Electricity Taxation and Firm Competitiveness: Evidence from Renewable Energy Financing

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1 Electricity Taxation and Firm Competitiveness: Evidence from Renewable Energy Financing Andreas Gerster Stefan Lamp PRELIMINARY DRAFT - April 2018 Abstract Global concerns for climate change have led to a surge in subsidies for renewable energy sources (RES). Subsidies are typically financed through a levy on electricity prices that can represent an important cost factor for firms in the industry, negatively impacting their international competitiveness. This paper exploits a recent policy change in RES financing that considerably expanded the group of firms that are eligible for a levy exemption to test for the impact of a permanent change in electricity prices on plant-level electricity consumption, fuel input choices, and competitiveness indicators employing production data from the census of the German manufacturing industry. We find that plants that are exempt from the levy increase their electricity consumption by about 5-7.5%, translating to an own-price elasticity of demand for electricity of about to We find further evidence for fuel substitution from gas to electricity, which contributes to higher CO 2 emissions. We do not find evidence that the levy exemption increases international competitiveness, indicating that policies are not effective and result in wealth transfers to the energy-intensive industry. Keywords: electricity tariff; renewable energy; manufacturing industry; energy policy. JEL classification codes: D22; H23; Q41; L60. The authors would like to thank the workshop participants at the 32nd European Economics Association Meeting, the 6th Mannheim Energy Conference, the ZEW Workshop on Environmental Economic Analysis based on Industry Census Data, as well as seminar audiences at the Toulouse School of Economics for helpful comments, discussion, and suggestions. All errors are solely the responsibility of the authors. RWI - Leibnitz-Institute for Economic Research and Ruhr Graduate School in Economics, Hohenzollernstrasse 1-3, Essen, phone: , andreas.gerster@rwi-essen.de. Toulouse School of Economics, University of Toulouse Capitole, Manufacture des Tabacs, 21 Alleé de Brienne, Toulouse Cedex 6, phone: , stefan.lamp@tse-fr.eu.

2 1 Introduction The global threat of climate change has put actions to reduce carbon emissions high on the government agenda of many counties. 1 One key element in reducing carbon emissions, especially in electricity markets, is the development of generation capacities from renewable energy sources (RES). While RES technologies, such as solar and wind, can produce electricity at low marginal costs, they often face high upfront investment costs. To foster RES investment, many countries offer production incentives for green electricity generation in the form of feed-in tariffs (FITs). FITs pay a fixed price (premium) for electricity generated by RES and are typically financed through a levy on electricity prices. The implementation of such support policies has significantly increased RES deployment, at the cost of rising electricity prices. Despite potentially large indirect impacts of the levy on firm input choices and firm outcomes, relatively little is known about the impact of RES financing on industry energy choices and overall productivity. This paper takes advantage of a recent policy reform in Germany to study the impact of an exemption to the electricity levy (EEG levy) using administrative data from the census of German manufacturing plants. The EEG levy represents the single largest cost component of electricity prices in the industry. We study the impact of exemptions from this levy and identify their impact on electricity use, fuel switching, carbon emissions, and competitiveness indicators at the plant level. By doing so, the paper contributes to the public policy debate on RES financing. In general, detailed micro-economic evidence for significant cost variation in energy inputs, such as the one present in our dataset, is rare. The insights of this study can help to shape future environmental policies that aim at permanent price changes, such as the introduction of an economy-wide carbon tax. The potential impact of the electricity levy on (international) firm competitiveness has been at the center of a heated policy discussion that led to the exemption of medium-sized energyintensive manufacturing plants from the levy in For this group of firms, the levy represents up to one third of final electricity prices. Yet, little is known about its impacts on production decisions of manufacturing firms. On the one hand, lower electricity prices can sustain firms international competitiveness. Yet, lower prices may also reduce incentives to invest in energy 1 The United Nations Framework Convention on Climate Change, COP21 Paris agreement (December 2015) has been ratified by 174 out of 197 parties to the convention (January 30, 2018). 1

