Rodrigo Zeidan
New York University Shanghai and Fundação Dom Cabral (Brazil).
1555 Century Avenue, 200122 Shanghai, China.
Heiko Spitzeck
Fundação Dom Cabral (Brazil)
Av. Dr. Cardoso de Melo 1184, São Paulo, SP 04845-004, Brazil
Inertia and other obstacles to greener banking when the business case for sustainability is not enough.
Abstract
We won’t be able to legislate climate change away. Market-driven greener investments are a necessary condition to mitigate climate change, but sustainability-related policies in financial companies face many uphill obstacles. Business-as-usual is a major barrier to change in corporate behavior. We present evidence, built on a proof by exclusion argument, that inertia has been the main barrier on the implementation failures of a sustainability credit rating system in a multinational bank.
Current theory assumes that if there is a solid business-case for any kind of investments – including green investments, companies will adopt more sustainable practices. Our research shows, however, that the business case for sustainability is a necessary but not sufficient condition for greener investments. By analyzing practices of a bank in Brazil we find that a green ratings system has not been implemented despite a clear business case.
Quantitative data come from the first and only step towards the system’s implementation. Qualitative data come from interviews in 2018 with bank employees. The system was tried out on 296 companies from the sugar industry in Brazil. Statistical analyses indicate that ratings adequately discriminate companies by their environmental and social impact. All companies prompted in interviews as best performers regarding environmental and social outcomes are ranked in the 92th percentile. Results also show an imperfect correlation between credit and sustainability scores (and return on assets and operating margin). One surprising and relevant outcome is that sustainability risks (as measured by each companies’ ratings) are higher than regular credit risks.
We investigate why the system has not been broadly implemented. Alternative theoretical explications for the implementation failure are analyzed and excluded one by one. Evidence failure rules out the design of the system, its relation to the banks’ current operations and remuneration incentives as barriers. This allows us to postulate that organizational inertia the main reason for the system to be put aside after its initial trial.
As the investment community is integrating ESG evaluations in their portfolio decisions, recognizing and overcoming organizational inertia will be for the performance of investment portfolios; sluggish companies will be swept away by the moving (and rising) tide. Greener banking is important for climate change mitigation, but internal inertia needs to be overcome.
Main
We cannot just legislate climate change away. Market-driver greener investments are a necessary condition to mitigate climate change, but sustainability-related initiatives by financial companies face many uphill obstacles, even if the concept of triple-bottom line performance is commonplace[1]. Banks are usually overlooked players in market-based solutions to climate change mitigation[2], but in many instances get to decide which kind of business economy shall be (and receives funding) or shall not be (and does not receive funding). Any significant change in bank behavior that would emphasize environmental outcomes, e.g. lower interest rates or more access to lines of credit, could quickly propagate to millions of companies that rely on commercial banks to fund investments and operations (cost-effective climate mitigation[3]). Such changes in behavior come from regulation or competitive pressure[4]. Unlocking capital to greener investment would mean going beyond simple commitments to greener production and limiting the crowding out of investments in climate mitigation[5]. Individual organizations commitments number more than 11,000 in developed countries but are not legally binding[6] and most don’t turn into action, individual or collective[7]. Business-as-usual is a major barrier to change in individual and corporate behavior[8]. Inertia and employee disengagement impede actions even for companies that propagate their interest in improving their environmental outcomes[9].
Here we focus on the failures of implementation of a sustainability-related credit ratings system in a multinational bank, an initiative that is not a direct response to financial regulation, and was undertaken in a business-friendly environment. We investigate the mechanisms and the outcomes of a ratings system similar to the Sustainability Credit Score System, SCCS[10], designed to facilitate lending to more sustainable (or less unsustainable) companies. We take advantage of rare access to a financial institution’s data, managers and executives. The trial of the system was performed on companies in the sugar industry, due to its unique set of challenges (e.g. deforestation) and opportunities (e.g. ethanol and green plastic)10. Cheaper credit for more sustainable sugar-producing companies or to foster green investments in the sugar industry could potentially affect millions of smallholder farmers[11].
