To Trust or Not to Trust? Financial Inclusion and Fragmentation of Trust in Developing Countries

To Trust or Not to Trust? 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Financial Inclusion and Fragmentation of Trust in Developing Countries Jeremie BERTRAND, El Ghassem EL GHASSEM, Caroline PERRIN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9523032/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Does trust in banks operate uniformly, and does variability in this trust matter for financial inclusion? This paper introduces trust fragmentation, i.e. the degree of heterogeneity in trust levels within a society, as a critical but overlooked factor shaping financial inclusion outcomes. Using individual-level data from the Global Findex and trust measures from the World Values Survey, we show that higher fragmentation in trust within a country significantly reduces financial inclusion. This effect is particularly pronounced among individuals who are more sensitive to ambiguity, such as women, younger respondents, and those with higher levels of education, and attenuated in environments where ambiguity is reduced, where trust in banks and financial satisfaction are high. These findings suggest that fragmented trust creates ambiguity, deterring individuals from engaging with formal financial services. JEL Codes : G21, G51, Z10. Trust in Banks Financial Inclusion Culture Fragmentation 1. Introduction Trust in banks is a determinant of financial inclusion, defined as people's access and use of formal financial services (Heyert and Weill, 2025 ; Koomson et al., 2023a ; Xu, 2020 ). High levels of trust foster confidence in the safety and reliability of financial institutions, encouraging individuals to open accounts, save, and use financial services more frequently. Conversely, distrust acts as a significant barrier, leading to self-exclusion, even when financial services are accessible. In the 2017 Global Findex report, 13% of the non-banking population cited distrust in the financial system as the primary reason for not having a bank account (Demirgüç-Kunt et al., 2020 ). This percentage increased up to 25% in the 2021 Global Findex report 2021 (World Bank, 2021 ). This trust deficit results in a reliance on informal mechanisms, limiting economic opportunities and perpetuating financial exclusion for disadvantaged groups, such as women or minorities. Despite the importance of trust, understanding how it operates is far from straightforward. Although institution-based trust in banks has been positively linked to financial inclusion (Ghosh, 2021 ), the mechanisms underpinning this relationship remain overlooked. Why does financial inclusion vary, even in societies in which average trust is high? Could it be that what matters is not just how much people trust but how unevenly this trust is distributed? Consider a society in which trust in banks is uniformly high (low). In such a context, interactions tend to be uninfluential (impactful), as most individuals share a baseline level of confidence in financial institutions. This collective trust uniformly encourages (discourages) people to open accounts, save money, and utilize credit or other financial products without hesitation. This may create a virtuous (vicious) cycle in which the reliability of the banking system is reinforced (weakened) by widespread participation, enabling (hindering) financial institutions to thrive and innovate. Now imagine a contrasting scenario in which trust levels vary widely across groups within the same society. Some individuals highly trust banks, while others are more skeptical. In this fragmented environment, the dynamics shift significantly. Those who place high confidence in banks are more likely to engage with formal financial services and actively participate in saving, borrowing, and investing, as they view banks as safe and reliable, enabling them to seamlessly integrate into the financial ecosystem. In contrast, those who harbor distrust in banks may shy away from formal institutions altogether. Instead, they might turn to informal alternatives, such as savings groups or family networks. Upon first consideration, one might expect that such opposing behaviors would cancel each other out, leaving the average level of trust as the only relevant driver of financial inclusion. However, this overlooks the ambiguity created by trust fragmentation. When trust is highly dispersed, individuals are exposed to conflicting signals about the reliability of financial institutions. For ambiguity-averse individuals, this lack of normative clarity causes hesitation and disengagement. In such environments, fragmentation does not neutralize itself; instead, it compounds uncertainty, reducing the perceived stability of formal finance institutions and services and thereby diminishing financial inclusion. This is where the concept of institutional fragmentation becomes important. When individuals are exposed to conflicting institutional cues, i.e., formal rules that encourage financial inclusion on the one hand and informal norms or social signals that cast doubt on the trustworthiness of financial institutions on the other, ambiguity arises. In such environments, the institutional landscape is no longer coherent or predictable but fragmented and uncertain (Casson et al., 2010 ). This ambiguity undermines individuals’ ability to form stable expectations about financial engagement, especially when trust in banks is not uniformly shared. Building on the insights of Ellsberg ( 1961 ), such ambiguity is particularly problematic for ambiguity-averse individuals who prefer known risks to unknown ones and are more likely to withdraw from situations with unclear probability structures. Therefore, institutional fragmentation, especially in trust, does not merely reflect heterogeneity but creates an interpretive vacuum that amplifies perceived risks and inhibits financial inclusion. We empirically investigate this phenomenon by analyzing the extent to which fragmentation in trust within a society affects financial inclusion outcomes. Using microdata collected on an individual level across 51 countries and provided by the Global Findex combined with trust measures from the World Values Survey (WVS), we show that trust fragmentation is strongly and negatively associated with the three dimensions of financial inclusion: formal account ownership, savings, and usage. Furthermore, in line with ambiguity-aversion literature, we show that trust fragmentation affects more women, younger, and high-educated individuals. Going further, we also show that the environment has a role to play, as a high level of trust in banks and a high level of satisfaction counterbalance the effect of fragmentation by reducing initial ambiguity. This paper makes two key contributions to the extant literature. First, we introduce the concept of trust fragmentation into the analysis to enrich the understanding of trust in banks. Unlike existing studies that treat trust as a uniform societal trait, we demonstrate that heterogeneity in trust within a society, i.e. institutional fragmentation, affects financial inclusion outcomes. By highlighting the effects of trust fragmentation, we challenge the conventional view of trust as a homogenous construct and emphasize the importance of accounting for cultural heterogeneity in financial behaviors. Second, we identify trust heterogeneity as a critical yet previously overlooked factor, contributing to the literature on the determinants of financial inclusion. The results underscore the need for targeted policies that address trust disparities within society, particularly those supporting marginalized groups in accessing and using formal financial services. The remainder of the article is structured as follows: Section 2 presents the literature background. Section 3 discusses the data sources, variable definitions, and methodology. Section 4 presents the results of the analysis. Section 5 explains the mechanisms underlying our results. Section 6 presents the battery of robustness checks. Finally, Section 7 concludes the paper. 2. Literature Review 2.1. Trust and its different forms Trust remains a concept without a universally accepted definition (Hosmer, 1995 ). It cannot be reduced to a purely rational logic, which would confuse it with knowledge or power (Becker, 1996 ), nor assimilated to cooperation, which may stem from trust but can also exist independently (Good, 2000 ). Mayer et al. ( 1995 ) define it as the willingness to accept vulnerability to another’s actions, based on positive expectations rather than control, thereby highlighting its non-rational and risk-related dimensions. From an experimental standpoint, Deutsch ( 1958 ) views trust as the decision to act despite risks where potential losses outweigh possible gains, distinguishing interpersonal trust, which fosters openness, from mere predictability, which often leads to suspicion. Serva et al. ( 2005 ) emphasize mutual trust as a dynamic loop shaped by reciprocal behaviors, while Schoorman et al. ( 1996 ) underline that trust remains based on individual perceptions, which may create asymmetries. In this sense, trust can be seen as a psychological state that combines accepted vulnerability with positive expectations (Lewicki et al., 2006 ). Trust can be conceptualized along two main dimensions: interpersonal and institutional. Interpersonal trust refers to confidence placed in other individuals and typically evolves through different stages depending on the willingness to accept risk. In early interactions, calculus-based trust prevails, grounded in cost–benefit assessments, contracts, and sanctions, yet it is easily undermined when incentives shift (Lewicki and Bunker, 1996 ). With repeated exchanges, knowledge-based trust emerges, relying on behavioral predictability and communication, although it remains vulnerable when expectations are unmet. At a more advanced stage, identification-based trust develops, rooted in shared values and goals, which can foster strong cooperation and substitute for formal safeguards (Ring and Van den Ven, 1994 ). However, the consequence of this strong relationship of trust is that in the event of breaches it can be particularly destructive, because individuals may feel betrayed. Finally, a further form of interpersonal trust, so-called naïve trust, reflects spontaneous and uncalculated confidence in others’ goodwill, often present in early encounters but associated with heightened vulnerability because it represents a gamble for the individual displaying this type of confidence, and there is no past experience to moderate disappointment in the event of a problem. (Wicks et al., 1999 ). By contrast, institution-based trust denotes confidence not in specific individuals but in broader institutional frameworks, such as trust in banks or the legal system (Rousseau, 2005 ). This form of trust is essential in large-scale or impersonal transactions, where institutional safeguards raise the costs of opportunistic behavior and thus render cooperation more likely (Rousseau et al., 1998 ). Importantly, institution-based trust can reinforce interpersonal trust by creating an enabling environment for exchanges (Omeihe and Omeihe, 2021 ), though institutional breakdowns often erode both simultaneously. Moreover, this type of trust is sensitive to institutional cohesion: when institutions are fragmented, citizens’ trust in them diminishes (Schneider et al., 2025 ), or may itself become fragmented, with trust and distrust coexisting within the same institutional domain (Kujala et al., 2016 ). 2.2. Institutional fragmentation Institutional fragmentation refers to the presence of multiple, overlapping, and often conflicting institutions, including formal regulatory frameworks, informal norms, and diverse enforcement mechanisms. Following Douglass North’s institutional theory (North, 1990 ), institutions are human-derived constraints—both formal (e.g., laws and regulations) and informal (e.g., trust, norms, and social conventions)—that structure human interaction in political, economic, and social domains. When these institutions lack coherence, mutual reinforcement, or clear enforcement, they generate ambiguity and unpredictability. This institutional incoherence undermines stable expectations and raises transaction costs, ultimately constraining development (Casson et al., 2010 ; North, 1990 ). Institutional fragmentation in finance extends beyond regulatory or legal multiplicity to encompass the coexistence of divergent normative frameworks, including culturally embedded expectations and varying levels of societal trust. Scholars, such as La Porta et al. ( 1997 ) and Aoki ( 2001 ), have highlighted how cultural and institutional heterogeneity foster inconsistencies in financial governance across countries or regions, contributing to uncertainty and misalignment between global financial practices and local norms. Fragmentation along normative dimensions can challenge the institutional coherence needed for stable market functioning, particularly when institutional logic based on trust, informal rules, or relational norms diverge from formal legal frameworks (Guiso et al., 2004 ). This divergence may result in mismatched expectations between financial institutions and their stakeholders, increasing the perceived opacity of institutional environments (Jackson and Deeg, 2008 ). Under such conditions, institutions are no longer viewed as neutral intermediaries but are interpreted considering historical grievances, socioeconomic inequalities, and cultural narratives. This normative fragmentation creates ambiguity by obscuring what constitutes legitimate financial behavior across different contexts, such as providing secure services and fair and ethical decisions. When norms related to trust and culture diverge across institutional fields, organizations encounter difficulties in navigating competing expectations, which undermines their ability to act confidently and consistently (Greenwood et al., 2011 ). For instance, in environments with low institutional trust or inconsistent cultural norms regarding authority, firms may struggle to gauge the credibility or enforceability of institutional demands (Beck et al., 2003 ). This ambiguity can hinder cross-border investment, erode stakeholder engagement, and foster institutional avoidance or strategic decoupling. As institutional actors interpret and negotiate competing logics, the lack of a unified cultural or trust-based foundation amplifies interpretive flexibility, enabling an opportunistic adaptation but at the cost of systemic uncertainty (Suddaby et al., 2013 ). Thus, institutional fragmentation, especially in its normative forms, reflects pluralism and acts as a generator of structural ambiguity in financial systems. 2.3. Trust and financial inclusion In the financial sector, trust is shaped not only by beliefs about stability or competence but also by experiences and perceptions of ethical conduct, fairness, and customer care (Alesina and La Ferrara, 2002 ; Bjørnskov, 2007 ). Ozili ( 2018 ) has extensively explored this topic, revealing that trust in financial institutions transcends the basic notion of fund security, instead encompassing broader perceptions of fairness, transparency, and responsiveness. These attributes are relevant, as they shape the willingness of individuals to engage with financial services. In this context, trust is not isolated but intrinsically linked to the cumulative experiences of individuals, influenced by cultural norms, historical interactions with financial systems, and the prevailing reputation of the financial sector. Empirical research has consistently demonstrated a significant positive correlation between trust in financial institutions and financial inclusion (Heyert and Weill, 2025 ; Koomson et al., 2023a ; Xu, 2020 ). The report by the World Bank in 2014 was seminal in highlighting the role of trust in financial decision-making processes, particularly in developing economies. It pointed out that a lack of trust constitutes a major barrier to the utilization of financial services, including bank accounts. The report underscored that trust, or the lack thereof, can be a more formidable barrier than traditional obstacles, such as income level or financial literacy. This correlation between trust and financial inclusion is not restricted to a single geographic or economic context. For instance, Demirgüç-Kunt et al. ( 2018 ) performed a comprehensive analysis across countries and showed that there is a corresponding increase in financial inclusion in regions where trust in financial institutions is higher. This trend was maintained across diverse economic landscapes, from densely populated urban areas in emerging economies to remote rural regions in developed nations. Similar results were observed by Xu ( 2020 ). The dynamics of trust and financial inclusion also encompass the role of government policies and regulatory frameworks. Trust in financial institutions is not solely dependent on the institutions themselves but also hinges on the perceived integrity and effectiveness of financial regulation and oversight. In regions where regulatory frameworks are robust and transparent, there is a higher likelihood of trust in the financial system, which fosters greater financial inclusion (Ghosh, 2021 ). 2.4. Institutional fragmentation, trust, and financial inclusion In this paper, the institution under study is the trust in banks and not the banks themselves; hence, institutional fragmentation, in our context, does not refer to the fragmentation of banking systems but rather to the dispersion in the level of trust that individuals place in financial institutions. In societies with highly fragmented trust, some individuals strongly trust banks and actively engage with them, while others remain highly skeptical and avoid financial institutions altogether. In a purely rational environment, one might expect this institutional fragmentation of trust in banks to have no impact on financial inclusion. Indeed, although individuals with a high level of trust in banks are more likely to open a bank account, this is offset by individuals with a low level of trust, who are wary of banks and, therefore, will not open a bank account. On average, these effects should cancel each other out, with only the average level of trust having an impact. However, this does not consider the ambiguity that such fragmentation creates for individuals as a whole. Ambiguity aversion, first introduced by Ellsberg ( 1961 ), describes an individual’s tendency to prefer known risks over unknown risks due to a lack of clear probability distributions. His famous experiment, the two urn problem, illustrated this behavior: participants were asked to bet on the color of a randomly drawn ball from one of two urns: Urn 1, which contained exactly 50 red and 50 black balls (a known probability distribution), and Urn 2, which contained 100 balls with an unknown proportion of red and black balls (an ambiguous probability distribution). According to the expected utility theory, individuals should be indifferent toward the two urns, as they lack enough information to favor one over the other. However, Ellsberg found that most people bet on Urn 1, avoiding Urn 2 due to the unknown probability, revealing a strong aversion to ambiguity. This phenomenon, now known as the Ellsberg Paradox , provides evidence that individuals systematically deviate from rational decision-making under uncertainty, contradicting the assumptions of classical expected utility models. Ambiguity aversion has been extensively documented in economic behavior research, particularly in that related to financial decision-making. For instance, Dimmock et al. ( 2016a ) found that individuals with higher levels of ambiguity aversion are less likely to participate in the stock market and tend to allocate a smaller portion of their financial wealth to equity. Bianchi and Tallon ( 2019 ) similarly provided empirical evidence that ambiguity aversion leads to a preference for familiar assets, contributing to the home bias phenomenon in investment portfolios. Of interest to this paper, Huo et al. ( 2024 ) showed that ambiguity aversion results in a systematic pessimistic bias in expectations, increasing sensitivity to signals under dispersed information. These findings explain why ambiguity-averse individuals tend to overreact to perceived risks and uncertainties. Institutional fragmentation in trust creates an environment where financial engagement is inherently uncertain, making ambiguity-averse individuals particularly hesitant to interact with formal banking institutions. When trust in financial institutions is highly dispersed within a society, individuals lack clear and stable expectations regarding the reliability and fairness of banks. In such a setting, ambiguity aversion amplifies the effects of institutional fragmentation by reinforcing individuals’ tendencies to avoid uncertain financial environments. Thus, we can formally posit the following hypothesis: Hypothesis Higher levels of fragmentation in trust in banks reduce financial inclusion. 3. Empirical Design 3.1. Data The analysis utilizes individual-level data from the Global Findex database, which includes data from surveys conducted in 2011, 2014, 2017, and 2021. Global Findex is a comprehensive dataset developed by Gallup Inc. in collaboration with the World Bank that covers over 130 countries. The target population includes individuals aged 15–99 years. The survey focuses on household financial inclusion, examining how political, cultural, geographic, and financial factors influence the extent of financial inclusion at the household level. Each country has a minimum of 1000 respondents, with larger sample sizes for countries with significant populations, such as India and China. The Global Findex database offers a wide range of indicators that capture how individuals engage with financial services, including payments, withdrawals, borrowing, and savings, through both formal and informal channels as well as their overall access to these services. The Global Findex is a good fit for our research question because it measures financial inclusion through multiple dimensions, and it is determined on the individual level. For trust variables, we also utilize data from the WVS (Inglehart et al., 2022 ), which offers comprehensive insights into individual values and preferences, enabling a precise measurement of heterogeneity within a country. WVS is a large-scale research initiative conducted over six waves across different periods, covering more than 100 countries and societies. The survey encompasses over 85,000 respondents, making it one of the most extensive datasets available for the study of cultural dimensions. Although the specific questions asked in each wave of the WVS may vary, culture-related questions are consistently included, enabling a robust analysis of cultural factors globally. The WVS sample is randomly selected, ensuring representation across a wide range of demographic factors and providing a diverse cross-section of respondents. We incorporate additional control variables at the country level using a combination of databases, namely the International Monetary Fund’s Financial Access Survey and the World Bank’s Global Indicators. These databases provide a crucial context for analyzing financial access and development among nations. We used World Income Group classification of economies to define ‘developing countries’ as low-, lower-middle-, or upper-middle-income. Our final data sample includes 51 countries from all over the world, with 200,334 observations. 