metadata: authors: 0: author: Amann,Juergen
1: author: Grover,Arti Goswami
count: Chile,World
docna: 0: docna: The Effects of Energy Prices on Firm
            Competitiveness : Evidence from Chile
docty: Policy Research Working Paper
abstracts: cdata!: This paper analyzes the impact of
            changes in energy prices on the competitiveness of
            manufacturing firms in Chile. Using the Chilean Annual
            National Industrial Survey data, the paper illustrates that,
            first, increases in energy prices generally do not hurt firm
            competitiveness. Second, the impact of energy prices depends
            on the fuel type&#x2014;while electricity price
            increases are negatively correlated with firm outcomes,
            fossil fuel price increases have a positive association with
            investment and firm productivity, a result that is
            consistent with the strong version of the Porter hypothesis.
            Third, these effects are heterogeneous and vary by firm
            attributes such as size, ownership and location. Fourth,
            investigating non-linear patterns in firm outcomes based on
            the level of energy prices, the findings show that the
            positive correlation between fossil fuel price increases and
            capital upgrading is particularly pronounced when energy
            prices are at relatively low levels.
display_title: The Effects of Energy Prices on Firm
            Competitiveness : Evidence from Chile
listing_relative_url: /research/2023/05/34058645/effects-energy-prices-firm-competitiveness-evidence-chile
topicv3: Energy Efficiency m1326852 533,Carbon Pricing m1327308 1846,Firm Dynamics and Productivity Growth
            m1504195 2864
publishtoextweb_dt: 2023-05-08T00:00:00Z
dois: 10.1596/1813-9450-10436
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2132862
1: author_key: 1066720
model_extractions: Policy Research Working Paper 10436 The Effects of Energy Prices on Firm Competitiveness

# Evidence from Chile

_Juergen Amann Arti Grover_ Finance, Competitiveness and Innovation Global Practice May 2023
Policy Research Working Paper 10436

# **Abstract**

This paper analyzes the impact of changes in energy prices on the competitiveness of manufacturing firms in Chile. Using the Chilean Annual National Industrial Survey data, the paper illustrates that, _first,_ increases in energy prices generally do not hurt firm competitiveness. _Second,_ the impact of energy prices depends on the fuel type—while electricity price increases are negatively correlated with firm outcomes, fossil fuel price increases have a positive association with investment and firm productivity, a result that is consistent with the strong version of the Porter hypothesis. _Third,_ these effects are heterogeneous and vary by firm attributes such as size, ownership and location. _Fourth,_ investigating non-linear patterns in firm outcomes based on the level of energy prices, the findings show that the positive correlation between fossil fuel price increases and capital upgrading is particularly pronounced when energy prices are at relatively low levels.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The of Prices on Firm Effects Energy Competitiveness: Evidence from Chile<sup>∗</sup>

Juergen Amann (University of Nottingham)<sup>†</sup> Arti Grover (International Finance Corporation, World Bank Group)<sup>‡</sup> JEL Classification: D21; D24; L60; O14; Q41; Q55.

Keywords: Energy prices; Fossil fuels; Firm productivity; Firm-level technology adoption, Competitiveness.

> ∗This research was completed under the guidance of Marianne Fay, Country Director for Chile. The authors are grateful to Doerte Doemeland and Yira J. Mascaro for their strategic guidance, and to Esperanza Lasagabaster, Vincent Palmade, Richard Record, Erik von Uexkull and several colleagues from the Ministry of Finance in Chile for their useful comments.

> †email: amannj.work@gmail.com

> ‡Corresponding author, email: agrover1@ifc.org
# **1 Introduction**

Green growth requires green technologies, that is, production techniques that economize on exhaustible resources and emit fewer greenhouse gases. Yet, investment in such technological changes is inhibited because carbon, usually used as shorthand for greenhouse gases (GHGs), is highly mispriced.<sup>1</sup> For instance, fossil fuels subsidies and the failure to implement taxes or controls for internalizing the risks of climate change result in a substantially lower user cost of carbon than is appropriate from a long-term societal perspective. Pricing carbon to reflect its true cost remains challenging due to political reasons.<sup>2</sup> In addition, assessing the effects of energy price variation on firm-level outcomes is difficult due to extensive data requirements and identification issues. Consequently, empirical work analyzing the impact of energy prices on firm competitiveness is still relatively scarcer in developing countries compared to developed economies (Burke et al., 2016).

This paper contributes to understanding the effects of energy price increases on manufacturing firm-level outcomes in Chile, an emerging economy, which has undergone fast-paced economic development in recent years.<sup>3</sup> Chile is an interesting case as the country has experienced a substantial variation in primary energy inputs, including hydro, coal and gas, as a primary source of energy production in the past; see Figure A.1.<sup>4</sup> The country has also committed to carbon neutrality by 2050, but its industry continues to depend heavily on fossil fuels despite some progress. In the context of its broader tax reform agenda, the Boric administration that took office in March 2022 is considering corrective taxes and subsidy reforms to incentivize the use of fossil fuel-saving technologies. At the same time, the government has announced

> 1Note that in this study, we combine various types of fossil fuels which may vary notably in the amount of GHG emissions.

> 2Industrial policies have historically supported the provision of subsidized energy, a major input in manufacturing, especially in markets where cost-competitiveness is particularly important (El-Katiri and Fattouh, 2017). Consequently, policy makers may fear the adverse reactions of interventions targeting a reduction of greenhouse gas emissions through changes in the price-setting mechanism. Climate change mitigation in emerging and emerging economies might pose a challenge from a development perspective if it requires more costly, low-carbon energy sources (Jakob and Steckel, 2014).

> 3Following the historic World Bank Country and Lending Groups classification (last access: December 2022), Chile was an upper middle-income country at the beginning of our sample period in 1995 and became a high-income country in 2012.

> 4Between 1995-2013, more than 95% of Chilean manufacturing firms used electricity, followed by diesel, with 35%-54% using this fuel type. The consumption of other fuels such as LPG and petrol is lower; on average 20%-30% firms use these fuels. A relatively smaller share of firms uses natural gas, coke, kerosene and pipe gas.

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various measures to promote investment, innovation and productivity growth. Welldesigned policy intervention that combines carbon pricing instruments with targeted adjustment support to overcome initial obstacles to adopting new technologies can yield a win-win outcome of higher productivity and lower emissions. However, the current evidence base for policy design is limited. This paper contributes precisely to this gap by assessing the effects of energy price changes on firm outcomes.

To this end, the paper uses the publicly available version of the Chilean _Annual National Industrial Survey_ to study the effects of energy prices on firm outcomes. The data contains 100,803 observations on 12,169 plants, with an average of 4,200 unique firms observed across survey years from 1995 to 2015. We estimate separate elasticities of electricity and fossil fuel for various firm-level outcomes such as productivity, energy efficiency, employment, wages, and profits employing fixed-effects (FE) panel estimations.<sup>5</sup> We also estimate the heterogeneity in responses by firm attributes and non-linearities conditional on energy price levels. We add to the robustness of our results through an Instrumental Variable approach where we utilize the regional energy distribution cost to account for unobserved reverse causality.

This paper makes the following broad contributions: _First_ , it finds that overall increases in energy prices generally do not hurt firm competitiveness. _Second_ , it confirms notable heterogeneity in how energy price surges affect firm performance of the Chilean manufacturing sector in line with other recent works. These depend on the type of energy input, i.e. electricity vs fossil fuels, (Amann et al., 2021; Cal`ı et al., 2022, among others) and hint at various degrees of substitutability between distinct production inputs conditional on firm characteristics (Brucal and Dechezleprˆetre, 2021, among others). For instance, while electricity price increases negatively correlate with production and employment, fossil fuel price increases are not necessarily detrimental to firm performance. For example, a 10% rise in fossil fuel prices correlates significantly with a capital investment increase of 3.1% and an increase in output per worker of 0.8%. In other words, our results support the _strong_ version of the Porter Hypothesis (Porter, 1991; Porter and Van der Linde, 1995) for fossil fuel increases but not for electricity price increases. _Third_ , it points out extensive heterogeneities of energy price hikes along many dimensions. We find heterogeneous effects by firm attributes such as size, ownership and location. The heterogeneity analysis suggests that the strong version of the Porter Hypothesis can only be observed for large firms (with 50 employees or above). By comparison, small firms observe a negative correlation with surges in energy prices in general and electricity prices in particular. Our results also indicate that the Porter-type adoption mechanism is

> 5We construct a harmonized unit price index for fossil fuels by combining information on coal, gas, diesel, petrol, LPG and kerosene use of Chilean manufacturing firms.

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more pronounced for exporting firms and firms under foreign ownership, while firms that only operate domestically and are under domestic ownership are more adversely affected by hikes in electricity prices. _Fourth_ , it presents explanatory evidence that is suggestive of a notable change in the relationship between competitiveness indicators and fossil fuel price changes conditional on the price level. We find that the positive correlation between fossil fuel price increases and capital upgrading is more pronounced when fuel prices are low. At higher fuel price levels, this effect becomes smaller and eventually insignificant. Similar non-linear effects cannot be observed for electricity prices.

The structure of this paper is as follows: section 2, provides an overview of the empirical firm-level literature on the energy prices-productivity nexus while section 3 sets a background of the institutional changes pertaining to energy prices for Chilean manufacturing firms. We then introduce the _Estructura de la industria manufacturera_ data (INE, 2015) in section 4, after which we turn to our empirical strategy and the results in sections 5 and 6, respectively, before concluding in section 7.

# **2 Literature**

The literature identifies four main transmission channels for how firms may respond to energy price interventions (Rentschler et al., 2017; Coste et al., 2018): One strategy, where firms _innovate_ by adopting more efficient technologies and reap economic benefit, is often referred to as the Porter Hypothesis.<sup>6</sup> Additionally, firms may pursue other coping strategies, such as cost _pass-on_ to customers and other firms, _absorption_ or adjusting production inputs, i.e., _substitution_ .

Evaluating these transmission channels, the empirical firm-level literature finds broad support for the Porter-type _innovation_ hypothesis, where policy-led price interventions lead to innovation and notable business upgrading. Brucal and Dechezleprˆetre (2021) show that large and energy-intensive sectors in Indonesian manufacturing tend to reduce energy consumption most significantly in response to an energy price hike. Plants react to higher energy prices by updating their capital stock and investing in new and presumably more energy-efficient technology. Using firm-level data of small and micro enterprises from Indonesia for 2013, Rentschler and Kornejew (2018) explore the impact of input price changes of electricity and

> 6The literature distinguishes between different forms of the Porter Hypothesis (Ambec et al., 2013): Under its _”weak”_ form, environmental regulations spur innovations, while under the _”strong”_ version, environmental regulations will lead to increases in firm competitiveness. The _”narrow”_ interpretation states that more flexible regulations may be better at providing the preferable incentive structures to firms than more prescriptive forms.

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various fossil fuels on firm performance. The authors find that higher prices for all energy types are associated with higher energy efficiency. Cal`ı et al. (2023) evaluate energy prices’ direct and indirect impact on firms’ economic performance for eleven developing countries between 2002 and 2013. Using World Bank firm-level survey data, the authors find that higher energy prices do not necessarily hamper economic performance. They also report considerable heterogeneities, as energy-intensive firms report a smaller performance effect in response to a rise in energy prices than their less energy-intensive counterparts. Cal`ı et al. (2022) focus on medium- to large-sized manufacturing firms in two emerging economies (Mexico and Indonesia). They find that while surges in electricity price harm plants’ performance, increases in fuel prices yield positive outcomes for labor and total factor productivity and profits. The effects are particularly pronounced for capital-intensive manufacturing sectors. In the same vein, Amann et al. (2021) analyze the effects of energy price hikes on manufacturing firms in Oman. They show that increases in fossil fuel prices are causally linked to improvements in productivity and efficiency and lead to notable business upgrading. As Cal`ı et al. (2022), the Oman study only observes such innovation effects for fossil fuel price increases but not for electricity hikes, which are more detrimental to firm performance.

