Entrepreneurship Dynamics: Assessing the Role of Macroeconomic Variables on New Business Density in Euro Area

Abstract

This article examines the impact of the macroenvironment on enterprises in euro-area countries over the period 2006–2020. Our study builds on important works and theories in the field of business, including the work of Kar and Özsahin. We employ the Panel Least Squares method to estimate the coefficient of selected variables. We identify political, institutional (government effectiveness index, regulatory quality index, rule of law, market capitalization of company, control of corruption, political stability and absence of violence) and financial (financial development index, gross domestic product, inflation rate, unemployment rate, public debt) determinants that can have an effect on entrepreneurship. The article aims to fill a gap in the existing literature by providing new insights from the Eurozone and updated data that were not included in previous literature reviews and studies. In this way, we contribute to expanding knowledge about the relationship between macroeconomic factors and entrepreneurial activities in this specific geographical area, considering the lack of current analyses. According to our results, there is a positive statistically significant relationship between entrepreneurship and gross domestic product per capita, regulatory quality index, and market capitalization of the company and a negative statistically significant relationship between entrepreneurship and rule of law, and public debt.

1. Introduction

Business development is influenced by a multitude of factors. According to many authors, we can study the macroeconomic situation in the economy from different views. Three groups of entrepreneurial studies can be categorized according to the study. The first group provides explanations for why some individuals choose to work for themselves while others choose to work for others. Entrepreneurship has a microeconomic perspective. The second and third groups of studies are focused on macroeconomic perspectives. These studies are interested in the economic performance of a country, savings, investments, inequality, and monetary and fiscal policies (Quadrini 2009). Other studies look for another factor, which can affect the macroeconomic state of entrepreneurship. Many authors studied these factors in different countries. The rate of inflation, foreign direct investment, access to funding, and the total tax rate have a significant impact on the total entrepreneurship rate (Rusu and Roman 2017). Macroeconomic factors, entrepreneurial performance, and economic growth are studied in the article by Mezentceva and Mezentceva (2017). They studied macroeconomic conditions in emerging markets. Another study is interested in the effect of social, cultural, and economic factors on entrepreneurship (Castoño et al. 2015). The authors look at the factors that affect entrepreneurship in 15 European countries, 12 countries from Latin America and the Caribbean. The study tests three main hypotheses. The first hypothesis is that “a suitable social structure consisting of a solid rule of law and economic freedom positively affects entrepreneurship”. The second hypothesis is said: “Societies with less corruption, and better training and education have higher levels of entrepreneurial activity”. The third hypothesis is: “Entrepreneurial activity and economic performance have a positive correlation”. Hypotheses were tested by a model that combines principal component analysis and multiple regressions. Authors confirmed hypotheses H1, H2, and H3. They use economic factors such as economic policy measures, openness, innovation, and economic performance; social factors such as economic freedom degree, and rule of law; cultural factors such as control of corruption and the law. In the article is said that the correlation between economic factors and entrepreneurship is stronger in Latin America and Caribbean countries than in European countries.

The relationship between social and economic factors and the inclination of students towards entrepreneurship is being investigated and quantified in a study (Castoño et al. 2015). The authors used a clustering principle to investigate dependencies among factors and characteristics. Another research defines and quantifies the significant factors (social, economic, macroeconomic) that determine the perceptions of the entrepreneurial propensity of students for starting a new business in the Czech Republic, the Slovak Republic, and Poland (Dvorský et al. 2019). Loukil’s study examines which factors in the macroeconomic environment can stimulate entrepreneurial activity in emerging and developing countries (Loukil 2019). The author verified using the GMM method that demand and institutional framework are the primary factors that influence entrepreneurship. She finds that financial development and unemployment have no significant impact on entrepreneurship and its activity.

Our study is inspired by Kar and Özsahin (2016), who studied role of financial development on entrepreneurship by employing panel data estimation methods for 17 emerging countries over the period 2004–2009. The results of our study are compared with Kar and Özsahin’s study results (Kar and Özsahin 2016).

In our study, we are focusing on Eurozone countries because we believe that this set of nations has not been utilized in a similar study of this nature previously. The data spans from 2006 to 2020, as we aimed to utilize the most recent data available; however, data for the years 2021 and 2022 were not yet accessible. The primary research question addressed in this study is: “Does the combination of economic, political and institutional factors collectively have a significant impact on the density of new business in the Eurozone?” The article is organized as follows. The initial section provides an introduction to entrepreneurship and the macroeconomic conditions of countries (Section 1). Next, we summarize the theoretical background of entrepreneurship and entrepreneur, their definition, and factors that may impact entrepreneurship based on the literature review (Section 2). We give an overview of the methodology and data used in the empirical part of the article in Section 3. Section 4 involves conducting research, summarizing descriptive statistics of the variables we used, and summarizing the key results. The results in Section 5 are compared to the outcomes of the most crucial empirical studies in the field of research and we suggest potential future research in this area.