3 efficiency, with negative impacts on environmental indicators, such as carbon emissions. 2 Finally, increasing the amount of electricity that is no longer subject to the levy leads to distributional concerns, as households and other commercial accounts need to finance the largest share of RES deployment. We base our analysis on Germany where the increasing penetration of RES has led to an substantial rise in the EEG levy from 0.19 cent per kilowatt hour (kwh) in 2000 to 6.24 cent per kwh in The levy imposes a non-negligible cost on the manufacturing industry, giving firms strong incentives to apply for an exemption if eligible. The 2012 policy reform that we study in this paper lowered the threshold for application from 10 Gigawatt hours (GWh) to 1 GWh and considerably extended the number of exempt plants in the manufacturing sector from 683 to 1,663. To estimate the causal impact of electricity price changes on plant-level outcomes, we compare plants that that were affected by the 2012 policy reform and pay only about 15% of the total levy with plants that share a similar history in terms of electricity consumption and economic outcomes but are not affected by the exemption and continue to pay the full levy. For plants to be exempt in 2013, they need to apply in 2012, based on their electricity consumption in To be eligible for the exemption, plants need to verify their electricity consumption between 1 and 10 GWh, and need to show that they are energy-intensive, i.e. the ratio of the total payment for electricity to gross value added needs to be at least 14%. There are several issues that need to be addressed in order to estimate the causal impact of RES levy exemptions. First, as participation is voluntary, plants might self-select into treatment. 3 To mitigate this concern, we take advantage of the policy change as natural experiment and estimate the intention-to-treat (ITT) effect based on a standard difference-in-differences (DiD) framework. More specifically, we compare two groups of plants that are similar in size and share a common history, yet of which only one is newly eligible for the exemption. The first group of 2 While large industrial plants with an annual electricity use above 10 Gigawatt hours (GWh) must provide environmental certification in order to be eligible for the exemption, this is not true for medium-sized plants with an annual electricity consumption between 1-10 GWh. 3 This is a rather hypothetical possibility as the exception leads to a large drop in input costs of about 22% in the plant s electricity bill and profit-maximizing firms should apply. In fact, data from the federal ministry of the environment in Germany (BAFA) shows that the rejection to application ratio has been exceptionally high in 2012 (19%) in comparison to previous years (4-10%), indicating that many plants tried to obtain the exemption even if they did not fully qualify. 2

4 plants, with an annual electricity consumptions between 5 and 10 GWh, was not eligible for the exemption before the policy change in 2012, while the second group, plants with 10 to 20 GWh electricity consumption were already eligible from before. This ITT provides us with a lower bound for the true average treatment effect (ATE) for the group of 5-10 GWh plants. Second, our main econometric specification combines semi-parametric conditioning (matching) and 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 on pre-treatment covariates that directly affect plant 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 plant characteristics to recover the average treatment effect of the treated (ATT) under weaker identifying conditions. Third, we test for heterogeneous treatment effects for plants with a high electricity intensity that should benefit most from the levy exemptions, and plants that have a high export share, which allows us to say something about targeting of the policy. We employ administrative plant level data (Amtliche Firmendaten in Deutschland, AfiD) from the German manufacturing sector that contains a large set of detailed economic variables (employment, sales, exports, etc.). Additionally, it provides information on investments, production, energy usage, and cost structure. We combine the AfiD data with the list of plants that have been exempt from the EEG levy. We observe plants for up to 5 years prior to the policy change in 2012 and one year thereafter. Our main findings show that there is a positive and significant impact for treated plants on electricity consumption, i.e. plants that have been exempt from the levy in 2013 consume significantly more electricity compared to plants in the control group. Using the group DiD strategy, we find a lower bound (ITT) effect of 2.6%. Focusing on the semi-parametric DiD specification, we find that the levy exemption leads to an ATT of approximately 5-7.5%. These figures translate to a short-run own-price elasticity of electricity of about to We find that exempt plants substitute between input fuels and use more electricity at the expense of gas in their total energy mix. We also find some evidence that treated plants are less likely to engage in own electricity generation. Focusing on competitiveness indicators, we do not find any evidence that the levy exemption increases plant productivity. On the other hand, our findings indicate a small but sig- 3

5 nificant negative impact on employment. We find that EEG exempt plants reduce employment by 2-3% more than the control group in 2013, which can be explained by the increasing use of gas (and cogeneration technologies) in control plants. Moreover, regarding induced carbon dioxide emissions, we show that exempt plants significantly increase their CO 2 emissions, which is mainly driven by the German electricity-mix that heavily relies on carbon and lignite technologies. Finally, a back-of-the-envelope calculation reveals that the EEG levy reform in 2012 led to a redistribution of about 190 million Euros from the group of electricity consumers to the group of newly exempt manufacturing plants. As we do not find any competitiveness enhancing impacts, our results indicate that the current policy design predominantly affects energy input choices and leads to rent transfers. The study relates to several strands of literature. First and foremost, it contributes 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 (see for example Abrell, Ndoye Faye, and Zachmann 2011, Martin, Muûls, De Preux, and Wagner 2014, Martin, Muûls, and Wagner 2016, Petrick and Wagner 2014, Wagner, Muûls, Martin, and Colmer 2014) and provided evidence for a significant impact of the ETS on emissions in the early phases of its implementation. There is also a broader literature that focuses on the impact of environmental regulation on the industry and firm competitiveness (Greenstone 2002, Greenstone, List, and Syverson 2012, Hanna 2010, Martin, De Preux, and Wagner 2014, Levinson 1996). Martin, De Preux, and Wagner (2014) is the first study to analyze the impact of a climate change levy (carbon tax) on firms in the UK manufacturing sector, employing detailed micro data. They find that electricity use patterns and energy intensity are affected significantly by the tax, while economic covariates are not impacted. Compared to their study, the present work focuses on the indirect effects of renewable energy financing on the industry. The present paper takes advantage of a policy reform on RES financing to investigate the impact of permanent change in electricity prices on plant level outcomes. This setup allows us to take advantage of a non-marginal price change in electricity cost for identification. The timing of the policy change allows us furthermore to distinguish between the realized price effect and the change in price expectations. In 2012, plants know already whether they will be exempt from the RES levy in the next year but still have to pay the levy. Furthermore, 4