The extent to which energy inputs can serve as _substitutes_ depends on the country and policy context. Rentschler and Kornejew (2018) find that while electricity can be substituted away by a blend of other energy sources, it plays a minor role in replacing fossil-based energy sources. Amann et al. (2021) evaluate the substitutability of electricity and aggregated fossil fuels and find that Omani firms significantly increase their kWh consumption of electricity in response to rising fuel prices.

Firm-level evidence suggests that the effectiveness of the _absorption_ channels depends on firm characteristics and sectoral attributes. Particularly firms in energyintensive sectors respond more strongly to price hikes. Rentschler and Kornejew (2018) highlight that firm-level response patterns of Indonesian micro-firms are very different depending on the analyzed sector. Brucal and Dechezleprˆetre (2021) report that for Indonesian manufacturing firms, energy price increases are causally linked to upticks in plant exit and employment contraction, particularly for energy-intensive and large firms. The authors also report job reallocation from energy-intensive to energy-efficient firms and sectors. Kumar and Prabhakar (2020) analyze how Indian energy-intensive exports react to changes in energy prices and find adverse trade effects, asserting that carbon leakage concerns are not necessarily unfounded. Furthermore, energy-intensive sectors are affected more severely by energy price hikes, most notably the non-ferrous metal and machinery industry. In turn, Cal`ı et al. (2023) do not find statistical evidence that price hikes lead to employment losses 5
across eleven developing economies between 2002 and 2013.

Numerous studies identify notable cost _pass-through_ from firms to the consumer indicating firms only bear part of the carbon costs (Lin and Jiang, 2011; Arlinghaus, 2015; Joltreau and Sommerfeld, 2019). However, firm-level evidence on this transmission channel remains relatively scarce for emerging economies. Rentschler and Kornejew (2018) confirm that higher prices for all energy types are associated with higher long-run sales in Indonesia. Cal`ı et al. (2023) do only find significant evidence for an aggregated cost pass-through effect for firms experiencing power outages.

# **3 Institutional Setting**

In 1998, Chile suffered a severe drought, which had a notable impact on the energy generation of the Central Interconnected System (SIC) network.<sup>7</sup> This event, in conjunction with disruptions in three natural gas plants, caused blackouts between November 1998 and April 1999, forcing the government to ration supply (Serra, 2022). The subsequent period of the late-1990s to mid-2000s was characterized by heavy use of natural gas in electricity generation and massive natural gas imports from Argentina (Cansino et al., 2018). However, the gas-heavy energy production episode abruptly stopped in 2005 when Argentina began restricting and taxing natural gas exports due to domestic deficits leading to a notable price surge in Chile.

In response, Chile saw the construction of new coal plants to close the wedge between reduced energy supply and growing demand. Coal plants played a crucial role in this endeavor given their comparably lower cost of electricity generation (Serra, 2022). The country also diversified its energy mix in terms of energy type and destination. For example, in 2009/2010, Chile imported natural gas from Algeria, Equatorial Guinea, the Arab Republic of Egypt and Indonesia and constructed largescale liquefied natural gas terminals (Mundaca et al., 2015), leading to an overall reduction in the relative weight of hydropower (Cansino et al., 2018).

Technological advancements and growing environmental concerns have led to a stronger focus on green and renewable energy in recent years. By the 2010s, increasing public opposition and stricter environmental requirements halted the construction of additional coal-fired plants (Serra, 2022), and new policy measures promoting non-conventional renewable energies were put into practice (MEFR, 2008; Cansino

> 7Throughout the analyzed period, Chile had four main electricity grids: the Northern Interconnected System (SING), the Central Interconnected System (SIC), the Aysen System (SEA), and the Magallanes System (SEM), with the former being integrated into the National Electricity System (SEN). Within these grids, numerous state-owned and private energy producers have been in charge of energy generation (Serra, 2022).

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et al., 2018). The year 2014 also saw the passing of carbon tax legislation with the _General Tax Reform Bill_ .<sup>8</sup>

# **4 Data**

The Chilean _Annual National Industrial Survey_ (ENIA from now on) is made available by the _National Statistical Institute_ (INE, 2015). ENIA data is publicly available and spans the period 1995-2019. From 1995 to 2007, ENIA offers a harmonized panel data set (referred to as the _combined_ data) which identifies the same manufacturing firm with a unique ID linking plants over time. At the time of this study, unique firm IDs linking panel data were unavailable and, until recently, ENIA had microdata available from 2016-2019 in separate annual waves (without a unique firm ID over time). We refer to these separate waves as the _raw_ version on ENIA.<sup>9</sup> The combined ENIA data set suffers from a lack of regional and manufacturing sector-level identifiers. To recover information on the location and the ISIC 4-digit codes for each plant, we resort to the raw data version of ENIA, which contains both desired variables. We match plants in the combined and raw data by their common characteristics and apply the same matching techniques to expand the panel structure in the post-2007 period. This procedure yields a final data set containing 100,803 observations on 12,169 plants, with an average of 4,200 unique firms observed across survey years. We provide a more extensive discussion on the matching algorithm in Appendix C. As illustrated in Figure A.2, the matching algorithm achieves a stable firm sample over the entire sample horizon and a low attrition rate.

The combined data also exhibit irregularities potentially stemming from misreported electricity and fossil fuel profiles. We address this issue by employing a simple cleaning algorithm, which provides a rule-based procedure to identify and correct inter-temporal changes in reported quantities and unit prices of pre-defined magnitudes. We provide a more extensive discussion on the cleaning algorithm in Appendix D.

> 8With its inception in 2014, the tax reform (Ley 20.780) falls too close to the end of the sample period to merit an empirical analysis. At the time of writing, the implementation of the policy remains an ongoing process; see the World Bank’s Results Brief 15 February 2021 and project Market Instruments for Climate Change Mitigation in Chile (last access: September 2022).

> 9The authors are working on a revision of the results using the newly available data.

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# **5 Empirical Methodology**

## **5.1 Baseline Estimation**

**Model Structure.** Our empirical setup is similar to that of other works in the literature such as Amann et al. (2021); Cal`ı et al. (2022). We estimate:

_′_ where **x** _it_ = � _up_<sup>_EL_</sup> _it_<sup>_, upFF_</sup> _it_ � denote the unit prices for electricity ( _EL_ ) and fossil fuels ( _FF_ ) for firm _i_ and period _t_ , respectively. The calculation of the electricity unit prices is straightforward: where _value_ denotes the purchased value (in real LCU) and _quantity_ denotes the purchased quantity of energy type _m_ = _{EL}_ .<sup>10</sup> For the fossil fuel aggregate, we calculate kWh-equivalent quantities for all _n_ = _{coal, gas, diesel, petrol, LPG, kerosene}_ fossil fuel types according to the conversion rates in Table B.1 as follows: where _q_ ̃ _it_<sup>_n_denotesthequantity(inkWhequivalents)offossilfueltype</sup><sup>_n_forfirm</sup> _i_ and period _t_ , and _wit_<sup>_n_correspondstoitsfixed-quantityweight,respectively.The</sup> calculation of the individual _up_<sup>_n_</sup> _it_<sup>seriesisidenticaltoEquation2for</sup><sup>_m_=</sup><sup>_n_.The</sup> two energy unit price series we derive in this way are visualized in Figure A.3.

**Dependent Variables.** We analyze the effect of energy price variations on firmlevel outcomes, _yit_ . To this end, we compartmentalize the outcome variables by applying the framework introduced in section 2 to the extent to which this is possible with the open-access version of ENIA.<sup>11</sup> We define these domains as: _invest-_

> 10All monetary series are deflated using ISIC 2-digit industry-level following Haraguchi and Amann (2023).

> 11A more detailed analysis of additional variables of interest such as TFPQ or other coping mechanisms, particularly the cost _pass-through_ dimension, was not possible due to current data

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_ment_ and _productivity_ / _survival_ , _substitution_ and _absorption_ . On the _competitiveness_ / _innovation_ effects, we evaluate the strong and weak form of the Porter Hypothesis by computing various measures of productivity, including labor productivity and Total Factor Productivity (TFP) using the Ackerberg-Caves-Frazer (ACF) method (Ackerberg et al., 2015). _Substitution_ effect is measured by the elasticity of substitution across various energy sources due to changes in their prices. _Absorption_ effect is measured through the impact of energy prices on firm size (employment), other input costs (e.g., wages), profit margins and return on sales, normalized by net income, costs and the changes of fixed assets. We provide a complete list of analyzed firm-level outcome variables _yit_ alongside their definition and descriptive statistics in Table B.2 as well as a list of dependent variables analyzed by other papers in this literature in Table B.3.

# **5.2 Heterogeneity in Responses**

To analyze heterogeneity in the response patterns, we adjust Equation 1 employing a dummy identifier of the form: where _I_ ( _·_ ) is an indicator function which takes the value one if condition _Z_ is met for a particular observation in our data _zit_ . We analyze heterogeneities in firm-level responses by breaking down the sample based on the firm-level characteristics of firm size (small, medium, large; see panel I in Table B.4 for a summary statistics and definition of the respective cut-offs), ownership (foreign and domestic; see panel II), export status (exporter and non-exporter; panel III) and energy intensity (intensive and non-intensive; panel IV).

# **5.3 Non-linearity in Responses**

We investigate non-linear patterns by extending the model in Equation 1 and estimating a panel quantile regression model as proposed by Machado and Silva (2019) to investigate how the correlation between firm-level outcomes and energy price changes varies depending on the actual observed price level. The implication is that a firm’s limitations. This leaves numerous highly policy-related questions unanswered. For example, one of the most direct measures of cost pass-through is the unit price of final products sold to the market; however, this is not observed in the open-access version of ENIA. A more extensive analysis could be conducted in the presence of access to the full data.

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optimal response to energy price hikes may vary depending on the energy price it observes. For example, firms may be more wasteful in their energy use at lower energy price levels and retain less efficient production processes because of low opportunity costs. Conversely, they might find it more difficult to adjust their production at higher price levels as they may have already exhausted some of the more straightforward energy-saving strategies when transitioning away from an initially low energy-price regime. The linear specification for the conditional quantile of dependent variable _y_ and explanatory variables **_x_** _it_ is given by: where _β_ ( _τ_ ) is a vector of the coefficients related to the _τ_ th quantile.

# **5.4 Endogeneity Issues**

Endogeneity in estimating the impact of energy prices on firm outcomes can emerge due to omitted variables bias, selection effects and reverse causality. We address the various sources as much as possible in the current setup.

First, omitted variable bias can be due to time-invariant variables (e.g., access to specific energy sources due to regional or industry-specific attributes or policies contingent on such criteria). It can also vary over time and be either idiosyncratic (e.g., managerial ability to adjust energy use based on prices optimally) or more systemic (e.g., time-trends such as structural changes due to global market shocks or technological change). Issues of reverse causality arise in our context because energy prices are measured at the firm level. Firms with higher productivity or other outcomes are likely the ones with better capabilities (e.g., management). In turn, these can affect energy prices at the firm level through several channels. For example, by easing information and coordination frictions, management may help reduce “hidden” administrative and time costs associated with energy use through efficiency-enhancing investments in programs or equipment that can affect firm-level energy prices per unit (Gillingham and Palmer, 2014). Management may also help firms overcome uncertainty, for instance, in energy price fluctuations or the magnitude of future cost savings, by enabling firms to target and track historical energy use and accurately forecast future needs and payoffs. In particular, savvy firms may be able to optimally time energy purchases when prices are low, e.g., by employing forecasting tools or using financial vehicles.<sup>12</sup> Productive firms also implement more 12Even difficult-to-store energy carriers such as electricity are often subject to differences in fixed 10
energy-centric management practices (Grover and Karplus, 2020), which may raise energy efficiency and thereby reduce the per-unit price.