2. Entrepreneurship and Its Determinants: Literature Review

Entrepreneurship is in the dictionary defined as “the state of being an entrepreneur, or a person who organizes, manages, and assumes the risk of a business with the goal of generating economic value” (Costa 2023).

Figure 1 shows the evolution of the term entrepreneur. The French dictionary used the term entrepreneur for the first time in 1437, according to the literature (Sendra-Pons et al. 2022). The term evolved over time. Earlier in the literature, there was the term defined as a risk manager. Later, economists in the 18th and 19th centuries used the term capitalist. Schumpeter and Backhaus (1934) used the term innovator in 1934, Kirzner (1973) defined an entrepreneur as an alert seeker of opportunities, and other authors (Casson 1982; Hébert and Link 1988) as coordinator of limited resources. Long (1983) used the definition of entrepreneurs as a creative search for opportunities, deliberate risk-taking, and professional competence.

In

Table 1. Definition of entrepreneurship according to different authors.

Author (Year)	Definition
Drucker (1985)	“It is the process of extracting profits from new, unique, and valuable combinations of resources in an uncertain and ambiguous environment”.
Schumpeter (1934)	“It is the process of creating new combinations of factors to produce economic growth”
Gartner (1989)	“It is the process by which new organizations emerge”.
Timmons (1989)	“It is the ability to create and build something from practically “nothing”
Stevenson and Jarillo (1990)	“It is the process by which individuals— either on their own or inside organizations —pursue opportunities without regard to resources they currently control”.
Kao (1993)	“It is the process of doing something new and something different for the purpose of creating wealth for the individual and adding value to society”.
Shane and Venkataraman (2000)	“It is an activity that involves the discovery, evaluation, and exploitation of opportunities to introduce new goods and services, ways of organizing, markets, processes, and raw materials through methods that did not previously exist”.
Coulter (2001)	“It is the process whereby an individual or a group of individuals use organized efforts and means to pursue opportunities to create value and grow by fulfilling wants and needs through innovation and uniqueness, no matter what resources are currently controlled”.
Johannisson (2002)	“It is where the interplay of internal and external forces creates a future”.
Eisenmann (2013)	According to Professor Howard Stevenson, one of the godfathers of entrepreneurship research, “Entrepreneurship is the pursuit of opportunity beyond resources controlled”.

Source: Based on (Sendra-Pons et al. 2022).

, there are definitions of entrepreneurship that have emerged over time. Similarly to the term entrepreneur, there is no consensus on the term entrepreneurship. While definitions have evolved over the years, there is something in common. In general, we can define entrepreneurship as a process that is led by entrepreneurs for the purpose of generating value.

The literature shows that there is a different effect of many factors on entrepreneurship and its activity in different countries. Halis et al. (2007) talk about the value concept. The concept of value is defined as the processes of social and individual development, which are important contributors to economic, technological, cultural and political theories. Entrepreneurship is influenced by factors such as GDP per capita, financial development, inflation, unemployment, and others.

Gross domestic product per capita can have a positive or negative effect on entrepreneurship. There are studies that show there is not a significant effect of GDP per capita on entrepreneurship (Ovaska and Sobel 2004). Dvouletý and Mareš (2016) find that there is a positive correlation between entrepreneurial activity and GDP per capita. This research was provided on the data of the Czech Republic. Other studies show that there is a significantly positive relationship between GDP per capita and entrepreneurship (Guerrero et al. 2016; Van Stel et al. 2004). Van Stel et al. (2004) find that there is a relationship between entrepreneurial activity and economic growth, but the level of effect depends on the level of per capita income.

According to the literature, there is an impact of financial development on entrepreneurship. Different viewpoints are considered when examining this relationship. Dutta and Meierrieks (2021) investigate the effect of financial development on entrepreneurship for a panel of 136 countries. They stated that financial development contributes to entrepreneurial activity In response to demand. They confirmed their hypothesis that a higher level of financial development is associated with a higher level of entrepreneurship. Dutta and Sobel (2018) offer a different perspective on financial development and entrepreneurship. By conducting their research, they contributed to previous research on human capital, financial development, and the link between entrepreneurship and human capital. Results show that human capital and financial development act as substitutes with regard to their impact on entrepreneurship. Bianchi (2012) researched the relationship between financial development and entrepreneurship as a difference between the self-employed and employees, which increases with financial development. Other authors studied the effects of differences in local financial development and its effect on entrepreneurship, concrete in Italy or in China (Guiso et al. 2004; Jiang et al. 2019).