6 while the previous literature has focused on more stringent regulation, i.e. carbon tax, this paper investigates the impact of a price drop on plant level fuel input choices and competitiveness outcomes. The present work furthermore complements the previous literature by looking at a distinct institutional setting and market. As pointed out by Ganapati, Shapiro, and Walker (2016) there are large differences in pass-through of energy costs depending on market structure in the industry. Second, this paper relates to recent work that analyzes electricity taxation and plant level outcomes in the German manufacturing sector (Flues and Lutz 2015, Gerster 2017). Both studies use a regression discontinuity design (RDD) to identify causal treatment effects. Flues and Lutz (2015) focuses on the electricity tariff design to identify the impact of marginal electricity price changes on firm outcomes. Gerster (2017), on the other hand, focuses on the original EEG levy threshold of 10 GWh and exploits local randomization around that threshold during the financial crisis to estimate the causal effects of the exemptions. His results show that exempt firms substantially increase their electricity use and substitute it for other fossil energy fuels, such as natural gas. While the RDD design only allows comparing plants close to the 10 GWh threshold, the current paper expands this analysis to study the group of medium-sized companies in the German manufacturing sector. Employing matching DiD techniques, allows us furthermore to focus on additional outcomes, such as own-electricity generation, and to study how different plants are affected heterogeneously by the policy change, which can provide important additional insights for policy design. More broadly, we contribute to a literature that aims at identifying elasticities for fuel inputs and fuel substitution in the industry (see for instance Bernstein and Madlener 2015, Hyland and Haller 2015, Neenan and Eom 2008, Paul, Myers, and Palmer 2009). These papers typically focus on time-series variation and find a short-run elasticity for electricity of about to (higher in individual manufacturing sub-sectors). Our estimates rely on a natural experiment and matching DiD methodology, which allows us to contrast these earlier estimates of electricity elasticities. Insights from this study makes hence an important contribution on future policy design concerning RES financing. The remainder of this paper is structured as follows. The next section explains in detail the institutional setting of the EEG levy design. Section 3 develops the empirical strategy, while 5

7 section 4 introduces the data and tests for the underlying identification assumptions. Section 5 presents the main results, robustness checks, and elaborates on heterogeneity. Finally, section 6 concludes. 2 Institutional Setting The introduction of the renewable energy act (Erneuerbare Energien Gesetz, EEG) in 2000 in Germany established feed-in tariffs (FITs) for private investors to incentivize the deployment of RES technologies. FIT pay a fixed price (premium) for every kwh of electricity produced by renewable resources and are financed through a levy on electricity prices. 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 (see Figure A.1). Figure 1 displays the average industry electricity prices in 2013 for plants with an annual electricity use between 0.2 GWh and 200 GWh, highlighting the contribution of the EEG levy to the electricity prices in the industry. In 2013, the EEG levy exemption is the single largest cost component of electricity, accounting for about 30.7% of the industry electricity price. 4 The figure highlights the role of surcharges on electricity prices in the industry: plants that have to pay all type of levies and surcharges (non-exempt) face one of the highest electricity prices in Europe, while plants that are eligible to exemptions (fully-exempt) have one of the lowest electricity prices in Europe. 5 The introduction of the EEG levy has led to a heated policy debate concerning the loss of international competitiveness of the German manufacturing industry. As a result, the policymaker allowed for exemptions from the levy for energy-intensive plants of the manufacturing, mining and railway sector as early as These exemptions initially focused on large industrial plants exceeding 10 GWh electricity consumption that qualified to be energy intensive, i.e. that have a ratio of electricity cost to gross value added at the firm level above 15%. Firms apply on an annual basis for the exemption based on certified accounts. Exempt plants paid a reduced EEG levy of 0.05 cent per kwh for all of the plant s electricity use exceeding 10% of the baseline use 4 Electricity prices based on survey data (Source: German federal network agency). As our analysis focuses on medium-sized manufacturing plants consuming between 1 and 10 GWh of electricity, the EEG levy is likely to represent an even larger share of total electricity prices. 5 Source: Eurostat. 6

