The effects of the energy transition on power sector employment in Latin America

López, David et al. Working Paper The effects of the energy transition on power sector employment in Latin America IDB Working Paper Series, No. IDB-WP-01435 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: López, David et al. (2023) : The effects of the energy transition on power sector employment in Latin America, IDB Working Paper Series, No. IDB-WP-01435, Inter-American Development Bank (IDB), Washington, DC, https://doi.org/10.18235/0004715 This Version is available at: https://hdl.handle.net/10419/289972 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode The Effects of the Energy Transition on Power Sector Employment in Latin America David López Mariana Weiss José Francisco Pessanha Karla Arias Livia Gouvea Gomes Michelle Hallack IDB-WP-01435 Energy Division TECHNICAL NOTE Nº January 2023 The Effects of the Energy Transition on Power Sector Employment in Latin America David López Mariana Weiss José Francisco Pessanha Karla Arias Livia Gouvea Gomes Michelle Hallack January 2023 Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library The effects of the energy transition on power sector employment in Latin America / David López, Mariana Weiss, José Francisco Pessanha, Karla Arias, Livia Gouvea Gomes, Michelle Hallack. p. cm. — (IDB Working Paper Series ; 1435) Includes bibliographic references. 1. Energy transition-Latin America. 2. Job creation-Latin America. 3. Coronavirus infectioins-Economic aspects-Latin America. I. López Soto, David. II. Weiss, Mariana. III. Pessanha, José. IV. Arias, Karla. V. Gouvea, Livia. VI. Hallack, Michelle, 1983-. VII. Inter-American Development Bank. Infrastructure and Energy Sector. VIII. Series. IDB-WP-1435 JEL Codes: LL25, D25, M51, Q40, Q42 Topics: Firm's Job creation, Energy Transition, Post-Covid Economic Recovery, Latin A merica, Industrial Organization. Copyright © Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 Attribution- NonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose. No derivative work is allowed. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license. Note that link provided above includes additional terms and conditions of the license. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. http://www.iadb.org 2023 David López Mariana Weiss José Francisco Pessanha Karla Arias Livia Gouvea Gomes Michelle Hallack The Effects of the Energy Transition on Power Sector Employment in Latin America TABLE OF CONTENT INDEX Inter-American Development Bank Abstract 1. Introduction 2. Post-COVID Economic Recovery and Job Creation through the Energy Transition in Latin America 3. Drivers of Job Creation in an Energy Sector in Transition to a Sustainable Future 4. Data and Methodology 5. Results and Discussion 6. Conclusion References Appendix 1. Exploratory Statistical Analysis of the Error Components after the Clustering Exercise Appendix 2. Robustness Check for Cross-Section Employment Data 3.1 Firm Size and Potential Hiring 3.2 Firm Technology or Activity Area 3.3 Employee Educational Level 3.4 Corporate Social Responsibility (CSR) Policies 4.1 Energy Employment Variables 4.2 Model and Estimation Methodology 04 05 08 12 12 13 13 15 16 16 19 21 26 28 33 35 The present study analyzes the relationship between energy transition and job creation potential in Latin America. It looks at companies' characteristics to infer potential hiring process drivers in forthcoming years. The analysis is based on an econometric model applied to cross-sectional data to explain the dependent variable "potential hiring rate" depending on the firm's size (based on the number of clients), area of activity or technology, employees' educational levels, and labor policies. The data came from 338 interviewed companies, including energy generation, transmission, distribution, and new energy services, oil and gas, and construction companies in six Latin American Countries (Bolivia, Chile, Costa Rica, Mexico, Panama, and Uruguay). The econometric study focused on 135 companies that declared they would be hiring new employees in the next year when they were interviewed. The results show that the smaller energy companies with a larger participation of skilled workforce will tend to have a higher expected hiring rate in the forthcoming year, implying an inverse relationship between firm size and potential hiring rate. The model findings convey that the higher the number of skilled employees in the workforce, the greater the expected expansion of the company's labor force, particularly in renewable generation companies. Another aspect worth considering about the factors behind the company's potential hiring rate is the