Amid the sustainability of entrepreneurs in question, this study aims to examine the role of institutional quality, investor sentiment and uncertainty avoidance in entrepreneurship development in emerging and developed markets.
In the presence of heteroscedasticity, autocorrelation and cross-sectional dependence, the Panel Corrected Standard Error model is used to analyze the data. Feasible Generalized Least Squares and Driscoll–Kraay are used for robustness checks.
Uncertainty avoidance culture turns out to be the most significant factor in promoting new business density. Investor sentiment and uncertainty acceptance together build a bullish environment, boosting entrepreneurship. Although institutions positively affect entrepreneurship, inflexible institutional environments can reverse it. The results slightly differ between emerging and developed markets. Although the impact of institutional quality and uncertainty avoidance, by and large, remains the same, the role of investor sentiment becomes insignificant in emerging economies.
This study highlights that an uncertainty avoidance culture hinders entrepreneurship regardless of a country’s economic and financial status. However, the investor sentiment, especially in developed markets, can foster entrepreneurship. Furthermore, while maintaining reasonable quality regulations and having an effective government, there is a need to maintain transparency in governance.
This study examines the combined effects of institutional quality, investor sentiment and uncertainty avoidance culture on entrepreneurship development. While doing so, it compares the impacts of these critical factors on developed and emerging markets. Examining the impacts of individual dimensions of institutional quality on entrepreneurship and the overregulated institutions provides valuable insights into the specific areas needing attention in each country.
1. Introduction
Entrepreneurship is known to have a positive impact on economic growth and employment. It brings regional development and growth in labor productivity (Galindo-Martín et al., 2021). Entrepreneurs carry new and innovative ideas to the table, discovering the most optimal ways to use available resources (McMullen et al., 2008). Of late, there has been a surge in entrepreneurship growth in emerging and developed economies alike. The global new business density has increased from 1.9 in 2010 to 3.5 in 2020 per 1,000 population. However, the growth in new registrations is mainly concentrated in developed and upper-middle-income economies (World Bank, 2020). In emerging markets, it is primarily confined to necessity entrepreneurship (Estrin et al., 2018). As necessity entrepreneurship is more challenging to sustain than opportunity entrepreneurship, it is vital to study the factors that affect entrepreneurs in these markets (Belda and Cabrer-Borrás, 2018).
The factors affecting entrepreneurship are diverse and can be classified into organizational (structure, size, history and system), environmental (economic, political, sociological and technological) and individual characteristics (skill, risk appetite, age and personality). In essence, a set of external and internal factors affects the incidence of entrepreneurship (Hansen and Wernerfelt, 1989). The attitude to face uncertainty, learning from failures and the ability to perceive opportunities are part of an entrepreneurial vision (Crecente-Romero et al., 2016). From a country’s perspective, the quality of institutions, economic growth, education level, economic freedom, inflation rate and government size are considered either critical drivers or deterrents to entrepreneurial growth (Sendra-Pons et al., 2022; Kaivanto and Zhang, 2022; Ahmed and Alae, 2016). Especially in emerging markets, immature formal structures like institutions and financial markets hinder entrepreneurship (Foo et al., 2020).
Among all, institutions play a pivotal role in promoting entrepreneurship in both developed and emerging economies. As North (1990) defined, institutions are the formal rules made on social, cultural and regulatory fronts. This theory considers formal rules and regulations, like institutions and informal values and behavioral expectations that shape individual and organizational mindsets in an economy. The influence of formal institutions on entrepreneurship can be seen through rules and regulations prevalent in an economy, which are measured through critical dimensions like controlling corruption, government effectiveness, maintaining rules and laws and political stability (Xie et al., 2021). However, it is interesting to state that though institutional quality generally exerts a positive impact on entrepreneurship, burdensome regulations with complex bureaucratic rules deter entrepreneurship. Corruption is commonly considered an inhibitor, yet revisionists consider it a catalyst (Mitchell and Campbell, 2009; Méon and Sekkat, 2005). According to them, despite adverse impacts, corruption at times helps circumvent existing cryptic regulations that are too complex to comprehend.
In addition to the formal values of society, informal values and behavioral expectations are important, needing equal attention. Informal values are reflected in the culture and social norms existing in society (Holmes et al., 2011). In the early stages of entrepreneurial development, cultural-cognitive aspects, along with past behavior and intentions, become critical determinants (Graham and Bonner, 2024). The tolerance toward risk is lower in a society with an uncertainty avoidance culture. It creates a stigma toward business failure, leading to slow progress in entrepreneurship. A UAE study shows that students lack interest in entrepreneurship due to a hostile cultural environment toward businesses (Facchini et al., 2020). Countries with low uncertainty avoidance are often associated with more innovation and, thus, greater entrepreneurship (Hayton et al., 2002).
From entrepreneurs’ viewpoints, investor sentiment carries significant ramifications. Investor sentiment captures the essence of behavioral expectations and, in turn, influences market returns (Kaivanto and Zhang, 2022). Generally measured through the Business Confidence Index (BCI), it is known to provide future information on production, procurement and inventories through opinion polls, thus building business expectations (Olkiewicz, 2022). Business confidence, along with macroeconomic fundamentals like gross domestic product (GDP) and inflation, affects new businesses (Kaivanto and Zhang, 2022). With a positive investor sentiment, access to financial capital tends to ease, especially for businesses working on green innovation (Dong et al., 2024). Interestingly, investor sentiment is mainly popular in the context of finance and behavioral studies, but its role in entrepreneurship has not been much explored (Dicks and Fulghieri, 2021).