**Fixed-effects Specification.** _First_ , we use a fixed-effects (FE) setup to capture endogeneity arising from variations in time-invariant firm attributes, regional and industry-specific market conditions and technology-related developments (Mundlak, 1978; Wooldridge, 2005). To this end, the error term in Equation 1 has the following structure:

where _αi_ denotes firm-specific intercepts while industry-year interactions ( _Dst_ ) capture industry-specific market conditions and technology-related developments. We also include region-year interactions ( _τr_ ) to capture the evolution of region-specific long-run development dynamics in local governance, infrastructure, development, innovation, etc.

_Second_ , we rule out endogeneity resulting from autocorrelation or heteroscedasticity by using clustered standard errors at the plant-year level as residuals in Equation 6 (Petersen, 2009), which can be estimated consistently with a sufficiently large number of clusters (Wooldridge, 2010; Cameron et al., 2011). We employ this FE specification for all baseline estimations, including the heterogeneity and non-linearity analysis.

**Instrumental Variables.** We address time-varying unobserved variation at the plant level remains a source of endogeneity by employing an Instrument Variable (IV) design commonly used in this field (e.g., Amann et al., 2021; Cal`ı et al., 2022). The first stage IV estimating plant-level energy prices is:

where _inst_<sup>_m_</sup> _it_<sup>refers to the instrument of energy unit price</sup><sup>_upit_of energy source</sup><sup>_m_.By</sup> employing a spatial/leave-one-out instrument, we intend to exploit the geographical variation in energy prices resulting from the cost of distributing energy to a particular province. For the validity of our instrument, we require it to be correlated with plantlevel energy prices without directly affecting performance. However, the validity of spatial instruments has been criticized for various reasons (Betz et al., 2018).

costs based on use and time-of-day pricing, including in developing countries, creating opportunities for arbitrage by shifting production to off-peak hours.

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Spatial instruments are effective if unit prices of other plants do not directly or indirectly affect plant _i_ ’s outcome variable. As energy prices drive firm performance, they can indirectly feed into the outcomes of other plants through _spillover effects_ . To minimize this effect, our spatial instrument considers energy prices for _i_<sup>_′_</sup> plants within a region that are _not_ operating in the same ISIC 2-digit sector as plant _i_ in IV specification 1 ( _IV_ 1). At the same time, energy prices of firms within the same sector can be aligned due to policies pertaining to subsidies in a given sector. If we consider prices of firms in other ISIC 2-digit sectors as instruments, they may not be strongly correlated with energy prices of a firm in another sector, while spillover effects may be most prominent in industries that are most closely related in terms of activity and location. Consequently, and as a robustness check, we provide an alternative IV specification ( _IV_ 2), which considers the energy prices for _i_<sup>_′_</sup> plants within a region that are _not_ operating in the same ISIC 4-digit sector as plant _i_ but can operate in the same wider ISIC 2-digit sector.

We recover the cost of regional energy distribution as follows: _In step one_ , for firm _i_ unit price index for fuel type _m_ , _up_<sup>_m_</sup> _it_<sup>, we calculate the average unit prices paid</sup> by all _other_ firm _i_<sup>_′_</sup> in the same region _r_ in a given year. The instrumented energy unit prices are calculated as: where _Nrts_ denotes the number of plants in period _t_ populating the same region as firm _i_ , _i ∈ r_ that are not engaged in the same manufacturing activity, i.e., the same ISIC _k_ -digit sector _s_<sup>_k_</sup> , _s_<sup>_k_</sup> _i_<sup>=</sup><sup>_sk_</sup> _i_<sup>_′_,</sup><sup>_k_=</sup><sup>_{_2</sup><sup>_,_4</sup><sup>_}_.</sup><sup>_Insteptwo_,wenormalizethis</sup> unit price in equations 8 and 9 by the national average of the same year to minimize the potential correlation of plant-level outcomes with aggregate shocks affecting each energy type: 12
where _up_ ̄<sup>_m,k_</sup> _t_ is the average, national unit price of energy source _m_ in year _t_ and instrument of type _k_ . This instrument can be interpreted as a proxy for the cost of energy distribution to a particular region: it is correlated with plant-level energy prices while mitigating the effect of idiosyncratic plant-level attributes by excluding the plant itself when calculating regional averages in equations 8 and 9.

Some concerns about the validity of spatial instruments may remain. _First_ , it is possible that the energy unit price of firm _i_ can, at least partially, be influenced by the unit price of other plants. Given the market structure of the Chilean energy sector, we argue that plants typically enter the market as price takers. Consequently, they cannot negotiate lower prices based on other plants’ energy bills, even if they know the energy bill of their competitors. Yet, not all of our analyzed outcome variables are subject to the same endogeneity concerns. For example, if plants _i_<sup>_′_</sup> become more efficient and consume less coal, it is unlikely that this will affect firm _i_ ’s coal consumption. _Second_ , spatial instruments may be more of a concern in cases where the substitutability of inputs is highly dependent on local conditions in the labor market. For instance, the hiring practices of plants _i_<sup>_′_</sup> may influence the bargaining power of job seekers and, therefore, affect the wage bill of other plants. While we cannot rule out all such dynamics, the consistency in our findings across model specifications (including both IV designs) may indicate that our empirical setup accounts for most of these effects. Finally, as an additional check for our instrumental variable design, we follow the recommendations in Chao and Swanson (2005) and provide test statistics as part of the regression tables to illustrate that none of our models suffers from weak identification.

# **6 Results**

## **6.1 Baseline FE Estimates**

Table 1 present results from baseline estimation, which we discuss along with the identified firm-level coping mechanism.

_Investment & productivity/survival._ In line with global evidence, our FE estimations suggest a positive correlation between productivity and investment with fossil fuel prices (panel I) and a small trade-off between productivity and energy prices (panel II). There are some noteworthy differences in firm-level response by fuel types. Higher electricity prices do not significantly correlate with a reduction in investment in assets (panel I) and lower productivity (panel II). However, increases in fossil fuel prices correlate positively with investments in machinery (column 1), the balance of net assets (4), output per worker (5) and higher wages (13), and negatively with exit 13
probability (8). Overall, the results on fossil fuel price hikes pertaining to investments support a weak version of the Porter Hypothesis, while those on productivity are in favor of the strong version. These results align with other studies differentiating firm-level coping strategies by energy type and underscore the importance of a disaggregated analysis by fuel type.

_Substitution effect_ . Panel III in Table 1 suggests that increases in prices of electricity correspond to a significant reduction in consumed quantities of electricity (9). In terms of magnitude, the quantified effects for fossil fuel are comparable in magnitude to other studies with a similar estimation setup (Amann et al., 2021). However, the results hint at more pronounced price elasticities of both energy types. We also observe similar responses across all energy types in response to a surge in fossil fuel hikes, which are significant. Firms’ consumption of fossil fuels correlates positively with a price hike in electricity (9), but the degree of substitution between fossil fuels and electricity varies by fuel type (11 vs 12).

_Absorption effect_ . Panel IV of Table 1 suggests that fossil fuel increases positively correlate with wages (13) but not employment (15 & 16). Wage effects emanate primarily from the non-production worker sub-sample, suggesting some reallocation of workers towards higher skill usage or premium due to changes in fossil fuel prices (Brucal and Dechezleprˆetre, 2021; Dechezleprˆetre et al., 2020). By comparison, electricity price hikes seem to force firms to shed employment (15) and, more so, production workers (16). Panel V of Table 1 confirms that energy price increases negatively correlate with firms’ profit margins. However, absorption and pass-through again vary by energy type. While fossil fuel price hikes are associated with cost increases, electricity surges affect profit margins through a reduction in sales (perhaps through a reduction in firms’ production workers). The results for return on sales are consistent with the findings on sales costs and asset evolution, showing a consistent negative correlation with increases in energy prices.

To conclude, while electricity price increases are associated with reduced production and a decline in firm size, fossil fuel price hikes have a positive association with capital investment, possibly manifesting in improved productivity, thereby supporting the _strong_ version of the Porter Hypothesis. However, these productivity increases may not translate into higher profitability (at least in the short-run), indicating that the pass-through of such price hikes is limited and at least partially absorbed by firms.<sup>13</sup> 13Evidence suggests firms pass at least parts of the energy costs on to consumers (Chateau et al., 2018), which may not be desirable from the point of the policy design and may have notable welfare implications (Maruejols et al., 2022). Given the current data limitations, it is impossible to quantify this effect in this study.

14
# Table 1: Baseline results - FE

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity<br>|-0.009<br>(0.026)<br><sup>_∗∗_</sup>|-0.053<br>(0.132)<br><sup>_∗_</sup>|0.027<br>(0.033)<br><sup>_∗_</sup>|-0.024<br>(0.023)<br><sup>_∗∗∗_</sup>|
|Fossil Fuel|0.031<br>(0.015)|0.102<br>(0.048)|0.042<br>(0.021)|0.030<br>(0.008)|
|RMSE<br>R<sup>2</sup><br><sup>2</sup>|1.2286<br>0.47207<br>|0.95837<br>0.69099<br>|0.98344<br>0.69640<br>|0.71812<br>0.72267<br>|
|Adjusted R<br>F-test, p-value|0.34371<br>1.0000|0.34659<br>1.0000|0.55930<br>0.99914|0.67233<br>0.99853|
|Observations<br>f|39,922<br>9097|2,143<br>1153|18,719<br>6602|63,920<br>11609|
|# rms|,|,|,|,|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Exit<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity|-0.017<sup>_∗_</sup><br>0009|-0.018<sup>_∗_</sup><br>0009|-0.004<sup>_∗∗_</sup><br>0002|0.010<sup>_∗∗∗_</sup><br>0004|
||(.)<br><sup>_∗∗_</sup>|(.)<br>|(.)<br>|(.)<br><sup>_∗∗∗_</sup>|
|Fossil Fuel|0.008<br>(0.003)|0.004<br>(0.005)|0.001<br>(0.0007)|-0.003<br>(0.0009)|
|RMSE<br>|0.36376|0.56556|0.03251|0.22088|
|R<sup>2</sup><br><sup>2</sup>|0.88466<br>|0.74716<br>|0.95606<br>|0.305195<br>|
|Adjusted R<br>F-test, p-value<br>|0.86477<br>0.29276|0.70221<br>0.99203|0.93808<br>0.99089|0.18521<br>0.98324|
|Observations<br># frms|67,982<br>12,228|65,502<br>12,232|13,470<br>4,363|53,887<br>9,207|
|_Panel III_<br>|Qnt. Electricity<br>|Qnt. Fossil Fuel<br>|Qnt. Diesel<br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity|-0.626<sup>_∗∗∗_</sup><br>(0.181)|0.004<br>(0.034)|0.025<br>(0.039)|0.082<sup>_∗_</sup><br>(0.043)|
|FilFl|0173<sup>_∗∗∗_</sup>|0584<sup>_∗∗∗_</sup>|0512<sup>_∗∗∗_</sup>|0501<sup>_∗∗∗_</sup>|
|oss ue|.<br>(0.014)|-.<br>(0.044)|-.<br>(0.043)|-.<br>(0.073)|
|RMSE<br>|0.85200|1.0077|0.95725|0.85209|
|R<sup>2</sup><br>Adjusted R<sup>2</sup><br>|0.85820<br>0.83374<br>|0.76343<br>0.71868<br>|0.76835<br>0.71857<br>|0.75485<br>0.69437<br>|
|F-test, p-value<br>Observations|0.54180<br>68,242|0.97990<br>57,561|0.96935<br>42,925|0.98297<br>31,197|
|# frms|12,284|12,285|7,538|6,074|
|_Panel IV_<br>|Wages all workers<br>|Wages production workers<br>|Emp. all workers<br>|Emp. production workers<br>|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity|0.002<br>(0.006)|-0.014<br>(0.020)|-0.026<sup>_∗∗∗_</sup><br>(0.008)|-0.035<sup>_∗∗∗_</sup><br>(0.011)|
|FossilFuel|0005<sup>_∗∗_</sup>|00008|0004|0005|
||.<br>(0.002)|.<br>(0.006)|.<br>(0.004)|.<br>(0.005)|
|RMSE<br>|0.23644|0.56824|0.33527|0.39471|
|R<sup>2</sup>|0.86734|0.75367|0.91187|0.89357|
|Adjusted R<sup>2</sup><br>F-test, p-value<br>|0.84448<br>0.46209<br>|0.70288<br>0.99805<br>|0.89667<br>0.08628<br>|0.87515<br>0.21396<br>|
|Observations<br># frms|67,967<br>12,207|45,782<br>8,955|68,004<br>12,285|67,575<br>12,285|
|_Panel V_<br>|Proft margin<br><br>|Sales/output<br>|Costs/output<br>|Return on Sales<br>|
|_Absorption - business metr_|_cs_<br>(17)|(18)|(19)|(20)|
|Electricity|-0.016<sup>_∗_</sup><br>(0.008)|-0.009<sup>_∗∗_</sup><br>(0.003)|0.003<br>(0.005)|-0.043<sup>_∗_</sup><br>(0.023)|
|Fossil Fuel|-0.010<sup>_∗∗_</sup><br>(0.004)|0.001<br>(0.002)|0.005<sup>_∗_</sup><br>(0.002)|-0.018<sup>_∗_</sup><br>(0.010)|
|RMSE<br>|0.48343|0.23065|0.24169|0.78155|
|R<sup>2</sup><br>|0.48955|0.60731|0.54986|0.82711|
|Adjusted R<sup>2</sup><br>F-test, p-value<br>|0.39786<br>1.0000<br>|0.53855<br>1.0000<br>|0.47223<br>1.0000<br>|0.77826<br>0.79504<br>|
|Observations<br># frms|64,959<br>12,277|64,742<br>11,728|68,228<br>12,277|35,097<br>12,255|
|_Fixed-efects_|||||
|Plant<br>|Yes<br>|Yes<br>|Yes<br>|Yes<br>|
|Industry-Year<br>Region-Year|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|Yes<br>Yest|