question of job quality. The results suggest that the firms hiring more are those with fewer policies. It can be explained by the fact that more traditional companies, such as hydrocarbon and utility companies, tend to have better-established policies but necessarily the highest potential job creation rates. This takeaway raises a discussion about whether a change in job quality is associated with the energy transition or merely with new entrants who will become traditional in the coming years. Moreover, it also helps to explain some of the political economies of the labor market that may play a role in the energy transition process. Therefore, one of the present study's main takeaways is the need to analyze more in-depth and promote job quality in smaller energy companies. Inter-American Development Bank JEL Codes: L25, D25, M51, Q40, Q42 Keywords: FIRM'S JOB CREATION, ENERGY TRANSITION, POST-COVID ECONOMIC RECOVERY, LATIN AMERICA, INDUSTRIAL ORGANIZATION. Abstract More than 75% of global greenhouse emissions result from energy use (Energy Hub, 2022a). Consequently, the energy transition is central to achieving the Paris Agreement Goals. Moreover, in the COVID-19 green recovery, the energy transition has become a promise of investments and employment (IRENA, 2019). According to the International Labor Organization (ILO, 2018), the shift towards sustainable practices may create 18 million new jobs worldwide by 2030. WEF (2021) also suggests that renewable energy and energy efficiency jobs are geographically more diversified, gender diverse, and more likely to employ young people. In Latin America, the renewable energy sector already provided jobs (directly and indirectly) for at least 1.7 million people. This clean energy employment in the region is concentrated in liquid biofuels, hydropower, and solar and wind energy (IRENA, 2021). The economic contraction caused by the COVID-19 pandemic was reflected in electricity demand and, consequently, in new investment decisions. In Latin America, the impact on the economy was even greater. According to the World Bank Group (2022), the Latin American gross domestic product (GDP) dropped by 6.7% in 2020, while the global GDP slowed by 3.3%. Despite increased threats to investment decisions, the International Energy Agency – IEA (2020a, 2021a) argued that investment levels in clean energy technologies remained resilient during COVID-19. At the end of 2021, governments worldwide mobilized funds in rebates, grants, loans, and tax incentives/exemptions to mitigate the effects of the COVID-19 crisis. Governments have approved US $480 billion for clean energy investments between 2021-2023 (IEA, 2021b). Those investments include energy efficiency, public transport, clean transport, low carbon power, clean fuels, innovative technologies, grid extension and reinforcement, and storage. That is extremely important, especially because electricity demand is already upward (c et al., 2021). Many studies have also highlighted that the energy transition can aid post-pandemic economic recovery (IEA, 2020b; Urdiales et al., 2021, Hallack et al., 2021). The broad vision of those studies defends the idea that post-COVID economic recovery plans must be consistent with countries' energy transition strategies because these investments have greater potential to reduce greenhouse gas emissions and create jobs and income. In its sustainable recovery plan for the health crisis, the International Energy Agency (IEA, 2020b) even proposes that this is a unique opportunity to reboot economies and open up many new employment opportunities while accelerating toward a more resilient and cleaner energy scenario. According to different studies, US $1 million invested by an energy transition firm can contribute, on average, to the creation of two to 45 direct jobs, depending on the technology, sector, and country (IEA, 2020b; ACEEE, 2011; Garrett-Peliter, 2017; Pollin & Garrett-Peltier, 2009; Janssen & Staniaszek, 2012). IADB then conducted firm-level surveys in three Latin American countries (Bolivia, Chile, and Uruguay) to understand the profile of companies with higher job creation potential (Ravillard et al., 2021). Inter-American Development Bank 1. Introduction 4 Inter-American Development Bank 3. Drivers of Job Creation in an Energy Sector in Transition to a Sustainable Future The literature suggests that the relationship between job creation and energy transition is more complex and could be affected and changed by different factors such as emerging firms, electricity demand growth projections, energy market diversification impacting technology costs (operational and capital costs), adequate labor supply with the necessary skills avoiding misalignments with demand, and availability of national and local policies addressing the changes of a decarbonized economy (Czako, 2020; ILO, 2018). The following section discusses preliminary evidence for some of the determinants that, in theory, can affect potential job creation and hiring. Although there is a wide range of studies in industrial organizations that explore the interaction of these variables, we will focus on the information available in firm surveys in Latin America. A more detailed discussion of the methodology, data sources, and econometric analysis will be given in Section 4. 