metadata: authors: 0: author: Grover,Arti Goswami
1: author: Karplus,Valerie Jean
count: World
docna: 0: docna: The Energy-Management Nexus in Firms : Which
            Practices Matter, How Much and for Whom ?
docty: Policy Research Working Paper
subtopic: Energy Demand,Energy and Mining,Energy and Environment,Plastics & Rubber Industry,Pulp & Paper Industry,General Manufacturing,Food & Beverage Industry,Common Carriers Industry,Textiles, Apparel & Leather Industry,Construction Industry,Business Cycles and Stabilization Policies,Disability,Services & Transfers to Poor,Economic Assistance,Energy Policies & Economics,Access of Poor to Social Services
teratopic: Industry,Social Protections and Labor,Macroeconomics and Economic Growth,Energy,Poverty Reduction
abstracts: cdata!: Management practices matter for firm
            performance. As energy is one input in firm production,
            management practices may interact with energy use. Using a
            comprehensive firm-level database covering 31 countries,
            this study documents the link between structured management
            practices, energy use, and firm performance. The paper
            reports several findings. First, although management is
            negatively correlated with energy expenditure, it bears a
            positive (or 0) relationship with physical energy use,
            suggesting that management effort is directed toward saving
            costs but not reducing environmental impact. These results
            are primarily driven by the manufacturing sector. Second,
            among the structured management practices examined, those
            relating to target-setting are associated with reduced
            energy expenditure intensity. Third, generic management
            practices are correlated with greater discipline around
            energy management. Finally, while generic practices are
            correlated with stronger firm performance in manufacturing
            and services, energy-centric practices show a positive
            association only in services. Vast heterogeneity in adoption
            and outcomes suggests that targeted approaches to encourage
            energy management practices in firms may be more effective
            than uniform ones.
repnme: repnme: The Energy-Management Nexus in Firms :
            Which Practices Matter, How Much and for Whom ?
display_title: The Energy-Management Nexus in Firms
            : Which Practices Matter, How Much and for Whom
listing_relative_url: /research/2020/09/32403393/energy-management-nexus-firms-practices-matter-much
topicv3: Manufacturing Sectors m1326521 1281,Energy and Extractives m1326595 507,Power and Electricity Sector m1327094 515,Fuel Subsidies m1327095 1628,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2020-09-14T00:00:00Z
historic_topic: Industry,Social Protections and Labor,Macroeconomics and Economic Growth,Energy,Poverty Reduction
origu: EFI-FCI-TIC-Firms,Entrep,&Innov. (ETIFE)
no_of_pages: 60
author_keys: 0: author_key: 1066720
1: author_key: 1905574
model_extractions: Policy Research Working Paper 9397 The Energy-Management Nexus in Firms Which Practices Matter, How Much and for Whom?

_Arti Grover Valerie J. Karplus_ Finance, Competitiveness and Innovation Global Practice September 2020
Policy Research Working Paper 9397

# **Abstract**

Management practices matter for firm performance. As energy is one input in firm production, management practices may interact with energy use. Using a comprehensive firm-level database covering 31 countries, this study documents the link between structured management practices, energy use, and firm performance. The paper reports several findings. _First,_ although management is negatively correlated with energy expenditure, it bears a positive (or null) relationship with physical energy use, suggesting that management effort is directed toward saving costs but not reducing environmental impact. These results are primarily driven by the manufacturing sector. _Second,_ among the structured management practices examined, those relating to target-setting are associated with reduced energy expenditure intensity. _Third,_ generic management practices are correlated with greater discipline around energy management. _Finally,_ while generic practices are correlated with stronger firm performance in manufacturing and services, energy-centric practices show a positive association only in services. Vast heterogeneity in adoption and outcomes suggests that targeted approaches to encourage energy management practices in firms may be more effective than uniform ones.

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@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
# The Energy-Management Nexus in Firms: Which Practices Matter, How Much and for Whom?

Arti Grover<sup>_∗_</sup> Valerie J. Karplus<sup>_†_</sup>

**Acknowledgments:** This paper has been financed by the World Bank’s Climate Innovation Trust Fund supported by the governments of Australia, Denmark, the United Kingdom, the Netherlands, and Norway. We are deeply grateful to Denis Medvedev for providing intellectual guidance for this project, and to Manuela Francisco, Michael Ehst, Gonzalo Varela, Adja Mansora Dahourou, and Rafay Khan for their kind support. The project has benefited from excellent research assistance by Yiran He, Jasdeep Mandia, and Joanna Slusarewicz. We thank Joshua Wimpey for his guidance in using the World Bank Enterprise Survey data, in particular the Green Economy module.<sup>1</sup>

> _∗_ Please address correspondence to: Arti Grover, World Bank, 1818 H Street, Washington DC, USA, E-mail: agrover1@worldbank.org

> _†_ Carnegie Mellon University, Scott Hall 5113, 5000 Forbes Avenue, Pittsburgh, PA 15213 USA, E-mail: vkarplus@andrew.cmu.edu

> 1 **Disclaimer:** 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.
# **1 Introduction**

The persistence in productivity differences among firms, even within the same narrowlydefined industry, is striking and puzzling. For example, within four-digit industries in the United States, labor productivity in manufacturing plants at the 90th percentile is four times as high as it is in plants at the 10th percentile (Syverson, 2011). A growing literature suggests that management practices play a key role in explaining the large variation in productivity across industries and countries (Bloom and Van Reenen, 2007; Bloom et al., 2012b). In a world facing climate change, patterns of energy use and associated greenhouse gas emissions have taken center stage in discussions on firm performance. To this end, methodologies have been adapted to study the relationship between management practices and energy demand at the firm level, albeit mostly in developed countries (Bloom et al., 2010a; Boyd and Curtis, 2014; Martin et al., 2012; Schweiger and Stepanov, 2019). Our paper builds on this nascent literature to explore the link between firms’ management practices and energy use.

Despite the implications of business decisions for climate change, and climate change for business, the firm as a unit of analysis is not common in the literature on energy efficiency. Energy use by firms is a major driver of greenhouse gas emissions worldwide. Direct emissions from manufacturing and construction account for 20% of global CO2-equivalent emissions from fossil fuel combustion (World Bank Data, 2020), and is much larger once indirect (e.g., electricity and heat) emissions are included. Firm energy use decisions not only have implications for firms’ private costs, but also affect the global and local environmental impacts of economic activity. Prior studies focus heavily on individual decisions, or aggregated outcomes at the industry or country level, such as total production, given that energy data at the firm level are often aggregated into a firm’s materials inputs or not reported at all.

Management practices could interact with energy decisions via several channels. By easing information and coordination frictions, management practices may help reduce “hidden” administrative and time costs associated with energy efficiency investments (e.g. in identi1
fying and implementing programs or equipment for energy saving) (Gillingham and Palmer, 2014). Management practices may also help firms overcome uncertainty, for instance in energy price fluctuations or in the magnitude of future cost savings, if these disciplines enable firms to target and track historical energy use and accurately forecast future needs and payoffs. For instance, savvy firms may be able to optimally time energy purchases when prices are low.<sup>2</sup> Energy-centric management practices may raise energy efficiency by requiring managers to apply low hurdle rates and payback periods equivalent to or longer than those for traditional investments when assessing energy efficiency opportunities, increasing the likelihood of adoption (Martin et al., 2012). While management practices can contribute to reduced energy intensity, some generic practices, such as setting production targets or monitoring related indicators, serve other functions within the firm that may conflict with energy intensity reduction. Thus, the relationship between generic management practices and firms’ energy intensity is an open empirical question.

The literature suggests that though general management practices are associated with lower output-normalized expenditures on energy in firms, the extent and drivers of variation in this relationship remain an empirical puzzle. Most prior work focused on developed countries (Bloom et al., 2010a; Boyd and Curtis, 2014; Martin et al., 2012). Bloom et al. (2010a) find an inverse relationship between measures of management practices and energy (expenditure) intensity, for instance, in the United Kingdom. In the United States, Boyd and Curtis (2014) find that conditional on adopting general (not energy-specific) targetsetting practices, all other management disciplines, that is, operations, monitoring, and incentive practices, are associated with increased energy (expenditure) intensity. Martin et al. (2012) found that a set of climate-friendly management practices are associated with lower energy intensity, more efficiency-oriented R&D, and higher productivity in a sample of 190 randomly-sampled manufacturing firms in the United Kingdom.<sup>3</sup> The only study to focus

> 2Even difficult-to-store energy carriers such as electricity are often subject to differences in fixed costs based on use and in time-of-day pricing, including in developing countries, creating opportunities for arbitrage by shifting production to off-peak hours.

> 3These practices are distinct from the energy management practices we evaluate in the present analysis.

2
on manufacturing firms in a broad set of countries (mainly in Eastern Europe) found that the negative correlation between management practices and energy (expenditure) intensity was much higher in magnitude when energy prices are low (subsidized) compared to when they are high (Schweiger and Stepanov, 2019). The basic argument is that management’s effect on energy intensity depends on factor costs; better managed firms conserve in the face of high prices but intensify use to take advantage of lower prices.

Firms in developing countries, where the energy-management nexus has been rarely explored, may show a different relationship when compared to their counterparts in the developed world. _First_ , industry composition often varies with the level of development. Developing countries may possess inexpensive labor or natural resources, which may encourage relatively energy-intensive industrial production and discourage energy-saving investments. _Second_ , firms in developing countries face more heavily subsidized costs of fossil energy. Nine out of the top ten countries that accounted for 61% (US $272 billion) of the total fossil fuel subsidies globally in 2017 were developing or emerging markets (Taylor, 2020). _Third_ , firms in developing countries may prioritize growth over environmental protection, although they may also be more sensitive to input costs. Evidence from a randomized controlled trial in Shandong Province, China, suggests that firms rarely considered energy efficiency interventions with a payback period longer than one year (Karplus and Zhang, 2020). In Ryan (2018), an experiment that raised energy productivity of Indian manufacturing plants led to longer operating hours and greater demand for skilled labor and electricity, which given the country’s high reliance on coal generation likely increased environmental impact.

The purpose of this study is to develop a deeper understanding of the link between management practices (both generic and energy-centric) and energy intensity in a large and diverse sample of firms operating globally. We use the data collected in a recent wave of World Bank Enterprise Surveys (WBES), which provides a representative cross-section of firms in 31 countries. We observe complete entries for over 6,000 out of more than 20,000 total firms, a sample three times larger than prior studies. A unique feature of this wave of 3
the WBES is that it includes not only a module measuring management practices of firms but an additional “Green Economy” module that solicits responses on firms’ energy use, type of pollution emissions, and (a limited set of) practices relating to energy conservation (e.g., energy target-setting, monitoring, and external audits).

Our analysis advances the literature on the energy-management nexus in the following ways. _First_ , we characterize the variation in energy intensity with respect to generic management practices as well as energy-centric management practices. _Second_ , the WBES Green Economy Module provides information on the physical quantity of energy used, which allows us to measure the association of management practices with a close proxy for environmental impact. _Third_ , the presence of information on both general and energy-specific management practices within the same survey allows us to consider their separate and combined influence, since either could be an omitted variable when examining relationship with energy intensity. _Fourth_ , our data allows us to explore the relationship of generic and energy-centric management practices and firm outcomes in services, a sector that remains largely ignored in such discussions. _Finally_ , of the sample of 31 countries in WBES dataset, eight are in the “Lower Middle” or “Low” income categories. This enables us to also explore the similarities and differences in the relationship between management practices and energy intensity in advanced and developing economies.

We report four main findings. _First_ , while generic management practices negatively correlate with energy expenditure, they bear either a positive or no relationship to physical energy use, suggesting that management can help firms save energy costs without significantly reducing environmental impacts. _Second_ , among the structured management practices examined, target-setting practices are most strongly associated with reduced output-indexed energy expenditure. In both cases, these results are mainly driven by manufacturing firms, while the relationship between management practices and energy intensity is weaker in services.<sup>4</sup> _Third_ , general management practices are correlated with greater discipline around

> 4The coefficient on target-setting practices was negative but insignificant for services firms, with much noisier estimates.

