1. Introduction
Financial development is a broader yardstick of access, depth, and efficiency of the financial system in a country (Svirydzenka, 2016). The finance–growth nexus explains a strong connection between financial development and economic growth, given that there are functioning banking and market-based financial systems that help reduce external financial constraints while supporting economic progress (Levine, 2004). The micro-foundation of financial development in a country depends strongly on the openness, human capital, savings culture, financial lifestyle, risk management practices, and consumption pattern of the users of the financial system, and the quality of the institutions (Ibrahim & Sare, 2018; Pal & Mahalik, 2024; Samargandi et al., 2015; Nadler & Breuer, 2019). Using panel data from 1984 to 2020 and the PMG-ARDL model, Pal and Mahalik (2024) examine the impact of remittances, FDI, and institutional quality on financial development in top (Europe and Central Asia) and bottom (sub-Saharan Africa) developing regions. The study finds that remittances, FDI, and institutional quality stimulate financial development in the top region but reduce it in the bottom region. However, when moderated by institutional quality, remittances and FDI are positively associated with financial development in both regions. The paper also confirms the positive influence of economic growth on financial development. Goodell et al. (2023) present a systematic review and bibliometric analysis of culture research in finance journals. It summarizes publication and citation trends, identifying key contributors, and outlines major themes such as governance, financial decision-making, investing, social trust, religion, and cultural distance. Current themes include individualism and risk-taking, regulations, religiosity and venture capital, and organizational legitimacy.
Despite a growing and fundamental influence of culture on finance the literary strength on the cultural financial development nexus is very limited (Khan et al., 2022).
Culture involves the passing down of knowledge, values, and behavioral influences from one generation to the next through teaching and imitation (R. Boyd & Richerson, 1988). The transformative nature of culture is added to its ability to differentiate among people sharing the cultural dimensions. Hofstede (1980) mentioned, Culture is the shared mental framework that differentiates one group of people from another. Because of the collective similarity of attributes, culture plays a meaningful role in representing the social, economic, and political decision frameworks of groups of people. People sharing cultural attributes tend to choose similar banks, invest in familiar financial assets, and share typical risk management attributes (Vanheusden et al., 2024).
Cultural values are closely related to the wealth of nations (Schwartz, 1994; Franke et al., 1991). Cultural dimensions also explain the foreign bias in international portfolio holdings in international economics (Karolyi, 2016). Several cultural dimensions, such as the power distance, uncertainty avoidance, individualism, and masculinity scores connect strongly to companies considering new debt and equity issuance from the financial markets (Rashid et al., 2023). Similarly, national culture determines cross-country differences in financial sector development (Khan et al., 2022). Culture strongly moderates financial decisions made by individuals, households, and firms (Aggarwal et al., 2016). Cultural distances between two nations carry strong implications for financial market development, as found by de Jong and Semenov (2002) after examining the OECD countries. Culture plays the role of the modifier during crisis periods, despite very limited evidence on this nexus. Fernandez-Perez et al. (2021) investigated the nexus between COVID-19, market volatility, and culture, and found that countries with low individualism and high uncertainty avoidance had higher volatility globally during the first few weeks of the pandemic.
Despite a rise in studies on culture and growth nexus, this study finds a critical gap in the literature on culture and financial development. The study contributes to a very limited set of studies on culture and financial development nexus. Most notably, Khan et al. (2022) investigated the impact of the four Hofstede’s (1980) dimensions of national culture (e.g., individualism, uncertainty avoidance, masculinity, and power distance) on financial sector development (FSD) in emerging and developing economies during the period 1984 to 2018. Their results suggest that a higher level of individualism and masculinity enhance FSD, but higher uncertainty avoidance in an economy impedes FSD. This study forwards us with three takeaways that demand further investigation. First, the nations with risk-averse investors will keep aside financial market engagement (investments, borrowings, and depositing in banks), particularly during the crisis time. Second, FSD is dominated by male-centric, non-collective cultural characteristics in emerging markets. Third, the study investigated four dimensions of the culture, while there are two more Hofstede cultural dimensions: long versus short-term orientation and indulgence versus restraints—missing in analysis. Hence, the approach of the current study helps investigate additional cultural dimensions and their influence on financial development.
The results of this study are also instrumental in explaining the influence institutional quality has on financial development. Institutional quality helps recover the financial system during the crisis period (Sever, 2022). A country with a strong rule of law is more likely to have stable formal institutions, while a country with a culture of corruption is more likely to have unstable formal institutions. Institutional volatility, which is the instability of formal and informal institutions, in a country can be affected by its national culture (Henisz, 2004).
We use Hofstede’s 6-D model of national culture (Hofstede, 1983, 1991; Hofstede et al., 2010). Hofstede states that culture changes very gently over time, and these cultural measures maintain their validity over a long period. The dimension scores for each country do not indicate an absolute score; instead, it is a relative score with respect to other countries, which hardly swings even if culture changes (Haga et al., 2018; Beugelsdijk et al., 2015; Hofstede, 2001). This suggests relative stability in societies such that data collected from year-to-year does not substantively change. We also include the role of formal and informal institutions and analyze the asymmetric impact of financial crises on our findings.
We investigate the direct and indirect impact of the cultural variables on the financial expansion of the 33 countries from 2000 to 2021. The control variables that we use are inflation, economic growth, the real effective exchange rate, and institutional quality. Following the approach of previous investigations that have sought to mitigate the endogeneity concern between financial development and economic growth, this research utilizes the two-step generalized method of moments (GMM) estimator (Calderón & Liu, 2003; Guiso et al., 2004; Levine & Zervos, 1996; Masten et al., 2008; Wachtel et al., 2006). The study has utilized a host of tests based on Ordinary Least Squares (OLS), two-steps dynamic system GMM, PVAR, and other tests to check robustness of the findings.
The study presents relevant past literature in Section 2. Section 3 explains data and develops methodology. Section 4 analyzes the results, and Section 5 concludes the study.