Ratings, based on the Analytical Hierarchy Process methodology, were based on a 30-question questionnaire answered by account managers or other bank employees on 296 companies, in 2016 and 2017. The system has not been implemented, either in parallel or in complement to the regular credit ratings system of the bank, despite showing clear business benefits. Evidence on its implementation failures has important implications for addressing issues of organizational change for sustainability[12]. We build our argument on a proof by exclusion argument, ruling out alternative explanations for implementation failure that are not related to institutional inertia. Quantitative data come from the first and only step towards the system’s implementation. Qualitative data come from interviews in 2018 with senior executives and account managers, the latter responsible for feeding or managing the system with the required information for building the credit-score of evaluated companies.
The first salient result is the imperfect correlation of sustainability and economic credit ratings (0.63 in our sample of 296 companies). Opportunities to direct credit to more sustainable companies (high sustainability, low credit rating) or restrict credit to less sustainable ones (low sustainability, low credit rating) exist, as figure 1 shows. The ratings system is also able to discriminate companies that combine financial and environmental performance (at least in comparison to competing firms in the same industry). Table 1 presents the correlations between the regular credit and sustainability ratings and two measures of financial performance: return on assets and operating margin. Significantly, the system brings new information about companies, as the mean and standard deviation are statistically distinct from the credit rating, even after both are normalized (see Methods). Additionally, sustainability ratings are lower than pure credit scores, revealing that even in the context of the financial system, firms are perceived to pose significant environmental and social risk (the sustainability rating of each company is lower than its credit score, on average, at 99% confidence). Moreover, according to the results from the semi-structured survey, the eight companies cited as best in the industry in their management of environmental and social outcomes were ranked at least in the 92th percentile of the SCSS.
Table 1 – Correlation between ratings systems and financial metrics.
| Credit rating | SCSS | Return on assets | Operating margin | |
| Credit rating | 1 | 0.63 | 0.87 | 0.80 |
| SCSS | 0.63 | 1 | 0.78 | 0.72 |
| Return on assets | 0.87 | 0.78 | 1 | n.a. |
| Operating margin | 0.80 | 0.72 | n.a. | 1 |
The main ex-ante concern of executives was on the relationship between the sustainability ratings system and lending rates. Lending policies at the micro level tend to eschew ESG (environmental, social and governance) screening factors because such screening is more useful as red flagging[13]. Given that it usually leads to only two potential decisions: to lend or not to lend; a system that only consider ESG risks decreases lending. Proper incentives to managers should include the facilitation or price of credit[14]. A system that only reduces the probability that a loan is approved is unlikely to be implemented.
Internal obstacles to the incorporation of a new ratings system or more general organization change include technical shortcomings[15], misaligned incentives[16], productivity loss associated with a learning curve[17], and regulatory approval[18]. Our argument by exclusion that corresponds to the existing literature is illustrated in Figure 2.
Figure 2: Exclusion Argument
Regarding technical shortcomings, the system has been designed specifically for the bank with inputs for both the sustainability and risk management areas. In the survey, all interviewees answer that the system is compatible with the internal risk management processes of the bank. There is also unanimous agreement that incentives for account managers were aligned, given that lower interest rates and higher lines of credit would have been directed at companies with better ratings regarding their environmental and social outcomes, if the system were not abandoned soon after the implementation of the first phase.
Data for the training of account managers reveal that the median time to complete the 30-question questionnaire for each company is 37 minutes (with a standard deviation of 6 minutes). This does not take into account the gathering information aspect of the risk management process, but the regular credit risk management also require such a step. Interviewed account managers state that there would be a learning curve but, according to one of them: “learning if the farm maintains its protected area properly according to the legal reserve regulation or burns dry sugar cane straws is easier than making sense of most of the existing financial statements from farmers, when they exist”.
