Multi-Criteria Evaluation of the Institutional and Tax Environment for Business in the EU Economies

Abstract

The purpose of this study is to evaluate the institutional and tax environment for businesses in 21 countries of the European Union (EU), using a multi-criteria analysis and, in particular, the PROMETHEE II method, based on thirteen evaluation criteria and the ranking of the European economies. Through the research, the importance of factors related to the efficiency of the tax administration, the tax capacity of the country, the growth rate of the economy, the quality of institutions, the level of corruption, the tax burden, the time of tax compliance and the political stability of the country is studied, in the formation of an attractive environment for the development of entrepreneurship in the EU economies. Moreover, the results of the analysis revealed the existence of significant fiscal and institutional differences between the EU economies under investigation, regarding the development of entrepreneurship. These differences are due to the size of the economy, the institutions, the history, the economic crises of each country and a wide range of dissimilar policy recommendations, which reflect asymmetric policy responses. The study concludes by offering theoretical and political recommendations to improve the institutional and fiscal environment in EU economies so that entrepreneurship can play an important role in improving the economic and social situation of a country.

1. Introduction

The business environment is influenced by many factors, mainly economic, such as the growth rate of the economy, the tax policy, the level of the tax burden, the tax rates and the time for tax compliance (Baumol 1990; Boettke and Coyne 2009; Wennekers et al. 2010). Entrepreneurship is considered as a tool for economic development and innovation in all countries regardless of the stages of development (Knoll and Zloczysti 2012). In many ways, entrepreneurship is critical to the well-being of society (Amorós and Bosma 2014; Pietrzak et al. 2017; Kaur and Srivastava 2017; Petrenko et al. 2017). According to Buno et al. (2015), the business environment includes economic, political, institutional, legal, technological and cultural conditions that enable and shape the entrepreneurial activities of firms (Ohanyan and Androniceanu 2017; Gavurova et al. 2016). Chládková (2015) argues that the business environment is influenced by a wide range of conditions in the area of legislative power, institutional infrastructure (Draskovic et al. 2017) and market functions.

A balanced and business-friendly tax environment will positively influence the development of entrepreneurship and contribute to the promotion of investment projects, being an important factor in stimulating the economy and competition (Lee and Gordon 2005). Many scholars agree that the taxation of business profits plays a crucial role in the assessment of the business environment, arguing that low tax rates contribute to high rates of economic growth, improve business conditions and create an attractive business climate (Anastasiou 2009; Zervoyianni and Anastasiou 2009; Kormendi 1983; Boettke and Coyne 2009; Murphy et al. 1991). At the same time, the implementation of an effective tax system to combat tax evasion requires the modernization and digitization of tax administration, the autonomy of tax authorities and the simplification of tax legislation (Parker 2009). Also, factors related to combating corruption, enhancing the quality of institutions and political stability are considered to guarantee higher business returns (Makhdoom et al. 2021). Similarly, administrative complexity increases the entry cost for business and is negatively associated with entrepreneurship (Gurley-Calvez and Bruce 2008). These facts increase the interest in developing methods to assess the business environment due to the increasing mobility of capital and the international presence of businesses.

Moreover, the institutional and fiscal environment affects the development of entrepreneurship in such a way that institutional and fiscal asymmetries between formal and informal institutions, such as corruption and tax evasion, hinder the development of economically and socially productive entrepreneurship. In order to reduce these asymmetries, there is a need for reforms aimed at developing and aligning formal and informal institutions and policies (Williams and Vorley 2015; Ostapenko and Williams 2016; Williams and Vorley 2017).

In this context, the aim of the study is to assess the quality of the institutions and the tax environment for businesses in 21 countries of the European Union, through the application of the PROMETHEE II method and their ranking, taking into account thirteen basic evaluation criteria for the year 2019. Through this, the establishment of a common framework for evaluating the business environment in terms of tax treatment and the quality of the institutions is achieved, which allows the formulation of a commonly accepted point of view among the interested parties, such as international organizations, national agencies, rating agencies, investment companies, etc., on the quality of the business environment in a country. With these data, the contribution of the paper focuses on the application of an integrated method for the quantitative assessment of the tax and institutional environment for business in EU economies and the analysis of the relationships between institutional weaknesses, fiscal characteristics and entrepreneurship. In addition, the research seeks a better understanding of institutional asymmetries and their influence on the level of entrepreneurship in EU economies.

This paper contributes to the existing empirical literature on the main determinants of entrepreneurship in European economies by using a broad data set and taking into account factors such as tax policy, corruption and the institutional environment, which were previously ignored. In addition, knowledge about the level of attraction of business initiatives in an economy helps economic policy makers to implement the correct development policy, offering a tool for the comparative analysis and evaluation of the position of each country in the European business context. Also, the study contributes to the investigation of the level of convergence between the EU countries, in terms of promoting entrepreneurship.