_Note:_ Estimates according to fixed-effects model described in equations 1 and 6 include firm-effects and industry-year and region-year effects. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 15
# **6.2 Heterogeneity in Responses**

This section highlights the significant heterogeneity in response to energy price hikes across various firm attributes by estimating Equation 4.

**Firm Size.** Considering three categories of firm-sizes: large (more than 50 employees), medium (between 20 and 49) and small (fewer than 20),<sup>14</sup> we draw the following conclusions on the heterogeneity in responses based on the results presented in Table 2: _First_ , the overall positive correlations of fossil fuel price changes with firm upgrading and productivity are primarily driven by large firms (panel I). In response to the withdrawal of fossil fuel subsidies, large firms upgrade both machinery _and_ ICT equipment, consistent with the results from Oman, Mexico and Indonesia (Amann et al., 2021; Cal`ı et al., 2022). _Second_ , the aggregate negative productivity effects observed for electricity price hikes are primarily driven by small and medium-sized firms (panel II). Large firms seem to be unperturbed by electricity price increases, both in terms of their productivity and profitability. However, large firms also observe negative correlations of fossil fuel price hikes with profit margins. _Third_ , firms of all sizes are sensitive to energy prices and substitute away from the energy source whose price increases and towards the alternative source. Intra-fuel elasticities do not notably differ by firm size (panel III).

**Foreign Ownership.** We differentiate between nationally-owned (domestic) and foreign firms.<sup>15</sup> Results from Table 3 suggest that although investment correlates positively and significantly with fossil fuel fuel hikes for both firm types, the magnitude is generally higher for foreign firms (panel I). It is not surprising, then, that fuel price increases correspond more strongly to productivity increases for foreignowned firms than domestic ones (panel II). Furthermore, electricity price hikes are negatively and significantly correlated with domestic firms’ productivity and profit margins. Regarding substitution and absorption effects, foreign firms’ substitute more strongly in response to increases in fossil fuel prices (panel III) while they sharply reduce employment following electricity price hikes (panel IV).

> 14We provide summary statistics for the size cut-offs used in the analysis in Table B.4. In ENIA the classification by firm size varies between the raw and combined data sets and complicates the firm matching algorithm described in Appendix C slightly; see Table C.7 for more information on the respective cut-offs in the two ENIA data sets.

> 15We define a firm as _domestic_ if it records a foreign capital formation of zero in all periods; see Table B.4 panel II for the corresponding summary statistics.

16
Table 2: Results by firm size - FE

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>Vehicles/output<br>(2)<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|
|Electricity _×_ Large|0.003<br>|-0.068<br>0.002<br><br>|-0.046<br>|
||(0.035)<br>|(0.139)<br>(0.040)<br><br>|(0.043)<br>|
|Electricity _×_ Medium|-0.065<br>0041|-0.050<br>0.045<br>0286<br>0068|-0.027<br>0025|
|Electricity _×_ Small|(.)<br>0.059<br>(0.062)|(.)<br>(.)<br>-1.17<br>0.047<br>(0.723)<br>(0.070)|(.)<br>0.004<br>(0.022)|
|Fossil Fuel _×_ Large|0.042<sup>_∗∗∗_</sup><br>(0.014)|0.096<sup>_∗_</sup><br>0.039<br>(0.047)<br>(0.025)|0.040<sup>_∗∗∗_</sup><br>(0.011)|
|Fossil Fuel _×_ Medium|0.007<br>(0.030)|0.211<br>0.051<br>(0.154)<br>(0.035)|0.032<sup>_∗∗_</sup><br>(0.013)|
|Fossil Fuel _×_ Small|0.015<br>(0.038)|-0.318<br>0.059<br>(0.513)<br>(0.050)|-0.005<br>(0.017)|
|RMSE<br>R<sup>2</sup>|1.2280<br>0.47246|0.95530<br>0.98143<br>0.69301<br>0.69771|0.71779<br>0.72292|
|Adjusted R<sup>2</sup><br>F-test, p-value|0.34405<br>1.0000|0.34796<br>0.56102<br>1.0000<br>0.99948|0.67259<br>0.99920|
|Observations<br># firms|39,901<br>9,097|2,142<br>18,709<br>1,153<br>6,602|63,920<br>11,609|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>TFP<br><br>|Profit margin<br>|
|_Productivity/survival_|(5)|(6)<br>(7)|(8)|
|Electricity _×_ Large|-0.013<br>|0.010<br>-0.002<br><br>|0.013<br>|
|Electricity _×_ Medium|(0.013)<br>-0.032<sup>_∗∗_</sup><br>|(0.016)<br>(0.002)<br>-0.036<sup>_∗∗_</sup><br>-0.003<br><br>|(0.014)<br>-0.020<br>|
|Eltiit_×_Sll|(0.013)<br>0030<sup>_∗∗_</sup>|(0.014)<br>(0.003)<br>0054<sup>_∗∗_</sup><br>0008|(0.012)<br>0053<sup>_∗∗∗_</sup>|
|ecrcy ma|-.<br>(0.012)|-.<br>-.<br>(0.020)<br>(0.005)|-.<br>(0.016)|
|Fossil Fuel _×_ Large|0.016<sup>_∗∗_</sup><br>(0006)|0.007<br>0.01<sup>_∗_</sup><br>(0008)<br>(0007)|-0.024<sup>_∗∗∗_</sup><br>(0006)|
|Fossil Fuel _×_ Medium|.<br>0.012<sup>_∗∗_</sup><br>(0.005)|.<br>.<br>0.007<br>0.002<br>(0.007)<br>(0.002)|.<br>-0.007<br>0.006|
|Fossil Fuel _×_ Small|-0.004|0.004<br>-0.003|0.015<sup>_∗_</sup>|
||(0.007)|(0.010)<br>(0.002)|0.008)|
|RMSE<br>R<sup>2</sup>|0.35874<br>0.88783|0.56311<br>0.03221<br>0.74942<br>0.95686|0.48318<br>0.49022|
|Adjusted R<sup>2</sup>|0.86847|0.70482<br>0.93917|0.39854|
|F-test, p-value|0.30055|0.99457<br>0.00110|1.0000|
|Observations<br># firms|67,940<br>12,228|65,462<br>13,470<br>12,232<br>4,363|64,920<br>12,277|
|_Panel III_<br>|Qnt. Electricity<br>|Qnt. Fossil Fuels<br>Qnt. Diesel<br><br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)<br>(11)|(12)|
|Electricity _×_ Large|-0.652<sup>_∗∗∗_</sup><br>|-0.077<sup>_∗_</sup><br>-0.103<sup>_∗_</sup><br><br>|0.062<br>|
|Electricity _×_ Medium|(0.210)<br>-0.579<sup>_∗∗∗_</sup><br>|(0.043)<br>(0.053)<br>0.062<br>0.023<br><br>|(0.048)<br>0.105<sup>_∗∗_</sup><br>|
|Electricity _×_ Small|(0.180)<br>-0.609<sup>_∗∗∗_</sup><br>(0.139)|(0.036)<br>(0.038)<br>0.120<sup>_∗∗_</sup><br>0.068<br>(0.047)<br>(0.052)|(0.049)<br>0.138<sup>_∗∗_</sup><br>(0.055)|
|Fossil Fuel _×_ Large|-0.154<sup>_∗∗∗_</sup><br>(0015)|-0.596<sup>_∗∗∗_</sup><br>-0.468<sup>_∗∗∗_</sup><br>(0045)<br>(0038)|-0.535<sup>_∗∗∗_</sup><br>(0072)|
|Fossil Fuel _×_ Medium|.<br>-0.174<sup>_∗∗∗_</sup>|.<br>.<br>-0.550<sup>_∗∗∗_</sup><br>-0.496<sup>_∗∗∗_</sup>|.<br>-0.467<sup>_∗∗∗_</sup>|
|Fossil Fuel _×_ Small|(0.023)<br>-0.223<sup>_∗∗∗_</sup>|(0.048)<br>(0.054)<br>-0.517<sup>_∗∗∗_</sup><br>-0.517<sup>_∗∗∗_</sup>|(0.071)<br>-0.372<sup>_∗∗∗_</sup>|
||(0.020)|(0.051)<br>(0.059)|(0.067)|
|RMSE<br>R<sup>2</sup>|0.84499<br>0.86055|1.0014<br>0.95058<br>0.76631<br>0.77141|0.84883<br>0.75677|
|Adjusted R<sup>2</sup>|0.83646|0.72204<br>0.72221|0.69667|
|F-test, p-value|0.58448|0.98508<br>0.97667|0.98821|
|Observations<br># firms|68,200<br>12,284|57,525<br>42,895<br>12,285<br>7,538|31,180<br>6,074|
|_Panel IV_|Wages all workers<br>|Wages production workers<br>Emp. all workers<br><br>|Emp. production workers<br>|
|_Absorption - workers_|(13)|(14)<br>(15)|(16)|
|Electricity _×_ Large|0.013<br>|-0.005<br>-0.006<br><br>|0.004<br>|
|Electricity _×_ Medium|(0.014)<br>-0.020<br>|(0.005)<br>(0.008)<br>-0.014<sup>_∗∗_</sup><br>0.001<br><br>|(0.035)<br>-0.022<br>|
|Electricity _×_ Small|(0.012)<br>-0.053<sup>_∗∗∗_</sup><br>(0.016)|(0.006)<br>(0.007)<br>-0.009<br>0.017<sup>_∗∗_</sup><br>(0.006)<br>(0.006)|(0.029)<br>-0.075<sup>_∗∗_</sup><br>(0.032)|
|Fossil Fuel _×_ Large|-0.024<sup>_∗∗∗_</sup><br>(0006)|0.001<br>0.010<sup>_∗∗_</sup><br>(0005)<br>(0003)|-0.008<br>(0017)|
|FossilFuel_×_Medium|.<br>-0007|.<br>.<br>0002<br>0002|.<br>-0011|
|<br>FossilFuel_×_Small|.<br>(0.006)<br>0015<sup>_∗_</sup>|.<br>.<br>(0.003)<br>(0.003)<br>00004<br>00008|.<br>(0.016)<br>-0019|
||.<br>(0.008)|.<br>.<br>(0.006)<br>(0.004)|.<br>(0.023)|
|Adjusted R<sup>2</sup><br>WithinAdjustedR<sup>2</sup>|0.39854<br>000351|0.53877<br>0.47258<br>000998<br>000452|0.78191<br>002166|
|<br>F-testp-value|.<br>0.55207|.<br>.<br>0.99955<br>0.02593|.<br>0.12069|
|, <br>Observations<br># firms|64,612<br>12,158|42,433<br>64,642<br>8,904<br>12,235|64,218<br>12,235|
|_Fixed-efects_||||
|Plant|Yes|Yes<br>Yes|Yes|
|Industry-Year<br>Region-Year|Yes<br>Yes|Yes<br>Yes<br>Yes<br>Yes|Yes<br>Yes|

