have been exempt from the FIT levy. Moreover, we combine the administrative data with financial covariates from the Bureau Van Dyke database of publicly listed companies in Germany (DAFNE). This allows us to include financial information on equity, liabilities and debt structure (based on both balance sheets and earnings statements) to the analysis. Preliminary results of our main matching estimator show that there exists a positive and significant impact for treated firms on electricity consumption, i.e. firms that have been exempt from the EEG levy in 2013 consume significantly more electricity compared to firms in the control group. In contrast, we do not find effects on employment or turnover. We are currently analyzing the impacts on energy use patterns and emissions as well as investigating heterogeneous treatment effects. The study relates to a growing body of literature that aims at identifying the impact of environmental regulation on firm outcomes. Recent research in this area has mainly focused on the impact of the EU Emissions Trading Scheme (ETS) on the manufacturing industry (Martin, Muûls, and Wagner 2016, Petrick and Wagner 2014, Wagner, Muûls, Martin, and Colmer 2014) and provided evidence for a significant effect of the ETS on emissions in the early phases of its implementation. Recent papers that have made use of German manufacturing data to look at the competitiveness impacts of electricity prices include Flues and Lutz (2015) and Gerster (2015), that both use a regression discontinuity design approach to identify causal effects. Flues and Lutz (2015) take advantage of marginal tax rate changes for identification and do not find any effect of electricity prices on firm outcomes. Gerster (2015), on the other hand, uses the exemption rules of the early FIT design and finds that large manufacturing plants increase their electricity use in response of being exempt from the FIT levy. In a related work, Martin, De Preux, and Wagner (2014) analyses the impact of the UK climate change levy on firms in the UK manufacturing sector. They find that electricity use patterns and energy intensity is affected significantly by the 2
tax. Main contributions This paper makes three main contributions. First, the growing body of literature analyzing the impact of energy taxes, carbon trading, and the renewable financing mechanism on firm outcomes has not yet come to a conclusion concerning the impact of these policies on the industry. Analyzing the impact of the EU-ETS on firm outcomes using the same methodology Wagner, Muûls, Martin, and Colmer (2014) find a sizable and significant impact of the EU-ETS on employment in French manufacturing firms, while Petrick and Wagner (2014) do not find any employment effects for Germany. On the other hand, Flues and Lutz (2015) and Gerster (2015) find different impacts of electricity taxation on firm outcomes in German manufacturing. Taking advantage of the 2012 policy reform, affecting the range of firms that consume between 1-10 GwH, we are able to contribute to this ongoing discussion by providing well-identified estimates that allows us to precisely quantify the impact of electricity taxation (EEG levy) on mid-sized manufacturing firms. Second, our main econometric strategy, matching difference-in-difference method, allows us to analyze heterogeneous treatment effects, in different dimensions (size of firm, energy intensity, financials, etc.). This will help us to understand how electricity taxation impacts different sub-sectors in manufacturing; an important point for policy considerations. Finally, compared to Martin, De Preux, and Wagner (2014), this paper contributes to the discussion on effective renewable energy financing mechanisms. Evidence for fuel substitution in industrial processes from cleaner natural gas to dirtier electricity 2 suggests that the EEG exemption of manufacturing firms might not only have an important impact on firm competitiveness, but also impact greenhouse gas emissions. 2 The German electricity mix is dominated by brown technologies. Coal and ignite account for 45% of final electricity consumption in 2013. 3
Price, in Cent/kWh 0 5 10 15.19 5.86.25 6.22.36 6.50.37 7.61.54 8.38.65 9.08.78 10.75 1.01 10.40 1.12 12.13 1.33 10.07 2.05 10.02 3.53 10.51 3.59 10.74 5.28 9.83 6.24 9.32 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Electricity price without EEG levy EEG levy Source: BDEW 2014 Figure 1: Average industry electricity prices in Germany and share of EEG levy. 2 Background on the EEG levy The introduction of the Renewable Energy Sources Act (Erneuerbare-Energien-Gesetz, EEG) in 2000 established one of the world s most ambitious renewable energy support regime in Germany. It obliges transmission system operators to pay fixed feed-in tariffs to producers of green electricity and to pass on their additional cost to household and business customers by charging a per kilowatt hour (kwh) levy on electricity. Due to increasing RES deployment, the surcharge has soared from 0.19 cent per kwh in 2000 to 6.24 cent per kwh in 2014. Figure 1 displays the average industry electricity price for plants with annual electricity uses between 160 and 200,000 megawatt hours (MWh), highlighting the contribution of the EEG levy to the electricity prices in the industry. 3 3 As we are focusing our analysis of firms between 1 and 10 GWh with individual electricity contract, the EEG levy might represent an even larger share of total electricity prices. 4