Given the above backdrop, the present study delves into the combined impact of uncertainty avoidance culture, investor sentiment and institutional quality on business density across countries. It also attempts to understand whether the role of institutions varies between developed and emerging economies. Consequently, the study includes 15 emerging and 16 developed nations, which are geographically diverse and cover most leading stock markets. The study first generates combined results, then compares the two markets.
This empirical study concludes that uncertainty avoidance culture is a prime deterrent to entrepreneurship in both emerging and developed markets. Furthermore, investor sentiment favorably affects new businesses, indicating the importance of the people’s mindset and the bullish environment set by the investors. While improved institutional quality plays a favorable role, highly restrictive environments can reverse the effects. Corruption control, government effectiveness and rule of law exhibit the most significant impacts. Comparing across countries, it is established that, other than culture, corruption control, rule of law and voice and accountability play a significant role in emerging markets, while investor sentiment and government effectiveness turn out to be significant in developed markets. Interestingly, among the controls, GDP, patents and market openness are critical for emerging markets, whereas education is significant for the developed markets.
Consequently, the findings make several important contributions. First, it ascertains how risk-averse behavior deters entrepreneurial growth, suggesting measures to incentivize prospective entrepreneurs for undertaking new ventures. Second, as the impacts of the variables of interest vary between developed and emerging economies, specific interventions according to the relative significance of the factors may help realize the goals. Third, as excessive regulations, especially over corruption control and governance effectiveness, become counterproductive, it argues for moderate institutional measures for sustained business growth.
Accordingly, the remaining part of the paper is organized as follows. Section 2 presents the literature review. Section 3 outlines data and methodology. Results are presented and discussed in Section 4. Section 5 concludes the study and offers implications.
2. Review of literature
2.1 Institutional quality and entrepreneurship
An entrepreneur is one who has the courage to try new combinations in the production processes while looking for profitable opportunities (Fritsch, 2017). Several theoretical bases exist to explain how entrepreneurship is promoted in an economy. The prominent ones are the institutional theory, eclectic theory, need for achievement theory and agency theory. While the institutional and eclectic theories consider macrolevel sociocultural factors and the demand- and supply-side factors, respectively, the need for achievement and agency theories put emphasis on microlevel behavioral and demographic factors (Castaño et al., 2015; Solomon et al., 2021a).
The institutional theory supports the influence of social, regulatory and cultural aspects of formal and informal institutions on the survival of an organization or entrepreneurship (Roy, 1997). The prime indicators capturing formal institutions are political stability, rule of law, level of corruption, access to credit and the strictness of institutions. In the inception stage, the rule of law plays a critical role (Mickiewicz et al., 2021). The impact of different institutional parameters seems to vary according to the levels of development. Evidently, the institutional quality is relatively better in high-income countries, which helps promote entrepreneurship. However, the propensity for corruption and the absence of the rule of law are prominent in low-income nations (Sendra-Pons et al., 2022).
Countries with low corruption levels, a stricter rule of law, effective government, straightforward procedures and better access to credit tend to promote entrepreneurship. Contrarily, if the institutions are weak, signifying corruption and weak regulations, it leads to resource diversion from productive activities and increased business costs (Chambers and Munemo, 2017). According to Park and Shin (2022), in a corrupt society, people feel discouraged because of unfair competition by the elite, creating “sand effects,” thereby deterring entrepreneurial growth. Even the positive impacts of education, GDP growth, financial services and technology on entrepreneurship can be hampered in a corrupt environment (Ahmed and Alae, 2016).
It may, however, be said that higher levels of institutional control may also have a negative impact. Overregulation, complexities in rules and procedures and authoritarianism tend to exert negative impacts, especially on new businesses (Scarpetta et al., 2002). Moreover, while corruption per se is problematic from a business perspective, it may sometimes generate some “greasing effects” at a low and tolerable level (Mohamadi et al., 2017).
2.2 Uncertainty avoidance and entrepreneurship
Informal institutions include an economy’s social values and norms and how they shape the perception about individuals’ capabilities and opportunities (Xie et al., 2021). Culture reflects stable informal institutions that take time to change (Holmes et al., 2011). Uncertainty avoidance is a cultural trait people exhibit in a given society. In entrepreneurship, it entails the level at which society considers entrepreneurial behavior, including self-supporting and risk-taking attitudes, desirable (Hayton et al., 2002). It is the extent to which a society tends to avoid unknown situations, influencing the risk-taking appetite (Hofstede et al., 2004). Japan is a case in point, which, despite being so developed, experiences a low incidence of entrepreneurship, thanks to a risk-averse and uncertainty-avoidant culture. For women, low capability perception and less opportunity are internal forces, while a hostile culture against entrepreneurship is an external force demotivating them (Xie et al., 2021).
