Taking ESG strategies for achieving profits: A dynamic panel data analysis

Useche, Alejandro J.; Martínez-Ferrero, Jennifer; Reyes, Giovanni E. Article Taking ESG strategies for achieving profits: A dynamic panel data analysis Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Useche, Alejandro J.; Martínez-Ferrero, Jennifer; Reyes, Giovanni E. (2025) : Taking ESG strategies for achieving profits: A dynamic panel data analysis, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Leeds, Vol. 30, Iss. 59, pp. 61-78, https://doi.org/10.1108/JEFAS-02-2023-0030 This Version is available at: https://hdl.handle.net/10419/319672 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/4.0/ Taking ESG strategies for achieving profits: a dynamic panel data analysis Alejandro J. Useche School of Management, Universidad del Rosario, Bogota, Colombia Jennifer Mart� ınez-Ferrero Accounting and Finance Department, Instituto Multidisciplinar de Empresa (IME), Universidad de Salamanca, Salamanca, Spain, and Giovanni E. Reyes School of Management, Universidad del Rosario, Bogota, Colombia Abstract Purpose –The goal is to investigate the relationship between financial performance and environmental, social and governance (ESG) indicators and disclosures for a sample of Latin American firms. Design/methodology/approach –Dynamic panel data regressions are used to analyze a sample of 114 companies listed on the Latin American Integrated Market, MILA (Chile, Colombia, Mexico and Peru) for the period 2011–2020. The Altman Z-score and Piotroski F-score are used as indicators of the probability of default and comprehensive financial strength. Models are developed in which the relationship between economic value added (EVA) and Jensen’s alpha are evaluated against firms’ ESG practices. Findings –A direct relationship between ESG strategies and financial performance was found. Better practices and transparency in ESG are related to lower probability of bankruptcy, greater financial strength, greater EVA and superior risk-adjusted returns. Research limitations/implications –ESG data were obtained from the Bloomberg system based on a methodology that may differ from other sources. The sample covers four Latin American countries and large corporations. Independent variables were selected for their perceived validity, given their frequent use in previous studies. Practical implications –Evidence for company management regarding the importance of strengthening ESG practices and reporting should be part of their balanced scorecards. For investors, the results support the importance of evaluating ESG practices in asset selection. Originality/value –The present study is the first research to present empirical evidence on the relationship between ESG scores and disclosures for MILA countries, using a comprehensive set of financial performance indicators (Altman Z-scores, Piotroski F-scores, EVA and Jensen’s alpha). Keywords ESG, Corporate social responsibility, Financial performance, Dynamic data panel, Developing markets Paper type Research paper Journal of Economics, Finance and Administrative Science 61 © Alejandro J. Useche, Jennifer Mart� ınez-Ferrero and Giovanni E. Reyes. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode The authors thank Universidad del Rosario (Bogota, Colombia) for the academic support. The authors are grateful to the Junta de Castilla y Le� on and the European Regional Development Fund (No: CLU-2019-03) for funding the Research Unit of Excellence “Economic Management for Sustainability” (GECOS) and the Multidisciplinar Institute of Enterprise and also grateful to the MICIU/AEI/10.13039/ 501100011033/ and FEDER UE (No. PID2021-122419OB-I00-GELESMAT). The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2077-1886.htm Received 6 February 2023 Revised 2 February 2024 29 May 2024 2 August 2024 Accepted 5 September 2024 Journal of Economics, Finance and Administrative Science Vol. 30 No. 59, 2025 pp. 61-78 Emerald Publishing Limited 2077-1886 DOI 10.1108/JEFAS-02-2023-0030 1. Introduction A central tenet of neoclassical financial theory is that a business should maximize value for its shareholders, and any concern for the interests of, and impacts on different groups and communities (stakeholders) is implicit; an investment is worthwhile only as long as it generates positive free cash flows (Bolton, 2015). However, management strategies have evolved in recent decades to include the environmental, social and governance (ESG) aspects of a business. This change has also permeated the view of investors, who are increasingly aware of the relevance of including ESG considerations in analyzing the performance of the companies in which they invest (Hyrske et al., 2022;Sherwood and Pollard, 2023). Although there are debates about the relationship between ESG practices and financial results, most