Assuming that the process of filling the questionnaire and entering the data in the system would take 2 hours for the initial ratings creation and an additional 30 minutes for cross-referencing and validating the final ratings, the bank would spend roughly 5,000 hours to build the first round of ratings for the roughly 2,000 sugar industry companies in their portfolio. Subsequent rounds should marginally increase administrative costs as updating the ratings would enter the managers’ regular routine work.
When asked if there is a lack of proper incentives, account managers (88%) and executives (100%) answer no. Two managers expressed concerns about how a potential bankruptcy of farmers and manufacturers might reduce their remuneration if they were to overestimate the fallen companies’ sustainability ratings (a similar concern to the overestimation of regular credit ratings is common and is the reason why the incentives of account managers are aligned with those of the risk management area). Surveyed executives (100%) did not fear regulatory disapproval. On the contrary, one executive observed that incorporating sustainability into their credit models would improve the banks’ standing with regulatory bodies.
Inertia, short-termism, and competition from other more profitable internal opportunities[19] seem to be the main obstacles to widespread use (in the interviews, arguments such as: “we chose to focus our time and attention in lowering delinquency instead”, mirroring the result that capital is allocated based on “gut feel” and the personal reputation of the manager running a given division[20] were common). Of the three main obstacles polled, 74% of the bank employees chose focus on other lending, 15% poor design of the system, and 11% lack of financial incentives as the main reasons for the implementation failure.
In the context of the management for sustainability, the deployment of a complementary ratings system focusing on social and environmental criteria is a multidimensional, wicked challenge2. Failures to implement sustainable solutions can impart lessons for future projects. Moving from business-as-usual will take more than diplomacy[21] and well-designed instruments (the implementation of the SCSS would meet some of potential regulatory concerns such as the role of capital requirements to mitigate risks, the bluntness of climate-aligned prudential policy and the overburden of assessment exercises)6.
The case study of a bank in Brazil failing to implement greener investment decisions points to organizational inertia, which needs to be understood better, if we want to assist banks in making better investments on one side for their shareholders and on the other side for society. There is already some evidence that banks may become proactively engaged in sustainable activities[22] and that this could lead to cost-effective climate mitigation without reducing banks’ profits[23]. After all, climate change can increase defaults with adverse effects on bank leverage and profits[24], leaving portfolios vulnerable if climate-related information is ignored. Greener banking can lead to significant change, including, in the case of the Brazilian sugar industry, better land use.10 Our results reinforce the notion that the business case for sustainability is a necessary but not sufficient condition for companies to diminish their negative impact on society.
Methods
The methodology follows that of rigorous case studies that are carried out in close contact with practitioners, which is ideally-suited to creating scientific and managerially-relevant knowledge[25]. Case studies are of particular importance when current theory does not apply; scientific models don’t change only incrementally, and innovative behavior is hard to capture longitudinally. In addition, integrating ESG criteria and other kinds of sustainability-related information in regular risk models is an emerging field; other models do not exist or are sufficiently rare to make comparisons almost impossible. We argue that the present case study, that of a new environmental risk management model, qualify as a black swan in relation to the current understanding of how companies
The present article is a study of a multinational bank operating in Brazil, which aims to improve lending efficiency by implementing a ratings system that classifies companies in relation to their impact on the environment and the rest of society. The initial implementation classified companies in the Brazilian sugar industry.
The sustainability ratings system is applied to 296 companies, in 2016-17. Because of confidentiality issues, we do not have access to all the raw data and some estimations are performed by data scientists at the bank (data on each anonymized companies’ ratings and regular credit rating are available from the author upon request). The main variables of interest are the sustainability and the regular credit rating for each company, alongside two financial variables: return on assets and operating margin.
The final sustainability credit score10 is based on matrices for the six sustainability dimensions, each matrix , in which , and a weighting matrix for the six dimensions.