Uncertainty Avoidance can be measured through an index developed by Hofstede (2001). The other indices measuring different dimensions of culture include power distance, individualism, motivation toward achievement, long-term orientation and indulgence (Hunt and Levie, 2003). While power distance is about the acceptance of unequal distribution of power by the less powerful members of society, individualism is the level of interdependence among the members. The uncertainty avoidance dimension deals with the risk-taking appetite embedded in a society, while the long-term orientation describes how conventional the society is and its readiness to change with future needs (Hofstede, 2001).
The impacts of such factors seem to vary according to the entrepreneurial types. Necessity entrepreneurs prefer collectivism and greater power distance, while opportunity entrepreneurs prefer less power distance and good regulatory quality (Sambharya and Musteen, 2014). Contrary to the belief that individualistic culture supports entrepreneurship, Stephan and Uhlaner (2010) proved otherwise. Similarly, Zhao et al. (2012) ascertained that collectivism, low uncertainty avoidance and humane orientation are essential to support early-stage entrepreneurial activity. Although high uncertainty avoidance has a negative influence on entrepreneurship, people tend to switch to entrepreneurship even when they are risk-averse due to dissatisfaction with society and democracy (Hofstede et al., 2004). Thus, the impact of uncertainty avoidance can vary according to external macroeconomic conditions. Sometimes, the desire to have autonomy and increasing chances of becoming wealthier can also overshadow the heightened stress, long working hours, increased risk and uncertainty associated with a business (Noorderhaven et al., 2004).
2.3 Investor sentiment and entrepreneurship
The term “sentiment” reflects an individual’s views, opinions and emotions. It tends to affect the markets irrespective of whether they are rational or irrational (Thorp, 2004). Keynes (1936) defined “sentiment’ as spontaneous optimism or animal spirits dimmed due to hopes about the future. Investor sentiment measures the behavioral expectations of individuals (Kaivanto and Zhang, 2022) and plays a significant role in an entrepreneur’s journey. When the markets are bullish, investors feel motivated to pour in money (Arif and Lee, 2014). The increased money flow excites potential entrepreneurs to start new ventures and the established owners to plan expansion (Samila and Sorenson, 2011).
In recent years, sentiment analysis of news and social media platforms has occupied center stage in analyzing people’s reactions (Boppuru and Ramesha, 2018). Investor sentiment, in particular, has the power to alter stock prices. The propensity to speculate about stocks, with or without any fundamental backdrop, affects businesses (Hao et al., 2017). It is also found that high stock prices positively influence the sourcing of funds from venture capitalists, fostering entrepreneurship (Dicks and Fulghieri, 2021). However, positive investor sentiment may not always guarantee a rise in investment. While with low sentiment, small firms and highly volatile markets tend to receive greater returns, large and stable firms are less likely to be impacted (Chen et al., 2013).
The existing studies on investor sentiment are mostly confined to forecasting stock prices. Recently, attempts have been made to analyze subjective texts, product reviews and research and development (R&D) decisions of the firms. With high market sentiment, financially constrained firms invest more in R&D than their unconstrained counterparts (Dang and Xu, 2018). Just as sentiment plays a vital role in financial markets where traders are classified as being fundamental-based or market noise-based, entrepreneurs are largely influenced by macroeconomic fundamentals along with market sentiments (Kaivanto and Zhang, 2022).
From the literature review, the following gap areas are identified. The literature on the possible linkages between investor sentiment and entrepreneurship, making a comparative analysis between emerging and developed economies, is scarce. The studies on the impact of an overregulated institutional environment are also limited. Besides, comprehensive studies that consider formal institutions, culture and investor sentiment are hardly found in a single study. The present study, thus, addresses these critical aspects comprehensively.
3. Data and methodology
This study analyzes how institutions, uncertainty avoidance culture and investor sentiment influence entrepreneurship in emerging and developed markets. While doing so, it also examines how the impact of institutions on entrepreneurship changes with stringent regulations and restrictive environments. The analysis is also done separately for emerging and developed markets to see whether the impacts vary according to economies. We use secondary data sourced from the World Bank, the Organization for Economic Cooperation and Development (OECD) and the Heritage Foundation to address these aspects. Based on the Morgan Stanley Capital International index, we consider 15 emerging and 16 developed economies. Due to missing data, we have an unbalanced panel of 343 observations from 2006 to 2020.
3.1 Variable selection
3.1.1 Dependent variable.
The dependent variable is “new business density,” which is often used as a proxy for entrepreneurship rate (Chambers and Munemo, 2017; Nguyen et al., 2021). It is calculated as the number of new firms registered per 1,000 people in the age group of 15–64 years.
3.1.2 Independent variables.
3.1.2.1 Institutional quality.
This study considers World Governance Indicators, which encompass six different dimensions, to measure the quality of institutions. They are control of corruption(CC), government effectiveness(GE), political stability and absence of violence(PSAV), regulatory quality(RQ), the rule of law(RL) and voice and accountability(VAA) (Kaufmann et al., 2008). We construct an institutional quality index (IQI) considering all six indicators with the help of principal component analysis (PCA) to examine its impact on new business density. PCA can transform the correlated variables into a set of linearly uncorrelated components, hence addressing multicollinearity problems, if present, among the indicators (Abdi and Williams, 2010). As the impact of each dimension is of equal interest, six different models are also run, incorporating individual dimensions separately. Evidently, the impacts of various institutional quality indicators on entrepreneurship can vary (Sendra-Pons et al., 2022; Chambers and Munemo, 2017). Furthermore, very few studies emphasize the notion that, after a threshold, institutions become rigid and overly restrictive, which can hamper entrepreneurial growth. Thus, this study also incorporates the squared terms of the IQI along with its individual indicators.