2. Literature Review
2.1. Financial Development: Common Determinants
A range of traditional factors, including inflation, economic growth, and the consumer price index, that significantly explain the development of the financial system have been documented in prior research (J. H. Boyd et al., 2001; Aluko & Ajayi, 2018; Khan et al., 2019; Zaidi et al., 2019; Khan et al., 2022). Inflation and economic growth serve as the primary determinants of financial development. Inflation represents an increase in the overall price level within an economy, indicating a degree of macroeconomic instability. Financial sector activities are hypothesized to decline with an increase in the inflation rate (Huybens & Smith, 1999). Numerous empirical studies have documented the negative correlation between inflation and the development of the banking sector (Aggarwal et al., 2011; Gwama, 2015; Mahawiya, 2015). Economic growth enhances demand for enterprises, subsequently fostering the development of the financial sector (Aluko & Ajayi, 2018). Song et al. (2021) analyze the relationship between corruption, economic growth, and financial development across 142 countries from 2002 to 2016, concluding that economic growth positively influences financial development. Numerous studies indicate a positive relationship between economic growth and the development of the banking sector (Falahaty & Hook, 2013; Le et al., 2016). Institutional quality, the quality of formal institutions, and the nature of government intervention significantly affect financial development through distorted controls on prices, capital flows, and foreign exchange (Kwok & Tadesse, 2006).
2.2. Institutional Quality and Financial Development
Institutional theory posits that quality institutions are characterized by socially established rules and norms that facilitate the attainment of legitimacy in their operations (Scott & Meyer, 1983). Institutional quality encompasses the framework of rules and regulations that oversee institutional activities within a nation. An institutional mechanism characterized by transparency, stability, and accountability can mitigate corruption and rent-seeking, thereby liberating resources for productive investment.
Upreti (2015) examines the interplay among financial development, institutional quality, and economic growth within a cohort of eight South Asian nations from 1980 to 2010. The research indicates a positive relationship between financial development and economic growth, with institutional quality serving as a moderating factor in this effect. Ali et al. (2018) analyzed the interaction among financial inclusion, financial development, and institutional quality in a sample of 72 countries from 1995 to 2015. The research indicates that financial inclusion positively influences financial development, with institutional quality serving as a moderating factor. Khan et al. (2019) investigate the correlation between institutional quality and financial development across a sample of 100 developing and emerging economies from 1984 to 2016. The research indicates that institutional quality positively and significantly influences financial development. Khan et al. (2024) analyze the interplay among the quality of financial institutions, innovation, and financial development across 22 emerging markets. The interaction between innovation, technology, and effective institutions can expedite financial development. This study presents policy implications aimed at enhancing competitiveness and sustaining financial development in emerging markets. According to the existing literature, a positive relationship between institutional quality and financial development is anticipated.
2.3. Hofstede Cultural Dimensions and Financial Development
2.3.1. Power Distance Index and Financial Development
Power distance is defined as the extent to which the less powerful members of institutions and organizations within a country expect and accept that power is distributed unequally (Hofstede et al., 2010). Power distance measures the degree of inequality in a society (Ashraf et al., 2016). As such, high power distance societies accept the unequal distribution of power and authority and have a more dependent relationship between their leaders and subordinates. High power distance societies, characterized by a hierarchical social structure and an acceptance of unequal power distribution, tend to exhibit lower levels of social trust. This lack of trust can elevate transaction costs and foster a culture averse to risk-taking, ultimately impeding financial development (Bjørnskov, 2008; Das & Teng, 2004; Growiec & Growiec, 2014; Khan et al., 2022). Based on these considerations, we expect a negative association between power distance and financial development and define the following hypothesis (see model 2):
Power distance has a negative effect on financial development.
2.3.2. Individualism Versus Collectivism and Financial Development
Individualism and collectivism differentiate between decision-making processes that prioritize group dynamics and those that emphasize individual autonomy. Individualistic civilizations prioritize accomplishments and provide recognition for them. The concept of collectivism, as explored by Earley (1989), Hofstede (1980), and Hui (1988), encompasses groups characterized by a strong emphasis on sharing, cooperation, and the prioritization of group well-being. Chui et al. (2010) establish a connection between individualism and overconfidence. Risk-taking behavior is more pronounced in cultures that emphasize individual success and achievement compared to collectivist cultures that prioritize group welfare. Shleifer and Vishny (1997) advocate for individualism and a high-risk approach as essential for successful stock trading. According to Kyriacou (2016), individualism enhances governance and stimulates economic growth. Ang (2019) demonstrates that individuality enhances financial sector development in various countries. Khan et al. (2022) indicate that individualism and masculinity enhance financial sector development in emerging and developing economies. Individualism, long-term orientation, and indulgence enhance financial capabilities (Bialowolski et al., 2023). Raji et al. (2024) indicate that nations characterized by individualism and masculinity exhibit greater financial inclusion. Therefore, we expect a positive link between individualism and financial development and analyze the following hypothesis (See model 3):
Individualism has a positive impact on financial development.
2.3.3. Masculinity Versus Femininity and Financial Development
Masculinity evaluates male characteristics, including assertiveness and achievement. Masculinity evaluates how much society rewards male traits like competition, assertiveness, and achievement (Hofstede, 1980, 2001). Material achievement is frequently linked to males (Zheng et al., 2012). Masculine nations prioritize achievement, confrontation, and intellectual independence, in contrast to solidarity, cooperation, and morality (Aggarwal et al., 2012). Masculinity presents a contrast between being assertive and being modest. Khan et al. (2022) demonstrated that masculinity enhances the development of the financial sector. In a masculine society, individuals tend to exhibit overconfidence and engage in riskier behaviors, particularly in financial contexts. Risk-taking increases the demand for financial services, thereby enhancing financial growth (Khan et al., 2022). Engaging in financial risks is not universally advisable; however, certain studies have associated masculinity with such behavior (Mihet, 2013; Powell & Ansic, 1997). Engaging in risks solely for personal benefit may lead to financial depletion (Meier-Pesti & Penz, 2008). Women exhibit a higher level of risk awareness compared to men (Barke et al., 1997; Bord & O’Connor, 1997; Lundeberg et al., 1994). Barke et al. (1997) investigate gender differences in how scientists perceive nuclear risk. Previous research shows that the general public has gender disparities in risk perception, with women expressing greater concern about health and environmental hazards. This research examines a mail survey of scientists from the American Association for the Advancement of Science in Colorado and New Mexico. The results indicate that male scientists perceive significantly less risk from nuclear technologies compared to female scientists. This difference is not explained by differences in scientific knowledge or attitudes toward technology or nature. Instead, gender and field of research have an additive effect, with female scientists and life scientists perceiving the greatest risks. Raji et al. (2024) demonstrate that nations characterized by masculinity and individualism exhibit higher levels of financial inclusion. Also, nations with greater female representation are likely to experience enhanced economic growth. Therefore, the study hypothesizes the following (see model 4):
Masculinity has a positive impact on financial development.
