metadata: authors: 0: author: Aterido,Reyes
1: author: Iootty De Paiva Dias,Mariana
2: author: Melecky,Martin
count: Central Asia
docna: 0: docna: Energy Prices, Energy Intensity, and Firm Performance
docty: Policy Research Working Paper
theme: Financial Sector oversight and
            policy/banking regulation & restructuring,Mitigation,Infrastructure Finance,Human Development and Gender,Financial Stability,ICT,Economic Policy,ICT Solutions,Fiscal Policy,Environment and Natural Resource Management,Disease Control,Finance for Development,Finance,Enterprise Development,Private Sector Development,Pandemic Response,MSME Development,Fiscal sustainability,Climate change,Adaptation,Disaster Risk Finance,Financial Sector Integrity
abstracts: cdata!: This paper estimates the effect of
            electricity prices on firm performance, focusing on firm
            productivity, sales, and employment. Using the World Bank
            Business Pulse Survey data for a sample of 24 emerging
            markets and developing economies during 2019&#x2013;23,
            the paper estimates the average effect and the heterogeneous
            effects across industries of varying energy intensity and
            firms that implemented (or did not implement) energy
            efficiency measures (self-reported in the Business Pulse
            Survey). The findings show that increasing electricity
            prices by 1 percent reduces employment at firms in
            energy-intensive industries that did not adopt energy
            efficiency measures by about 1.5 percent, compared with
            similar firms in energy-non-intensive sectors. In parallel,
            energy-intensive firms may increase sales and productivity
            but this result is robust to all alternative specifications.
            Firms may increase sales while reducing employment after
            energy price hikes, by adopting energy-efficient
            technologies and by passing through costs to consumers in
            inelastic markets while reducing employment in
            energy-intensive sectors due to cost pressures. These
            results highlight the adoption of energy efficiency measures
            by firms as an important employment protection policy action
            to cope with future volatility in energy (electricity) prices.
display_title: Energy Prices, Energy Intensity, and
            Firm Performance
listing_relative_url: /research/2025/02/34457929/energy-prices-energy-intensity-firm-performance
topicv3: Economic Shocks and Vulnerability m1326506 702,Firm Dynamics and Productivity Growth
            m1504195 2864
publishtoextweb_dt: 2025-02-19T00:00:00Z
dois: 10.1596/1813-9450-11069
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 37
author_keys: 0: author_key: 688368
1: author_key: 1051061
2: author_key: 915923
model_extractions: Policy Research Working Paper 11069 Energy Prices, Energy Intensity, and Firm Performance _Reyes Aterido Mariana Iootty Martin Melecky_ Finance, Competitiveness and Innovation Global Department February 2025
Policy Research Working Paper 11069

# **Abstract**

This paper estimates the effect of electricity prices on firm performance, focusing on firm productivity, sales, and employment. Using the World Bank Business Pulse Survey data for a sample of 24 emerging markets and developing economies during 2019–23, the paper estimates the average effect and the heterogeneous effects across industries of varying energy intensity and firms that implemented (or did not implement) energy efficiency measures (self-reported in the Business Pulse Survey). The findings show that increasing electricity prices by 1 percent reduces employment at firms in energy-intensive industries that did not adopt energy efficiency measures by about 1.5 percent, compared with similar firms in energy-non-intensive sectors. In parallel, energy-intensive firms may increase sales and productivity but this result is robust to all alternative specifications. Firms may increase sales while reducing employment after energy price hikes, by adopting energy-efficient technologies and by passing through costs to consumers in inelastic markets while reducing employment in energy-intensive sectors due to cost pressures. These results highlight the adoption of energy efficiency measures by firms as an important employment protection policy action to cope with future volatility in energy (electricity) prices.

This paper is a product of the Finance, Competitiveness and Innovation Global Department. 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 mmelecky@worldbank.org, miootty@worldbank.org, and raterido@worldbank.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
# **Energy Prices, Energy Intensity, and Firm Performance**

Reyes Aterido, Mariana Iootty, and Martin Melecky _World Bank_

**Keywords** : World Bank Business Pulse Survey Data, Firm-level Panel Data, Energy Prices, Energy Intensity of Industries and Firms, Firm Performance, Productivity, Sales, Employment.

**ion** : D22, L25, Q41, Q43, O13.
# **1. Introduction**

The link between rising energy costs and firm performance has garnered increasing attention in academic and policy discussions due to the broader adoption of carbon pricing mechanisms and the gradual elimination of energy subsidies. As these policy changes elevate nonrenewable energy prices—a crucial input in the production processes of many industries—firms may experience notable performance implications. Higher energy costs generally affect profitability and operational efficiency by increasing production expenses, particularly in energy-intensive sectors. Firms with greater energy dependence often face heightened stock market volatility due to increased uncertainty and lower expected cash flows (Sadorsky 1999; Henriques & Sadorsky 2008). Moreover, rising energy costs may constrain corporate investment strategies, leading to reduced capital expenditures, especially in industries reliant on energy inputs (Kilian 2008; Bloom 2009).

link between energy price fluctuations and firm performance. Cali et al. (2022) show that, in Indonesia and Mexico, increases in electricity prices harm manufacturing plants’ performance. However, fuel price hikes result in higher productivity and profits of manufacturing plants. Fuel prices incentivize plants to replace inefficient fuel-powered with more productive electricity-powered capital equipment. Their results help to re-evaluate the policy trade-off between reducing carbon emissions and improving economic performance, particularly in countries with large fuel subsidies, such as Indonesia and Mexico. Cali et al. (2023) studied firms from 12 sectors and 11 middle-income countries during 2002–2013, using World Bank Enterprise Survey data. The study did not consistently find that higher energy prices negatively affect economic performance; they may even enhance it in some cases. Firms might be able to offset increased energy costs through innovation or new market strategies. The impact of energy prices on firms is a multifaceted issue, depending on several characteristics, including energy intensity, firm size, ownership type, and previous experience with electricity outages—which made companies less sensitive to price increases, likely due to their familiarity with managing energy and input shortages.