4
energy management. _Finally_ , the general management score is correlated with stronger firm performance for both manufacturing and services, while the energy management score shows a positive association for services firms only.<sup>5</sup> This paper beyond this point is organized as follows. In the next section, we discuss the related literature on general and energy management in firms. The third section discusses the preparation of our data set and presents descriptive insights. The fourth section describes our regression specifications and results. The final section concludes with policy implications of our results and directions for future work.

# **2 Related Literature**

## **2.1 General Management Practices**

While persistent differences in productivity can be explained by a range of factors besides management, such as higher quality labor and capital, differential investment in information technology and research and development (R&D), learning by doing, firm structure, productivity spillovers, regulatory behavior, and differences in competitive regime, the quality of management remains a prominent explanation. Prior work has suggested that management practices may function as a “technology,” whereby management is conceptualized as intangible capital in which output is monotonically increasing (Bloom et al., 2016). Differences in managerial quality are important in explaining the cross-country differences in income levels, productivity, innovation and firm dynamism. For example, differences in productivity across firms in Asia, Europe, and North and South America have been attributed to the heterogeneity in management practices (Bloom et al., 2010b). Likewise, a study by Bloom and co-authors suggest that management practices account for nearly 30% of the differences

> 5The observed relationships between (generic or energy-centric) management practices and firm outcomes (energy intensity or output) do not imply causality. Although our empirical analysis adheres to the state of the art in the field, existing studies have also admitted the possibility that omitted variables could be correlated with both management and energy use. An advantage of our study is that both generic and energy-centric practices are observed, and can be controlled for in regressions.

5
in TFP across countries. Here, management practices are found to explain at least a fifth of within-country heterogeneity in firm performance, thereby suggesting that firm- and sectorspecific factors are at least as important as the general business environment in shaping firm performance (Bloom et al., 2016).

The four sets of practices that comprise an overall management score in the World Management Survey include: operations, performance monitoring, incentives, and target-setting (Bloom and Van Reenen, 2007). Questions in the operations category seek to understand the production process and the adoption of lean manufacturing techniques, while those in monitoring category solicit responses on the types of production and input data that are collected as well as how the performance of capital and workers is measured. Questions on practices relating to the hiring and firing of workers as well as the criteria for bonuses and promotions comprise the incentives category. Finally, the target-setting category evaluates practices relating to defining and communicating organizational goals, setting a time frame for implementation, whether or not these goals are demanding, and if there are consequences for failing to achieve them. In prior studies, these scores are averaged to obtain an overall management score.

In addition to focusing on the composite management score, studies have also considered specific categories of management practices. Weaknesses in management practices, for example, relating to data driven performance monitoring cripples the overall management score in Croatia (Grover et al., 2019). Monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and track them periodically over time. By comparison, Croatia firms’ average score on practices relating to target setting and incentives is relatively closer to that of United States firms. Incentive practices relate to how managers and non-managers are awarded bonuses and promotions. The findings for Croatia and the Russian Federation regarding the weaknesses of these subindices are similar, that is, monitoring practices are weaker (Grover et al., 2019; Grover and Torre, 2019). By comparison, in the United States and Mexico manufacturing firms 6
are stronger in monitoring vis-`a-vis structured management practices relating to workers’ incentives and target setting (Bloom et al., 2019a,b).

Structured management practices may affect productivity by interacting with a firm’s input choices. Prior literature has focused mainly on labor and capital. Ichniowski and Shaw (1999) highlight the effectiveness of labor management practices, including problemsolving teams, orientation, training, job rotation, employment security, and profit sharing, and linked them to economic performance. Management practices also correlate positively with improved labor conditions in firms (Distelhorst et al., 2017). Bloom et al. (2016) find that a high capital-labor ratio is systematically correlated with higher monitoring and target scores, relative to incentive scores, suggesting that management styles may differ, depending upon the nature of a firm’s productive activities.

A subset of this literature has attempted to explicitly discern the association of management with energy use. Bloom et al. (2010a) found that management practices were inversely correlated with energy intensity (measured as the proportion of expenditure on energy relative to the value of output), leading the authors to conclude that management may also be good for the environment. Boyd and Curtis (2014) conducted a similar study in the United States using the Annual Census of Manufactures, and found similarly that management practices are negatively related to energy intensity, but that the negative effect was conditional on a firm’s level of broad target-setting practices. Using BEEPS data, Schweiger and Stepanov (2019) examine the relationship between generic management practices and energy intensity in 2,000 firms from 40 countries, differentiating between firms in countries with high and low fuel subsidies. They find a 23% reduction in fuel intensity as management score improves from the 25th to the 75th percentile of the distribution. In the face of high fuel subsidies, this decline in energy intensity is only 2%. Table A5 provides a survey of the available literature that relates management practices to firm outcomes.

7
## **2.2 Energy-Centric Management Practices**

In contrast to general management practices, specialized management practices target a specific aspect of the firm’s operations. Some examples of energy management practices include the introduction of target-setting and monitoring practices related to energy use, an explicit requirement to adhere to energy conservation norms, rewards for employees that promote energy efficiency, emphasis on energy management as a priority, and encouraging innovation in controlling energy use. Based on data from 190 firms in the United Kingdom, Martin et al. (2012) find that climate-friendly management practices are associated with lower energy intensity and higher productivity. Climate-friendly practices in this setting included 33 practices, including the stringency of external GHG reduction targets faced, adaptation measures undertaken, and energy use target-setting and monitoring. Using surveys of environmental management practice adoption at 500 facilities, Delmas and Toffel (2008) find that distinct external constituents prompt firms to adopt specialized environmental management practices. In both cases, the emphasis is on studying the correlation of environment-friendly management practices with energy use, using a limited sample size. We are unaware of any study that explicitly measures energy-specific practices, such as tracking physical quantity alongside expenditures and encouraging cost-effective investments in energy efficiency. To the best of our knowledge, our study is the first to examine the emergence of these practices using data that include many developing country firms, and in this sense our work makes an important contribution to this literature.

# **3 Data Preparation and Exploration**

## **3.1 Data Preparation**

The recent wave of the World Bank Enterprise Surveys conducted in 2018 and 2019 for 31 countries collected nationally-representative firm data on a number of financial, operational, 8
managerial, and business environment measures. Within the WBES, the Green Economy module is rolled out by the World Bank Group, jointly with the EBRD-EIB.

_Generic and Energy-centric Management Scores_ . General management scores are calculated using 11 questions from the management practice module. These questions map to eight questions from the U.S. Management and Organizational Practices Survey (U.S. Census Bureau, 2015). They cover four categories of practices: target-setting, monitoring, incentives, and operations (see Table A1). Firms’ responses to questions on management practices are aggregated into a single management score following Bloom et al. (2019a) in two steps. _First_ , the responses to each of the management practice questions are normalized on a 0-1 scale: the response associated with the most structure is normalized to one, and the one associated with the least structured is normalized to zero. _Second_ , overall management score is calculated as the unweighted average of the individual question scores as in Bloom et al. (2012c) in computing the average based on the z-scores of individual questions. We use these scores to compute descriptive statistics. About 2% of the firms with non-missing entries report a zero average score, while the average for all firms is 0.48 (standard deviation of 0.21). For regressions, we take the z-scores of individual questions, and compute the unweighted average. We then once again take the z-score to obtain a composite management score. General management score also separates the overall management index into four subindices to separately assess the importance of monitoring, incentives, targets and operations practices.

Energy management scores use questions from the Green Economy Module of the WBES that relate to monitoring, target setting, and relative emphasis on energy efficiency. Questions and scoring scales are listed in Table 1. All scores are normalized to a 0-1 scale. Six out of nine questions in the Green Economy Module require binary responses. More structured practices are those that are more specific, formal, frequent, or comprehensive and hence are assigned a higher score. Overall scores are computed as an unweighted average. Given that energy-centric practices are likely specific to firm energy requirements, it is not surprising 9
Table 1: Summary statistics and response rate for energy management questions.

|Question|Scoring|Mean|Std Dev|Med|p25|p75|Score = 0|Responses|
|---|---|---|---|---|---|---|---|---|
|(1) Did the establishment<br>monitor energy consump-<br>tion?|0 - No or don’t know, 1 - Yes|0.568|0.495|1.0|0.0|1.0|8,431|19,511|
|(2) How often did the estab-<br>lishment monitor its energy<br>consumption?|0 - Don’t know or no monitoring,<br>increments of 0.125 from annu-<br>ally (0.125) to hourly (1)|0.253|0.245|0.3|0.0|0.5|8,493|19,518|
|(3) Was an external energy<br>audit completed?|0 - No or don’t know, 1 - Yes|0.265|0.441|0|0|1.0|8,146|11,087|
|(4) Did the establishment<br>have targets for energy con-<br>sumption?|0 - No or don’t know, 1 - Yes|0.276|0.447|0|0|1.0|14,120|19,511|
|(5) What was targeted?|0 - Don’t know, refuse to answer,<br>or no targets, 0.5 - Quant or exp<br>only, 1 - Both quant and exp|0.220|0.382|0|0|0.5|14,199|19,513|
|(6) Did the establishment<br>adopt energy management?|0 - No or don’t know or refuse to<br>answer, 1 - Yes|0.308|0.462|0|0|1.0|13,494|19,511|
|(7) Did the establishment<br>adopt any measures to en-<br>hance energy efciency?|0 - No or don’t know or refuse to<br>answer, 1 - Yes|0.334|0.472|0|0|1.0|12,987|19,511|
|(8) Were any measures de-<br>veloped by establishment?|0 - No or don’t know or refuse to<br>answer, 1 - Yes|0.252|0.386|0|0|0.5|12,987|19,516|
|(9)<br>What<br>was<br>the<br>pay-<br>back period vs. non-energy-|0 - Did not implement, 0.25 -<br>Don’t know, 0.5 - Shorter, 0.75 -|0.106|0.269|0|0|0|12,987|15,214|
|efciency measures?|Equal payback, 1 - Longer||||||||
|E Mgmt Average||0.287|0.292|0.167|0|0.5|6,540|18,621|

**10**
that nearly one-third of the firms report zero score, while the average for all firms is 0.28 (standard deviation of 0.29).

_Energy Intensity_ . Energy intensity is the amount of energy used (expenditure or physical quantity) normalized by output, measured using last fiscal-year sales. Studies suggest large heterogeneity in energy intensity within narrowly defined sectors, and these variations are higher than that in other inputs (Lyubich et al., 2018). This is larger than the productivity spread for relatively homogeneous industries, such as cement (Syverson, 2004).

Energy intensity in expenditure (and physical quantity) is calculated by summing energy expenditure (quantity) for firms that report electricity use and at least one out of three possible primary fuel types. More than half of the firms in the ( _>_ 20,000 firm) sample report non-missing values for all fuel types. Summary statistics for both energy intensity and management measures are reported in Appendix Table A3. For a description of data preparation and cleaning, as well as the components of the generic and energy-centric management practices, see Appendix Section A1.

# **3.2 Stylized Facts about Management Practices and Energy Use**

After cleaning data from the World Bank Enterprise Surveys for 31 countries, our sample includes 14,713 firms reporting energy consumption in value terms and 8,272 firms reporting energy consumption in physical quantity terms. For 8,476 firms, the generic management score can be computed, contingent on firms responding to at least six of the eight aggregated questions from the management practices module. The energy-centric management score can be computed for 18,621 firms that answer at least eight of the nine energy management practice questions from the Green Economy module. Appendix Table A3 presents descriptive statistics for key variables included in our regression analysis. In this section, we highlight the main stylized facts on management practices and energy use that emerge in our data.<sup>6</sup>

> 6For our regression, we use data that are winsorized at the top and bottom 1 percent. The composition of firms across countries and two-digit sectors before and after winsorization is similar. Notably, services firms do not generally report capital stock and share of high-skilled labor, and hence we omit these variables

11
_There is striking variation in energy intensity within narrowly-defined sectors._ Figure 1 shows that this heterogeneity is larger in terms of physical quantity per unit of output (shown in red) compared to expenditure per unit of output (shown in blue). Heterogeneity is nearly as large within three-digit sectors (panels (b)-(f)) as it is in the full sample (panel (a)), underscoring the importance of within-sector variation in energy use patterns, and the fact that sector definitions do not always neatly delineate firm energy requirements. Even after data cleaning (described in Appendix Section A1), a few firms with high physical energy intensity (outside the range shown) result in a sample mean that exceeds the median by two orders of magnitude for the energy intensity quantity measure (see summary statistics in Appendix Table A3). Sector panels are ordered from most (cement) to least (other retail) energy intensive. The distributions of expenditure- and physical use-based energy intensity generally grow narrower when moving from more to less energy intensive sectors. This may correspond to increased homogeneity in energy needs (due to, for instance, reliance primarily on electricity to power equipment and buildings) in light manufacturing and retail.