3.1.2.2 Uncertainty avoidance (UA).
Uncertainty avoidance entails people’s risk-taking behavior, with low values representing risk-loving behavior and high values representing risk-averse behavior. To measure it, uncertainty avoidance (UA) index is considered, which is one of the six indices proposed under Hofstede’s Cultural Index. Here, “uncertainty avoidance” is calculated by taking the average of all the values. Then a binary dummy is constructed where the above-average values are marked as 1, representing risk-averse economies, and below-average values as 0, representing risk-loving economies.
3.1.2.3 Business confidence index.
To capture the role of investor sentiment, we consider BCI given by OECD. BCI is commonly used to measure market sentiment. It provides information regarding the prospects of future markets. An index of 100 and above indicates positive and bullish markets, whereas any value below 100 reflects bearish and pessimistic scenarios (Khan and Upadhayaya, 2020). As the data on BCI is available on a monthly basis, this study derives the average for 12 months to convert it into a yearly data set.
3.1.3 Control variables.
The control variables for this study include gross tertiary enrollment (GTE) to control for education level; market openness (MO) indicating trade, investment and financial freedom; patent-to-GDP ratio capturing innovation; and log of GDP per capita (logGDP-PC) reflecting an economy’s economic status.
3.2 Empirical specification
We conducted diagnostic tests (Supplementary S3 and S4) before selecting an appropriate empirical model for the analysis. The Breusch–Pagan/Cook–Weisberg test confirmed the heteroskedasticity in the data set, and the Wooldridge test confirmed the presence of serial correlation. The cross-sectional dependence was found through the Pesaran CD test (Supplementary S1-S5). Therefore, to tackle the problem of heteroscedasticity, autocorrelation and cross-sectional dependence, Panel Corrected Standard Error (PCSE) model is used. This model, by and large, addresses these problems and works well with a panel data set having N > T. PCSE provides more accurate standard error estimates with a small T, better size properties in hypothesis testing and is less sensitive to model misspecification compared to Feasible Generalized Least Squares (FGLS) and Driscoll–Kraay Standard Error (DKSE) (Hoechle, 2007; Nguyen et al., 2021; Beck and Katz, 1995). Unlike the ordinary least squares method, which assumes the error terms to be homoskedastic and uncorrelated, PCSE estimates a variance–covariance matrix of the errors across panels, takes care of contemporaneous correlation between panels and addresses possible panel-level AR(1) autocorrelation (Beck and Katz, 1995). Hence, the present study prefers PCSE for the analysis. However, FGLS and DKSE are used only for robustness checks (Supplementary Tables A–D). The panel data model can, thus, be expressed as:
In equation (1), the dependent variable is the new business density, with country and time. The independent variables are BCI (UA (and IQI (. The control variables include GTE (), GDP per caipta (), Patent-to-GDP ratio and market openness (). is the error term. As the impact of each dimension of the institutional quality is proposed to be examined along with IQI, seven separate models are run. As mentioned earlier, it also helps us overcome any possible multicollinearity problems. Further, the square terms of the institutional quality are introduced to measure the possible differential impacts of the overregulated institutions. The operationalization, preliminary descriptions and summary statistics of new business density, along with all explanatory variables of interest and all the control variables, are provided in Table 1.
Operationalization of variables
| Variables | Definition/operationalization | Source | Acronyms | Obs. | Mean | SD | Min. | Max. |
|---|---|---|---|---|---|---|---|---|
| Dependent variable | ||||||||
| New business density | Number of new firms per 1,000 people between the age of 15 and 64 | World Bank’s Entrepreneurship Database | NBD | 403 | 4.88 | 4.42 | 0.07 | 25.04 |
| Independent variables | ||||||||
| Business confidence index | Provides information on future financial environments (bullish or bearish) using opinion surveys. | OECD data | BCI | 465 | 99.89 | 1.82 | 92.53 | 104.04 |
| Uncertainty avoidance (dummy) | The extent to which people of the community feel threatened by unknown future measured as dummy | Hofstede’s Cultural Index | UA | 465 | 0.55 | 0.50 | 0 | 1 |
| Institutional quality index | Includes six sub-indicators: | World Bank governance indicators | ||||||
| Control of corruption | CC | 465 | 0.90 | 0.98 | −0.95 | 2.46 | ||
| Government effectiveness | GE | 465 | 1.00 | 0.73 | −0.52 | 2.35 | ||
| Regulatory quality | RQ | 465 | 1.01 | 0.69 | −0.48 | 2.08 | ||
| Rule of law | RoL | 465 | 0.94 | 0.85 | −0.70 | 2.12 | ||
| Voice and accountability | VAA | 465 | 0.88 | 0.71 | −1.75 | 1.74 | ||
| Political stability and absence of violence | PSAV | 465 | 0.34 | 0.84 | −2.01 | 1.56 | ||
| Control variables | ||||||||
| Gross tertiary enrollment | The ratio of the number of students enrolled in tertiary education (regardless of age) to the population of the age group | World Bank | GTE | 392 | 62.87 | 24.58 | 11.43 | 143.31 |
| Log GDP per capita | The sum of gross value added by all the producers in the country minus taxes plus subsidies/total population | World Bank | LogGDP-PC | 465 | 10.01 | 0.99 | 6.69 | 11.55 |
| Patents/GDP ratio | The ratio of total patent applications filed in a country and GDP per capita | World Bank | Patent-GDP ratio | 465 | 5.41 | 21.93 | 0 | 165.36 |
| Market openness | Includes trade, investment, and financial freedom | Heritage Foundation | MO | 465 | 73.09 | 11.91 | 34.67 | 89.27 |
4. Results and discussion
Before diving into panel regression estimates, Figure 1 presents the new businesses registered annually in both emerging and developed markets. It is evident that the number of new businesses has continuously increased in both markets during the study period, with a steep increase since 2017.
