Nonlinear Nexus Between ESG Scores and Corporate Performance of Insurance Companies in the MENAT Region: Moderating the Effect of Institutional Quality

Abstract

Although the relationship between ESG performance and firm performance has been the subject of several studies, the nonlinear relationship between ESG performance and the corporate performance of insurance companies remains less explored, specifically in the Middle East, North Africa, and Turkey (MENAT) region. Moreover, the moderating effect of institutional quality on this relationship has not been examined. To fill this gap, this paper investigates the nonlinear impact of ESG performance on the financial performance of insurance companies in the MENAT region, as well as the moderating effect of institutional quality. To achieve this, a sample of 31 insurance companies located in the seven MENAT countries was constructed over the period 2017–2022. The sample was selected based on the completeness and availability of ESG-related data. This ensured a standardized dataset to enhance the reliability of the results. To estimate this relationship, the System Generalized Method of Moments (SGMM) was used. This technique was used to address endogeneity issues. The empirical results indicate that the performance of the insurance companies is better for those with better ESG performance. Moreover, the quality of institutions is an even more important factor in enhancing the ESG practices–corporate performance nexus. More in-depth analysis is needed to show how these various relationships might be altered with ESG criteria. The findings of this research would, therefore, be beneficial to insurers in terms of an increased understanding of how effective integration of ESG practices, both at the institutional and company level, could be streamlined to enhance their long-term competitiveness and profitability.

1. Introduction

The continuous inflows of capital in environmental, social, and governance (ESG) criterion investment funds spur the insurance companies to an ongoing adoption of policies centered on that very dimension as an instrument of access, at the current levels, and an increase of financing sources under heightened investor expectation and competitiveness boosters. Growth of ‘green’ and sustainable funds encourages them to actively monitor and improve environmental, social responsibility, and governance ratings, which are seen as an indication of a firm’s commitment towards sustainable development (Bressan 2023). These practices now increasingly appear to be an integral part of an important strategic lever that attracts capital with lower cost, improved reputation, and strengthens the resilience of companies to the challenges emerging within their natural and social environment and institutional setup (Yusuf et al. 2024).

By applying the Principles for Sustainable Insurance (PSI) formulated by the United Nations Environment Programme Finance Initiative (UNEPFI), it would be imperative that these criteria are integrated into strategic decision-making to achieve long-term sustainability in a sector where regulatory pressure mounts and societal expectations further evolve (e.g., Heinkel et al. 2001; Bressan 2023). However, while the introduction of these practices can enhance competitiveness and access financial markets, the costs associated therewith are similarly high, specifically in terms of compliance costs, technological investments, or data transparency costs, which, if badly managed may, in fact, reduce the overall performance of companies (Attia et al. 2023).

Theoretically, there are different interpretations of the relationship between ESG performance and financial performance. Agency theory suggests a negative relationship, as ESG practices can lead to extra costs in green technologies and social responsibility without immediate financial returns, seen as a drain on shareholder resources (Jensen and Meckling 1979). In contrast, stakeholder theory views ESG practices positively, enhancing company reputation, attracting loyal customers and socially responsible investors, thereby boosting long-term profitability and reducing risk. The influence of ESG on financial performance depends on organizational context and implementation strategies (Freeman 1984). Empirical research has significantly evolved, examining how ESG dimensions impact corporate performance, but the consensus remains varied and often conflicting (e.g., Shang 2024; Alghafes et al. 2024; Gonzalez-Ruiz et al. 2024; Bagh et al. 2024; Jin 2025; Wang et al. 2025). Most studies focus on the banking and industrial sectors, with limited research on insurance companies (e.g., Bressan 2023; Di Tommaso and Mazzuca 2023; Yusuf et al. 2024), leaving this area largely unexplored.

By building empirical studies on the impact of ESG practices on company performance, the literature partially addresses how institutional quality enhances the ESG financial performance or sustainability of firms (Attia et al. 2023; Abozeid et al. 2025). The influence of these practices is linked to the quality of institutions where companies operate; a solid institutional framework provides clear regulations, transparent governance, and respect for stakeholder rights, aiding ESG integration into strategies. In countries with high institutional quality, firms benefit from better legal and economic infrastructures, facilitating ESG practices, reducing compliance costs, and improving investor and customer perceptions. Conversely, in weak institutional contexts, companies may face regulatory obstacles, limiting the positive impact of ESG initiatives on performance. Thus, institutional quality is a vital moderator in the connection between ESG practices and financial performance, highlighting the significance of the macroeconomic and regulatory environment (Attia et al. 2023).

Therefore, this study’s dual objectives are to explore the curvilinear relationship that may exist between the ESG practices and the performance of the insurance companies within the Middle East, North Africa, and Turkey (MENAT) region. The institutional quality can also be considered, which may have moderating influences on an existing nonlinear relationship between ESG performance and corporate performance.

Several reasons underpin the choice for the study. This study investigates the nonlinear relationship between ESG performance and the financial performance of insurance companies in the MENAT region with the quality of institutions acting as a moderating factor. First, ESG criteria have turned into a symbolic signal of sustainability and competitiveness for companies, particularly in the financial sector, where insurance companies are central to risk management and long-term financing. Second, the MENAT region is unique due to the special challenges concerning sustainability, institutional inequalities, and governance. Therefore, this area represents an appropriate framework to analyze how the adoption of ESG commitments may influence the economic and financial performance of companies. The period 2017–2022 is also strategic, corresponding to an intense phase of regulatory reforms and increased awareness of ESG issues in this region. Moreover, the insurance sector is at the center of sustainable investment activities and constitutes a privileged observation vector for this study. Lastly, adding the moderating effect of institutional quality enables the assessment of institutional differences between countries in the MENAT region and the role these might play in enhancing ESG practices toward increasing firm value. This will indeed further embellish the understanding of the area under review.

Based on these reasons, this research has sought to seek answers to the following research questions in attempting to achieve the above-mentioned objectives: In what way can ESG practice improve corporate performance? Is institutional quality a forerunner in combining ESG and financial performance in insurance companies in MENAT?

This paper contributes to two areas: first, there is a need to study the curvilinear link between ESG practices and insurance companies’ financial performance that has not been tackled so far in the literature. This could be seen as the first paper to assess, to my knowledge, the moderating role of institutional quality in ESG and corporate performance in MENAT countries. Using the System Generalized Method of Moments (SGMM), the main results show that the relationship between ESG and corporate performance is U-shaped. In addition, ESG performance reduces corporate performance up to an optimal point. These results are relevant for regulators and bank managers in the MENAT region.

This research paper is structured as follows. Section 2 elaborates on the developed hypotheses in relationship to the associated literature. The research model used is explained within Section 3. Section 4 explains, discusses, and presents the main results. In Section 5, a brief summary of the findings is presented as a conclusion.