_There is wide heterogeneity in management practices, both generic and energy-centric._ As shown in Figure 2, there is large variation in both general management (shown in blue) and energy-specific management (shown in red).<sup>7</sup> The general management distribution is normally distributed with slightly greater density in the left tail of the distribution, similar to prior studies (Bloom et al., 2012a). The shape of the distribution does not differ dramatically across sectors. The energy management score distribution is generally much flatter, with a much higher density at zero.

_On average, manufacturing firms are better managed than services firms both in terms of generic and energy-centric management practices._ On average, a services firm scores 0.514 (SD 0.182) in the adoption of the generic management practices, compared to an average of 0.524 (SD 0.178) for manufacturing firms.<sup>8</sup> The difference in average scores is statistically

> from the services regressions.

> 7Please note that the general and energy management scores are measured for overlapping but not identical subsets of firms in WBES.

> 8The distribution of scores by sub-categories of services and manufacturing is shown in Appendix Figure

12
<!-- Start of picture text --> (a) All firms (b) Cement (c) Plastics<br>o Oo<br>2 2 2%<br>Fa® Fa® 2®<br>aL AL on<br>oO oO oO<br>-10 5 0 5 -10 5 0 5 -10 5 0 5<br>Log dev mean Log dev mean Log dev mean<br>(d) Machinery (e) Apparel (f) Other Retail<br>i=® i=® c®<br>OW on OL<br>; nN ; :<br>Oo Oo Oo<br>-10 -5 0 5 -10 -5 0 5 -10 -5 0 5<br>Log dev mean Log dev mean Log dev mean<br>————_ El - Expenditure ———— El - Quantity<br><!-- End of picture text -->
<!-- Start of picture text --> (a) All Firms (b) Cement (c) Plastics<br>wNwoNtdN<br>NN N<br>pe > ><br>aot oT oT<br>[7= [}c c(3)<br>a~ a~ a~<br>aed © aed<br>oO oO oO<br>0 2 4 6 8 1 0 2 4 6 8 1 0 2 4 6 8 1<br>Score (0-1) Score (0-1) Score (0-1)<br>(d) Machinery (e) Apparel (f) Other Retail<br>aaa<br>NN N<br>Fae pe ><br>ccc<br>®® ®<br>as a~ a*<br>re 0 0<br>Oo Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1 0 2 4 6 8 1<br>Score (0-1) Score (0-1) Score (0-1)<br>————_ Generic management —-——— Energy-centric management<br><!-- End of picture text -->
for energy management practices, if energy requirements and associated costs are very small relative to total costs or sales and thus have little impact on profit. Generic management practices, by contrast, may be more universal in their potential to help diverse firms excel. Moreover, energy is a subset of the strategic and operational concerns of a firm; stronger generic management practices may thus correlate with a propensity to invest in specialized energy-centric management practices. However, the need for energy-specific practices may be endogenous to energy requirements implied by a firm’s capital equipment choices or a function of factors beyond a firm’s control, such as local energy system characteristics.

_Across high, upper middle, and lower middle income groups, average management scores are not statistically different, with high variation within country groups (see Appendix Table A4)._ Using the winsorized sample and computing generic management score as a straight average of non-missing entries, firms in the high income group average a generic management score of 0.491 (SD 0.209), upper middle a score of 0.481 (SD 0.211), and lower middle 0.477 (SD 0.200); differences are not statistically significant. Large standard deviations are consistent with the finding in (Bloom et al., 2012b) that score heterogeneity within countries is often greater than between them. All countries have roughly the same share of zeros, while high and upper middle income countries have a slightly larger share of firms scoring above 0.75 (4% versus 3%). For energy management, the high income group firms’ energy-centric management score average is 0.291 (SD 0.304), upper middle 0.237 (SD 0.273), and lower middle 0.365 (SD 0.296); the differences are statistically significant. Appendix Table A4 summarizes generic and energy-centric management scores by income group. Interestingly, firms in lower middle income countries have fewer zero entries for energy management score, and also have a higher share of high-scoring firms. This could be due either to the fact that these countries have higher shares of energy-intensive manufacturing firms, or firms in these settings may be more motivated to invest in energy management practices, e.g. to mitigate the impact of volatile prices or supply disruptions. Distributions of generic and energy management scores by country income group are shown in Appendix Figure A2.

15
_Larger firms are better managed, both in terms of generic and energy-centric management practices._ Appendix Figure A3 shows that within energy- / knowledge-intensity categories of manufacturing and services firms, there is a nearly monotonic increasing relationship between firm size and general management score, as prior work has noted (Bloom and Van Reenen, 2007). This finding is similar to that found in the United States by Bloom et al. (2014) who show that better managed firms are significantly larger: in their study, a one standard deviation increase in generic management is associated with a 40 log point increase in employment size. In this analysis, we find that a similar pattern holds for both general and energy management. It is also visible within income groups (see Appendix Figure A4).

# **4 Energy-Management Nexus: Empirical Analysis**

The remainder of this paper focuses on four research questions. _First_ , do general management practices matter for firms’ use of energy in terms of both expenditure and physical units? _Second_ , which practices matter most, and for which categories of firms? Are there significant differences between firms in the services and manufacturing sectors? _Third_ , do better managed firms also adopt disciplines to conserve energy? _Fourth_ , do better managed firms, either in terms of general, or energy-centric practices, or both, show superior performance?

## **4.1 Regression Specifications**

Our main regression specification (Equation 1) focuses on the relationship between general or energy management ( _Mi_<sup>_g,e_</sup> where M is a firm-specific measure of generic management _Mi_<sup>_g_</sup> or energy-centric management ( _Mi_<sup>_e_))andenergyintensityinexpenditureorphysicalunits</sup> divided by output ( _EI_ ( _v_ ) _i_ and _ei_ ( _q_ ) _i_ , respectively). We further allow energy intensity to vary according to capital and labor inputs, gross output, and a vector of age, sector, and country controls ( **x** _it_ ). Capital _ki_ , labor _li_ , and gross output _yi_ are expressed in logs, as is 16
physical energy intensity, as indicated by the lower case variables in Equation 1. Energy intensity in value terms is expressed as a percentage (the total value of energy use is divided by total output, and multiplied by 100). All variables are observed only once for each firm, in year 2019.

We follow Bloom et al. (2010a) and Schweiger and Stepanov (2019) to examine the relationship between management practices and energy intensity. Our approach differs from the two-step methodology of Boyd and Curtis (2014), which requires the use of more detailed industry level data (at the six-digit industry code level). Boyd and Curtis (2014) first create a firm-level measure of relative energy intensity by comparing each firm to others in the same narrow industry group and then in the second step regress this relative intensity measure on management practices and other controls. This methodology resolves much of the variation in energy intensity due to industry differences, such as equipment- or processrelated energy needs.<sup>10</sup> We are unable to implement the methodology due to lack of six-digit industry specification information. Our analysis should therefore be interpreted as the net effect of both sources of variation, after controlling for 3-digit industry- and country-specific effects. Compared to Schweiger and Stepanov (2019), our approach is very similar but the specifications include different firm-level controls.<sup>11</sup> We examine the relationship of firm performance to generic and energy-centric management separately and also in a specification that includes both. In all settings, the production function estimation includes labor and (for manufacturing firms) capital.<sup>12</sup> This approach is

> 10Our approach captures both between and within variation across sectors, although we control for sector and country fixed effects, as well as firm age, in all specifications.

> 11In addition to country and sector fixed effects and firm age (Schweiger and Stepanov, 2019) include percentage of employees with college degree, percentage of self-generated electricity, geo-location of firm, mean temperatures and night-lights around the firm, an indicator for the firm being listed, a measure of credit constraints, indicator variable for foreign and state ownership, exporter status, and electricity as a major or very severe obstacle.

> 12We do not consistently observe materials costs and therefore include only capital and labor, following other studies.

17
similar to Bloom and Van Reenen (2007), in which management enters directly into the production function as a covariate. The coefficient on management score _βZ_ suggests how much of the variation in output is explained by generic or energy-centric management practices.

# **4.2 Energy Intensity and Generic Management Practices**

Table 2 shows the relationship between generic management practices and energy (expenditure) intensity. All columns control for age and fixed effects pertaining to three-digit sector and country. Energy expenditure intensity is computed as energy divided by output, then multiplied by 100, as in Bloom et al. (2010a). Estimated coefficients for management score in columns (1)-(3) show that generic management practice scores are negatively correlated with energy expenditure in both manufacturing and services. However, in specifications shown in columns (4)-(6), which additionally control for employees, capital stock (only available for manufacturing firms), and sales, the coefficient on management becomes insignificant for manufacturing firms. Adding output (sales) in particular is strongly negatively correlated, with a 1% increase in firm sales reducing energy intensity by 1.7 percentage points. This suggests large economies of scale with respect to energy use, which is not surprising given the high fixed costs of shifting to energy-intensive production equipment and the sensitivity of energy requirements to utilization. In the most demanding specification (columns (4)-(6)), an increase in management score from the 25th to 75th percentile is associated with a decline in energy intensity expense by 15% in services (column (6)), and a 2.5% decline in manufacturing (in line with prior studies, but not statistically significant). Appendix Table A6 shows the results broken down by country income group, revealing that the negative effect is strongest among firms in lower middle income countries. Splitting this estimation, we do not find the relationship between management and energy expenditure intensity to vary much by sectoral energy intensity, although high energy-intensity firms and high knowledge-intensity 18
Table 2: Relationship between management and energy intensity (value).

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|General|-0.708<sup>_∗∗∗_</sup>|-0.424<sup>_∗_</sup>|-1.092<sup>_∗∗∗_</sup>|-0.015|0.017|-0.266|
||(0.185)|(0.222)|(0.356)|(0.184)|(0.256)|(0.341)|
|No. Employees||||1.107<sup>_∗∗∗_</sup>|0.920<sup>_∗∗∗_</sup>|0.967<sup>_∗∗∗_</sup>|
|||||(0.129)|(0.171)|(0.218)|
|Log(Sales)||||-1.576<sup>_∗∗∗_</sup>|-1.652<sup>_∗∗∗_</sup>|-1.670<sup>_∗∗∗_</sup>|
|||||(0.098)|(0.130)|(0.172)|
|Capital Stock|||||0.406<sup>_∗∗∗_</sup><br>(0.074)||
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|6143|3880|2063|6133|3000|2059|
|Adjusted _R_<sup>2</sup>|0.218|0.125|0.297|0.280|0.223|0.348|
|df<br>~~m~~|118|82|63|120|85|65|

Standard errors in parentheses Capital, labor, and output enter in logs. Management scores are the unweighted average of the z-scores for individual practices. We primarily observe capital input for firms in the manufacturing sector, and therefore have omitted capital from the pooled regression to avoid dropping services firms. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 services firms show the larger coefficients. See Appendix Tables A7 and A8.

Turning to energy intensity in physical quantity terms, we find a striking divergence from the energy expenditure results. Here the coefficient on management score is positive and significant, primarily driven by the manufacturing sector (columns (2) and (5)) (see Table 3. These results suggest that although better management practices enable manufacturing firms to limit the cost of energy use, they may not have the same effect on the consumption of physical quantity. One potential reason is that using greater quantities of energy may help a better managed firm substitute away from increasingly expensive labor, or enable automation, which may be a particular concern for the manufacturing sector. Management efforts in this case may be directed towards optimizing energy purchasing conditions, to 19
Table 3: Relationship between management and energy intensity (quantity).