_Note:_ Estimates according to fixed-effects model described in equations 1 and 4 and include firm-effects and industry-year and region-year effects as well as interactions and levels of the respective firm-level heterogeneity variables. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 17
Table 3: Results by ownership - FE

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity _×_ Domestic|-0.013<br>(0.028)|-0.113<br>(0.182)|0.050<br>(0.040)|-0.020<br>(0.012)<br>|
|Electricity _×_ Foreign|0.032|0.204|-0.124|-0.064<sup>_∗∗_</sup>|
||(0.058)|(0.336)|(0.085)|(0.032)|
|Fossil Fuel _×_ Domestic<br>|0.022<sup>_∗_</sup><br>(0.013)<br><sup>_∗∗∗_</sup>|0.109<br>(0.073)<br>|0.045<sup>_∗∗_</sup><br>(0.019)<br>|0.025<sup>_∗∗∗_</sup><br>(0.006)<br><sup>_∗∗∗_</sup>|
|Fossil Fuel _×_ Foreign|0.078<br>(0.026)|0.055<br>(0.166)|0.029<br>(0.036)|0.065<br>(0.013)|
|RMSE<br><sup>2</sup>|1.2285<br>|0.95723<br>|0.98320<br>|0.71800<br>|
|R<br>|0.47217|0.69172|0.69654|0.72276|
|Adjusted R<sup>2</sup><br>F-test, p-value<br>Obi|0.34377<br>1.0000<br>39922|0.34621<br>1.0000<br>2143|0.55941<br>0.99939<br>18719|0.67242<br>0.99891<br>63920|
|servatons<br># frms|,<br>9,097|,<br>1,153|,<br>6,602|,<br>11,609|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Proft margin<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity _×_ Domestic|-0.021<sup>_∗∗_</sup><br>|-0.024<sup>_∗∗_</sup><br>|-0.004<sup>_∗∗_</sup><br>|-0.017<sup>_∗_</sup><br>|
|Electricity_×_Foreign|(0.009)<br>0.022|(0.010)<br>0.051|(0.002)<br>0.001|(0.009)<br>-0.008|
||(0.033)|(0.039)|(0.003)|(0.028)|
|Fossil Fuel _×_ Domestic|0.007<sup>_∗_</sup><br>(0.003)|0.004<br>(0.004)|0.0007<br>(0.0008)|-0.009<sup>_∗∗∗_</sup><br>(0.001)|
|Fossil Fuel _×_ Foreign|0.016<sup>_∗_</sup><br>0009|0.004<br>0016|0.003<sup>_∗∗_</sup><br>0001|-0.014<br>0010|
||(.)|(.)|(.)|(.)|
|RMSE<br>R<sup>2</sup>|0.36361<br>0.88475|0.56550<br>0.74721|0.03249<br>0.95611|0.48342<br>0.48955|
|Adjusted R<sup>2</sup><br>|0.86488<br>|0.70226<br>|0.93813<br>|0.39783<br>|
|F-test, p-value<br>Observations|0.31509<br>67,982|0.99378<br>65,502|0.00105<br>13,470|1.0000<br>64,959|
|# frms|12,228|12,232|4,363|12,277|
|_Panel III_<br>|Qnt. Electricity<br>|Qnt. Fossil Fuels<br>|Qnt. Diesel<br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity _×_ Domestic|-0.634<sup>_∗∗∗_</sup>|0.025|0.011|0.085<sup>_∗_</sup>|
||(0.176)<br><sup>_∗∗_</sup>|(0.035)<br><sup>_∗_</sup>|(0.041)<br><sup>_∗_</sup>|(0.042)<br>|
|Electricity _×_ Foreign|-0.516<br>(0.245)|-0.157<br>(0.076)|0.183<br>(0.088)|0.070<br>(0.121)|
|Fossil Fuel _×_ Domestic|0.173<sup>_∗∗∗_</sup><br>(0015)|-0.558<sup>_∗∗∗_</sup><br>(0044)|-0.477<sup>_∗∗∗_</sup><br>(0045)|-0.472<sup>_∗∗∗_</sup><br>(0072)|
||.<br><sup>_∗∗∗_</sup>|.<br><sup>_∗∗∗_</sup>|.<br><sup>_∗∗∗_</sup>|.<br><sup>_∗∗∗_</sup>|
|Fossil Fuel _×_ Foreign|0.161<br>(0.019)|-0.666<br>(0.051)|-0.566<br>(0.055)|-0.631<br>(0.073)|
|RMSE|0.85190|1.0072|0.95684|0.85131|
|R<sup>2</sup><br>|0.85823|0.76368|0.76855|0.75529|
|Adjusted R<sup>2</sup>|0.83377|0.71896|0.71879|0.69489|
|F-test, p-value<br>Observations|0.56986<br>68242|0.98363<br>57561|0.97478<br>42925|0.98616<br>31197|
|# frms|,<br>12,284|,<br>12,285|,<br>7,538|,<br>6,074|
|_Panel IV_|Wages all workers|Wages production workers|Emp. all workers|Emp. production workers|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity _×_ Domestic|-0.003<br>(0.006)|-0.019<br>(0.017)|-0.024<sup>_∗∗_</sup><br>(0.009)|-0.032<sup>_∗∗_</sup><br>(0.012)|
|Electricity _×_ Foreign|0.048<sup>_∗∗_</sup><br>(0.019)|0.043<br>(0.054)|-0.043<sup>_∗∗_</sup><br>(0.016)|-0.063<sup>_∗∗∗_</sup><br>(0.020)|
|FilFlDi|<sup>_∗∗_</sup>||||
|oss ue _×_ omestc|0.006<br>|0.003<br>|0.003<br>|0.003<br>|
|Fossil Fuel _×_ Foreign|(0.002)<br>-0.002|(0.007)<br>-0.013|(0.004)<br>0.015|(0.005)<br>0.016|
||(0.008)|(0.020)|(0.009)|(0.010)|
|RMSE|023635|056818|033525|039468|
|R<sup>2</sup>|.<br>0.86744|.<br>0.75373|.<br>0.91188|.<br>0.89359|
|Adjusted R<sup>2</sup><br>F-test, p-value<br>|0.84459<br>0.48889<br>|0.70292<br>0.99853<br>|0.89668<br>0.09625<br>|0.87516<br>0.23316<br>|
|Observations<br># frms|67,967<br>12,207|45,782<br>8,955|68,004<br>12,285|67,575<br>12,285|

_Note:_ Estimates according to fixed-effects model described in equations 1 and 4 and include firm-effects and industry-year and region-year effects as well as interactions and levels of the respective firm-level heterogeneity variables. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 18
**Exporter Status.** We separate firms based on their international market engagement.<sup>16</sup> The results presented in Table 4 can be summarized as follows: Capital upgrading among exporters is more strongly and positively associated with fossil fuel price increases (panel I). Non-exporting firms, in turn, experience a negative correlation between productivity indicators and electricity price increases (panel II). The same effect is absent for exporters. Exporters have a positive association with productivity and fossil fuel hikes. Considered jointly, our results suggest that the strong version of the Porter Hypothesis holds for exporting firms. For non-exporters, the empirical evidence lends support to the weak version of the Porter Hypothesis instead.<sup>17</sup> Energy substitution and absorption are comparable across both firm types (panels III and IV).

**Energy Intensity.** Finally, we evaluate the impact of energy price variations conditional on energy consumption. We do so by classifying the manufacturing sectors as either energy-intensive or non-intensive.<sup>18</sup> The results presented in Table 5 suggest that capital upgrading is more prevalent for energy-intensive sectors; however, evidence for the weak Porter Hypothesis exists for energy-intensive and non-intensive sectors in response to fossil fuel price surges (panel I). Further evidence in support of the strong version of the Porter Hypothesis is again observed, primarily for the energy-intensive sub-sectors (panel II). Intra-fuel substitution and absorption of energy price hikes are also more prevalent for energy-intensive sectors (panels III) and IV).

> 16We define a firm as an exporter if it reports a positive net value of export earnings of self-made products during any period. For more information and summary statistic see Table B.4 panel III.

> 17The estimated signs for non-exporters are also in line with the strong version of the Porter Hypothesis yet remain insignificant.

> 18Energy-intensive sectors are food; pulp and paper; basic chemicals; refining; iron and steel; nonferrous metals; non-metallic minerals following the sector classification of EIA (2022).See Table B.4 panel IV for a cross-tabulation by groups.