The chart presents mean new businesses registered for emerging economies and advanced economies from 2006 to 2020. Emerging economies increase overall from about 60000 in 2006 to about 140000 in 2020 with noticeable growth after 2014 and strong increases in 2019 and 2020. Advanced economies increase from about 75000 in 2006 to about 135000 in 2020 with steady growth after 2011. Advanced economies show higher values than emerging economies from 2006 to 2018, while emerging economies exceed advanced economies in 2019 and remain higher in 2020.Year wise trends of new businesses registered (emerging and advanced economies)
Source: World Bank Entrepreneurship Database, 2022
The chart presents mean new businesses registered for emerging economies and advanced economies from 2006 to 2020. Emerging economies increase overall from about 60000 in 2006 to about 140000 in 2020 with noticeable growth after 2014 and strong increases in 2019 and 2020. Advanced economies increase from about 75000 in 2006 to about 135000 in 2020 with steady growth after 2011. Advanced economies show higher values than emerging economies from 2006 to 2018, while emerging economies exceed advanced economies in 2019 and remain higher in 2020.Year wise trends of new businesses registered (emerging and advanced economies)
Source: World Bank Entrepreneurship Database, 2022
The correlation matrix (Supplementary) shows a positive relation of entrepreneurship with IQI and BCI, confirming previous findings (Nguyen et al., 2021; Chambers and Munemo, 2017) and a negative correlation with UA. Further empirical analysis can provide conclusive findings. For this, PCSE results are given in Table 2.
Determinants of new business density (PCSE method)
| Variables | Model_overall | Model_1 | Model_2 | Model_3 | Model_4 | Model_5 | Model_6 |
|---|---|---|---|---|---|---|---|
| BCI | 0.060** [0.025] | 0.059** [0.027] | 0.062** [0.026] | 0.059** [0.026] | 0.060** [0.028] | 0.059** [0.026] | 0.059** [0.028] |
| UA | −4.697*** [0.827] | −4.107*** [0.826] | −4.568*** [0.693] | −4.006*** [0.692] | −3.821*** [0.723] | −4.170*** [0.848] | −3.996*** [0.691] |
| Institutional variables | |||||||
| IQI | 0.009 [0.205] | ||||||
| (IQI)2 | −0.146*** [0.049] | ||||||
| CC | 1.485*** [0.452] | ||||||
| (CC)2 | −0.556** [0.278] | ||||||
| GE | 1.003* [0.528] | ||||||
| (GE)2 | −0.784*** [0.287] | ||||||
| PSAV | 0.163 [0.272] | ||||||
| (PSAV)2 | −0.211 [0.169] | ||||||
| RQ | 1.231 [0.826] | ||||||
| (RQ)2 | −0.209 [0.454] | ||||||
| RoL | 1.047** [0.464] | ||||||
| (RoL)2 | −0.490 [0.394] | ||||||
| VAA | 0.792* [0.412] | ||||||
| (VAA)2 | −0.376 [0.345] | ||||||
| Control variables | |||||||
| GTE | 0.025*** [0.009] | 0.022*** [0.008] | 0.023*** [0.009] | 0.0212** [0.009] | 0.022** [0.009] | 0.021** [0.009] | 0.023** [0.009] |
| Pat-GDP ratio | 0.021** [0.001] | 0.017 [0.010] | 0.014 [0.010] | 0.018* [0.011] | 0.022** [0.010] | 0.017* [0.010] | 0.038*** [0.012] |
| MO | 0.031 [0.023] | 0.034 [0.024] | 0.042* [0.025] | 0.042* [0.025] | 0.038 [0.024] | 0.036 [0.025] | 0.042 [0.026] |
| LogGDP-PC | 0.384 [0.421] | 0.240 [0.403] | 0.749** [0.355] | 0.570 [0.358] | 0.199 [0.386] | 0.476 [0.390] | 0.559 [0.378] |
| Constant | −5.860 [4.272] | −5.713 [4.059] | −10.950*** [3.647] | −9.260** [3.825] | −6.501 [4.065] | −8.081** [4.004] | −9.618** [3.875] |
| No. of obs. | 343 | 343 | 343 | 343 | 343 | 343 | 343 |
| R-squared | 0.361 | 0.365 | 0.369 | 0.357 | 0.366 | 0.355 | 0.361 |
| Wald-chi2 | 74.88*** | 67.080*** | 80.430*** | 68.380*** | 75.440*** | 61.750*** | 73.230*** |
Standard errors in brackets. *p < 0.1; **p < 0.05; ***p < 0.01
Among all the explanatory variables, the role of UA culture turns out to be the strongest. The impact of UA is almost four times (−4.697 and −4.1 in Models 1 and 2, respectively) higher than CC (1.4), the latter being the next strongest factor. This result is also consistent in separate studies for emerging and developed markets. It shows that as a country moves from being risk-loving to a risk-averse culture, the new businesses get negatively impacted. It indicates that countries with a substantial UA culture tend to fear uncertainty and avoid unknown situations. People become less tolerant of external influences and their ideas, fearing failure (Hayton et al., 2002).