2.3.4. Uncertainty Avoidance Index and Financial Development
Uncertainty avoidance quantifies a society’s tolerance for ambiguity and unpredictability. de Jong and Semenov (2002) indicate that stock markets are generally more developed in societies characterized by reduced uncertainty avoidance and elevated masculinity levels. Dutta and Mukherjee (2012) utilize data on financial development sourced from the Beck et al. (2001) database. The authors establish that a country’s financial development is significantly negatively correlated with uncertainty avoidance. Aggarwal and Goodell (2014) identify a significant negative correlation between uncertainty avoidance and access to finance. Countries with low uncertainty avoidance have greater access to credit compared to those with high levels of uncertainty avoidance. Satt et al. (2019) demonstrate that the level of information during Ramadan affects the optimism of analysts’ recommendations, specifically noting that high uncertainty results in fluctuating levels of optimism. Khan et al. (2022) indicate that uncertainty avoidance hinders the development of the financial sector. Bialowolski et al. (2023) find a negative correlation between uncertainty avoidance and financial capability across a sample of 137 countries. Based on the mentioned empirical evidence, we expect a negative relationship between uncertainty avoidance and financial development (see model 5):
Uncertainty Avoidance has a negative impact on financial development.
2.3.5. Long-Term Versus Short-Term Normative Orientation and Financial Development
A fifth cultural dimension is referred to as time orientation. Long-term orientation refers to a focus on future outcomes, whereas short-term orientation emphasizes the significance of the present and past over future considerations (Hofstede et al., 2010). A short-term orientation emphasizes the importance of tradition and fulfilling social obligations, whereas a long-term orientation prioritizes persistence and reflects a pursuit of societal morality (Dahl, 2004; Darsono et al., 2021). Darsono et al. (2021) examine the influence of four cultural dimensions—power distance index, individualism, uncertainty avoidance index, and long-term orientation—on sustainable investment returns in Asian stock exchanges from 2015 to 2019. The findings demonstrate that power distance, individualism, uncertainty avoidance, and long-term orientation significantly and positively affect market returns. The authors conclude that a long-term oriented investor in the sustainable stock exchange within the Asian region would contribute to higher stock returns. Investors tend to select assets with long-term returns to achieve portfolio diversification (Anderson et al., 2011). Bialowolski et al. (2023) utilized data from 137 countries and identified a positive relationship between individualism, long-term orientation, and indulgence with the financial capability of those countries. We hypothesize the following (see model 6):
Long-Term Orientation has a positive impact on financial development.
2.3.6. Indulgence Versus Restraint and Financial Development
Indulgence refers to a societal framework that permits a degree of unrestricted satisfaction of fundamental human impulses associated with enjoyment and leisure. Restraint refers to a societal framework that curtails the fulfillment of needs and governs it through stringent social norms. The Indulgence versus Restraint dimension, a key component of Hofstede’s cultural framework, has garnered increasing attention for its potential to explain variations in organizational behavior and financial outcomes. While Aleqedat (2021) notes a general influence of cultural dimensions on Jordan’s corporate landscape and a scarcity of local research on Indulgence, this paper underscores its importance. Sun et al. (2019) examine the role of indulgence versus restraint as a moderating variable in the relationship between corporate social performance (CSP) and corporate financial performance (CFP). The research indicates that in indulgent nations, social performance has a positive impact on financial performance. de Oliveira (2016) indicates that countries characterized by high levels of indulgence exhibit higher dividend payout ratios. Bialowolski et al. (2023) find a positive correlation between indulgence and the financial capability of the nations examined in their study. Considering the preceding discussion, we offer the following hypothesis (refer to model 7):
Indulgence (as opposed to restraint) has a positive impact on financial development.
To complement these related studies, we present Table 1 below, which summarizes relevant literature associated with Hofstede’s analysis.
3. Empirical Design
3.1. Data and Measurements
In this study we utilize several models to discover the direct and indirect influence of the national culture variables on financial development of 33 developed and developing countries in our sample from 2001 to 2021. Table 2 indicates more details about our observations based on MSCI Market Classification Framework. To provide context for the study, this overview highlights the diverse economic and cultural characteristics of the selected countries (Table 2), which significantly influence financial behaviors and market dynamics. The sample includes Developed Economies such as Australia and the United States, characterized by mature economies, advanced financial systems, and individualistic cultures; Emerging Economies such as Brazil and China, marked by rapid growth, developing financial sectors, and a range of cultural values; and Frontier Economies such as Estonia and Slovakia, representing smaller economies with growth potential and evolving financial markets. This diverse selection allows for a comprehensive analysis of how National Culture and Institutional Quality interact to influence Financial Development.
To measure financial development, this study employs the IMF Financial Development (FD) index, which assesses the depth, access, and efficiency of a country’s financial institutions and markets. In contrast to previous research, such as Shehzad et al. (2022), which has used domestic credit to the private sector (% of GDP) as a proxy, the FD index was selected for its comprehensive nature. We identify four traditional control factors, beginning with the inflation rate (INF), which is derived from fluctuations in the Consumer Price Index (Izadi, 2015; Aluko & Ajayi, 2018). High inflation contributes to financial underdevelopment (J. H. Boyd et al., 2001); consequently, we anticipate a negative coefficient for the inflation rate. Second, in accordance with Zaidi et al. (2019) and Khan et al. (2022), we employ a proxy for economic growth (GDPGR). Additionally, we utilize a proxy to assess an individual country’s currency value in relation to other major currencies in the index, based on their trade balance (EXCH) (Khan et al., 2022). EXCHit represents the real effective exchange rate for country ‘i’ at time ‘t’, which is calculated as the weighted average of the country’s currency against an index or basket of other significant currencies. The weights are established by assessing the relative trade balance of a nation’s currency in relation to each country included in the index. The fourth variable is Institutional Quality (IQ), derived from the World Governance Indicators (WGI) by Kaufmann et al. (2011). While previous research, such as Shehzad et al. (2023), has utilized governance index using nine governance indicators, this study employs the Worldwide Governance Indicators (WGI) comprise six composite indicators that assess various dimensions of governance across more than 200 countries since 1996: Control of Corruption, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Voice and Accountability. The indicators derive from several hundred variables sourced from 31 distinct data providers, reflecting governance perceptions reported by survey respondents, non-governmental organizations, commercial information providers, and public sector entities globally.