The relationship between governance effectiveness and entrepreneurship was examined by Friedman (2011) in his study. There is evidence that countries with higher governance effectiveness have less favorable attitudes towards, and lower levels of, entrepreneurship when compared to countries with less effective governance. The author explained the findings that developed countries with higher GDP per capita have fewer opportunities for entrepreneurs. In developed countries, the market for new businesses is saturated compared to developing countries, which would lead to a decrease in motivation for nascent business and entrepreneurship overall. To sum up, there is a negative relationship between governance effectiveness in a country and its level of entrepreneurship. Another paper examines the role government policy plays in the development of entrepreneurship and its impact on economic development. The success of the country in entrepreneurship is a result of the government’s policy behavior. In particular developing countries wants the government to carry out economic development, for example through various political programs in various forms (Obaji and Olugu 2014).

He (2011) in his study looked at the relationship between inflation and entrepreneurship. There is evidence, that a higher inflation rate will induce more people to become entrepreneurs instead of workers, and then more entrepreneurs generate more innovation and higher growth in the country. Inflation and entrepreneurship can have a positive relationship, as shown by other studies (Sayed and Slimane 2014; Vidal-Suñé and Lopez-Panisello 2013; Shapero 1979; Gibb and Ritchie 1982). According to these studies, if inflation increases, it can be recorded as an increase in business opportunities because increased expectations of entrepreneurs’ earnings can be a result of higher price levels for products and services. On the other hand, some studies have found that there is a negative relationship between inflation and entrepreneurship, because inflation can discourage entrepreneurship because the business environment is considered riskier, and it increases the costs of starting a business (Arin et al. 2015; Djankov et al. 2010; Perotti and Volpin 2004; Singh and De Noble 2003).

Nistotskaya and Cingolani (2016) examined in their study the relationship and effect of regulatory quality in countries on entrepreneurship. According to their findings, bureaucratic structure has a direct and indirect impact on entrepreneurship rates through improved regulatory quality, while also exerting a direct independent effect. Other authors examined the effect of corruption on entrepreneurship in developing countries (Abd Rashid et al. 2023). According to them, the impact of corruption on entrepreneurship is unique because of the poor regulatory quality in most developing countries. The importance of the rule of law in underdeveloped areas is greater than in developed central-north areas, while regulatory quality is more relevant in developed central-north areas. In times of crisis, institutional quality becomes less important in the determination of inputs, which become more sensitive to the propensity to innovate and to human capital and infra-structural endowments Agostino et al. (2020).

Mickiewicz et al. (2021) found that changes in the rule of law have more pronounced effects than regulation changes.

The relationship between an unemployment rate and entrepreneurship was examined by many authors (Thurik 2003; Baptista and Thurik 2007; Musa and Semasinghe 2013; Cueto et al. 2015). According to Thurik (2003), entrepreneurship can contribute to the reduction of unemployment. He discovered that the relationship between entrepreneurship and unemployment in the UK is unique, as entrepreneurship contributes less than in other areas to alleviate the unemployment problem. When compared to the effects of entrepreneurship on unemployment with the OECD average, Portugal is an outlier (Baptista and Thurik 2007). Musa and Semasinghe (2013) explored the relationship between the concept of entrepreneurship and unemployment. The push effect is the theory that higher unemployment will increase early-stage activity. The authors believed that boosting entrepreneurial activity led to a decrease in unemployment, but high unemployment rates resulted in a slowdown or decrease in entrepreneurial activity in an economy. The results of other studies show small direct and indirect effects of unemployment and entrepreneurship. The increase in unemployment in a region results in a decrease in self-employment, but the increase in unemployment in neighboring regions results in a rise in incentives for self-employment (Cueto et al. 2015).

Dutta and Sobel (2016) investigated the link between corruption and entrepreneurship, and they found that corruption has a negative impact on entrepreneurship.

The impact of the public debt of a country on the development of entrepreneurship was examined (Zhuravlov et al. 2021). Macroenvironmental factors, particularly financial ones, play a role in the sustainable development of entrepreneurship. The increase in the public debt of a country leads to a tight tax policy: increase in taxes, increase in the range of taxes, abolition of benefits and subsidies, and constant tax monitoring of business activities. This is a disincentive to business development. The reputation capital of countries is also decreasing, which has a significant impact on the reputation capital of entrepreneurs in the global competitive market. All this leads to social destabilization at the macroeconomic level and, as a result, to an increase in migration abroad, a reduction in birth rates, and a reduction in labor resources, both qualitatively and quantitatively.

3. Data and Methodology

In our article, there is a panel data estimation method used to test the relationship between selected financial, selected political factors and entrepreneurship. Our sample includes 19 countries in the euro area (Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Portugal, Slovenia, Slovakia, and Spain) for the period 2006–2020. The Economic and Monetary Union encompasses all EU countries. 19 of the countries listed above have replaced their national currencies with a single currency, the euro. These countries belong to the euro area, which is also known as the Eurozone.