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|General|0.162<sup>_∗_</sup>|0.294<sup>_∗∗∗_</sup>|-0.082|0.353<sup>_∗∗∗_</sup>|0.518<sup>_∗∗∗_</sup>|0.087|
||(0.085)|(0.112)|(0.130)|(0.087)|(0.132)|(0.134)|
|No. Employees||||0.496<sup>_∗∗∗_</sup>|0.312<sup>_∗∗∗_</sup>|0.459<sup>_∗∗∗_</sup>|
|||||(0.061)|(0.089)|(0.102)|
|Log(Sales)||||-0.561<sup>_∗∗∗_</sup>|-0.619<sup>_∗∗∗_</sup>|-0.567<sup>_∗∗∗_</sup>|
|||||(0.041)|(0.061)|(0.069)|
|Capital Stock|||||0.217<sup>_∗∗∗_</sup><br>(0.037)||
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|3587|2338|1155|3582|1837|1153|
|Adjusted _R_<sup>2</sup>|0.262|0.266|0.261|0.312|0.331|0.316|
|df<br>~~m~~|110|79|58|112|81|60|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 ensure that the increased energy requirement does not result in a higher share of energy expenditure.<sup>13</sup> The positive effect is concentrated among firms in high and upper middle income countries (see Appendix Table A9). As shown in Appendix Table A8, moderate and low energy-intensive manufacturing as well as high knowledge-intensive services all show a positive effect. To our knowledge, we are among the first to examine this relationship. More study is needed, especially to understand this effect in services firms<sup>14</sup> Which management practices matter for energy use? Examining the correlation between

> 13Although the samples used for estimation in Tables 2 and 3 are different, our results are robust to considering the set of firms that consistently report both energy value and quantity. These additional results are available upon request.

> 14(Karplus and Zhang, 2020) find a negative relationship between management score and energy intensity in quantity terms in a relatively small sample of firms in the metal machining sector in Shandong Province, but smaller in magnitude compared to the effect on energy intensity in value terms.

20
specific sub-components of generic management scores and energy intensity in value and quantity terms, we find that the practices that matter most seem to differ for manufacturing and services sectors. These practices also influence energy intensity in expenditure and physical units differently. Specifications (1)-(3) in Table 4 show the relationship between management sub-scores (target-setting, monitoring, operations and incentives) and energy intensity in value terms. While target-setting practices matter for reducing energy intensity (expenditure) in manufacturing, these practices have statistically insignificant correlation for energy use in services. None of the other management practices seem to matter for energy use in services. Our results differ from Boyd and Curtis (2014) for the United States, who document that effective monitoring, incentive structures and particularly lean manufacturing operations are associated with a reduction in energy use, while the implementation of production targets (conditional on other practices) is found to increase energy consumption. The reason may be due to the fact that the World Management Survey, which was used to measure management practices in the United States in Boyd and Curtis (2014), refers to production targets, whereas the WBES definition is broader and explicitly includes examples such as “production volume, quality, efficiency, waste, or on-time delivery” in the question on target-setting practices. Appendix Table A10, columns (1)-(5), reveals that the target-setting practices matter especially for moderate and low energy intensive firms.<sup>15</sup> Specifications (4)-(6) in Table 4 show that there is a positive relationship in manufacturing firms between monitoring practices and energy intensity in physical quantity terms. While target-setting improves energy efficiency in value terms, monitoring production and performance of firms is actually associated with a worsening of the physical energy intensity in manufacturing, perhaps because more energy-intensive firms are more motivated to invest in monitoring capabilities. As for services, we do not find evidence of association of management with energy efficiency, both in value and in physical unit terms.

> 15Unlike high energy intensity firms, moderate and low energy intensive firms’ energy requirements are largely determined by core activities and long-lived capital equipment purchases, and thus may have less flexibility to adjust energy use through target setting.

21
Table 4: Relationship between management (all sub-scores) and energy intensity in value (1)-(3) and quantity (4)-(6) terms.

||(1)<br>All Firms|(2)<br>Manufacturing|(3)<br>Services|(4)<br>All Firms|(5)<br>Manufacturing|(6)<br>Services|
|---|---|---|---|---|---|---|
|Targets z-score|-0.211<sup>_∗_</sup>|-0.584<sup>_∗∗∗_</sup>|0.007|0.060|0.020|0.076|
||(0.114)|(0.144)|(0.227)|(0.057)|(0.077)|(0.102)|
|Monitor z-score|0.006|-0.013|-0.155|0.187<sup>_∗∗∗_</sup>|0.268<sup>_∗∗∗_</sup>|0.072|
||(0.095)|(0.124)|(0.198)|(0.052)|(0.075)|(0.092)|
|Operations z-score|-0.003|-0.043|0.074|0.078<sup>_∗_</sup>|0.043|0.086|
||(0.094)|(0.134)|(0.167)|(0.042)|(0.065)|(0.066)|
|Incentives z-score|0.023|0.218|0.054|-0.018|-0.054|0.010|
||(0.124)|(0.171)|(0.240)|(0.060)|(0.085)|(0.103)|
|No. Employees|1.124<sup>_∗∗∗_</sup>|0.835<sup>_∗∗∗_</sup>|1.138<sup>_∗∗∗_</sup>|0.465<sup>_∗∗∗_</sup>|0.286<sup>_∗∗∗_</sup>|0.427<sup>_∗∗∗_</sup>|
||(0.130)|(0.176)|(0.221)|(0.062)|(0.091)|(0.103)|
|Log(Sales)|-1.603<sup>_∗∗∗_</sup>|-1.679<sup>_∗∗∗_</sup>|-1.851<sup>_∗∗∗_</sup>|-0.549<sup>_∗∗∗_</sup>|-0.597<sup>_∗∗∗_</sup>|-0.575<sup>_∗∗∗_</sup>|
||(0.098)|(0.134)|(0.174)|(0.041)|(0.063)|(0.065)|
|Capital Stock||0.567<sup>_∗∗∗_</sup>|||0.234<sup>_∗∗∗_</sup>||
|||(0.079)|||(0.042)||
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|6124|3011|2055|3513|1805|1135|
|Adjusted _R_<sup>2</sup>|0.264|0.215|0.339|0.299|0.323|0.310|
|df<br>~~m~~|124|88|69|116|85|63|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 22
Monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and appropriately track them periodically over time. The most energy-intensive firms may adopt monitoring practices to manage their sales, inventory, and costs as well as the performance of capital and workers, which may interfere with energy conservation, especially if there is no monitoring of energy use. These results can be viewed as an added dimension to the evidence on “energy-management gap” noted in Boyd and Curtis (2014) whereby the very management practices that improve firm performance may have eroded energy efficiency measured in physical units. In Appendix Table A10, columns (6)-(10) show that monitoring practices are positively correlated with energy use manufacturing, irrespective of sectoral energy intensity (columns (6)-(8)).

# **4.3 Generic and Energy-Centric Energy Management**

Do firms with more developed management practices also have a higher propensity to develop specialized practices? In Table 5, we examine the correlation between generic and energy-centric management scores in manufacturing and services firms. Interestingly, overall manufacturing firms have a higher correlation between the two scores, compared to services firms, and this difference is statistically significant based on a two-sided t-test. Energy management practices concern whether or not the firm sets targets for energy use and monitors progress towards them, and the relative focus on conserving energy among a firm’s priorities. These are related to generic management practices and it appears that a firm evolves to adapt generic practices that are well-suited for its production setting into those that can also help conserve energy use, if conditions require it. In the case of services, the lower correlation may reflect the fact that there is greater variation in the value of energy intensity within services, given the wide heterogeneity in activities and in energy requirements in the sector.<sup>16</sup>

> 16As shown in Appendix Table A11, the positive association of energy management with generic management holds irrespective of sectoral energy intensity (in manufacturing) or knowledge intensity (in services).

23
Table 5: Relationship between general management practices and energy management practices.

||(1)|(2)|(3)|
|---|---|---|---|
||All Firms|Manufacturing|Services|
|General|0.416<sup>_∗∗∗_</sup>|0.482<sup>_∗∗∗_</sup>|0.322<sup>_∗∗∗_</sup>|
||(0.017)|(0.027)|(0.028)|
|No. Employees|0.053<sup>_∗∗∗_</sup>|0.055<sup>_∗∗∗_</sup>|0.030|
||(0.011)|(0.018)|(0.018)|
|Capital Stock||0.034<sup>_∗∗∗_</sup><br>(0.007)||
|Log(Sales)|0.033<sup>_∗∗∗_</sup>|0.018|0.036<sup>_∗∗∗_</sup>|
||(0.007)|(0.012)|(0.011)|
|3-digit sector|✓|✓|✓|
|Age|✓|✓|✓|
|Country|✓|✓|✓|
|Observations|7080|3162|2363|
|Adjusted _R_<sup>2</sup>|0.244|0.300|0.180|
|df<br>m|121|85|66|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

# **4.4 Energy Intensity and Energy Management Practices**

Table 6 indicates a negative and significant relationship between energy management practices and energy intensity (expenditure) for manufacturing firms (columns (2) and (5)). Increasing the energy management score from the 25th to the 75th percentile is associated with lower energy intensity (in value terms) by about 10% in manufacturing. While this relationship is also negative and significant for services in (3), the significance is lost once additional covariates are added in (6). Controlling for generic management practices reduces the sample size substantially. The relationship between energy management and energy expenditure intensity is no longer statistically significant when manufacturing is broken down by energy intensity and services by knowledge intensity (see Table A12).

24
Table 6: Relationship between energy management and energy intensity in value terms.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|Energy|-0.392<sup>_∗∗∗_</sup>|-0.379<sup>_∗∗∗_</sup>|-0.362<sup>_∗∗_</sup>|0.106|-0.284<sup>_∗∗_</sup>|0.235|0.330<sup>_∗∗∗_</sup>|0.107|0.286|
||(0.089)|(0.110)|(0.159)|(0.089)|(0.128)|(0.156)|(0.120)|(0.155)|(0.245)|
|General|||||||-0.164|-0.049|-0.370|
||||||||(0.193)|(0.265)|(0.355)|
|No. Employees||||1.299<sup>_∗∗∗_</sup>|0.883<sup>_∗∗∗_</sup>|1.465<sup>_∗∗∗_</sup>|1.095<sup>_∗∗∗_</sup>|0.914<sup>_∗∗∗_</sup>|0.973<sup>_∗∗∗_</sup>|
|||||(0.077)|(0.111)|(0.126)|(0.129)|(0.173)|(0.219)|
|Capital Stock|||||0.513<sup>_∗∗∗_</sup>|||0.409<sup>_∗∗∗_</sup>||
||||||(0.059)|||(0.074)||
|Log(Sales)||||-1.793<sup>_∗∗∗_</sup>|-1.802<sup>_∗∗∗_</sup>|-1.952<sup>_∗∗∗_</sup>|-1.591<sup>_∗∗∗_</sup>|-1.659<sup>_∗∗∗_</sup>|-1.689<sup>_∗∗∗_</sup>|
|||||(0.062)|(0.091)|(0.101)|(0.098)|(0.130)|(0.171)|
|3-digit sector|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations|13472|7378|5734|13442|5567|5720|6094|2978|2047|
|Adjusted _R_<sup>2</sup>|0.157|0.096|0.207|0.241|0.205|0.285|0.281|0.225|0.347|
|df<br>~~m~~|123|84|66|125|87|68|121|86|66|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**25**
Table 7: Relationship between energy management and energy intensity in quantity terms.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|Energy|0.064|0.054|0.068|0.192<sup>_∗∗∗_</sup>|0.097|0.186<sup>_∗∗∗_</sup>|0.261<sup>_∗∗∗_</sup>|0.163<sup>_∗_</sup>|0.227<sup>_∗∗_</sup>|
||(0.040)|(0.053)|(0.063)|(0.040)|(0.061)|(0.060)|(0.060)|(0.088)|(0.094)|
|General|||||||0.240<sup>_∗∗∗_</sup>|0.435<sup>_∗∗∗_</sup>|0.015|
||||||||(0.090)|(0.139)|(0.138)|
|No. Employees||||0.457<sup>_∗∗∗_</sup>|0.319<sup>_∗∗∗_</sup>|0.478<sup>_∗∗∗_</sup>|0.480<sup>_∗∗∗_</sup>|0.304<sup>_∗∗∗_</sup>|0.447<sup>_∗∗∗_</sup>|
|||||(0.034)|(0.054)|(0.053)|(0.061)|(0.089)|(0.101)|
|Capital Stock|||||0.210<sup>_∗∗∗_</sup>|||0.211<sup>_∗∗∗_</sup>||
||||||(0.027)|||(0.037)||
|Log(Sales)||||-0.534<sup>_∗∗∗_</sup>|-0.591<sup>_∗∗∗_</sup>|-0.569<sup>_∗∗∗_</sup>|-0.573<sup>_∗∗∗_</sup>|-0.626<sup>_∗∗∗_</sup>|-0.574<sup>_∗∗∗_</sup>|
|||||(0.024)|(0.039)|(0.036)|(0.041)|(0.061)|(0.068)|
|3-digit sector|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations|7666|4352|3166|7653|3326|3161|3582|1837|1153|
|Adjusted _R_<sup>2</sup>|0.241|0.241|0.228|0.300|0.319|0.299|0.315|0.332|0.318|
|df<br>~~m~~|121|84|63|123|86|65|113|82|61|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**26**
In the case of energy intensity in physical units (Table 7), we find significant positive correlations with energy-centric management practices in all sectors, after controlling for a firm’s generic management practice level. Positive correlations with energy management measures likely reflect the fact that energy costs are more salient for these firms. To the extent that energy management helps firms disproportionately save costs relative to quantities of energy used, this positive correlation is expected to be even stronger. Interestingly, we see that correlations are positive for both generic and energy-centric management for manufacturing firms, but not for services firms. Given that on average manufacturing firms are more energy intensive than services firms, management practices may be tuned to address energy, whereas for services, only those firms that are relatively energy intensive develop energy management practices. In Table A13, we see that the effect in manufacturing runs mainly through energy management for high energy intensity firms (as they rely on energyintensive capital equipment that may require specialized knowledge to realize improvements). However, for moderate and low energy intensity firms, generic management plays a dominant role, perhaps because energy costs are less salient and these practices are sufficient for the firm to achieve its energy conservation objectives. In services, the effect runs through high knowledge intensity firms, perhaps because sophisticated managers of relatively energy intensive firms see value in developing practices to minimize cost or other burdens energy imposes on daily operations.