To establish a connection between Hofstede’s Cultural Dimensions and Financial Growth, we incorporate the following variables into our models: power distance index (PDI), individualism versus collectivism (IDV), masculinity versus femininity (MAS), uncertainty avoidance index (UAI), long-term orientation versus short-term normative orientation (LTO), and indulgence versus restraint (IVR).
Table 3 describes the variables and Table 4 shows descriptive statistics of the variables investigated in the current study.
Table 4 shows that the average financial development for the sample is 0.65. The range of economic growth observed is from −14.72 to 25.55 percent annually, with an average of 2.46 percent for the sample analyzed. The mean inflation rate is 2.91 percent, closely aligning with the optimal rate for developed countries; however, it varies from −2.98 to 54.2. The averages of the power distance index, masculinity versus femininity, and indulgence versus restraint for the sample countries during the study period are 48, 52, and 49, respectively. Statistics indicate that, on average, there is no significant gap between the wealthy and the poor, nor between tough and tender cultures, and between free gratification and strict social norms in our sample. The means for individualism versus collectivism, uncertainty avoidance index, and long-term versus short-term normative orientation are 58, 68, and 55, respectively, all exceeding the natural point. The statistics suggest a preference for a loosely structured social framework within our sample. The findings indicate that, on average, the countries in our sample exhibit discomfort with uncertainty and ambiguity, promoting thrift and efforts in modern education as strategies for future preparedness. This study examines the influence of cultural variables on financial development.
3.2. Econometric Models
To examine the impact of national culture variables on financial development, we utilize Ordinary Least Squares (OLS) and the two-step dynamic system Generalized Method of Moments (GMM). Financial development is posited to stimulate economic growth (Levine & Zervos, 1996; Wachtel et al., 2006). This study employs the two-step dynamic system-GMM estimator to address the endogeneity issue in Equations (1)–(7). In accordance with the works of Love and Zicchino (2006), Canova and Ciccarelli (2013), Izadi (2021), and Izadi et al. (2023), we employ the panel vector autoregressive (PVAR) model and examine impulse response functions (IRFs) to elucidate both static and dynamic interdependencies between financial development (FINDEV) and national culture over time. PVAR is utilized as a robustness test in Section 4.5 Models (2) to (7) incorporate national culture variables individually to address collinearity issues.
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit + uit(1)
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit+ β5 × LPDIit + uit (2)
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit + β5 × LIDVit + uit(3)
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit + β5 × LMASit + ui(4)
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit + β5 × LUAIit + ui(5)
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit + β5 × LLTOit + ui (6)
FINDEVit = c + β1 × INFit + β2 × GDPGrit + β3 × EXCHit + β4 × WGIit + β5 × LIVRit + ui (7)
Following Economou (2019) and Izadi et al. (2022), we appraised our desired variables’ influences on financial growth inside the crisis period as well as outside; Dummy variables were included in Equation (1) to mitigate the impact of the global financial crisis on our findings. The variables’ respective two values are (a) ‘1’ in the period 2007 to 2009 and (b) ‘0’ otherwise. These were selected from the significance results of the regression analysis that included crisis dummies. Variables within the crisis period were given as follows:
iVar = DUM × Var(8)
Variables outside the crisis period, in turn, were created as follows:
i2Var = (1 − DUM) × Var(9)
The results of Equations (10) and (11) indicate if the crisis critically affected the desired variables on FINDEV.
FINDEVit = c + β1 × iVarit + β2-6 × Other Varsit + uit(10)
FINDEVit = c + β1 × i2Varit + β2-6 × Other Varsit + uit(11)
4. Results and Discussions
4.1. Summary of the Main Findings
This section presents the results of the OLS regressions for models (1) through (7). Table 5 presents the findings of an Ordinary Least Squares (OLS) model with FINDEV as the dependent variable. In all models, inflation has a negative impact on FINDEV, while the exchange rate exerts a positive influence. Previous studies have reported a significant impact of inflation on a nation’s financial development (Rousseau & Wachtel, 2002; Hondroyiannis & Papapetrou, 2001). J. H. Boyd et al. (2001) examine how inflation disrupts the financial sector’s efficiency in allocating resources. It finds a significant negative relationship between inflation and both banking sector development and equity market activity. This relationship is non-linear, with the negative impact of inflation decreasing as inflation rises. Arcand and Fafchamps (2012) and Law and Singh (2014) identify an inverted U-shaped relationship, suggesting that financial development yields positive effects only to a certain threshold, beyond which it becomes detrimental. EXCH serves to assess the value of a specific country’s currency in relation to other major currencies within the index.
A stronger home currency typically draws increased foreign direct investments (Khamphengvong et al., 2018), which subsequently influences financial development. The relationship between GDP growth and FINDEV is negatively correlated across all models, with significance observed in models 1 through 6. WGI exhibits a positive correlation with FINDEV in models 1 through 6. A nation achieves greater financial development when there is a legal and regulatory institutional framework that safeguards investor rights and guarantees contract enforcement (Khan et al., 2019).
Individualism versus collectivism and indulgence positively influence financial development, while masculinity versus femininity and the uncertainty avoidance index have a negative impact. The findings of model 3 indicate that individualistic societies exhibit greater financial development. The extant literature, as evidenced by Shleifer and Vishny (1997), Dutta and Mukherjee (2012), and Bialowolski et al. (2023), indicates a positive association between a nation’s degree of individualism and its inclination towards risk-taking, a factor that subsequently facilitates the expansion of its financial system. Individualistic societies demonstrate a stronger correlation between financial attitudes and financial satisfaction (Çera et al., 2020). Cultures characterized by high individualism tend to reward personal achievements and successes, with greater incentives for risk-taking compared to collectivist societies, which prioritize collective interests (Chui et al., 2010). Consequently, they undergo enhanced financial development.
The findings align with expectations derived from existing literature. Lal (1999) argues that the Western concept of the autonomous, self-reliant individual is historically and culturally specific, contrasting it with the emphasis on social roles, duties, and hierarchies in other major civilizations. He highlights the significance of Western individualism in explaining the expansion of markets in the West. Chui et al. (2010) establish a connection between individualism and overconfidence, positing that cultures with high individualism tend to reward personal achievements and successes.