19
Table 4: Results by exporter status - FE

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity _×_ Exporter|-0.0004<br>|-0.264<br>|-0.029<br>|-0.035<br>|
|Electricity _×_ Non-exporter|(0.034)<br>-0.014|(0.213)<br>0.076|(0.043)<br>0.058|(0.034)<br>-0.020|
||(0.035)|(0.151)|(0.048)|(0.022)|
|FossilFuel_×_Exorter|0045<sup>_∗∗_</sup>|0150<sup>_∗_</sup>|0040|0042<sup>_∗∗∗_</sup>|
|p<br>|.<br>(0.021)<br>|.<br>(0.072)<br>|.<br>(0.027)<br>|.<br>(0.014)<br><sup>_∗∗_</sup>|
|Fossil Fuel _×_ Non-exporter|0.020<br>(0.019)|0.064<br>(0.065)|0.043<br>(0.025)|0.025<br>(0.009)|
|RMSE<br><sup>2</sup>|1.2285<br>|0.95855<br>|0.98298<br>|0.71808<br>|
|R<br>|0.47216|0.69087|0.69669|0.72270|
|Adjusted R<sup>2</sup><br>F-testp-value|0.34376<br>10000|0.34441<br>10000|0.55962<br>099938|0.67234<br>099892|
|, <br>Obti|.<br>39922|.<br>2143|.<br>18719|.<br>63920|
|servaons<br># frms|,<br>9,097|,<br>1,153|,<br>6,602|,<br>11,609|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Proft margin<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity _×_ Exporter|-0.020<br>(0.014)|0.003<br>(0.018)|-0.0008<br>(0.002)|0.026<br>(0.016)|
|Electricity_×_Non-exporter|-0015|-0024<sup>_∗∗_</sup>|-0006<sup>_∗∗_</sup>|-0029<sup>_∗∗∗_</sup>|
||.<br>(0.010)|.<br>(0.011)|.<br>(0.002)|.<br>(0.010)|
|Fossil Fuel _×_ Exporter|0.016<sup>_∗∗_</sup><br>(0.006)|0.005<br>(0.009)|0.002<sup>_∗_</sup><br>(0.0008)|-0.023<sup>_∗∗∗_</sup><br>(0.007)|
|Fossil Fuel _×_ Non-exporter|0.004<br>0004|0.004<br>0005|0.0003<br>00010|-0.004<br>0005|
||(.)|(.)|(.)|(.)|
|RMSE<br>|0.36336|0.56549|0.03243|0.48328|
|R<sup>2</sup><br>AdjtdR<sup>2</sup>|0.88491<br>086506|0.74722<br>070227|0.95627<br>093835|0.48986<br>039819|
|use <br>F-test, p-value<br>|.<br>0.31354<br>|.<br>0.99377<br>|.<br>0.00103<br>|.<br>1.0000<br>|
|Observations<br># frms|67,982<br>12,228|65,502<br>12,232|13,470<br>4,363|64,959<br>12,277|
|_Panel III_|Qnt. Electricity<br>|Qnt. Fossil Fuels<br>|Qnt. Diesel<br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity _×_ Exporter|-0.725<sup>_∗∗∗_</sup>|-0.097|-0.132<sup>_∗_</sup>|0.037|
||(0.172)<br><sup>_∗∗∗_</sup>|(0.058)<br>|(0.067)<br>|(0.056)<br><sup>_∗∗_</sup>|
|Electricity _×_ Non-exporter|-0.592<br>(0.183)|0.042<br>(0.034)|0.005<br>(0.038)|0.104<br>(0.042)|
|Fossil Fuel _×_ Exporter|0.154<sup>_∗∗∗_</sup><br>(0012)|-0.608<sup>_∗∗∗_</sup><br>(0043)|-0.491<sup>_∗∗∗_</sup><br>(0038)|-0.550<sup>_∗∗∗_</sup><br>(0075)|
||.<br><sup>_∗∗∗_</sup>|.<br><sup>_∗∗∗_</sup>|.<br><sup>_∗∗∗_</sup>|.<br><sup>_∗∗∗_</sup>|
|Fossil Fuel _×_ Non-exporter|0.180<br>(0.018)|-0.555<br>(0.049)|-0.491<br>(0.052)|-0.461<br>(0.070)|
|RMSE|0.85080|1.0085|0.95672|0.85131|
|R<sup>2</sup><br><sup>2</sup>|0.85860|0.76305|0.76861|0.75530|
|Adjusted R|0.83420|0.71821|0.71886|0.69489|
|F-test, p-value<br>Observations|0.56640<br>68242|0.98523<br>57561|0.97472<br>42925|0.98616<br>31197|
|# frms|,<br>12,284|,<br>12,285|,<br>7,538|,<br>6,074|
|_Panel IV_|Wages all workers|Wages production workers|Emp. all workers|Emp. production workers|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity _×_ Exporter|0.004<br>(0.009)|0.025<br>(0.030)|-0.032<sup>_∗∗_</sup><br>(0.014)|-0.044<sup>_∗∗_</sup><br>(0.016)|
|Electricity _×_ Non-exporter|0.001<br>(0.007)|-0.026<br>(0.019)|-0.023<sup>_∗∗_</sup><br>(0.009)|-0.032<sup>_∗∗_</sup><br>(0.013)|
|Fossil Fuel _×_ Exporter|0.007<br>|-0.007<br>|0.001<br>|0.006<br>|
|Fossil Fuel _×_ Non-exporter|(0.004)<br>0.004|(0.009)<br>0.004|(0.006)<br>0.005|(0.007)<br>0.004|
||(0.003)|(0.007)|(0.004)|(0.005)|
|RMSE|023635|056815|033427|039391|
|R<sup>2</sup>|.<br>0.86744|.<br>0.75376|.<br>0.91240|.<br>0.89400|
|Adjusted R<sup>2</sup><br>Fttl|0.84459<br>048888|0.70296<br>099853|0.89728<br>009345|0.87565<br>022952|
|-es, p-vaue<br>|.<br>|.<br>|.<br>|.<br>|
|Observations<br># frms|67,967<br>12,207|45,782<br>8,955|68,004<br>12,285|67,575<br>12,285|

_Note:_ Estimates according to fixed-effects model described in equations 1 and 4 and include firm-effects and industry-year and region-year effects as well as interactions and levels of the respective firm-level heterogeneity variables. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 20
Table 5: Results by energy intensity - FE

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity _×_ Energy-intensive|-0.008<br>|-0.024<br>|0.004<br>|-0.022<br>|
|Electricity _×_ Non-intensive|(0.033)<br>-0.010|(0.135)<br>-0.118|(0.036)<br>0.089<sup>_∗_</sup>|(0.053)<br>0.003|
||(0.041)|(0.227)|(0.046)|(0.031)|
|Fossil Fuel _×_ Energy-intensive|0.037<sup>_∗∗_</sup><br>|0.092<br>|0.048<sup>_∗∗_</sup><br>|0.034<sup>_∗∗∗_</sup><br>|
|Fossil Fuel _×_ Non-intensive|(0.015)<br>0.015|(0.058)<br>0.126|(0.023)<br>0.021|(0.005)<br>0.041<sup>_∗∗∗_</sup>|
||(0.029)|(0.106)|(0.035)|(0.011)|
|RMSE<br><sup>2</sup>|1.2286<br>|0.95828<br>|0.98331<br>|0.71808<br>|
|R<br>|0.47210|0.69105|0.69648|0.72270|
|Adjusted R<sup>2</sup><br>F-testp-value|0.34368<br>10000|0.34477<br>10000|0.55931<br>099939|0.67234<br>099892|
|, <br>Observations<br>|.<br>39,922<br>|.<br>2,143<br>|.<br>18,719<br>|.<br>63,920<br>|
|# frms|9,097|1,153|6,602|11,609|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Proft margin<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity _×_ Energy-intensive|-0.015<sup>_∗_</sup><br>(0.009)|-0.011<br>(0.010)|-0.003<sup>_∗_</sup><br>(0.002)|-0.033<sup>_∗∗_</sup><br>(0.014)|
|Electricity_×_Non-intensive|-0.022<sup>_∗_</sup>|-0.036<sup>_∗∗_</sup>|-0.005|-0.009|
||(0.011)|(0.015)|(0.003)|(0.010)|
|Fossil Fuel _×_ Energy-intensive|0.009<sup>_∗_</sup><br>|0.010<br>|0.002<sup>_∗∗∗_</sup><br>|-0.014<sup>_∗_</sup><br>|
|FossilFuel_×_Non-intensive|(0.004)<br>0.003<sup>_∗_</sup>|(0.007)<br>-0.010|(0.0008)<br>-0.003|(0.007)<br>-0.009|
||(0.002)|(0.007)|(0.002)|(0.005)|
|RMSE<br>|0.36356|0.56550|0.03248|0.48336|
|R<sup>2</sup><br>Adjusted R<sup>2</sup><br>|0.88478<br>0.86491<br>|0.74721<br>0.70226<br>|0.95614<br>0.93817<br>|0.48968<br>0.39798<br>|
|F-test, p-value<br>|0.31481<br>|0.99378<br>|0.00105<br>|1.0000<br>|
|Observations<br># frms|67,982<br>12,228|65,502<br>12,232|13,470<br>4,363|64,959<br>12,277|
|_Panel III_|Qnt. Electricity<br>|Qnt. Fossil Fuels<br>|Qnt. Diesel<br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity _×_ Energy-intensive|-0.624<sup>_∗∗∗_</sup>|0.002|-0.007|0.085<sup>_∗_</sup>|
||(0.181)<br><sup>_∗∗∗_</sup>|(0.039)<br>|(0.037)<br>|(0.045)<br>|
|Electricity _×_ Non-intensive|-0.628<br>(0.182)|-0.005<br>(0.042)|-0.102<br>(0.062)|0.074<br>(0.058)|
|Fossil Fuel _×_ Energy-intensive|0.172<sup>_∗∗∗_</sup><br>(0015)|-0.565<sup>_∗∗∗_</sup><br>0112)|-0.514<sup>_∗∗∗_</sup><br>(0046)|-0.491<sup>_∗∗∗_</sup><br>(0065)|
|Fossil Fuel _×_ Non-intensive|.<br>0.171<sup>_∗∗∗_</sup><br>(0.020)|.<br>-0.516<sup>_∗∗∗_</sup><br>(0.111)|.<br>0.402<sup>_∗∗∗_</sup><br>(0.048)|.<br>-0.499<sup>_∗∗∗_</sup><br>(0.088)|
|RMSE|0.85098|1.1555|0.95668|0.85076|
|R<sup>2</sup><br><sup>2</sup>|0.85854|0.81396|0.76863|0.75561|
|Adjusted R|0.83413|0.78190|0.71889|0.69529|
|F-test, p-value<br>Observations|0.56697<br>68242|0.87892<br>68326|0.97470<br>42925|0.98596<br>31197|
|# frms|,<br>12,284|,<br>12,285|,<br>7,538|,<br>6,074|
|_Panel IV_|Wages all workers|Wages production workers|Emp. all workers|Emp. production workers|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity _×_ Energy-intensive|-0.004<br>(0.007)|0.037<sup>_∗_</sup><br>(0.019)|-0.050<sup>_∗∗∗_</sup><br>(0.010)|-0.063<sup>_∗∗∗_</sup><br>(0.013)|
|Electricity _×_ Non-intensive|0.004<br>(0.007)|-0.032<br>(0.023)|-0.016<sup>_∗_</sup><br>(0.009)|-0.025<sup>_∗∗_</sup><br>(0.012)|
|Fossil Fuel _×_ Energy-intensive|-0.003<br>|0.003<br>|-0.0008<br>|-0.005<br>|
|Fossil Fuel _×_ Non-intensive|(0.003)<br>0.008<sup>_∗∗∗_</sup>|(0.009)<br>-0.002|(0.006)<br>0.006|(0.007)<br>0.008|
||(0.003)|(0.008)|(0.005)|(0.006)|
|RMSE|023640|056646|033467|039363|
|R<sup>2</sup>|.<br>0.86738|.<br>0.75522|.<br>0.91218|.<br>0.89415|
|Adjusted R<sup>2</sup><br>Fttl|0.84452<br>048951|0.70472<br>099840|0.89703<br>009459|0.87583<br>022821|
|-es, p-vaue<br>|.<br>|.<br>|.<br>|.<br>|
|Observations<br># frms|67,967<br>12,207|45,782<br>8,955|68,004<br>12,285|67,575<br>12,285|

_Note:_ Estimates according to fixed-effects model described in equations 1 and 4 and include firm-effects and industry-year and region-year effects as well as interactions and levels of the respective firm-level heterogeneity variables. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 21
# **6.3 Non-linearity in Responses**

Next, we turn to the issue of potential non-linearities in the firm-level responses to energy price increases. We examine if firms’ optimal response to energy price hikes may depend on the energy price it observes. Firms may resort to different energysaving strategies at low energy price levels compared to higher prices. For example, firms may invest in updated production technologies to increase efficiency at lower price levels. Still, this coping mechanism may not be accessible to firms once they have exhausted the saving potential stemming from capital upgrading, for example, at higher energy price levels. It is, therefore, crucial to analyze to what extent the previously established correlation patterns are themselves determined by the actual price levels of energy.

The results of this analysis are summarized in Figure 1 and show that the positive correlation between fossil fuel price increases and capital upgrading is more pronounced when fuel prices are low (panel I). At higher fuel price levels, this effect becomes smaller and eventually insignificant. Similar effects cannot be observed for electricity prices.<sup>19</sup> These patterns may be suggestive of the cost of upgrading electricity-based technology relative to fuel-based machinery (panel I, Figure 1a). Furthermore, the previously identified Porter-type innovation hypothesis for fossil fuels seems most pronounced at low(er) energy price levels but becomes statistically insignificant at higher fuel levels (panel II). By comparison, the _substitution_ channel does not exhibit a non-linear relationship as correlations between electricity and fossil fuel prices and consumed quantities of either energy source are similar during lowand high-price energy regimes (panel III). Lastly, the positive wage effect correlating with a surge in fossil fuel prices becomes smaller and insignificant at higher fuel price levels (panel IV).

These findings suggest that firms may resort to different coping strategies depending on energy price levels. At lower energy price levels, adjustment is notably through the _innovation_ channel, while other mechanisms, such as _absorption_ , may become more relevant at even higher energy price levels.