2. Literature Review

2.1. Agency Theory and ESG Scores

In the light of the recent academic literature, several controversial issues, among which are growing ESG performance and financial corporate performance, stand out. In this regard, two great theories, i.e., agency and stakeholder theories, set forth important frameworks for an in-depth analysis that could justify such a linkage and the mechanisms underlying it.

In this respect, agency theory postulates that ESG performance weakens the performance of the firms due to the agency cost and overinvestment threat (Friedman 1970; Jensen and Meckling 1979), whereas the stakeholder theory contrasts with the assumption that ESG performance enhances corporate performance by further strengthening the relationship with stakeholders (Freeman 1984).

Specifically, as Jensen and Meckling (1979) pointed out, one potential source of a principal–agent problem can be the relationship between the shareholders of a firm (principal) and the managers of a firm (agent). In such a context, ESG initiatives can be seen as unnecessary costs that do not directly add value for the shareholders themselves. From this perspective, managers may invest in ESG projects because doing so builds their brand and/or keeps outsiders off their back, regardless of whether the project improves the profitability of the underlying business. In this way, the result may be added to agency costs reducing the overall profitability of the firm (Friedman 1970). In this context, Attia et al. (2023) note that ESG initiatives could lead to overinvestment, a situation whereby managers invest beyond the optimal level of ESG projects. In such a situation, overinvestment will negatively affect financial performance because those resources tied up in ESG projects could have been used elsewhere for greater value creation. The implementation costs associated with ESG policies, such as audits, compliance with regulations, and reporting, may also add to operational costs and thus dent profitability in the short run (Khémiri and Alsulami 2023). This is mainly due to information asymmetries that exacerbate ESG investment issues. Shareholders may not perceive the real-world consequences of ESG practices, which may further create conflicts about the strategic objectives of the company.

Stakeholder theory, on the other hand, suggests the reverse: that those companies which tend to listen to the concerns of their stakeholders will be able to improve their economic performance over the long term. This theory considers that businesses should not be oriented only towards shareholders’ interests but also to customers, employees, suppliers, local communities, and regulators (Freeman 1984). In addition, Carroll (1979) argues that the social responsibilities of companies go beyond simple profit maximization and involve ethical and philanthropic responsibilities. According to this perspective, ESG performance can strengthen a company’s legitimacy and improve its relations with stakeholders. These improved relationships can result in greater customer loyalty, more motivated employees, reduced legal risks and an improvement in the company’s overall reputation (Clarkson 1995). In addition, Kramer and Porter (2006) introduce the concept of ‘Shared Value’ and claim that companies can create economic value and social value at the same time by incorporating ESG issues into their core strategy. The argument here is that ESG initiatives can enable companies to distinguish themselves from competitors and attract socially responsible investors, thereby reducing the cost of capital. Donaldson and Preston (1995) also suggest that proactive stakeholder management can also reduce friction as well as contribute to organizational stability. By meeting the various expectations of one’s stakeholders, companies can become better equipped to resist a crisis and achieve greater long-term economic success. Wood (1991) adds that corporate social performance should be assessed by its impact on the social system and environment, rather than by events in the short-term financial bottom line.

2.2. Agency Theory and Insurance Performance

Agency theory, which examines the relationship between stakeholders (owners) and agents (managers), is of great importance in the insurance sector. This theory deals with the conflicts that arise when agents, who are supposed to act in the best interests of stakeholders, instead pursue their own objectives. In the context of insurance, these conflicts can manifest themselves in different ways, notably through a misalignment of incentives between insurance companies and their agents or brokers. For example, agents may prefer to sell policies that are likely to generate higher commissions rather than those that best meet policyholders’ needs (Fields and Tirtiroglu 1991).

The performance of insurance companies can be significantly affected by their ability to manage agency conflicts effectively. Appropriate organizational structures and governance mechanisms are essential to mitigate these conflicts. For example, mutual insurance companies, which are owned by policyholders, generally have incentive structures that are distinct from those of stock insurance companies, which are owned by shareholders. Mutual companies may focus more on customer satisfaction and long-term stability, while stock companies may focus more on profitability and shareholder returns (Fields and Tirtiroglu 1991). Therefore, the choice of organizational form can have a significant impact on the overall performance and strategic direction of an insurance company.

Regulatory frameworks and corporate strategies also have a significant influence on the performance of insurance companies. Regulations such as Solvency II in the EU aim to ensure that insurance undertakings maintain adequate capital reserves to meet their liabilities, thereby minimizing the risk of insolvency. These standards may also affect insurers’ investment strategies and risk management practices. On the other hand, corporate strategies that align with the principles of agency theory, such as robust monitoring and incentive systems, can improve the efficiency and performance of insurance companies. By addressing conflicts of interest and aligning the interests of all stakeholders, insurance companies can achieve better financial results and maintain the trust of their policyholders (Maggioni and Turchetti 2024).

2.3. Hypothesis Development

2.3.1. The Nexus Between ESG Scores and Insurance Performance

In practice, some studies have analyzed the nexus between ESG performance and corporate performance, showing contradictory results. Some papers highlight the existence of a positive correlation between ESG practices and company performance (e.g., Shang 2024; Alghafes et al. 2024; Gonzalez-Ruiz et al. 2024). For instance, Shang (2024) finds evidence that the effective management of ESG practices helps Chinese companies to maximize their performance, thereby reducing financing constraints. It follows from this that the optimization of profitability in Chinese companies needs to be underpinned by strengthening their ESG practices. Furthermore, Alghafes et al. (2024) show that ESG components differently influence the performance of Islamic banks in GCC countries. Whereas the social dimension strengthened several of the financial indicators, governance is improving ROE, and the environmental component has its influence on Tobin’s Q. In any case, ESG practices are still barely integrated by these banks, although they have great potential to support the goals of sustainable development.

In addition, few studies have been performed in the insurance industry. Bressan (2023) illustrates that insurers that adhere to high ESG criteria tend to pay more taxes, which is associated with low profitability as compared to their low-ESG peers. This may simply mean that while ESG practices enhance sustainability, they can also be costly for firms. In addition, Yusuf et al. (2024) find that ESG is perceived to be a competitive advantage and thus helps in enhancing the financial performance of insurance companies in Indonesia. This means that the acceptance of the principles of sustainable finance, together with the development of environmentally friendly service products, helps an organization ensure its sustainability and viability for the future. Di Tommaso and Mazzuca (2023) demonstrate that ESG ratings have had a powerful effect on the evolution of the share prices of insurance companies in Europe, fluctuating accordingly. Their study indicates that this market, sensitive to these variations even before the signature of the Paris Agreement, represents how the inclusion of ESG criteria can develop important support for sustainable investments.