3.1 Firm Size and Potential Hiring The relationship between company size and employment generation is a common theme for all sectors, and the energy sector is no exception. Today the energy sector comprises a wide range of actors, not only large, well-established companies such as regulated utilities and traditional engineering firms but also new independent power producers and startups of various sizes (IRENA, 2019). So, could a firm's size be a determinant for potential hiring in the energy sector? Previous conventional studies on firm dynamics, productivity growth, and job creation in developing countries exclude many micro- and small enterprises, many of which are informal. However, the typically excluded firms may be associated with a large share of total employment in developing countries. Li, Y., & Rama, M. (2015) found that micro- and small enterprises account for a greater share of gross job creation. Their study also reveals a greater dispersion of firm productivity, a weaker correlation between firm productivity and firm size, and a smaller contribution of within-firm productivity gains to aggregate productivity growth. In addition, Dogan et al. (2017) found that smaller firms contribute to job creation. These findings pointed to new directions in the data and research efforts needed to understand the role of micro- and small enterprises and to identify policies with the potential to foster job creation and the sustainability of these jobs over time in developing countries. The study of Malik et al. (2021) sheds some additional light on this estimator. At a global level, this study found that solar-rooftop installations, small hydro, and microgrids are likely to 12 Inter-American Development Bank employ many more workers per MW than large solar utility and hydropower projects. This conclusion suggests that firm size plays a role in potential hiring practices in the energy sector. Additional evidence from ILO (2018) found that decoupling a firm's economic growth from greenhouse gas (GHG) emissions does not limit the ability of enterprises to grow and generate employment, emphasizing that the results remain largely unchanged after considering the age and size of enterprises. A decade ago, ILO (2010) presented a G20 training strategy, "A skilled workforce for strong, sustainable and balanced growth," in which it mentioned that establishing solid bridges between vocational education, training and skills development, and the world of work makes it more likely that workers will learn the "right" skills, namely those required by the evolving 3.3 Employee Educational Level 3.2 Firm Technology or Activity Area Energy transition involves different employment factors across economic activities and technologies. For instance, several studies in OECD (Organisation for Economic Co-operation and Development) countries found that the employment factors for renewable energy technologies lead to higher employment than those of fossil fuel-based technologies (Malik et al., 2021; del Río and Burguillo, 2008; Wei et al., 2010). Employment factors in solar PV installation and manufacturing components lead to higher employment than in other renewable energies (Malik et al., 2021; Cameron and van der Zwaan, 2015). Studies also find large variations in employment factors across different technologies such as wind, geothermal and hydro (Breitschopf et al., 2012; Cameron and van der Zwaan, 2015), while the type of the activity also differs (resource extraction vs. manufacturing and services) (Wei et al., 2009). Unclear boundaries between direct and indirect jobs, including several activities in the supply chain (local, imports, and exports) and country contexts with different job components, explain the large variety of employment factors within technologies (Cameron and van der Zwaan, 2015; IRENA, 2019; Malik et al., 2021). For instance, a key factor when estimating energy-related jobs is the supply chain costs of technology and activity. Therefore, technology differentiations are good determinants for estimating potential hiring because the more expensive variations have employment factors that tend to create more jobs (Malik et al., 2021). Despite the exponential growth of employment, it is important to highlight that the jobs created by renewable energy generation are accompanied by decreasing marginal increments due to improvements in labor productivity and lower capital costs (Wei et al., 2009). 