3.2. Econometric specifications To evaluate the impact of trust in bank fragmentation on financial inclusion, we use the probit estimation to model binary dependent variables. Our model is as follows: $$\:P\left(Financial\:Inclusio{n}_{i,t}\right)=\alpha\:+\beta\:*{Trust\:Fragmentation}_{j}+\delta\:*Control{s}_{i,j,t}+{\epsilon\:}_{i,j,t}$$ where i refers to the individual, j to the country where the individual lives, and t to the year; and ε is an idiosyncratic error term. 3.3. Variables 3.3.1. Financial inclusion Financial inclusion is measured in two dimensions: access and usage. The access dimension is measured by having an account at a formal financial institution, while usage is measured by two variables: using the bank account to save and the frequency of using the formal account. Allen et al. ( 2016 ) argue that the frequency dimension is important because of single usage individuals, who receive remittances or wages and withdraw them immediately and, thus, do not really access the services of financial institutions. Nonetheless, the questions we use to obtain the data might differ from 2011 to 2021 in terms of phrasing, but they all have the same overall meaning. The questions below are from the Global Findex in 2011. First, to determine account ownership, we use the following question: “ Do you, either by yourself or together with someone else, currently have an account at any of the following places? An account can be used to save money, to make or receive payments, or to receive wages and remittances. Do you currently have an account at a bank, credit union, or the post office? ” Based on this question, we construct a binary variable ( Formal Account ), which is equal to 1 if the respondent has an account at a formal financial institution and 0 otherwise. 3.3.2. Trust in bank fragmentation We measure trust in banks using a variable that captures individuals’ self-reported trust in the banking sector within their country. This measure is based on the survey question, “I am going to name a number of organizations. For each one, could you tell me how much confidence you have in them: Banks - is it a great deal (1), quite a lot (2), not very much (3), or none at all (4)?” For ease of interpretation, we reverse-coded the responses so that the variable ranges from 1 (no confidence) to 4 (high confidence). Similar items are used to assess trust in other institutions, such as the government, the judicial system, and law enforcement, enabling comparative analysis across key institutional domains. This operationalization is consistent with previous studies (Fungáčová et al., 2022 ; Heyert and Weill, 2025 ) and serves as a proxy for institutional trust in actors involved in the management and oversight of financial transactions. To construct the Trust Fragmentation variable, we compute the standard deviation of individual responses to the trust in banks question at the country level. Although the mean captures the average level of trust in the banking sector within a country, the standard deviation provides a measure of dispersion around the mean. This dispersion reflects the extent to which individuals within a country diverge in their trust in banks. A high standard deviation indicates substantial heterogeneity in trust levels, suggesting the presence of both highly trusting and highly distrustful individuals, which we interpret as a high degree of fragmentation. Conversely, a low standard deviation implies that most individuals report levels of trust close to the national average, indicating relatively homogenous trust. 3.3.3. Control variables On an individual level, we account for potential confounding factors that may influence financial inclusion. First, we control for the respondent’s gender ( Male ), as prior research suggests that women are less financially included than men (Zins and Weill, 2016 ). We also include the respondent’s age and its square ( Age and Age² ) to capture the non-linear relationship between age and financial inclusion. Age is typically positively correlated with financial inclusion, although this tends to decline in older individuals (Zins and Weill, 2016 ). Education is another key determinant of financial inclusion; thus, we incorporate two dummy variables for educational attainment. The first indicates whether the respondent has completed elementary education or less ( Primary ), and the second reflects those who have completed secondary education and possibly some post-secondary education ( Secondary ), with the highest level of education ( Tertiary ) serving as a reference group. We also account for income by including four dummy variables to represent the respondent’s income quintile, from the first quintile ( Poorest 20% ) to the fourth quintile ( Fourth 20% ), with the richest quintile serving as the reference group (Aslan et al., 2017 ). This controls for the influence of income on financial inclusion, given that individuals with higher incomes are generally more likely to access formal financial services. Following the financial inclusion literature, we control for several macroeconomic and legal variables to account for the broader country-level context in which households operate. First, as financial inclusion is shaped by supply-side factors, such as the availability of financial services (Allen et al., 2016 ), we include the average number of bank branches and ATMs per 1000 km 2 ( ATM Penetration ). Then, we also control for national income using the logarithm of GDP per capita . Third, recognizing the importance of legal rights and their enforcement in promoting financial inclusion (Osili and Paulson, 2008 ), we include a measure of the Rule of Law . Beyond macroeconomic and legal fundamentals, we also control for the trust environment and macro shocks that shape households’ willingness to engage with banks. First, we add a banking-crisis indicator ( Banking Crisis ) as trust in financial institutions falls during crises (van der Cruijsen et al., 2020). Second, we add the mean level of Trust in Banks in the analysis, as it has a direct impact on financial inclusion (Heyert and Weill, 2025 ). Third, we add confidence in government and courts to capture institutional trust. Prior work shows these dimensions correlate with trust in financial institutions and can reduce the role of bank-specific trust in driving inclusion (van der Cruijsen et al., 2020). Finally, we include Financial Satisfaction , a household-level assessment of economic well-being that correlates with trust and perceptions of service quality; accounting for it helps separate preference/ambivalence channels from institutional ones (van der Cruijsen et al., 2020). Third, we control for Ethnic Fragmentation , measured as the probability that two randomly drawn individuals belong to different ethnic groups (Fearon, 2003 ). The fragmentation of trust may also be linked to the structure of the country's population. In countries where several ethnic groups exist, clusters of behaviour can be expected, explaining the fragmentation. Furthermore, prior work links greater ethnic fragmentation to lower financial development in developing economies (Amin and Murshed, 2025 ). Lastly, to complement ethnic fragmentation, we also add the country surface area as a control for within-country heterogeneity of values and preferences, which is associated with larger territories. A full description of all variables is given in Appendix A. Finally, we add region and year as fixed effects to control for unobservable spatial and temporal heterogeneity. 3.3.4. Descriptive Statistics Table 1 provides detailed descriptive statistics for the sample. Table 1 Summary statistics This table provides descriptive statistics for the variables used in this study. Country level variables are per country observations. Appendix A contains variable definitions. Financial Inclusion Variables Obs. Mean Std. Dev. Min Max Median Formal Account 200,334 0.488 0.499 0 1 0 Formal Savings 163,181 0.202 0.402 0 1 0 Frequency 144,131 0.511 0.499 0 1 1 Formal Account (LiTS) Independent Variable Trust Fragmentation 51 0.863 0.115 0.555 1.025 0.884 Individual Characteristics Male 200,334 0.466 0.498 0 1 0 Age 200,334 39.755 16.658 15 99 37 Primary 200,334 0.352 0.477 0 1 0 Secondary 200,334 0.507 0.499 0 1 1 Tertiary 200,334 0.140 0.347 0 1 0 Poorest 20% 200,334 0.168 0.374 0 1 0 Second 20% 200,334 0.176 0.380 0 1 0 Middle 20% 200,334 0.194 0.395 0 1 0 Fourth 20% 200,334 0.210 0.407 0 1 0 Richest 20% 200,334 0.250 0.433 0 1 0 Country Characteristics ATM Penetration 51 26.413 30.699 0.793 160.344 15.428 Log(GDP Capita) 51 11.690 2.877 6.506 18.981 11.013 Rule of Law 51 -0.510 0.579 -1.686 1.263 -0.532 Trust in Banks 51 2.585 0.390 2.018 3.386 2.493 Trust in Government 51 2.421 0.504 1.645 3.739 2.405 Trust in Court 51 2.470 0.446 1.644 3.440 2.499 Financial Satisfaction 51 5.875 0.874 3.351 7.191 6.103 Banking Crisis 51 0.014 0.078 0 0.504 0 Ethnic Fragmentation 51 0.457 0.229 0.039 0.879 0.484 Country Surface 51 1,399,814 2,878,737 10,450 1.71e + 07 513,120 Instrument Variables Parents’ Crisis Experience 200,334 0.021 0.051 0 1 0 Daily Internet Use 51 0.351 0.419 0.218 0.481 0.367 Daily TV Use 51 0.768 0.432 0.481 0.891 0.653 {Please insert Table 1 around here} In terms of financial inclusion variables, only about 48.8% of the respondents have a bank account. Considering the individual characteristics of our sample, about 47% of our respondents are male, and the average age is 39 years old, ranging from 15 to 99. For education level, only about 14% of our sample have completed tertiary or higher education, and 36% have a primary education level or less. Regarding income level, 25% of our respondents fall into the category of the richest 20% in the sample. Our sample characteristics are similar to those of Heyert and Weill ( 2025 ). At the country level, if we consider the independent variables, the countries have an average trust in banks, which is in the middle level at 2.58. The variable measure originally ranged from 1 to 4, with the countries in our sample having an average ranging from 2.018 to 3.386. On the level of variation in trust, the sample shows an average variation score of 0.863, with a range of 0.55–1.025. Appendix B shows that the correlation between trust in banks (mean) and its fragmentation (standard deviation) is equal to -0.49, which is not a perfect relation. In our sample, India illustrates this pattern clearly: despite exhibiting one of the highest levels of trust in banks (3.387), it records one of the lowest fragmentation scores (0.815), comparable to that of Argentina, the country with the lowest trust in banks (2.018). Conversely, Tunisia provides the opposite case, with one of the lowest trust levels (2.102) but a fragmentation score of 0.913, which is higher than that of Myanmar (0.814), a country with the second-highest trust score in the sample (3.179). 4. Trust Fragmentation and Financial Inclusion Table 2 presents the results on the link between trust fragmentation and financial inclusion, testing our hypothesis. We begin by testing the relationship between account ownership and trust fragmentation, controlling for all covariates, including the average level of trust in the banking system (column 1). To assess the robustness of our findings, we then re-estimate the regression without including average trust (column 2). This step is motivated by the relatively strong correlation between trust fragmentation and average country-level trust (–0.4943, see Appendix B), which raises potential concerns of collinearity that could bias the estimates. Finally, although there is no strict theoretical justification for clustering standard errors at the country level in our model, it is reasonable to assume that individuals within the same country may react differently to trust fragmentation depending on contextual factors such as culture. We therefore further test the robustness of our results by clustering standard errors at the country level (column 3). Table 2 Impact of Trust Fragmentation on Financial Inclusion This table reports probit regressions at the individual level. The dependent variable is Formal Account . In column (1), we display our main estimation with all control variables. In column (2), we rerun our main estimation by removing the Trust in Banks variable. Finally, in column (3), we rerun our main estimation by clustering standard errors at the country level. In all columns, we control for region and year fixed effects. P-values are reported in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions. (1) (2) (3) Formal Account Formal Account Formal Account Trust Fragmentation -1.525*** -1.525*** -1.525* (0.000) (0.000) (0.080) Trust in Banks 0.001 0.001 (0.963) (0.998) Male 0.135*** 0.135*** 0.135*** (0.000) (0.000) (0.000) Age 0.046*** 0.046*** 0.046*** (0.000) (0.000) (0.000) Age² -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) Primary -1.037*** -1.037*** -1.037*** (0.000) (0.000) (0.000) Secondary -0.575*** -0.575*** -0.575*** (0.000) (0.000) (0.000) Poorest 20% -0.557*** -0.557*** -0.557*** (0.000) (0.000) (0.000) Second 20% -0.468*** -0.468*** -0.468*** (0.000) (0.000) (0.000) Middle 20% -0.350*** -0.350*** -0.350*** (0.000) (0.000) (0.000) Fourth 20% -0.219*** -0.219*** -0.219*** (0.000) (0.000) (0.000) ATM Penetration 0.002*** 0.002*** 0.002 (0.000) (0.000) (0.505) Log(GDP Capita) -0.013*** -0.013*** -0.013 (0.000) (0.000) (0.612) Rule of Law 0.331*** 0.331*** 0.331*** (0.000) (0.000) (0.010) Banking Crisis 0.204*** 0.204*** 0.204 (0.000) (0.000) (0.250) Trust in Government -0.201*** -0.200*** -0.201 (0.000) (0.000) (0.389) Trust in Court 0.157*** 0.157*** 0.157 (0.000) (0.000) (0.671) Financial Satisfaction 0.016*** 0.016*** 0.016 (0.007) (0.007) (0.849) Ethnic Fragmentation 0.359*** 0.359*** 0.359 (0.000) (0.000) (0.219) Country Surface 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) Constant 0.795*** 0.796*** 0.795 (0.000) (0.000) (0.563) Cluster - - Country Region FE Yes Yes Yes Year FE Yes Yes Yes Observations 200,334 200,334 200,334 Pseudo R 2 0.167 0.167 0.167 {Please insert Table 2 around here} The results indicate a statistically significant and negative association between Trust Fragmentation and our financial inclusion variable, Formal Account , in all specifications. This means that the greater the fragmentation of trust in banks within countries, the more likely individuals are to not have a bank account. This result validates our main hypothesis. As outlined in our conceptual framework, classical expected utility theory suggests that such fragmentation should have no aggregate effect, as high-trust individuals engage with banks, while low-trust individuals abstain, thereby offsetting each other. However, our findings show that this is not the case and that trust fragmentation does have a significant negative effect on financial inclusion. Turning to the average Trust in Banks variable, the results show a consistently positive but not significant effect on Formal Account. This finding is particularly noteworthy as it contrasts with previous studies that identified a positive correlation between trust in banks and financial inclusion (Heyert and Weill, 2025 ). Once trust fragmentation is taken into account, however, the direct effect of overall trust in banks disappears, suggesting that fragmentation plays a more critical role in shaping financial inclusion outcomes. 1 Turning to other control variables, consistent with prior findings in the literature (Allen et al., 2016 ; Lu et al., 2021 ; Zins and Weill, 2016 ), the individual-level characteristics in our sample exhibit expected relationships with financial inclusion outcomes. Gender plays a significant role, as being male is positively and significantly associated with higher levels of formal account ownership. Age also has a positive and statistically significant effect, although the inclusion of a squared age term indicates a concave relationship, suggesting that the likelihood of financial inclusion increases with age but at a decreasing rate. Educational attainment emerges as a strong determinant, as individuals with only a primary or secondary education are significantly less likely to be financially included compared to those with higher education levels, underscoring the relevance of financial literacy and human capital. Income level also has an effect, with individuals in the lowest and second income quintiles being systematically less likely to be included in the financial system and higher-income individuals exhibiting greater engagement across all financial inclusion indicators, confirming the central role of economic resources in accessing and using formal financial services. On the supply side, infrastructure-related variables show that ATM penetration is positively associated with account ownership, indicating that the physical accessibility of financial services facilitates transactional engagement. Regarding institutional and economic conditions, experiencing a recent systemic banking crisis is surprisingly associated with significantly higher levels of account ownership, although these effects lose significance once clustering at the country level is applied. Institutional trust also plays a heterogeneous role. Confidence in government is negatively associated with financial inclusion, except for clustered standard errors. On the opposite trust in the judicial system is positively related to formal accounts, suggesting that perceptions of legal fairness is an important determinant of having an account, which is also consistent about the result on the rule of law. Self-reported satisfaction with household financial conditions is positively and significantly associated with financial inclusion, reinforcing the importance of subjective economic well-being in shaping individuals’ engagement with the formal financial sector. Interestingly, Ethnic Fragmentation and Country Surface are positively linked to financial inclusion. This result is consistent with the literature, which shows that in an environment with high ethnic diversity, banks tend to adapt their offerings to these different ethnic groups in order to best meet their needs, leading to an increase in account ownership (Zeitz and Leblang, 2021 ). Another explanation comes from Koomson et al. ( 2023b ), who demonstrate that diversity stimulates entrepreneurship, job creation and therefore demand for accounts to receive salaries. 5. Understanding the Mechanism of Ambiguity We have therefore demonstrated that the greater the fragmentation of trust, the more reluctant individuals are to open a bank account. As explained before, our intuition is that in highly fragmented environment, individuals face inconsistent signals about the reliability and fairness of financial institutions, creating ambiguity, which is interpreted pessimistically by ambiguity-averse individuals. As shown by Huo et al. ( 2024 ), such ambiguity increases sensitivity to uncertainty, leading to systematic avoidance behavior. If ambiguity aversion is the primary mechanism driving the relationship between institutional trust fragmentation and financial inclusion, we would expect systematic differences in how demographic groups respond to uncertainty and how environmental characteristics moderate this relationship, in line with the existing literature. 5.1. Demographic characteristics Research suggests that ambiguity aversion is not uniformly distributed among individuals; instead, it tends to vary by gender and age. Previous studies have indicated that women exhibit higher levels of ambiguity aversion than men, particularly in financial decision-making contexts (Chew et al., 2012 ; Croson and Gneezy, 2009 ). Women are generally more risk- and ambiguity-averse than men, suggesting that they may be more sensitive to the lack of a clear and consistent institutional framework in societies with high fragmentation in trust. Consequently, female respondents are expected to be more discouraged from engaging with financial institutions when trust in banks is fragmented. Similarly, ambiguity aversion tends to decrease with age as individuals accumulate more financial experience and develop heuristics to overcome uncertain environments (Osmont and Cassotti, 2023 ). Older individuals have likely encountered multiple financial crises and regulatory changes, making them less sensitive to ambiguous signals about the reliability of financial institutions. Therefore, if ambiguity aversion explains the effect of fragmentation in trust on financial inclusion, we should observe that older individuals are less deterred by trust fragmentation than younger individuals. In contrast, younger individuals who have had fewer financial interactions and rely more on social cues to assess institutional credibility should exhibit greater hesitation in fragmented trust environments. Finally, ambiguity aversion also appears to be related to individuals’ educational attainment. Interestingly, prior research suggests that more highly educated individuals tend to display greater aversion to ambiguity (Dimmock et al., 2016a ; Butler et al., 2014 ). One possible explanation, offered by Soo Hong et al. ( 2018 ), is that aversion to ambiguity requires the ability to recognize ambiguity in the first place. Accordingly, individuals with lower levels of education may exhibit less sensitivity to ambiguity simply because they find it more difficult to identify. To test this, we extend our analysis by examining how sex, age and education individually condition the relationship between institutional trust fragmentation and financial inclusion. Thus, socio-demographic moderators are incorporated into our estimation. First, we interact our trust fragmentation variable with a dummy, Male , which is equal to 1 if the respondent is male. Second, we introduce two age-based dummy variables: Young , which is equal to 1 if the respondent falls within the bottom 25th percentile of the age distribution (28 years or younger), and Old , which is equal to 1 if the respondent belongs to the top 25th percentile (55 years or older). Finally, we use two control variables, Primary and Tertiary , which take the value 1 if the respondent’s highest level of educational attainment is primary or tertiary, respectively, and we then interact these variables with our measure of trust fragmentation {Please insert Table 3 around here} Table 3 Moderating effect of socio-demographic variables This table presents the results of probit regression about the moderating effect of socio-demographic variables. In column (1), we interact Trust Fragmentation with Male a dummy equal to 1 if the respondent is a male. In column (2), we interact Trust Fragmentation with Young , a dummy equal to 1 if the individual is less than or equal to the 25th percentile of Age , and with Old , a dummy equal to 1 if the individual is greater than or equal to the 75th percentile of Age . In column (3), we interact Trust Fragmentation with Primary and Tertiary education, dummies equal to 1 if the respondent has attained respectively primary or tertiary educational level. In all columns, we control for all control variables as in Table 2 and for region and year fixed effect. P-values are reported in parentheses. Standard errors are robust to heteroscedasticity. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. (1) (2) (3) Formal Account Formal Account Formal Account Trust Fragmentation -1.672*** -1.860*** -1.560*** (0.000) (0.000) (0.000) Male -0.128*** (0.007) Trust Fragmentation x Male 0.304*** (0.000) Young -0.065 (0.300) Trust Fragmentation x Young -0.359*** (0.000) Old -1.193*** (0.000) Trust Fragmentation x Old 1.371*** (0.000) Primary -1.495*** (0.000) Trust Fragmentation x Primary 0.192*** (0.001) Tertiary 0.869*** (0.000) Trust Fragmentation x Tertiary -0.332*** (0.001) Constant 0.917*** 2.261*** 1.122*** (0.000) (0.000) (0.000) Control Variables All All All Region FE Yes Yes Yes Year FE Yes Yes Yes Observations 200,334 200,334 200,334 Pseudo R 2 0.168 0.168 0.168 As shown in Table 3 , the effect of trust fragmentation exhibits significant heterogeneity by gender (column 1), age (column 2), and educational attainment (column 3). Specifically, the negative impact of trust fragmentation on financial inclusion is less pronounced among male respondents. With respect to age, younger individuals appear more vulnerable to trust fragmentation, whereas older individuals display greater resilience. Turning to education, respondents with only primary education seem relatively unaffected by fragmentation, while those with tertiary education experience a stronger negative effect. These patterns are consistent with prior evidence showing that ambiguity aversion decreases with age (Osmont and Cassotti, 2023 ) and among men (Chew et al., 2012 ) but increases with higher levels of educational attainment (Soo Hong et al., 2018 ). 5.2. Environmental characteristics Research also suggests that ambiguity aversion is influenced by individuals’ trust and satisfaction in the financial sector. Empirical evidence indicates that when trust in financial institutions is low, individuals perceive greater ambiguity in financial interactions and consequently display higher levels of ambiguity aversion (Dimmock et al., 2016b ). Ambiguity reduces trust in investment environments and discourages engagement with financial products (Li et al., 2019 ), while higher trust is associated with increased willingness to accept ambiguous financial prospects (Guiso et al., 2008 ). Going further, in environments where individuals are satisfied with their financial situation, ambiguity toward banks is reduced because personal financial stability provides a buffer against uncertainty. When people feel economically secure, they are more likely to positively interpret institutional signals and perceive financial institutions as less risky. Satisfaction with one’s financial condition reinforces trust and reduces the psychological salience of conflicting information, thus mitigating ambiguity aversion in decision-making (Dimmock et al., 2016b ). Conversely, dissatisfied customers are more likely to interpret financial offerings as ambiguous and respond with greater hesitation, thereby amplifying the effect of ambiguity aversion on financial inclusion. To examine the moderating role of trust and financial satisfaction in the relationship between trust fragmentation and financial inclusion, we construct three variables. First, we define High Trust in Banks as a dummy variable equal to one if the respondent lives in a country where the average level of trust in banks is above the sample mean. This variable captures whether the effect of trust fragmentation depends on the prevailing institutional trust environment. Second, following Fungáčová et al. ( 2019 ), we compute relative trust , defined as the difference between trust in banks and trust in court. From this measure, we create the dummy variable High Relative Trust , which equals one if the respondent is in a country with above-average relative trust. The rationale is that confidence in banks should not be assessed in isolation but relative to trust in other institutions. When relative trust is high, reliance on banks is more pronounced, so fragmentation is likely to have a stronger effect. Finally, we incorporate financial satisfaction, measured as respondents’ self-reported satisfaction with their financial situation. We define High Financial Satisfaction as a dummy equal to one if the country’s average financial satisfaction is above the sample mean. The logic here is that individuals who are more financially satisfied may be less exposed to the adverse effects of trust fragmentation, as their personal circumstances reduce dependence on institutional conditions. Conversely, in context of low satisfaction, fragmentation may exacerbate exclusion from financial services. From an empirical standpoint, if the mechanism at work is indeed ambiguity, each moderating variable should attenuate the negative effect of trust fragmentation. Accordingly, we expect the interaction terms to load positively, offsetting the negative coefficient on fragmentation. {Please insert Table 4 around here} Table 4 Moderating effect of trust and satisfaction in institutions This table presents the results of probit regression about the moderating effect of trust in Banks, relative trust, and financial satisfaction variables. In column (1), we interact Trust Fragmentation with High Trust in Banks (a dummy equal to 1 if trust in banks is higher than the average in our sample, 0 otherwise). In column (2), we interact Trust Fragmentation with High Relative Trust (a dummy equal to 1 if the difference between Trust in Banks and Trust in Court is higher than average, 0 otherwise). In column (3), we interact Trust Fragmentation with High Financial Satisfaction (a dummy equal to 1 if the household financial satisfaction is higher than its average, 0 otherwise). In all columns, we control for all control variables as in Table 2 and for region and year fixed effect (FE). P-values are reported in parentheses. Standard errors are robust to heteroscedasticity. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. (1) (2) (3) Formal Account Formal Account Formal Account Trust Fragmentation -3.842*** -3.753*** -2.505*** (0.000) (0.000) (0.000) High Trust in Banks -2.733*** (0.000) Trust Fragmentation x High Trust in Banks 2.747*** (0.000) High Relative Trust -3.093*** (0.000) Trust Fragmentation x High Relative Trust 3.047*** (0.000) High Financial Satisfaction -0.790*** (0.000) Trust Fragmentation x High Financial Satisfaction 1.668*** (0.000) Constant 1.590*** 2.356*** 3.166*** (0.000) (0.000) (0.000) Control Variables All All All Region FE Yes Yes Yes Year FE Yes Yes Yes Observations 200,334 200,334 200,334 Pseudo R 2 0.172 0.172 0.178 The interaction terms between trust fragmentation and the three additional variables are consistently positive and statistically significant. These results suggest that the adverse consequences of trust fragmentation are attenuated when baseline trust in banks is already high, when individuals report higher relative trust in banks compared to other institutions, and when satisfaction with financial institutions is strong. In such contexts, the trust fragmentation does not fully translate into disengagement, as the broader trust environment cushions its negative impact. Moreover, these results provide further support for our theoretical proposition that ambiguity aversion is a key mechanism through which institutional trust fragmentation affects financial behavior and that this mechanism operates differentially across demographic groups and the level of ambiguity in the environment. 6. Robustness Analyses 6.1. Additional control variables Cultural dimensions, such as those measured by Hofstede’s six indices (Hofstede, 2011 )—uncertainty avoidance, power distance, individualism, masculinity, long-term orientation, and indulgence—may significantly influence financial inclusion by shaping societal values, behaviors, and attitudes toward financial systems. For example, cultures with high individualism are more likely to encourage personal financial responsibility and autonomy, leading to higher financial inclusion (Lu et al., 2021 ). Conversely, cultures with strong power distance or uncertainty avoidance may foster hierarchical or risk-averse norms, which could discourage individuals, particularly women, from engaging with formal financial systems. However, we do not include cultural dimensions in the main estimations because they can introduce issues of multicollinearity. Cultural factors are deeply intertwined with institutional and legal development, meaning that they could overlap with variables, such as trust in banks, and rule of law, potentially distorting the relationships we aim to study. Additionally, cultural measures tend to remain stable over time, limiting their explanatory power in dynamic analyses of financial inclusion. Thus, these dimensions are included in this robustness check to ensure that our results are not confounded by unobserved cultural factors. {Please insert Table 5 around here} Table 5 Robustness Tests This table reports the results of the robustness tests. In column (1), we use probit regression and control for additional control variables at the country-level using Hofstede dimensions: Uncertainty Avoidance, Power Distance, Individualism, Masculinity, LT Orientation, and Indulgence. In column (2), we use OLS regressions to estimate our models at a country-year level. Finally in column (3), we display results of our main analysis dropping overrepresented countries in our sample. P-values are reported in parentheses. Standard errors are robust to heteroscedasticity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. (1) (2) (3) Additional control Country-level Dropping overrepresented countries Formal Account Formal Account Formal Account Trust Fragmentation -1.090*** -0.377** -1.374*** (0.000) (0.029) (0.000) Uncertainty 0.009*** (0.000) Power Distance -0.006*** (0.000) Individualism 0.004*** (0.000) Masculinity 0.004*** (0.000) Long-term Orientation 0.019*** (0.000) Indulgence 0.020*** (0.000) Constant 0.070 0.517 0.694*** (0.677) (0.494) (0.000) Control Variables All All All Region FE Yes Yes Yes Year FE Yes Yes Yes Observations 127,826 175 165,457 Pseudo R 2 0.213 0.152 R 2 0.591 Adj. R 2 0.528 As shown in Table 5 column (1), controlling for cultural dimensions does not alter our main findings. Trust fragmentation remains strongly negative and statistically significant on formal account ownership. 6.2. Country-level analysis To further verify the robustness of our results, we perform an additional analysis at the country level using aggregated data per country and year. Although the individual-level estimations link trust fragmentation and financial inclusion outcomes, country-level analysis evaluates whether the relationships observed hold in an aggregate context, accounting for broader institutional and macroeconomic dynamics. Trust Fragmentation remains negatively associated with formal account ownership, in line with our main results (Table 5 – column (2)). 6.3. Removal of overrepresented countries A potential source of bias that could affect our results relates to the representativeness of the countries included in our study. If certain countries are overrepresented in our sample, they may disproportionately influence the results. To address this concern, we excluded 3 of 51 countries contributing, namely China, India, and Morocco (nearly 5% of the sample), as they contributed more than 14% of the total observations. The majority of countries in our sample have between 2000 and 3000 observations, with few countries having 1000 observations. China, India, and Morocco had 15,309, 12,478, and 7,090 observations, respectively. As shown in Table 5 column (3), after removing these overrepresented countries, the results remain consistent with the main findings for Formal Account , confirming the robustness of our estimations. 6.4. Alternative financial inclusion variable Demirgüç-Kunt and Klapper ( 2013 ) point out that in developing countries, approximately 10 percent of individuals with an account leave it inactive, making no deposits or withdrawals. Furthermore, when trust is weakened or fragmented, individuals who already possess a bank account may respond to the resulting uncertainty not by closing the account, but by leaving it inactive and ceasing to use it altogether. Therefore, it is important to consider not only the ownership of a bank account but also its use, whether in terms of withdrawals or savings. To do so we construct two new variables. First, Formal Savings , using information provided in the following question: “ In the past 12 months, have you saved or set aside money by using an account at a bank, credit union or another financial institution? ”. We construct a dummy variable equal to 1 if the respondent has deposited money into his/her personal account(s) and 0 otherwise. Second, we construct the variable Frequency , which is a dummy equal to 1 if the respondent has withdrawn money from his/her personal account(s) at least twice per month and 0 otherwise, using the question “ in a typical month, how many times is money taken out of your personal account(s)? this includes cash withdrawals, electronic payments or purchases, checks, or any other time money is removed from your account(s) by yourself or others? ”. {Please insert Table 6 around here} Table 6 – Alternative dependent variables This table reports probit regressions at the individual level. The dependent variable is Formal Savings in columns (1) to (3) and Frequency in columns (4) and (6). In columns (1) and (4), we display the estimations with all control variables as in Table 2 . In columns (2) and (5), we rerun the main estimation by removing the Trust in Banks variable. Finally, in columns (3) and (6), we rerun the main estimation by clustering standard errors at the country level. In all columns, we control for region and year fixed effects. P-values are reported in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions. (1) (2) (3) (4) (5) (6) Formal Savings Formal Savings Formal Savings Frequency Frequency Frequency Trust Fragmentation -2.012*** -2.281*** -2.012*** -0.798*** -0.584*** -0.798** (0.000) (0.000) (0.005) (0.000) (0.000) (0.028) Trust in Banks 0.255*** 0.255 -0.239*** -0.239 (0.000) (0.269) (0.000) (0.353) Male 0.064*** 0.064*** 0.064*** 0.009 0.009 0.009 (0.000) (0.000) (0.004) (0.274) (0.293) (0.619) Age 0.028*** 0.028*** 0.028*** 0.028*** 0.029*** 0.028*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Age² -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Primary -0.765*** -0.740*** -0.765*** -0.837*** -0.860*** -0.837*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Secondary -0.397*** -0.389*** -0.397*** -0.487*** -0.493*** -0.487*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Poorest 20% -0.664*** -0.669*** -0.664*** -0.495*** -0.489*** -0.495*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Second 20% -0.524*** -0.528*** -0.524*** -0.418*** -0.412*** -0.418*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Middle 20% -0.362*** -0.365*** -0.362*** -0.328*** -0.324*** -0.328*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Fourth 20% -0.232*** -0.233*** -0.232*** -0.195*** -0.194*** -0.195*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Ethnic Fragmentation 0.131*** 0.302*** 0.131 0.213*** 0.065*** 0.213 (0.000) (0.000) (0.521) (0.000) (0.000) (0.474) Trust in Government -0.250*** -0.180*** -0.250 0.068*** -0.003 0.068 (0.000) (0.000) (0.218) (0.001) (0.874) (0.783) Trust in Court -0.081*** 0.001 -0.081 -0.221*** -0.280*** -0.221 (0.003) (0.965) (0.782) (0.000) (0.000) (0.487) Financial Satisfaction 0.043*** 0.037*** 0.043 0.102*** 0.100*** 0.102 (0.000) (0.000) (0.588) (0.000) (0.000) (0.117) Banking Crisis -0.124*** -0.131*** -0.124 0.172*** 0.195*** 0.172 (0.000) (0.000) (0.380) (0.000) (0.000) (0.386) ATMs/1,000km2 0.003*** 0.003*** 0.003 0.002*** 0.002*** 0.002 (0.000) (0.000) (0.123) (0.000) (0.000) (0.602) Rule of Law 0.250*** 0.239*** 0.250** 0.310*** 0.320*** 0.310** (0.000) (0.000) (0.020) (0.000) (0.000) (0.015) Log(GDP Capita) 0.005*** 0.004** 0.005 0.003* 0.003 0.003 (0.004) (0.015) (0.801) (0.064) (0.140) (0.882) Country Surface 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000 (0.000) (0.000) (0.001) (0.000) (0.000) (0.324) Constant 1.762*** 2.269*** 1.762 3.210*** 2.785*** 3.210** (0.000) (0.000) (0.100) (0.000) (0.000) (0.017) Cluster - - Country - - Country Region FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Observations 163,181 163,181 163,181 144,131 144,131 144,131 Pseudo R 2 0.169 0.168 0.169 0.420 0.419 0.420 Table 6 presents the results of our main analysis using the two new financial inclusion variables. As before, we test our specification by including all control variables (columns (1) and (4)), by removing Trust in Banks to control for a potential collinear effect (columns (2) and (5)) and by clustering our standard errors by country (columns (3) and (6)). We can observe that in all cases our Trust Fragmentation variable is negative and significant, proving that a fragmentation of trust not only reduces account ownership, but also reduces its use, whether for saving money or withdrawing it. 6.5. Alternative sample In line with Heyert and Weill ( 2025 ), we rely on the Life in Transition Survey (LiTS) as a robustness check to confirm that the identified relationships are not driven by dataset-specific characteristics. Although LiTS provides valuable insights into the relationship between trust in banks and financial inclusion, we do not use it for our main estimations for several reasons. First, the regional coverage of LiTS is limited to 28 countries in Central and Eastern Europe and Central Asia. Although diverse, it does not capture the global heterogeneity of financial inclusion and institutional trust. The Global Findex dataset offers broader coverage, including countries from all regions of the world, ensuring a more representative analysis. Moreover, LiTS spans only three survey years (2006, 2010, and 2016) compared to four for the Global Findex. {Please insert Table 7 around here} Table 7 – Alternative sample: LiTS survey This table provides results of probit regression using an alternative database, the LiTS database for 2006, 2010, and 2016. The dependent variable is Formal Account , which is measured by at least one member of the household having a bank account or using a debit/credit card. In column (1), we display our main estimation with all control variables. In column (2), we rerun our main estimation by removing the Trust in Banks variable. Finally, in column (3), we rerun our main estimation by clustering standard errors at the country level. In all columns, we control for region and year fixed effects. P-values are reported in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions. (1) (2) (3) Formal Account Formal Account Formal Account Trust Fragmentation -1.627*** -1.630*** -1.627** (0.000) (0.000) (0.038) Trust in Banks 0.091*** 0.091*** (0.000) (0.000) Male 0.107*** 0.102*** 0.107*** (0.000) (0.000) (0.001) Age 0.026*** 0.025*** 0.026*** (0.000) (0.000) (0.000) Age Square -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) Primary -0.573*** -0.581*** -0.573*** (0.000) (0.000) (0.000) Secondary -0.386*** -0.388*** -0.386*** (0.000) (0.000) (0.000) Poorest 20% -0.238*** -0.246*** -0.238** (0.000) (0.000) (0.021) Second 20% 0.020 0.021 0.020 (0.591) (0.580) (0.831) Middle 20% 0.186*** 0.193*** 0.186** (0.000) (0.000) (0.018) Fourth 20% 0.308*** 0.321*** 0.308*** (0.000) (0.000) (0.000) Ethnic Fragmentation 0.362*** 0.391*** 0.362 (0.000) (0.000) (0.476) Trust in Government -0.038*** -0.019*** -0.038* (0.000) (0.001) (0.099) Trust in Court 0.010* 0.035*** 0.010 (0.084) (0.000) (0.702) Financial Satisfaction -0.086*** -0.077*** -0.086*** (0.000) (0.000) (0.000) Banking Crisis -0.241*** -0.269*** -0.241 (0.000) (0.000) (0.377) ATMs/1,000km2 0.004*** 0.003*** 0.004* (0.000) (0.000) (0.077) Rule of Law 0.895*** 0.894*** 0.895*** (0.000) (0.000) (0.000) Log (GDP Capita) 0.011*** 0.016*** 0.011 (0.000) (0.000) (0.787) Country Surface 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) Constant 1.515*** 1.617*** 1.515 (0.000) (0.000) (0.211) Cluster - - Country Year FE Yes Yes Yes Observations 70,271 70,271 70,271 Pseudo R 2 0.263 0.259 0.263 The regression results using this alternative dataset are presented in Table 7 . In all specifications, financial inclusion is measured at the household level, with at least one household member having a bank account or using a debit/credit card, providing a comprehensive indicator of formal account ownership. The first specification focuses on the core explanatory variables, Trust Fragmentation , alongside basic individual-level controls (e.g., gender, age, and education). The subsequent specifications progressively include macroeconomic controls, such as rule of law, GDP per capita, as well as institutional trust measures, such as trust in government and justice systems and household-level satisfaction variables and finally ethnic fragmentation. Although the inclusion of these controls slightly alters coefficient magnitudes, the overall relationships remain consistent, with trust fragmentation having a strong and significant negative impact on financial inclusion. 