Other recent studies have investigated the influence of other factors on ESG performance (e.g., Bai et al. 2024; Al Amosh et al. 2024; Paolone et al. 2024; Adeneye et al. 2024). For example, Bai et al. (2024) prove that corporate culture, especially innovation and quality cultures, improves the ESG performance of listed Chinese companies. It encourages green innovation, increases transparency, and works more effectively in non-state companies or ones in uncertain environments. This paper uncovers the significant contribution of informal institutions to ESG practices and sustainable development. Al Amosh et al. (2024) find that debt financing enhances the ESG performance of Jordanian companies, while equity financing has no significant effect. This helps the companies to restrict the power of opportunistic shareholders, which in turn reduces agency costs and motivates investment in ESG activities. Furthermore, Guo and Pang (2025) indicate that digital transformation significantly improves ESG performance in listed companies in China. This process, they show, is mediated by an increase in the level of green innovation along with increased media attention. Wang et al. (2025) prove that, generally, a positive relation of higher ESG scores to financial performance in China, especially regarding ROA and Tobin’s Q, may mean an enhancement in financial returns from ESG concerns in several industries, including insurance.

More recently, other studies have tested the nonlinear impact of ESG performance on firm performance (or other variables) in different industries (e.g., Khémiri and Noubbigh 2020; Franco et al. 2020; Attia et al. 2023; Bagh et al. 2024; Jin 2025; Wang et al. 2025). For instance, Franco et al. (2020) find that hospitality firms invest in corporate social responsibility to improve stakeholder relationships and enhance performance. However, CSR has both costs and benefits. The study finds that the relationship between CSR and financial performance is U-shaped. It starts as a cost but then generates increasing benefits when stakeholder relationships become strong. Using hotel and tourism companies from 2012 to 2018, Attia et al. (2023) show the correlation between CSR and financial performance is not linear. This relationship follows the shape of a U for the CSR–ROE and CSR–Tobin’s Q link, but it is inversely U-shaped for the CSR–ROA link. The authors conclude from here that an optimum balance between costs and benefits of social responsibility will contribute to maximizing the wealth of the company.

In the same vein, Khémiri and Alsulami (2023) depict that the there is a quadratic relationship between CSR disclosure on Islamic Bank stability of GCC countries. In addition, in a comparative study between China and the US, Bagh et al. (2024) find that overall ESG performance has a nonlinear effect on sustainable growth. Jin (2025) shows that ESG pillars graded differently impact the financial performances of Korean listed companies, measured by ROE and ROA. The main results indicate the existence of positive relationships and nonlinear correlations between each pillar and firm performance. He finds that environmental and governance performance improves firm performance, while the environmental pillar imparts the greatest impact. In contrast, social performance reduces firm performance. The study indicates that the environmental pillar has proven to be the best predictor of financial performance. Wang et al. (2025), on the other hand, explore how industrial competition, regional marketization, and executives’ green perception change the nonlinear relationship between economic policy uncertainty and ESG performance. The first hypothesis will therefore be the following:

Hypothesis 1.

The nexus between ESG scores and insurance performance is nonlinear.

2.3.2. The Moderating Role of Institutional Quality

According to North (1990), the quality of institutions has played a fundamental role in the process of economic development and business performance. High institutional quality reduces uncertainty, favors the cooperation of economic agents and increases performance. In practice, some studies have explored the moderating effect of institutional quality on certain variables. For instance, Franco et al. (2020) establish that quality management is a moderator in the relationship between CSR and firm performance. In the same vein, Attia et al. (2023) prove that governance practices—institutional quality and governance structure moderate the nonlinear link between CSR and performance. In this respect, Abdulla and Jawad (2024) found proof that with ESG, the score acts variably regarding the differentiation of non-financial listed firms in the MENA region; there is a negative impact on performance from an operational, financial perspective, and a positive impact on market performance. The analysis also unpacks the following in detail: how climate risk moderates the relationship, with ESG performing better in high climate-exposure countries. However, results are very mixed, especially regarding net-zero initiatives and the environmental performance index impact of a company’s performance. Shawat et al. (2024) note that ESG performance greatly improves the financial performance of non-financial firms in the MENA region and therefore confirms stakeholder theory. In addition, size positively moderates the nexus between sustainability practices and financial performance. Khémiri and Alsulami (2023) investigated how governance structure and institutional quality influence the nonlinear nexus between CSR and Islamic Bank stability. More recently, Guo and Pang (2025) found that environmental uncertainty negatively moderates the link between digital transformation and companies’ ESG performance. In addition, Wang et al. (2025) confirm the presence of an inverted U-shaped nexus between economic policy uncertainty and ESG performance of Chinese firms, with moderate levels of economic policy uncertainty facilitating ESG engagement and excessive uncertainty leading to a decline in ESG practices.

In other words, the findings by Xu et al. (2021) indicate that ESG performance has a positive moderating effect on the correlation between R&D investment and green innovation. Their findings showed that enhancing green innovation attracts the creation of innovative activities and investment in ESG practices, thus sustaining the companies of emerging countries. Xiang et al. (2024) proved that ESG mediates the nexus between digital finance and the financial performance of Chinese companies. It improves this relationship in both the short and long run. It has been indicated that the depth of use of digital finance is the main factor that increases this performance, especially for large companies and state-owned enterprises. On one side, green innovation and digital transformation enhance this positive effect on ESG. On the other hand, the breadth of coverage in digital finance extends to more small and non-public companies. This paper highlights the key role that digital finance could play in sustainability practices. Saeed AbdelNour and Elfaky (2024) indicated that various financial indicators like solvency and profitability are intermediated by the dimensions of ESG, from which it can then be inferred that good management of ESG may ease financing constraints and improve the financial performance of Egyptian insurance companies. Thus, the second hypothesis is as follows:

Hypothesis 2.

Institutional quality moderates the nexus between ESG and insurance performance.

3. Research Design

3.1. Sample and Data

This study used a sample of 31 insurance companies operating in seven countries in the MENAT region, namely Bahrain, Kuwait, Morocco, Qatar, Saudi Arabia, Turkey, and United Arab Emirates for the period 2017–2022. Insurance data were obtained from the annual individual insurance companies’ balance sheets, as published in Refinitiv Eikon’s database (https://eikon.refinitiv.com/ (accessed on 2 February 2025)). In addition, country-specific data (i.e., macroeconomic variables) were collected from the World Bank database. The sample of 31 insurance companies was chosen primarily because of the availability of ESG score data. It is essential to understand that ESG data is not uniformly available for all companies, which restricted the selection to reliable and comprehensive ESG metrics. Although the sample size may seem modest, it represents the best data available for this study. The sample generally appears to be sufficient to offer meaningful insights into the ESG performance of insurance companies. Insurance companies in seven different countries are likely to introduce variability due to different regulatory environments, economic conditions and cultural factors. To address this, the GMM system method was employed, which aims to remedy the problem of heterogeneity.