# **4.5 Management Practices and Productivity**

Results from the previous section suggest that both generic and energy-centric management practices, to a certain extent, can help mitigate the output-indexed cost of energy use. But can adoption of these practices help firms realize productivity gains? Table 8 shows that generic management is positively correlated with output, suggesting that better-managed firms produce more after controlling for their input use. Said differently, structured management explains variation in firm productivity, which is the residual of the production function, 27
consistent with prior studies. When energy-centric management scores enter into the regression with or without generic management, they exhibit a modest positive correlation with output in services but not in manufacturing. It may be that energy-saving opportunities in manufacturing firms are highly capital intensive, which imply large initial outlays and uncertain payback periods. By contrast, services firms may achieve energy savings through better coordination of knowledge and practice dissemination within the firm, without capital equipment modifications. The effect in the services sector is strongest among low knowledge intensity activities, which is comprised of transport, construction, retail trade, and hotels and restaurants (see Table A14). In these sectors, much of the energy use to be managed is related to building energy efficiency and electricity conservation; explicit practices may translate into changes in employee behavior and cost savings for the firms without machinery or infrastructure upgrades, driving productivity gains.

28
Table 8: Relationship between general management, energy management, and output.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|All Firms|Manufacturing|Services|
|General|0.305<sup>_∗∗∗_</sup>|0.093<sup>_∗∗_</sup>|0.347<sup>_∗∗∗_</sup>||||0.265<sup>_∗∗∗_</sup>|0.074|0.310<sup>_∗∗∗_</sup>|
||(0.031)|(0.044)|(0.054)||||(0.032)|(0.045)|(0.056)|
|Energy||||0.158<sup>_∗∗∗_</sup>|0.009|0.178<sup>_∗∗∗_</sup>|0.099<sup>_∗∗∗_</sup>|0.044|0.112<sup>_∗∗∗_</sup>|
|||||(0.015)|(0.022)|(0.024)|(0.021)|(0.028)|(0.035)|
|No. Employees|0.971<sup>_∗∗∗_</sup>|0.641<sup>_∗∗∗_</sup>|0.924<sup>_∗∗∗_</sup>|1.022<sup>_∗∗∗_</sup>|0.609<sup>_∗∗∗_</sup>|1.021<sup>_∗∗∗_</sup>|0.963<sup>_∗∗∗_</sup>|0.638<sup>_∗∗∗_</sup>|0.916<sup>_∗∗∗_</sup>|
||(0.015)|(0.028)|(0.026)|(0.008)|(0.022)|(0.013)|(0.015)|(0.028)|(0.026)|
|Capital Stock||0.088<sup>_∗∗∗_</sup>|||0.084<sup>_∗∗∗_</sup>|||0.086<sup>_∗∗∗_</sup>||
|||(0.015)|||(0.012)|||(0.015)||
|Materials||0.302<sup>_∗∗∗_</sup>|||0.361<sup>_∗∗∗_</sup>|||0.301<sup>_∗∗∗_</sup>||
|||(0.026)|||(0.020)|||(0.026)||
|3-digit sector|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations|7141|2265|2380|15627|4046|6579|7080|2246|2363|
|Adjusted _R_<sup>2</sup>|0.623|0.809|0.606|0.679|0.854|0.652|0.624|0.808|0.607|
|df<br>~~m~~|120|84|65|125|86|68|121|85|66|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**29**
# **5 Conclusion**

“Is management good for the environment, or just hot air?”<sup>17</sup> Using firm-level data from 31 countries, our results suggest that management practices are associated with a reduction in energy expenditure intensity. However, these cost savings do not translate into reduced physical energy use, a close proxy for environmental impacts.

There may be several reasons for the divergence between the relationship of management practices to physical and expenditure measures of energy intensity. _First_ , managers may be focused on saving costs, not limiting the environmental impacts of energy use _per se_ . To the extent that management practices help managers reach their goals more expediently, it is perhaps not surprising that the correlation runs only through the energy expenditure intensity measure. _Second_ , management may enable firms to take advantage of lower prices, e.g. through investments in self-generation or by allocating production hours to take advantage of favorable time-of-day pricing. Larger one-time or repeat energy purchases may be subject to subsidies or preferential pricing, relative to smaller outlays. For some firms, it may be economically attractive to increase physical energy use in order to save costs on other productive inputs, for instance, by automating certain processes. Therefore, our results suggest that although management practices are associated with reduced energy intensity in value terms, it does not mean that physical energy use, and its related environmental impacts, will follow the same pattern.

Turning to focus on energy management practices, we find they have no additional effect on reducing the intensity of energy expense over and above generic management practices. It is possible, given the diversity of firms in our sample, that the coefficient reflects reverse causality: energy-intensive firms have stronger incentives to develop energy management. The decision to develop energy management practices may be the result of selection by subset of firms most likely to profit from them. Supporting this notion, we find that energy management scores are on average higher in energy-intensive sectors. Energy-intensive man-

> 17This is the title of the paper by (Bloom et al., 2010a).

30
ufacturing firms also have a lower rate of “0” energy management scores at 2.6%, compared to 3.2% for moderate and 3.1% for low energy intensity firms. This is unsurprising, as energy costs are likely to be more salient for energy intensive firms, relative to the value of their output, leading them to invest in energy-specific management practices for their own sake, rather than benefiting from them as a spillover from generic management effort. By contrast, among services firms, the high and low knowledge intensity groups report 43% and 41% “0” scores, respectively.

Before controlling for generic management practices, our findings for manufacturing are similar to the results of Martin et al. (2012) (see Table 6, column (5)), who find that 190 U.K. firms exhibited lower energy expenditure intensity at higher levels of “climate-friendly” management practices. This study included a much longer list of practices, including exposure to climate policies, among them carbon pricing under the European Emissions Trading System, although negative effects were found for practice categories similar to the ones we examine, such as target-setting practices. It is difficult to assemble such detailed practices for a large number of firms in a cross-country setting. In this context, the Green Economy module can be improved to include additional practices relating to energy management, and to use a finer scoring scale, rather than relying on binary adoption measures.<sup>18</sup> Finally, the generic management score is correlated with stronger firm performance for both manufacturing and services, while the energy-centric management score is only positively related in (low knowledge intensive) services. Given the high rate of “0” scores concentrated among small firms in services, it is possible that this pattern reflects that firms only consider introducing energy-centric practices once they are sufficiently large. Manufacturing firms, by contrast, show no relationship between energy management and productivity, suggesting limited incentive for firms to make further investments in energy-centric practices. Introducing CO2 pricing or other pro-climate constraints on firm energy use may alter the calculus firms confront when deciding whether or not to invest in specialized energy

> 18For suggestions on how the energy management questions in the Green Economy Module of the WBES could be improved, please see Section A4.

31
management.

Taken together, our findings suggest that interventions aimed at improving energy management should be tailored to firm circumstances. Whether generic or energy-centric management translates into energy expenditure savings varies depending upon a firm’s circumstances, such as the extent to which reducing energy use requires capital expenditures or equipment upgrades, or can be adjusted through, for instance, team- or employee-level incentives for conservation. A deeper investigation of the barriers (e.g., administrative costs) that prevent firms from adopting energy management practices could aid in the design of informational or financial interventions that could help to overcome the energy-efficiency gap. However, without policy interventions that alter fundamental relationships among input prices or increase the salience of energy costs and climate concerns, management is likely to play a limited role in reducing energy-related environmental impacts.

# **References**

Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., Saporta-Eksten, I., and Van Reenen, J. (2019a). What drives differences in management practices? _American Economic Review_ , 109:1648–1683.

Bloom, N., Genakos, C., Martin, R., and Sadun, R. (2010a). Modern management: Good for the environment or just hot air? _Econ. J._ , 120(544):551–572.

Bloom, N., Genakos, C., Sadun, R., and Reenen, J. V. (2012a). Management practices across firms and countries. _Acad. Manage. Perspect._ , 26(1):12–33.

Bloom, N., Genakos, C., Sadun, R., and Van Reenen, J. (2012b). Management Practices Across Firms and Countries. _Academy of Management Perspectives_ , 26(1):12–33.

Bloom, N., Iacovone, L., Pereira-Lopez, M., and Van Reenen, J. (2019b). Management in Mexico: Market Size, Frictions and Misallocation.

Bloom, N., Lemos, R., Sadun, R., Scur, D., and Van Reenen, J. (2014). The new empirical economics of management. _Journal of the European Economic Association_ , pages 835–876. Bloom, N., Mahajan, A., McKenzie, D., and Roberts, J. (2010b). Why do firms in developing countries have low productivity? _American Economic Review_ , 100(2):619–623.

Bloom, N., Sadun, R., and Van Reenen, J. (2016). Management as a Technology? Bloom, N., Schweiger, H., and Van Reenen, J. (2012c). The land that lean manufacturing forgot? _Economics of Transition and Institutional Change_ , 20(4):593–635.

Bloom, N. and Van Reenen, J. (2007). Measuring and explaining management practices across firms and countries. _Q. J. Econ._ , 72(4):1352–1408.

Boyd, G. A. and Curtis, M. E. (2014). Evidence of an “Energy-Management Gap” in US 32
- manufacturing: Spillovers from firm management practices to energy efficiency. _J. Environ. Econ. Manage._ , 68(3):467–479.

- Delmas, M. A. and Toffel, M. W. (2008). Organizational Responses to Environmental Demands: Opening the Black Box. _Strategic Management Journal Strat. Mgmt. J_ .

- Distelhorst, G., Hainmueller, J., and Locke, R. M. (2017). Does Lean Improve Labor Standards? Management and Social Performance in the Nike Supply Chain. _Management Science_ , 63(3):707–728.

- Gillingham, K. and Palmer, K. (2014). Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence. _Review of Environmental Economics and Policy_ , 8(1):18–38.

- Grover, A., Iacovone, L., and Chakraborty, P. (2019). Management Practices in Croatia: Drivers and Consequences for Firm Performance. Technical Report 9067, The World Bank. Policy Research Working Papers.