8 that determines eligibility. As the EEG levy continued to rise continuously in the late 2000s, the political pressure increased to extend the eligibility criteria to include also smaller energy-intensive manufacturing plants. The eligibility rules were thus revised in an 2012 amendment to the original EEG act, decreasing the eligibility cutoffs to 1 GWh of electricity in the previous business year and to a share of electricity cost to gross value added at the firm level of at least 14%. The updated eligibility criteria were first employed in To be exempt in 2013, plants must apply in 2012, based on their electricity consumption in As a result of this reform, the number of exempt plants in the manufacturing sector grew considerably from 683 in 2012 to 1,663 in Furthermore, the reform also impacted the payment schedule for the EEG levy. According to the 2012 law, all plants pay the full EEG levy for the first GWh use of electricity and, if exempt, they pay 10% of the levy for any consumption between 1 and 10 GWh, and 1% for consumption above 10 GWh. Figure 2 depicts the marginal levies that exempt and non-exempt plants have to pay. As the EEG levy corresponds to the largest component of industrial electricity prices, representing approximately 30% of the total electricity price, plants have strong incentives to apply for an exemption if eligible. 7 This paper exploits the 2012 amendment to the EEG to investigate the causal effects of the EEG levy exemptions in As the revision of the eligibility rules passed the legislative process in the summer of 2011, we do not expect plants to have strategically influenced their electricity consumption that year to be declared eligible. 8 Moreover, the revision of the EEG levy payment schedule decreased the incentives for firms to place directly above a certain size threshold, as the reduced rates apply only to the marginal consumptions above the 1 and 10 GWh thresholds. 6 While larger plants, with an annual consumption of more than 10 GWh, need to provide environmental certification on the energy-efficiency potential, smaller plants are not subject to this legal requirement. 7 The policy change 2012 led to a strong increase in plants seeking exemptions, but that did not fulfill the selection criteria (BAFA, 2013). 8 Electricity is typically used as intermediate input in the production process and highly dependent on output. It is unlikely that firms made capital investments to increase their level of electricity consumption in the last months of 2011 based on the the announcement of the law as they did not know their eligibility with certainty. We provide descriptive evidence of pre-treatment electricity trends at plant level to contrast this hypothesis and estimate treatment effects based on the pre-announcement year

9 3 Research Design We aim at identifying the causal impact of the EEG levy exemptions on energy input choices, CO 2 emissions, and competitiveness indicators for German manufacturing plants in In line with Rubin s (1974) potential outcomes framework, let D it denote a treatment indicator that equals unity if plant i in year t is exempt and zero otherwise. Potential outcomes are denoted by Y it (1) if plant i is treated and Y it (0) if it is not treated, i.e. continues to pay the full EEG levy. The time subscripts t and t denote pre-treatment and post-treatment observations, respectively. In addition, the vector X it represents a set of covariates of plant i in year t that are predetermined relative to FIT exemptions. We are interested in estimating the average treatment effect of the treated (ATT), given by α AT T = E[Y it (1) Y it (0) D it = 1], where E[ ] denotes the expectations operator. The fundamental problem of causal inference (Holland 1986) is that only Y it (0) or Y it (1) can be observed, yet not both, so that we cannot directly estimate the ATT. Several challenges to identification need to be addressed to estimate the the causal impact of the EEG levy exemption. First, not all plants are eligible for the exemptions and plants need to apply one year prior to treatment. This might create selection effects for treatment as more productive plants are more likely to apply and plants might select into treatment based on future growth expectations. Second, the levy exemption might have heterogeneous effects for the group of treated plants. Finally, there might be anticipation effects to the policy change. We exploit several features of the EEG levy design, in particular the 2012 policy change in combination with semi-parametric conditioning methods and DiD analysis, to address the abovementioned challenges and to bound our estimates for the causal impact of the EEG levy exemption on plants in the manufacturing industry. 3.1 Natural experiment We start by exploiting the exogenous shift in the eligibility threshold from 10 GWh to 1 GWh in a classic DiD framework. As eligibility is a necessary condition for being exempt, we can focus on the group of plants that becomes newly eligible for the exemption in 2013 and estimate the intention-to-treat (ITT) effect. More specifically, we define the treatment indicator Z i = 1 for plants that consume between 5-10 GWh of electricity in 2011 and thus can become eligible for 8

10 the exemption in The control group, Z i = 0, consists of plant that consume between GWh in 2011 and whose eligibility in 2013 remained unaffected by the reduction of the threshold from 10 to 1 GWh. 9 The ITT can be interpreted as a lower bound estimator for the ATT for the group of 5-10 GWh plants. 10 Accordingly, we identify the ITT by the estimate ˆα IT T from the following DiD equation: where Y i = Y it Y it Y i = α + α IT T Z i + ɛ i, (1) denotes the difference in the outcome variable between the posttreatment year 2013 (t) and the pre-treatment year 2011 (t ), Z i equals 1 for plants with an electricity consumption of 5-10 GWh in 2011, and ɛ i represents an idiosyncratic error term. Estimating equation 1 in first-differences allows us to take into account unobserved time-invariant factors at the plant level, such as the average productivity. Taking advantage of the natural experiment allows us to estimate a lower bound effect for the ATT. Importantly, as the change in cutoff is clearly exogenous, the DiD approach exploits a source of variation that is unrelated to firms selection into treatment. The ITT is identified as long as the trends in potential outcomes do not differ between the group of eligible plants and control plants. While untestable in the data, this assumption is more credible if pre-treatment tests leading up to the policy change have been parallel. Moreover, the stable unit treatment value assumption (SUTVA) has to hold, i.e. the composition of treatment group and control group is stable over time. The precision of the estimates can be improved 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. Finally, this approach as- 9 We define the treatment and control groups for plants of similar size (5-10 GWh vs GWh), as it is more likely that they share common unobservable trends. We test for parallel pre-treatment trends in Section As we cannot determine the second eligibility criterion with full precision, the ratio of electricity cost to gross value added of 14%, we set eligibility Z i to 1 for plants with an electricity use of more than 1 GWh. The interpretation of the ITT as a lower bound estimate remains unchanged by focussing on only one eligibility criterion. This is true even in the case that the change in the selection criteria might have led to additional exempted plants in the control group. 9