The choice of insurance companies becomes even more relevant at this juncture in view of the current challenges facing the industry. Indeed, the insurance industry itself, a necessary player for long-term risk management and sustainable financing, has ESG criteria impinging directly upon it. To be sure, insurance companies are among those increasingly being challenged to responsibly embed new practices within their business strategies, which makes their factors affecting profitability and resiliency well worth considering. The period 2017–2022 is even more relevant because it corresponds to a period of major transformation, marked by the gradual adoption of regulations relating to environmental and social issues in the MENAT region. This period has also seen an increase in investor and consumer awareness of sustainable practices. Besides, the MENAT region is a specific environment with particular challenges concerning governance and sustainability issues, hence the best field of research for these dynamics.

Finally, the analysis of the moderating effect of institutional quality within this context will provide further understanding of the underlying mechanisms through which institutions exert an impact on the possibility for companies to internalize ESG criteria into their practices and then improve their economic performance. In this sense, the present research offers new insights on how ESG and institutional factors interact in determining the performances of insurance companies in the region under examination.

3.2. Variables

3.2.1. Dependent Variable

ROA and ROE are the two variables used to measure the financial performance of insurance companies in this research study. The key measure of financial performance, Return on Assets, defined as the ratio between net income and total assets, shows how well a firm is using its assets to generate profits. It is one of the important profitability ratios wherein higher profitability refers to good financial performance. In addition, ROA has its alternative measure, known as Return on Equity. ROE is the relation of net profit to the shareholders’ equity and expresses the capability of the company to generate profits for its shareholders. This measure is especially useful in assessing financial profitability from the investor’s point of view and supplements the ROA measure by giving a different perspective on the overall performance of the company.

3.2.2. Main Independent Variable

ESG performance is the overall score showing the extent to which companies are committed to sustainability and responsibility. The score ranges over a wide set of dimensions, from environmental initiatives on carbon emission reduction to social responsibilities related to diversity policy and management transparency. Inclusion of ROE and ROA in the analysis, therefore, enriches the analysis of financial performance and its relation with ESG performance, enabling an enriched view of dynamics underpinning insurance companies’ sustainability and profitability.

3.2.3. Moderator Variable

To analyze the moderating effect of institutional quality on ESG practices and financial performance nexus, I used a composite measure (IQ) including all six dimensions of worldwide governance indicators. The six dimensions of IQ are defined as follows: VA: Voice and accountability, PS: Political stability, GE: Government quality, RQ: Regulatory quality, RL: Rule of law, CC: Control of corruption. A high score is better in terms of country governance. Kaufmann et al. (2011) also found a strong correlation between dimensions of IQ; we therefore used PCA to develop an amalgam measure of IQ.

3.2.4. Control Variables

Loans are represented by the ratio of total loans to total assets, which enables us to indicate the extent of indebtedness of insurance companies and their capability to meet financial liabilities. Firm size is expressed as the natural logarithm of total assets; this transformation captures the scale of activities of the firm and reduces the impact of extreme values. Revenue (REV) is the ratio of total revenue to total assets and reflects the efficiency of firms in using their assets to generate revenue. Finally, macroeconomic variables add a contextual dimension to the analysis. The GDP growth rate is the annual percentage change of economic output in real terms; strong GDP growth is usually associated with a favorable business environment. The inflation rate is measured as the consumer price index, annual percent: this is percentage change in the general price level, where moderate inflation is believed to positively affect economic growth and high inflation to dampen business profitability. In essence, these variables allow an in-depth understanding of dynamics that influence ESG and the financial performance of insurance companies in diversified macroeconomic and institutional contexts.

Table 1. Definition of variables.

Variable	Acronyms	Definition	Source
Return on assets	ROA	Net income to total assets	Refinitiv Eikon
ESG performance	ESG	Economic, social, and governance score	Refinitiv Eikon
Institutional quality	IQ	Composite measure, which calculates the six dimensions of country governance using PCA. The six dimensions of country governance (CLG) are defined as follows: Voice and accountability (VA), political stability (PS), government quality (GE), regulatory quality (RQ), rule of law (RL), and control of corruption (CC) (Kaufmann et al. 2011). A higher score means better country governance.	Kaufmann et al. (2011) and authors’ own calculation
Loans	Debt	Total loan to total assets	Refinitiv Eikon
Firm size	Size	Natural logarithm of total assets	Refinitiv Eikon
Revenue	REV	Total revenue to total assets	Refinitiv Eikon
GDP growth	GDP	GDP growth rate (annual %) (gross domestic product growth rate (at constant 2015 prices))	WDI, Word Bank
Inflation rate	INF	Consumer prices index (annual %)	WDI, Word Bank

summarizes the definition of variables.

3.3. Econometric Models

Following Attia et al. (2023), Bagh et al. (2024), Jin (2025), and Wang et al. (2025), I investigate the nonlinear nexus between ESG and insurance performance for the MENAT region and the moderating role of institutional quality in this relationship. The basic model tries to check the inverted U-shaped nexus between ESG and bank performance, related to the first hypothesis. To this end, I applied the GMM technique in a system suggested by Blundell and Bond (1998). The functional form of the bank performance model can be specified as follows:

$$RO{A}_{cit}={\alpha }_i+{\beta }_{1}RO{A}_{cit-1}+{\beta }_{2}ES{G}_{cit}+{\beta }_{3}ES{G}_{cit}^2+{\displaystyle \sum _{n=4}^6{\beta }_n{X}_{cit}}+{\displaystyle \sum _{n=7}^8{\beta }_n{M}_{ct}}+{\epsilon }_{it}$$

(1)

where $ES{G}_{cit}$ is the ESG score for country/bank i at time t; $ES{G}_{cit-1}$ is the lag on one year of $ES{G}_{cit}$; $RO{A}_{cit}$ is the return on assets; $RO{A}_{cit}^2$ is the quadratic term of $RO{A}_{cit}$; $X_{cit}$ is the vector of control variables; $M_{ct}$ is the vector of macroeconomic variables.

Table 2. Descriptive Statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
ROA%	186	1.023	6.278	−36.871	10.142
ESG%	186	16.225	21.201	0	81.047
IQ	186	1.116	2.044	−1.578	4.795
SIZE	186	20.099	1.237	17.934	23.208
DEBT%	186	3.00	0.069	1.23	41.7
REV%	186	8.3	0.366	−1.051	84.80
INF%	186	2.823	8.209	−2.54	72.309
GDP%	186	2.201	3.827	−7.178	11.439

summarizes the definition of variables.

Numerous studies have employed the System Generalized Method of Moments (SGMM) approach to examine corporate performance, including works by Attia et al. (2023), Bagh et al. (2024), Alghafes et al. (2024), Jin (2025), and Wang et al. (2025). SGMM offers distinct advantages. Primarily, it addresses issues such as omitted variable bias, measurement inaccuracies, dynamic heterogeneity across panels, and potential endogeneity between independent variables and the error term. This methodology is particularly valuable in scenarios where the time dimension of a panel is relatively limited. Our study observes T equal to 6, in contrast to a cross-sectional dimension of N equal to 31. Furthermore, the second-order autocorrelation assessment, employing the method of Arellano and Bond (1991), yielded insignificant outcomes for the AR model (2), indicating the absence of autocorrelation. This implies that the model exhibits a well-structured offset, suggesting that a single offset for the insurance performance variable is adequate. The precision of the GMM method within the system can be bolstered by appropriately conditioning the values at time periods t−1 and t−2 for the difference equation, alongside a single lag in the level equation. The instruments applied are deemed reliable, as evidenced by Hansen’s J statistic, which evaluates over-identification restrictions.