Interestingly, the IQI does not turn out to be statistically significant. However, its square term is negative and significant in the aggregate sample (−0.146). The result is intriguing but not surprising. It indicates that after controlling for other variables, institutional quality below a threshold does not seem to matter much to the prospective entrepreneurs. However, beyond a threshold, it creates discouraging effects among the budding entrepreneurs as they are not immune to overregulations (Djankov et al., 2002). It may also be noted that the composite index may have limitations, as individual indicators within could create counterbalancing effects, suppressing the overall effect at a low level. However, at a higher level, negative effects become stronger. In this context, it is, thus, imperative to understand the impacts of individual dimensions of institutional quality. This would help us identify specific institutional quality indicators that matter the most, paving the way for targeted policy interventions.
The results of the individual IQI indicators suggest that CC (1.485), GE (1.003), RoL (1.047) and VAA (0.792) exhibit visible positive effects. Entrepreneurs get a positive push if the policies are executed with a commitment to promoting enterprises while emphasizing these aspects (Chambers and Munemo, 2017). However, an overregulated institutional environment may hinder the prospects of entrepreneurship. This is evident from the fact that the coefficients of the square terms of CC (−0.556) and GE (−0.784) are negative, corroborating earlier findings (Dreher and Gassebner, 2013; Anokhin and Schulze, 2009).
The impact of BCI is positive (0.0602 and 0.0586 in Models 1 and 2, respectively), indicating that a bullish business environment encourages entrepreneurs to start and expand businesses (Kaivanto and Zhang, 2022). However, its impact is relatively low compared to formal institutions and uncertainty avoidance. Among control variables, education and the patent-to-GDP ratio have favorable impacts on entrepreneurship across several models. At the same time, logGDP-PC and MO exhibit positive impacts in a few cases.
Two separate models are run to understand whether the impacts vary between emerging and developed markets (Tables 3 and 4). The results indicate that UA has the strongest negative impact (−4.990 and −4.896 in models 1 and 2, respectively) in the emerging markets, i.e. nearly four times the impact of VAA and GDP. In the case of institutional quality, CC, RoL and VAA remain significant. Furthermore, an upward-sloping concave relationship exists between institutional quality indicators and entrepreneurship (Anokhin and Schulze, 2009). However, BCI does not turn out to be significant. One of the possible reasons, inter alia, could be that the value of stocks traded in emerging markets is just half that of developed markets (World Federation of Exchanges database, 2022). Also, the stock markets in emerging economies are highly volatile and less rational. Consequently, venture capitalists feel discouraged from investing in these markets (Li and Song, 2025). The MO, logGDP-PC and patents-to-GDP ratio show a positive and significant impact in emerging markets, while education becomes insignificant, though positive.