The remainder of the paper is structured as follows: In Section 2, the relevant literature is reviewed and the empirical results of previous research on the factors that enhance entrepreneurship are analyzed, in Section 3 the methodology to be used is presented, in Section 4 the evaluation indicators are described and the research data are presented, while Section 5 analyzes the empirical results of the survey which are discussed at length in Section 6. Finally, in Section 7, useful conclusions are drawn and specific policies and possible extensions of the research are proposed.

2. Literature Review

The research activity in the field of developing and applying methods to assess the business environment is considered limited, but the general conclusions of studies dealing with the improvement of entrepreneurship and the strengthening of economic activity in a country are considered important as they highlight critical factors in terms of tax policy and institutional organization, related to the choice of evaluation criteria, highlighting issues related to the functioning of the public administration, the fiscal policy, the institutional adequacy of a country and the room for improvement. Gedeon (2010) conducted a thorough literature review and concluded that the level of entrepreneurship varies systematically across countries. It has been argued that economic conditions and institutions are important determinants of entrepreneurial prosperity (Bettignies and Brander 2007; McMillan and Woodruff 2002).

Romer and Romer (2010) showed that there is a close causal relationship between the level of economic growth and changes in a country’s tax system. Gale and Orszag (2004) investigated the relationship between the tax burden and the level of consumption expenditure in an economy and found that an increase in the level of taxes will have a direct negative effect on the level of consumption. On the other hand, Andersen and Jordan (1968) argue that an increase in the level of economic activity is related to the level of government spending. Several studies support a highly significant relationship between total entrepreneurship and GDP per capita (Audretsch 2007; Baumol and Strom 2007). Moreover, empirical evidence has shown that this relationship is negative for all countries except the richest ones, where a positive relationship is observed, although less significant (Amorós and Bosma 2014).

Furthermore, experience from the analysis of OECD economies shows a negative relationship between income taxes and economic activity, failing to confirm conventional positions about a negative impact of the level of indirect taxes on the level of entrepreneurship (Widmalm 2001; Sanusi et al. 2017; Judith et al. 2022). Moreover, the related studies by Schwellnus and Arnold (2008) and Vartia (2008) support the existence of a negative effect of the level of corporate tax rates on the level of productivity. Similarly, the study by Da Rin et al. (2011) showed that the imposition of income tax on corporations has a significant negative effect on the creation of new businesses. In fact, the analysis of Baliamoune-Lutz and Garello (2014) supports the existence of a strong negative effect of the progressivity of taxes on above-average incomes on the development of entrepreneurship. Standard models of multinational enterprises argue that as a rule corporate taxation negatively affects FDI, but the final tax burden depends on the set of tax policies implemented by the host economies and mainly the regime of exemptions and exclusions, the amount of withholding tax on repatriated profits or dividends and the incentives for FDI. At the same time, it has been found that the implementation of strategies to postpone or avoid taxation through ‘triangular’ business activities aimed at exploiting tax policy differences between economies may lead to the elimination of the final tax burden (Grubert 2004; Altshuler and Grubert 2003), thus intensifying tax competition.

Moreover, the successful exercise of business activity requires, in many cases, the development of contemporary infrastructure such as roads, ports, telecommunications, energy, etc., which are mainly developed by the central government or at least through state participation supported by tax revenues (Anwar and Li 2021). In addition, taxation contributes to improving the living standards of citizens through its redistributive nature and the provision of public goods, thereby affecting the level of consumption expenditure (Fejzaj and Gjoni 2021). At the macro level, it is useful for policy makers to understand what motivates entrepreneurs to establish their business (Thai and Turkina 2014). In particular, knowing the institutional barriers to starting businesses can help them not only understand the current situation, but also take policy measures to keep their countries’ business development on track (Aidara et al. 2021). In this process, Bjørnskov and Foss (2008) and Wennekers et al. (2002) argue that governance plays a critical role in making this happen. In all EU countries, it is widely argued that good governance also helps the development of entrepreneurship (Haltiwanger et al. 2013; Friedman 2011; Amorós and Bosma 2014). However, promoting the development of entrepreneurship requires maintaining the effectiveness of government in the long term (Gugler and Chaisse 2009; Sanusi et al. 2017). Governments can take pro-business initiatives to improve the quality of governance. However, such measures may not be feasible for countries with a low level of economic development (Thai and Turkina 2014).

The relationship between entrepreneurship and tax evasion occupies an important place in the literature, focusing on issues of unequal competition, entrepreneurial intentions and the attitude of enterprises towards the risk of detection and punishment of non-tax compliance behaviors (Wiklund et al. 2003; Mickiewicz et al. 2019). Consequently, issues related to the efficiency of tax administration in the areas of combating tax evasion and facilitating entrepreneurship through the digitization of procedures and reduction of the time and cost of tax compliance become particularly importance in the given research context (Adžić et al. 2022).