11 sumes 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 semiparametric conditioning strategies, which we discuss next. 3.2 Semi-parametric conditioning We refine the ITT estimates by a program evaluation approach that combines matching and DiD techniques to recover the ATT (see for instance Calel and Dechezlepretre 2016, Petrick and Wagner 2014, Fowlie, Holland, and Mansur 2012). This approach allows us to exploit both the longitudinal structure of our dataset and the rich information on firm and plant-level characteristics to recover a consistent estimate of the treatment effect. By combining matching with DiD, the ATT can be identified under the weaker assumption of conditional independence between changes in the outcome variables and treatment status, conditional on pre-treatment covariates X it (Heckman, Ichimura, and Todd 1997). The ATT is given by the following expression: ˆα AT T = { 1 (Y it (1) Y it (0)) } W N0,N N 1 (i, k)(y kt (0) Y kt (0)), (2) 1 kɛi 0 iɛi 1 where Y it refers to the outcome of plant i in year t and Y i0 is the outcome variable in the base year. Furthermore, I 1 denotes the set of N 1 exempt plants, while I 0 and N 0 denote the corresponding control group values. The weight W with kɛi 0 W N0,N 1 (i, k) = 1 determines the weighting of counterfactual observation k. We employ propensity score matching to construct a control group of non-exempt plants that closely match treated plants in terms of pre-treatment covariates X it. To pair treated and control plants, we use distinct matching algorithms based on nearest neighbor (NN) matching, NN with caliper and replacement, and one-to-many matching with caliper and replacement. In case there is more than one control control plant matched to the treatment plant, more similar observations receive a higher weight W. The validity of the matching DiD estimator depends on three main identifying assumptions: conditional independence, overlap, and SUTVA. First, conditional independence requires that the distribution of the control outcome Y it (0), conditional on observable plant and firm-level characteristics, is the same among plants that are exempt to the EEG levy and the group of control 10

12 plants. Second, we require that the support of the distribution of the conditioning covariates overlaps for the treatment and control group. Finally, SUTVA has to hold, i.e. potential outcomes at one plant are independent of the treatment status of other plants. In practice this means that we rule out spillover effects and general equilibrium effects. While some of these assumptions are directly testable in the data, such as overlap, we will provide indirect evidence to show that both SUTVA and conditional independence are credible assumptions in this context. 3.3 Heterogeneity In a final step, we investigate whether treatment effects vary by For example, firms that have a high cost exposure to energy inputs, might be more likely to switch fuels in case they can take advantage of the large drop in electricity costs, related to the EEG levy exemption. We investigate treatment effect heterogeneity by estimating a regression model that follows the spirit of matching (Fowlie, Holland, and Mansur 2012): Y i = δ j + βx it + θx it D i + αd i + ε i, (3) where δ j refers to fixed-effects for group j, which comprises plant j and its m j closest matches. X it denotes the predetermined covariate of plant i in year t that we use for the estimation of treatment effects. To make the estimates comparable to our main results, we weight the observations as in the one-to-many matching DiD approach Data Our main data source is the AFiD (Amtliche Firmendaten in Deutschland) panel, which is an administrative dataset on German manufacturing plants. This dataset includes detailed firm- and plant-level outcomes collected by the statistical offices of the German federal states. It covers the universe of German plants from the manufacturing and industry sector with more than 20 employees and includes about 50,000 plant-level observations per year. The dataset contains a variety of plant-level characteristics, such as their sales, exports and the number of employees. 11 Counterfactual observations are weighted 1/M j, where M j is the number of counterfactual observations in group j. 11

13 It also comprises detailed information on a plant s energy use for various energy inputs, most notably electricity, gas, and oil. Based on the disaggregate information on energy use, we are able to calculate CO 2 emissions using (time-varying) average emission coefficients of the respective fuel types. Information on the energy cost and the gross value added at the firm level can be observed for a representative sample of firms. Our second data source is comprises the complete list of EEG exempt plants that has been published by the federal ministry of the environment in Germany (BAFA) since 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 Starting with the entire BAFA list, we drop the plants that do not belong to the manufacturing sector and are able to match close to 80% of the newly eligible plants. Using the years 2007 to 2013 from the AFiD panel, the combined dataset allows us to observe both plant-level outcomes and EEG levy exemptions for the period 2010 to Furthermore, we observe plant-level outcomes for three additional pre-treatment years. Table 1 presents the group of non-exempt plants, plants that fulfill the newly defined eligibility criteria for the EEG levy exemption (1-10 GWh) and that were exempt in 2013, and the entire group of plants that receive the levy exemption in The table clearly shows that the three groups are very different, as the EEG selection criteria are based on electricity consumption and are hence related to size and energy-input choices. We are able to match around 640 newly eligible plants belonging to the 1-10 GWh group. These plants typically belong to medium-sized manufacturing firms, with an average of 73 employees and 26 million Euros sales. The exempt plants use around 5.5 GWh of electricity in 2013 and their energy-mix is dominated by electricity (60%) and gas (28%). While electricity use in the group of non-exempt plants slightly decreased from 2011 to 2013 by 0.1 GWh, the group of newly exempt firms increased their electricity usage by 0.3 GWh. Assessment of identifying assumptions As elaborated in section 3, for the econometric strategy to hold, conditional unconfoundedness has to be fulfilled in both the natural experiment and the semi-parametric conditioning approach. While not directly testable in the data, we provide indirect evidence by focusing on parallel pre- 12