In addition, I analyze the moderating effect of institutional quality on the relationship between ESG and insurance performance in the MENAT region to draw further implications. For testing the second hypothesis, the basic model is reformulated. The following interaction terms are added to Equation (2):

$$\begin{array}{l}RO{A}_{cit}={\alpha }_i+{\beta }_{1}RO{A}_{cit-1}+{\beta }_{2}ES{G}_{cit}+{\beta }_{3}ES{G}_{cit}^2+I{Q}_{ct}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{4}ES{G}_{cit}\times I{Q}_{ct}+{\beta }_{5}ES{G}_{cit}^2\times I{Q}_{ct}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{n=6}^8{\beta }_n{X}_{cit}}+{\displaystyle \sum _{n=9}^{10}{\beta }_n{M}_{ct}}+{\epsilon }_{it}\end{array}$$

(2)

4. Empirical Results and Discussion

4.1. Statistical Analysis

Table 2 summarizes the descriptive statistics. The mean ROA has an average value of 1.023%, while in its extreme values lie variability in financial performance. On the other hand, the mean ESG performance is low at 16.225%, indicating poor diffusion of sustainable practices among all insurance companies but one. Quality institutions are highly dispersed, varying from −1.578 to 4.795. Marked differences in inflation, ranging from −2.54% to 72.309%, and GDP growth, ranging from −7.178% to 11.439%, reflect contrasting macroeconomic environments that influence company performance. These variations underline the importance of institutional quality in mitigating economic challenges and maximizing ESG impacts. Insurance companies are relatively homogeneous in size (SIZE, average 20.099), but their debt-to-equity ratio (DEBT) varies widely (1.23% to 41.7%). Revenues (REV), averaging 8.3%, show a significant disparity (−1.051% to 84.80%), reflecting differences in operating efficiency. Finally, inflation (−2.54% to 72.309%) and GDP growth (−7.178% to 11.439%) indicate conflicting macroeconomic environments.

It is observed from

Table 3. Correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) ROA	1.000
(2) ESG	0.043 *	1.000
(3) IQ	0.029 *	−0.194 *	1.000
(4) SIZE	0.202 *	0.323 *	0.056	1.000
(5) DEBT	0.109	−0.061	0.123	−0.088	1.000
(6) REV	−0.109	−0.015	−0.012	−0.025	−0.103	1.000
(7) INF	0.058	0.375 *	−0.211 *	0.088	−0.085	0.042	1.000
(8) GDP	−0.104	0.287 *	−0.095	−0.023	−0.066	0.071	0.245 *	1.000

Note: * indicates significant correlation at the 5% level.

that the empirical models studied are not plagued by the multicollinearity problem (Gujarati (2002)). Specifically, the correlation coefficients between exogenous variables are below 0.80, reflecting low correlation between these variables. In

Table 4. VIF correlation.

Variable	VIF	1/VIF
ESG	1.41	0.710
INF	1.22	0.818
SIZE	1.16	0.860
GDP	1.14	0.878
IQ	1.09	0.913
DEBT	1.04	0.962
REV	1.02	0.985
Mean VIF	1.15

, the VIF result is less than 10, meaning that the variables are not afflicted by multicollinearity.

4.2. Baseline Results

4.2.1. The Nonlinear Nexus Between ESG Performance and Financial Performance

This subsection presents the results of the curvilinear nexus between ESG performance and company performance in the MENAT region. The different results are represented in

Table 5. Nonlinear nexus between ESG performance and corporate performance.

	(1)	(2)
	ROA	ROA
Variables	Linear	Curvilinear
ROAt-1	0.529 ***	0.203 ***
	(0.014)	(0.071)
ESG	0.041 ***	−0.112 ***
	(0.004)	(0.031)
ESG2		0.002 ***
		(0.0001)
SIZE	0.590 ***	0.359
	(0.082)	(0.373)
DEBT	0.293	0.143 ***
	(0.246)	(4.535)
REV	−2.786	−5.555 ***
	(0.030)	(1.602)
INF	0.006	0.039 ***
	(0.004)	(0.012)
GDP	−0.234 ***	−0.061 *
	(0.033)	(0.035)
Constant	−0.121 ***	−0.596
	(0.016)	(0.077)
Observations	154	154
Number of companies	31	31
Number of instruments	30	24
AR (1) (p-value)	0.007	0.006
AR (2) (p-value)	0.236	0.278
Hansen test (p-value)	0.133	0.148
Inflexion point		0.312

Notes: Standard errors are displayed in brackets. *** and * denote statistical significance at the 1% and 10% levels, respectively.

below. Results for two models suggest that GMM is an appropriate estimator for this study since the J-Hansen statistic confirms that the instruments are significantly different from each other. Furthermore, the results of AR (1) and AR (2) also support the null hypothesis of first and second order correlation. Model (1) shows the linear relationship between ESG performance and ROA, while model (2) presents the curvilinear or nonlinear relationship between these two variables.

Specifically, from the results, it is revealed that the coefficient of the ROA performance lagging variable, i.e., ROA_t-1, is positively and significantly accepted in both models at a 1% significance level, hence indicating an increase in the ROA of the assuring company in the current year by the previous year’s accounting return. From the economic point of view, the positive influence of the previous year’s ROA on the present one in insurance companies reflects the inertia effect of good asset management, continuity of income derived from successfully placed investments, and growing customer and investor confidence. Past good performance favors organic growth through reinvestment of profits and operational efficiency, strengthening the future results. This effect is, however, bounded by exogenous factors or unexpected disruptions.

Regarding ESG, the estimates from model 1 show that ESG performance has a positive and significant impact on ROA. Economically, it means that ESG performance increases the ROA of insurance companies because it reduces their financial risks and optimizes the cost of operations, making the companies more attractive for customers and investors. It decreases the costs resulting from disruption or disputes, enlarges margins thanks to sustainability and secures advantageously priced financing. Also, good ESG performance provides increased reputation and fosters customer loyalty to create a predictable revenue flow in a self-sustaining, virtuous cycle that assures above-average, sustained profitability and general efficiency.

This result best explains the U-shaped relationship that exists between ESG performance and ROA, which is examined here using Franco et al. (2020) and Attia et al. (2023). I further apply the test of the U-shaped relationship proposed by Lind and Mehlum (2010). From Table 5, model 2, it appears that the association between ESG and ROA is curvilinear—the inflection point in ESG performance is estimated at 0.312, which is U-shaped and hence confirms hypothesis 1.