In addition, Anokhin and Schulze (2009) argue that corruption and the quality of institutions in an economy are critical factors in assessing the size of inequalities in the level of entrepreneurship and innovation between countries. The intense presence of corruption in economic and social life forms an unhealthy and risky business environment, increasing the cost of operating the business (Marinescu and Jora 2013). On the contrary, the reduction of corruption phenomena allows the efficient functioning of the state and control mechanisms and contributes to the reduction of the operating cost of the business through the market mechanism (Rose-Ackerman 2001; Hoang Vu et al. 2021). A notable study by Dove (2015) showed that the independence of the judiciary in the field of anti-corruption has a significant impact on entrepreneurship. Moreover, institutions have the potential to exert a significant influence on the allocation of activity between productive, unproductive and destructive entrepreneurship (Baumol 1990; Boettke and Coyne 2009; Murphy et al. 1991). Several studies argue that institutional characteristics related to administrative complexity, such as bureaucracy, corruption, the time and cost of tax compliance, political stability and government efficiency, affect the level of entrepreneurship (Bjørnskov and Foss 2008; Fogel et al. 2008). Kitching et al. (2015) state that regulation is one of the most dynamic conditions for SMEs, creating opportunities and barriers for businesses; however, it could only be beneficial for large businesses as it creates barriers for smaller businesses due to the high compliance costs. In addition, the business environment is strongly influenced by firms’ innovation performance (Zizlavsky 2016). Innovation barriers that limit firms’ innovation performance are financial/organizational constraints and external factors, such as market size and saturation, a well-established market position and regulation (Božić and Rajh 2016; Nam and Bao Tram 2019).

Detailed information on the quality of the business environment and its individual characteristics is offered by a series of indicators, such as the Global Competitiveness Index, the Economic Freedom Index, the Corruption Perception Index and others that constitute the multi-criteria evaluation method of the country’s competitiveness (Belanová 2014). Körner et al. (2002) classify business environment quality indicators based on the indicator constructs (single and composite indicators), the nature of the data used (subjective and objective indicators) and the data sources (external experts and local entrepreneurs). Given this context, the authors state that almost all indicators used to measure the quality of the business environment are subjective, as it is impossible to assess a whole range of aspects using objective data.

The Greek literature on the assessment of the tax environment for businesses is also limited, as Greek studies focus mainly on tax evasion, the shadow economy and the efficiency of tax authorities, such as the studies of Athanasios et al. (2020, 2021a, 2021b, 2022a, 2022b, 2023) that focus on evaluating the efficiency of tax administration and its contribution to attracting foreign direct investment.

3. The Methodology

Multi-criteria decision analysis (MCDA) is a powerful tool for decision makers, as it helps to simultaneously consider multiple criteria in complex scenarios. Its aim is to identify and compare different policy options, and to assess their outcomes, performance, impacts and trade-offs in various settings, such as in business, government and scientific settings (Scott et al. 2012; Marttunen et al. 2017). It also aims to evaluate multiple conflicting criteria, such as cost, quality, safety, energy savings, returns and risks in decision making and to explicitly consider conflicting criteria in a single criterion (André et al. 2010). A separate analysis of each level of the hierarchy of criteria and sub-criteria is carried out and then combined multiplicatively, leading to a single-criterion problem with specific efficient solutions at corner points of the set of available solutions (Dodgson et al. 2009). The goal of multi-criteria analysis is to identify and rank or choose between alternatives, to provide a systematic approach to support complex decisions, to solve a multi-criteria design problem, to find the best alternative for a decision maker or a set of good alternatives and the ranking of alternatives (De Bruin et al. 2009). Therefore, MCDA can be applied to many possible applications and is suitable for complex decision-making problems involving multiple and conflicting objectives and criteria, as it allows decision-makers to integrate the various options, which reflect the views of stakeholders, in a prospective or retrospective context, and explore trade-offs between different options.

The weighting of the criteria is linked to the importance attributed to the criteria by stakeholders and scientists (Stagl 2006). Thus, a set of criteria can be created where the same importance will not be assigned to each criterion (Garmendia and Gamboa 2012). The evaluation of the scenarios according to each criterion and the prioritization of the scenarios, in the multi-criteria decision analysis, are distinguished in three methods: (a) the complete aggregation method, (b) the outranking method and (c) the iterative, trial–error methods (Maystre et al. 1994; Gamper and Turcanu 2007; André et al. 2010). In the complete aggregation method, all criteria are aggregated into a single, composite performance vector to achieve scenario comparison (André et al. 2010). In the outranking method, scenarios are compared against predefined criteria through a preferential reference system (Gamper and Turcanu 2007; André et al. 2010). Iterative methods explore the feasibility of scenarios through discussion with decision makers (Gamper and Turcanu 2007). Among the outranking methods is the PROMETHEE II method, which is generally applied to solve discrete choice problems, focusing on pairwise comparisons between different options (Belton and Stewart 2002).