Building upon the research question posed in the introduction, we present both the null and alternative hypotheses to be tested in our analysis in the following table (

Table 2. Null and alternative hypotheses of the research study.

Hypothesis
H0	None of the economic, political, or institutional factors has a significant impact on the density of new businesses in the Eurozone.
H1	At least one of the economic, political, or institutional factors has a significant impact on the density of new businesses in the Eurozone.

).

In this section, we look at how certain factors affect entrepreneurship. Kar and Özsahin (2016) inspired us to select certain factors for our research. In their research, they investigate the impact of financial development on entrepreneurship by utilizing panel data estimation methods in 17 emerging markets economies over the period 2004–2009.

In the analysis, we used the following financial factors:

• Financial development index (FDI);
• Gross domestic product per capita (GDP);
• Inflation rate (INF);
• Unemployment rate (UNRATE);
• Public debt (PD).
• In the analysis, we used the following political and institutional factors:
• Government effectiveness index (GEI);
• Regulatory quality index (RQI);
• Rule of law (RoL);
• Market capitalization of companies (MCC);
• Control of corruption (CC);
• Political stability and absence of violence (PSAV).

All data are annual. The financial, political and institutional factors mentioned above are independent variables used in our analysis. New business density (ENT) is used in the analysis as a dependent variable. Data used for entrepreneurship (ENT), gross domestic product per capita, inflation rate, unemployment rate, market capitalization of companies, public debt, and factors of political effects (control of corruption, government effectiveness index, regulatory quality index, rule of law, political stability and absence of violence) are retrieved from World Bank World Development Indicators online database (The World Bank 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2023g). Financial development index data are retrieved from the International Monetary Fund database (International Monetary Fund 2023b).

We examine the relationship between entrepreneurship (ENT) and financial, political and institutional determinants. To estimate the coefficients of selected variables we use Panel Least Squares method as was used in the previous research (Kar and Özsahin 2016) with the following model specification:

$$\begin{gathered} {ENT}_{it}={\beta }_0+{\beta }_1{FDI}_{it}+{\beta }_2{GDP}_{it}+{\beta }_3{GEI}_{it}+{\beta }_4{INF}_{it}+{\beta }_5{RQI}_{it}+{\beta }_6{RoL}_{it}+{\beta }_7{UNRATE}_{it}+{\beta }_8{MCC}_{it}+{\beta }_9{CC}_{it}+ \\ {\beta }_{10}{PD}_{it}+{\beta }_{11}{PSAV}_{it}+u_{it}, \end{gathered}$$

(1)

where i denotes the each country of euro area (i = 1, 2, 3, …, 19) and t denotes the time period (t = 2006–2020). β is the estimated coefficient of each variable. u_i is individual and time effects and the error term, for which the following equation applies:

$$u_{it}={\mu }_{it}+{\lambda }_{it}+{\nu }_{it}$$

(2)

where µ_it is the unobservable individual effect, λ_it is the unobservable time effect, and ν_it is the error term. Individual effects allow for accounting variations among individuals that are not directly observable or measurable but have a significant impact on our analysis. Time effects enable us to capture trends or variations over time, which can be crucial for our study. The general error term encompasses all other factors not accounted for individual or time effects.

By using this model, we can estimate the coefficients of selected factors, which can have an effect on entrepreneurship. The results of the research show us which factors have a significant impact on entrepreneurship in euro-area countries. In the previous research, the influence of a group of factors on a business had not been investigated. The benefits of this research can be seen in the factors we tested in the Eurozone countries for the current period until 2020.

The variables used in the equation are as follows:

ENT_it is a new business density. It is an entrepreneurship indicator and dependent variable in our analysis. According to The World Bank (2023b), the new business density is defined as “the number of newly registered corporations per 1000 working-age people (those ages 15–64)”. Data are retrieved from World Bank World Development Indicator Database.

FDI_it is the financial development index. It belongs to the group of financial factors, that affect entrepreneurship. Data are retrieved from International Monetary Fund, Financial Development Index Database (International Monetary Fund 2023a, 2023b). According to IMF (International Monetary Fund 2023a) the index is defined as “a relative ranking of countries on the depth, access, and efficiency of their financial institutions and financial markets. It is the aggregate of the Financial Institutions index and the Financial Markets index”.

GDP_it is gross domestic product per capita. It is one of the financial factors in our analysis. Data are retrieved from the World Bank, World Bank national accounts data, and OECD National Accounts data files (The World Bank 2023c). GDP per capita is a measurement of the approximate value of a country’s gross domestic product contributed by each member of its population. It is calculated by taking a country’s GDP and dividing it by the country’s population. GDP refers to the total value of all goods and services produced within a country’s borders during a period of time, usually annually. There must be Purchasing power parity (PPP) used while comparing GDP per capita between countries (The World Bank 2023a).