Each dimension Ai is composed of 5 questions, and the inputs come from a questionnaire of 30 questions with four possible alternatives in relation to the relative position of a company in its industry: below average; average; above average; and top of the industry. Bank employees are given training to be able to differentiate between the four criteria, but it should be noted that this kind of questionnaire is typical of regular credit ratings systems, and checks are in place to make sure that data are consistent. The resulting score is then normalized to fall between the 0.5-1 interval; the best score for any company is given a score of 1 and the lowest value a score of 0.5. The regular credit score is also normalized in the same interval for comparison. The correlation between the two types of score is 0.63, with the dispersion illustrated in figure 1.
We hypothesize that data on sustainability allows for a better discrimination of companies alongside the credit dimension. Companies are divided in four categories, related to either low or high economic and sustainability credit. High economic and sustainability credit companies should have been incentivized with cheaper and more readily available credit; the bank should have made credit more expensive and scarcer to companies with low credit and sustainability ratings. We use a t-test to investigate the null hypothesis of the means of both ratings being equal (thus, providing the same kind of information for decision makers). Results indicate, with 99% confidence, that the means of both ratings are distinct (in fact, the mean of sustainability ratings is inferior to credit ratings, at the same confidence level, something expected if we consider that most companies pose bigger environmental than credit risks).
Table 2. Two-sample t test with equal variances
| Variable | Obs | Mean | Std.err. | Std.dev. | [99% conf.interval] | |
| credit rating | 296 | 0.797 | 0.007 | 0.128 | [0.778; 0.816] | |
| SCSS | 296 | 0.743 | 0.007 | 0.120 | [0.725; 0.761] | |
| combined | 592 | 0.770 | 0.005 | 0.127 | [0.756; 0.783] | |
| diff | 0.054017 | 0.010 | 0.028 | 0.080 | ||
| diff = mean(v1) – mean(v) | t = 5.301 | |||||
| Ho: diff = 0 | degrees of freedom = 590 | |||||
| Ha: diff < 0 | Ha: diff = 0 | Ha: diff > 0 | ||||
| Pr(T < t) = 1.00 | Pr(|T| > |t|) = 0.00 | Pr(T > t) = 0.00 | ||||
We validate the statistical analysis with data from a semi-structured survey with 18 bank employees, divided in: employees of the sustainability department (4), responsible for designing and monitoring the implementation of the sustainability ratings system; senior managers (3), two of whom oversaw its implementation (1), and account managers (11) that input or verify the data (data are fed similarly to regular credit ratings systems).
Interviews have been conducted via phone or in person in two periods, August and December, 2018 (questions are in appendix 1, excluding open ended discussions on the subject). An optional question is to name at most two companies in the sugar industry that were, in their opinion, the least damaging to the environment. Only eight employees choose to speculate, and of the twelve named companies five are repeated by at least two employees. All seven companies mentioned rank at least in the 92th percentile of the SCSS, reinforcing the ability of the system to discriminate between better and worse environmental performance companies.
Limitations
The present work uses a single case study to induce new forms of theorizing on sustainability-related action. Case studies can be made rigorous25 but they still compare unfavorably to large longitudinal, experimental and/or panel studies. Moreover, given that integrating ESG criteria into financial evaluations is an emerging field, data are limited, as are possible comparisons, even to other case studies.
Another limitation is the less than ideal setting for cross-company comparisons. Account managers were trained for inputting data and the final scores were validated by the bank’s risk management team, but we do not have access to each account managers’ evaluations to cross-check for bias in the assessment of each companies’ operations. Finally, because of confidentiality reasons only part of the present results can be replicated.
Data availability
The primary non-confidential data are available from the corresponding author upon request.
Code availability
The workfiles used in the statistical analysis are available from the corresponding author upon request.
Conflict of Interests The authors declare no competing interests.
[1] Hussain, N., Rigoni, U., & Orij, R. P. (2018). Corporate governance and sustainability performance: Analysis of triple bottom line performance. Journal of Business Ethics, 149(2), 411-432.
[2] Campiglio, E. (2016). Beyond carbon pricing: The role of banking and monetary policy in financing the transition to a low-carbon economy. Ecological Economics, 121, 220-230.
[3] Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., … & Woodbury, P. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645-11650.