Andre et al. (2023) analyze firm-level data from 21 OECD countries (1995–2020) and show that energy price shocks impact firm productivity through immediate cost increases and longer-term adjustments. The study highlights sectoral and firm-level heterogeneity, finding that energy price hikes initially reduce productivity, particularly in energy-intensive sectors and firms under financial constraints. However, medium-term productivity improvements often follow as firms adapt, typically through increased investment. These findings align with the "pollution haven" hypothesis, which suggests that higher energy costs erode competitiveness and may drive firms to relocate (Copeland & Taylor 2004), and the Porter hypothesis, which posits that higher carbon prices can enhance productivity by incentivizing efficiency and innovation (Porter & van der Linde 1995). This dual perspective underscores the nuanced impact of energy price shocks on firm outcomes, blending short-term losses with potential long-term gains.

The recent energy price surge, triggered by the strong economic recovery following COVID-19 and the Russian invasion of Ukraine, has sparked renewed interest in the subject. Battistini et al. (2022) focus on EU economies during 2020-2022 and highlight a few interesting aspects. Energy price shocks disproportionately affect firms based on their energy dependence and hedging strategies. Energyintensive sectors are particularly hard-hit, facing greater financial stress and profitability challenges due to heightened costs and supply chain disruptions. Spiking energy prices have triggered a significant 2
worsening of the energy terms of trade in the euro area. This has impacted companies by reducing their purchasing power and increasing operational costs, affecting their profitability and investment decisions. Ari et al. (2022) employed the IMF-World Bank CPAT model and found clear evidence of a regressive impact on European households’ finances. However, the effects on European businesses are less clear-cut, with mixed results regarding the loss of competitiveness. The increase in energy costs for energy-dependent and trade-sensitive industries within the European Union may not be disproportionately higher than for non-EU countries due to variations in natural gas reliance or the use of other energy products across different nations.

When evaluating the effects of fluctuating energy prices, the role of fossil fuel subsidies must be carefully considered. Accurately pricing fossil fuels is essential for the efficient allocation of an economy's scarce resources and investment across sectors and activities. An efficient price incorporates both the supply costs and the environmental externalities of fuel consumption (Coady et al. 2019). However, substantial subsidies or underpricing distort market signals, leading to the overconsumption of fossil fuels, exacerbating global warming, and intensifying local environmental degradation. For instance, Black et al. (2023) report that global fossil fuel subsidies reached an unprecedented $7 trillion in 2023, as governments sought to shield consumers and businesses from energy price spikes triggered by the Russian Federation’s invasion of Ukraine and the post-pandemic economic recovery. These subsidies, while aimed at providing short-term relief, may have inadvertently delayed firms' and households' consideration of environmental costs in consumption and investment decisions. As a result, the adoption of energy-efficient technologies and sustainable practices has likely been postponed, undermining long-term climate goals (Bovenberg & de Mooij 1994; Parry et al. 2021).

Against this backdrop, this paper contributes to the existing body of research by focusing on the impact of country-specific energy price fluctuations during 2019-2023 on firms in emerging economies using the World Bank Business Pulse Survey—a dataset not used yet for this type of analysis in the literature— together with the CPRS classification of Battiston et al. (2022) to gauge the risk exposure of sectors to (the intensity of treatment by) the energy price fluctuations. Furthermore, the paper contributes to the literature by shedding light on the specific context of emerging economies, employing rapid survey data to grasp the nuances of business cycles, using the quarterly country-specific electricity price tariffs for businesses, matching the latter with the month in which a firm was surveyed, and highlighting the differentiated effects of sectoral energy intensity in conjunction with firm-level energy efficiency measures as indicators of a firm's exposure to energy price fluctuations and preparedness to manage energy price shocks.

Cali et al. (2022, 2023) that electricity price increases, on average, lead to higher productivity and sales for firms. However, the magnitude of this positive effect is significantly reduced for firms that have not implemented energy efficiency measures, suggesting that such initiatives are critical for firms to adapt to evolving energy market trends. In addition, when the baseline results are subjected to a battery of robustness tests the results do not survive in all alternative specifications. By contrast, we find that rising electricity prices exert a negative effect on employment, particularly in energy-intensive industries where energy efficiency measures are absent. This result highlights the vulnerability of labor markets in sectors heavily reliant on energy (electricity) inputs, especially in developing economies where the adoption of efficiency-enhancing technologies often lags.

3
disproportionately affected by energy price increases due to their limited flexibility in reducing energy consumption. Similarly, Sato et al. (2019) emphasize the employment risks in sectors with high energy dependency, particularly in regions with limited policy support for energy efficiency. Our study’s integration of industry-level energy intensity with firm-level adaptations provides a novel contribution by offering deeper insights into how electricity price changes propagate through different economic layers. Compared to earlier studies, such as Martin et al. (2014), which primarily focused on advanced economies or treated energy price impacts in aggregate terms, this paper uniquely highlights the heterogeneity of these effects across firms in developing countries, where structural constraints and varying levels of technological adoption exacerbate disparities in energy price impacts.

productivity and sales, but also as a critical buffer against adverse employment effects associated with rising electricity prices. This aligns with evidence from Bloom et al. (2010), who emphasize that firm-level innovations, including energy efficiency improvements, are key to maintaining competitiveness during economic transitions. Our findings also complement those by Newell et al. (2021), which highlight the role of energy efficiency in mitigating the labor market disruptions associated with volatile energy prices, particularly in energy-intensive sectors.

By focusing on developing economies, this study expands the literature on energy price shocks—which has predominantly centered on advanced economies—and addresses critical gaps in understanding the shocks’ differentiated effects. Consistent with Popp (2019), our results suggest that fostering widespread adoption of energy efficiency practices is essential for mitigating the employment risks of transitioning to greener energy systems. Particularly for energy-intensive industries in developing countries, these measures are crucial for safeguarding jobs and ensuring firms remain competitive amid fluctuating electricity prices and shifting global energy markets.