6.6. Instrumental variable regression Finally, our analysis may be subject to potential endogeneity bias. Financial inclusion may impact trust in banks, leading to potential simultaneous causality. Additionally, as trust impacts many economic outcomes, it is likely that there variables are omitted that simultaneously impact both the diversity of trust in the environment and financial inclusion. To address potential endogeneity concerns related to trust fragmentation, we implement a shift-share (Bartik-style) instrumental variable strategy based on cross-country differences in demographic composition. The instrument exploits systematic differences in trust in banks across observable demographic groups, such as gender, age, education, and income, as documented in the literature (e.g., Fungáčová et al., 2019 ). We first estimate group-specific propensities to trust banks using individual-level survey data, excluding observations from the country under consideration (leave-one-country-out approach) to avoid mechanical correlation. These estimated propensities capture persistent, cross-country patterns in trust across demographic groups. We then combine these “shifts” with country-wave specific demographic shares (“shares”) to construct a predicted distribution of trust in each country. The instrumental variable is defined as the predicted dispersion (standard deviation) of trust across demographic groups within a country. This shift-share design provides a transparent decomposition of the source of identifying variation. The “shares” correspond to the demographic composition of each country, while the “shifts” reflect group-specific trust propensities estimated from external variation. Identification therefore comes from the interaction between predetermined demographic structure and exogenous differences in trust across groups. In line with recent contributions on the econometrics of shift-share instruments, the validity of our approach relies on the exogeneity of either the shares or the shocks (Goldsmith-Pinkham et al., 2020 ; Borusyak et al., 2022 , 2025 ). In our setting, the identifying assumption is that, conditional on controls, the estimated group-level trust propensities are orthogonal to country-specific unobserved determinants of financial inclusion. The leave-one-country-out procedure ensures that these shifts are not contaminated by within-country variation, thereby reinforcing their exogeneity. The relevance of the instrument follows from the well-established relationship between demographic characteristics and trust in financial institutions, implying that differences in population structure generate systematic variation in the dispersion of trust. Regarding the exclusion restriction, the instrument combines demographic shares with externally estimated group-specific trust propensities. A potential concern is that demographic composition may directly affect financial inclusion through channels unrelated to trust, such as financial literacy, income, or preferences for financial services. To address this, our empirical specification includes a rich set of individual-level controls (age, gender, education, and income), which absorb the direct effects of these characteristics on financial participation. As a result, the identifying variation in the shift-share instrument does not stem from demographic composition per se, but from differences in predicted trust across demographic groups. Conditional on these controls, demographic structure can influence financial inclusion only through its effect on the dispersion of trust, rather than through direct demographic channels. The instrument therefore isolates the component of trust fragmentation driven by heterogeneity in trust across groups, supporting the plausibility of the exclusion restriction. Finally, we note that our setting is particularly well suited to a shift-share design, as the object of interest is the dispersion of beliefs across individuals within countries. The instrument therefore directly maps into the theoretical mechanism under investigation, namely that heterogeneity in trust across social groups affects financial inclusion outcomes. Overall, this approach provides a plausibly exogenous and conceptually coherent source of variation in trust fragmentation. As an additional instrument, we exploit the fragmentation of trust in government institutions. This strategy is grounded in a well-established literature showing that trust in different institutions is strongly interrelated and reflects a common underlying component of institutional trust. Evidence from psychology and political economy indicates that individuals tend to form generalized beliefs about institutions, such that trust in one institution co-moves with trust in others (Newton and Norris, 2000 ; Devos et al., 2002 ). In the economic literature, Stevenson and Wolfers ( 2011 ) and Buriak et al. ( 2019 ) document that trust in political institutions is closely correlated with trust in economic and financial institutions. Following this approach, we construct a measure of fragmentation in trust in government and use it as an instrument for trust fragmentation in banks. The relevance condition is supported by the fact that heterogeneity in institutional trust tends to be pervasive across domains: societies characterized by polarized or heterogeneous beliefs about public institutions are also likely to exhibit dispersion in trust toward financial institutions. A potential concern is that trust in government institutions may directly affects financial inclusion through channels unrelated to trust in banks, for instance via confidence in regulation, public policies, or the broader institutional environment. To mitigate this concern, our empirical specification includes a comprehensive set of individual-level controls (age, gender, education, income) and macroeconomic characteristics, which absorb the main determinants of financial participation. In addition, we control for the average level of institutional trust, ensuring that identification relies on the dispersion of trust rather than its level. Therefore, the remaining variation captured by the instrument reflects differences in the heterogeneity of institutional beliefs across individuals, rather than overall confidence in public institutions. Under this assumption, fragmentation in trust in government affects financial inclusion only through its impact on trust fragmentation in banks, by shaping the broader environment of beliefs about institutions. This interpretation is consistent with the view that trust operates as a generalized social norm influencing multiple domains, while our empirical strategy isolates its bank-specific transmission channel. {Please insert Table 8 around here} Table 8 – Instrumental variable regression This table reports results of the instrumental variable (IV) regression. Column (1) displays the results of the first stage of the IV, using an OLS regression in which Shift-Share Instrument and Trust in Government Fragmentation are used as instruments for Trust Fragmentation . The exogeneity test (H-test), overidentification test (J-test), and relevance test (F-test) appear at the bottom of the first column. Second stage is displayed in column (2) for Formal Account. In all columns, we control for all control variables as in Table 2 and for region and year fixed effect (FE). P-values are reported in parentheses. Standard errors are robust to heteroscedasticity *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions. First-stage Second-stage (1) (2) Trust Fragmentation Formal Account Shift-Share Instrument 0.050*** (0.000) Trust in Government Fragmentation 0.488*** (0.000) Trust Fragmentation* −0.369*** (0.000) Control variables All All Observations 192,344 192,344 Pseudo R² 0.194 R² 0.810 Adjusted R² 0.810 Exogeneity (H-test) 72.26 (0.000) Overidentification (J-test) 2.841 (0.521) Relevance (F-test of excluded instruments) 101.83 (0.000) Table 8 displays the results of our instrumental variable regression. All instrumental variables are statistically significant. Regarding the quality of the first-stage regression in column (1), we can see that the H- and J-tests are not significant, while the F-test of the excluded instruments is highly significant, suggesting that the instruments are both relevant and sufficiently strong. In the second stage of the instrumental variable estimation in column (2), the coefficient associated with Trust Fragmentation remains negative and statistically significant. These results confirm the robustness of our main findings and reinforce the interpretation that greater dispersion in trust in banks has a detrimental effect on financial inclusion 2 . 7. Conclusion This paper investigates the relationship between institutional fragmentation in trust and financial inclusion. Although trust in banks is widely recognized as a key driver of financial inclusion, we demonstrate that the coherence, or lack thereof, in trust perceptions within societies shape access to and usage of formal financial services. Using individual-level data from the Global Findex database combined with trust measures from the WVS, we provide robust evidence that a higher fragmentation in trust is associated with lower financial inclusion outcomes. Our results contribute to a better understanding of financial inclusion dynamics. We confirm that while trust in banks is a factor that positively impacts financial inclusion, its fragmentation within countries negatively impacts it. Our intuition is that this fragmentation in trust undermines the predictability of financial behavior and reinforces ambiguity about the credibility and fairness of formal institutions. Hence, in societies in which trust is fragmented, individuals are less likely to engage with formal financial systems due to the ambiguity generated by conflicting institutional signals. To validate this interpretation, we analyze both individual and contextual dimensions identified in the literature as shaping attitudes toward ambiguity. On the individual side, ambiguity-averse groups (specifically women, younger individuals, and those with higher educational attainment) are less likely to open bank accounts, reflecting their greater sensitivity to ambiguity. On the environmental side, contexts that mitigate ambiguity toward banks, such as those characterized by high institutional trust or greater financial satisfaction, attenuate the negative impact of trust fragmentation on financial inclusion. These findings reinforce our interpretation that trust fragmentation affects financial inclusion by heightening ambiguity toward the financial system. Our analysis highlights the limits of relying solely on average levels of trust to explain financial inclusion. By focusing on the distribution of trust, rather than its mean alone, we provide an explanation for why inclusion can remain low even where average trust appears high: heterogeneous beliefs create ambiguity, and ambiguity-averse households shy away from formal intermediation. Furthermore, we demonstrate that, when this fragmentation is taken into account, the average level of trust alone no longer explains individuals' financial inclusion. From a policy perspective, our results have significant implications for addressing financial inclusion gaps. Policymakers and financial institutions need to recognize that heterogeneity in trust within societies cannot be ignored when designing inclusive financial systems. Efforts to build trust in financial institutions must be targeted, context-specific, and cognizant of cultural and institutional dynamics. Initiatives, such as financial literacy programs, increased transparency, and tailored financial products, can help address the specific trust needs of diverse societal subgroups. Additionally, financial institutions operating in societies with high institutional fragmentation should focus on reducing ambiguity toward banks among individuals. This can be achieved by improving communication strategies, addressing perceptions of inequity, and innovating financial products that align with the expectations of trust. For instance, introducing community-based banking initiatives and leveraging trusted intermediaries could engage populations that remain skeptical of formal financial systems. Addressing trust fragmentation will require sustained efforts to create inclusive environments that promote trust in the safety, reliability, and fairness of financial institutions. This paper also opens avenues for future research. While we provide robust evidence of the negative impact of institutional trust fragmentation on financial inclusion, important questions remain. For instance, what are the long-term consequences of trust fragmentation for financial stability and resilience? How does the rise of digital finance (mobile money or fintech solutions) interact with fragmented trust perceptions? Which types of policy interventions, from financial literacy initiatives to regulatory reforms, are most effective in reducing ambiguity in contexts of high trust heterogeneity? And finally, to what extent do cultural and institutional settings condition these dynamics, and how can policies be tailored accordingly? Declarations Ethical statement The Authors declare that there is no conflict of interest Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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Financial Inclusion and Fragmentation of Trust in Developing Countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTrust in banks is a determinant of financial inclusion, defined as people's access and use of formal financial services (Heyert and Weill, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Koomson et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Xu, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). High levels of trust foster confidence in the safety and reliability of financial institutions, encouraging individuals to open accounts, save, and use financial services more frequently. Conversely, distrust acts as a significant barrier, leading to self-exclusion, even when financial services are accessible. In the 2017 Global Findex report, 13% of the non-banking population cited distrust in the financial system as the primary reason for not having a bank account (Demirg\u0026uuml;\u0026ccedil;-Kunt et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This percentage increased up to 25% in the 2021 Global Findex report 2021 (World Bank, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This trust deficit results in a reliance on informal mechanisms, limiting economic opportunities and perpetuating financial exclusion for disadvantaged groups, such as women or minorities.\u003c/p\u003e \u003cp\u003eDespite the importance of trust, understanding how it operates is far from straightforward. Although institution-based trust in banks has been positively linked to financial inclusion (Ghosh, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the mechanisms underpinning this relationship remain overlooked. Why does financial inclusion vary, even in societies in which average trust is high? Could it be that what matters is not just how much people trust but how unevenly this trust is distributed?\u003c/p\u003e \u003cp\u003eConsider a society in which trust in banks is uniformly high (low). In such a context, interactions tend to be uninfluential (impactful), as most individuals share a baseline level of confidence in financial institutions. This collective trust uniformly encourages (discourages) people to open accounts, save money, and utilize credit or other financial products without hesitation. This may create a virtuous (vicious) cycle in which the reliability of the banking system is reinforced (weakened) by widespread participation, enabling (hindering) financial institutions to thrive and innovate. Now imagine a contrasting scenario in which trust levels vary widely across groups within the same society. Some individuals highly trust banks, while others are more skeptical. In this fragmented environment, the dynamics shift significantly. Those who place high confidence in banks are more likely to engage with formal financial services and actively participate in saving, borrowing, and investing, as they view banks as safe and reliable, enabling them to seamlessly integrate into the financial ecosystem. In contrast, those who harbor distrust in banks may shy away from formal institutions altogether. Instead, they might turn to informal alternatives, such as savings groups or family networks. Upon first consideration, one might expect that such opposing behaviors would cancel each other out, leaving the average level of trust as the only relevant driver of financial inclusion. However, this overlooks the ambiguity created by trust fragmentation. When trust is highly dispersed, individuals are exposed to conflicting signals about the reliability of financial institutions. For ambiguity-averse individuals, this lack of normative clarity causes hesitation and disengagement. In such environments, fragmentation does not neutralize itself; instead, it compounds uncertainty, reducing the perceived stability of formal finance institutions and services and thereby diminishing financial inclusion.\u003c/p\u003e \u003cp\u003eThis is where the concept of institutional fragmentation becomes important. When individuals are exposed to conflicting institutional cues, i.e., formal rules that encourage financial inclusion on the one hand and informal norms or social signals that cast doubt on the trustworthiness of financial institutions on the other, ambiguity arises. In such environments, the institutional landscape is no longer coherent or predictable but fragmented and uncertain (Casson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This ambiguity undermines individuals\u0026rsquo; ability to form stable expectations about financial engagement, especially when trust in banks is not uniformly shared. Building on the insights of Ellsberg (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1961\u003c/span\u003e), such ambiguity is particularly problematic for ambiguity-averse individuals who prefer known risks to unknown ones and are more likely to withdraw from situations with unclear probability structures. Therefore, institutional fragmentation, especially in trust, does not merely reflect heterogeneity but creates an interpretive vacuum that amplifies perceived risks and inhibits financial inclusion.\u003c/p\u003e \u003cp\u003eWe empirically investigate this phenomenon by analyzing the extent to which fragmentation in trust within a society affects financial inclusion outcomes. Using microdata collected on an individual level across 51 countries and provided by the Global Findex combined with trust measures from the World Values Survey (WVS), we show that trust fragmentation is strongly and negatively associated with the three dimensions of financial inclusion: formal account ownership, savings, and usage. Furthermore, in line with ambiguity-aversion literature, we show that trust fragmentation affects more women, younger, and high-educated individuals. Going further, we also show that the environment has a role to play, as a high level of trust in banks and a high level of satisfaction counterbalance the effect of fragmentation by reducing initial ambiguity.\u003c/p\u003e \u003cp\u003eThis paper makes two key contributions to the extant literature. First, we introduce the concept of trust fragmentation into the analysis to enrich the understanding of trust in banks. Unlike existing studies that treat trust as a uniform societal trait, we demonstrate that heterogeneity in trust within a society, i.e. institutional fragmentation, affects financial inclusion outcomes. By highlighting the effects of trust fragmentation, we challenge the conventional view of trust as a homogenous construct and emphasize the importance of accounting for cultural heterogeneity in financial behaviors. Second, we identify trust heterogeneity as a critical yet previously overlooked factor, contributing to the literature on the determinants of financial inclusion. The results underscore the need for targeted policies that address trust disparities within society, particularly those supporting marginalized groups in accessing and using formal financial services.\u003c/p\u003e \u003cp\u003eThe remainder of the article is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the literature background. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses the data sources, variable definitions, and methodology. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the analysis. Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5\u003c/span\u003e explains the mechanisms underlying our results. Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the battery of robustness checks. Finally, Section \u003cspan refid=\"Sec26\" class=\"InternalRef\"\u003e7\u003c/span\u003e concludes the paper.