13 Inter-American Development Bank 3.1 Firm Size and Potential Hiring demands of labor markets, enterprises, and workplaces in different economic sectors and industries. Education and skills policies are more effective when well-coordinated with employment, social protection, and industrial and investment policies. The energy transition will demand investments in traditional utilities and new small energy producers in digitalization, demand management, manufacturing components of renewable technologies, and all new infrastructures for generation, storage, transportation, and distribution. Development of these investments will require increasing inflows of new workers with specific professional skill sets and different educational levels and delivering specific workforce training for workers without such skills, who would be most directly impacted (Foster et al., 2020). Thus, the educational and qualification level of the labor force will be a key factor in potential employment outcomes for the energy transition in the next few years (Czaco, 2020; ILO, 2018, Malik et al., 2021). In the case of the European Union, it is expected that jobs created by the transition will be filled by low- to medium-educated employees, even for the less advanced tasks (Czaco, 2020). Evidence from Czaco (2020) also suggests that higher-skilled roles are initially preferred, but demand will also increase for lower-skilled workers during the energy transition. That is the case in the renewable energy sector, where job demand is geared toward medium- and high-skilled workers in connection with technological advancements. Czaco (2020) concludes that skills mismatches will be a key factor inhibiting the green energy transition in Europe and globally. Recent studies have highlighted educational and training gaps in the renewable energy labor supply and the fact that this phenomenon is more acute in developing countries (Lucas et al., 2018). In India, for example (ILO, 2018), the potential for employment creation is conditioned to the domestic capacity of technology manufacturing and the establishment of vocational training programs and certification schemes. The ILO (2018) has identified skill development regulations and policies in the labor market as key elements for a successful green energy transition. Indeed, early applications of such policies in Denmark, Estonia, France, and Germany suggest the rising demand for skilled workers in different green economy sectors. As a policy recommendation, the European Union report (Czako, 2020) suggests that employers improve and adapt STEM education profiles, improve visibility and wider perception, and incentivize STEM education for both men and women, creating opportunities in the green transition to build gender equity in the male-dominated energy sector. This policy recommendation echoes actions by companies operating in the sector that has also been implementing their corporate policies (usually referred to as corporate social responsibility), adapting their businesses to government policies and even looking at improving their social and environmental engagement within the organization and society (Sighn et al., 2020; Pfajfar et al., 2022; Mbanyele et al., 2022). 14 Inter-American Development Bank 3.1 Firm Size and Potential Hiring Associated with business ethics, corporate social responsibility (CSR) policies are shared value mechanisms companies use to help enhance society and the environment instead of contributing negatively. CSR policies can benefit the firm's employees, the community, and the environment. Singh & Misra (2021) separate CSR policies into three public dimensions: employees, customers, and the community. CSR policies for employees include all of the company's socially responsible activities and spending for the well-being of employees, such as health insurance, retirement plans, internal training courses and inclusive gender and diversity policies. CSR policies for customers consist of the promotion of high-quality services by the company, as well as the provision to customers of all necessary information, quick resolution of complaints, and actions to improve customer satisfaction. Finally, community-oriented CSR policies can involve giving charity to communities, improving quality of life, providing the community with financial support (for the arts, culture, education, and