The rest of the paper is organized as follows. Section 2 describes the employed data. Section 3 explains the estimation methodology and identification. Section 4 discusses the estimation results and their robustness checks. Section 5 concludes.

# **2. Data**

Our analysis uses data from the Business Pulse Surveys (BPS) developed by the World Bank to monitor the impact of the COVID-19 pandemic on the private sector. The questionnaire collects information on several dimensions of firm performance, spanning from basic economic indicators—such as the operating status of the business, year of establishment, sales, and employment—as well as firm-specific practices such as managerial practices, technology adoption, and implementation of energy-efficient measures. Firms were surveyed from April 2020 (about end-2019 performance) to June 2023 in several waves, <mark>providing a unique perspectve on the private sector's response to the pandemic and the subsequent economic disturbances caused by the fast post-COVID-19 recovery, Russia’s invasion of Ukraine, and the associated energy price surges and fuctuatons.</mark> Our sample comprises an unbalanced panel of 63,716 observations drawn from 24 countries across four regions (Eastern and Central Europe, Latin America and the Caribbean, South Asia and the Pacific, and Sub4
Saharan Africa).<sup>1</sup> Due to data availability issues, the analysis is limited to 16 countries. This reduction stems primarily from misreporting in the sales variable but is also due to incomplete data for control variables, and electricity prices; the latter is, for example, not available for Tajikistan. Furthermore, self-reported energy efficiency measures are not available in all countries. Consequently, regressions that include energy efficiency measures are based on data from 13 countries. The sample covers micro, small, medium, and large businesses across all main sectors (agriculture, manufacturing, retail, and other services, including construction).<sup>2</sup> <mark>Table A1 (a-c) in the Annex describes the sample distributon across years, sectors, and frm sizes at the country level. This breakdown is instrumental for categorizing sectors by their energy usage intensity. The sectors are metculously disaggregated to the most granular level the sampling methodology allows. Within the sample, agriculture accounts for 6 percent of the frms, manufacturing for 25 percent, wholesale and retail for 24 percent, and a diverse array of other services for 45 percent. Within the service sector, food services and accommodaton represent 23 percent, transportaton and informaton and communicaton technology (ICT) account for 13 percent, and constructon for 12 percent. The remaining frms are engaged in fnancial actvites, real estate, educaton, health, and other sectors.</mark> Table A2 in the Annex shows the summary statistics of the main variables used in the analysis. The dependent variables used in the estimations are employment, sales, and sales per worker as a measure of labor productivity.<sup>3,4</sup> The median firm has sales of $12,312, 6 workers,<sup>5</sup> with a labor productivity of $1,950 per worker, and has been operating for 15 years. Figure 1 depicts the trends of our outcome variables, labor productivity, sales, and employment.

critical aspects of the data. The first aspect involves classifying sectors based on their energy intensity following the Climate Policy Relevant Sectors (CPRS) framework introduced by Battiston et al. (2017). The CPRS taxonomy identifies energy-intensive sectors through a multifaceted lens, considering (i) the emissions produced by each sector's economic activities, (ii) the sector's contribution to the Greenhouse Gas (GHG) emissions chain, (iii) the sector's engagement in specific policy processes, including its lobbying capacity; and (iv) the transition risk associated with the sector, which inversely relates to the level of fuel substitutability. A sector is deemed energy-intensive if its activities result in emissions, utilizes fuel (or a mix of fuels) as a primary input, and has a low potential for substituting these fuels. The sector classification by energy usage is systematically outlined in Table A3 in the Annex.

1 For each country, the sample frame was based on the statistical data from the National Statistical Committees of each country at the time of the survey's first implementation. For the later survey waves, the list of companies was updated using lists of business associations and internal lists of entrepreneurs from the survey firm.

2 It is also worth acknowledging that firm weights are unavailable, so the sample is not representative. However, the estimations control for firm characteristics, muting to some extent the composition effects.

3 Sales per worker is a widely used proxy for labor productivity, offering a practical measure of output per unit of labor input, especially when value-added data are unavailable. Bloom et al. (2010) emphasize its effectiveness in capturing labor efficiency and management practices, while Syverson (2011) notes that it may reflect other factors, such as market power or capital intensity, which could distort comparisons. Despite these limitations, Bartelsman et al. (2004) highlight its importance in developing economies, where sales data often provide the most accessible alternative for productivity analysis.

4 Sales and sales per worker are winsorized at the 5 and 95 percentiles.

5 5
<mark>The second element is measuring energy price shocks. We use the commercial electricity rate shown in Figure 1, compiled quarterly by Global Petrol Prices for each country. For the countries included in the sample, electricity rates, on average, fell during 2020 and 2021, reaching their nadir in the last quarter of 2021. Subsequently, there was a recovery in the rates during 2022. The price fuctuaton ranged from $0.03 to $0.38 per kWh. In the case of oil prices, the range was between $33.7 and $113.</mark> The third aspect refers to fossil fuel subsidies. We measure this aspect using the IMF data (Black et al., 2023), which provide, for 170 countries, the estimates of explicit subsidies for fossil fuels, i.e., undercharging for the supply cost of fossil fuels. All but five countries in the dataset, among them Tajikistan, have no (zero) petroleum subsidies. In the case of explicit subsidies for electricity, only three countries in the dataset do not have subsidies. Considering countries with explicit subsidies, the petroleum subsidies range from 0.00004 to 3.58 percent of GDP, while electricity subsidies range from 0.04 to 9.76 percent of GDP. The distributions of such subsidies overtime are depicted in Figures 3 (panels a and b).