Determinants of new business density (emerging markets) using PCSE method
| Variables | Model_overall | Model_1 | Model_2 | Model_3 | Model_4 | Model_5 | Model_6 |
|---|---|---|---|---|---|---|---|
| BCI | −0.030 [0.029] | −0.029 [0.028] | −0.027 [0.027] | −0.029 [0.029] | −0.029 [0.029] | −0.028 [0.029] | −0.026 [0.027] |
| UA | −4.990*** [1.261] | −4.896*** [1.308] | −4.792*** [1.393] | −4.933*** [1.269] | −5.180*** [1.415] | −4.891*** [1.360] | −4.748*** [1.312] |
| Institutional variables | |||||||
| IQI | 0.227 [0.446] | ||||||
| (IQI)2 | −0.069 [0.093] | ||||||
| CC | 1.513*** [0.451] | ||||||
| (CC)2 | 0.023 [0.474] | ||||||
| GE | 0.295 [0.432] | ||||||
| (GE)2 | −0.100 [0.398] | ||||||
| PSAV | 0.326 [0.313] | ||||||
| (PSAV)2 | −0.140 [0.175] | ||||||
| RQ | 1.342 [0.903] | ||||||
| (RQ)2 | −0.424 [0.726] | ||||||
| RoL | 0.837** [0.373] | ||||||
| (RoL)2 | −0.054 [0.552] | ||||||
| VAA | 1.100*** [0.412] | ||||||
| (VAA)2 | 0.531 [0.401] | ||||||
| Control variables | |||||||
| GTE | 0.006 [0.007] | 0.005 [0.007] | 0.005 [0.007] | 0.004 [0.007] | 0.007 [0.007] | 0.004 [0.007] | 0.005 [0.008] |
| Pat-GDP ratio | 0.025** [0.01] | 0.019* [0.011] | 0.020* [0.011] | 0.020* [0.010] | 0.023** [0.011] | 0.020* [0.011] | 0.027** [0.012] |
| MO | 0.067*** [0.023] | 0.055** [0.023] | 0.067*** [0.024] | 0.072*** [0.023] | 0.067*** [0.024] | 0.065*** [0.023] | 0.064*** [0.023] |
| LogGDP-PC | 1.034** [0.435] | 1.069** [0.424] | 1.321*** [0.442] | 1.350*** [0.431] | 1.124*** [0.426] | 1.217*** [0.455] | 1.176*** [0.434] |
| Constant | −4.006 [3.861] | −4.552 [3.626] | −7.887** [3.323] | −7.932** [3.459] | −6.005 [3.653] | −6.751* [3.669] | −7.219** [3.421] |
| No. of obs. | 170 | 170 | 170 | 170 | 170 | 170 | 170 |
| R-squared | 0.282 | 0.28 | 0.23 | 0.258 | 0.254 | 0.254 | 0.262 |
| Wald-chi2 | 64.680*** | 49.250*** | 37.690*** | 44.480*** | 43.680*** | 46.200*** | 47.140*** |
Standard errors in brackets; *p < 0.1; **p < 0.05; ***p < 0.01
Determinants of new business density (developed markets) using PCSE method
| Variables | Model_Overall | Model_1 | Model_2 | Model_3 | Model_4 | Model_5 | Model_6 |
|---|---|---|---|---|---|---|---|
| BCI | 0.166*** [0.054] | 0.175*** [0.059] | 0.160*** [0.050] | 0.166*** [0.052] | 0.179*** [0.059] | 0.162*** [0.053] | 0.174*** [0.059] |
| UA | −5.876*** [0.774] | −5.381*** [0.701] | −5.905*** [0.858] | −5.333*** [0.817] | −4.741*** [0.709] | −5.352*** [0.870] | −5.755*** [0.815] |
| Institutional variables | |||||||
| IQI | −0.528 [0.322] | ||||||
| CC | −0.274 [0.598] | ||||||
| GE | −1.624*** [0.564] | ||||||
| PSAV | −0.805 [0.606] | ||||||
| RQ | 0.986 [0.896] | ||||||
| RoL | −0.723 [0.717] | ||||||
| VAA | −2.280 [1.864] | ||||||
| Control variables | |||||||
| GTE | 0.049** [0.019] | 0.047** [0.020] | 0.049*** [0.019] | 0.049** [0.020] | 0.045** [0.019] | 0.049** [0.021] | 0.046** [0.019] |
| Pat-GDP ratio | 2.187 [2.374] | 2.830 [2.491] | 1.873 [2.279] | 1.947 [2.293] | 3.170 [2.447] | 2.434 [2.296] | 2.643 [2.448] |
| MO | −0.009 [0.039] | −0.009 [0.039] | −0.015 [0.039] | −0.021 [0.040] | −0.022 [0.039] | −0.007 [0.040] | −0.011 [0.038] |
| LogGDP-PC | −1.062 [0.953] | −1.583* [0.939] | −0.896 [0.867] | −1.003 [0.940] | −1.932** [0.963] | −1.084 [0.921] | −1.405 [0.971] |
| Constant | 0.677 [10.21] | 4.881 [9.941] | 1.592 [9.357] | 0.711 [9.858] | 7.167 [10.66] | 1.045 [10.01] | 6.055 [10.01] |
| No. of obs. | 173 | 173 | 173 | 173 | 173 | 173 | 173 |
| R-squared | 0.446 | 0.445 | 0.456 | 0.443 | 0.449 | 0.442 | 0.449 |
| Wald-chi2 | 82.830*** | 85.450*** | 80.740*** | 76.830*** | 83.750*** | 69.810*** | 81.840*** |
Standard errors in brackets; *p < 0.1; **p < 0.05; ***p < 0.01
In the case of developed markets also, UA culture has the strongest negative impact (−5.381) on entrepreneurship. Interestingly, its impact is higher in developed markets than in their developing counterparts. From the perspectives of formal institutions, except for GE, no other indicators are statistically significant. Moreover, it exerts a negative impact. This could be attributed to the fact that in developed markets, GE, after a certain threshold, hinders entrepreneurship due to excessive regulations (Lobonț et al., 2022). Interestingly, although other IQI indicators are not significant, all have negative coefficients except for regulatory quality. Unlike emerging markets, BCI exerts a positive impact on entrepreneurship in developed markets. This is because stock markets and investor sentiment have a much stronger impact on businesses in developed markets, as they are bigger in both volume and quantity (Belaid et al., 2021). In developed markets, the square terms have been dropped from the analysis as the institutional quality has already improved significantly.