The multi-criteria analysis and the PROMETHEE II method allow the evaluation of different alternatives and the selection of the best solution. The method allows the integration of subjective information obtained from local actors and the evaluation of scenarios that initially seem incomparable (Sauvé et al. 2022). The PROMETHEE II method is a stable and widely used multi-criteria decision-making tool in various management contexts, which provides decision-makers with a structured, transparent and integrated analysis. This method allows the impact of each alternative to be assessed against each criterion, which helps to identify conflicts and synergies between alternatives and raises awareness among end-users. It also allows the use of different evaluation scales depending on the nature of each criterion, which improves the accuracy of the results. The identified and weighted criteria can provide an indication of stakeholders’ overall priorities and can identify conflicts and agreements within the respondent group (Sauvé et al. 2022). In addition, the PROMETHEE II method allows multiple choices of alternatives under constraints as the sensitivity analysis process helps to analyze how changes in preferences or weights affect the ranking of alternatives (Brans and Mareschal 2005). The implementation of the PROMETHEE II methodology by DECISIONLAB software can also be used to assist decision makers in numerical analysis (Brans and Mareschal 2005). The net flow, which is the sum of the output and input flows, represents an overall ranking of alternatives where changes in the evaluation results are translated into a preference index through a preference function. The multi-criteria index is the weighted sum of the preference index. Thus, the PROMETHEE II method helps decision makers by providing output and input flows that express the degree to which an alternative outperforms all other alternatives (Sauvé et al. 2022). Therefore, multi-criteria analysis and the PROMETHEE II method are important tools for decision makers as they provide a more comprehensive ranking of alternatives and allow evaluation against different criteria.

In this study we use the PROMETHEE II outranking method. The family of PROMETHEE methods is one of the most popular approaches for multicriteria decision analysis, with numerous applications in various areas (Brans and de Smet 2016). The PROMETHEE II method is based on a pairwise comparison between $\mathrm{m}$ different alternatives $\mathrm{a},\mathrm{b},\dots .\mathrm{m}$ against each criterion ${\mathrm{g}}_{\mathrm{k}}$ from a defined set of evaluation criteria ${\mathrm{g}}_1,{\mathrm{g}}_2,\dots ,{\mathrm{g}}_{\mathrm{n}}$. The method uses the concept of a generalized criterion to model the value that a decision maker might assign to the range of difference ${\mathrm{g}}_{\mathrm{j}}\left(\mathrm{a}\right)-{\mathrm{g}}_{\mathrm{j}}\left(\mathrm{b}\right)$ to the criterion. ${\mathrm{g}}_{\mathrm{j}}$ for a pair of actions (a, b). The variations in the evaluation results Δj(a, b) related to the criterion ${\mathrm{g}}_{\mathrm{j}}$, are translated into a preference index P[Δ_j (a, b)] through a preference function, defined as follows:

$${\mathrm{P}}_{\mathrm{j}}\left(\mathrm{a},\mathrm{b}\right)={\mathrm{F}}_{\mathrm{j}}\left[{\mathrm{d}}_{\mathrm{j}}\left(\mathrm{a},\mathrm{b}\right)\right]$$

where:

$${\mathrm{d}}_{\mathrm{j}}\left(\mathrm{a},\mathrm{b}\right)={\mathrm{g}}_{\mathrm{j}}\left(\mathrm{a}\right)-{\mathrm{g}}_{\mathrm{j}}\left(\mathrm{b}\right)$$

$$0\le {\mathrm{P}}_{\mathrm{j}}\left(\mathrm{a},\mathrm{b}\right)\le 1$$

$${\mathrm{P}}_{\mathrm{j}}\left(\mathrm{a},\mathrm{b}\right)=0\mathrm{when}{\mathrm{g}}_{\mathrm{j}}\left(\mathrm{a}\right)-{\mathrm{g}}_{\mathrm{j}}\left(\mathrm{b}\right)\le 0$$

The value 1 indicates a strong preference and 0 indicates no preference. When ${\mathrm{g}}_{\mathrm{j}}\left(\mathrm{a}\right)-{\mathrm{g}}_{\mathrm{j}}\left(\mathrm{b}\right)>0$ the preference function takes different values, depending on the choice made by the researcher among six predefined types of generalized criteria (Brans and Mareschal 2005). In this research, the standard preference function was used, where the highest values are preferred over the lowest values.

Subsequently, for each pair of alternative actions (α, b) the weighted preference index is defined as follows:

$$\mathsf{\pi }\left(\mathrm{a},\mathrm{b}\right)={\displaystyle \sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{j}}{\mathrm{P}}_{\mathrm{j}}\left(\mathrm{a},\mathrm{b}\right)$$

which expresses the degree of total preference of alternative (α) over alternative (b) (Σίσκος 2008). This achieves the ranking of alternative actions from the best to the worst by comparing the strengths and weaknesses of each alternative action against all of the corresponding actions. The strengths are estimated by calculating the positive outranking flow as the overall preference of alternative action (α) over all other alternative actions, as follows:

$${\mathsf{\phi }}^{+}\left(\mathsf{\alpha }\right)=\frac{1}{\mathrm{n}-1}{\displaystyle \sum }_{\mathrm{x}\in \mathrm{A}}\mathsf{\pi }\left(\mathrm{a},\mathrm{x}\right)$$