The rising EEG surcharge led to a heated policy debate concerning the loss of international competitiveness of the German manufacturing industry. While the largest electricity users were exempt from the surcharge from the onset of the policy, the number of eligible firms grew importantly over time due to lobbying pressure of the industry. The first reform, passed in 2003, extended the eligibility criteria to firms that exceed 10 GWh of electricity consumption and that had a proven ratio of electricity cost to gross value added at the firm level of at least 15%. This paper makes use of the 2012 reform that increased the number of eligible firms considerably by lowering the application criteria for firms. According to the revised rules, all plants with more than 1 GWh electricity consumption can apply for the exemption if their share of electricity expenditure to gross value added at the firm level is at least 14%. As a result of this reform, the number of exempt firms grew considerably from 734 in 2012 to 1,716 in 2013. The reform led to a non-linear payment schedule for the EEG levy: all firms pay the full EEG levy for up to 1 GWh use of electricity, if exempt, firms pay 10% of the levy for any consumption between 1 and 10 GWh, and 1% for consumption above 10 GWh. To apply for the exemption, electricity contracts and bills from the previous year, as well as a calculation of gross value added, have to be confirmed by a certified accountant and then be sent to the Federal Office for Economic Affairs and Export Control (Bundesamt für Wirtschaft und Ausfuhrkontrolle, BAFA). The BAFA decides upon exemptions for the following year, so that eligibility is determined two years before an exemption becomes effective. In our case this means that in order to be exempt in 2013, firms have to apply in 2012 based on 2011 data. As the EEG 2012 bill passed the legislative process only in July 2011, we don t expect firms to have had the possibility to strategically influence their electricity consumption in 2011. 4 Firms have to apply on an annual basis for the exemption and prove that they fulfill the requirements. The main focus of this paper is on the group of firms that consume between 1 and 10 GWh electricity annually. This group of firms were not eligible for the exemption before the 2012 EEG reform, however had 4 Electricity is typically used as intermediate input in the production process. It is unlikely that firms were able to make capital investments required to increase electricity consumption in the last few months of 2011 to strategically increase their consumption to be declared eligible. We will provide descriptive evidence of pre-treatment electricity trends at firm level to contrast this hypothesis. 5
the possibility to apply thereafter. As the EEG levy sums up to about one third of total electricity prices in 2013 (see Figure 1), we expect profit maximizing firms to apply in case of eligibility. 3 Data Our main data source is the administrative plant-level data, AFiD (Amtliche Firmendaten in Deutschland), for the German manufacturing industry. This dataset includes detailed firm and plant level outcomes collected by the statistical offices of the German federal states and only accessible under strict confidentiality rules. It covers the entirety of German plants from the manufacturing and industry sector with more than 20 employees and includes about 50,000 plant-level observations per year. Because electricity from own-generation facilities is not subject to the levy, our analysis focusses solely on plants without own-generation facilities. The dataset contains a variety of plant-level characteristics, such as their gross output, exports or the number of employees. It also comprises detailed information on a plant s energy use for various energy sources, most notably electricity, which allows to observe whether the first eligibility criterion is met. Based on the disaggregated information on energy use, we calculate CO2 emissions using the emission coefficients of the respective fuel types, as for example described in Petrick and Wagner (2014). Information on the energy cost and the gross value added at the firm level can be observed for a representative sample of firms. Unfortunately, the firm s electricity cost is unobserved, so that we cannot precisely determine whether a firm s electricity cost to gross value added ratio exceeds 14%, as required by the second eligibility criterion. Our second data source is a list of exempt plants that has been published by the BAFA since 2010. Using Bureau van Dijk identifiers, tax identification numbers and official municipality keys, we merge this information to the AFiD panel for the years 2010 to 2013. Starting with the entire BAFA list, we drop the plants that do not belong to the manufacturing sector. We are able to match close to 80% of the newly eligible plants. Using the years 2007-2013 from the AFiD panel, the combined dataset allows to observe both plant-level outcomes and EEG levy exemptions for 6