- Grover, A. and Torre, I. (2019). Management Capabilities and Performance of Firms in the Russian Federation. Technical Report 8996, The World Bank. Policy Research Working Papers.

- Ichniowski, C. and Shaw, K. (1999). The Effects of Human Resource Management Systems on Economic Performance: An International Comparison of U.S. and Japanese Plants. _Management Science_ , 45(5):704–721.

- Karplus, V. and Zhang, D. (2020). Management practices and energy efficiency in industrial firms: Experimental evidence from China. Unpublished manuscript.

- Lyubich, E., Shapiro, J. S., and Walker, R. (2018). Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy. _AEA Papers and Proceedings_ , 108:136–142.

- Martin, R., Muˆuls, M., de Preuxe, L. B., and Wagner, U. J. (2012). Anatomy of a paradox: Management practices, organizational structure and energy efficiency. _J. Environ. Econ. Manage._ , 63(2):208–223.

- Ryan, N. (2018). Energy productivity and energy demand: Experimental evidence from Indian manufacturing plants. _NBER Working Paper 24619_ .

- Schweiger, H. and Stepanov, A. (2019). Energy Subsidies, Energy Intensity and Management Practices. _SSRN Electronic Journal_ .

- Syverson, C. (2004). Market structure and productivity: A concrete example. _J. Polit. Econ._ , 112(6):1181–1222.

- Syverson, C. (2011). What determines productivity? _J. Econ. Lit._ , 49(2):326–365.

- Taylor, M. (2020). Energy Subsidies: Evolution in the Global Energy Transformation to 2050. Technical report, International Renewable Energy Agency, Abu Dhabi.

- U.S. Census Bureau (2015). Management and Organizational Practices Survey (MOPS). `https://www.census.gov/programs-surveys/mops.html` .

- World Bank (2020a). Enterprise Surveys. `https://www.enterprisesurveys.org/en/ enterprisesurvey` .

- World Bank (2020b). Exchange Rate. https://databank.worldbank.org/source/worlddevelopment-indicators.

- World Bank Data (2020). World Bank Country and Lending Groups Classification. `https://datahelpdesk.worldbank.org/knowledgebase/articles/ 906519-world-bank-country-and-lending-groups` .

33
# **Supplementary Material**

# **A1 Data Preparation**

## **A1.1 World Bank Enterprise Surveys**

We compile our data set from the World Bank Enterprise Surveys (World Bank, 2020a), a firm-level survey of a representative sample of an economy’s private sector, including both manufacturing and services firms. We used the 2019 surveys, which were conducted in 31 countries in Europe, the Middle East and North Africa, and the Russian Federation and Central Asia. In each country, firms responded to either the Manufacturing survey or the Services survey based on their self-identified sectors. The survey includes productive inputs, performance measures, business indicators, management practice scores, and a green economy module. This module covers energy and environmental impacts as well as energyand climate-related management practices. We use the energy practices in the green economy module to compute the energy management score. This score is designed to capture the extent to which a firm has implemented structured practices aimed at reducing or raising the efficiency of energy use. The green economy module also covers the physical quantities of various forms of energy consumption such as electricity, petroleum, coal, and natural gas. We use these energy data to compute energy intensity in physical terms. To the best of our knowledge, this paper is the first to analyze the impact of general and energy management practices on physical and value measures of energy intensity.

Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank classification (World Bank Data, 2020), only one country is classified as low-income (Tajikistan); we therefore group countries into three income categories: high, upper middle, and lower middle.

## **A1.2 Merging**

We merge the firm-level responses from all the 31 countries into a combined dataset. For categorical variables such as region that are country-specific and thus inconsistent across surveys, we tagged the possible responses by country throughout the merge to ensure that the responses remained distinct in the final dataset. Additionally, for each country, we converted all financial variables into US dollars using the average market exchange rate in 2018 from World Bank (2020b). After merging, our sample included 20,156 observations.

## **A1.3 Management Measures**

We develop a measure of structured management practices that takes 11 survey questions that quantify practices. These management practices can be divided into operations (1 practice), monitoring (2 practice), targeting (4 practices), and incentives (4 practices). The table below lists these management practices and the scoring scale applied for each. To better align with the methodology from (Bloom et al., 2010a), we map these 11 questions to the 8 questions used by the U.S. MOPS survey (U.S. Census Bureau, 2015). Thus, we combine: 34
- The two monitoring questions by keeping the latter

- ”Did this establishment have production targets?” with ”What best describes the time frame of production targets at this establishment?” to make ”What best describes the company’s production goals calendar?” (scored 1-4)

- ”Did this establishment have performance bonuses for managers?” with ”What were managers’ performance bonuses mostly based on?” by only keeping the latter

We scale all scores between 0 and 1, with 1 best and 0 worst on performance. We apply a z-score normalization. First, each practice category is z-scored across all observations. Then the average is taken of these z-scored practices, conditional on at most 2 missing responses out of 8 management z-scores.

Table A1: Management Questions

|**Category**|**Question**|**Scoring Scale**|
|---|---|---|
|Operations|What response best describes what<br>happened in the company when a prob-<br>lem arose in its processes?|0 - No action, 0.33 - Fixed,<br>0.67 - Prevention, 1 - Con-<br>tinuous improvement|
|Monitoring|How many key performance indicators<br>were monitored in the company?|0 - None, 0.33 - 1-2, 0.67 -<br>3-9, 1 - 10 or more|
||Did this establishment have production<br>targets? Examples of production tar-<br>gets are: production volume, quality,<br>efciency, waste, or on-time delivery.|0 - No, 1 - Yes|
||What best describes the time frame<br>of production targets at this establish-<br>ment?|0 - No targets, 0.33 - Less<br>than one year, 0.67 - One<br>year or more, 1 - Combina-<br>tion of short- and long-term|
|Targeting|How easy or difcult was it for this es-<br>tablishment to achieve its production<br>targets overall?|0 - Not achieved,<br>0.2 -<br>Extraordinary efort, 0.4 -<br>More than normal, 0.6 -<br>Some efort, 0.8 - Normal ef-<br>fort, 1 - Not much efort|
||Who was aware of the production tar-<br>gets at this establishment?|0 - Only senior manages,<br>0.33 - Most managers and<br>some workers, 0.67 - Most<br>managers and most workers,<br>1 - All managers and most<br>workers|

35
What were the performance bonuses for 0 - No bonuses, 0.25 - Firm managers based on? performance, 0.5 - Establishment performance, 0.75 - Team performance, 1 - Their own performance What were the criteria with which the 0 - Non-managers not non-managers were promoted in the promoted, 0.33 - Factors company? such as tenure or family connections, 0.67 - Partly Incentives performance and ability and partly other factors, 1 - Solely performance and ability When a non-manager had a bad per0 - Rarely or never, 0.5 - Afformance, was he or she reassigned or ter 6 months, 1 - Within 6 dismissed? months

# **A1.4 Energy Management Measures**

For energy management, we identified seven management practices which can be categorized in monitoring (2 practices), targeting (1 practice), incentives (1 practice) and prioritization of energy management (3 practices). Each practice is scaled between 0 and 1, with 1 representing best management practice and 0 representing the worst. We use z-score normalization with the mean. First, each practice is z-scored across all observations. Then the average is taken of these z-scored practices. Energy management practices, scoring scale, and a detailed summary of responses are included in Table 1.

36
Table A2: Energy Management Questions

|**Category**|**Question**<br>**Scoring Scale**|
|---|---|
|Monitoring|How often did this establishment monitor its energy con-<br>sumption?<br>1-8<br>Did this establishment complete an external audit of its<br>energy consumption?<br>1-2|
|Targeting|What sort of targets for energy consumption did this<br>establishment have?<br>1-4|
|Incentives|Is the manager responsible for environmental and cli-<br>mate change issues evaluated against how well the estab-<br>lishment performs on energy consumption, CO2 emis-<br>sions or other pollution or environmental targets?<br>1-2|
||Did this establishment adopt any measures to enhance<br>energy efciency?<br>1-2|
|Energy as a Priority|Were any of these measures developed by the establish-<br>ment?<br>1-2<br>Was there a maximum time period for the return on<br>investment of the adopted energy efciency measures?<br>1-2<br>Was this maximum payback time longer than, equal to,<br>or shorter than the average time applied to non-energy<br>efciency related measures?<br>1-3|

# **A1.5 Energy Intensity**

We use two measures of energy intensity, based on expenditure or physical quantities divided by output. A firm is included if expenditure or quantity is non-missing for electricity and at least one fuel type. To compute the energy intensity in value terms, we divide the total energy cost, summed over all energy types, by the sales of the firm. The total energy cost is computed by the sum of the reported total annual cost of electricity and fuel after conversion to the US$. If either of the reported values is missing or marked as “don’t know,” we assume it to be zero and then sum it. We multiply the energy intensity by 100 to represent the energy intensity value measure in percentage.

We compute the energy intensity using physical quantities by dividing total energy consumed (in energy-equivalent units) by the sales of the firm. The total energy consumption in British Thermal Units (BTU) is found by first converting reported consumption in electricity, coal, natural gas and fuel made from petroleum to standard energy units (BTU) and then summing across them. Then if either of reported value is missing or don’t know, we assume it to be zero. We sum up these values if at least one of the values is non-zero. To 37
compute the energy intensity in physical terms, the total energy consumed is divided by the sales of the firm if the total energy consumed is non-zero. The units of energy intensity in physical terms is BTU per US$.

# **A1.6 Treatment of Outliers**

We winsorize observations in the main sample by excluding the top and bottom 1 percentile on energy intensity (physical), energy intensity (expenditure), and output measures. We retain 19,521 observations, although not every firm reports energy expenditure, physical energy use, and output. The management and green economy survey modules were administered to a subset of the randomly-selected firms in the full sample. Constructing our data set thus required trading off its size against a requirement that all major variables be observed. We do not systematically drop firms with missing observations of key variables, but instead remain mindful that our regression samples differ in composition. For instance, in the winsorized sample, we observe energy intensity (physical) for 8,272 firms and energy intensity (value) for 14,713 firms. We observe at least six out of eight general management practices for 8,476 firms, and energy management practices for 6,178 firms. Firms that have non-missing values for all covariates are included in each regression. If we limit the sample to only firms that observe both measures of energy intensity plus output, the sign of the results remains mostly unchanged (not shown). However, firms that report both physical energy use and expenditures may differ systematically from those that report only one or the other. For instance, these firms may have better developed general management or energy management practices that support monitoring of input use. The composition of firms across countries and two-digit sectors before and after winsorization is similar.

# **A1.7 Descriptive Statistics**

The composition of firms across countries and two-digit sectors before and after winsorization is similar. Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank classification (World Bank Data, 2020), only one country is classified as low-income (Tajikistan); we therefore group countries into three income categories: high, upper middle, and lower middle.