Also, the weaknesses of alternative action α are estimated as the sum of all preference indices π(x, α) for all alternative actions by calculating the negative outranking flow, as follows:

$${\mathsf{\phi }}^{-}\left(\mathsf{\alpha }\right)=\frac{1}{\mathrm{n}-1}{\displaystyle \sum }_{\mathrm{x}\in \mathrm{A}}\mathsf{\pi }\left(\mathrm{x},\mathrm{a}\right)$$

This leads to the net flow index of the following form:

$$\mathsf{\phi }\left(\mathsf{\alpha }\right)={\mathsf{\phi }}^{+}\left(\mathsf{\alpha }\right)-{\mathsf{\phi }}^{-}\left(\mathsf{\alpha }\right)=\frac{1}{\mathrm{n}-1}{{\displaystyle \sum }}_{\mathrm{x}\in \mathrm{A}}\left[\mathsf{\pi }\left(\mathrm{a},\mathrm{x}\right)-\mathsf{\pi }\left(\mathrm{x},\mathrm{a}\right)\right]$$

The net flow index for alternative action (b) is calculated in the same way. In this way, the alternatives are ranked from the best to the worst. If $\mathsf{\phi }\left(\mathsf{\alpha }\right)=\mathsf{\phi }\left(\mathrm{b}\right)$, then the action (α) is indifferent to action (b), and if $\mathsf{\phi }\left(\mathsf{\alpha }\right)>\mathsf{\phi }\left(\mathrm{b}\right)$, then (α) is preferred to (b).

4. Data

The study focuses on the assessment of the tax and institutional environment for businesses in 21 European economies in 2019, and subsequently, on the ranking of these countries through the application of the PROMETHEE II method. The analysis includes data from Austria, Belgium, Bulgaria, Croatia, the Czech Republic, Denmark, Estonia, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Malta, the Netherlands, Portugal, Romania, the Slovak Republic, the Republic of Slovenia and Sweden. The selection of the 21 countries was made with the main criterion of their accession to the European Union and the development of a common economic policy and strategy in the field of entrepreneurship and the attraction of foreign investments, and at the same time the implementation of a specific tax convergence policy in the field of fiscal stability. The other countries of the European Union are not included in the survey due to a lack of data. Specifically, when selecting the European countries participating in the research, it was found that there is no published data on the variable that refers to the hit rate of tax audits (HRA) in the countries of Cyprus, Finland, France, Spain, Luxembourg and Poland, through their participation in the IOTA survey. Given the importance of this factor in the research results, it was decided that these countries should be excluded from the study, even though published data were available for the remaining indicators. The study refers to the year 2019, which is also the last year for which data is available, before the recent health crisis of COVID-19 which is considered to have caused a clear alteration of the comparability of the data due to the different extraordinary economic, fiscal and institutional measures taken in response to the crisis by the governments of the European countries under consideration. For this reason, the study was limited to the evaluation of the institutional and tax environment of the economies before the health crisis in order to obtain reliable and comparable data. The survey data were collected from official public sources [Intra-European Organization of Tax Administration (IOTA), PwC, World Bank Group, International Transparency, Eurostat and United States Agency for International Development (USAID)] and are considered sufficient to assess the business climate in the sectors under consideration as they cover important aspects of the level of institutional and fiscal adequacy of the economies. For the application of the method, 13 criteria (indicators) were used, which appear in

Table 1. Indicators.

Indicators	Definition	Source
Hit rate of tax audits	Number of tax audits where a tax adjustment was made/Number of tax audits completed × 100	IOTA
Time to prepare and pay taxes (hours)	The time, in hours per year, it takes to prepare, file and pay (or withhold) three major types of taxes: the corporate income tax, the value added or sales tax and labor taxes, including payroll taxes and social security contributions.	PwC
GDP per capita	A country’s GDP divided by its total population.	Eurostat
GDP growth	Annual percentage growth rate of GDP at market prices based on constant local currency.	Eurostat
Total tax and contribution rate	The amount of taxes and mandatory contributions payable by businesses after accounting for allowable deductions and exemptions as a share of commercial profits.	Eurostat
Tax capacity as percentage of GDP	Τhe maximum capacity that a country can contribute by the way of taxation both in ordinary and extraordinary circumstances.	USAID
Tax buoyancy	Τhe efficiency and responsiveness of revenue mobilization in response to Gross Domestic Product growth.	USAID
Tax effort	Τhe ratio between the share of the actual tax collection and the predicted tax revenue.	USAID
Control of corruption estimation	Perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. It also measures the strength and effectiveness of a country’s policy and institutional framework to prevent and combat corruption.	International Transparency
Government effectiveness	Perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation and the credibility of the government’s commitment to such policies.	World Bank Group
Political stability and absence of violence/terrorism	Perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism.	World Bank Group
Regulatory quality	Perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.	World Bank Group
Rule of law	The extent to which countries adhere to the rule of law in practice.	USAID

.