empirical studies have found a direct relationship between them (Derwall et al., 2005;Mart� ınez-Ferrero and Fr� ıas-Aceituno, 2015). Although the importance of taking ESG criteria into account in defining a business strategy is conceptually clear, the evidence shows that efforts to include ESG criteria are insufficient, particularly in Latin America (Global Sustainable Investment Alliance, 2021). At the same time, research on the ESG practices of companies in the region and its financial impact is scarce. Motivated by this backdrop, the central objective of this research is to study the relationship between ESG practices and various financial indicators for companies in Latin America. We focus on member countries of the Latin American Integrated Market (MILA) (Chile, Colombia, Mexico and Peru), the largest transnational stock market integration in this part of the Americas. Thus, the study sample includes 114 companies across various sectors, covering the 10-year period from 2011 to 2020. Using a dynamic panel data analysis, we find a direct relationship between ESG practices and disclosures and financial solvency as measured by Altman’s Z-score, as well as evidence of superior overall financial strength based on the Piotroski F-score. Additionally, we find that stronger ESG results are related to greater economic value creation and superior stock performance based on risk-adjusted returns. The remainder of this study is organized as follows: Section 2 presents our theoretical framework and hypotheses. Section 3 explains the methodology, including the data and sample selection process, the empirical measures used and the proposed regression models. Section 4 presents descriptive statistics for the sample and the results obtained from the regression models, along with a discussion of the results. Finally, the conclusions, main findings, contributions and limitations of the study are presented in Section 5. 2. Literature review and hypotheses development Many studies, both theoretical and empirical, examine the relationship between an organization’s ESG performance and its financial results, generating mixed arguments, evidence and conclusions (Wood, 2010). On the one hand, studies such as Levitt (1958) and Friedman (1970) argue that a company’s only responsibility is to maximize the wealth of its owners. From this perspective, investing in activities with societal benefits represents a cost that interferes with the goal of optimizing corporate profits. This argument is consistent with an inverse relationship between corporate social investment and financial performance, as proposed by Waddock and Graves (1997), who argue that some managers tend to reduce spending on ESG activities when financial results are adequate to improve reported profits in the short term and, therefore, to receive higher bonuses. An opposing point of view is presented by theories in the field of corporate social responsibility (CSR), which recognize that businesses have a commitment to the societies in which they operate (Carroll, 1979). In this stream of the literature, Jones (1980) and Freeman (1984) lay the foundations of stakeholder theory, affirming that managers must consider a firm’s moral duty to consumers, employees, suppliers, communities and society JEFAS 30,59 62 in general, including the environment. A growing body of literature presents recent evidence of a positive correlation between ESG ratings and superior financial performance in terms of operating results, risk/return profiles and stock returns (Friede et al., 2015; Valor, 2005;Cherkasova et al., 2023), lowering the cost of capital and boosting a company’s brand or overall reputation (Mart� ınez-Ferrero, 2014;Villar� on-Peramato et al., 2018;Wei et al., 2018). However, it is important that companies not only receive acceptable ESG scores from third-party providers but also supply adequate and timely disclosures or sustainability reports. There are opposing arguments on this issue. On the one hand, the cost of capital perspective (Buallay, 2019) argues that investing in ESG activities increases operational costs and reduces profits, thus reducing market value, at least in the short term (Dalal and Thaker, 2019). On the other hand, a second approach, known as the value creation perspective, posits that investing in ESG initiatives can help companies to create competitive advantages and strengthen financial performance (Eccles et al., 2014;Goss and Roberts, 2011). From this perspective, more complete disclosures regarding ESG activities and outcomes tend to increase revenue and reduce costs, promoting financial stability and improving strategic decision-making (Eccles et al., 2015;Eccles and Saltzman, 2011). Beyond contributing to better financial performance in the short term, ESG disclosures contribute to value creation in the long