14 treatment trends. Natural experiment: We first focus on the parallel pre-treatment trends for the natural experiment of a change in eligibility criteria. To increase the similarity of treated and control plants, we focus on the group of plants with 5-10 GWh annual electricity consumption in 2011, treated, and those with GWh electricity consumption in 2011, as control plants. While the treated group was not eligible for exemption in 2013, the control group consists of similar plants, that have been eligible for exemption from Figure 3 assess the common trends assumption graphically and find supporting evidence as the trends for both groups of plants are very similar prior to The figure plots the unconditional trends for six key-variables: electricity use, sales, employment, export share, wage, and electricity share. All variables are measured in logs (but export share and electricity share) and are normalized with respect to The individual data points can be interpreted as growth rates with respect to the base-year 2011 that determines the EEG levy exemption eligibility. We formally test for the differences in pre-treatment growth rates in the two-years leading up to the policy change ( and ) in Table 2. The table confirms the graphical impression and shows that the growth rates are very similar. In fact we only find a statistically difference between the two groups in employment growth two years prior to the treatment, which can be explained by the potential heterogeneous effect of the economic crisis on different firm sizes. Yet, as we find very similar rebound in employment the year thereafter and also other economic and energy-related variables in the year prior to eligibility, we are confident that the two groups are indeed very similar and that conditional unconfoundedness holds. Semi-parametric conditioning: We estimate two alternative propensity score specifications to match treatment and control plants, based on pre-treatment variables from 2011 that either determine the treatment status or that are pre-determined. In specification (1), we condition on variables such as baseline electricity use, the number of employees, sales, the export share and the average wage payments, both in linear and in quadratic terms. In addition, we also exploit the rich longitudinal structure of our data to condition on 2010 and 2009 electricity uses, which eliminates systematic differences in electricity use trend for treated and control observations As electricity use is highly output related, conditioning on past electricity consumption allows us to match firms that share a similar economic history. The presence of the economic crisis in makes this feature especially desirable. Alternatively, we also experiment with a minimum propensity score definition, that only takes into account 13

15 Furthermore, our specification includes dummies for two-digit sector classifications, which captures potential sectoral effects. We estimate the propensity score by logistic regressions. The results are presented in Table A.4. In specification (2), we condition only on the number of employees, sales, and electricity use in 2011, as well as lagged electricity use in 2009, 2010, with strict matching at a two-digit sectoral level. Strict matching forces all treatment-control pair to be of the same sector, which leads to slightly worse balance in terms of covariates, but ensures that differences in trends by sector cannot confound our estimates. As there is a limited number of treated observations in each manufacturing sub-sector, for the propensity score estimation, we regroup plants according to their average energy intensity in the pre-treatment period into 5 sectors and estimate a separate logistic regression for each of the groups. 13 The results are presented in Table A.5. In both specification (1) and (2), we consider only plants with electricity uses in 2011 between 1 and 10 GWh. This data trimming leaves as potential matches plants that are very similar to treated plants in terms of their electricity use, which already improves covariate balance considerably. In Figure A.2, we illustrate the advantage of trimming in terms of covariate balance for the example of electricity. The validity of the semi-parametric conditioning rests on the following three identifying assumptions: First, for conditional independence to hold, we require changes in outcome variables to be independent of the treatment status, conditional on covariates X it. This assumption is equivalent to the common trend assumption of the DiD model and is particularly plausible when conditioning on a rich set of covariates is possible. While untestable in principle, this assumption is more plausible if outcome trends are parallel in the years leading up to the policy intervention. Figure 4 illustrates that we cannot detect differences in trends prior to 2011 for any of our main variables. This result is confirmed when conducting statistical tests on trend differences by treatment status (Table 3). We do not find statistically different trends for electricity use, employment, the export share and wages from 2011 to 2010 and 2010 to 2009 using either Specification (1) or Specification (2) to estimate propensity scores. We reject the null hypothesis of no trend difference at the 95% level only in one out of 12 hypothesis tests in specification (1). Again, the the economic sub-sector and electricity-to-sales ratio in Our results are robust to this choice of the propensity score. 13 We provide robustness to this regrouping, estimating the two propensity scores on three digit sub-sectoral definitions (WZ 2008 definition). 14