our pre-treatment period 2010-2012. 5 Finally, in order to control for additional firm characteristics such as rentability and debtto-equity ratios, etc., we merge data from the Amadeus (DAFNE) dataset that contains official firm-level balance sheet and earnings statement variables for all listed firms for the years 2008-2013. - Add table on firm descriptives here - 4 Empirical strategy Taking advantage of the 2012 FIT reform, we aim at identifying the causal impact of electricity taxation on firm competitiveness in the manufacturing industry. In line with the potential outcomes framework we denote plant i s outcome Y i (1) if treated and Y i (0) if untreated, i.e. the plant continues to pay the EEG levy. The treatment indicator is denoted D i, the set of covariates is summarized by the vector X, and the time subscripts t and t denote pre-treatment and post-treatment respectively. We are interested in estimating the average treatment effect of the treated (ATT), given by ˆα = E[Y it (1) Y it (0) X, D i = 1] (1) As Y it (0) is not directly observed in the data, we follow two strategies to recover this information and to estimate the true (unbiased) treatment effect. 4.1 Difference-in-Difference In order to get a first estimate of the main treatment effect, we employ a naive difference-indifference (DiD) estimator, comparing the outcome for firms that had to pay the EEG levy in 2012 but are exempt in 2013. The unconditional estimator might be biased in case factors that are related to plant-level outcomes vary across treatment and control group. Improved estimates can 5 We are hence able to observe if firm s had plants that were already eligible for an exemption under the previous rules. 7
be obtained by controlling for pre-treatment electricity consumption, firm demographics, as well as industry classification. However, even assuming that we were able to control for all relevant covariates, the conditional DiD might lead to problems with limited overlap in the distribution of the covariate space X. In case the functional form is misspecified, missing observations are incorrectly imputed (see Fowlie, Holland, and Mansur 2012). Finally, this approach assumes equal weights for all control observations, even though treatment and control firms are not equally similar to treatment plants. A potential solution to these concerns is given by semi-parametric conditioning strategies. 4.2 Semiparametric Conditioning Second, in order to refine our estimates, we follow the recent program evaluation literature (see for instance Calel and Dechezlepretre 2016, Petrick and Wagner 2014, Fowlie, Holland, and Mansur 2012) and apply an estimator that combines matching and difference-in-difference techniques to recover the true causal impact. Our empirical strategy rests on a comparison of the subgroup of firms that became exempt from the levy in 2013 ( treated ) and compare their outcomes to a group of matched control firms. Combining both techniques, the ATT can be identified under the weaker assumption of conditional independence between changes in the outcome variables and treatment status (Heckman, Ichimura, and Todd 1997). Moreover, this approach allows us to exploit both the longitudinal structure of our dataset and the rich information on firm characteristics to recover a consistent estimate of the treatment effect. More specifically, the ATT is defined as ˆα = { 1 (Y it (1) Y i0 (0)) } W N0,N N 1 (i, k)(y kt (0) Y k0 (0)) 1 kɛi 0 iɛi 1 (2) where Y it refers to the outcome of plant i in year t and Y i0 is the outcome variable in the base year. I 1 is the set of N 1 EEG exempt firms and the weight W with kɛi 0 W N0,N 1 (i, k) = 1 determines the weighting of counterfactual observation k. As we are able to observe the pre-reform period 2007-2012, we aim at matching firms based 8