> 19The correlations between machinery investment and electricity price increases follow a less pronounced yet still non-linear pattern. A positive correlation with innovation is observed at higher electricity prices, while lower electricity prices correspond to a negative correlation with investment in machinery.

22
<!-- Start of picture text --> ua<br><!-- End of picture text -->
# **6.4 Robustness Checks**

**Instrumental Variables Estimation.** We address potential endogeneity by employing a spatial/leave-one-out instrument to exploit the geographical variation in energy prices from the cost of distributing energy to a particular province to eliminate time-varying unobserved variation in the energy prices faced by firm _i_ . As we discuss in section 5.4, we provide two variations of the spatial instrument where either excluding the same ISIC 2-digit sector ( _IV_ 1) or 4-digit sector ( _IV_ 2) to address potential spillover effects that could potentially invalidate our IV design. 20 The results of the IV estimations are in line with that of the baseline model and similar in magnitude.

Results from instrumental variable _IV_ 1 and _IV_ 2, presented in Table 6 and Table 7 generally confirm the robustness of our OLS results in baseline model. The most notable differences are that the effect of fossil fuel increases on ICT capital upgrading (2) and wages (13) are now insignificant in the two IV specifications. The same is true for the negative correlation of electricity price hikes with the profit margin (17). In contrast, the IV estimators report a slightly higher price elasticity of electricity. Finally, wages by production workers are now found to significantly contract in response to an electricity price hike (14) with _IV_ 1. This effect is statistically insignificant in the baseline model and in the _IV_ 2 specification.

> 20We provide the First Stage estimates for both variations of the instrument in tables B.5 and B.6, respectively. Across all estimated models, the F-statistics lie between 118 and 148, suggesting that the selected instrument is strong. In both cases, the instrument has a positive association with electricity prices consistent with the results reported for Indonesia in Cal`ı et al. (2022). Since plants’ electricity is generated by utilizing fuel, higher fuel prices are implicitly included in electricity prices.

24
Table 6: Instrumental Variable Results - _IV_ 1

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity|-0.020<br>|-0.062<br>|0.013<br>|-0.026<br>|
|Fossil Fuels|(0.027)<br>0.028<sup>_∗_</sup>|(0.186)<br>0.051|(0.034)<br>0.045<sup>_∗_</sup>|(0.023)<br>-0.032<sup>_∗∗∗_</sup>|
||(0.015)|(0.066)|(0.022)|(0.009)|
|_Fit statistics_<br>|||||
|Adjusted R<sup>2</sup><br><sup>2</sup>|0.35121<br>|0.37544<br>|0.56574<br>|0.67408<br>|
|Within Adjusted R<br>Observations|0.00115<br>39,922|0.00139<br>2,143|0.00224<br>18,719|0.01570<br>63,920|
|Weak id. test|59.4|102.0|69.8|110.1|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Exit<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity|-0.022<sup>_∗∗_</sup><br>|-0.021<sup>_∗∗_</sup><br>|-0.005<sup>_∗_</sup><br>|0.010<sup>_∗∗_</sup><br>|
|Fossil Fuels|(0.009)<br>0.008<sup>_∗∗_</sup>|(0.010)<br>0.003|(0.003)<br>-0.0004|(0.003797)<br>-0.003<sup>_∗∗∗_</sup>|
||(0.004)|(0.005)|(0.001)|(0.0007308)|
|_Fit statistics_<br>|||||
|Adjusted R<sup>2</sup>|0.86930|0.71151|0.81888|0.18521|
|Within Adjusted R<sup>2</sup><br>|0.00397<br>|0.00281<br>|0.00209<br>|0.00622<br>|
|Observations<br>Weak id. test|67,982<br>92.6|65,502<br>83.7|13,470<br>43.7|53,887<br>43.9|
|_Panel III_|Qnt. Electricity<br>|Qnt. Fossil Fuels<br>|Qnt. Diesel<br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity|-0.860<sup>_∗∗∗_</sup><br>0064|0.030<br>0035|0.0006<br>0042|0.070<br>0043|
||(.)<br><sup>_∗∗∗_</sup>|(.)<br><sup>_∗∗∗_</sup>|(.)<br><sup>_∗∗∗_</sup>|(.)<br><sup>_∗∗∗_</sup>|
|Fossil Fuels|-0.183<br>(0.012)|-0.543<br>(0.044)|-0.451<br>(0.039)|-0.491<br>(0.063)|
|_Fitstatistics_|||||
|<br>Adjusted R<sup>2</sup><br>|0.88009|0.75431|0.76921|0.70066|
|Within Adjusted R<sup>2</sup><br>Obti|0.12624<br>68242|0.09820<br>57561|0.07439<br>42925|0.08329<br>31197|
|servaons<br>Weak id. test|,<br>81.9|,<br>73.8|,<br>116.3|,<br>73.5|
|_Panel IV_<br>|Wages all workers<br>|Wages production workers<br>|Emp. all workers<br>|Emp. production workers<br>|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity|-0.003|-0.029<sup>_∗_</sup>|-0.025<sup>_∗∗_</sup>|-0.037<sup>_∗∗_</sup>|
||(0.006)<br>|(0.015)<br>|(0.010)<br>|(0.014)<br>|
|Fossil Fuels|0.004<br>(0.002)|0.002<br>(0.007)|0.002<br>(0.004)|0.005<br>(0.004)|
|_Fit statistics_|||||
|Adjusted R<sup>2</sup><br>|0.85280|0.69545|0.90048|0.87769|
|Within Adjusted R<sup>2</sup><br>Observations|0.00139<br>67967|0.00848<br>45782|0.00179<br>68004|0.00182<br>67575|
|Weak id. test|,<br>111.5|,<br>55.9|,<br>38.6|,<br>62.9|
|_Panel V_|Proft margin|Sales/output|Costs/output|Return on Sales|
|_Absorption - business metri_|_cs_<br>(17)|(18)|(19)|(20)|
|Electricity|-0.014|-0.009<sup>_∗∗_</sup>|0.002|-0.043<sup>_∗_</sup>|
|FossilFuels|(0.008)<br>-0010<sup>_∗∗_</sup>|(0.003)<br>00006|(0.005)<br>0005<sup>_∗_</sup>|(0.024)<br>-0019<sup>_∗_</sup>|
||.<br>(0.004)|.<br>(0.003)|.<br>(0.003)|.<br>(0.010)|
|_Fit statistics_|||||
|AdjustedR<sup>2</sup>|0.40214|0.54410|0.47755|0.77830|
|<br>Within Adjusted R<sup>2</sup>|0.00098|0.00091|0.00064|0.00137|
|Observations<br>Weak id. test|64,959<br>119.5|64,742<br>90.7|68,228<br>95.2|35,097<br>81.2|
|_Fixed-efects_|||||
|Plant<br>|Yes<br>|Yes<br>|Yes<br>|Yes<br>|
|Industry-Year<br>Region-Year|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|Yes<br>Yest|

_Note:_ Estimates are based on FE model described in equations 1 and 7 and include firm-effects and industry-year and region-year effects. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 Weak identification test statistic for instrumental variables following Chao and Swanson (2005).

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Table 7: Instrumental Variable Results - _IV_ 2

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity|-0.028<br>|-0.113<br>|0.027<br>|-0.030<br>|
|FossilFuels|(0.026)<br>0.027<sup>_∗_</sup>|(0.195)<br>0.068|(0.036)<br>0.052<sup>_∗∗_</sup>|(0.023)<br>-0.034<sup>_∗∗∗_</sup>|
||(0.015)|(0.070)|(0.020)|(0.008)|
|_Fit statistics_<br>|||||
|Adjusted R<sup>2</sup><br><sup>2</sup>|0.35031<br>|0.29992<br>|0.56827<br>|0.67441<br>|
|Within Adjusted R<br>Observations|0.00106<br>39,922|0.00235<br>2,143|0.00166<br>18,719|0.01534<br>63,920|
|Weak id. test|42.6|52.9|97.3|107.4|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Exit<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity|-0.017<br>|-0.016<br>|-0.004<br>|0.010<sup>_∗∗_</sup><br>|
|Fossil Fuels|(0.010)<br>0.007<sup>_∗_</sup>|(0.011)<br>0.004|(0.002)<br>0.0002|(0.003797)<br>-0.003<sup>_∗∗∗_</sup>|
||(0.004)|(0.005)|(0.0009)|(0.0007308)|
|_Fit statistics_<br>|||||
|Adjusted R<sup>2</sup>|0.86884|0.71060|0.88852|0.18521|
|Within Adjusted R<sup>2</sup><br>|0.00375<br>|0.00267<br>|0.00505<br>|0.00622<br>|
|Observations<br>Weak id. test|67,982<br>102.0|65,502<br>63.2|13,470<br>42.0|53,887<br>43.9|
|_Panel III_|Qnt. Electricity<br>|Qnt. Fossil Fuels<br>|Qnt. Diesel<br>|Qnt. LPG<br>|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity|-0.900<sup>_∗∗∗_</sup><br>0034|0.032<br>0035|0.004<br>0042|0.077<sup>_∗_</sup><br>0044|
||(.)<br><sup>_∗∗∗_</sup>|(.)<br><sup>_∗∗∗_</sup>|(.)<br><sup>_∗∗∗_</sup>|(.)<br><sup>_∗∗∗_</sup>|
|Fossil Fuels|-0.189<br>(0.010)|-0.567<br>(0.047)|-0.478<br>(0.043)|-0.503<br>(0.062)|
|_Fitstatistics_|||||
|<br>Adjusted R<sup>2</sup><br>|0.88145|0.73719|0.74428|0.69602|
|Within Adjusted R<sup>2</sup><br>Obti|0.12460<br>64866|0.09927<br>54776|0.07439<br>40567|0.08509<br>30069|
|servaons<br>Weak id. test|,<br>59.3|,<br>55.2|,<br>67.7|,<br>66.7|
|_Panel IV_<br>|Wages all workers<br>|Wages production workers<br>|Emp. all workers<br>|Emp. production workers<br>|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity|0.003|-0.019|-0.025<sup>_∗∗_</sup>|-0.037<sup>_∗∗_</sup>|
|ll|(0.007)<br>|(0.018)<br>|(0.010)<br><sup>_∗_</sup>|(0.014)<br><sup>_∗_</sup>|
|Fossi Fues|0.004<br>(0.002)|0.002<br>(0.007)|0.007<br>(0.004)|0.009<br>(0.004)|
|_Fit statistics_|||||
|Adjusted R<sup>2</sup><br>|0.85010|0.69563|0.89863|0.87635|
|Within Adjusted R<sup>2</sup>|0.00150|0.00851|0.00159|0.00173|
|Observations<br>Wkid|67,967<br>7|45,782<br>|68,004<br>|67,575<br>|
|ea . test|3.5|95.9|59.8|50.3|
|_Panel V_|Proft margin|Sales/output|Costs/output|Return on Sales|
|_Absorption - business metri_|_cs_<br>(17)|(18)|(19)|(20)|
|Electricity|-0.013|-0.012<sup>_∗∗∗_</sup>|0.001|-0.040|
|FossilFuels|(0.009)<br>-0010<sup>_∗∗_</sup>|(0.004)<br>00008|(0.005)<br>0004|(0.023)<br>-0017|
||.<br>(0.004)|.<br>(0.003)|.<br>(0.002)|.<br>(0.010)|
|_Fit statistics_|||||
|AdjustedR<sup>2</sup>|0.39903|0.54065|0.47633|0.77868|
|<br>Within Adjusted R<sup>2</sup>|0.00092|0.00088|0.00052|0.00108|
|Observations<br>Weak id. test|64,959<br>96.3|64,742<br>111.9|68,228<br>72.9|35,097<br>106.3|
|_Fixed-efects_|||||
|Plant<br>|Yes<br>|Yes<br>|Yes<br>|Yes<br>|
|Industry-Year<br>Region-Year|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|Yes<br>Yest|

_Note:_ Estimates are based on FE model described in equations 1 and 7 and include firm-effects and industry-year and region-year effects. Clustered (plant & year) standard-errors in parentheses. Signif. Codes: ***: 0.01, **: 0.05, *: 0.1 Weak identification test statistic for instrumental variables following Chao and Swanson (2005).