38
Table A3: Summary statistics of winsorized sample.

|**Variable**|Obs|Mean|St. Dev|Min|p5|Median|p95|Max|
|---|---|---|---|---|---|---|---|---|
|Energy intensity exp (% of output)|14,076|5.04|7.39|0.03|0.16|2.36|20.19|51.75|
|Energy intensity quant (log) (BTU/USD)|7,695|6.53|2.52|-0.45|2.14|6.57|10.68|15.51|
|Elec intensity exp (% of output)|13,777|2.81|4.70|0.00|0.07|1.14|11.36|50.76|
|Elec intensity quant (log) (kWh/USD)|6,132|-5.74|3.16|-17.77|-11.23|-5.38|-1.16|4.39|
|Capital stock (log)|6,432|12.86|2.44|-1.07|8.96|12.83|16.89|33.66|
|Employees (log)|18,595|3.26|1.32|0|1.61|3.04|5.67|9.34|
|Sales (log)|16,410|13.70|2.09|4.31|10.32|13.66|17.17|19.01|
|General management score|9,853|0.48|0.21|0|0.08|0.51|0.78|1|
|Energy management score|17,347|0.28|0.29|0|0|0.17|0.83|1|

**39**
Table A4: Generic and energy-centric management score statistics for high, upper middle, and lower middle income groups. (G) indicates generic management; (E) indicates energy-centric management. Uses the winsorized sample and computes management scores using straight averages of non-missing questions scores.

|**Income Group**|Shr Obs = 0|Shr Obs _<_ 0.25|Shr Obs _>_ 0.75|Mean|SD|p10|Median|p90|
|---|---|---|---|---|---|---|---|---|
|(G) High|0.017|0.072|0.045|0.491|0.209|0.167|0.517|0.738|
|(G) Upper Middle|0.022|0.077|0.043|0.481|0.211|0.167|0.504|0.737|
|(G) Lower Middle|0.020|0.065|0.031|0.477|0.200|0.2|0.5|0.725|
|(E) High|0.345|0.209|0.122|0.291|0.304|0|0.167|0.778|
|(E) Upper Middle|0.387|0.283|0.070|0.237|0.273|0|0.167|0.719|
|(E) Lower Middle|0.213|0.162|0.102|0.365|0.296|0|0.359|0.806|

**40**
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# **A3 Supplementary Tables**

Table A5: Overview of prior literature on management practices, energy, and productivity.

|**Year**|**Authors**|**Country**|**Time Period**|**Outcome Variable**|**Methodology**|**Main Result**|
|---|---|---|---|---|---|---|
|2012|Bloom,<br>Schweiger<br>and<br>Van<br>Reenen|Belarus,<br>Bulgaria,<br>Ger-<br>many, India, Kazakhstan,<br>Lithuania, Poland, Roma-<br>nia,<br>Russian<br>Federation,<br>Serbia,<br>Ukraine,<br>Uzbek-<br>istan|October 2008 to<br>April 2010|operating<br>revenue,<br>proft<br>margin,<br>EBIDTA,<br>returns<br>to total assets|measuring<br>and<br>scor-<br>ing management prac-<br>tices, in areas of oper-<br>ations, monitoring, tar-<br>gets, and incentives|Average<br>management<br>score is positively and sig-<br>nifcantly correlated with<br>total factor productivity.|
|2010|Bloom,<br>Mahajan,<br>McKenzie<br>and Roberts|United<br>States,<br>United<br>Kingdom,<br>Japan,<br>France,<br>Germany, Greece, Poland,<br>Brazil, Colombia, Ecuador,<br>Morocco, China, Indonesia,<br>Philippines, India||sales per employee||Firms in developing coun-<br>tries are often badly man-<br>aged, which substantially<br>reduces their productivity.|
|2012|Bloom,<br>Genakos,<br>Sadun<br>and<br>Van Reenen|Argentina,<br>Australia,<br>Brazil,<br>Canada,<br>Chile,<br>China,<br>France,<br>Germany,<br>Greece, India, Italy, Japan,<br>Mexico,<br>New<br>Zealand,<br>Poland, Portugal, Ireland,<br>Sweden, United Kingdom,<br>United States|2002 to 2012|sales, return on cap-<br>ital<br>employed,<br>sales<br>growth, exit|double-blind<br>survey<br>technique for manage-<br>ment scoring on 18 key<br>practices|Higher management scores<br>are<br>robustly<br>associated<br>with better performance.|
|2010|Bloom,<br>Genakos,<br>Martin<br>and<br>Sadun|United Kingdom|summer 2006|real gross output (sales<br>&<br>inventory<br>changes<br>defated with a sectoral<br>producer price index)|match<br>management<br>data<br>to<br>production<br>and<br>energy<br>usage<br>information|Better managed frms are<br>signifcantly more produc-<br>tive.<br>Management prac-<br>tices that are associated<br>with improved productiv-<br>ity are also linked to lower<br>greenhouse gas emissions.|

**45**
|2007|Bloom<br>and<br>Van Reenen|United States, France,<br>Germany,<br>United<br>Kingdom|summer 2004|total factor productiv-<br>ity,<br>sales,<br>return on<br>capital employed|double-blind<br>survey<br>technique for manage-<br>ment scoring on 18 key<br>practices|Management practices are<br>always positively and sig-<br>nifcantly associated with<br>the longer-run component<br>of<br>TFP.<br>Management<br>also has a signifcant and<br>positive association with<br>ROCE.|
|---|---|---|---|---|---|---|
|2013|Bloom,<br>Eifert,<br>Mahajan,<br>McKenzie<br>and Roberts|India|August 2008 to<br>August<br>2010;<br>August 2011 to<br>November 2011|quality, inventory, out-<br>put, and total factor<br>productivity (TFP)|exogenous<br>consulting<br>treatment intervention|The<br>treatment<br>interven-<br>tion led to signifcant im-<br>provements in quality, in-<br>ventory, and output.|
|2017|Bloom,<br>Sadun<br>and<br>Van Reenen|34 countries|2004 to 2014|performance:<br>value<br>added|original survey data on<br>management<br>practices<br>over<br>11,000<br>frms<br>in<br>four survey waves|Diferences<br>in<br>manage-<br>ment practices account for<br>about 30% of total factor<br>productivity<br>diferences<br>both<br>between<br>countries<br>and<br>within<br>countries<br>across frms.|
|2018|Schweiger<br>and Stepanov|38 countries in Cen-<br>tral and Eastern Eu-<br>rope, Central Asia and<br>Middle East and North<br>Africa|2011 to 2016|energy intensity value<br>measure|frm-level data on man-<br>agement practices and<br>energy expenditures in<br>about 2,000 manufac-<br>turing frms|An improvement in man-<br>agement from the 25th to<br>the 75th percentile is as-<br>sociated with a 23% fuel<br>intensity reduction when<br>fuel subsidies are low (neg-<br>ative) and with a 2% fuel<br>intensity reduction when<br>fuel subsidies are high.|

**46**
|2014|Boyd<br>and<br>Curtis|United States|2006 to 2007|energy<br>expenditure<br>over gross output in<br>percentage|merging survey of frm<br>management<br>practices<br>with US Census data<br>on manufacturing|Better managed frms pro-<br>duce more output and are<br>larger.<br>At best, fndings<br>show a modest relation-<br>ship between management<br>and energy efciency, but<br>standard errors are large.|
|---|---|---|---|---|---|---|
|2019|Grover,<br>Ia-<br>covone,<br>and<br>Chakraborty|Croatia|2017|sales<br>per<br>employee,<br>proft|survey on frm capabil-<br>ities in Croatia, includ-<br>ing a module for cap-<br>turing management ca-<br>pabilities|Manufacturing frms are<br>better managed than ser-<br>vice frms. Better manage-<br>ment is consistently and<br>strongly associated with<br>better performance.|
|2019|Bloom, Bryn-<br>jolfsson, Fos-<br>ter,<br>Jarmin,<br>Patnaik,<br>Saporta-<br>Eksten,<br>Van<br>Reenen|United States|2010 to 2015|total factor productiv-<br>ity|survey<br>of<br>structured<br>management<br>practices<br>in 35,000 manufactur-<br>ing plants|The study fnds large dif-<br>ferences<br>in<br>productivity<br>associated with variations<br>in management practices.|
|2019|Bloom,<br>Iacovone,<br>Pereira-<br>Lopez,<br>Van<br>Reenen|Mexico, United States|2014 to 2015|total factor productiv-<br>ity, proft|survey of management<br>practices,<br>combined<br>with data from Mex-<br>ico’s National Survey<br>on<br>Productivity<br>and<br>Competitiveness<br>in<br>2015|Management<br>score<br>is<br>strongly<br>and<br>positively<br>correlated<br>with<br>frm<br>productivity.|
|2020|Karplus<br>and<br>Zhang|China|March<br>and<br>April 2016|energy efciency, total<br>energy use|two-part<br>survey<br>on<br>management<br>and<br>en-<br>ergy<br>management;<br>electricity<br>use<br>and<br>production information<br>from SECA survey|Moving<br>from<br>the<br>25th<br>to<br>75th<br>percentile<br>on<br>management score reduces<br>electricity<br>intensity<br>by<br>40% in value terms and<br>by 33% in physical terms.|