This result is economically significant since it suggests that investment in ESG initiatives could generate a potential conflict between management and shareholders. Such examples of early-stage costs that are usually thought to divert funds from short-term profitability goals include regulatory compliance, the implementation of clean technologies, and special training. In such a scenario, these short-run non-productive expenditures can reduce ROA—a negative consequence of ESG performance. However, where ESG performance improves, and overshoots a threshold value (estimated at 0.312), the principles of the stakeholder theory win out. Indeed, by satisfying the expectations of main stakeholders, namely customers, investors, regulators, and employees, companies strengthen their reputation, attractiveness, and trust relations with these contributors. This comprises benefits in terms of improved risk management, scale economies via more sustainable processes, better customer and employee loyalty, and preferential access to green finance at a lower cost. All these cumulative effects enable ESG performance to have a positive impact on ROA in the long term. Therefore, such nonlinearities exist in ESG and ROA because there has been a dynamic equilibrium between initial costs related to agency theory and the long-term benefit supported by the stakeholder theory; this will eventually explain the impact changing from negative to positive whenever high levels in ESG performance are reached.

Turning now to the control variables, it should be seen from Model 1 that, on average, the coefficient for the variable of Size is positive and significant at 1%, thus implying that larger insurance firms, with their economies of scale in operation, may improve the ROA positively. Large companies have economies of scale, which lower the average cost per unit of business; they are also diversified to bear risks due to larger and more varied insurance portfolios. They also have better access to finance on advantageous terms, boosting their capacity for investment and growth. Noticeably, their prestige and dominant positions in the marketplace attract more clients and shareholders, swelling revenues, hence favorably impacting utilization of assets through better efficiency drives that realize higher ROA.

In model 1, the coefficient of the debt variable is positive and statistically significant at the 1% level, thus showing that the positive influence of debt on ROA for insurance companies in the MENAT region can be justified by well-managed financial leverage. This is made possible by the debt financing of their activities, which expands the ability to invest in profitable opportunities such as diversified asset portfolios or growth projects. So long as the debt cost is less than the return on these investments, the profitability of the underlying assets will rise. Besides, debt can discipline better management of resources as inefficient practices are reduced and operations performance is at an optimum, hence increasing ROA.

However, in model (2), the coefficient in the revenue variable (REV) is negative and statistically significant at the 1% level. Either poor management of revenues or changes in the mix of revenues can explain the negative effect of revenues on ROA for insurance companies in the MENAT region. Low or unstable revenues could indicate falling demand or poor performance of the insurance portfolios that reduce asset efficiency. Moreover, if the revenues come from low-profitability segments or require high operating costs, this could lead to pressure on profit margins. This leads to suboptimal asset utilization, which in turn lowers ROA.

In addition, it seems that country-specific factors are more influential in determining ROA. The coefficient of the inflation variable (INF) assumes a positive and significant impact at the 1% significance level in model 2. Inflation could favorably contribute to the ROA of insurance companies in MENAT when it results in higher insurance premiums and investment returns. During periods of inflation, insurance companies may increase their premiums to account for the increased cost of covering the risk. In the same vein, inflation may boost investment returns, especially those that are related to real assets, such as property or commodities, which appreciate with a rise in prices. This will enable the company to increase its revenues and better optimize its assets, which can translate into a higher ROA.

Nevertheless, in both models, the signs of the coefficients of the GDP growth variable are negative, with significant effects, though at different degrees of significance. From an economic point of view, the negative impact of the growth of GDP on ROA for insurance companies in the MENAT region could be explained through an overcapitalization phenomenon. In periods of economic growth, companies may invest and build up assets too fast, leveraging market opportunities at the expense of proper resource allocation, which may cause overexpansion. Additionally, during growth, increased competition contributes to insurance premium reduction, which reduces the profit margins, hence pressuring asset profitability. These two factors together constrain asset efficiency and reduce ROA, even with economic growth.

The findings have significant practical recommendations for managers. Executives should develop a comprehensive ESG framework that aligns with the organization’s overall strategy and integrates with business unit strategies, risk management and governance structures. Regular ESG risk assessments are essential to identify and measure the potential impacts of ESG factors, thereby informing decision-making and prioritizing initiatives. Involving key stakeholders, including employees, investors, customers and regulators, in the ESG strategy development process ensures alignment with stakeholder expectations. Finally, implementing a process of continuous ESG performance improvement, which involves regularly reviewing and updating ESG policies, procedures and practices in response to feedback and changing conditions, is essential. These steps provide a structured approach enabling managers to effectively address ESG risks and opportunities in real-life applications.

4.2.2. The Moderating Effect of Institutional Quality

In this subsection, I will discuss the moderating role of institutional quality in the association between ESG and ROA. The evidence is also tabulated in

Table 6. Moderating effect of institutional quality on ESG and corporate performance.

	(1)	(2)	(3)
Variables	ROA	ROA	ROA
ROAt-1	0.298 ***	0.524 ***	0.457 ***
	(0.028)	(0.034)	(0.021)
ESG		0.041 ***	0.112 **
		(0.013)	(0.047)
ESG2			−0.001 **
			(0.001)
IQ	0.261 **	0.591 **	0.370
	(0.125)	(0.274)	(0.290)
ESG * IQ		−0.021 **	−0.089 ***
		(0.008)	(0.018)
ESG2 * IQ			0.001 ***
			(0.0001)
SIZE	1.116 ***	0.434 *	0.581 **
	(0.137)	(0.254)	(0.215)
DEBT	3.729	2.926	4.512
	(2.678)	(2.801)	(3.636)
REV	−4.011 ***	−5.274 *	−8.505
	(0.879)	(2.639)	(2.801)
INF	0.047 ***	0.007	0.043 ***
	(0.014)	(0.015)	(0.012)
GDP	−0.158 ***	−0.140 ***	−0.143 ***
	(0.022)	(0.038)	(0.032)
Constant	−0.104 ***	−0.635*	−0.110 ***
	(0.007)	(0.087)	(0.017)
Observations	154	154	154
Number of companies	31	31	31
Number of instruments	29	25	30
AR (1) (p-value)	0.009	0.008	0.008
AR (2) (p-value)	0.277	0.269	0.234
Hansen test (p-value)	0.174	0.181	0.187

Notes: Standard errors are displayed in brackets. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

, which shows that the direct impact of institutional quality on the performance of a firm is also positive and highly significant at a 5% level in both models 1 and 2.

It is therefore clear that, given its positive framework towards improving performance, institutional quality remains a determinant for improving insurance firms’ performance in the MENAT region. Moreover, better institutional capacity through good governance and a better regulatory framework for business, as well as good investors’ rights, mitigate risks that uncertainty and corruption raise, adding their voice toward confidence for various stakeholders. These conditions promote operational efficiency, facilitate access to finance, and encourage long-term investment. As such, insurance companies enjoy improved profitability, greater stability, and an enhanced ability to attract and retain customers in a competitive environment.