on pre-treatment trends. Given the double selection criteria that firms have to fulfill for the exemption, we can construct a suitable control group by using the fact that some plants exhibit similar electricity consumption patterns but do not meet the second criterion regarding the share of total electricity cost to gross value added. To pair treated and control firms, we use nearest neighbor matching (NN) algorithms based on an estimated propensity scores. 6 The validity of semi-parametric conditioning depends on three main assumptions: conditional independence, stable unit treatment value assumption (SUTVA), and overlap. Conditional independence (unconfoundedness) as stated in equation (3) requires changes in outcome variables to be independent of the treatment status. While untestable in the data, this assumption is more plausible if outcome trends are parallel in the years leading up to the policy intervention for which we can provide visual tests. E(Y(0, t) Y(0, t ) X, D = 1) = E(Y(0, t) Y(0, t ) X, D = 0) (3) While we can directly test for the overlap assumption, it is impossible to test for SUTVA. However, we can present indirect evidence that these assumptions are likely to hold in our empirical setting. In order to test for potential spill-overs at firm level, we contrast our main estimates at plant level with firm aggregated data as well as single-plant firms. Finding similar size effects suggests that SUTVA is likely to hold. 4.3 Heterogeneity Finally, in order to test for the hypothesis that treatment effects vary systematically across plants with different size and financial covariates, we estimate a model similar to Fowlie, Holland, and Mansur (2012). Y it Y it0 = δ j + βx i + θx i D i + αd i + ε i (4) 6 The propensity score is based on variables that are related to the treatment status and pre-treatment outcome variables. Our current baseline specification includes the industry sector, as well as electricity consumption, sales, employment and their squares in 2011. 9
δ j refers to group specific fixed-effects, where group j comprises plant j and its m j closest matches. In order to make these results comparable to our main results, we weight the observations as in the matching approach. We interact the treatment indicator D i with covariates related to size and firm financials. Finally, we test for equality of the θ coefficients. 4.4 Potential concerns Even though the semi-parametric conditioning method allows us to overcome the main issues that are present in the standard DiD framework, two main concerns remain that we would like to address here. Selection - As the BAFA lists allow us to recover only the set of firms that received the EEG exemption but not the entire set of firms that applied for the exemption, there might be some concerns concerning selection. However, our main specification should be able to take this into account using comparable pre-treatment criteria to define plant eligibility. In addition, we are currently experimenting with the use of instruments that are correlated with the propensity of firms to apply for exemption, but exogenous to other outcomes (see also Martin, De Preux, and Wagner 2014). Growth expectations - One additional concern is that firm growth expectations might play an important role in the decision for firms to apply. This is particularly relevant for small firms with close to 1 GWh of electricity consumption that might benefit only little from current exemptions. As we are able to observe pre-treatment trends in inputs and outputs, we can classify firms according to average growth rates leading up to the policy reform and test for our main estimates to hold. 5 Main Results (preliminary and incomplete) This section presents the main results for the impact of the EEG levy exemption on plants in the German manufacturing sector. We present some preliminary results for our main specification, based on matching difference-in-difference and nearest neighbor (NN) matching only. For each 10
treated plant, we keep the closest match of the control plant. 7 Dependent Variable (in log differences) Treatment Control Difference SE T stat Electricity -0.014-0.132 0.118*** 0.0259 4.55 Sales -0.004 0.025-0.029 0.029-0.97 Employment 0.005 0.009-0.004 0.028-0.14 N 563 563 Table 1: ATT for the main variables of interest. Trimmed sample (1-10 GWh electricity consumption in 2011). Standard errors do not take into account that propensity score is estimated. For the preliminary results we have been able to match 563 treated plants in 2013 to a control plant. 8. Table 1 shows log differences for the main dependent variables electricity, sales, and employment. While both the treated and control plants have reduced their electricity consumption from 2012 to 2013, the drop has been larger for control plants (that are subject to the levy). Plants that received the exemption, on the other hand, hardly reduced their electricity consumption leading to a difference in outcomes of 11.8%, which is statistically significant at the 1% level. Sales and employment do not show any significant differences in the preliminary specification. We are in the process of extending this preliminary analysis, performing different matching algorithms in particular with respect to 1:N matches, NN matching with caliper, and alternative specifications of the propensity score. This will allow us to provide robust estimates of the ATT. 5.1 Robustness In addition to the robustness checks pointed out in the main section, we will perform several robustness tests concerning the main underlying assumptions of the semi-parametric condition- 7 As outlined in Section 4, the NN matching is based on propensity scores. The propensity score included the following variables: industry sector, electricity, sales, employment and their squares in 2011. 