<mark>The fourth data aspect concerns frms' adopton of energy efciency practces. This is a crucial factor because it can signifcantly infuence a frm's resilience to energy price fuctuatons. To account for these practces, we draw on data from the BPS, which features a module focused on frms' energy efciency measures. The survey queries frms on implementng technologies or methods to improve energy usage efciency. Those who have adopted energy efciency solutons are prompted to detail the specifc technologies or practces in use. These practces encompass questons about LEED certfcaton for buildings, adoptng efcient lightng systems, adherence to ISO 14001 or 50001 standards, or engagement in carbon trading schemes. About 38 percent of the surveyed frms acknowledge that they do not employ energy efciency technologies or practces. Yet, this fgure masks considerable cross-country heterogeneity. For example, in Armenia, 99 percent of the surveyed frms reported using at least one energy-saving approach. By contrast, in Paraguay, a striking 86 percent of frms indicated they do not engage in any such practces. For a more detailed breakdown, see Table A2 in the Annex.</mark>

# **3. Estimation methodology**

<mark>To assess the impact of energy shocks on frm performance, we utlize the BPS sample for 24 developing countries over the 2019-2023 period, covering large fuctuatons in country-level electricity prices. We hypothesize that a frm's susceptbility to the energy price shock is contngent on the shock's magnitude at a specifc tme, the frm's energy requirements for its operatons that we assume are sector-specifc, and any energy efciency measures or lack thereof that can make the frm-level energy efciency deviate from its sector’s average.</mark> <mark>As explained in the data secton, the variatons in the shock intensity are quantfed through the monthly electricity data for each country matched to frm surveys by the month of the frms’ interviews. The BPS data's unique advantage lies in recording the precise survey date for each frm, introducing frm-level variaton in shock exposure across countries. This aspect is critcal as the shock's magnitude is shown to vary from month to month (Figure 2). The energy usage intensity is determined based on CPRS classifcaton; sectors such as manufacturing, constructon, and transportaton are categorized as energyintensive, in contrast to agriculture, retail, and other services, which are classifed as less energy-intensive using the Batston et al. (2022) methodology which is predetermined at the global level and not infuenced by frm or sector energy technology adopton at the country level.</mark> 6
<mark>We evaluate frm performance using three metrics: labor productvity (sales per worker) and, separately, sales and employment. Labor productvity is used due to the unavailability of value-added data</mark> (Bartelsman et al., 2004) <mark>.</mark><sup>6</sup> <mark>We hypothesize that frms with a higher degree of exposure to energy price increases may experience a reducton in sales, are likely to downsize and cut jobs, and could experience a drop in productvity because frm costs may rise faster than revenues, and some supply chain disruptons may reduce frms’ ability to make proft-maximizing decisions. Additonally, we hypothesize that frms operatng in energy-intensive (CPRS) sectors will be disproportonately afected, with the efect of energy prices on their performance further amplifed.</mark> <mark>The baseline regression to assess the efect of energy prices on frm performance is described in equaton (1) where we focus on the energy price efect associated with fuctuatons in electricity prices that we observe at the country level and can beter identfy and use the global prices of oil only as a control variable:</mark> p (1) where pppii,jj,kk,tt = αα∙p is the performance of firm ppkk,tt + ββ∙ccp ccjj + γγ∙p _i_ in sector ppkk,tt ∙ccp _j_ in country ccjj + ρρ∙XX _k_ ii,tt at time + ππjj + θθ _t_ , pkk + μμ is the price of electricity kk,tt + ωωtt + εεii,jj,kk,tt that varies over time at the country level, is the sector-specific classification according to energy ii,jj,kk,tt pp pp usage intensity, is a vector of firm control characteristics, including age and size clusters (micro, small, ccp cc medium, large)<sup>7</sup> as well as the monthly global price of oil, are sector-specific dummies to account for ii,tt XX sector-specific characteristics other than energy usage intensity, are country-level fixed effects to jj μμ control for country characteristics that are invariant in the short term, such as institutions or market kk θθ structures, are country-time fixed effects to control for time-varying macroeconomic conditions common for all firms in a given country that vary over time, are the time-fixed effects to control for kk,tt μμ time-varying global factors that affect all countries, sectors, and firms. The equation is estimated by OLS tt ωω using robust standard errors. <mark>Weights are employed in the analysis to equalize the emphasis on smaller versus larger countries.</mark> <mark>Note that relevant frm characteristcs are carefully considered for each regression: t</mark> o reduce omitted variable concerns, <mark>size dummies are incorporated when assessing labor productvity o</mark> r sales <mark>as the dependent variable</mark> s, while sales quartiles are used when employment is the dependent variable <mark>. The age of frms is controlled for because older frms may utlize energy efciency practces and generate outcomes diferent from younger frms.</mark><sup>8</sup> <mark>The overall efect of electricity price fuctuatons on a frm in the energyintensive (CPRS) sector is then given by: α + γ .</mark>

> 6 Bartelsman et al. (2004) highlight its importance in developing economies, where sales data often provide the most accessible alternative for productivity analysis.

> 7 Micro (1 to 5 employees), small (6 to 19), medium (20 to 99), large (100 or more employees); the omitted category in the regressions is “small”. When the dependent variable is employment, size is determined based on the quartiles of sales, with the lowest quartile serving as the omitted category.

> 8 The age of a firm significantly influences its performance, reflecting variations in experience, resource accumulation, and adaptability. Young firms often exhibit higher growth rates but face greater financial constraints and survival risks, as noted by Haltiwanger et al. (2013). In contrast, older firms benefit from established customer bases and operational efficiencies but may encounter innovation inertia (Huergo & Jaumandreu, 2004). Coad et al. (2016) find that firm age interacts with industry dynamics, where mature firms in competitive sectors must innovate to sustain performance.