term (Jensen, 2000,2001). Studies such as Fatemi et al. (2018) and Li et al. (2018) verify that companies with ESG strengths and high levels of disclosure tend to increase their value and vice versa; that is, there is a two-way relationship between these aspects. Most studies on the relationship between ESG performance and financial results use return on assets (ROA) or Tobin’s Q as dependent variables (Barnett and Salomon, 2012; Buallay, 2019;Van der Laan et al., 2008). Despite their widespread use in the literature, these traditional indicators only partially evaluate an organization’s financial performance and risks or its book value relative to its market value. The field of financial analysis has developed other metrics that provide a more comprehensive view of an organization’s financial condition, which is why we define the following as dependent variables in testing our hypotheses: (1) Altman’s Z score, a measure of a company’s probability of default or bankruptcy (Altman, 1968,2013;Altman et al., 1977); (2) Piotroski’s F score, which expresses the degree of a company’s financial strength (Piotroski, 2000); (3) economic value added (EVA), an indicator of the economic profit or wealth created per period (Stern and Shiely, 2001), and (4) Jensen’s alpha, a measure of the difference between a stock capital asset pricing model (CAPM) expected return and its realized market return (Mayo, 2011;Sharpe, 1964). Very few empirical works analyze the relationship between ESG indicators and financial performance in Latin American markets, and their scope in terms of variables studied and time periods is limited. Correa-Garc� ıa and V� asquez-Arango (2020),Duque-Grisales and Aguilera-Caracuel (2021) and Rodr� ıguez-Garc� ıa et al. (2022) use ROA or Tobin’s Q as dependent variables, studying periods ranging from five to seven years. Garz� on-Jim� enez and Zorio-Grima (2021) as well as Ram� ırez et al. (2022) focus only on the impact of ESG performance on the cost of capital. Lavin and Montecinos-Pearce (2021) study the relationship between board characteristics and ESG disclosure in the context of a single country. It is therefore important to carry out deeper empirical research in the context of Latin America, a developing region in which academic contributions on environmental sustainability and social welfare can strengthen awareness about corporate responsibility with respect to natural resources, the fight against poverty and corruption (Blowfield, 2005) and societal and government commitments to improving ESG practices that significantly impact the socioeconomic environment (Visser, 2008). Journal of Economics, Finance and Administrative Science 63 Based on the above discussion, the following two opposite-sign hypotheses are proposed: H1a. The better a company’s ESG performance and reporting, the better its financial performance in the form of a lower probability of bankruptcy, greater financial strength, increased shareholder value and superior risk/return performance. H1b. The better a company’s ESG performance and reporting, the worse its corporate financial performance, expressed as a higher probability of bankruptcy, the weaker its financial strength and ability to generate shareholder value and the worse its risk/return performance. 3. Method 3.1 Data and variables To create the study sample, we selected the companies from the main stock market indices of the four countries that comprise the MILA (Chile, Colombia, Mexico and Peru) for the period 2011–2020. These indices are as follows: the � Indice de Precios Selectivo de Acciones (IPSA) index for Chile (28 companies), the Colombia Investor Relations index (COLIR) for Colombia (23 companies), the Mexican Stock Exchange Price and Quotation Index (MEXBOL) for Mexico (35 companies) and the S&P Lima General Index for Peru (28 companies). The selection criterion for these indices was representativeness, i.e. an index considered the main reference for each country was chosen. The data were obtained from the Bloomberg information system, consisting of an array of 1,150 observations over a 10-year period. The distribution of the sample by country and sector is shown in Table 1. Dependent variables for this study are (1) Altman Z-score, z_altman; (2) Piotroski F-score, f_piotroski; (3) EVA, ln_eva and (4) Jensen’s alpha, alpha. To investigate our hypotheses, we propose two explanatory variables, ESG_score and ESG_disc, referring to ESG performance proxies and disclosure of ESG information, respectively. Data were obtained from the Bloomberg information system. A detailed explanation of Bloomberg ESG performance methodology, including the variables evaluated in the ESG pillars (19 themes and 46 subtopics), is presented in the Online Appendix. 