In Model 3, the nonlinear relationship between ESG performance and corporate performance is obvious. The curve has turned into a U-shape, indicating that insurers are risk-averse. We can then ascribe this result to the addition of the institutional quality variable in Model 3.

More precisely, the nonlinear effect of ESG on ROA of insurance companies, taking the form of an inverted U when institutional quality is present, depicts a more complex dynamic. First, ESG initiatives boost financial performance through the effects of enhanced reputation, attraction of responsible investors, and operational efficiency. However, if ESG investments are too excessive, then costs can surpass the expected benefits and bring about a fall in performance. The introduction of institutional quality therefore alters this relationship: strong institutions amplify the efficiency of ESG initiatives by reducing slacking and the risks of overinvestment. This hump-shaped relationship now implies an optimum beyond which ESG becomes counterproductive.

We notice that in the same model, the interaction term between asset profitability and institutional quality, ESG*IQ, exerts a nonlinear effect on business performance. The relationship is U-shaped, reflecting that insurers in the MENAT region are risk-takers. In fact, the nonlinear influence of the interaction between ESG and institutional quality on the financial performance of insurance companies takes a U shape, reflecting a significant transition. Given that there is moderate institutional quality, the initial high costs of implementing ESG initiatives may undermine financial performance due to incomplete adaptation or excessive burdens. All the same, strengthening the efforts concerning ESG²*IQ-points to an improvement in institutional quality which governs and brings about transparency and effectiveness within the ESG policies, and is thus beneficial. This leads to a gradual improvement in financial performance, which would attest that the interaction between ESG and institutional quality is turning into a long-term value creation lever.

4.3. Robustness Analysis

4.3.1. Change in Dependent Variable

Following Franco et al. (2020) and Attia et al. (2023), I utilize ROE as an alternative measure of financial performance. The results found are similar to those reported in previous regressions (

Table 7. Change in dependent variable.

	(1)	(2)
	ROE	ROE
Variables	Curvilinear	Moderating
ROEt-1	0.155 ***	0.066 ***
	(0.056)	(0.006)
ESG	−0.456 ***	1.115 ***
	(0.112)	(0.292)
ESG2	0.012 ***	−0.009 **
	(0.002)	(0.004)
IQ		1.336 ***
		(0.390)
ESG * IQ		−1.433 ***
		(0.345)
ESG2 * IQ		0.020 ***
		(0.004)
SIZE	3.115 *	7.747 ***
	(1.763)	(2.008)
DEBT	0.320	0.353
	(0.004)	(0.005)
REV	−4.266 **	−4.805 *
	(1.840)	(2.595)
INF	−0.088	0.327 ***
	(0.077)	(0.103)
GDP	−0.998 ***	−0.582 ***
	(0.211)	(0.195)
Constant	−0.599	−0.174 ***
	(0.357)	(0.004)
Observations	154	154
Number of companies	31	31
Number of instruments	29	25
AR (1) (p-value)	0.009	0.008
AR (2) (p-value)	0.277	0.269
Hansen test (p-value)	0.174	0.181

Notes: Standard errors are displayed in brackets. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

).

4.3.2. Change in Independent Variable

To explore in depth the influence of ESG practices on financial performance, I also assessed the impact of the three ESG dimensions (Environmental, Social, and Governance) on the financial performance of insurance companies. To do this, I follow Jin (2025) and Alghafes et al. (2024).

In models 1, 3, and 5 (

Table 8. Changes in independent variables.

	(1)	(2)	(3)	(4)	(5)	(6)
	ESGS		ESGG		ESGE
Variables	Curvilinear	Moderating	Curvilinear	Moderating	Curvilinear	Moderating
ROEt-1	0.186 ***	0.176 **	0.333 ***	0.603 ***	−0.486 *	0.489 ***
	(0.056)	(0.073)	(0.117)	(0.028)	(0.281)	(0.048)
ESG	−0.083	0.434 ***	0.490 *	−0.105 *	1.009 *	0.194 **
	(0.080)	(0.150)	(0.257)	(0.059)	(0.507)	(0.090)
ESG2	0.002	−0.004 **	−0.007 *	0.001 *	−0.022 *	−0.002 **
	(0.001)	(0.002)	(0.004)	(0.001)	(0.011)	(0.001)
IQ		1.882 *		−0.742		0.721 ***
		(1.035)		(0.471)		(0.215)
ESG * IQ		−0.218 ***		0.078 **		−0.086 ***
		(0.066)		(0.037)		(0.030)
ESG2 *IQ		0.002 ***		−0.001 *		0.001 **
		(0.001)		(0.001)		(0.001)
SIZE	0.314	−0.293	0.075	0.821 ***	2.374	0.147
	(0.458)	(0.553)	(0.812)	(0.213)	(1.529)	(0.328)
DEBT	0.125 *	0.136 **	−0.905	0.547	0.847	0.554
	(0.062)	(0.062)	(1.380)	(0.519)	(1.441)	(0.631)
REV	−5.045 **	−2.191	−3.698	−1.843	−7.855 *	−4.184
	(1.955)	(2.945)	(3.002)	(2.624)	(4.199)	(5.330)
INF	−0.027	0.043	−0.003	0.006	0.212	0.005
	(0.076)	(0.046)	(0.031)	(0.026)	(0.164)	(0.040)
GDP	−0.127 **	−0.187 ***	−0.385 **	−0.162 ***	−0.140 *	−0.158 ***
	(0.057)	(0.044)	(0.156)	(0.045)	(0.070)	(0.057)
Constant	−0.539	0.336	−0.105	−0.149 ***	−0.455	−0.421
	(0.093)	(0.101)	(0.159)	(0.038)	(0.300)	(0.637)
Observations	155	155	154	154	154	154
Number of companies	31	31	31	31	31	31
Number of instruments	17	15	15	27	25	28
AR (1) (p-value)	0.008	0.009	0.006	0.005	0.004	0.008
AR (2) (p-value)	0.272	0.309	0.243	0.183	0.278	0.261
Hansen test (p-value)	0.145	0.188	0.183	0.165	0.173	0.180

Notes: Standard errors are displayed in brackets. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

), the nonlinear relationship between ESG criteria and company performance seems stronger, especially regarding governance and environment. Such a nonlinear relationship, however, appears to fade when it relates to the social ESG criterion, which no longer exerts a significant effect on performance. Such an outcome suggests that the nonlinear association between environmental and governance criteria with performance can be explained by a growing effect until an optimum is reached: companies that invested in good governance and environmental initiatives initially benefited thanks to better risk management and increased attractiveness for responsible investors. On the other hand, excessive effort in these areas would result in costs outweighing the benefits and, consequently, lowering performance.

On the contrary, the absence of a nonlinear relationship for the social criterion may reflect different perceptions by the different stakeholders or some methodological problems in its measurement. Social initiatives, though relevant, may be regarded as complementary factors to performance without yielding comparable returns with respect to governance or the environment. This could justify their nonlinear effect disappearing.