16 statistical difference in 2010 is likely related to the sales rebound after the 2009 economic crisis. Second, the overlap assumption requires that the propensity scores distribution of both treatment and control group observations overlaps, i.e. that there are no treated observations with propensity score values that are not reached by any control group observation. This assumption can easily be verified graphically and, as Figure A.3 proves, it is met in our application. Third, SUTVA allows only for direct treatment effects on treated plants, but not for indirect effects on control group observations. Such indirect effects can occur for example when multiple plants are operated by a single firm and production capacity is reallocated. In addition, we need to exclude general equilibrium effects of the policy. While it not possible to formally test for SUTVA, we present indirect evidence that these assumptions are likely to hold in our empirical setting and conduct robustness checks using only single-plant firms. Moreover, while the exemption of large industrial plants (> 10 GWh) can have an important influence on the level of the EEG levy, the 2012 policy reform only marginally increased the levy by 0.04 Euro-cent / kwh as the total electricity consumption of newly eligible plants for the exemption is small Main Results 5.1 Natural experiment We first focus on the results for the unconditional DiD estimator for the sample of plants that consumed between 5 and 20 GWh of electricity in Table 4 lists the main results for electricity use and competitiveness indicators. Each row of the table corresponds to an individual regression of the outcome variable on a treatment dummy that is one for a plant belonging to the group of newly eligible plants (5-10 GWh electricity consumption in 2011). The control group is composed of plants that have been already eligible for the exemption since 2003 (10-20 GWh electricity in 2011) Source: BMU (2013). 15 As the EEG levy increased over the years (Figure A.1), plants had strong incentives to apply if eligible. We assume that this group of plants has successfully adjusted their electricity demand to the reduced rates. Gerster (2017) analyzes the previous EEG levy exemption for larger plants above the 10 GWh threshold and finds a large effect of 40% increase in electricity consumption for exempt plants. This increase in line with long-run estimates for electricity elasticities in manufacturing. 15

17 Focusing on electricity use first, we find that the EEG levy exemption leads to an increase in electricity use of 2.6% in the year following the exemption.as discussed above, this estimate can be interpreted as the ITT, a lower bound for the true ATT for the group of 5-10 GWh plants. The increase in 2013 is a short-term response to a decrease of the electricity price by approximately 22-25%. 16 In line with this result, we find that the electricity share in total energy use shows a positive sign, yet is not statistically significant. Finally, focusing on competitiveness indicators, we find that non of the key-variables, including employment, sales, export share, and investment are affected in the first year of exemption. We move to detailed plant-level data analysis next. 5.2 Semi-parametric conditioning Table 5 presents the main estimates for the ATT, using the two main propensity score estimators introduced above. Each of the columns employs a different matching algorithm for nearest neighbor matching, nearest neighbor matching with caliper and replacement, and 1:20 matching with caliper and replacement. The main outcome variables are differentiated between the treatment year (2013) and the year that determines eligibility (2011). The table is divided in three main groups of outcome variables: electricity use, fuel input choices and related carbon emissions, and competitiveness indicators. Focusing first on electricity use, we find that the EEG exemption has led on an increase in electricity consumption of about 5-7.5%. The main electricity effect are highly aligned and are significant in all specifications at least at the 5% significance level. For an average treated plant with 5.5 GWh electricity consumption in 2013, the levy exemption represents abound 23% reduction in electricity input costs. The short-run fuel elasticity for electricity is hence to be estimated between and Following these estimates, a price reduction of 10% translates to an increase in electricity demand of 2-3%. We also test for differences in gas and the propensity for a plant to have their own electricity generation. Electricity production in manufacturing is often a benefit of co-generation (gas) units. While the individual point estimates for gas show a similar magnitude as electricity, yet with a negative sign, estimates are not statistically significant. 16 The range is for plants that consume between 5 and 10 GWh of electricity, assuming that plants pay the average electricity price of cents / kwh before the exemption. In absolute terms the EEG levy exemption saves plants in the 5-10 GWh size group between 190,000 and 420,000 Euros of annual electricity expenditure (2013). 16