3.2 Research design/model To test our hypothesis, two basic models are proposed below to explore the relationships between ESG performance (Models 1–4A) and transparency in ESG disclosure (Models 1–4B) Sector Chile Colombia Mexico Peru Total Percentage % Communications 1 1 3 0 5 4.4 Consumer discretionary 2 0 3 0 5 4.4 Consumer staples 5 2 8 4 19 16.7 Energy 1 3 0 0 4 3.5 Financials 5 8 7 4 24 21.1 Health care 0 0 1 1 2 1.8 Industrials 1 2 6 2 11 9.6 Materials 3 3 6 13 25 21.9 Real estate 3 0 1 1 5 4.4 Technology 1 0 0 0 1 0.9 Utilities 6 4 0 3 13 11.4 Note(s): Sample: 1,150 firm-year observations from 2011–2020 Source(s): Authors’ own work Table 1. Sample composition by country and sector JEFAS 30,59 64 as well as the probability of insolvency (Model 1), measured by the Altman Z-score, and financial strength (Model 2), using the Piotroski F score. In complementary analyses, EVA and Jensen’s alpha are used as dependent variables in Models 3 and 4, respectively: Model 1A�B:zaltman ¼β1ESG score=ESG discit þβ2Sizeit þβ3Leverageit þβ4WACCit þβ5Betait þβ6Countryiþβ7Sectori þβ8Yeartþ η iþ ε it Model 2A�2B:f piotroski ¼β1ESG score=ESG discit þβ2Sizeit þβ3Leverageit þβ4WACCit þβ5Betait þβ6Countryiþβ7Sectori þβ8Yeartþ η iþ ε it Model 3A�3B:ln eva ¼β1ESG score=ESG discit þβ2Sizeit þβ3Leverageit þβ4WACCit þβ5Betait þβ6Countryiþβ7Sectoriþβ8Yeart þ η iþ ε it Model 4A�4B:alpha ¼β1ESG score=ESG discit þβ2Sizeit þβ3Leverageit þβ4WACCit þβ5Betait þβ6Countryiþβ7Sectori þβ8Yeartþ η iþ ε it where iand trepresent the company and time period, respectively; η iis unobservable heterogeneity; ε it is the error term; Size represents the size of the company, expressed as the natural logarithm of its assets; Leverage is the degree of leverage measured by the ratio of debt to equity; WACC is the weighted average cost of capital and Beta is the systematic risk of each asset. 3.3 Analytical procedures Before selecting the appropriate estimator and analysis technique for the proposed regression models, it is necessary to consider the nature of the dependent variable. Initially, either a fixedor random-effects estimator could be used, but it is necessary to select which one to use. To this end, the Hausman test is used under the null hypothesis of the existence of non-systematic differences between estimators. The result of the Hausman test shows that the p-value does not allow the rejection of H0 at 95% confidence (it is not significant), so we choose random effects. However, it is also necessary to examine whether the model suffers from the classical econometric problems: heteroscedasticity, autocorrelation and endogeneity. In the specific context of enterprises, their ESG scores and financial results are mutually dependent; that is, better ESG indicators or reports tend to promote better financial results, while organizations with better financial indicators tend to encourage strong ESG practices. This creates the possibility of endogeneity problems, the result of reverse causality between the variables under study (Wooldridge, 2010). In addition, when problems of heteroscedasticity and serial autocorrelation are present, the ordinary least squares (OLS) regression method cannot be used because it does not obtain consistent and efficient coefficients. In relation to heteroscedasticity, we resort to the modified Wald test under the null hypothesis of homoscedasticity. The test result shows that the null hypothesis at 99% confidence is rejected; there is a problem of heteroscedasticity. Regarding the serial autocorrelation, the Wooldridge test is proposed under the null hypothesis of no-first autocorrelation problems. Its p-value allows to reject the null hypothesis for a 99% Journal of Economics, Finance and Administrative Science 65 confidence level, supporting the existence of autocorrelation problems. Finally, endogeneity could exist as a result of reverse causality (Wooldridge, 2010) and arises when the proposed research models suffer from self-selection bias. To test the existence of endogeneity, Davidson and MacKinnon (1993) suggest an augmented regression test (Durbin–Wu–Hausman test), which can easily be formed by including the residuals of each endogenous variable, as a function of all exogenous variables, in a regression of the original model. We obtain the residuals of this estimate and subsequently perform an augmented regression where the residuals of the previous model are incorporated as an explanatory variable in our basic model. Since the coefficient obtained in the regression is different from 0, there is an endogeneity problem, and despite the selection of random effects, the OLS estimate is not consistent, and it is necessary to use instrumental variables (IV). IV methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model, thus solving the selfselection bias. Initially, in this step, the possible use of IV will solve the endogeneity problem. However, the conventional IV