7
p Equation (2) introduces the energy inefficiency variable, , which identifies firms that do not implement any action to manage or reduce energy usage. Notice that the variable does not vary over time ii ee because it is only asked in the last wave of the follow-up surveys. There are three additional terms pertaining to p : interacted with the electricity price, p , interacted with the energy intensity dummy, , and in a triple interaction with both the energy price and energy intensity vector. Because ii kk,tt ee pp firms applying techniques to manage or reduce energy use will become less dependent on energy, we jj ccp cc expect these firms to suffer less from the energy price increase. The interactions with electricity price, with the energy intense dummy, and the triple interaction will indicate whether firms that are more exposed to energy price shocks but have not introduced energy savings suffered more from the shock. We therefore expand equation (1) as follows: p ∙p ∙p + ∙p + ii,jj,kk,tt kk,tt kk,tt jj kk,tt ii kk,tt jj ii pp = αα∙p pp + γγ∙p pp ∙ccp cc + σσ∙p pp ee + φφ∙p pp ∙ccp cc ee (2) jj ii jj ii ii,tt jj kk kk,tt tt ββ∙ccp cc + δδ∙p ee + ττ∙ccp cc ee + ρρ∙XX + ππ + θθ + μμ + ωω ii,jj,kk,tt Note that we do not use firm-level fixed effects in this specification because they are perfectly correlated εε with the p variable that varies only across firms. The overall effect of electricity price fluctuations on a firm in an energy-intensive sector that did not implement any energy efficiency measures will be thus ii given by: ee . αα+ γγ+ σσ+ φφ

# **4. Estimation results**

Table 1 reports the estimation results using labor productivity (sales per worker) and separately sales and employment as the dependent variables. The baseline estimation suggests that firms in energy-intensive sectors tend to be more productive and have higher sales (columns 1 and 2). Sales and productivity in energy-intensive industries can be higher due to economies of scale, technological advancements, and market dynamics. High capital intensity reduces per-unit costs as production scales (Hall & Weiss, 1967), while innovations enhance efficiency (Caves et al., 1982). Inelastic demand and energy-saving technologies further boost competitiveness (Pindyck, 1981; Aldy & Pizer, 2015).

Similarly, firms in energy-intensive sectors tend to employ more full-time workers than firms of similar age, size, and sectoral characteristics in non-energy-intensive industries. This result could also be associated with the anecdotal evidence that more firms in energy-intensive industries are state-owned, with stateowned firms being known for over-employment. Evidence suggests that energy-intensive industries often have a higher proportion of state-owned enterprises (SOEs), which are commonly associated with overemployment. For instance, Lin, Cai, and Li (1998) highlight the prevalence of SOEs in sectors like steel production and coal mining, driven by social objectives such as employment stabilization rather than efficiency. Jefferson and Rawski (1994) and Dong and Putterman (2000) find that SOEs frequently maintain larger-than-necessary workforces, resulting in inefficiencies compared with privately-owned firms. This phenomenon is particularly evident in countries like China, where SOEs dominate energy-intensive sectors (Yusuf, Nabeshima, & Perkins, 2006), often prioritizing social stability over economic optimization.

Increases in global oil prices are associated with a rise in firm labor productivity as firms increase sales and reduce employment. Firms may increase sales and reduce employment after oil price hikes in various ways, for instance, by adopting energy-efficient technologies that trigger productivity gains and passing8
through costs to consumers in inelastic markets (Aldy & Pizer, 2015; Pindyck, 1981). Simultaneously, cost increases might also lead to labor reductions, especially in energy-intensive sectors (Caves et al., 1982).

For increases in electricity prices, we observe a negative effect on labor productivity in contrast to the positive effect observed for oil prices. Unlike global oil prices, electricity prices are country-specific and reflect the average commercial electricity rates by country, allowing for better identification. The negative association between electricity prices and labor productivity may be due to rising electricity prices increasing operating costs and reducing competitiveness, leading to a drop in sales, especially in energyintensive sectors (Aldy & Pizer, 2015). Similarly, firms may cut production to manage costs in competitive markets, hindering output and profitability (Pindyck, 1981; Caves et al., 1982).

negative effect. Petroleum and electricity subsidies can lower firm employment by encouraging capitalintensive production methods over labor-intensive ones. By reducing energy costs, subsidies incentivize firms to adopt automation and energy-efficient technologies, which reduces the need for workers. This shift towards automation is particularly evident in energy-intensive industries, where subsidies encourage investment in machinery instead of hiring labor (IMF, 2013). Additionally, subsidies can distort market signals, hindering the reallocation of resources to more labor-intensive sectors. As noted by the World Bank, these subsidies can lead to inefficiencies, promoting industries that rely more on capital than on labor, thus reducing overall employment opportunities (World Bank Group, 2024).

and productivity. Electricity subsidies can enhance firm sales and productivity by lowering operational costs, allowing firms to invest in energy-efficient technologies and increase production. This reduction in energy costs enables firms to allocate resources more effectively, boosting output (Stern, 2013). In contrast, petroleum subsidies often support capital-intensive industries, distorting market signals and hindering resource reallocation (IMF, 2013). As a result, while electricity subsidies promote efficiency, petroleum subsidies may not have the same effect on firm performance (Aldy & Pizer, 2015).

# **_Introducing interactions of energy prices and energy dependence_**

<mark>Next, we introduce the double interacton of country-level electricity prices with industry-specifc energy dependence (intensity) which helps us identfy a more causal relatonship than the estmated associatons presented in Table 1. This is because we assume that country-level electricity prices are exogenous relatve to frm-level performance and so is the industry-level energy-intensity measure (CPRS classifcaton) based on global experience.</mark> The results of double interactions reported in Table 2 clarify the final effect of energy prices on firm performance. They reveal that increases in electricity and oil prices are associated with increased sales and productivity of firms in energy-intensive sectors. This finding is consistent with the earlier hypothesis, based on the results in Table 1, that increasing oil prices can raise labor productivity. Firms may increase sales while reducing employment after oil price hikes by adopting energy-efficient technologies that lead to productivity gains and by passing-through costs to consumers in inelastic markets <mark>while reducing employment in energy-intensive sectors due to cost pressures</mark> (Aldy & Pizer, 2015; Caves et al., 1982; Pindyck, 1981). <mark>In sum, our estmaton results reveal a notable decline in employment across frms due to increasing electricity prices. This negatve employment response is partcularly accentuated in frms categorized as energy-intensive according to the CPRS classifcaton and could be even more pronounced</mark> 9
<mark>if the policy response includes an increase in electricity subsidies.</mark> By contrast, rising electricity prices boost firm sales and productivity. These gains could be even more significant if a concurrent policy response introduces subsidies, which are fiscally costly and may transfer taxpayers' resources to firm owners and investors.