GEI_it is the government effectiveness index (GEI). It is one of the political factors used in our analysis. Annual data are retrieved from the World Bank database (The World Bank 2023g). The index measures the quality of public services, civil service, policy formulation, policy implementation, and the credibility of the government’s commitment to raising these qualities or keeping them high (Millenium Challenge Corporation 2023).

INF_it is the inflation rate. It belongs to financial factors. The data are retrieved from the World Bank database (The World Bank 2023d). The annual rate of inflation is the price of the total basket in a given month compared with its price in the same month one year previously. The basket represents an item with all the goods and services consumed by households during the year. Every product in this basket has a price, which can change over time (European Central Bank 2023).

RQI_it is the regulatory quality index. It is a technical tool to measure the regulatory performance of a given country, developed in Greece by Markos Dragoumis. The index is composed of 67 variables, from which 50 of them are evaluated. The total score of each law is given to 0 to 100 scale (KEFIM 2023). The data are retrieved from the World Bank database (The World Bank 2023g).

RoL_it is the rule of law. This index is a quantitative assessment tool designed to offer a detailed and comprehensive picture of the extent to which countries adhere to the rule of law in practice. The index covers 128 countries (Insightsias 2023). The data are retrieved from the World Bank database (The World Bank 2023g).

UNRATE_it in the unemployment rate. The unemployed are people of working age who are without work, are available for work, and have taken specific steps to find work. The uniform application of this definition results in estimates of unemployment rates that are more internationally comparable than estimates based on national definitions of unemployment. This indicator is measured in the number of unemployed people as a percentage of the labor force and it is seasonally adjusted. The labor force is defined as the total number of unemployed people plus those in employment. The indicator is estimated by International Labour Organization (OECD Data 2023). Data are retrieved from the World Bank database (The World Bank 2023f).

MCC_it is the market capitalization of the company. It is one of the institutional factors used in our analysis. It refers to the total dollar market value of a company’s outstanding shares of stock (Fernando et al. 2023). The data are retrieved from the World Bank database (The World Bank 2023e).

CC_it is control of corruption. It is an index that can have a score from −2.5 to 2.5, while −2.5 mean weak control of corruption in a country and 2.5 mean strong control of corruption (The Global Economy 2023). The data are retrieved from the World Bank database as one of the political factors that affect entrepreneurship (The World Bank 2023g).

PD_it is public debt (central government debt in a country) as a percentage of GDP. Data are retrieved from the World Bank database (The World Bank 2023a).

PSAV_it is political stability and absence of violence index. It is one of the political factors used in our analysis. It measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Percentile rank indicates the country’s rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Percentile ranks have been adjusted to correct for changes over time in the composition of the countries covered by the WGI. Percentile Rank Upper refers to an upper bound of a 90 percent confidence interval for governance, expressed in percentile rank terms (Trading Economics 2023). The data are retrieved from the World Bank database (The World Bank 2023g).

In our analysis, we employ the Panel Least Squares (PLS) model as a robust framework for examining the relationships within our dataset. To determine whether we should employ Fixed Effects, Random Effects, or Pooled Effects in our model, we rely on a battery of diagnostic tests tailored for panel data analysis.

Several tests are particularly suitable for this purpose, including:

• Jarque-Bera Test: The Jarque-Bera test assesses the normality of error terms, providing insights into the distributional assumptions of the model. Hypothese for the Jarque-Bera test is the following (Wooldridge 2015):

H₁. 

The errors in the model follow a normal distribution.

2.

Breusch-Pagan Test: The Breusch-Pagan test examines the presence of heteroskedasticity (variance differences) in the error terms, helping us evaluate the assumptions related to constant error variance. Hypothese for the Breusch-Pagan test is the following (Greene 2012):

H₂. 

There is no heteroskedasticity in the data (the error variance is constant).

3.

Hausman Test: The Hausman test is a pivotal tool for selecting between Fixed Effects and Random Effects models. It examines whether the individual-specific effects (fixed effects) are correlated with the independent variables, helping us decide on the model’s appropriateness. Hypothese for the Hausman test is the following (Baltagi 2013):

H₃. 

The individual-specific effects (fixed effects) are not correlated with the independent variables, indicating that random effects are more appropriate.

These tests play a crucial role in guiding our modeling decisions, ensuring the selection of the most suitable panel data approach for our research questions and dataset characteristics.

4. Results

In order to examine the relationship between entrepreneurship and independent variables in Equation (1) and estimate the coefficients of the model, first we display descriptive statistics of variables, such as mean, median, maximum, minimum, standard deviation, skewness, kurtosis and number of observations of the used panel data.

In

Table 3. Descriptive statistics of dependent variables and some independent variables.