Indulgence refers to a societal framework that permits considerable freedom in the gratification of fundamental human drives associated with enjoyment and leisure. Bialowolski et al. (2023) investigate the relationship between national culture and financial capability at the country level, arguing that cultural differences are as important as socio-economic factors. Using data from 137 countries, it examines how Hofstede’s six cultural dimensions correlate with an aggregate index of financial behavior and knowledge. The results indicate that individualism, long-term orientation, and indulgence are positively related to financial capability, while uncertainty avoidance is negatively related. Furthermore, these relationships are often non-linear, with the influence of each dimension varying across different levels of cultural scores. Sun et al. (2019) examine the role of indulgence versus restraint as a moderating variable in the relationship between corporate social performance (CSP) and corporate financial performance (CFP). The research indicates that in indulgent nations, social performance has a positive effect on financial performance, albeit the relationship is moderate.
Masculinity exhibits a negative correlation with financial development, contradicting hypothesis 3. This result indicates that the preference for modest behavior, typically associated with femininity, in contrast to assertive behavior, often linked to masculinity, enhances financial development. Masculinity denotes the degree to which a culture emphasizes attributes like achievements, financial incentives, and productivity (Gleason et al., 2000). In countries characterized by high masculinity, male assertiveness is emphasized, with competition, challenges at work, success, and achievement serving as primary standards. Conversely, in high femininity countries, the predominant standards focus on aiding others, valuing relationships over financial gain, modesty, and caring for the vulnerable (Khan et al., 2022).
Numerous studies have established a positive correlation between masculinity and financial risk-taking (Mihet, 2013; Powell & Ansic, 1997); however, engaging in financial risks may not consistently yield optimal outcomes. Engaging in risk-taking solely for its own sake can significantly undermine an individual’s financial resources (Meier-Pesti & Penz, 2008). Empirical studies indicate that women exhibit greater concern and sensitivity to risks than men (Barke et al., 1997; Bord & O’Connor, 1997; Lundeberg et al., 1994). Consequently, nations exhibiting greater femininity are likely to undergo enhanced financial development.
de Jong and Semenov (2002) investigate the positive and significant influence of masculinity on the stock market development within OECD countries. A closer relationship between firms and investors is anticipated in a feminine society, as managers are more inclined to relinquish independence. The existing literature, as illustrated by Pagano and Volpin (1999), Rajan and Zingales (2000), and Dutta and Mukherjee (2012), indicates that political variables exert a substantial impact on the evolution of a country’s financial system. Consequently, it can be concluded that nations exhibiting lower masculinity tend to demonstrate higher political decorum, which is essential for economic growth.
Our results discover a negative and significant relationship between uncertainty avoidance and financial development, which is supported by Raji et al. (2024). Ashraf et al. (2016) indicate that banks in countries characterized by higher uncertainty avoidance tend to engage in lower risk-taking and issue fewer loans compared to those in countries with lower uncertainty avoidance. However, our findings reveal no significant coefficients for the power distance index and the comparison between long-term orientation and short-term orientation.
4.2. Tracking Endogeneity Problem
The two-step dynamic system-GMM estimator is a consistent and efficient estimator for dynamic panel data models. The GMM estimator uses the estimated residuals from the first step as instruments for the lagged dependent variables in the level model. Table 6 presents the results of running the GMM for Equations (2)–(7) that are consistent with our previous results.
4.3. Asymmetric Impact of the Financial Crisis
In accordance with Izadi et al. (2022), we established a dummy variable for the financial crisis, defined as DUM = ‘1’ if 2007 ≤ TIME ≤ 2009 and DUM = ‘0’ otherwise. iINF is equivalent to INF multiplied by a dummy variable representing inflation during a financial crisis. Conversely, i2INF is defined as INF multiplied by (1—dummy variable) and signifies inflation external to the financial crisis (see Equations (8)–(11)). Employing the identical methodology, we produce crisis factors for GDP growth, EXCH, WGI, and NC, including them into Models (1) to (5) in Table 7. Table 7 reveals that iINF and iGDPGR lack significance; however, i2INF and i2GDPGR are significant. Consequently, we noted that the financial crisis altered the significance of inflation and GDP growth in influencing FINDEV. This section’s findings demonstrate that institutional quality and national culture are consistently favorable predictors of financial development in both pre- and post-crisis contexts.
4.4. Robustness Tests—KAOPEN as Alternate Dependent Variable
First, we employ KAOPEN1 or financial openness as an alternative measurement of financial development. The larger R-squared presented in this table (compared to the ones for Table 1) confirms that the models with KAOPEN as the measurement of financial development have higher explanatory power. The results of all models of Table 8 are mostly consistent with the results of Table 5 with FINDEV as a dependent variable except model 2.
Models (2) in Table 8 indicate a negative and significant coefficient for the power distance index, whereas this coefficient was negative but insignificant in Table 5. Hofstede identifies a positive correlation between power distance index and national size (de Jong & Semenov, 2002), while Gleason et al. (2000) contend that power distance and masculinity are significantly influenced by geographical latitude and historical factors. Power distance relates to democracy and the acceptance of power disparities. A culture characterized by a low power distance places a higher value on equality and interpersonal trust. According to Hypothesis 1 (H1), countries with high power distance scores exhibit a more dependent relationship between leaders and subordinates, resulting in lower financial development.
In conclusion, this study provides evidence that cultural dimensions significantly influence financial development. Our analysis supports the hypothesis that higher individualism and indulgence, along with lower power distance and uncertainty avoidance, are associated with greater financial development. Specifically, H1, H2, H4, and H6 were accepted. However, the hypothesized positive impacts of masculinity (H3) and long-term orientation (H5) on financial development were not supported by our findings. These results underscore the importance of considering cultural factors in shaping financial systems and have implications for policymakers seeking to foster financial growth.
4.5. Robustness Tests: PVAR and Impulse Response Function
We employ additional estimation methods as further robustness checks. Panel Vector Autoregressive (PVAR) models and Impulse Response Functions (IRFs) are widely utilized statistical techniques in the fields of economics and finance. PVAR represents a variant of vector autoregression (VAR) models, employed to analyze the dynamic interrelationships among multiple variables within a panel data framework. Impulse response functions measure the dynamic response of one variable to shocks in another variable. The coefficients of the PVAR model, being a non-theoretically structural model, lack practical significance. Consequently, this section does not involve an analysis of the regression coefficients; rather, it focuses on the evaluation of the impulse response functions. IRFs illustrate the magnitude and temporal dynamics of shocks transmitted between variables. Impulse response functions (IRFs) are derived through a Cholesky decomposition of the variance–covariance matrix, utilizing orthogonalized shocks (Holtz-Eakin et al., 1988).