estimator (although consistent) is inefficient in the presence of the heteroscedasticity and autocorrelation problems previously confirmed by the Wald and Wooldridge tests, respectively. The solution is to employ an IV estimator that guarantees that the three problems are controlled (endogeneity, heteroskedasticity and autocorrelation). To this end, we use the generalized method of moments (GMM) (Arellano and Bond, 1991) and concretely the two-step GMM estimator that produces consistent and unbiased results and eliminates any potential unobserved firm-specific effects by exploiting the dynamic nature of relationship using internal instruments (Roodman, 2009). In this respect, note that although the dependent variable, the irresponsible ESG indicator, is an index coded from 0 to 100 and the Tobit estimator should be employed, the technique should resolve the endogeneity problem that our regression models suffer. To this aim and following the procedure of Hillier et al. (2011), we employ the dynamic panel GMM (Arellano and Bond, 1991), which allows us to address the abovementioned problems and obtain consistent and unbiased results (Greene, 2019), using Stata 17 for analysis. The panel data methodology allows including observations for various companies across multiple time periods, identifying and measuring effects not detectable by other processes and reducing the collinearity between explanatory variables, thereby increasing the efficiency of econometric indicators (Biørn, 2017;Pesaran, 2015). Panel data were used in studies with similar objectives and sample sizes, such as Atan et al. (2018),Dalal and Thaker (2019),Fakoya and Malatji (2020) and Landi and Sciarelli (2019). 4. Results 4.1 Descriptive statistics Table 2 shows descriptive statistics for the dependent and independent variables for the total sample. Altman Z-score allows to quantitatively assess a company’s probability of bankruptcy: low if Z> 3.0, medium when 1:8≤Z≤3:0and high in cases where Z< 1.8. Given that the average Altman Z-score is 3.13, we conclude that the mean probability of bankruptcy for the companies in the sample is low, although the dispersion of the indicator (5.16) is high. Regarding the Piotroski F-score, a result between 0 and 2 indicates substantial financial weakness, a value between 3 and 5 means the firm is fairly weak, a value between 6 and 7 indicates the firm is relatively strong and a value between 8 and 9 is associated with good financial strength. The average Piotroski F-score of 4.55 with a standard deviation of 1.61 indicates that the financial condition of the MILA companies is relatively weak when average profitability, leverage, liquidity and operational efficiency are evaluated. JEFAS 30,59 66 A positive Jensen’s alpha value, α i, means that investors earned a higher return than what the CAPM predicted, given the level of risk of the asset or portfolio and overall market conditions. Although the average α ifor the whole sample is positive (2.52), indicating the companies studied delivered excess returns on average, the differences among the individual results are considerable and include both positive and negative values. As for EVA (the economic profit that was created or destroyed over the period analyzed), the results are also mixed; however, despite significant dispersion across the sample, on average, the companies have a positive EVA, indicating they created value during the period analyzed. The results in Table 2 show that companies in MILA countries still have a long way to go in terms of CSR activities and results, yielding an average score of 31.64 in terms of overall ESG ratings, which can range from 0 to 100. The average scores were 27.69 in the environmental category, 34.27 for social and 32.94 for corporate governance. Similarly, ESG disclosure is still low in the region, with an average score of 26.14 out of 100, with ratings of 21.55 for environmental, 27.82 for social and 32.97 for corporate governance. These results show that, among the three categories, the environmental category has both the lowest performance and information reporting scores. Table 3 and Figure 1 show a consistent pattern across the average ESG variables by country for the period studied. In both ESG scores and information reporting, Colombia is in first place, followed by Mexico, Chile and Peru. Variable Mean Std. dev. Min. Max. z_altman 3.1336 5.1602 �1.0111 64.7701 f_piotroski 4.5545 1.6106 0.0000 8.0000 Alpha 2.5161 28.1203 �135.2719 171.6094 ln_eva 8.3150 3.9864 0.1235 16.3543 ESG_score 31.6400 27.2793 0.0000 84.0780 E_score 27.6850 32.1226 0.0000 100.0000 S_score 34.2736 31.6598 0.0000 95.4546 G_score 32.9397 27.6539 0.0000 82.4324 ESG_discl 26.1425 20.8564 0.0000 70.2479 E_discl 21.5505 21.3775 0.0000 84.6572 S_discl 27.8168 23.8473 0.0000 82.4561 G_discl 32.9714 22.8372 0.0000 89.8600 Size 