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Trust and its different forms\u003c/h2\u003e \u003cp\u003eTrust remains a concept without a universally accepted definition (Hosmer, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). It cannot be reduced to a purely rational logic, which would confuse it with knowledge or power (Becker, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), nor assimilated to cooperation, which may stem from trust but can also exist independently (Good, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Mayer et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) define it as the willingness to accept vulnerability to another\u0026rsquo;s actions, based on positive expectations rather than control, thereby highlighting its non-rational and risk-related dimensions. From an experimental standpoint, Deutsch (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1958\u003c/span\u003e) views trust as the decision to act despite risks where potential losses outweigh possible gains, distinguishing interpersonal trust, which fosters openness, from mere predictability, which often leads to suspicion. Serva et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) emphasize mutual trust as a dynamic loop shaped by reciprocal behaviors, while Schoorman et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) underline that trust remains based on individual perceptions, which may create asymmetries. In this sense, trust can be seen as a psychological state that combines accepted vulnerability with positive expectations (Lewicki et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTrust can be conceptualized along two main dimensions: interpersonal and institutional. Interpersonal trust refers to confidence placed in other individuals and typically evolves through different stages depending on the willingness to accept risk. In early interactions, calculus-based trust prevails, grounded in cost\u0026ndash;benefit assessments, contracts, and sanctions, yet it is easily undermined when incentives shift (Lewicki and Bunker, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). With repeated exchanges, knowledge-based trust emerges, relying on behavioral predictability and communication, although it remains vulnerable when expectations are unmet. At a more advanced stage, identification-based trust develops, rooted in shared values and goals, which can foster strong cooperation and substitute for formal safeguards (Ring and Van den Ven, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). However, the consequence of this strong relationship of trust is that in the event of breaches it can be particularly destructive, because individuals may feel betrayed. Finally, a further form of interpersonal trust, so-called na\u0026iuml;ve trust, reflects spontaneous and uncalculated confidence in others\u0026rsquo; goodwill, often present in early encounters but associated with heightened vulnerability because it represents a gamble for the individual displaying this type of confidence, and there is no past experience to moderate disappointment in the event of a problem. (Wicks et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, institution-based trust denotes confidence not in specific individuals but in broader institutional frameworks, such as trust in banks or the legal system (Rousseau, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This form of trust is essential in large-scale or impersonal transactions, where institutional safeguards raise the costs of opportunistic behavior and thus render cooperation more likely (Rousseau et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Importantly, institution-based trust can reinforce interpersonal trust by creating an enabling environment for exchanges (Omeihe and Omeihe, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though institutional breakdowns often erode both simultaneously. Moreover, this type of trust is sensitive to institutional cohesion: when institutions are fragmented, citizens\u0026rsquo; trust in them diminishes (Schneider et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), or may itself become fragmented, with trust and distrust coexisting within the same institutional domain (Kujala et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Institutional fragmentation\u003c/h2\u003e \u003cp\u003eInstitutional fragmentation refers to the presence of multiple, overlapping, and often conflicting institutions, including formal regulatory frameworks, informal norms, and diverse enforcement mechanisms. Following Douglass North\u0026rsquo;s institutional theory (North, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), institutions are human-derived constraints\u0026mdash;both formal (e.g., laws and regulations) and informal (e.g., trust, norms, and social conventions)\u0026mdash;that structure human interaction in political, economic, and social domains. When these institutions lack coherence, mutual reinforcement, or clear enforcement, they generate ambiguity and unpredictability. This institutional incoherence undermines stable expectations and raises transaction costs, ultimately constraining development (Casson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; North, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInstitutional fragmentation in finance extends beyond regulatory or legal multiplicity to encompass the coexistence of divergent normative frameworks, including culturally embedded expectations and varying levels of societal trust. Scholars, such as La Porta et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and Aoki (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), have highlighted how cultural and institutional heterogeneity foster inconsistencies in financial governance across countries or regions, contributing to uncertainty and misalignment between global financial practices and local norms. Fragmentation along normative dimensions can challenge the institutional coherence needed for stable market functioning, particularly when institutional logic based on trust, informal rules, or relational norms diverge from formal legal frameworks (Guiso et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This divergence may result in mismatched expectations between financial institutions and their stakeholders, increasing the perceived opacity of institutional environments (Jackson and Deeg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Under such conditions, institutions are no longer viewed as neutral intermediaries but are interpreted considering historical grievances, socioeconomic inequalities, and cultural narratives.\u003c/p\u003e \u003cp\u003eThis normative fragmentation creates ambiguity by obscuring what constitutes legitimate financial behavior across different contexts, such as providing secure services and fair and ethical decisions. When norms related to trust and culture diverge across institutional fields, organizations encounter difficulties in navigating competing expectations, which undermines their ability to act confidently and consistently (Greenwood et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For instance, in environments with low institutional trust or inconsistent cultural norms regarding authority, firms may struggle to gauge the credibility or enforceability of institutional demands (Beck et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This ambiguity can hinder cross-border investment, erode stakeholder engagement, and foster institutional avoidance or strategic decoupling. As institutional actors interpret and negotiate competing logics, the lack of a unified cultural or trust-based foundation amplifies interpretive flexibility, enabling an opportunistic adaptation but at the cost of systemic uncertainty (Suddaby et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, institutional fragmentation, especially in its normative forms, reflects pluralism and acts as a generator of structural ambiguity in financial systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Trust and financial inclusion\u003c/h2\u003e \u003cp\u003eIn the financial sector, trust is shaped not only by beliefs about stability or competence but also by experiences and perceptions of ethical conduct, fairness, and customer care (Alesina and La Ferrara, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bj\u0026oslash;rnskov, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Ozili (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) has extensively explored this topic, revealing that trust in financial institutions transcends the basic notion of fund security, instead encompassing broader perceptions of fairness, transparency, and responsiveness. These attributes are relevant, as they shape the willingness of individuals to engage with financial services. In this context, trust is not isolated but intrinsically linked to the cumulative experiences of individuals, influenced by cultural norms, historical interactions with financial systems, and the prevailing reputation of the financial sector.\u003c/p\u003e \u003cp\u003eEmpirical research has consistently demonstrated a significant positive correlation between trust in financial institutions and financial inclusion (Heyert and Weill, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Koomson et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Xu, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The report by the World Bank in 2014 was seminal in highlighting the role of trust in financial decision-making processes, particularly in developing economies. It pointed out that a lack of trust constitutes a major barrier to the utilization of financial services, including bank accounts. The report underscored that trust, or the lack thereof, can be a more formidable barrier than traditional obstacles, such as income level or financial literacy.\u003c/p\u003e \u003cp\u003eThis correlation between trust and financial inclusion is not restricted to a single geographic or economic context. For instance, Demirg\u0026uuml;\u0026ccedil;-Kunt et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) performed a comprehensive analysis across countries and showed that there is a corresponding increase in financial inclusion in regions where trust in financial institutions is higher. This trend was maintained across diverse economic landscapes, from densely populated urban areas in emerging economies to remote rural regions in developed nations. Similar results were observed by Xu (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dynamics of trust and financial inclusion also encompass the role of government policies and regulatory frameworks. Trust in financial institutions is not solely dependent on the institutions themselves but also hinges on the perceived integrity and effectiveness of financial regulation and oversight. In regions where regulatory frameworks are robust and transparent, there is a higher likelihood of trust in the financial system, which fosters greater financial inclusion (Ghosh, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Institutional fragmentation, trust, and financial inclusion\u003c/h2\u003e \u003cp\u003eIn this paper, the institution under study is the trust in banks and not the banks themselves; hence, institutional fragmentation, in our context, does not refer to the fragmentation of banking systems but rather to the dispersion in the level of trust that individuals place in financial institutions. In societies with highly fragmented trust, some individuals strongly trust banks and actively engage with them, while others remain highly skeptical and avoid financial institutions altogether.\u003c/p\u003e \u003cp\u003eIn a purely rational environment, one might expect this institutional fragmentation of trust in banks to have no impact on financial inclusion. Indeed, although individuals with a high level of trust in banks are more likely to open a bank account, this is offset by individuals with a low level of trust, who are wary of banks and, therefore, will not open a bank account. On average, these effects should cancel each other out, with only the average level of trust having an impact. However, this does not consider the ambiguity that such fragmentation creates for individuals as a whole.\u003c/p\u003e \u003cp\u003eAmbiguity aversion, first introduced by Ellsberg (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1961\u003c/span\u003e), describes an individual\u0026rsquo;s tendency to prefer known risks over unknown risks due to a lack of clear probability distributions. His famous experiment, the two urn problem, illustrated this behavior: participants were asked to bet on the color of a randomly drawn ball from one of two urns: Urn 1, which contained exactly 50 red and 50 black balls (a known probability distribution), and Urn 2, which contained 100 balls with an unknown proportion of red and black balls (an ambiguous probability distribution). According to the expected utility theory, individuals should be indifferent toward the two urns, as they lack enough information to favor one over the other. However, Ellsberg found that most people bet on Urn 1, avoiding Urn 2 due to the unknown probability, revealing a strong aversion to ambiguity. This phenomenon, now known as the \u003cem\u003eEllsberg Paradox\u003c/em\u003e, provides evidence that individuals systematically deviate from rational decision-making under uncertainty, contradicting the assumptions of classical expected utility models.\u003c/p\u003e \u003cp\u003eAmbiguity aversion has been extensively documented in economic behavior research, particularly in that related to financial decision-making. For instance, Dimmock et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e) found that individuals with higher levels of ambiguity aversion are less likely to participate in the stock market and tend to allocate a smaller portion of their financial wealth to equity. Bianchi and Tallon (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) similarly provided empirical evidence that ambiguity aversion leads to a preference for familiar assets, contributing to the home bias phenomenon in investment portfolios. Of interest to this paper, Huo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that ambiguity aversion results in a systematic pessimistic bias in expectations, increasing sensitivity to signals under dispersed information. These findings explain why ambiguity-averse individuals tend to overreact to perceived risks and uncertainties.\u003c/p\u003e \u003cp\u003eInstitutional fragmentation in trust creates an environment where financial engagement is inherently uncertain, making ambiguity-averse individuals particularly hesitant to interact with formal banking institutions. When trust in financial institutions is highly dispersed within a society, individuals lack clear and stable expectations regarding the reliability and fairness of banks. In such a setting, ambiguity aversion amplifies the effects of institutional fragmentation by reinforcing individuals\u0026rsquo; tendencies to avoid uncertain financial environments. Thus, we can formally posit the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003e \u003cem\u003eHigher levels of fragmentation in trust in banks reduce financial inclusion.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Empirical Design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data\u003c/h2\u003e \u003cp\u003eThe analysis utilizes individual-level data from the Global Findex database, which includes data from surveys conducted in 2011, 2014, 2017, and 2021. Global Findex is a comprehensive dataset developed by Gallup Inc. in collaboration with the World Bank that covers over 130 countries. The target population includes individuals aged 15\u0026ndash;99 years. The survey focuses on household financial inclusion, examining how political, cultural, geographic, and financial factors influence the extent of financial inclusion at the household level. Each country has a minimum of 1000 respondents, with larger sample sizes for countries with significant populations, such as India and China. The Global Findex database offers a wide range of indicators that capture how individuals engage with financial services, including payments, withdrawals, borrowing, and savings, through both formal and informal channels as well as their overall access to these services. The Global Findex is a good fit for our research question because it measures financial inclusion through multiple dimensions, and it is determined on the individual level.\u003c/p\u003e \u003cp\u003eFor trust variables, we also utilize data from the WVS (Inglehart et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which offers comprehensive insights into individual values and preferences, enabling a precise measurement of heterogeneity within a country. WVS is a large-scale research initiative conducted over six waves across different periods, covering more than 100 countries and societies. The survey encompasses over 85,000 respondents, making it one of the most extensive datasets available for the study of cultural dimensions. Although the specific questions asked in each wave of the WVS may vary, culture-related questions are consistently included, enabling a robust analysis of cultural factors globally. The WVS sample is randomly selected, ensuring representation across a wide range of demographic factors and providing a diverse cross-section of respondents.\u003c/p\u003e \u003cp\u003eWe incorporate additional control variables at the country level using a combination of databases, namely the International Monetary Fund\u0026rsquo;s Financial Access Survey and the World Bank\u0026rsquo;s Global Indicators. These databases provide a crucial context for analyzing financial access and development among nations. We used World Income Group classification of economies to define \u0026lsquo;developing countries\u0026rsquo; as low-, lower-middle-, or upper-middle-income. Our final data sample includes 51 countries from all over the world, with 200,334 observations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Econometric specifications\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of trust in bank fragmentation on financial inclusion, we use the probit estimation to model binary dependent variables. Our model is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:P\\left(Financial\\:Inclusio{n}_{i,t}\\right)=\\alpha\\:+\\beta\\:*{Trust\\:Fragmentation}_{j}+\\delta\\:*Control{s}_{i,j,t}+{\\epsilon\\:}_{i,j,t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e refers to the individual, \u003cem\u003ej\u003c/em\u003e to the country where the individual lives, and \u003cem\u003et\u003c/em\u003e to the year; and \u003cem\u003eε\u003c/em\u003e is an idiosyncratic error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Variables\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Financial inclusion\u003c/h2\u003e \u003cp\u003eFinancial inclusion is measured in two dimensions: access and usage. The access dimension is measured by having an account at a formal financial institution, while usage is measured by two variables: using the bank account to save and the frequency of using the formal account. Allen et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) argue that the frequency dimension is important because of single usage individuals, who receive remittances or wages and withdraw them immediately and, thus, do not really access the services of financial institutions. Nonetheless, the questions we use to obtain the data might differ from 2011 to 2021 in terms of phrasing, but they all have the same overall meaning. The questions below are from the Global Findex in 2011.\u003c/p\u003e \u003cp\u003eFirst, to determine account ownership, we use the following question: \u0026ldquo;\u003cem\u003eDo you, either by yourself or together with someone else, currently have an account at any of the following places? An account can be used to save money, to make or receive payments, or to receive wages and remittances. Do you currently have an account at a bank, credit union, or the post office?\u003c/em\u003e\u0026rdquo; Based on this question, we construct a binary variable (\u003cem\u003eFormal Account\u003c/em\u003e), which is equal to 1 if the respondent has an account at a formal financial institution and 0 otherwise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Trust in bank fragmentation\u003c/h2\u003e \u003cp\u003eWe measure trust in banks using a variable that captures individuals\u0026rsquo; self-reported trust in the banking sector within their country. This measure is based on the survey question, \u003cem\u003e\u0026ldquo;I am going to name a number of organizations. For each one, could you tell me how much confidence you have in them: Banks - is it a great deal (1), quite a lot (2), not very much (3), or none at all (4)?\u0026rdquo;\u003c/em\u003e For ease of interpretation, we reverse-coded the responses so that the variable ranges from 1 (no confidence) to 4 (high confidence). Similar items are used to assess trust in other institutions, such as the government, the judicial system, and law enforcement, enabling comparative analysis across key institutional domains. This operationalization is consistent with previous studies (Fung\u0026aacute;čov\u0026aacute; et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Heyert and Weill, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and serves as a proxy for institutional trust in actors involved in the management and oversight of financial transactions.\u003c/p\u003e \u003cp\u003eTo construct the \u003cem\u003eTrust Fragmentation\u003c/em\u003e variable, we compute the standard deviation of individual responses to the trust in banks question at the country level. Although the mean captures the average level of trust in the banking sector within a country, the standard deviation provides a measure of dispersion around the mean. This dispersion reflects the extent to which individuals within a country diverge in their trust in banks. A high standard deviation indicates substantial heterogeneity in trust levels, suggesting the presence of both highly trusting and highly distrustful individuals, which we interpret as a high degree of fragmentation. Conversely, a low standard deviation implies that most individuals report levels of trust close to the national average, indicating relatively homogenous trust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Control variables\u003c/h2\u003e \u003cp\u003eOn an individual level, we account for potential confounding factors that may influence financial inclusion. First, we control for the respondent\u0026rsquo;s gender (\u003cem\u003eMale\u003c/em\u003e), as prior research suggests that women are less financially included than men (Zins and Weill, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We also include the respondent\u0026rsquo;s age and its square (\u003cem\u003eAge and Age\u0026sup2;\u003c/em\u003e) to capture the non-linear relationship between age and financial inclusion. Age is typically positively correlated with financial inclusion, although this tends to decline in older individuals (Zins and Weill, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Education is another key determinant of financial inclusion; thus, we incorporate two dummy variables for educational attainment. The first indicates whether the respondent has completed elementary education or less (\u003cem\u003ePrimary\u003c/em\u003e), and the second reflects those who have completed secondary education and possibly some post-secondary education (\u003cem\u003eSecondary\u003c/em\u003e), with the highest level of education (\u003cem\u003eTertiary\u003c/em\u003e) serving as a reference group. We also account for income by including four dummy variables to represent the respondent\u0026rsquo;s income quintile, from the first quintile (\u003cem\u003ePoorest 20%\u003c/em\u003e) to the fourth quintile (\u003cem\u003eFourth 20%\u003c/em\u003e), with the richest quintile serving as the reference group (Aslan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This controls for the influence of income on financial inclusion, given that individuals with higher incomes are generally more likely to access formal financial services.