Descriptive Statistics	ENT	FDI	GDP	GEI	INF	RQI
Mean	6.33	0.58	37,043.20	1.21	1.72	1.24
Median	4.05	0.63	31,244.93	1.17	1.50	1.20
Maximum	39.04	0.90	123,678.70	2.23	15.40	2.05
Minimum	0.31	0.20	9230.71	0.16	−4.48	0.14
Std. Dev.	6.17	0.19	22,698.20	0.46	1.96	0.39
Skewness	1.98	−0.53	1.91	−0.08	2.13	−0.09
Kurtosis	7.51	2.04	7.21	2.38	14.07	2.27
Observations	263	285	285	285	285	285

Source: own calculations.

we can see the descriptive statistics of variables used in our model. As displayed, the average value of variable ENT is 6.33, the median value is 4.05, and the maximum value of ENT in euro area countries is 39.04, this value of ENT was in Cyprus in 2007. The minimum value of ENT is 0.31, which was observed in Greece in 2009. Skewness is 1.98 and kurtosis is 7.51. The number of observations of ENT is 263, because there are some unavailable data. In Table 1 we can see descriptive statistics of some independent variables of our model: FDI, GDP, GEI, INF, and RQI. The mean value of the data financial development index (FDI) is 0.58, the median value is 0.63, the maximum value is 0.90. Maximum value was observed in Spain in 2020 and minimum value was observed in Estonia in 2020 (0.20). The standard deviation of the FDI data set is 0.19, skewness is −0.53 and kurtosis is 2.04. There are 285 observations of FDI in our data set. The mean value of the data set gross domestic product (GDP) is 37,043.20, the median value is 31,244.93, the maximum is 123,678.70, observed in Luxembourg in 2014, and the minimum value was observed in Lithuania in 2006 (9230.71). The standard deviation is 22,698.20, skewness is 1.91, and kurtosis of the GDP data set is 7.21. In our data set are 285 observations of the GDP variable. The mean value of the government effectiveness index (GEI) is 1.21, the median is 1.17, the maximum is 2.23 (observed in Finland in 2011), the minimum is 0.16 (observed in Greece in 2016), the standard deviation is 0.46, skewness is −0.08 and kurtosis is 2.38. There are 285 observations of GEI data. The inflation rate is another independent variable in our model. Descriptive statistics of the inflation rate follows: the mean is 1.72, the median is 1.50, the maximum is 15.40 (observed in Latvia in 2008), the minimum is −4.48 (observed in Ireland in 2009), the standard deviation is 1.96, skewness is 2.13, kurtosis is 14.07. Number of observations of INF is 285. The mean value of the regulatory quality index (RQI) is 1.24, the median value is 1.20, the maximum value was observed in the Netherlands in 2017 (2.05), and the minimum value in Greece in 2016 (0.14). The skewness of RQI data is −0.09 and the kurtosis is 2.27. There are 285 observations of RQI in the data set.

In

Table 4. Descriptive statistics of independent variables of the model.

Descriptive Statistics	RoL	UNRATE	MCC	CC	PD	PSAV
Mean	1.22	9.01	51.08	1.19	79.41	0.76
Median	1.18	7.74	41.47	1.07	80.30	0.79
Maximum	2.13	27.47	321.94	2.45	252.52	1.51
Minimum	0.07	3.14	4.57	−0.19	4.23	−0.47
Std. Dev.	0.49	4.75	38.84	0.67	47.84	0.39
Skewness	−0.19	1.60	2.46	−0.02	0.84	−0.53
Kurtosis	2.10	5.57	14.82	1.89	4.15	3.19
Observations	285	285	202	285	135	285

Source: own calculations.

we can see descriptive statistics of the rest of the independent variables used in our model. The mean value of the rule of law (RoL) is 1.22, the median value is 1.18, the maximum value is 2.13 (observed in Finland in 2014), and the minimum value is 0.07 (observed in Greece in 2017). The standard deviation of the RoL data set is 0.49, skewness is −0.19, kurtosis is 2.10. The number of observations of RoL is 285. The unemployment rate is another independent variable of our model Mean value is 9.01, the median value is 7.74. The highest unemployment was recorded in Greece in 2013 (27.47) and the lowest unemployment in Germany in 2019 (3.14). The standard deviation of UNRATE data set is 4.75, skewness is 1.60 and kurtosis is 5.57. Number of observations is 285. The mean value of the market capitalization of the company (MCC) is 51.08, the median value is 41.47, the maximum value of MCC was observed in Luxembourg in 2007 (321.94) and the minimum value was observed in Slovakia in 2010 (4.57). The standard deviation of the MCC data set is 38.84, skewness is 2.46 and kurtosis is 14.82. There are 202 observations of MCC in our data set, because some of the data are unavailable. The mean value of control of corruption (CC) is 1.19, the median value is 1.07, the highest value of control of corruption was observed in Finland in 2006 (2.45), and the lowest value of control of corruption was observed in Greece in 2012 (−0.19). The standard deviation of control of corruption is 0.67, skewness is −0.02, and kurtosis is 1.89. There are 285 observations of variable CC in our data set. The mean value of public debt is 79.41, the median is 80.30. The highest public debt was observed in Greece in 2020 (252.52) and the lowest public debt was in Estonia in 2007 (4.23). The standard deviation of PB data set is 47.84, skewness is 0.84 and kurtosis is 4.15. Some data on public debt are not available, so there are 135 observations in euro-area countries. The mean value of political stability and absence of violence index is 0.76, the median value is 0.79. The maximum of PSAV is 1.51 (observed in Luxembourg in 2008) and the minimum value is −0.47 (observed in Spain in 2009). The standard deviation of political stability and absence of violence index is 0.39, skewness is −0.53, and kurtosis is 3.19. There are 285 observations of political stability and absence of violence index in the data set of euro area countries.