The selection of the specific evaluation criteria proposed in this research is based on international literature (Audretsch 2007; Baumol and Strom 2007; Amorós and Bosma 2014; Widmalm 2001; Da Rin et al. 2011; Rose-Ackerman 2001; Bjørnskov and Foss 2008) and is considered to represent a significant number of factors that shape the entrepreneurship environment in an economy.

Table 2. Evaluation criteria (seven variables).

Country	Control of Corruption	Hit Rate of Tax Audits	Tax Buoyancy	Tax capacity (Percentage of GDP)	Tax Effort (Ratio)	GDP per Capita	GDP Growth (Annual %)
Austria	0.441022184	3.314186005	−3.912023005	3.813748436	−0.494296322	10.75036563	0.41699082
Belgium	0.393296072	3.317815773	−2.207274913	3.855875845	−0.478035801	10.67124312	0.806858936
Bulgaria	−1.84530196	4.478472533	−0.673344553	3.734330717	−0.713349888	9.016129173	1.395784736
Croatia	−2.558787765	4.248495242	−0.510825624	3.726175239	−0.430782916	9.551935995	1.229934523
Czech Republic	−0.577690742	3.797733859	−1.272965676	3.795938526	−0.820980552	9.913755934	1.108522574
Denmark	0.769213681	4.238444906	−2.813410717	3.81242364	0.029558802	10.95436211	0.401113626
Estonia	0.443579813	3.481240089	−1.46967597	3.823847315	−0.733969175	9.916876279	1.319007181
Germany	0.641889008	4.286341385	−0.562118918	3.794814805	−0.616186139	10.67555225	0.055059895
Greece	−3.252187185	3.869115504	0.113328685	3.689129423	−0.385662481	9.853188612	0.633578574
Hungary	−2.88815495	4.110873864	−2.659260037	3.789629357	−0.579818495	9.621301781	1.580584088
Ireland	0.399524305	3.077312261	−0.798507696	3.944490196	−1.049822124	11.22842902	1.69395135
Italy	−1.345975385	4.502029427	0.165514438	3.688379329	−0.314710745	10.37705669	−0.72732812
Latvia	−0.678056006	4.333361463	−0.776528789	3.744787086	−0.673344553	9.684699529	0.943790673
Lithuania	−0.364736368	3.126760536	−0.94160854	3.800867956	−0.798507696	9.7555511	1.531498671
Malta	−1.445606252	4.143134726	−2.659260037	3.932021692	−0.733969175	10.2532733	1.777887785
Netherlands	0.6433722	3.214867803	0.336472237	3.854182006	−0.597837001	10.78815824	0.670691128
Portugal	−0.255570003	4.136765278	−2.207274913	3.745496055	−0.527632742	9.981254363	0.986846086
Romania	−1.545104605	4.473921899	−0.287682072	3.69162568	−0.994252273	9.328371636	1.348894586
Slovak Republic	−1.543180111	4.324132656	−0.223143551	3.852697539	−0.867500568	9.811509042	0.924137123
Slovenia	−0.085250387	2.493205453	−2.995732274	3.828858764	−0.755022584	10.09102018	1.238687007
Sweden	0.755286436	4.188138442	−0.967584026	3.780546643	−0.261364764	10.88725658	0.686221188

and

Table 3. Evaluation criteria (six variables).

Country	Government Effectiveness	Political Stability and Absence of Violence/Terrorism	Regulatory Quality	Rule of Law	Total Tax and Contribution Rate	Time to Prepare and Pay Taxes
Austria	0.419463645	−0.08692273	0.376768264	0.639399671	3.939638172	4.875197323
Belgium	0.133042008	−0.74701741	0.254163019	0.31239955	4.014579594	4.912654886
Bulgaria	−1.597948822	−0.5466929	−0.609881412	−4.26815344	3.342861805	6.089044875
Croatia	−0.71352955	−0.36427526	−0.574876512	−0.98763593	3.020424886	5.327876169
Czech Republic	−0.051421183	−0.05774832	0.217924132	0.045269525	3.83081295	5.438079309
Denmark	0.643355974	−0.0043844	0.446931045	0.624897201	3.169685581	4.882801923
Estonia	0.155756229	−0.45688011	0.46350054	0.242431511	3.867025639	3.912023005
Germany	0.421708408	−0.55458844	0.543258832	0.477868315	3.887730313	5.384495063
Greece	−1.064832156	−1.70518802	−0.638747847	−1.71698446	3.94931879	5.262690189
Hungary	−0.710250491	−0.25856597	−0.508177977	−0.65094853	3.634951112	5.624017506
Ireland	0.25128085	−0.03418871	0.470265796	0.31922435	3.261935314	4.40060302
Italy	−0.736748086	−0.90538766	−0.047920297	−1.20804539	4.079230924	5.472270674
Latvia	0.094369848	−0.81313594	0.174817399	0.007643146	3.640214282	5.12693575
Lithuania	0.03676114	−0.24870152	0.144699695	0.01646069	3.751854253	4.553876892
Malta	−0.159155499	0.024286032	−0.047204517	−0.05537205	3.784189634	4.934473933
Netherlands	0.585634849	−0.1654059	0.620411383	0.570573805	3.718438256	4.779123493
Portugal	0.150836694	0.064129565	−0.032523567	0.123777525	3.683866912	5.493061443
Romania	−1.679775662	−0.57678348	−0.778181963	−0.82006307	2.995732274	5.093750201
Slovak Republic	−0.543837436	−0.40472881	0.005035219	−0.64752988	3.906004933	5.257495372
Slovenia	0.072684145	−0.21389991	0.007082831	0.106079673	3.433987204	5.451038454
Sweden	0.531982281	0.038713565	0.587933719	0.600984247	3.893859035	4.804021045