Another intriguing result is that the relationship of logGDP–PC with entrepreneurship is negative in developed markets compared to a positive impact in emerging markets. This is probably because, in developed economies, the opportunity cost is higher for entrepreneurs than the returns due to higher real wages. Especially as job opportunities in these economies increase, the number of necessity entrepreneurs reduces (Wennekers et al., 2005; Solomon et al., 2021b). Education exerts a positive impact on entrepreneurship, while the patent-to-GDP ratio and MO are not significant. Thus, developed markets seem to be influenced more by education, the overall business environment and a risk-loving culture.
5. Conclusion and implications
This study attempted to examine the impact of institutional quality, investor sentiment and uncertainty avoidance culture on entrepreneurship, controlling for factors like GDP, education, innovation and market openness. The study also captured the impact of overrestrictive institutions and examined whether the effects of these critical factors vary between emerging and developed markets. The study broadly explored the roles of three significant aspects: institutional, behavioral and cultural factors. At the same time, it examined how education or human capital and income per capita, supported by R&D and market openness, help economies create or hinder new businesses.
It was established that uncertainty avoidance culture had a strong adverse effect on new business density across countries. However, the impact turned out to be relatively higher in developed economies than in emerging ones. Therefore, our study affirms earlier evidence while proving that a country, despite being developed with better institutions and infrastructure, can still be low on entrepreneurship if the people are risk-averse. Moreover, in cultures exhibiting uncertainty avoidance, people are risk-averse, giving a greater premium to stability and safety and avoiding new ventures (Hofstede et al., 2004).
Conversely, a positive investor sentiment in the market supported by a risk-loving culture can create a more growth-promoting environment. Especially in the early stages of becoming an entrepreneur, the risk-loving culture supports individuals in starting their businesses. The bullish attitude of the market further motivates people to explore entrepreneurship as a viable option (Samila and Sorenson, 2011). It is, thus, important to keep the investment environment bullish to restore people’s confidence in new and innovative ideas. With encouragement for potential entrepreneurs, a bullish environment brings more investments from both domestic venture capitalists and international investors. It receives a boost if the cultural environment is risk-loving. The bullish market characterizes a scenario of easy funding, credit availability and a positive outlook among established entrepreneurs, thus encouraging to-be entrepreneurs to come forward.
The findings further confirmed that institutional quality has, by and large, a favorable impact on entrepreneurship. Controlling corruption, providing an effective government, maintaining the rule of law and promoting voice and accountability are prerequisites for new business promotion. However, overregulated systems may create detrimental effects. Such phenomena may be attributed to complex procedures and inflexible rules that control corruption, creating inconveniences (Mohamadi et al., 2017). Moreover, the misuse of power and the authoritarian approach may discourage prospective entrepreneurs from undertaking businesses. It is, therefore, pertinent to strike a balance, ensuring that institutional quality is improved and inflexible regulations are avoided at the same time. Maintaining transparency in processes with simplistic steps can prove to be beneficial. Especially, the measures taken to control corruption and make governance effective must not be counterproductive, as excessive vigilantism has already proven to be so. India’s demonetization measure could be a case in point to substantiate such arguments. While the intention behind the action cannot be questioned, the inconveniences and uncertainties emanating from this measure are a testament to such eventualities.
For emerging markets, maintaining the rule of law and voice and accountability are significant, while strong government control negatively impacts entrepreneurship. Thus, emerging markets can boost entrepreneurship by providing safe environments to start and run businesses and improving the enforcement of contract procedures and property rights issues. On the other hand, developed markets must bring transparency to their public services and improve the quality of forming and implementing policies.
Investor sentiment exerts a strong influence only on developed markets. The size difference between the two types of economies and the high volatility present in emerging economies can be the plausible reasons for investor sentiment not becoming significant therein. The rise in logGDP–PC deterring business density in the developed markets could be attributed to the saturation of such markets, as most consumer needs are already met. Besides, as real wages and formal job opportunities are relatively higher in these economies, the following discouraging effects may occur. First, the opportunity cost of giving up a high, well-paying job to start a business is high, forcing people to stay away from a risky path of entrepreneurship (Solomon et al., 2021s). Second, higher labor costs in these markets discourage prospective entrepreneurs from venturing into new businesses (Djankov et al., 2002). Despite these explanations, there is still room for further research to substantiate these findings.
To conclude, it may be stated that a strong bullish sentiment in the marketplace can add positivity for both prospective and established entrepreneurs, reducing the uncertainty avoidance culture. Furthermore, changing entrepreneurial perspectives with stringent policies that aim to improve institutions may be counterproductive. Hence, a balanced approach to bringing improvement to institutions would be more effective. Along with efforts to encourage a risk-loving culture, from the institutional quality perspective, a balance in power and a straightforward and simplistic rule of law can help build trust among individuals. Ensuring corruption control, effective governance and reasonable quality regulations can benefit entrepreneurship in both emerging and developed markets. Emerging economies must especially encourage greater innovation and market openness. Besides, their efforts to realize higher income growth would help them attract more new businesses. To bring about fundamental changes in people’s mindsets, connecting with the young and educated ones can be beneficial. This bottom-up approach can go a long way in promoting new entrepreneurship. Further studies need to explore how country-specific cultural traits can be aligned with risk-taking behavior. Also, other parameters that measure culture, like individualism and power-distance, may be examined to add new implications.
References
Supplementary material
The supplementary material for the article can be found online.