In

Table 5. Results of key diagnostic tests.

Jarque-Bera Test
p-value	0.853
Breusch-Pagan Test
p-value	0.452
Hausman Test
Chi-square Statistic	31.563
Chi-Square Statistic Probability	0.000

Source: own calculations.

, we present the results of key diagnostic tests: the Jarque-Bera test (testing for the normality of errors), the Breusch-Pagan test (assessing heteroskedasticity), and the Hausman test (examining fixed effects). This table provides a comprehensive overview of the statistical assessments that have been performed to validate the underlying assumptions of our model and guide our modeling decisions. The outcomes of these tests are pivotal in ensuring the robustness and reliability of our analytical approach, enhancing the overall rigor of our research findings. The results displayed in Table 3 provide valuable insights into the statistical tests conducted to evaluate our model. Notably, the p-values associated with Equation (1) exceed the significance threshold of 0.05. This observation indicates that the data originate from a normal distribution. Subsequently, we executed the Breusch-Pagan test, yielding results that do not offer substantial evidence in support of heteroscedasticity within the model. Consequently, we uphold the null hypothesis, signifying that the assumption of homoscedasticity is satisfied. In the following step, we perform a Hausman test for Equation (1) to determine the most appropriate method for conducting regression analysis on the dataset. Importantly, the p-value in both equations falls below 0.05, or 5%. In light of this outcome, we accept H0, indicating that the fixed effects model is better suited to our study.

In

Table 6. Results of model estimations.

Variable	Coefficient	Standard Error	Probability
Financial development index	−0.081827	1.318715	0.9507
Gross domestic product per capita	0.000107	2.31 × 103	0.0000 ***
Government effectiveness index	0.321307	1.079076	0.7667
Inflation rate	−0.131394	0.091185	0.1537
Regulatory quality index	4.404643	1.155839	0.0003 ***
Rule of law	−5.684555	1.008695	0.0000 ***
Unemployment rate	0.041417	0.037236	0.2695
The market capitalization of the company	0.035232	0.008901	0.0002 ***
Control of corruption index	−0.763063	0.978850	0.4381
Public debt	−0.013804	0.004627	0.0038 **
Political stability and absence violence index	0.872407	0.700781	0.2170
c	−0.029672	1.388223	0.9830
R-squared		0.783952

Note: “**” = the variable is statistically significant on the 1% level of significance (99% confidence interval); “***” = the variable is statistically significant on the 0.1% level of significance (99.9% confidence interval). Source: own calculations.

we can see the results of our model estimations. In the first column, there are variables used in our model (financial and political and institutional). In the second column, there are estimated coefficients of the variables, and in the third column of the table, there are written probabilities of the estimated coefficients. The sign “*” there is indicated what the probability is.

In the last line of Table 6, there is R-squared written, which means that the model indicates 78% of the variance in the dependence in the dependent variable that the independent variables explain collectively. R-squared measures the strength of the relationship between the model and the dependent variable on a convenient 0–100% scale.

We can see in Table 3, that there are variables, which are statistically significant: gross domestic product per capita, regulatory quality, rule of law, market capitalization of company, and public debt. Gross domestic product per capita, regulatory quality, rule of law, and market capitalization of the company are statistically significant on the 0.1% level of significance. The public debt variable is statistically significant on the 1% of significance.

5. Discussion and Conclusions

Our article examines the relationship between entrepreneurship, which is measured by new registrations per thousand people between the ages of 15 and 64, and some selected variables (financial, political and institutional), which can affect entrepreneurship in euro-area countries. As mentioned in Section 2, there are many studies on the relationship between entrepreneurship and different factors.

According to our results, there is a statistically significant relationship between entrepreneurship (ENT) and financial variables (GDP, PD), and political and institutional variables (RQI, RoL, MCC). There is no statistically significant relationship between entrepreneurship and financial variables (FDI, GEI, INF, UNRATE), and political and institutional variables (CC, PSAV).