12.7030 3.3701 4.4634 19.5928 Leverage 260.3544 427.6335 2.0776 9650.7030 WACC 8.4405 3.1937 1.2396 24.6047 Beta 0.8124 0.4211 0.0075 2.8907 Note(s): Sample: 1,150 firm-year observations from 2011–2020 Source(s): Authors’ own work Score Disclosure ESG E S G ESG E S G Chile 31.96 29.80 35.53 30.54 28.90 25.22 31.67 33.48 Colombia 41.36 36.60 46.61 40.77 34.26 27.41 37.67 41.09 Mexico 39.21 34.79 40.34 42.51 31.44 26.39 31.98 41.19 Peru 13.89 9.51 15.39 16.77 10.33 7.32 10.98 15.59 Note(s): Sample: 1,150 firm-year observations from 2011–2020 Source(s): Authors’ own work Table 2. Descriptive statistics Table 3. Average ESG score and disclosure by country, 2011–2020 Journal of Economics, Finance and Administrative Science 67 Studying the data by industry, Table 4 and Figure 2 show that the highest average ESG scores are seen in the consumer staples (40.14) and energy (38.29) sectors, while the lowest are in the health care (0.00) and technology (0.00) sectors (on a scale of 0–100). When disaggregating the scores, the two highest outcomes in each category are (1) consumer staples (41.51) and financial services (34.34) for environmental topics; (2) energy (44.02) and consumer staples (42.07) for social and (3) consumer discretionary (40.32) and financial services (40.18) for corporate governance. In each category, the lowest scores were seen in the health care and technology sectors, which had scores of 0.00. In terms of disclosure and reporting of ESG information, Table 4 indicates that the energy and consumer staples sectors have the highest scores (39.81 and 30.76, respectively), while the technology and industrial sectors have the lowest (17.10 and 17.74, respectively). Disaggregating the overall scores, the energy sector has the best disclosure rating in the three ESG categories, while consumer staples is in second place for environmental (28.77), communications is in second place for social (34.04) and health care is in second place for corporate governance (38.79). Source(s): Authors’ own work Score Disclosure Industry ESG E S G ESG E S G Communications 21.31 11.67 24.02 28.24 28.58 21.19 34.04 37.95 Consumer discretionary 29.10 19.61 27.35 40.32 22.06 16.12 21.74 32.96 Consumer staples 40.14 41.51 42.07 36.85 30.76 28.77 31.57 34.36 Energy 38.29 34.30 44.02 36.54 39.81 34.30 44.47 47.43 Financials 37.53 34.34 37.98 40.18 24.21 16.96 23.66 34.67 Health care 0.00 0.00 0.00 0.00 28.69 22.53 29.61 38.79 Industrials 22.14 12.98 21.74 31.71 17.74 11.92 18.00 29.52 Materials 31.97 28.02 36.44 31.44 26.45 23.76 28.67 30.13 Real estate 15.02 15.83 17.71 11.53 21.25 15.47 26.22 28.69 Technology 0.00 0.00 0.00 0.00 17.10 8.40 17.67 34.28 Utilities 29.79 23.35 37.83 28.18 27.35 23.99 32.39 29.80 Note(s): Sample: 1,150 firm-year observations from 2011–2020 Source(s): Authors’ own work Figure 1. Average ESG score and disclosure by country, 2011–2020 Table 4. Average ESG score and disclosure by industry, 2011–2020 JEFAS 30,59 68 F-score is regularly applied to all industries, the Altman Z-score may have greater relevance for nonfinancial companies. Sixth, a characteristic of the study is that the stock market index of each market (IPSA, COLIR, MEXBOL and S&P Lima) was selected following a criterion of representativeness, i.e. an index considered a general reference for each country was taken, with another alternative being the use of criteria of securitization, capitalization or social responsibility. Future research could include more countries and companies from elsewhere in Latin America as well as other developing regions, and the results could be disaggregated by economic sector. It would also be interesting to include other independent variables and analyze their mediating effects. Including data from small and medium-sized companies would shed light on the broad nature of ESG phenomena, their interrelationships and impacts, although ESG information on such companies is currently very limited and difficult to obtain. 6. Conclusions The dynamic panel data analysis in this research, applied to 114 companies listed on MILA exchanges (Chile, Colombia, Mexico and Peru) for the period 2011–2020, allowed us to verify a direct relationship between ESG practices and a company’s financial strength. The results obtained corroborate our hypothesis regarding how better ESG scores and transparency are related to a lower probability of bankruptcy, greater overall financial strength, greater economic value created and superior risk-adjusted performance. 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(2010), Econometric Analysis of Cross Section and Panel Data, 2nd ed., MIT Press, Cambridge, MA. Supplementary material The supplementary material for this article can be found online. Corresponding author Alejandro J. Useche can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] JEFAS 30,59 78