[4] Campiglio, E., Dafermos, Y., Monnin, P., Ryan-Collins, J., Schotten, G., & Tanaka, M. (2018). Climate change challenges for central banks and financial regulators. Nature Climate Change, 8(6), 462.
[5] Pollitt, H., & Mercure, J. F. (2018). The role of money and the financial sector in energy-economy models used for assessing climate and energy policy. Climate Policy, 18(2), 184-197.
[6] Hsu, A., Cheng, Y., Weinfurter, A., Xu, K., & Yick, C. (2016). Track climate pledges of cities and companies. Nature News, 532(7599), 303.
[7] Schwarz, G. M., & Bouckenooghe, D. (2017). A Collective Action Process Model For Attitudes To Organizational Change. In Academy of Management Proceedings (Vol. 2017, No. 1, p. 10346).
[8] Wright, C., & Nyberg, D. (2017). An inconvenient truth: How organizations translate climate change into business as usual. Academy of Management Journal, 60(5), 1633-1661.
[9] Stål, H. I. (2015). Inertia and change related to sustainability–An institutional approach. Journal of cleaner production, 99, 354-365.
[10] Zeidan, R., Boechat, C., & Fleury, A. (2015). Developing a sustainability credit score system. Journal of Business Ethics, 127(2), 283-296.
[11] Cui, Z., Zhang, H., Chen, X., Zhang, C., Ma, W., Huang, C., … & Gao, Q. (2018). Pursuing sustainable productivity with millions of smallholder farmers. Nature, 555(7696), 363.
[12] Etzion, D. (2018). Management for sustainability. Nature Sustainability, 1(12), 744-749.
[13] van Duuren, E., Plantinga, A., & Scholtens, B. (2016). ESG integration and the investment management process: Fundamental investing reinvented. Journal of Business Ethics, 138(3), 525-533.
[14] Mathiesen, K. (2018) Rating climate risks to credit worthiness. Nature Climate Change, 8(6), 454–456.
[15] Alwan, Z., Jones, P., & Holgate, P. (2017). Strategic sustainable development in the UK construction industry, through the framework for strategic sustainable development, using Building Information Modelling. Journal of Cleaner Production, 140, 349-358.
[16] Scharfstein, D. S., & Stein, J. C. (2000). The dark side of internal capital markets: Divisional rent‐seeking and inefficient investment. The Journal of Finance, 55(6), 2537-2564.
[17] Morgan, H. M., & Ngwenyama, O. (2015). Real options, learning cost and timing software upgrades: Towards an integrative model for enterprise software upgrade decision analysis. International Journal of Production Economics, 168, 211-223.
[18] Claessens, S., Law, A., & Wang, T. (2018). How do credit ratings affect bank lending under capital constraints?. BIS Working Papers, 747.
[19] Stein, J. C. (1997). Internal capital markets and the competition for corporate resources. The Journal of Finance, 52(1), 111-133.
[20] Graham, J. R., Harvey, C. R., & Puri, M. (2015). Capital allocation and delegation of decision-making authority within firms. Journal of Financial Economics, 115(3), 449-470.
[21] Hsu, A., Moffat, A. S., Weinfurter, A. J., & Schwartz, J. D. (2015). Towards a new climate diplomacy. Nature Climate Change, 5(6), 501.
[22] Aras, G., Tezcan, N., & Furtuna, O. K. (2018). Multidimensional comprehensive corporate sustainability performance evaluation model: Evidence from an emerging market banking sector. Journal of cleaner production, 185, 600-609.
[23] von der Osten, F. B., Kirley, M., & Miller, T. (2017). Sustainability is possible despite greed-Exploring the nexus between profitability and sustainability in common pool resource systems. Scientific reports, 7(1), 2307.
[24] Dafermos, Y., Nikolaidi, M., & Galanis, G. (2018). Climate change, financial stability and monetary policy. Ecological Economics, 152, 219-234.
[25] Gibbert, M., Ruigrok, W., & Wicki, B. (2008). What passes as a rigorous case study?. Strategic Management Journal, 29(13), 1465-1474.