18 This is mainly related to the large standard errors, indicating that there is a considerable amount of heterogeneity in gas usage in manufacturing sub-sectors. Finally, in line with the increased gas usage in the control group, we find that there are more plants engaging in own electricity production in the control group. As the results refer to the difference between the outcome year 2013 and the year determining treatment eligibility, 2011, the differences can be driven by both, a short-term response to the realized price change in 2013 and a response to the expectation of electricity prices, that may have led firms to invest in different type of machinery as early as We will elaborate on these two channels below. In a second step, we evaluate the fuel input substitution and carbon emissions at the plant level. In line with the main effects from panel (a), we find that the point estimate for the share of electricity in the fuel mix of a plant is positive in all specifications. However, due to the small percentage change, this estimate shows only up to be significant in the one-to-many matching in column 3. On the other hand, we find more robust evidence that the gas share decreases for plants that are EEG exempt, with a magnitude of %. Oil, on the other hand, constitutes an interesting robustness check, as it is only an important fuel input in specific manufacturing sub-sectors and usage should not respond to changes in electricity prices due to the low substitutability between the two types of fuels. Finally, based on the individual energy inputs, we calculate the difference of CO 2 emissions between the treated and control group. While total CO 2 takes into account all types of fuels consumed by the plant (including electricity), direct CO 2 refers only to fuels consumed at the plant level, such as oil, gas, and coal. 17 Given the observed fuel-substitution in treated plants, and the fossil-fuel dominated electricity mix in Germany, our estimates indicate that the average fuel emissions increase by %. Finally, as the EEG levy reform was originally motivated with concerns about international 17 To calculate CO 2 emissions for electricity, we use the average carbon factor of the German fuel mix in each year. Ideally, we would be able to calculate the marginal emissions induced by the policy change. However, in order to calculate this information precisely, we need information on the marginal production technology at each point in time, as well as information on the timing of consumption changes. This information is not available. Yet, the decrease in coal prices and weak electricity demand in , has led to hard coal plants dominating the marginal price setting in Germany, with an increasing number of hours with lignite at the margin (Source: Timera Energy Blog, 20 October, 2014). Using the average emission factor can thus be seen as a lower bound for the true CO 2 emissions effect. 17

19 competitiveness for the manufacturing industry and related job-security, our third set of results focuses on competitiveness indicators. More precisely, we estimate the change in employment, sales, export share, and investment. We find that the EEG levy exemption has no impact on sales, export share or investment. Yet, we find a small negative, and significant, impact of the EEG levy exemption on employment. According to our estimates, employment decreased by around 2-3% in exempt plants. This finding may reflect our finding from above that treated plants engage less often in own electricity generation, compared to a control group, which implies lower levels of employment. We elaborate further on mechanisms in Section 5.5 on heterogeneity. 5.3 Price expectations For our main analysis, we are measuring the differences between the base year 2011, that determines treatment status and the year when the policy enters into force, As plants need to apply already in the first half of 2012 for the exemption, they typically receive the confirmation for being exempt in 2013 during the business year Knowing that the exemption will permanently reduce electricity cost in the future, plants might invest and change energy inputs based these expectations. 18 While a response to changes in price expectations is no threat to our identification strategy, from a policy perspective it is interesting to distinguish between the short-run price response and the impact related to changes in price expectations. To distinguish between this two channels, we first focus on the short-run price response in Table 6. For ease of representation, we only present the matching estimators for nearest neighbor and 1:20 with caliper and replacement. Again, 2013 is the first year in which prices for exempt firms have been lower. The results show that the response to electricity is between 2.7% and 5%, and are hence about 2.5 percentage points below the range of estimates that we found in Table 5, indicating that both the actual price change and the change in price expectations can play an important role in plant-level responses. The estimates relating to fuel inputs are in line with this main effect, showing strong evidence for fuel switching. 18 Even though plants need to apply on an annual level, once they have verified their electricity accounts and have obtained the exemption, it will be easy for them to predict whether they will be eligible to apply in the following year. Further background information on the EEG levy exemption can be found in BMU (2013). 18

20 5.4 Robustness We perform robustness checks to test for the credibility of the main identifying assumptions, in particular SUTVA and potential anticipation effects to the policy reform. First, Table 7 tests for possible intra-firm spillovers. It might be the case that multi-plant firms are able to adjust their production decisions, shifting production towards plants that are exempt. Having detailed plant level data, we estimate the treatment effect only with single-plant firms. In our sample, we observe a total of 369 single-plant firms that have been newly EEG exempt in We find that the point estimates for electricity are highly aligned with the ones found in the main effect table and are statistically significant at the 5% level in 3 out of 4 matching estimators. Also the remaining estimates are highly aligned with the main results reported in Table 5, yet the smaller sample size leads to less precision and wider standard errors. Second, Table 8 tests for the possibility that plants anticipate the 2012 policy change in the 2011 business year to make strategic investments to manipulate their electricity consumption to be declared eligible for the exemption. We hence re-do the matching based on the year Conditioning on the electricity consumption (and other covariates) in the year 2010 allows us to exclude the possibility of anticipation as the amendment to the law did only pass the legislative process in the summer of Conditioning on the pre-selection year 2010, we confirm our main estimates and find a significantly higher electricity consumption for the treated firms in the years Again, other point estimates are highly aligned with the main estimates, showing that anticipation does not corrupt our main findings. Finally, we provide additional robustness in the appendix. Tables A.1 shows the treatment effect only for the plants with an energy consumption between 5 and 10 GWh. First, this comparison helps us to align the plant-level estimates with the ITT estimates. Second, as this group of firms is benefitting most from the exception, it is likely that we can identify the treatment effects more precisely. The results confirm the main effects from Table 5. Moreover, Tables A.2 and A.3 perform some robustness concerning the matching strategy. Table A.2 presents the results when refraining from sub-sector aggregations, i.e. we use 3-digit sub-sectoral definitions. Table A.3 shows the main results relying on a reduced propensity score estimation that does only condition on the electricity-to-sales ratio in a given manufacturing sub-sector in Both tables provide evidence for the robustness of our main results. 19