26
**Contemporaneous versus Medium-term Effects.** We are concerned that it might take time for firms to make decisions on their investments that would, in turn, translate into productivity effects with further lags. By comparison, substitution and absorption effects could potentially be instantaneous, although they may show further adjustments over the medium to long run.<sup>21</sup> To this end, we experiment with lags of energy prices changes in Equation 1 to analyze the intertemporal component of the response patterns in investment and productivity effects. We estimate the model:

where _l_ = _{_ 1 _,_ 3 _,_ 5 _}_ , i.e., we compare the contemporaneous effect with the one-, threeand five-year lagged observations of the electricity and fossil fuel prices, respectively.

The results presented in Figure 2 suggest that the negative correlation of electricity price hikes on investment indicators is no longer significant for lagged models. At the same time, productivity effects are stronger for five year period when considering electricity prices (panels 2a and 2b).<sup>22</sup>

**Sensitivity to Sample Period.** Finally, we confirm the robustness of our results by providing a sensitivity analysis where we remove the final three years of the sample where we observe a stronger deviation of the reported raw data sample vis-a-vis our reconstructed data set. We do this to ensure that the results of our analysis are not driven by our matching algorithm while acknowledging that the correlations between the variables may change over time. This implies that a further truncation of the data set may, therefore, pick up intertemporal variations rather than eliminate any potential contagion stemming from our data generation). The results in Table 8 confirm the previously established relationships between energy prices and firm-level outcome variables.

> 21Historically, lagged values of the explanatory variables are also used as valid instruments although subject to some criticism. The literature on whether lagged explanatory variables are effective in surmounting endogeneity concerns is scarce. Among the few contributions, Reed (2015) and Bellemare et al. (2017) evaluate the practice of replacing an endogenous variable with its lag. Reed (2015) focuses on situations where endogeneity stems from simultaneity between _y_ and _x_ . In turn, Bellemare et al. (2017) identify the lack of serial correlation in the potentially endogenous explanatory variable and serial correlation among the unobserved sources of endogeneity as conditions that could lead to incorrect inferences when employing lagged variables as instruments. Consequently, we refrain from employing such a lagged IV setup in this study.

> 22Results pertaining to substitution and absorption effects are instantaneous in most cases with stronger adjustments in five-year periods.

27
Figure 2: Contemporaneous vs medium-term Effects <!-- Start of picture text --> Lags Lags<br>1−period 1−period<br>3−period 3−period<br>5−period 5−period<br>Advertisement/output<br>Machinery/output<br>Vehicles/output<br>Gross addition/output<br>−0.2 −0.1 0.0 0.1 0.2 0.3 −0.05 0.00 0.05 0.10<br>Investment<br><!-- End of picture text --> <!-- Start of picture text --> (a) Investment effects electricity prices (b) Investment effects fossil fuel prices<br><!-- End of picture text --> <!-- Start of picture text --> Lags Lags<br>1−period 1−period<br>3−period 3−period<br>5−period 5−period<br>Output/worker<br>Value−added/worker<br>TFP (ACF CD)<br>−0.02 0.00 0.02 0.04 0.06 0.08 0.10 −0.015 −0.005 0.005 0.010 0.015<br>(c) Productivity effects of electricity prices (d) Productivity effects of fossil fuel prices<br>Productivity<br><!-- End of picture text --> _Note:_ Estimates based on Equation 11. Scatters and error bars: FE point estimates and 90% confidence intervals.

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Table 8: Results Sub-sample

|_Panel I_<br>_Investments_|Machinery/output<br>(1)|ICT/output<br>(2)|Vehicles/output<br>(3)|Asset balance/output<br>(4)|
|---|---|---|---|---|
|Electricity|-0.027|-0.080|0.002|-0.029|
|FilFl|(0.029)<br>0034<sup>_∗∗_</sup>|(0.173)<br>0074|(0.036)<br>0048<sup>_∗_</sup>|(0.022)<br>0029<sup>_∗∗∗_</sup>|
|oss ues|.<br>(0.015)|.<br>(0.081)|.<br>(0.025)|-.<br>(0.010)|
|_Fit statistics_<br>RMSE<br><sup>2</sup>|1.2359|0.95829|0.97830|0.66044|
|R<br>Adjusted R<sup>2</sup>|0.48196<br>0.33775|0.69104<br>0.34670|0.71242<br>0.56107|0.75643<br>0.70543|
|F-test, p-value<br>Observations|1.0000<br>29204|1.0000<br>2143|0.99973<br>13559|0.99859<br>46779|
|# frms|,<br>7,284|,<br>1,153|,<br>5,146|,<br>9,427|
|_Panel II_<br>|Output/worker<br>|Value-added/worker<br>|TFP<br>|Exit<br>|
|_Productivity/survival_|(5)|(6)|(7)|(8)|
|Electricity|-0.022<sup>_∗∗_</sup><br>|-0.026<sup>_∗∗_</sup><br>|-0.003<sup>_∗_</sup><br>|0.009<sup>_∗∗_</sup><br>|
|Fossil Fuels|(0.009)<br>0.007<sup>_∗_</sup>|(0.010)<br>0.002|(0.002)<br>0.001|(0.005)<br>-0.004<sup>_∗∗∗_</sup>|
||(0.003)|(0.006)|(0.0007)|(0.001)|
|_Fit statistics_|||||
|RMSE<br>R<sup>2</sup>|0.34847<br>089978|0.55134<br>076786|0.03177<br>096045|0.22433<br>040215|
|<sup>2</sup>|.<br>|.<br>|.<br>|.<br>|
|Adjusted R<br>|0.87996<br>|0.72052<br>|0.94321<br>|0.26693<br>|
|F-test, p-value<br>|0.43245<br>|0.99581<br>|0.00974<br>|1.0000<br>|
|Observations<br># frms|49,814<br>9,958|47,875<br>9,960|10,660<br>3,535|36,817<br>8,079|
|_Panel III_|Qnt. Electricity|Qnt. Fossil Fuels|Qnt. Diesel|Qnt. LPG|
|_Substitution_|(9)|(10)|(11)|(12)|
|Electricity|-0.857<sup>_∗∗∗_</sup><br>(0068)|0.036<br>(0034)|0.015<br>(0042)|0.082<sup>_∗_</sup><br>(0039)|
|Fossil Fuels|.<br>-0.178<sup>_∗∗∗_</sup><br>|.<br>-0.518<sup>_∗∗∗_</sup><br>|.<br>-0.433<sup>_∗∗∗_</sup><br>|.<br>-0.458<sup>_∗∗∗_</sup><br>|
||(0.013)|(0.047)|(0.041)|(0.068)|
|_Fit statistics_<br>RMSE<br>|0.92019|1.0023|0.95935|0.88167|
|R<sup>2</sup><br>|0.85004|0.76064|0.76184|0.75881|
|Adjusted R<sup>2</sup><br>F-test, p-value<br>Observations|0.82036<br>0.84734<br>50057|0.70778<br>0.99689<br>42373|0.70264<br>0.99568<br>32677|0.68901<br>0.99631<br>22207|
|# frms|,<br>10,015|,<br>10,016|,<br>6,447|,<br>4,885|
|_Panel IV_<br>|Wages all workers<br>|Wages production workers<br>|Emp. all workers<br>|Emp. production workers<br>|
|_Absorption - workers_|(13)|(14)|(15)|(16)|
|Electricity|-0.009<br>|-0.031<sup>_∗_</sup><br>|-0.020<sup>_∗_</sup><br>|-0.030<br>|
|Fossil Fuels|(0.008)<br>0.003|(0.016)<br>0.0007|(0.011)<br>0.008<sup>_∗_</sup>|(0.022)<br>0.009|
||(0.003)|(0.007)|(0.004)|(0.006)|
|_Fitstatistics_|||||
|<br>RMSE<br><sup>2</sup>|0.22853<br>|0.56824<br>|0.32392<br>|0.35748<br>|
|R<br><sup>2</sup>|0.87331<br>|0.75367<br>|0.92032<br>|0.91481<br>|
|Adjusted R<br>F-test, p-value|0.84824<br>0.68804|0.70288<br>0.99805|0.90454<br>0.21807|0.89792<br>0.27194|
|Observations<br># frms|49,795<br>9,943|45,782<br>8,955|49,826<br>10,016|49,566<br>10,016|
|_PanelV_|Proftmargin|Sales/output|Costs/output|ReturnonSales|
|<br>_Absorption - business met_|<br>_rics_<br>(17)|(18)|(19)|<br>(20)|
|Fossil Fuels|-0.010<sup>_∗∗_</sup><br>|0.0008<br>|0.005<sup>_∗_</sup><br>|-0.021<sup>_∗_</sup><br>|
|Electricity|(0.004)<br>-0.016<sup>_∗_</sup><br>|(0.003)<br>-0.009<sup>_∗∗_</sup><br>|(0.003)<br>0.002<br>|(0.011)<br>-0.046<sup>_∗_</sup><br>|
||(0.008)|(0.003)|(0.005)|(0.024)|
|_Fit statistics_|||||
|RMSE<br>R<sup>2</sup>|0.47850<br>0.50904|0.21794<br>0.66051|0.23252<br>0.57203|0.74621<br>0.84141|
|AdjustedR<sup>2</sup>|040762|059240|048739|079057|
|<br>Fttl|.<br>10000|.<br>10000|.<br>10000|.<br>089491|
|-es, p-vaue<br>Observations<br>|.<br>47,529<br>|.<br>47,238<br>|.<br>50,066<br>|.<br>26,255<br>|
|# frms|10,007|9,506|10,008|9,983|
|Plant<br>Industry-Year|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|
|Region-Year|Yes|Yes|Yes|Yest|
|_Note:_<br>Estimates accordi|ng to FE model described|in equations 1 and 6 include f|rm-efects as well as|industry-year and|
|region-year efects. Clust|ered (plant & year) standa|rd-errors in parentheses. Signi|f. Codes: ***: 0.01,|**: 0.05, *: 0.1|
# **7 Conclusion**

This paper contributes to understanding the energy price-firm competitiveness nexus in an emerging economy context. Firms use four main coping mechanisms to navigate energy price increases: innovation and competitiveness, substitution across fuel types and other inputs, and absorption or pass-through. Our results indicate that while electricity price increases are associated with reduced production and firm size, fossil fuel price hikes result in increased capital investment, manifesting in improved productivity, thereby supporting the _strong_ version of the Porter Hypothesis. However, these productivity increases do not translate into higher profitability (at least not in the short run), indicating that such price hikes are at least partly absorbed by firms.

These broader results mask heterogeneity by firm attributes. The strong version of the Porter Hypothesis can only be observed in large firms. Small firms are more negatively affected by surges in energy prices. Likewise, exporters and foreignowned firms are also less affected compared to domestically oriented firms. Moreover, energy-intensive firms are also found to experience more extensive Porter-type innovations, and their business metrics and employment are typically more affected by energy price hikes than firms in non-energy-intensive sectors.

Given the firm heterogeneity in outcomes, policy reforms affecting energy prices and accompanying measures are best targeted based on solid micro-level analyses. The most vulnerable firms, such as the smaller and domestically oriented firms, may not have the means to adjust to energy price fluctuations and may need complementary support to undertake the necessary changes and investments. A better understanding of how existing inequalities interact with the risks posed by energy price policies, taking into account firm capabilities and management skills, will inform a more efficient policy design.<sup>23</sup>

> 23A word of caution is warranted here in over-interpreting. These results remain broadly illustrative as they build on the publicly available version of the ENIA data set, which includes firm-level panel data up to 2015. Additional work is required to merge post-2015 years to the panel dimension of the data set and include the introduction of the exogenous Carbon tax, which became effective in 2018, in the observation period.

30