# **_Accounting for energy efficiency measures_**

<mark>This secton introduces the triple interacton among energy prices, industry-level energy intensity, and frm-level energy efciency measures. It uses the instrumental variable (IV) approach to address a possible endogeneity of the frm-level energy-efciency measures vis-à-vis the frm-level outcomes. This endogeneity may rise because well-performing frms may be more likely to implement energy-efciency measures rather than only energy-efciency measures afectng frm performance—raising reverse causality concerns. We use three candidate IV variables for the frm-level energy-efciency measures. The validity of all three IV variables relies on the assumpton that one frm taking an energy-efciency measure cannot signifcantly infuence all other frms of the same size across countries, frms in the same industry across countries, and frms in the same locaton (country).</mark> <mark>Namely, for each frm, we calculate the average likelihood of energy efciency measure adopton across frms of similar size, excluding the given frms from the calculaton, and the analogous averages for frms in the same subsector and in the same locaton, always excluding the given frm from the sample. Then, we generate three candidate IVs interactng for a given frm: (i) the size and sector averages, (ii) the size and subsector averages, and (iii) the size, subsector, and locaton averages. From (i)-(iii), the instruments are assumed to show greater relevance because the richer interacton of averages creates a synthetc frm that is more similar to the given frm. Table A4 in the Annex reports the results of the frst-stage regression of frm-level efciency measures on the thus constructed instrument for each frm. All three computed instruments are relevant and signifcant at the 1 percent level. The size-subsector-locaton instrument is the most signifcant, statstcally and economically; therefore, we use it as our baseline IV in this secton. We use the other two instruments in robustness checks.</mark> <mark>Table 3 reports the estmaton results for equaton (2) where we introduce the triple interacton of electricity prices, energy intensity of industries, and energy efciency measures or lack thereof. The later frm-level variable is instrumented by the size-sector-local average computed for each (excluding it from the computaton sample). By controlling for double and triple interacton between global oil prices and country-level energy (commercial) electricity prices (oil and electricity), subsectoral energy intensity, and locaton (country) intensity, the estmaton results reveal additonal signifcant heterogeneites. We focus again on the interactons that include electricity prices because the data is more granular (i.e. countrylevel commercial electricity rates), and the energy efciency measures reported at the frm level, which are mostly related to electricity consumpton such as the LEED certfcaton for buildings, adoptng efcient lightng systems, adherence to ISO 14001 or 50001 standards.</mark> <mark>The interactons of energy prices and energy intensity remain signifcantly positve for sales and productvity—as was the case in the estmaton results using double interactons only (and no triple interacton). When controlling for additonal heterogeneity in the specifcaton of equaton (2), the double interacton of energy intensity and oil prices becomes signifcantly negatve in the employment regression. The double interacton of energy intensity and electricity prices remains negatve but loses signifcance at common levels. However, this loss of signifcance would be due to the signifcant heterogeneity identfed by the double interacton of electricity prices and energy-efciency measures and the triple interacton of</mark> 10
<mark>electricity prices, energy intensity, and energy-efciency measures. The estmated coefcient on the double interacton between electricity prices and energy efciency measures suggests that, afer electricity price increases, frms that did not adopt energy efciency measures reduce employment signifcantly more than other frms. The triple interacton is also estmated as signifcantly negatve. Together with the double interacton, it delivers an efect that is economically almost three tmes as large as the respectve double interacton efect in Table 2—suggestng that, afer electricity prices increase, frms in energy-intensive sectors that did not adopt energy efciency measures are those that markedly reduce employment.</mark> An increase in electricity prices by 1 percent can lead to a reduction in employment by about 1.5 percent in firms within energy-intensive industries that have not adopted energy efficiency measures. Conversely, these energy-intensive firms tend to see an increase in sales and productivity by approximately 1.4 percent, regardless of whether they have or have not implemented energy efficiency measures. These findings suggest that promoting the adoption of energy efficiency measures among firms could serve as an important employment protection policy during periods of electricity price volatility, which developing countries might consider prioritizing.

The impact of energy subsidies, which could serve as a complementary policy to mitigate the effects of energy price increases, is further elucidated by incorporating double and triple interactions and considering a broader range of heterogeneity. <mark>Namely, the efect of electricity subsidies on sales and productvity remains positve, and the efect of petroleum subsidies on sales and productvity becomes signifcantly negatve. Both electricity and petroleum negatvely afect employment but only the efect of electricity is statstcally signifcant, at the 1 percent level. Our results suggest that using energy subsidies to preserve employment during energy price shocks can be a counterproductve policy strategy. This result aligns with the literature and the possible negatve efect of subsidies on the pace of structural adjustment and the long-term distortonary efect of employment. Such subsidies can distort market signals to both consumers and frms, leading to inefcient energy consumpton, misdirected investments, misallocaton of resources, and future resilience to energy price shocks (Coady et al., 2024; Aldasoro & Faia, 2024; Coady et al., 2024). This inefciency can hinder economic growth and delay necessary adjustments to energy price changes. Furthermore, energy subsidies can strain public fnances, limitng the government's ability to invest in other critcal areas that support employment and economic growth (Clements et al., 2024).</mark> <mark>Finally, we explore the correlaton between country-level energy subsidies and the frm-level adopton of energy efciency measures. Table 4 reports the estmaton results, which suggest that the short-term (oneyear-lagged (t-1)) and medium-term (the preceding fve-year average (t-1 ... t-5)) subsidies signifcantly discourage frms from adoptng energy efciency measures—with the regression controlling for frm size, subsector, and country’s economic development. The medium-term efect of petroleum subsidies comes out as the strongest. However, even the short-term efect of petroleum and electricity subsidies is consistently signifcantly negatve, highlightng that even short-term subsidies can delay the much-needed structural adjustment when possibly sustained energy price shocks hit countries, industries, and frms. These estmated correlatons align with the literature highlightng that short- and medium-term subsidies negatvely impact energy efciency adopton (Stefanski, 2024; Davis, 2023). Subsidized frms ofen delay technological investments, worsening inefciencies (Van den Bergh & Delarue, 2023). Subsidies risk longterm harm to growth and employment.</mark>