health), and implementing sustainability policies for environmental conservation. Evidence of CSR as a driver of green investments and, thus, more jobs is limited, and further research is needed (Mbanyele et al., 2022; Singh & Misra, 2021). While some studies reveal that credible CSR policies may draw highly skilled employees and low-cost funding, which stimulates firms to allocate more resources towards green technologies (Freeman, 1984; Dhaliwal et al., 2012; Lins et al., 2017; and as cited in Mbanyele et al., 2022), other studies have suggested that it may not affect the firms'' green innovation they allocate CSR actions to other areas such as employee protection, philanthropy and public relations (Masulis et al., 2015; as cited in Singh & Misra, 2021). Omitted variables may bias the correlation between CSR policies and green innovation actions. For example, green innovation could reduce a firm's spending on other CSR initiatives; therefore, the correlation can also be biased by this reverse causality. In conclusion, CSR commitments should not be treated as a mandatory determinant of a firm's good performance. However, mandatory CSR rules (issued by government bodies or regulators) can effectively alter company behavior and performance (Mbanyele et al., 2022). 3.4 Corporate Social Responsibility (CSR) Policies 15 Based on the literature review on how a firm's size, technology or area of activity, employee education, and corporate social responsibility policies impact the potential hiring rate of energy sector companies, the next sections introduce the methodology and data used to understand the determinants that could affect employment and potential hiring in the energy sector in upcoming years. Inter-American Development Bank 4. Data and Methodology This section presents the data and methodology used to study job creation potential in electricity markets with a sample of Latin American countries. Company-level surveys conducted in six Latin American countries were the main data source for energy employment variables. The survey looks at each company's employment structure, worker profiles, the number of stated corporate social responsibility policies, new investments, and employment projections. Surveyed companies belonged to the broad spectrum of the energy sector and were classified into the following five categories: Renewable generators Network companies (which includes transmission and distribution) Non-renewable generators Oil companies Others, particularly including construction companies Energy service companies That has made for a unique database and a useful instrument to explore potential variables explaining job creation in the energy sector. The section presents energy employment variables and their statistical description, followed by the econometric methodology and estimation procedure used to address a company's potential hiring. The survey made it possible to explore the different electricity markets in greater detail. Information was collected between 2020 and 2021 from 338 companies and distributed as follows. In 2020, Bolivia, Chile, and Uruguay contributed with 26, 82 and 83 companies, respectively. In 2021, 74 companies responded to the questionnaire in Costa Rica, 42 companies responded in Mexico, and 31 companies responded in Panama. The survey instrument collected the same information on the number of employees, employees' sociodemographic data, the firm's costs and investments, and capital estimate forecasts. We used only those questionary questions that enabled us to construct the variables that, according to the literature review, could help us study job creation in the electricity sector. Table 2 below presents these variables and their descriptions. The main study variable, the potential job creation rate, measures the ratio of the total number of employees expected to 4.1 Energy Employment Variables 16 Inter-American Development Bank 3.1 Firm Size and Potential Hiring to be hired for the coming year and the total number of currently employed employees. The employment survey was applied once in each of the selected countries, so this rate is not meant to be a growth rate, per se, but rather a potential creation rate according to the firm's investment outlook. Three hundred thirty-eight (338) companies were surveyed in two different years. Nevertheless, the model adjustment was conducted with a sample of one hundred thirty-five (135) companies due to outliers and missing data. These companies' commonality is that they all declared their intentions to hire employees in the upcoming years. Table 3 shows the descriptive statistics for the main sample of companies. 