present the values of the thirteen evaluation criteria in the selected EU countries for the year 2019. From a brief overview of the values of the individual research criteria, it is observed that the economies of Sweden and Denmark record significant performances on the evaluation criteria related to political stability, government effectiveness and quality of institutions. In addition, in the area of GDP per capita, the economies of Ireland, Austria, Belgium, Denmark, Germany, the Netherlands, Belgium, Denmark, Germany, the Netherlands and Sweden have a remarkable performance, with values above 10.50. In addition, in terms of the hit rates of tax audits carried out by the tax authorities, the tax administrations of Italy and Bulgaria show a significant performance. In terms of the level of corporate taxation, the economies of Romania and Croatia have the lowest tax rates, while in the area of the time of tax compliance, the tax administrations of Ireland and Lithuania perform very well, in contrast to those of Bulgaria. Finally, in the field of tax effort, a remarkable performance of the Danish and Swedish economies is observed.

From the analysis of the levels of the variables under investigation, the following useful conclusions can be found: There is a convergence of the level of tax capacity (as a percentage of GDP), the time of compliance and the level of the tax rates of the European economies. On the contrary, significant discrepancies are observed regarding the growth rate of GDP, tax buoyancy, the control of corruption and the hit rate of tax audits. In addition, common European dynamics are identified in matters of governmental efficiency, adherence to the rules of law, political stability and the quality of institutions.

5. Empirical Results

Since the presentation of the criteria individually does not help to determine the position of each economy, the multi-criteria methodology based on the PROMETHEE II method was used to rank the countries on the basis of an overall assessment index which takes into account all of the criteria and indicators mentioned above.

Table 4. Score and ranking of the 21 European economies based on the PROMETHEE II multi-criteria analysis.

Country	Score	Ranking
Austria	0.1246	6
Belgium	0.0188	9
Bulgaria	−0.3347	20
Croatia	−0.1543	16
Czech Republic	−0.1231	14
Denmark	0.4784	1
Estonia	0.1234	7
Germany	0.1548	5
Greece	−0.4510	21
Hungary	−0.2695	18
Ireland	0.4045	4
Italy	−0.2770	19
Latvia	−0.0867	12
Lithuania	−0.0331	11
Malta	0.0617	8
Netherlands	0.4287	3
Portugal	0.0008	10
Romania	−0.2550	17
Slovak Republic	−0.1478	15
Slovenia	−0.1071	13
Sweden	0.4438	2

shows the score and ranking of the European economies under investigation resulting from the application of the PROMETHEE II method with regard to the importance of the 13 criteria mentioned above.

From the ranking of the European economies, the following emerges: Denmark is in the 1st position, which is mainly due to the increased tax effort, the high GDP per capita of the economy, the high efficiency of the government, the increased level of quality of the country’s institutions and political stability, the strong commitment to the rule of law, the low level of tax rates, the high hit rates of tax audits and the shorter time needed to fulfill the tax obligations of companies, combined with the fact that it does not particularly lag behind in the other criteria. The economies of Sweden and the Netherlands rank second and third, respectively, due to their remarkable performance in most criteria, compared to the economies of the other countries, such as the hit rate of tax audits, government efficiency, political stability, quality of institutions, adherence to the rule of law and per capita income. Ireland’s economy is in a very good position with a score above 0.40, while the economies of Germany, Austria and Estonia could be considered as an intermediate ranking group, with an overall positive score above 0.10. The good ranking of these countries is justified by their high performance on some indicators and their low performance on others. For example, Ireland presents the highest tax capacity and tax effort and the highest per capita income and growth rate, while performing well on other criteria such as the quality of institutions, tax compliance time and government effectiveness. Also, Germany’s economy records satisfactory (but moderate) performance in the majority of criteria but low values in tax compliance time and growth rate.