> **5. Robustness checks**

11
This section carries out several robustness checks and reports the results. First, we test whether our results could change with the use of alternative instrumental variables and report the results in Table B1 in Annex B. Both alternative instruments—IV2 based on size-sector and IV3 based on size-subsector—confirm the robustness of our baseline results. Next, we perform a battery of other robustness checks and report them in Table B2. In columns 1-3, we include country-time fixed effects to check whether other country-level macroeconomic factors, other than energy prices, could have affected firm performance: productivity, sales, and employment, respectively. In columns 4-6, we cluster the standard errors by country-sector to allow for country- and sector-specific spillovers (the results do not change materially if we cluster only by country or only by sector). In columns 7-9, we drop the weights for country size to treat all firms in larger and smaller countries with equal importance and, in columns 10-12, we also drop India, the largest country in our sample. In columns 13-15, we replace the current monthly/quarterly price for electricity (and oil) with a 3-month lagged average (using a 6-month lagged average does not materially change the results).

Overall, the impact of electricity price shocks on productivity and sales is not robust across the considered alternative specifications. By contrast, the result that electricity price shocks significantly decrease employment at firms in energy-intensive sectors that did not adopt energy efficiency measures remains robust across all alternative specifications.

# **6. Conclusion**

<mark>The paper examined the impact of electricity price changes on frms' labor productvity, sales, and employment across 24 developing countries from 2019 to 2023. It used business-specifc country-level tarifs and considered the energy dependence of industries as per the methodology of Batston et al. (2022), as well as the energy efciency measures taken by individual frms. Our study corroborates the fndings of Cali et al. (2022) and Cali et al. (2023), showing that, on average, frms may experience an increase in productvity and sales when electricity prices rise but these results are not sufciently robust to alternatve model specifcatons. By contrast, a rise in electricity prices signifcantly reduces employment, partcularly impactng frms in energy-intensive industries that did not implement energy efciency measures. These results underscore the critcal role of integratng industry-level energy dependence with frm-level energy efciency initatves to fully comprehend the efects of energy price fuctuatons on frm performance. To safeguard quality jobs amid energy price fuctuatons, including during the green transiton, policy makers must encourage widespread adopton of energy efciency measures, especially by frms in energy-intensive sectors.</mark> 12
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15
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**Tables**

**Table 1: Estimation results based on Equation (1)**

||(1)<br>Salesper worker|(2)<br>Sales(log)|(3)<br>Employment(log)|
|---|---|---|---|
|Energy intense|0.240**|0.255***|0.235***|
||(0.0955)|(0.0964)|(0.0320)|
|Price oil, log|11.64***|11.20***|-2.044***|
||(0.450)|(0.452)|(0.140)|
|Price electricity, log|-1.818***|-1.978***|-0.734***|
||(0.295)|(0.302)|(0.0987)|
|Explicit petroleum subsidy (% GDP)|1.468|1.469|-0.587**|
||(1.010)|(1.031)|(0.298)|
|Explicit electricity subsidy (% GDP)|8.011***|7.852***|-0.463*|
||(1.132)|(1.131)|(0.269)|
|Constant|-54.12***|-50.26***|7.677***|
||(2.350)|(2.364)|(0.731)|
|Observatons|21,629|21,629|21,629|
|R-squared|0.225|0.331|0.471|
|Country FE|YES|YES|YES|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size Controls for size clusters<sup>[1]</sup> , age, wave, and subsector [1] Constructed using employment in columns (1) and (2), and sales in column (3) 18
**Table 2a: Estimation results based on Equation (2) with double interactions**

||(1)|(2)|(3)|
|---|---|---|---|
||Salesper worker(log)|Sales(log)|Employment(log)|
|Energy intense|-0.410|-0.543|-0.579**|
||(0.787)|(0.794)|(0.257)|
|Price oil, log|11.34***|10.90***|-1.997***|
||(0.455)|(0.457)|(0.142)|
|Price electricity, log|-1.923***|-2.064***|-0.594***|
||(0.301)|(0.308)|(0.101)|
|Explicit petroleum subsidy (% GDP)|1.309|1.316|-0.516*|
||(1.006)|(1.027)|(0.302)|
|Explicit electricity subsidy (% GDP)|8.107***|7.950***|-0.469*|
||(1.121)|(1.121)|(0.269)|
|log Price oil*Energy intense|0.483***|0.490***|-0.0398|
||(0.179)|(0.179)|(0.0542)|
|log Price elect*Energy intense|0.689***|0.633***|-0.473***|
||(0.172)|(0.174)|(0.0573)|
|Constant|-52.99***|-49.09***|7.733***|
||(2.382)|(2.399)|(0.744)|
|Observatons|21,629|21,629|21,629|
|R-squared|0.226|0.332|0.474|
|Country FE|YES|YES|YES|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size Controls for size clusters<sup>[1]</sup> , age, wave, and subsector Clusters constructed using employment in columns (1) and (2), and sales in column (3) 19
**Table 2b: Estimation results based on Equation (2) with double interactions including subsidies**