Figure 1 illustrates the impulse response functions along with the 5% error bands derived from the Monte Carlo simulation for the sample under consideration (Izadi, 2021; Noman & Naka, 2015). The findings suggest the following: (1) the FINDEV shocks are nearly negligible in the short term and will alter after the fifth period; (2) inflation shocks will positively influence FINDEV in the mid-term, with a decrease observed after period five at a 5% significance level; (3) GDP growth shocks exert a minimal, albeit temporary, effect on FINDEV; (4) exchange rate shocks have a significant positive impact on FINDEV after period five; (5) unexpected variations in the World Governance Indicator and Net Capital have a minor effect on FINDEV in period five at the 95% significance level, although the graph suggests a substantial negative impact in the long run at the 5% significance level. The results of the robustness check align with our initial findings.
4.6. Discussions and Implications
Results indicate important policy changes in several areas. First, the positive and consistent connections between institutional quality and cultural variables with financial development emphasize the role of formal and informal institutions in creating a more conducive environment for financial development. Countries with well-functioning institutions are more likely to be able to implement effective structural reforms that can boost productivity and long-term growth. These reforms can help to make the economy more resilient to crises by reducing vulnerabilities and increasing flexibility. This is particularly warranted for the developing countries that are characterized by political instability and high financial risk impacting investment climate (Okafor et al., 2022), and high cultural dominance on financing choices (Rashid et al., 2023).
Second, cultural variables should be considered dynamic modifiers. Cultural dimensions are difficult to implement in policy directly, but nations that promote innovation, equality, diversity, and acceptance of leisure tend to have more developed financial systems. Openness in the form of collectivism is connected to the stock market, while individualistic and masculine cultural characteristics are closely associated with debt and bank market growth (Rashid et al., 2023). On the other hand, higher individualism and lower masculinity are connected to higher capital flows into a country (Izadi et al., 2023). Developing markets that are affected by limited access to finance have experienced a close connection between individualism and lower financial constraints for firms (Boubakri & Saffar, 2016). Therefore, a careful mix of cultural identities and values will be a competitive advantage for countries. Efficient management of cultural diversity and openness is important for new investment, innovation, understanding the needs and skills of workers, and financial development.
5. Conclusions
This research examines the influence of Hofstede’s cultural dimensions on the financial growth of 33 countries from 2001 to 2021. This study aims to examine the impact of institutional quality and cultural dimensions on the understanding of financial development. The OLS results indicate negative and significant coefficients for ‘masculinity versus femininity’ and ‘uncertainty avoidance index’ in relation to financial expansion, while ‘individualism versus collectivism’ and ‘indulgence versus restraint’ exhibit a positive influence on financial development. Analysis of the asymmetric impact of financial crises reveals that institutional quality and national culture serve as consistent and positive determinants of financial development across pre-, during, and post-crisis periods. We employ a two-stage dynamic system GMM to address the endogeneity issue in economic growth, yielding results consistent with those obtained from the OLS model. The coefficients for the ‘power distance index’ are negative and significant, indicating that countries with more equitable power distribution tend to be financially more developed.
The findings suggest key policy implications. Strong institutional quality and supportive cultural values are crucial for fostering financial development. Well-functioning institutions enable effective reforms that enhance economic resilience, especially in developing countries facing political instability and cultural influences on financing. While culture is complex to manipulate directly, promoting innovation, equality, diversity, and openness can lead to more developed financial systems. A strategic blend of cultural identities is a potential competitive advantage, with efficient management of cultural diversity being essential for attracting investment, driving innovation, and facilitating financial development.
The findings are constrained by the application of Hofstede’s cultural dimensions, which inadequately encompass the multifaceted nature of culture, including elements such as history, religion, and geography. Future research could utilize the GLOBE framework to achieve a more comprehensive understanding of the relationship between culture and financial development. Furthermore, we contend that an analysis of the data during and following the COVID-19 period will yield valuable insights as additional data become accessible.
Conceptualization, S.I. and F.J.W.; methodology, S.I.; software, S.I.; validation, S.I.; formal analysis, S.I.; investigation, S.I.; resources, S.I.; data curation, S.I.; writing—original draft preparation, S.I.; review and editing, S.I., F.J.W. and M.R.; visualization, S.I.; supervision, S.I. All authors have read and agreed to the published version of the manuscript.
Not applicable.
The raw data supporting the conclusions of this article will be made available by the authors on request.
Selma Izadi acknowledges the support of the Tennessee State University Research and Sponsored Programs, which was instrumental in the completion and enhancement of this paper.
The authors declare no conflicts of interest.
Footnotes
1. KAOPENit measures financial openness for country i at time t. We used the Chinn–Ito index (KAOPEN) from
Footnotes
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Figure 1 Impulse Response graphs of the variables. Source: Produced by the authors.
Literature Review Summary.
| Study | Year | Context |
|---|---|---|
| 1983 | Hofstede added on culture as “…that part of our conditioning that we share with other members of our nation, region, or group, but not with members of other nations, regions, or groups”. | |
| 1988 | Social identity theory says that the social group membership such as nationality, ethnicity, religion and occupation are important to generate personal identity, and people tend to conform to the dominant values and behavior of the group. This paper provides another piece of evidence showing the economic outcomes of cultural effects. | |
| 2001 | Data on cultural dimensions are obtained from | |
| 2002 | The authors focus on the stock market development of OECD countries. They find that stock markets tend to be more developed in countries where inhabitants have lower levels of uncertainty avoidance and higher levels of masculinity. | |
| 2003 | The authors indicate that culture can exert its influence by affecting the predominant values, institutions, and resource allocation in a country. | |
| 2009 | The authors conclude that national culture is a key country-specific characteristic that has been considered as a possible determinant of FDI inflows. | |
| 2010 | The authors find that country-pairs exhibit higher linkages if they have smaller cultural distance. The result remains significant to alternative measures of linkage. Finally, the cultural effect seems to be more pronounced for active trading country-pairs than thin-trading country-pairs. | |
| 2012 | The authors deliver a basic idea that geographic and cultural distance acts as a proxy for transaction cost, information asymmetry and unfamiliarity effect. These effects will affect cross-border capital flows and price co-movement. | |
| 2018 | The authors investigate the global variation in economic preferences. They find that national culture influences an individual’s behaviors relating to saving decisions, entrepreneurial activities, time preference, etc. | |
| 2019 | The author reports that individualism plays a vital role in explaining the wide gap in the levels of financial development experienced by many countries around the world. | |
| 2021 | The authors perform an event study to explore the influence of national culture on stock market responses to COVID-19 pandemic. They find that countries with low individualism and high uncertainty avoidance have higher volatility and larger market drops, during the first few weeks of the COVID-19 infection. | |
| 2022 | The authors investigate the impact of | |
| 2023 | The study explores the relationship between national culture and financial capability at the country level. The results show that individualism, long-term orientation, and indulgence are positively correlated with financial capability, while uncertainty avoidance is negatively correlated. These relationships are non-linear and vary depending on the level of each cultural dimension. | |
| 2024 | The study examines how national cultural values influence financial inclusion levels in different countries. Using data from 40 countries between 2012 and 2021 the results show that cultural dimensions significantly impact financial inclusion. Countries with high uncertainty avoidance and power distance tend to have lower financial inclusion, while those with high individualism and masculinity tend to have higher financial inclusion. | |
| 2024 | The study examines the relationship between financial institution quality, innovation, and financial development in 22 emerging markets. It finds that the interaction of innovation and technology with well-functioning institutions can accelerate financial development. |
Observation Statistics.