\u003c/p\u003e \u003cp\u003eFollowing the financial inclusion literature, we control for several macroeconomic and legal variables to account for the broader country-level context in which households operate. First, as financial inclusion is shaped by supply-side factors, such as the availability of financial services (Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we include the average number of bank branches and ATMs per 1000 km\u003csup\u003e2\u003c/sup\u003e (\u003cem\u003eATM Penetration\u003c/em\u003e). Then, we also control for national income using the \u003cem\u003elogarithm of GDP per capita\u003c/em\u003e. Third, recognizing the importance of legal rights and their enforcement in promoting financial inclusion (Osili and Paulson, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), we include a measure of the \u003cem\u003eRule of Law\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eBeyond macroeconomic and legal fundamentals, we also control for the trust environment and macro shocks that shape households\u0026rsquo; willingness to engage with banks. First, we add a banking-crisis indicator (\u003cem\u003eBanking Crisis\u003c/em\u003e) as trust in financial institutions falls during crises (van der Cruijsen et al., 2020). Second, we add the mean level of \u003cem\u003eTrust in Banks\u003c/em\u003e in the analysis, as it has a direct impact on financial inclusion (Heyert and Weill, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, we add confidence in government and courts to capture institutional trust. Prior work shows these dimensions correlate with trust in financial institutions and can reduce the role of bank-specific trust in driving inclusion (van der Cruijsen et al., 2020). Finally, we include \u003cem\u003eFinancial Satisfaction\u003c/em\u003e, a household-level assessment of economic well-being that correlates with trust and perceptions of service quality; accounting for it helps separate preference/ambivalence channels from institutional ones (van der Cruijsen et al., 2020).\u003c/p\u003e \u003cp\u003eThird, we control for \u003cem\u003eEthnic Fragmentation\u003c/em\u003e, measured as the probability that two randomly drawn individuals belong to different ethnic groups (Fearon, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The fragmentation of trust may also be linked to the structure of the country's population. In countries where several ethnic groups exist, clusters of behaviour can be expected, explaining the fragmentation. Furthermore, prior work links greater ethnic fragmentation to lower financial development in developing economies (Amin and Murshed, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lastly, to complement ethnic fragmentation, we also add the country surface area as a control for within-country heterogeneity of values and preferences, which is associated with larger territories. A full description of all variables is given in Appendix A.\u003c/p\u003e \u003cp\u003eFinally, we add region and year as fixed effects to control for unobservable spatial and temporal heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed descriptive statistics for the sample.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary statistics\u003c/b\u003e This table provides descriptive statistics for the variables used in this study. Country level variables are per country observations. Appendix A contains variable definitions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFinancial Inclusion Variables\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal Savings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal Account (LiTS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATM Penetration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(GDP Capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanking Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,399,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,878,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e513,120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInstrument Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u0026rsquo; Crisis Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily Internet Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily TV Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn terms of financial inclusion variables, only about 48.8% of the respondents have a bank account. Considering the individual characteristics of our sample, about 47% of our respondents are male, and the average age is 39 years old, ranging from 15 to 99. For education level, only about 14% of our sample have completed tertiary or higher education, and 36% have a primary education level or less. Regarding income level, 25% of our respondents fall into the category of the richest 20% in the sample. Our sample characteristics are similar to those of Heyert and Weill (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the country level, if we consider the independent variables, the countries have an average trust in banks, which is in the middle level at 2.58. The variable measure originally ranged from 1 to 4, with the countries in our sample having an average ranging from 2.018 to 3.386. On the level of variation in trust, the sample shows an average variation score of 0.863, with a range of 0.55\u0026ndash;1.025.\u003c/p\u003e \u003cp\u003eAppendix B shows that the correlation between trust in banks (mean) and its fragmentation (standard deviation) is equal to -0.49, which is not a perfect relation. In our sample, India illustrates this pattern clearly: despite exhibiting one of the highest levels of trust in banks (3.387), it records one of the lowest fragmentation scores (0.815), comparable to that of Argentina, the country with the lowest trust in banks (2.018). Conversely, Tunisia provides the opposite case, with one of the lowest trust levels (2.102) but a fragmentation score of 0.913, which is higher than that of Myanmar (0.814), a country with the second-highest trust score in the sample (3.179).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Trust Fragmentation and Financial Inclusion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results on the link between trust fragmentation and financial inclusion, testing our hypothesis. We begin by testing the relationship between account ownership and trust fragmentation, controlling for all covariates, including the average level of trust in the banking system (column 1). To assess the robustness of our findings, we then re-estimate the regression without including average trust (column 2). This step is motivated by the relatively strong correlation between trust fragmentation and average country-level trust (\u0026ndash;0.4943, see Appendix B), which raises potential concerns of collinearity that could bias the estimates. Finally, although there is no strict theoretical justification for clustering standard errors at the country level in our model, it is reasonable to assume that individuals within the same country may react differently to trust fragmentation depending on contextual factors such as culture. We therefore further test the robustness of our results by clustering standard errors at the country level (column 3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eImpact of Trust Fragmentation on Financial Inclusion\u003c/b\u003e This table reports probit regressions at the individual level. The dependent variable is \u003cem\u003eFormal Account\u003c/em\u003e. In column (1), we display our main estimation with all control variables. In column (2), we rerun our main estimation by removing the \u003cem\u003eTrust in Banks\u003c/em\u003e variable. Finally, in column (3), we rerun our main estimation by clustering standard errors at the country level. In all columns, we control for region and year fixed effects. P-values are reported in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.525***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.525***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.525*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.080)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.998)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.046***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.037***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.037***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.037***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.575***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.575***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.575***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.557***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.557***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.557***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.468***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.468***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.468***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.350***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.350***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.350***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.219***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.219***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.219***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATM Penetration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.505)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(GDP Capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.013***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.612)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.331***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.331***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.331***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanking Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.250)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.201***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.200***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.389)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.157***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.671)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.849)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.795***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.796***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.563)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe results indicate a statistically significant and negative association between \u003cem\u003eTrust Fragmentation\u003c/em\u003e and our financial inclusion variable, \u003cem\u003eFormal Account\u003c/em\u003e, in all specifications. This means that the greater the fragmentation of trust in banks within countries, the more likely individuals are to not have a bank account. This result validates our main hypothesis. As outlined in our conceptual framework, classical expected utility theory suggests that such fragmentation should have no aggregate effect, as high-trust individuals engage with banks, while low-trust individuals abstain, thereby offsetting each other. However, our findings show that this is not the case and that trust fragmentation does have a significant negative effect on financial inclusion.\u003c/p\u003e \u003cp\u003eTurning to the average \u003cem\u003eTrust in Banks\u003c/em\u003e variable, the results show a consistently positive but not significant effect on \u003cem\u003eFormal Account.\u003c/em\u003e This finding is particularly noteworthy as it contrasts with previous studies that identified a positive correlation between trust in banks and financial inclusion (Heyert and Weill, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Once trust fragmentation is taken into account, however, the direct effect of overall trust in banks disappears, suggesting that fragmentation plays a more critical role in shaping financial inclusion outcomes.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTurning to other control variables, consistent with prior findings in the literature (Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zins and Weill, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the individual-level characteristics in our sample exhibit expected relationships with financial inclusion outcomes. Gender plays a significant role, as being male is positively and significantly associated with higher levels of formal account ownership. Age also has a positive and statistically significant effect, although the inclusion of a squared age term indicates a concave relationship, suggesting that the likelihood of financial inclusion increases with age but at a decreasing rate. Educational attainment emerges as a strong determinant, as individuals with only a primary or secondary education are significantly less likely to be financially included compared to those with higher education levels, underscoring the relevance of financial literacy and human capital. Income level also has an effect, with individuals in the lowest and second income quintiles being systematically less likely to be included in the financial system and higher-income individuals exhibiting greater engagement across all financial inclusion indicators, confirming the central role of economic resources in accessing and using formal financial services.\u003c/p\u003e \u003cp\u003eOn the supply side, infrastructure-related variables show that ATM penetration is positively associated with account ownership, indicating that the physical accessibility of financial services facilitates transactional engagement.\u003c/p\u003e \u003cp\u003eRegarding institutional and economic conditions, experiencing a recent systemic banking crisis is surprisingly associated with significantly higher levels of account ownership, although these effects lose significance once clustering at the country level is applied. Institutional trust also plays a heterogeneous role. Confidence in government is negatively associated with financial inclusion, except for clustered standard errors. On the opposite trust in the judicial system is positively related to formal accounts, suggesting that perceptions of legal fairness is an important determinant of having an account, which is also consistent about the result on the rule of law. Self-reported satisfaction with household financial conditions is positively and significantly associated with financial inclusion, reinforcing the importance of subjective economic well-being in shaping individuals\u0026rsquo; engagement with the formal financial sector. Interestingly, \u003cem\u003eEthnic Fragmentation\u003c/em\u003e and \u003cem\u003eCountry Surface\u003c/em\u003e are positively linked to financial inclusion. This result is consistent with the literature, which shows that in an environment with high ethnic diversity, banks tend to adapt their offerings to these different ethnic groups in order to best meet their needs, leading to an increase in account ownership (Zeitz and Leblang, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Another explanation comes from Koomson et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), who demonstrate that diversity stimulates entrepreneurship, job creation and therefore demand for accounts to receive salaries.\u003c/p\u003e"},{"header":"5. Understanding the Mechanism of Ambiguity","content":"\u003cp\u003eWe have therefore demonstrated that the greater the fragmentation of trust, the more reluctant individuals are to open a bank account. As explained before, our intuition is that in highly fragmented environment, individuals face inconsistent signals about the reliability and fairness of financial institutions, creating ambiguity, which is interpreted pessimistically by ambiguity-averse individuals. As shown by Huo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), such ambiguity increases sensitivity to uncertainty, leading to systematic avoidance behavior. If ambiguity aversion is the primary mechanism driving the relationship between institutional trust fragmentation and financial inclusion, we would expect systematic differences in how demographic groups respond to uncertainty and how environmental characteristics moderate this relationship, in line with the existing literature.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Demographic characteristics\u003c/h2\u003e \u003cp\u003eResearch suggests that ambiguity aversion is not uniformly distributed among individuals; instead, it tends to vary by gender and age. Previous studies have indicated that women exhibit higher levels of ambiguity aversion than men, particularly in financial decision-making contexts (Chew et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Croson and Gneezy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Women are generally more risk- and ambiguity-averse than men, suggesting that they may be more sensitive to the lack of a clear and consistent institutional framework in societies with high fragmentation in trust. Consequently, female respondents are expected to be more discouraged from engaging with financial institutions when trust in banks is fragmented.\u003c/p\u003e \u003cp\u003eSimilarly, ambiguity aversion tends to decrease with age as individuals accumulate more financial experience and develop heuristics to overcome uncertain environments (Osmont and Cassotti, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Older individuals have likely encountered multiple financial crises and regulatory changes, making them less sensitive to ambiguous signals about the reliability of financial institutions. Therefore, if ambiguity aversion explains the effect of fragmentation in trust on financial inclusion, we should observe that older individuals are less deterred by trust fragmentation than younger individuals. In contrast, younger individuals who have had fewer financial interactions and rely more on social cues to assess institutional credibility should exhibit greater hesitation in fragmented trust environments.\u003c/p\u003e \u003cp\u003eFinally, ambiguity aversion also appears to be related to individuals\u0026rsquo; educational attainment. Interestingly, prior research suggests that more highly educated individuals tend to display greater aversion to ambiguity (Dimmock et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e; Butler et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). One possible explanation, offered by Soo Hong et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), is that aversion to ambiguity requires the ability to recognize ambiguity in the first place. Accordingly, individuals with lower levels of education may exhibit less sensitivity to ambiguity simply because they find it more difficult to identify.\u003c/p\u003e \u003cp\u003eTo test this, we extend our analysis by examining how sex, age and education individually condition the relationship between institutional trust fragmentation and financial inclusion. Thus, socio-demographic moderators are incorporated into our estimation. First, we interact our trust fragmentation variable with a dummy, \u003cem\u003eMale\u003c/em\u003e, which is equal to 1 if the respondent is male. Second, we introduce two age-based dummy variables: \u003cem\u003eYoung\u003c/em\u003e, which is equal to 1 if the respondent falls within the bottom 25th percentile of the age distribution (28 years or younger), and \u003cem\u003eOld\u003c/em\u003e, which is equal to 1 if the respondent belongs to the top 25th percentile (55 years or older). Finally, we use two control variables, \u003cem\u003ePrimary\u003c/em\u003e and \u003cem\u003eTertiary\u003c/em\u003e, which take the value 1 if the respondent\u0026rsquo;s highest level of educational attainment is primary or tertiary, respectively, and we then interact these variables with our measure of trust fragmentation\u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eModerating effect of socio-demographic variables\u003c/b\u003e This table presents the results of probit regression about the moderating effect of socio-demographic variables. In column (1), we interact \u003cem\u003eTrust Fragmentation\u003c/em\u003e with \u003cem\u003eMale\u003c/em\u003e a dummy equal to 1 if the respondent is a male. In column (2), we interact \u003cem\u003eTrust Fragmentation\u003c/em\u003e with \u003cem\u003eYoung\u003c/em\u003e, a dummy equal to 1 if the individual is less than or equal to the 25th percentile of \u003cem\u003eAge\u003c/em\u003e, and with \u003cem\u003eOld\u003c/em\u003e, a dummy equal to 1 if the individual is greater than or equal to the 75th percentile of \u003cem\u003eAge\u003c/em\u003e. In column (3), we interact \u003cem\u003eTrust Fragmentation\u003c/em\u003e with \u003cem\u003ePrimary\u003c/em\u003e and \u003cem\u003eTertiary\u003c/em\u003e education, dummies equal to 1 if the respondent has attained respectively primary or tertiary educational level. In all columns, we control for all control variables as in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and for region and year fixed effect. P-values are reported in parentheses. Standard errors are robust to heteroscedasticity. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.672***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.860***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.560***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.304***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x Young\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.193***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x Old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.371***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.495***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x Tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.332***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.917***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.122***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the effect of trust fragmentation exhibits significant heterogeneity by gender (column 1), age (column 2), and educational attainment (column 3). Specifically, the negative impact of trust fragmentation on financial inclusion is less pronounced among male respondents. With respect to age, younger individuals appear more vulnerable to trust fragmentation, whereas older individuals display greater resilience. Turning to education, respondents with only primary education seem relatively unaffected by fragmentation, while those with tertiary education experience a stronger negative effect.