8 The matching rate from the list of BAFA exempt firms to the AfiD panel has been increased recently. We are now able to match close to 80% which allows us to include additional firms in the estimation 11
ing strategy. In order to contrast our main results we can redefine our treatment specification to make a falsification test, comparing firms that have been already exempt from the EEG levy in previous years (> 10 GWh) with our control group. Unconfoundedness requires to find a zero effect in first differences. 5.2 Heterogeneity - Add results on heterogeneity here - As pointed out in Section 4, we aim at estimating heterogeneous treatment effects, employing data on financials & balance sheet information from the DAFNE database. Heterogeneous treatment effects will help us to answer the question if different sub-sectors are affected in a diverse fashion. These insights will be useful for effective policy formulation. 6 Discussion This paper draft is yet preliminary and incomplete. As data access is limited to on-site and distance processing, we cannot show some of our results concerning a detailed description of the matching algorithm as well as detailed descriptives statistics concerning balancing. We expect a first set of results by the end of February 2017. Finally, we would like to extend our analysis to examine the precise mechanisms that lead to increases in electricity usage as well as analyzing the impact of fuel switching on greenhouse gas emissions. 7 Conclusion This paper analyses the impact of electricity taxation on firm competitiveness and a broader set of firm outcomes. Using exogenous variation in electricity prices, resulting from a reform of the renewable energy financing mechanism, we find that firms that have become exempt from paying the levy significantly increase their electricity consumption compared to a matched control group. We are in the process of extending the current analysis to investigate broader impacts on competitiveness, studying in particular heterogeneous treatment effects. 12
The paper is able to make an important contribution to the policy discussion, independent of the main findings: uncovering a causal link between electricity taxation and a broader set of firm outcomes, our results will reveal that current policies are in fact distorting competition in the manufacturing sector and should be adjusted. On the other hand, showing that electricity taxation has no impact on firm competitiveness, our evidence can motivate policymakers to decrease the number of exempt firms in order to broaden the tax basis and disburden residential electricity consumers from a heavy FIT levy. As the policy might induce fuel switching behavior, the environmental impacts from CO2 will be an important outcome variable to consider. 13
References Calel, R., and A. Dechezlepretre (2016): Environmental policy and directed technological change: evidence from the European carbon market, Review of economics and statistics, 98(1), 173 191. Flues, F. S., and B. J. Lutz (2015): The effect of electricity taxation on the German manufacturing sector: A regression discontinuity approach,. Fowlie, M., S. P. Holland, and E. T. Mansur (2012): What do emissions markets deliver and to whom? Evidence from Southern California s NOx trading program, The American economic review, 102(2), 965 993. Gerster, A. (2015): Do Electricity Prices Matter? Plant-Level Evidence from German Manufacturing, Unpublished mimeo. Heckman, J. J., H. Ichimura, and P. E. Todd (1997): Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme, The review of economic studies, 64(4), 605 654. Martin, R., L. B. De Preux, and U. J. Wagner (2014): The impact of a carbon tax on manufacturing: Evidence from microdata, Journal of Public Economics, 117, 1 14. Martin, R., M. Muûls, and U. J. Wagner (2016): The impact of the European Union Emissions Trading Scheme on regulated firms: what is the evidence after ten years?, Review of environmental economics and policy, 10(1), 129 148. Petrick, S., and U. J. Wagner (2014): The impact of carbon trading on industry: Evidence from German manufacturing firms,. Wagner, U. J., M. Muûls, R. Martin, and J. Colmer (2014): The causal effects of the European Union Emissions Trading Scheme: evidence from French manufacturing plants, in Fifth world congress of environmental and resources economists. 14