**47**
Table A6: Relationship between management and energy intensity (value), by country income group.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||High|Upper Middle|Lower Middle|High|Upper Middle|Lower Middle|
|General|-0.442<sup>_∗_</sup>|-0.389|-1.563<sup>_∗∗∗_</sup>|0.339|0.229|-1.238<sup>_∗∗_</sup>|
||(0.239)|(0.263)|(0.588)|(0.235)|(0.261)|(0.579)|
|No. Employees||||0.822<sup>_∗∗∗_</sup>|1.052<sup>_∗∗∗_</sup>|1.533<sup>_∗∗∗_</sup>|
|||||(0.224)|(0.173)|(0.298)|
|Log(Sales)||||-1.168<sup>_∗∗∗_</sup>|-1.727<sup>_∗∗∗_</sup>|-1.710<sup>_∗∗∗_</sup>|
|||||(0.194)|(0.145)|(0.185)|
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|2013|2664|1394|2012|2656|1393|
|Adjusted _R_<sup>2</sup>|0.266|0.234|0.135|0.302|0.306|0.208|
|df<br>m|85|89|73|87|91|75|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**48**
Table A7: Relationship between management and energy intensity (value) (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|-0.211|0.085|0.111|0.213|-1.050|
||(0.804)|(0.340)|(0.369)|(0.341)|(0.708)|
|No. Employees|0.921<sup>_∗∗_</sup>|0.909<sup>_∗∗∗_</sup>|1.029<sup>_∗∗∗_</sup>|1.251<sup>_∗∗∗_</sup>|1.021<sup>_∗∗_</sup>|
||(0.422)|(0.245)|(0.308)|(0.233)|(0.417)|
|Capital Stock|0.490<sup>_∗∗_</sup><br>(0.198)|0.335<sup>_∗∗∗_</sup><br>(0.087)|0.458<sup>_∗∗∗_</sup><br>(0.132)|||
|Log(Sales)|-1.536<sup>_∗∗∗_</sup>|-1.727<sup>_∗∗∗_</sup>|-1.672<sup>_∗∗∗_</sup>|-1.972<sup>_∗∗∗_</sup>|-1.576<sup>_∗∗∗_</sup>|
||(0.311)|(0.173)|(0.281)|(0.195)|(0.312)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|586|1693|666|1301|758|
|Adjusted _R_<sup>2</sup>|0.189|0.237|0.268|0.189|0.444|
|df<br>~~m~~|43|46|54|43|52|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 49
Table A8: Relationship between management and energy intensity (quantity) (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|0.358|0.416<sup>_∗∗_</sup>|0.975<sup>_∗∗∗_</sup>|0.296<sup>_∗_</sup>|-0.213|
||(0.347)|(0.169)|(0.286)|(0.158)|(0.274)|
|No. Employees|0.471<sup>_∗∗_</sup>|0.314<sup>_∗∗∗_</sup>|0.077|0.428<sup>_∗∗∗_</sup>|0.561<sup>_∗∗∗_</sup>|
||(0.213)|(0.117)|(0.178)|(0.117)|(0.178)|
|Capital Stock|0.362<sup>_∗∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|0.250<sup>_∗∗∗_</sup>|||
||(0.082)|(0.044)|(0.081)|||
|Log(Sales)|-0.700<sup>_∗∗∗_</sup>|-0.630<sup>_∗∗∗_</sup>|-0.482<sup>_∗∗∗_</sup>|-0.631<sup>_∗∗∗_</sup>|-0.563<sup>_∗∗∗_</sup>|
||(0.142)|(0.075)|(0.128)|(0.083)|(0.118)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|381|1016|410|712|441|
|Adjusted _R_<sup>2</sup>|0.371|0.320|0.359|0.332|0.293|
|df<br>~~m~~|42|46|45|42|47|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 50
Table A9: Relationship between management and energy intensity (quantity), by country income group.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||High|Upper Middle|Lower Middle|High|Upper Middle|Lower Middle|
|General|0.170|0.214|0.304<sup>_∗_</sup>|0.550<sup>_∗∗∗_</sup>|0.410<sup>_∗∗_</sup>|0.243|
||(0.131)|(0.160)|(0.166)|(0.139)|(0.164)|(0.165)|
|No. Employees||||0.416<sup>_∗∗∗_</sup>|0.412<sup>_∗∗∗_</sup>|0.704<sup>_∗∗∗_</sup>|
|||||(0.114)|(0.103)|(0.105)|
|Log(Sales)||||-0.561<sup>_∗∗∗_</sup>|-0.547<sup>_∗∗∗_</sup>|-0.617<sup>_∗∗∗_</sup>|
|||||(0.084)|(0.071)|(0.068)|
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|1218|1236|1080|1217|1233|1079|
|Adjusted _R_<sup>2</sup>|0.206|0.240|0.094|0.249|0.285|0.185|
|df<br>m|81|76|67|83|78|69|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**51**
Table A10: Relationship between management (all sub-scores) and energy intensity in value (1)-(5) and quantity (6)-(10) terms (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)<br>High EI|(2)<br>Moderate EI|(3)<br>Low EI|(4)<br>High KI|(5)<br>Low KI|(6)<br>High EI|(7)<br>Moderate EI|(8)<br>Low EI|(9)<br>High KI|(10)<br>Low KI|
|---|---|---|---|---|---|---|---|---|---|---|
|Targets z-score|-0.058|-0.780<sup>_∗∗∗_</sup>|-0.440<sup>_∗_</sup>|-0.083|0.533|0.352<sup>_∗_</sup>|-0.132|0.124|0.035|0.211|
||(0.426)|(0.193)|(0.229)|(0.244)|(0.451)|(0.194)|(0.108)|(0.138)|(0.119)|(0.196)|
|Monitor z-score|-0.165|-0.008|-0.080|0.042|-0.778<sup>_∗_</sup>|0.299|0.189<sup>_∗_</sup>|0.408<sup>_∗∗∗_</sup>|0.071|0.041|
||(0.376)|(0.162)|(0.230)|(0.215)|(0.407)|(0.183)|(0.099)|(0.146)|(0.111)|(0.167)|
|Operations z-score|-0.268|-0.024|0.215|0.069|0.154|-0.082|0.154<sup>_∗_</sup>|-0.048|0.209<sup>_∗∗∗_</sup>|-0.073|
||(0.420)|(0.161)|(0.204)|(0.167)|(0.345)|(0.145)|(0.084)|(0.144)|(0.080)|(0.118)|
|Incentives z-score|0.146|0.387<sup>_∗_</sup>|-0.202|-0.002|0.079|-0.288|0.027|-0.121|0.059|-0.106|
||(0.499)|(0.225)|(0.283)|(0.257)|(0.462)|(0.227)|(0.113)|(0.171)|(0.122)|(0.199)|
|No. Employees|0.697|0.841<sup>_∗∗∗_</sup>|0.979<sup>_∗∗∗_</sup>|1.311<sup>_∗∗∗_</sup>|1.360<sup>_∗∗∗_</sup>|0.313|0.318<sup>_∗∗∗_</sup>|0.014|0.387<sup>_∗∗∗_</sup>|0.498<sup>_∗∗∗_</sup>|
||(0.434)|(0.253)|(0.316)|(0.234)|(0.440)|(0.212)|(0.123)|(0.163)|(0.116)|(0.186)|
|Capital Stock|0.667<sup>_∗∗∗_</sup>|0.508<sup>_∗∗∗_</sup>|0.691<sup>_∗∗∗_</sup>|||0.380<sup>_∗∗∗_</sup>|0.174<sup>_∗∗∗_</sup>|0.333<sup>_∗∗∗_</sup>|||
||(0.204)|(0.096)|(0.143)|||(0.090)|(0.051)|(0.076)|||
|Log(Sales)|-1.435<sup>_∗∗∗_</sup>|-1.760<sup>_∗∗∗_</sup>|-1.754<sup>_∗∗∗_</sup>|-1.954<sup>_∗∗∗_</sup>|-2.084<sup>_∗∗∗_</sup>|-0.656<sup>_∗∗∗_</sup>|-0.604<sup>_∗∗∗_</sup>|-0.446<sup>_∗∗∗_</sup>|-0.607<sup>_∗∗∗_</sup>|-0.579<sup>_∗∗∗_</sup>|
||(0.311)|(0.184)|(0.279)|(0.197)|(0.333)|(0.147)|(0.081)|(0.120)|(0.077)|(0.118)|
|3-digit sector|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations|582|1680|688|1305|750|362|991|421|702|433|
|Adjusted _R_<sup>2</sup>|0.182|0.228|0.244|0.191|0.440|0.409|0.297|0.357|0.326|0.278|
|df<br>~~m~~|46|49|54|46|56|45|49|47|45|49|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**52**
Table A11: Relationship between general management practices and energy management practices (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|0.403<sup>_∗∗∗_</sup>|0.496<sup>_∗∗∗_</sup>|0.503<sup>_∗∗∗_</sup>|0.311<sup>_∗∗∗_</sup>|0.350<sup>_∗∗∗_</sup>|
||(0.057)|(0.037)|(0.062)|(0.035)|(0.051)|
|No. Employees|0.006|0.076<sup>_∗∗∗_</sup>|0.037|0.023|0.050<sup>_∗_</sup>|
||(0.036)|(0.024)|(0.039)|(0.023)|(0.030)|
|Capital Stock|0.049<sup>_∗∗∗_</sup>|0.022<sup>_∗∗_</sup>|0.049<sup>_∗∗∗_</sup>|||
||(0.014)|(0.010)|(0.017)|||
|Log(Sales)|0.031|0.026<sup>_∗_</sup>|0.002|0.038<sup>_∗∗_</sup>|0.024|
||(0.025)|(0.015)|(0.031)|(0.015)|(0.018)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|621|1790|694|1509|854|
|Adjusted _R_<sup>2</sup>|0.362|0.295|0.287|0.181|0.166|
|df<br>m|43|46|53|43|53|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 53
Table A12: Relationship between energy management and energy intensity in value terms (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)<br>High EI|(2)<br>Moderate EI|(3)<br>Low EI|(4)<br>High KI|(5)<br>Low KI|
|---|---|---|---|---|---|
|Energy|-0.015|0.053|-0.002|-0.049|0.568|
||(0.460)|(0.199)|(0.243)|(0.263)|(0.467)|
|General|-0.346|0.060|0.159|0.220|-1.283<sup>_∗_</sup>|
||(0.884)|(0.333)|(0.358)|(0.358)|(0.737)|
|No. Employees|0.860<sup>_∗∗_</sup>|0.910<sup>_∗∗∗_</sup>|1.052<sup>_∗∗∗_</sup>|1.261<sup>_∗∗∗_</sup>|1.044<sup>_∗∗_</sup>|
||(0.420)|(0.249)|(0.306)|(0.234)|(0.419)|
|Capital Stock|0.529<sup>_∗∗∗_</sup><br>(0.195)|0.333<sup>_∗∗∗_</sup><br>(0.088)|0.447<sup>_∗∗∗_</sup><br>(0.132)|||
|Log(Sales)|-1.534<sup>_∗∗∗_</sup>|-1.732<sup>_∗∗∗_</sup>|-1.673<sup>_∗∗∗_</sup>|-1.972<sup>_∗∗∗_</sup>|-1.619<sup>_∗∗∗_</sup>|
||(0.310)|(0.173)|(0.280)|(0.194)|(0.313)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|584|1684|656|1293|754|
|Adjusted _R_<sup>2</sup>|0.190|0.236|0.286|0.188|0.444|
|df<br>~~m~~|44|47|54|44|53|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 54
Table A13: Relationship between energy management and energy intensity in quantity terms (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)<br>High EI|(2)<br>Moderate EI|(3)<br>Low EI|(4)<br>High KI|(5)<br>Low KI|
|---|---|---|---|---|---|
|Energy|0.447<sup>_∗_</sup>|0.088|0.015|0.223<sup>_∗_</sup>|0.152|
||(0.245)|(0.113)|(0.178)|(0.116)|(0.162)|
|General|0.178|0.371<sup>_∗∗_</sup>|0.966<sup>_∗∗∗_</sup>|0.214|-0.248|
||(0.367)|(0.177)|(0.297)|(0.164)|(0.280)|
|No. Employees|0.492<sup>_∗∗_</sup>|0.307<sup>_∗∗∗_</sup>|0.076|0.420<sup>_∗∗∗_</sup>|0.550<sup>_∗∗∗_</sup>|
||(0.217)|(0.117)|(0.178)|(0.117)|(0.179)|
|Capital Stock|0.339<sup>_∗∗∗_</sup><br>(0.083)|0.157<sup>_∗∗∗_</sup><br>(0.043)|0.250<sup>_∗∗∗_</sup><br>(0.081)|||
|Log(Sales)|-0.735<sup>_∗∗∗_</sup>|-0.633<sup>_∗∗∗_</sup>|-0.482<sup>_∗∗∗_</sup>|-0.636<sup>_∗∗∗_</sup>|-0.568<sup>_∗∗∗_</sup>|
||(0.145)|(0.076)|(0.129)|(0.083)|(0.118)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|381|1016|410|712|441|
|Adjusted _R_<sup>2</sup>|0.376|0.320|0.357|0.334|0.293|
|df<br>~~m~~|43|47|46|43|48|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 55
Table A14: Relationship between general management, energy management, and output (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|0.035|0.048|0.135|0.285<sup>_∗∗∗_</sup>|0.393<sup>_∗∗∗_</sup>|
||(0.103)|(0.063)|(0.090)|(0.067)|(0.099)|
|Energy|0.056|0.085<sup>_∗∗_</sup>|-0.032|0.109<sup>_∗∗_</sup>|0.080|
||(0.064)|(0.036)|(0.061)|(0.043)|(0.060)|
|No. Employees|0.640<sup>_∗∗∗_</sup>|0.644<sup>_∗∗∗_</sup>|0.625<sup>_∗∗∗_</sup>|0.957<sup>_∗∗∗_</sup>|0.843<sup>_∗∗∗_</sup>|
||(0.066)|(0.038)|(0.065)|(0.030)|(0.053)|
|Capital Stock|0.028|0.094<sup>_∗∗∗_</sup>|0.101<sup>_∗∗∗_</sup>|||
||(0.035)|(0.020)|(0.032)|||
|Materials|0.412<sup>_∗∗∗_</sup>|0.288<sup>_∗∗∗_</sup>|0.255<sup>_∗∗∗_</sup>|||
||(0.045)|(0.029)|(0.066)|||
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|420|1234|549|1509|854|
|Adjusted _R_<sup>2</sup>|0.801|0.817|0.793|0.624|0.589|
|df<br>m|43|47|53|43|53|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 56
# **A4 Expanding the WBES Green Economy Module**

The WBES Green Economy Module largely focuses on binary measures of a firm’s effort to improve energy efficiency. It largely does not distinguish between economic and non-economic motivations for adopting energy management. The areas below represent additional lines of questioning that could be built into future versions of the WBES Green Economy Module.

- Measure relative extent of practice adoption, rather than binary questions about whether a practice has been adopted. Sample question(s): At what scale do you measure the firm’s physical energy use: by factory, production line, or specific equipment? How often do you hold meetings to review the firm’s energy use?

- Measure the extent to which employees at all levels of the organization are aware of energy-saving targets. Sample question(s): If you hold meetings to review energy use, who typically attends? If I asked one of your factory floor employees what measures they take to manage energy inputs, what would they say?

- Understand the organization of energy management functions within the firm. Sample question(s): Does the firm have a dedicated energy manager? If yes, who does that individual report to? Is responsibility for energy management part of the regular training for your employees? Please describe.

- Determine whether or not the firm faces government energy efficiency mandates, and how it has responded to them. Sample question(s): Is the firm required by the government to limit its energy consumption? What measures has the firm taken in response? How difficult is it for the firm to implement these policies?

- Assess ease of access to inputs and pricing at the firm level. Sample question(s): Does the firm face tiered pricing? Does the firm receive its energy at a discount? Does buying on contract versus via the spot market affect the energy price the firm receives? Are frequent power outages a common problem for the firm? How often do they occur? What do you do to mitigate them?

- Evaluate relative emphasis on energy conservation, relative to other goals. Sample question(s): What are the firm’s top five objectives against which top managers evaluate success? Is reducing energy cost is a high, medium, or low priority for the top management? Is conserving physical energy used is a high, medium, or low priority for the top management?

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