Moreover, under model 4, the relation of the interaction term–ESG governance criteria with institutional quality and company performance have become inverted U-shaped, reflecting thereby a dynamic of efficiency and saturation. First, good governance, supported by high-quality institutions, favors better decision-making and an efficient management of risks with increasing attractiveness for investors, thus at its end improving results. On the other hand, gains beyond a certain point might be hampered by extremely high administrative costs and organizational complexity induced by too-intense investments or too-strict government demands. This suggests the necessity of an optimal mix between ESG governance practices and the quality of institutions to maximize the performance of companies.

4.3.3. Change in Econometric Technique

For greater robustness, I used an alternative econometric technique: quantile regression.

Table 9. Quantile regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Variables	Q: 0.1	Q: 0.2	Q: 0.3	Q: 0.4	Q: 0.5	Q: 0.6	Q: 0.7	Q: 0.8	Q: 0.9
ESG	−0.178 ***	−0.163 ***	−0.155 ***	−0.136 ***	−0.114 ***	−0.096 ***	−0.085 ***	−0.082 ***	−0.075 ***
	(0.006)	(0.007)	(0.003)	(0.007)	(0.009)	(0.007)	(0.003)	(0.004)	(0.497)
ESG2	0.003 ***	0.003 ***	0.002 ***	0.002 ***	0.002 ***	0.002 ***	0.002 ***	0.002 ***	0.001 ***
	(0.0005)	(0.0004)	(0.0004)	(0.0003)	(0.0003)	(0.0004)	(0.0005)	(0.0005)	(0.0006)
SIZE	−0.358	−0.541	−0.630	−0.858	−1.127	−1.344	−1.478	−1.505	−1.588
	(3.675)	(2.958)	(2.661)	(2.168)	(2.318)	(2.944)	(3.455)	(3.563)	(4.011)
DEBT	0.610 *	0.569 **	0.549 **	0.498 ***	0.438 **	0.389	0.359	0.353	0.335
	(0.322)	(0.258)	(0.233)	(0.191)	(0.204)	(0.258)	(0.300)	(0.310)	(0.396)
REV	−2.169 ***	−1.613 ***	−1.339 ***	−6.431 ***	1.787 ***	0.841 ***	1.252 ***	1.333 ***	1.588 ***
	(0.199)	(0.161)	(0.144)	(0.118)	(0.126)	(0.116)	(0.188)	(0.194)	(0.214)
INF	0.046	0.045	0.044	0.042 *	0.040 *	0.039	0.038	0.038	0.037
	(0.038)	(0.031)	(0.028)	(0.023)	(0.024)	(0.031)	(0.036)	(0.037)	(0.041)
GDP	−0.184	−0.167	−0.159	−0.138	−0.114 ***	−0.094	−0.082	−0.079	−0.071 ***
	(0.175)	(0.141)	(0.127)	(0.104)	(0.011)	(0.140)	(0.164)	(0.169)	(0.002)
Number of companies	31	31	31	31	31	31	31	31	31

Notes: Standard errors are displayed in brackets. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

shows the results for different quantiles ranging from Q: 0.1 to Q: 0.9. The results found are similar to those reported in previous regressions.

4.4. Discussion

This is a discussion of the main findings of this paper. First, the results point out an inverted U-shaped association between ESG performance and company performance measured by ROA. In other words, high values of ESG performance led to decreased performance. Operating performance increases with an increase in ESG performance at the optimal level of 31.20%. In other words, the result means that insurance companies in the MENAT region should strategically incorporate ESG criteria to an optimal level of 31.20%. Too low a performance level may tarnish the image and attractiveness of these firms, whereas too high a level of ESG performance could increase costs beyond control, thereby decreasing profitability. A well-calibrated ESG investment can therefore maximize benefits in terms of reputation, compliance, and stakeholder satisfaction, while financial costs are included. Managers will have to regularly monitor this performance, which must balance the financial objectives with those of sustainability. This result confirms the predictions of agency and stakeholder theories, hence the validity of hypothesis H1. The results found confirm some studies that have explored the nonlinear relationship between ESG practices and corporate performance in different industries (e.g., Franco et al. 2020; Attia et al. 2023; Bagh et al. 2024; Jin 2025). For instance, Attia et al. (2023) found that the relationship between Corporate Social responsibility (CSR) and corporate performance (measured by ROE and Tobin’s Q) is nonlinear and U-shaped.

In addition, the results show that institutional quality significantly moderates the nonlinear relationship between ESG and firm performance. This effect suggests that the improvement in ESG performance accompanied by institutional quality still positively enhances the operational performance of insurance companies, hence validating hypothesis 2. In other words, the moderating effect of institutional quality in the nonlinear relationship between ESG performance and company performance means that strong institutions play a very important role in reaping the maximum benefit from ESG initiatives. The quality of the institution, if high, enables insurance companies to optimize their ESG strategy through risk reduction, enhanced transparency, and governance. Such institutional support reinforces the positive impact of ESG efforts on operational performance, enabling companies to realize full benefits from their sustainable investments while improving their profitability and competitiveness in the marketplace. Attia et al. (2023) demonstrates that institutional quality moderates the nonlinear nexus between CSR and financial performance.

5. Conclusions and Policy Implications

The objective of this paper is to analyze the nonlinear nexus between ESG performance, and the performance of insurance companies listed in the MENAT region, as well as the moderating role of institutional quality on such a nexus. These results point out an inverted U-shaped association between ESG performance and company performance measured by ROA. In addition, the results show that institutional quality significantly moderates the nonlinear relationship between ESG and firm performance.

From this perspective, insurers in the MENAT region would have to devise a balanced strategy with regards to ESG criteria and the quality of institutions that can help drive performance. On a managerial level, companies would have to increase the integration of ESG initiatives, especially for governance and environmental issues, but also keeping cost and efficiency under control. The implication is that there is an optimal threshold beyond which ESG benefits outweigh the costs, because excessive investments in ESG may become counterproductive. Additionally, managers must engage with public institutions to align their ESG practices with regulatory standards so that they will be perceived as consistent and relevant for the regional context.

While policymakers now need to start reinforcing institutional quality to improve on areas like transparency and freedom from corruption and develop regulatory frameworks friendly to ESG investments, they simultaneously permit the full development of the exploitation capabilities around companies, thus giving new impulses in environments favorable for growth. Above all, they will increase awareness and introduce various incentive schemes, which would further enable corporate participation in sustainable performance without setting their competitiveness at stake.

Nevertheless, my study has certain limitations. One-size-fits-all approaches might be less effective in institutional environments that are quite diverse within the MENAT region. In other words, due to the differences in institutional quality, some countries of the region might derive better benefits from ESG initiatives compared to others. The problem with the measurement of the impact that social criteria have on performance exacerbates managers’ capacity to assess and amend their strategy. Finally, the economic uncertainties and geopolitical challenges in the region impede consistent implementation of ESG and institutional policies, which would make the expected results more difficult to achieve.