\u003c/p\u003e \u003cp\u003eThese patterns are consistent with prior evidence showing that ambiguity aversion decreases with age (Osmont and Cassotti, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and among men (Chew et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) but increases with higher levels of educational attainment (Soo Hong et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Environmental characteristics\u003c/h2\u003e \u003cp\u003eResearch also suggests that ambiguity aversion is influenced by individuals\u0026rsquo; trust and satisfaction in the financial sector. Empirical evidence indicates that when trust in financial institutions is low, individuals perceive greater ambiguity in financial interactions and consequently display higher levels of ambiguity aversion (Dimmock et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e). Ambiguity reduces trust in investment environments and discourages engagement with financial products (Li et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while higher trust is associated with increased willingness to accept ambiguous financial prospects (Guiso et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGoing further, in environments where individuals are satisfied with their financial situation, ambiguity toward banks is reduced because personal financial stability provides a buffer against uncertainty. When people feel economically secure, they are more likely to positively interpret institutional signals and perceive financial institutions as less risky. Satisfaction with one\u0026rsquo;s financial condition reinforces trust and reduces the psychological salience of conflicting information, thus mitigating ambiguity aversion in decision-making (Dimmock et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e). Conversely, dissatisfied customers are more likely to interpret financial offerings as ambiguous and respond with greater hesitation, thereby amplifying the effect of ambiguity aversion on financial inclusion.\u003c/p\u003e \u003cp\u003eTo examine the moderating role of trust and financial satisfaction in the relationship between trust fragmentation and financial inclusion, we construct three variables. First, we define \u003cem\u003eHigh Trust in Banks\u003c/em\u003e as a dummy variable equal to one if the respondent lives in a country where the average level of trust in banks is above the sample mean. This variable captures whether the effect of trust fragmentation depends on the prevailing institutional trust environment. Second, following Fung\u0026aacute;čov\u0026aacute; et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we compute \u003cem\u003erelative trust\u003c/em\u003e, defined as the difference between trust in banks and trust in court. From this measure, we create the dummy variable \u003cem\u003eHigh Relative Trust\u003c/em\u003e, which equals one if the respondent is in a country with above-average relative trust. The rationale is that confidence in banks should not be assessed in isolation but relative to trust in other institutions. When relative trust is high, reliance on banks is more pronounced, so fragmentation is likely to have a stronger effect.\u003c/p\u003e \u003cp\u003eFinally, we incorporate financial satisfaction, measured as respondents\u0026rsquo; self-reported satisfaction with their financial situation. We define \u003cem\u003eHigh Financial Satisfaction\u003c/em\u003e as a dummy equal to one if the country\u0026rsquo;s average financial satisfaction is above the sample mean. The logic here is that individuals who are more financially satisfied may be less exposed to the adverse effects of trust fragmentation, as their personal circumstances reduce dependence on institutional conditions. Conversely, in context of low satisfaction, fragmentation may exacerbate exclusion from financial services.\u003c/p\u003e \u003cp\u003eFrom an empirical standpoint, if the mechanism at work is indeed ambiguity, each moderating variable should attenuate the negative effect of trust fragmentation. Accordingly, we expect the interaction terms to load positively, offsetting the negative coefficient on fragmentation.\u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eModerating effect of trust and satisfaction in institutions\u003c/b\u003e This table presents the results of probit regression about the moderating effect of trust in Banks, relative trust, and financial satisfaction variables. In column (1), we interact \u003cem\u003eTrust Fragmentation\u003c/em\u003e with \u003cem\u003eHigh Trust in Banks\u003c/em\u003e (a dummy equal to 1 if trust in banks is higher than the average in our sample, 0 otherwise). In column (2), we interact \u003cem\u003eTrust Fragmentation\u003c/em\u003e with \u003cem\u003eHigh Relative Trust\u003c/em\u003e (a dummy equal to 1 if the difference between Trust in Banks and Trust in Court is higher than average, 0 otherwise). In column (3), we interact \u003cem\u003eTrust Fragmentation\u003c/em\u003e with \u003cem\u003eHigh Financial Satisfaction\u003c/em\u003e (a dummy equal to 1 if the household financial satisfaction is higher than its average, 0 otherwise). In all columns, we control for all control variables as in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and for region and year fixed effect (FE). P-values are reported in parentheses. Standard errors are robust to heteroscedasticity. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.842***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.753***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.505***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Trust in Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.733***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x High Trust in Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.747***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Relative Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.093***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x High Relative Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Financial Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.790***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation x High Financial Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.668***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.590***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.356***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.166***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200,334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe interaction terms between trust fragmentation and the three additional variables are consistently positive and statistically significant. These results suggest that the adverse consequences of trust fragmentation are attenuated when baseline trust in banks is already high, when individuals report higher relative trust in banks compared to other institutions, and when satisfaction with financial institutions is strong. In such contexts, the trust fragmentation does not fully translate into disengagement, as the broader trust environment cushions its negative impact.\u003c/p\u003e \u003cp\u003eMoreover, these results provide further support for our theoretical proposition that ambiguity aversion is a key mechanism through which institutional trust fragmentation affects financial behavior and that this mechanism operates differentially across demographic groups and the level of ambiguity in the environment.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Robustness Analyses","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Additional control variables\u003c/h2\u003e \u003cp\u003eCultural dimensions, such as those measured by Hofstede\u0026rsquo;s six indices (Hofstede, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u0026mdash;uncertainty avoidance, power distance, individualism, masculinity, long-term orientation, and indulgence\u0026mdash;may significantly influence financial inclusion by shaping societal values, behaviors, and attitudes toward financial systems. For example, cultures with high individualism are more likely to encourage personal financial responsibility and autonomy, leading to higher financial inclusion (Lu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, cultures with strong power distance or uncertainty avoidance may foster hierarchical or risk-averse norms, which could discourage individuals, particularly women, from engaging with formal financial systems.\u003c/p\u003e \u003cp\u003eHowever, we do not include cultural dimensions in the main estimations because they can introduce issues of multicollinearity. Cultural factors are deeply intertwined with institutional and legal development, meaning that they could overlap with variables, such as trust in banks, and rule of law, potentially distorting the relationships we aim to study. Additionally, cultural measures tend to remain stable over time, limiting their explanatory power in dynamic analyses of financial inclusion. Thus, these dimensions are included in this robustness check to ensure that our results are not confounded by unobserved cultural factors.\u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eRobustness Tests\u003c/b\u003e This table reports the results of the robustness tests. In column (1), we use probit regression and control for additional control variables at the country-level using Hofstede dimensions: Uncertainty Avoidance, Power Distance, Individualism, Masculinity, LT Orientation, and Indulgence. In column (2), we use OLS regressions to estimate our models at a country-year level. Finally in column (3), we display results of our main analysis dropping overrepresented countries in our sample. P-values are reported in parentheses. Standard errors are robust to heteroscedasticity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdditional control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry-level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDropping overrepresented countries\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.374***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertainty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower Distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividualism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasculinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term Orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndulgence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.694***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165,457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e column (1), controlling for cultural dimensions does not alter our main findings. Trust fragmentation remains strongly negative and statistically significant on formal account ownership.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Country-level analysis\u003c/h2\u003e \u003cp\u003eTo further verify the robustness of our results, we perform an additional analysis at the country level using aggregated data per country and year. Although the individual-level estimations link trust fragmentation and financial inclusion outcomes, country-level analysis evaluates whether the relationships observed hold in an aggregate context, accounting for broader institutional and macroeconomic dynamics.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTrust Fragmentation\u003c/em\u003e remains negatively associated with formal account ownership, in line with our main results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026ndash; column (2)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e6.3. Removal of overrepresented countries\u003c/h2\u003e \u003cp\u003eA potential source of bias that could affect our results relates to the representativeness of the countries included in our study. If certain countries are overrepresented in our sample, they may disproportionately influence the results. To address this concern, we excluded 3 of 51 countries contributing, namely China, India, and Morocco (nearly 5% of the sample), as they contributed more than 14% of the total observations. The majority of countries in our sample have between 2000 and 3000 observations, with few countries having 1000 observations. China, India, and Morocco had 15,309, 12,478, and 7,090 observations, respectively.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e column (3), after removing these overrepresented countries, the results remain consistent with the main findings for \u003cem\u003eFormal Account\u003c/em\u003e, confirming the robustness of our estimations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.4. Alternative financial inclusion variable\u003c/h2\u003e \u003cp\u003eDemirg\u0026uuml;\u0026ccedil;-Kunt and Klapper (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) point out that in developing countries, approximately 10 percent of individuals with an account leave it inactive, making no deposits or withdrawals. Furthermore, when trust is weakened or fragmented, individuals who already possess a bank account may respond to the resulting uncertainty not by closing the account, but by leaving it inactive and ceasing to use it altogether. Therefore, it is important to consider not only the ownership of a bank account but also its use, whether in terms of withdrawals or savings.\u003c/p\u003e \u003cp\u003eTo do so we construct two new variables. First, \u003cem\u003eFormal Savings\u003c/em\u003e, using information provided in the following question: \u0026ldquo;\u003cem\u003eIn the past 12 months, have you saved or set aside money by using an account at a bank, credit union or another financial institution?\u003c/em\u003e\u0026rdquo;. We construct a dummy variable equal to 1 if the respondent has deposited money into his/her personal account(s) and 0 otherwise.\u003c/p\u003e \u003cp\u003eSecond, we construct the variable \u003cem\u003eFrequency\u003c/em\u003e, which is a dummy equal to 1 if the respondent has withdrawn money from his/her personal account(s) at least twice per month and 0 otherwise, using the question \u0026ldquo;\u003cem\u003ein a typical month, how many times is money taken out of your personal account(s)? this includes cash withdrawals, electronic payments or purchases, checks, or any other time money is removed from your account(s) by yourself or others?\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash; Alternative dependent variables\u003c/b\u003e This table reports probit regressions at the individual level. The dependent variable is \u003cem\u003eFormal Savings\u003c/em\u003e in columns (1) to (3) and \u003cem\u003eFrequency\u003c/em\u003e in columns (4) and (6). In columns (1) and (4), we display the estimations with all control variables as in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In columns (2) and (5), we rerun the main estimation by removing the \u003cem\u003eTrust in Banks\u003c/em\u003e variable. Finally, in columns (3) and (6), we rerun the main estimation by clustering standard errors at the country level. In all columns, we control for region and year fixed effects. P-values are reported in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal Savings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Savings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormal Savings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.281***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.798***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.584***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.798**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.255***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.239***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.353)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.619)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.765***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.740***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.765***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.837***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.860***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.837***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.397***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.389***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.397***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.487***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.493***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.487***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.664***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.669***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.664***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.495***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.489***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.495***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.524***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.528***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.524***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.418***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.412***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.418***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.362***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.365***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.362***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.328***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.324***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.328***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.232***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.232***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.194***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.195***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.131***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.302***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.474)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.250***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.783)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.221***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.280***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.487)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.100***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.588)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanking Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.124***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.131***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.386)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATMs/1,000km2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.602)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.250***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.239***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.250**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.310***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.320***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.310**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(GDP Capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.882)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.324)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.762***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.269***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.210***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.785***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.210**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e144,131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of our main analysis using the two new financial inclusion variables. As before, we test our specification by including all control variables (columns (1) and (4)), by removing \u003cem\u003eTrust in Banks\u003c/em\u003e to control for a potential collinear effect (columns (2) and (5)) and by clustering our standard errors by country (columns (3) and (6)). We can observe that in all cases our \u003cem\u003eTrust Fragmentation\u003c/em\u003e variable is negative and significant, proving that a fragmentation of trust not only reduces account ownership, but also reduces its use, whether for saving money or withdrawing it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.5. Alternative sample\u003c/h2\u003e \u003cp\u003eIn line with Heyert and Weill (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we rely on the Life in Transition Survey (LiTS) as a robustness check to confirm that the identified relationships are not driven by dataset-specific characteristics. Although LiTS provides valuable insights into the relationship between trust in banks and financial inclusion, we do not use it for our main estimations for several reasons. First, the regional coverage of LiTS is limited to 28 countries in Central and Eastern Europe and Central Asia. Although diverse, it does not capture the global heterogeneity of financial inclusion and institutional trust. The Global Findex dataset offers broader coverage, including countries from all regions of the world, ensuring a more representative analysis. Moreover, LiTS spans only three survey years (2006, 2010, and 2016) compared to four for the Global Findex.