Many authors studied the relationship between entrepreneurship and the financial development index (Dutta and Meierrieks 2021; Dutta and Sobel 2018; Cumming et al. 2014; Bianchi 2012; Guiso et al. 2004; Jiang et al. 2019). They showed that there is a positive effect of FDI on entrepreneurship in different countries. Our model estimate coefficient of the FDI variable: −0.081827. It indicates a negative relationship between entrepreneurship and financial development index in euro area countries, which is not in line with other studies, but this variable is not statistically significant in our model.

According to the literature, there is a positive (Dvouletý and Mareš 2016; Guerrero et al. 2016; Van Stel et al. 2004) or no significant effect (Ovaska and Sobel 2004) of entrepreneurship on gross domestic product. The estimated coefficient of GDP in our model is 0.000107 in euro area countries. It is in line with other authors (Dvouletý and Mareš 2016; Guerrero et al. 2016; Van Stel et al. 2004). This estimated variable is statistically significant on a 0.1% level of significance and it shows that there is a positive relationship between entrepreneurship and gross domestic product. We can say that as the wealth of the country increases, entrepreneurship in the country increases.

There is no statistically significant positive effect of government effectiveness on entrepreneurship in euro area countries (estimated coefficient is 0.321307). According to a study (Friedman 2011) there is a negative significant relationship between entrepreneurship and government effectiveness. According to another study (Obaji and Olugu 2014) there is a positive relationship between entrepreneurship and government effectiveness mostly in developing countries.

The estimated coefficient of the inflation rate is not statistically significant in our model, but it shows a negative relationship. This is in line with other studies (Arin et al. 2015; Djankov et al. 2010; Perotti and Volpin 2004; Singh and De Noble 2003).

The estimated coefficient of the regulatory quality index is 4.404643, this coefficient is statistically significant at a 0.1% level of significance in our model. The authors (Abd Rashid et al. 2023; Agostino et al. 2020) of the study drew a similar conclusion for developing countries.

The estimated coefficient of the rule of law in euro-area countries is −5.684555. This coefficient is statistically significant at a 0.1% level of significance. These results are in line with the authors (Abd Rashid et al. 2023; Agostino et al. 2020; Mickiewicz et al. 2021).

The results show a positive relationship between the unemployment rate and entrepreneurship, so with the increase in unemployment in euro-area countries there is an increase in entrepreneurship. The estimated coefficient of the unemployment rate is 0.041417. These results are in line with the studies of the authors (Musa and Semasinghe 2013; Cueto et al. 2015).

The estimated coefficient of the market capitalization of companies in euro-area countries is 0.035232. This coefficient is statistically significant at a 0.1% level of significance. We have not found previous studies about the relationship between the market capitalization of a company and entrepreneurship.

The estimated coefficient of control of corruption index in euro area countries is −0.763063. This coefficient shows that there is a negative relationship between these variables and entrepreneurship. According to other studies, there is shown corruption hurts entrepreneurship (Dutta and Sobel 2016).

The estimated coefficient of public debt is −0.013804. It means that there is a negative statistically significant effect of public debt on entrepreneurship. This result is in line with the study (Zhuravlov et al. 2021). Results show that as the public debt increases in the country, it leads to tight tax policy and it reduces the entrepreneurship in country.

The results show that there is a positive not significant relationship between political stability and absence of violence index and entrepreneurship.

Compared to the study of Kar and Özsahin (2016) there are similarities in some results. They investigate the effects of some factors on entrepreneurship in 17 emerging market economies over the period 2004–2009 using the panel data estimation method. Their results show that improvements in the financial system will promote entrepreneurial activity. There is a variable financial development index in our model, which has a negative no significant effect on entrepreneurship in euro-area over the period 2006–2020. Entrepreneurship is positively correlated with the development of banking systems and stock markets. Entrepreneurship is positively impacted by a high level of per capita income, as it increases the creation of new investment opportunities for potential entrepreneurs. Our model shows the effect of gross domestic product per capita on entrepreneurship is positive and statistically significant. Entrepreneurship is positively impacted by the gross domestic product per capita. Their results suggest that higher inflation can decrease entrepreneurial activity due to its negative effects on macroeconomic uncertainty and the expectations of entrepreneurs about the future. The impact of the inflation rate on the entrepreneurial spirit in our model is negative and statistically insignificant, too. While institutional factors, such as controlling corruption and political stability, have a positive impact on entrepreneurship as expected in their study, they do not have any statistical significance at all. Our findings indicate that political stability has a positive and insignificant impact on entrepreneurship, and controlling corruption has a negative and insignificant effect on entrepreneurship.

To sum up, there are some factors that affect entrepreneurship in euro area countries. According to our results, there is a statistically significant relationship between entrepreneurship (ENT) and gross development product, public debt, regulatory quality index, rule of law index, and market capitalization of the company.