| MSCI Market Classification | Countries | Freq. | % |
|---|---|---|---|
| Developed (19) | Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Japan, Netherlands, Portugal, Spain, Sweden, Switzerland, UK, USA | 323 | 57.58 |
| Emerging (11) | Brazil, Chile, China, Czech Republic, Greece, Hungary, South Korea, Poland, Russia, South Africa, Turkey | 187 | 36.67 |
| Frontier (3) | Estonia, Iceland, Slovakia | 51 | 9.09 |
Note: We used the MSCI market classification framework: Developed country = 19, developing country = 14 (emerging country = 11, and frontier country = 3).
Variables Description.
| Variables | Definition | Source |
|---|---|---|
| FINDEV | IMF financial development index developed by | IMF |
| KAOPEN | Financial openness: The Chinn–Ito index (KAOPEN) | |
| INF | Log of Consumer Price Index—Inflation Rate | The World Bank |
| GDPGR | Economic Growth—GDP Growth Rate | The World Bank |
| EXCH * | Real Effective Exchange Rate, Consumer Price Index | International Financial Statistics (IFS) |
| WGI | Institutional quality Index which is a weighted average of Control of Corruption, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Voice and Accountability. | The World Bank and |
| Market | Dummy Variable: 0 Developed; 1 Developing | MSCI Market Classification Framework |
| PDI | Power Distance Index | |
| IDV | Individualism Versus Collectivism | |
| MAS | Masculinity Versus Femininity | |
| UAI | Uncertainty Avoidance Index | |
| LTO | Long-Term Orientation Versus Short-Term Normative Orientation | |
| IVR | Indulgence Versus Restraint | |
| NC ** | A composite score of the six Hofstede cultural dimensions | Averaging pdi, idv, mas, uai, ltowvs and ivr |
Notes: EXCH * is the real effective exchange rate (REER), which is the weighted average of a country’s currency in relation to an index or basket of other major currencies. NC ** is a single number that represents the overall cultural profile of each country.
Descriptive Statistics.
| Variables | Mean | Maximum | Minimum | Std. Dev. | Obs. |
|---|---|---|---|---|---|
| FINDEV | 0.65 | 0.99 | 0.19 | 0.18 | 693 |
| KAOPEN | 1.41 | 2.37 | −1.90 | 1.34 | 693 |
| GDPGR | 2.46 | 25.55 | −14.72 | 3.53 | 693 |
| INF | 2.91 | 54.2 | −2.98 | 3.91 | 693 |
| EXCH | 96.61 | 156.97 | 44.40 | 13.43 | 693 |
| WGI | 62.67 | 117.62 | 15.02 | 18.95 | 693 |
| PDI | 48 | 93 | 11 | 18 | 693 |
| IDV | 58 | 91 | 18 | 21 | 693 |
| MAS | 52 | 95 | 5 | 21 | 693 |
| UAI | 68 | 100 | 23 | 23 | 693 |
| LTO | 55 | 100 | 21 | 22 | 693 |
| IVR | 49 | 78 | 14 | 14 | 693 |
Notes: FINDEV = financial development index, KAOPEN = Financial openness, GDPGr = GDP growth rate, Inf = Inflation rate, EXCH = Real Effective Exchange Rate, WGI = Institutional quality Index, PDI = Power distance index, IND = individualism versus collectivism, MAS = Masculinity versus femininity, UAI = Uncertainty avoidance index, LTO = long-term versus short-term orientation, IVR = Indulgence versus restraint. We collect FINDEV From IMF, Kaopen from
Ordinary Least Squared—Dependent Variable: FINDEV.
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| Variables | OLS | OLS | OLS | OLS | OLS | OLS | OLS |
| INF | −0.037 *** | −0.037 *** | −0.037 *** | −0.038 *** | −0.035 *** | −0.038 *** | −0.037 *** |
| (0.008) | (0.008) | (0.008) | (0.008) | (0.007) | (0.008) | (0.007) | |
| GDPGR | −0.004 ** | −0.004 ** | −0.004 * | −0.004 * | −0.006 *** | −0.005 ** | −0.003 |
| (0.0021) | (0.0021) | (0.0021) | (0.0021) | (0.0021) | (0.0021) | (0.0020) | |
| EXCH | 0.003 *** | 0.003 *** | 0.003 *** | 0.003 *** | 0.003 *** | 0.003 *** | 0.002 *** |
| (0.0004) | (0.0005) | (0.0005) | (0.0005) | (0.0005) | (0.0005) | (0.0004) | |
| WGI | 0.001 *** | 0.002 *** | 0.001 *** | 0.002 *** | 0.001 *** | 0.002 *** | −0.001 |
| (0.0003) | (0.0004) | (0.0004) | (0.0004) | (0.0004) | (0.0004) | (0.0004) | |
| PDI | −0.004 | ||||||
| (0.0155) | |||||||
| IDV | 0.046 *** | ||||||
| (0.0171) | |||||||
| MAS | −0.039 *** | ||||||
| (0.0116) | |||||||
| UAI | −0.083 *** | ||||||
| (0.0177) | |||||||
| LTO | −0.019 | ||||||
| (0.0162) | |||||||
| IVR | 0.172 *** | ||||||
| (0.0155) | |||||||
| C | 0.260 *** | 0.280 *** | 0.101 | 0.399 *** | −0.662 *** | 0.348 *** | −0.177 |
| (0.054) | (0.091) | (0.079) | (0.675) | (0.101) | (0.093) | (0.063) | |
| Obs. | 630 | 630 | 630 | 630 | 630 | 630 | 630 |
| R2 | 0.18 | 0.18 | 0.19 | 0.19 | 0.21 | 0.18 | 0.31 |
Notes: Robust standard errors in parentheses. Six culture variables were converted to log-natural form. Refer to
The Two-Steps Dynamic System-GMM.