17 Inter-American Development Bank Table 2. Variable Type and Description Source: Authors' elaboration based on IDB’s employment survey data. Potential Job Creation Rate Employees Education (%) Renewable Generation Female Labor Participation Rate CSR Policies Dependent Variable Explanatory Variable Explanatory Variable Explanatory Variable Explanatory Variable Explanatory Variable (1, ∞+) (0, ∞+) [0, 1] [0, 1] [0, ∞+) [0, 1] Variable Name Description RangeType The main study variable, represented by the ratio of the total number of employees expected to be hired in the next year and the total number of employees in the current year in company i The proportion of employees in company i with at least a technical or university education. Proxy for the skilled labor force. Dummy variable that assumes value 1 when the company is a renewable energy generator, and 0, otherwise. The proportion of female labor force in company i Number of corporate social responsibility policies company i said it had Total number of employees in the company i 18 Inter-American Development Bank Table 3. Descriptive Statistics The following multiple linear regression model is proposed to evaluate the impacts of the energy transition on the job creation potential in the post-COVID-19 economic recovery of the Latin American electricity sector. The specification was found using forward stepwise regression with the variables indicated by the literature review presented above as the initial explanatory variables. Where job creation potentiali is the ratio between the expected total number of employees in the next year and the total number of employees in the current year in company i. Poli is the number of corporate social responsibility policies adopted in company i. The variable employeesi stands for the total number of employees in company i. Educationi is the proportion of employees in company i with university or technical educational levels. The dummy variable renewablei is equal to 1 if company i adopts renewable generation technologies, or 0, if otherwise. The εi is the random term typically present in linear regression models. 4.2 Model and Estimation Methodology Potential Job Creation Rate Employees Education (%) Renewable Generation Female Labor Participation Rate CSR Policies 135.00 135.00 135.00 135.00 132.00 5.81 183.64 0.64 0.28 0.25 1.10 14.01 850.10 0.37 0.45 0.20 0.99 1.00 1.00 0.00 137.67 7,000.00 1.00 0.00 1.00 0.00 0.00 1.00 5.00 Variable N Std. Dev. Mean Min Max Source: Authors' own elaboration based on the IDB’s employment survey data. Log (potential job creation ratei ) ~ b0 + b1 Poli + b2 log(employeesi ) + b3 educationi + b4 educationi*renewablei + εi 19 Inter-American Development Bank One of the advantages and challenges of this study was counting the number of large traditional energy companies or generators and obtaining a representation of new businesses emerging from the energy transition. However, from the statistical point of view, the heterogeneity of firm sizes could bring some heteroscedasticity to the econometric analysis (Greene, 1999; Wooldridge, 2003). Therefore, besides their classification, these companies are also clustered into ten groups according to size, as measured by the number of employees (see Appendix 1 for more information on the explanatory statistical analysis and clustering exercise). Also, a check on the residuals resulting from the adjustment by ordinary least squares showed that the assumption of homoscedastic errors was not met. The logarithm of the explained variable was taken to overcome the problems arising from error heteroscedasticity. A logarithmic transformation on the number of employees variable was used to mitigate its distribution asymmetry. The clustered standard error estimator (Wooldridge, 2003) was used because it assumed a heteroscedasticity pattern in which the error variance is a function of the number of company employees, a variable associated with the company size. One of the energy transition characteristics is the adoption of renewable generation. The renewable generation dummy variable was included to assess the energy transition's impact on employment in the electricity sector. In this case, we considered an interaction between the renewable and education variables, given that the novelty of renewable generation technologies demands qualified professionals. 