\u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash; Alternative sample: LiTS survey\u003c/b\u003e This table provides results of probit regression using an alternative database, the LiTS database for 2006, 2010, and 2016. The dependent variable is \u003cem\u003eFormal Account\u003c/em\u003e, which is measured by at least one member of the household having a bank account or using a debit/credit card. In column (1), we display our main estimation with all control variables. In column (2), we rerun our main estimation by removing the \u003cem\u003eTrust in Banks\u003c/em\u003e variable. Finally, in column (3), we rerun our main estimation by clustering standard errors at the country level. In all columns, we control for region and year fixed effects. P-values are reported in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.627***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.630***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.627**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.573***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.581***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.573***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.386***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.388***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.386***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.238***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.246***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.238**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.831)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.186***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.193***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.186**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.308***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.321***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.308***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.362***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.391***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.476)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.038*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.099)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.702)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.086***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.086***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanking Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.241***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.269***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.377)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATMs/1,000km2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.895***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.895***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog (GDP Capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.787)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.515***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.617***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.211)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70,271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regression results using this alternative dataset are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. In all specifications, financial inclusion is measured at the household level, with at least one household member having a bank account or using a debit/credit card, providing a comprehensive indicator of formal account ownership. The first specification focuses on the core explanatory variables, \u003cem\u003eTrust Fragmentation\u003c/em\u003e, alongside basic individual-level controls (e.g., gender, age, and education). The subsequent specifications progressively include macroeconomic controls, such as rule of law, GDP per capita, as well as institutional trust measures, such as trust in government and justice systems and household-level satisfaction variables and finally ethnic fragmentation. Although the inclusion of these controls slightly alters coefficient magnitudes, the overall relationships remain consistent, with trust fragmentation having a strong and significant negative impact on financial inclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.6. Instrumental variable regression\u003c/h2\u003e \u003cp\u003eFinally, our analysis may be subject to potential endogeneity bias. Financial inclusion may impact trust in banks, leading to potential simultaneous causality. Additionally, as trust impacts many economic outcomes, it is likely that there variables are omitted that simultaneously impact both the diversity of trust in the environment and financial inclusion.\u003c/p\u003e \u003cp\u003eTo address potential endogeneity concerns related to trust fragmentation, we implement a shift-share (Bartik-style) instrumental variable strategy based on cross-country differences in demographic composition. The instrument exploits systematic differences in trust in banks across observable demographic groups, such as gender, age, education, and income, as documented in the literature (e.g., Fung\u0026aacute;čov\u0026aacute; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We first estimate group-specific propensities to trust banks using individual-level survey data, excluding observations from the country under consideration (leave-one-country-out approach) to avoid mechanical correlation. These estimated propensities capture persistent, cross-country patterns in trust across demographic groups. We then combine these \u0026ldquo;shifts\u0026rdquo; with country-wave specific demographic shares (\u0026ldquo;shares\u0026rdquo;) to construct a predicted distribution of trust in each country. The instrumental variable is defined as the predicted dispersion (standard deviation) of trust across demographic groups within a country.\u003c/p\u003e \u003cp\u003eThis shift-share design provides a transparent decomposition of the source of identifying variation. The \u0026ldquo;shares\u0026rdquo; correspond to the demographic composition of each country, while the \u0026ldquo;shifts\u0026rdquo; reflect group-specific trust propensities estimated from external variation. Identification therefore comes from the interaction between predetermined demographic structure and exogenous differences in trust across groups. In line with recent contributions on the econometrics of shift-share instruments, the validity of our approach relies on the exogeneity of either the shares or the shocks (Goldsmith-Pinkham et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Borusyak et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In our setting, the identifying assumption is that, conditional on controls, the estimated group-level trust propensities are orthogonal to country-specific unobserved determinants of financial inclusion. The leave-one-country-out procedure ensures that these shifts are not contaminated by within-country variation, thereby reinforcing their exogeneity.\u003c/p\u003e \u003cp\u003eThe relevance of the instrument follows from the well-established relationship between demographic characteristics and trust in financial institutions, implying that differences in population structure generate systematic variation in the dispersion of trust. Regarding the exclusion restriction, the instrument combines demographic shares with externally estimated group-specific trust propensities. A potential concern is that demographic composition may directly affect financial inclusion through channels unrelated to trust, such as financial literacy, income, or preferences for financial services. To address this, our empirical specification includes a rich set of individual-level controls (age, gender, education, and income), which absorb the direct effects of these characteristics on financial participation. As a result, the identifying variation in the shift-share instrument does not stem from demographic composition per se, but from differences in predicted trust across demographic groups. Conditional on these controls, demographic structure can influence financial inclusion only through its effect on the dispersion of trust, rather than through direct demographic channels. The instrument therefore isolates the component of trust fragmentation driven by heterogeneity in trust across groups, supporting the plausibility of the exclusion restriction.\u003c/p\u003e \u003cp\u003eFinally, we note that our setting is particularly well suited to a shift-share design, as the object of interest is the dispersion of beliefs across individuals within countries. The instrument therefore directly maps into the theoretical mechanism under investigation, namely that heterogeneity in trust across social groups affects financial inclusion outcomes. Overall, this approach provides a plausibly exogenous and conceptually coherent source of variation in trust fragmentation.\u003c/p\u003e \u003cp\u003eAs an additional instrument, we exploit the fragmentation of trust in government institutions. This strategy is grounded in a well-established literature showing that trust in different institutions is strongly interrelated and reflects a common underlying component of institutional trust. Evidence from psychology and political economy indicates that individuals tend to form generalized beliefs about institutions, such that trust in one institution co-moves with trust in others (Newton and Norris, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Devos et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In the economic literature, Stevenson and Wolfers (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Buriak et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) document that trust in political institutions is closely correlated with trust in economic and financial institutions.\u003c/p\u003e \u003cp\u003eFollowing this approach, we construct a measure of fragmentation in trust in government and use it as an instrument for trust fragmentation in banks. The relevance condition is supported by the fact that heterogeneity in institutional trust tends to be pervasive across domains: societies characterized by polarized or heterogeneous beliefs about public institutions are also likely to exhibit dispersion in trust toward financial institutions.\u003c/p\u003e \u003cp\u003eA potential concern is that trust in government institutions may directly affects financial inclusion through channels unrelated to trust in banks, for instance via confidence in regulation, public policies, or the broader institutional environment. To mitigate this concern, our empirical specification includes a comprehensive set of individual-level controls (age, gender, education, income) and macroeconomic characteristics, which absorb the main determinants of financial participation. In addition, we control for the average level of institutional trust, ensuring that identification relies on the dispersion of trust rather than its level. Therefore, the remaining variation captured by the instrument reflects differences in the heterogeneity of institutional beliefs across individuals, rather than overall confidence in public institutions.\u003c/p\u003e \u003cp\u003eUnder this assumption, fragmentation in trust in government affects financial inclusion only through its impact on trust fragmentation in banks, by shaping the broader environment of beliefs about institutions. This interpretation is consistent with the view that trust operates as a generalized social norm influencing multiple domains, while our empirical strategy isolates its bank-specific transmission channel.\u003c/p\u003e \u003cp\u003e \u003cem\u003e{Please insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cem\u003earound here}\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash; Instrumental variable regression\u003c/b\u003e This table reports results of the instrumental variable (IV) regression. Column (1) displays the results of the first stage of the IV, using an OLS regression in which \u003cem\u003eShift-Share Instrument\u003c/em\u003e and \u003cem\u003eTrust in Government Fragmentation\u003c/em\u003e are used as instruments for \u003cem\u003eTrust Fragmentation\u003c/em\u003e. The exogeneity test (H-test), overidentification test (J-test), and relevance test (F-test) appear at the bottom of the first column. Second stage is displayed in column (2) for \u003cem\u003eFormal Account.\u003c/em\u003e In all columns, we control for all control variables as in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and for region and year fixed effect (FE). P-values are reported in parentheses. Standard errors are robust to heteroscedasticity *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively. Appendix A contains variable definitions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst-stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond-stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormal Account\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShift-Share Instrument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in Government Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.488***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust Fragmentation*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.369***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192,344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192,344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo \u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExogeneity (H-test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOveridentification (J-test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelevance (F-test of excluded instruments)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e displays the results of our instrumental variable regression. All instrumental variables are statistically significant. Regarding the quality of the first-stage regression in column (1), we can see that the H- and J-tests are not significant, while the F-test of the excluded instruments is highly significant, suggesting that the instruments are both relevant and sufficiently strong. In the second stage of the instrumental variable estimation in column (2), the coefficient associated with \u003cem\u003eTrust Fragmentation\u003c/em\u003e remains negative and statistically significant. These results confirm the robustness of our main findings and reinforce the interpretation that greater dispersion in trust in banks has a detrimental effect on financial inclusion\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis paper investigates the relationship between institutional fragmentation in trust and financial inclusion. Although trust in banks is widely recognized as a key driver of financial inclusion, we demonstrate that the coherence, or lack thereof, in trust perceptions within societies shape access to and usage of formal financial services. Using individual-level data from the Global Findex database combined with trust measures from the WVS, we provide robust evidence that a higher fragmentation in trust is associated with lower financial inclusion outcomes.\u003c/p\u003e \u003cp\u003eOur results contribute to a better understanding of financial inclusion dynamics. We confirm that while trust in banks is a factor that positively impacts financial inclusion, its fragmentation within countries negatively impacts it. Our intuition is that this fragmentation in trust undermines the predictability of financial behavior and reinforces ambiguity about the credibility and fairness of formal institutions. Hence, in societies in which trust is fragmented, individuals are less likely to engage with formal financial systems due to the ambiguity generated by conflicting institutional signals.\u003c/p\u003e \u003cp\u003eTo validate this interpretation, we analyze both individual and contextual dimensions identified in the literature as shaping attitudes toward ambiguity. On the individual side, ambiguity-averse groups (specifically women, younger individuals, and those with higher educational attainment) are less likely to open bank accounts, reflecting their greater sensitivity to ambiguity. On the environmental side, contexts that mitigate ambiguity toward banks, such as those characterized by high institutional trust or greater financial satisfaction, attenuate the negative impact of trust fragmentation on financial inclusion. These findings reinforce our interpretation that trust fragmentation affects financial inclusion by heightening ambiguity toward the financial system.\u003c/p\u003e \u003cp\u003eOur analysis highlights the limits of relying solely on average levels of trust to explain financial inclusion. By focusing on the distribution of trust, rather than its mean alone, we provide an explanation for why inclusion can remain low even where average trust appears high: heterogeneous beliefs create ambiguity, and ambiguity-averse households shy away from formal intermediation. Furthermore, we demonstrate that, when this fragmentation is taken into account, the average level of trust alone no longer explains individuals' financial inclusion.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, our results have significant implications for addressing financial inclusion gaps. Policymakers and financial institutions need to recognize that heterogeneity in trust within societies cannot be ignored when designing inclusive financial systems. Efforts to build trust in financial institutions must be targeted, context-specific, and cognizant of cultural and institutional dynamics. Initiatives, such as financial literacy programs, increased transparency, and tailored financial products, can help address the specific trust needs of diverse societal subgroups. Additionally, financial institutions operating in societies with high institutional fragmentation should focus on reducing ambiguity toward banks among individuals. This can be achieved by improving communication strategies, addressing perceptions of inequity, and innovating financial products that align with the expectations of trust. For instance, introducing community-based banking initiatives and leveraging trusted intermediaries could engage populations that remain skeptical of formal financial systems. Addressing trust fragmentation will require sustained efforts to create inclusive environments that promote trust in the safety, reliability, and fairness of financial institutions.\u003c/p\u003e \u003cp\u003eThis paper also opens avenues for future research. While we provide robust evidence of the negative impact of institutional trust fragmentation on financial inclusion, important questions remain. For instance, what are the long-term consequences of trust fragmentation for financial stability and resilience? How does the rise of digital finance (mobile money or fintech solutions) interact with fragmented trust perceptions? Which types of policy interventions, from financial literacy initiatives to regulatory reforms, are most effective in reducing ambiguity in contexts of high trust heterogeneity? And finally, to what extent do cultural and institutional settings condition these dynamics, and how can policies be tailored accordingly?\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003eThe Authors declare that there is no conflict of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.B.: Conceptualization, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft, Project administration.E.E.: Conceptualization, Methodology, Formal Analysis, Resources, Writing - Original Draft. C.P.: Conceptualization, Methodology, Investigation, Writing \u0026ndash; Original Draft.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from Global Findex. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the author(s) with the permission of Global Findex.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlesina A, La Ferrara E (2002) Who trusts others? J Public Econ 85(2):207\u0026ndash;234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen F, Demirguc-Kunt A, Klapper L, Peria MSM (2016) The foundations of financial inclusion: Understanding ownership and use of formal accounts. 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Acad Manage Rev 24:99\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank (2021) \u003cem\u003eThe Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X (2020) Trust and financial inclusion: A cross-country study. Finance Res Lett 35:101310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeitz AO, Leblang DA (2021) Migrants as engines of financial globalization: the case of global banking. Int Stud Quart 65(2):360\u0026ndash;374\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZins A, Weill L (2016) The determinants of financial inclusion in Africa. Rev Dev Finance 6(1):46\u0026ndash;57\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e It is interesting to note that if we remove the \u003cem\u003eTrust Fragmentation\u003c/em\u003e variable from our analysis, the \u003cem\u003eTrust in Banks\u003c/em\u003e coefficient becomes positive and significant again, as in previous articles.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We also use the IV regression on the other financial inclusion variables (\u003cem\u003eFormal Savings\u003c/em\u003e and \u003cem\u003eFrequency\u003c/em\u003e) and results are also negative and significant. For the sake of brevity results are not displayed here but are available upon request to the authors.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trust in Banks, Financial Inclusion, Culture, Fragmentation","lastPublishedDoi":"10.21203/rs.3.rs-9523032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9523032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDoes trust in banks operate uniformly, and does variability in this trust matter for financial inclusion? This paper introduces trust fragmentation, i.e. the degree of heterogeneity in trust levels within a society, as a critical but overlooked factor shaping financial inclusion outcomes. Using individual-level data from the Global Findex and trust measures from the World Values Survey, we show that higher fragmentation in trust within a country significantly reduces financial inclusion. This effect is particularly pronounced among individuals who are more sensitive to ambiguity, such as women, younger respondents, and those with higher levels of education, and attenuated in environments where ambiguity is reduced, where trust in banks and financial satisfaction are high. These findings suggest that fragmented trust creates ambiguity, deterring individuals from engaging with formal financial services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes\u003c/strong\u003e: G21, G51, Z10.\u003c/p\u003e","manuscriptTitle":"To Trust or Not to Trust? Financial Inclusion and Fragmentation of Trust in Developing Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 13:28:24","doi":"10.21203/rs.3.rs-9523032/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68c72095-0ee4-4b95-8c49-804bd6235adc","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"327269430062010804187969783367826911844","date":"2026-05-11T06:48:27+00:00","index":8,"fulltext":""},{"type":"reviewersInvited","content":"2","date":"2026-05-07T22:43:31+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:28:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 13:28:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9523032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9523032","identity":"rs-9523032","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}