||(1)|(2)|(3)|
|---|---|---|---|
||Sales per worker<br>(log)|Sales (log)|Employment (log)|
|Energy intense|-0.334|-0.468|-0.590**|
||(0.787)|(0.794)|(0.257)|
|Price oil, log|12.21***|11.74***|-2.176***|
||(0.474)|(0.475)|(0.143)|
|Price electricity, log|-1.979***|-2.083***|-0.520***|
||(0.260)|(0.266)|(0.0923)|
|Explicit petroleum subsidy (% GDP)|63.85***|61.58***|-13.66***|
||(12.83)|(13.58)|(5.187)|
|Explicit electricity subsidy (% GDP)|-6.694***|-6.935***|1.567***|
||(1.561)|(1.599)|(0.545)|
|log Price oil*Energy intense|0.476***|0.482***|-0.0394|
||(0.179)|(0.180)|(0.0542)|
|log Price elect*Energy intense|0.712***|0.655***|-0.478***|
||(0.171)|(0.173)|(0.0573)|
|log Price elect*Subsidy elect (% GDP)|-6.125***|-6.195***|0.783***|
||(0.647)|(0.662)|(0.215)|
|log Price petroleum*Subsidy fuel (% GDP)|-13.59***|-13.09***|2.868***|
||(2.712)|(2.868)|(1.090)|
|Constant|-58.61***|-54.55***|8.842***|
||(2.391)|(2.404)|(0.752)|
|Observatons|21,629|21,629|21,629|
|<br>R-squared|0.230|0.335|0.474|
|<br>CountryFE|YES|YES|YES|
|Robust standard errors in parentheses||||
|<br>*** p<0.01, ** p<0.05, * p<0.1||||
|Weighted by country sample size||||
|Controls for size clusters<sup>[1]</sup>, age, wave, and subsector||||

[1] Constructed using employment in columns (1) and (2), and sales in column (3) 20
**Table 3: Estimation results based on Equation (2) with the triple interaction using IV approach**

||(1)<br>Sales per<br>worker<br>(log)|(2)<br>Sales (log)|(3)<br>Employm<br>ent (log)|
|---|---|---|---|
|Energy intense|0.873|0.808|0.266|
||(0.923)|(0.936)|(0.301)|
|Energy inefficient IV1|-0.745|-1.103|-4.469***|
||(1.141)|(1.165)|(0.419)|
|Price oil, log|12.08***|11.54***|-1.974***|
||(0.486)|(0.488)|(0.134)|
|Price electricity, log|-2.385***|-2.353***|0.217|
||(0.507)|(0.517)|(0.166)|
|Explicit petroleum subsidy (% GDP)|-1.996***|-1.918***|-0.0712|
||(0.679)|(0.679)|(0.179)|
|Explicit electricity subsidy (% GDP)|6.943***|6.690***|-1.092***|
||(0.868)|(0.871)|(0.243)|
|Energy intense*Energy inefficient IV1|-1.337|-2.146*|-1.542***|
||(1.249)|(1.275)|(0.495)|
|log Price oil*Energy intense|0.555***|0.594***|-0.110**|
||(0.191)|(0.191)|(0.0505)|
|log Price elect*Energy intense|1.368***|1.421***|-0.141|
||(0.322)|(0.327)|(0.118)|
|log Price elect*Energy inefficient IV1|-0.833|-1.008*|-0.835***|
||(0.548)|(0.559)|(0.192)|
|log Price elect*Energy intense*Energy inefficient IV1|-0.401|-0.839|-0.702***|
||(0.601)|(0.612)|(0.229)|
|Constant|-48.26***|-43.55***|10.10***|
||(2.628)|(2.656)|(0.787)|
|Observations|19,237|19,237|19,237|
|R-squared|0.233|0.335|0.571|
|CountryFE<br>|YES<br>|YES<br>|YES<br>|

Firm-level adoption of efficiency measures instrumented by average adoption of the measures by other firms in the same sector, of similar size and location. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size Controls for size clusters<sup>[1]</sup> , age, wave, and subsector [1] Constructed using employment in columns (1) and (2), and sales in column (3) 21
**Table 4: Correlation of country-level energy subsidies and firm-level adoption of energy efficiency measures**

||(1)|(2)|
|---|---|---|
||Energy Efficiency|Energy Efficiency|
|Petroleum subsidies (average previous 5 years)|3.091***||
||(0.274)||
|Electricity subsidies (average previous 5 years)|0.215***||
||(0.0131)||
|Petroleum subsidies (lagged)||0.602***|
|||(0.190)|
|Electricity subsidies (lagged)||0.275***|
|||(0.0187)|
|Micro|-0.300***|-0.449***|
||(0.0344)|(0.0339)|
|Medium|1.003***|1.040***|
||(0.0394)|(0.0390)|
|Large|1.188***|1.181***|
||(0.0678)|(0.0679)|
|Age (log)|0.287***|0.279***|
||(0.0227)|(0.0235)|
|Agriculture|0.0340|-0.0498|
||(0.0618)|(0.0647)|
|Construction|0.232***|0.165**|
||(0.0802)|(0.0801)|
|Retail|-0.162***|-0.164***|
||(0.0381)|(0.0375)|
|Transportation|0.0695|0.0113|
||(0.0803)|(0.0792)|
|Accommodation|0.138*|0.155**|
||(0.0714)|(0.0690)|
|Restaurants|-0.0186|-0.0413|
||(0.0557)|(0.0540)|
|IT|0.535***|0.513***|
||(0.0929)|(0.0933)|
|Financial|0.741***|0.751***|
||(0.0828)|(0.0836)|
|Education|-0.0861|-0.232**|
||(0.0911)|(0.0925)|
|Health|0.267**|0.196*|
||(0.119)|(0.117)|
|Other Services|-0.0513|-0.0858**|
||(0.0416)|(0.0413)|
|Constant|-1.230***|-1.029***|
||(0.0774)|(0.0750)|
|Observatons|13,706|13,706|
|Pseudo R2|0.3142|0.3027|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size. Controls for GDP per capita; Omitted variables: small and manufacturing 22