Greece is ranked last, which is justified by its particularly low performance in a large number of criteria, such as the level of government effectiveness (19th position), the quality of institutions (21st position), political stability (21st position), adherence to the rules of law (20th position), low tax capacity (21st position), high tax rates (19th position) and the increased time required to fulfill the tax obligations of businesses. The economies of Bulgaria and Italy are also ranked at the bottom, and their ranking is justified by their respective performances in the majority of the individual criteria. In particular, Bulgaria has the lowest GDP per capita, the lowest value of the index of adherence to the rules of law and the worst time of tax compliance of enterprises, while it is in the lowest positions in terms of the quality of institutions (19th position), government efficiency (20th position) and tax capacity (20th position). Also, Italy, despite a very good performance in the area of tax audits (1st position), shows the lowest growth rate, the lowest value in the indicators of the quality of institutions and tax capacity and the highest tax rates, while at the same time showing a high time for tax compliance (18th position). At this point, we should mention that the ranking of some countries in the last positions does not necessarily mean that they are facing problems, since the purpose of this multi-criteria method is not to evaluate economies in order to divide them into more and less efficient ones, but to rank them on the basis of a comparison between them, taking into account, at the same time, all of the evaluation criteria.

6. Discussion

The range of performance scores of the examined economies indicates a strong differentiation of the institutional and tax environment for businesses and thus significant differences within the EU economies. As shown in Figure 1 below, in which the efficiency scores of Table 4 are presented, there is a significant dispersion of the values of the economies’ performance scores in the area of the business environment, which indicates significant differences between economies at the institutional and tax level, despite the efforts to develop and implement convergence policies between economies. In particular, it is observed that ten economies have a positive efficiency score and only four economies have a score above 0.40. The remaining eleven countries surveyed have negative scores, of which five countries have scores below −0.2. The standard deviation of the values of the efficiency scores is determined to be 0.268 points and the range of the efficiency scores is from −0.4510 to 0.4784 points.

The above evidence reveals the existence of significant fiscal and institutional differences between the EU economies under investigation, regarding the development of entrepreneurship, mainly due to the size of the economy, institutions, history, economic crises in each country (e.g., Greece) and a wide range of disparate policy recommendations, reflecting asymmetric policy responses. These institutional differences in EU economies can negatively affect the level of productive entrepreneurship as they create a culture of aversion to healthy entrepreneurship and skepticism towards entrepreneurs, limiting the results of start-up programs and creating poor perceptions of business opportunities and entrepreneurship initiatives.

7. Conclusions

The purpose of this investigation was to assess the level of the institutional and fiscal environment for the development of entrepreneurship in 21 European economies, with the help of multi-criteria analysis and, in particular, with the use of the PROMETHEE II method, and their ranking based on thirteen evaluation criteria. The results of the method showed Denmark as the best performing economy, followed by the economies of Sweden and the Netherlands.

Furthermore, from the analysis and ranking criteria of the economies, it was found that factors such as the efficiency of the tax administration, the tax capacity of the country, the growth rate of the economy, the quality of institutions, the level of corruption, the tax burden, the time of tax compliance and the political stability of the country contribute to the creation of an attractive business climate to attract foreign investment. These results are in agreement with previous studies such as those of Romer and Romer (2010), Fejzaj and Gjoni (2021), Anokhin and Schulze (2009), Boettke and Coyne (2009) and Bjørnskov and Foss (2008), confirming the conclusions formulated in terms of the importance of factors related to the rate of growth, the quality of institutions, the level of corruption, the tax burden and political stability, in shaping the business environment in an economy. In addition, the range of values of the efficiency scores of the examined EU economies revealed the existence of significant fiscal and institutional differences between them, regarding the development of entrepreneurship, due to the size of the economy, institutions, history, economic crises of each country and a wide range of dissimilar policy recommendations, reflecting asymmetric policy responses and reinforcing the conclusion of significant divergences in the economic, institutional and political constitution of the European Union. Despite the reforms promoted in the formal institutions of the EU economies, it is found that institutional asymmetries remain as a result of informal institutions such as corruption, tax evasion and political instability, which hinder the development of productive entrepreneurship. In order to correct these institutional asymmetries, the development and alignment of institutions between EU countries is required, through a review of institutional adjustments and the convergence of European economies, recognizing, however, that such institutional reforms require long-run efforts that may be undermined by informal business activities that are favored by the existence of unproductive entrepreneurship.

The results of the research and the evaluation carried out may contribute to the creation of an effective tool for the continuous study of the degree of attraction of investment proposals within the European Union, and the identification of the institutional and fiscal factors that create obstacles to entrepreneurship. From a macroeconomic point of view, the proposed methodology could strengthen the effort to continuously monitor the level of convergence of the European economies and influence the process of taking appropriate fiscal measures to correct the business environment. Also at the institutional level, the implementation of the proposed method could support the decision-making, mainly through the legislative initiative, that will improve the institutional environment and will have a quick and targeted effect on observed discrepancies in efficiency and consistency with regard to the institutions of each member state of the EU.

Future research could investigate the effectiveness of the proposed multicriteria analysis method using data after the COVID-19 health crisis (e.g., 2024) in order to assess the robustness of the results and perform a comparative analysis of the economies’ position and the enhancement entrepreneurship. In this way, it would be possible to study the potential impact of policies to deal with the pandemic on the business environment of each country. Also, a different combination of evaluation criteria could be used or the research could be extended to a different set of economies.