| Variables | LPDI | LIDV | LMAS | LUAI | LLTO | LIVR |
|---|---|---|---|---|---|---|
| FINDEV.L1 | 0.841 *** | 0.808 *** | 0.757 *** | 0.737 *** | 0.828 *** | 0.747 *** |
| (0.000) | (0.209) | (0.029) | (0.028) | (0.026) | (0.027) | |
| INF | −0.0001 | −0.0001 | 0.0001 | 0.0001 | −0.0004 | 0.0001 |
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | |
| GDPGR | −0.001 | −0.001 | −0.001 | −0.001 * | −0.0006 | −0.001 |
| (0.001) | (0.001) | (0.0004) | (0.0004) | (0.0004) | (0.0004) | |
| EXCH | −0.000 | −0.000 | −1.0000 | −0.0001 | −0.0000 | −0.0001 |
| (0.001) | (0.001) | (0.0002) | (0.0002) | (0.0002) | (0.0002) | |
| WGI | −0.001 *** | −0.001 *** | −0.001 *** | −0.001 *** | −0.001 *** | −0.001 *** |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| LPDI | −0.045 *** | |||||
| (0.013) | ||||||
| LIDV | 0.062 *** | |||||
| (0.012) | ||||||
| LMAS | −0.063 *** | |||||
| (0.013) | ||||||
| LUAI | −0.101 *** | |||||
| (0.014) | ||||||
| LLTO | −0.030 | |||||
| (0.019) | ||||||
| LIVR | 0.101 *** | |||||
| (0.013) | ||||||
| C | 0.319 *** | −0.067 | 0.439 *** | 0.655 *** | 0.264 *** | −0.155 *** |
| (0.570) | (0.050) | (0.065) | (0.075) | (0.081) | (0.047) | |
| Obs. | 598 | 598 | 598 | 598 | 598 | 598 |
| Wald Chi2(4) | 1187 | 1240 | 1307 | 1362 | 1192 | 1352 |
| Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Notes: Robust standard errors in parentheses. Six culture variables were converted to log-natural form. Refer to
Asymmetric Impact of Financial Crises.
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| INF | −0.039 *** | −0.038 *** | −0.0398 *** | 0.0384 *** | |
| GDPGR | −0.004 ** | −0.004 * | −0.004 ** | −0.0041 * | |
| EXCH | 0.003 *** | 0.0032 *** | 0.0030 *** | 0.0031 *** | |
| WGI | 0.002 *** | 0.0020 *** | 0.002 *** | 0.0021 *** | |
| NC | 0.129 ** | 0.1269 ** | 0.130 ** | 0.1243 ** | |
| iINF | −0.023 | ||||
| i2INF | −0.039 *** | ||||
| iGDPGR | 0.0033 | ||||
| i2GDOGR | −0.0047 ** | ||||
| iEXCH | 0.004 *** | ||||
| i2EXCH | 0.003 *** | ||||
| iWGI | 0.0015 *** | ||||
| i2WGI | 0.0019 *** | ||||
| iNC | 0.1433 *** | ||||
| i2NC | 0.129 ** | ||||
| C | −0.261 | −0.257 | −0.267 | −0.230 | −0.269 |
| R2 | 0.19 | 0.19 | 0.19 | 0.19 | 0.19 |
| F-value | 23.53 *** | 23.73 *** | 24.44 *** | 23.53 *** | 24.45 *** |
Notes: This table shows the OLS results of the asymmetric impact of the Financial Crisis for 33 developed and developing countries from 2001 to 2021. In column 1–5 we include within and outside the financial crisis. Robust standard errors in parentheses. Six culture variables were converted to log-natural form. Refer to
Alternative dependent variable—KAOPEN.
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| Variables | OLS | OLS | OLS | OLS | OLS | OLS | OLS |
| INF | −0.359 *** | −0.327 *** | −0.354 *** | −0.363 *** | −0.350 *** | −0.359 *** | −0.358 *** |
| (0.053) | (0.050) | (0.050) | (0.052) | (0.053) | (0.053) | (0.052) | |
| GDPGR | −0.065 *** | −0.062 *** | −0.052 *** | −0.063 *** | −0.069 *** | −0.065 *** | −0.062 *** |
| (0.015) | (0.014) | (0.015) | (0.046) | (0.015) | (0.015) | (0.015) | |
| EXCH | −0.009 *** | −0.013 *** | −0.010 *** | −0.008 *** | −0.010 *** | −0.009 *** | −0.011 *** |
| (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.004) | |
| WGI | 0.021 *** | 0.013 *** | 0.013 *** | 0.020 *** | 0.020 *** | 0.021 *** | 0.017 *** |
| (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | (0.0030) | |
| LPDI | −0.821 *** | ||||||
| (0.101) | |||||||
| LIDV | 0.901 *** | ||||||
| (0.113) | |||||||
| LMAS | −0.243 *** | ||||||
| (0.080) | |||||||
| LUAI | −0.245 ** | ||||||
| (0.123) | |||||||
| LLTO | −0.012 | ||||||
| (0.112) | |||||||
| LIVR | 0.321 *** | ||||||
| (0.116) | |||||||
| C | 1.121 *** | 3.316 *** | −1.67 *** | 2.283 *** | 2.609 *** | 1.477 *** | 0.605 |
| (0.370) | (0.596) | (0.523) | (0.465) | (0.703) | (0.639) | (0.472) | |
| Obs. | 630 | 630 | 630 | 630 | 630 | 630 | 630 |
| R2 | 0.25 | 0.33 | 0.32 | 0.21 | 0.26 | 0.25 | 0.26 |
Notes: Robust standard errors in parentheses. Six culture variables were converted to log-natural form. Refer to
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; Weinberg, Frankie J 2
; Rashid Mamunur 3
1 Department of Economics and Finance, College of Business, Tennessee State University, Nashville, TN 37203, USA
2 Department of Management, College of Business, Loyola University New Orleans, New Orleans, LA 70118, USA; [email protected]
3 Christ Church Business School, Canterbury Christ Church University, Canterbury CT1 1QU, UK; [email protected]