20 18 Inter-American Development Bank 5. Results and Discussion Table 4. . Regression Results with Clustered Standard Errors Log (Employees) Education (%) Education*Ren. Gen CSR Policies Intercept R2 Obs. -0.274*** (0.053) 0.301* (0.168) 0.618* (0.363) -0.167*** (0.040) 1.65*** (0.251) 0.413 135 Dependent Variable: Log(Potential Job Creation Rate) Source: Authors' elaboration based on the regression model and IDB’s employment survey data. The paper developed a multiple linear regression model to understand the characteristics of the companies that expect to have a higher hiring rate in the upcoming years. The analysis focuses on the firm's size, area of activity, employees' level of education and number of corporate social policies. Table 4 below shows the results of the econometric regression to explain the potential job creation rate in upcoming years. Three key results are important to our discussion: The smaller the energy company, the larger the potential job creation rate. 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Firm Size by Cluster Inter-American Development Bank Appendix 1. Exploratory Statistical Analysis of the Error Components after the Clustering Exercise Figure A1 shows a clustering exercise. In the graph, the vertical axis depicts firm size as the logarithm of the number of current employees, and the horizontal axis shows ten clusters that group the same number of firms as much as possible. This visualization by clusters allows us to see the great heterogeneity of the companies. While in the first decile, we have small companies with at most two employees, in the tenth decile, we have companies with between 95 and 7,000 employees. That disparity within clusters is also one of the reasons why we project the logarithm and not the absolute values of current employees. Some 80% of companies are small to medium companies (between 3 and 95 employees). Source: Authors' Elaboration. 33 Inter-American Development Bank Figure A2. QQ Plot of Standardized Residuals from Regression Figure A2 compares theoretical error quantiles and the estimated residual quantiles. The diagonal line represents the theoretical residual's Gaussian (normal) distribution. If the residuals of a linear regression model represented in the graphic by the points lie approximately on the line, the residuals are Gaussian. Figure A2, therefore, suggests that the normality assumption for the error component of the regression model is reasonable. Source: Authors' Elaboration. 34 Table A1. Alternative Model Results Inter-American Development Bank Appendix 2. Robustness Check for Cross-Section Employment Data The data used constitute a cross-sectional database of energy firms where we observed many subjects at one point or period. For this purpose, our analysis might also have no regard for time differences, and it was impossible to apply panel regression methods—fixed effects included. However, we ran a model with dummies for countries as a proxy. The results are presented below in Table A1. Country coefficients are negative and significant, indicating that all countries tend to hire less than Chile (the reference category). The following two tables (Table A2 and Table A3) highlight the correlation of countries to the number of policies and the number of renewable generation firms. We are convinced that these correlations directed the dummy variables' sign and captured the effects of the number of policies variable and the interaction of education x_Ren .gen. R^2= 0.57683 Estimate Std. Error t value Pr (>|t|) (Intercept) 2.45642077 0.31653924 7.7602410 8.476813e-15 x_pol -0.01935982 0.05463491 -0.3543488 7.230775e-01 log(trabajadores_t) -0.24256776 0.05050587 -4.8027637 1.564905e-06 x_educacion 0.36808194 0.16766590 2.1953298 2.813995e-02 id_countryCRI -1.20137634 0.27325223 -4.3965839 1.099679e-05 id_countryMEX -1.50241061 0.39113095 -3.8411959 1.224363e-04 id_countryPAN -1.11945363 0.38642789 -2.8969277 3.768366e-03 id_countryURY -1.17885018 0.28721811 -4.1043728 4.054133e-05 x_educacion:x_gen_ren 0.10012130 0.36610741 0.2734752 7.844880e-01 35 Table A2. Contingency Number of Policies (x_pol) and Country Table A3. Contingency Number of Renewable Generation Firms (x_gen_ren) and Country Chi sq. test=103.85 (p-value = 2.557e-13) Chi sq. test=22.844 (p-value = 0.000136) Inter-American Development Bank x_pol 0 19 15 15 5 5 3 3 3 13 33 0 9 0 5 0 0 0 2 20 0 0 0 0 0 0 0 1 1 1 1 2 3 4 5 CHL CRI MEX PAN URY x_gen_ren 0 8 37 9 4 14 5 16 9 1 0 33 CHL CRI MEX PAN URY 36 Table A4 and Figure A2 below present the model without the interaction term and its normality test (p-value 0,592). Inter-American Development Bank R^2= 0.41103 Estimate Std. Error t value Pr (>|t|) (Intercept) 1.4849729 0.22679004 6.547787 5.839586e-11 x_pol -0.1628634 0.02968631 -5.486143 4.108053e-08 log(trabajadores_t) -0.2768385 0.05446357 -5.083003 3.715130e-07 x_educacion 0.5289158 0.15293421 3.458453 5.432866e-04 x_gen_ren 0.4336245 0.20740828 2.090681 3.655669e-02 Table A4. Alternative Model Results Figure A2. Residual Histograms -2 -1 0 0 10 30 1 2 3 Residuals Frequency 37