Economic freedom influences economic growth and unemployment: an analysis of the Eurozone

Abstract

Lack of economic growth and a high unemployment rate imply poverty and inequality. Economic freedom is considered a relevant explanatory factor for growth, employment and the distribution of income. The present research work addresses how policy makers should take into account economic freedom aspects to effectively enhance a country’s economic growth and lower its unemployment rate. This study analyses the effect of four components of economic freedom (business freedom, labour freedom, government integrity, tax burden) on the mean GDP growth of the last 5 years, and also on unemployment. A cross-national analysis, based on data from the 23 Eurozone3 (Eurozone and part of its enlargement) Member Countries, examines the causal configurations of GDP and unemployment by a fuzzy-set qualitative comparative analysis. The results suggest that the combination of high levels of business freedom, labour freedom and government integrity triggers high levels of economic growth and lowers the unemployment rate.

Keywords:

• Economic freedom
• fuzzy-set qualitative comparative analysis
• economic growth
• unemployment rate
• Eurozone

JEL CODES:

• G00; F00; E00; M00

1. Introduction

Many works in the literature measure countries’ development. They attempt to not only identify the factors that explain it, but to also detect a connection or casualty between economic growth and poverty cycles (Qin et al., 2021), and between economic growth and export and technology development (Sultanuzzaman et al., 2019). The main research question of this research work is how economic freedom affects economic growth. Several studies have addressed this link between economic freedom and economic growth (Carlsson & Lundström, 2002; Kreft & Sobel, 2005; De La Fuente-Mella et al., 2020), and many have found a positive relation (Barro, 1991; Barro, 1994; Barro, 1999; Scully & Slottje, 1991; Vanssay & Spindler, 1994; Torstensson, 1994). However, economic freedom is a complex construct with several dimensions that must be clearly defined before embarking on its analysis. Carlsson and Lundström, (2002) state that economic freedom does not mean freedom from any state intervention; states should only provide protection of freedom for individuals in society. In this vein, institutions play an important role and attract scholars and policy makers’ interest in their three main pillars: cognitive, normative and regulative forms (Scott, 1995; Kuckertz et al., 2016). However, the literature about this approach to economic freedom and research community is not abundant and offers very little information on the design of regulatory frameworks. From the institutional theory, Bouncken et al. (2020) broadly define an institution as a set of rules that govern actors’ behaviour. This concept of economic freedom, based on the integrity and efficacy of institutions’ freedom, has proved to strongly impact countries’ economic growth and development. Ha and Thanh (2022) remark on the importance of institutional quality and well-developed institutional systems in European Economics, while Ortigueira-Sánchez et al. (2022) stress the marked importance of government funding because it contributes directly to economic development. Škare et al. (2021) confirm a link between institutional structure and economic growth movement. They assess government effectiveness by means of six criteria, including rule of law and control of corruption.

With a broader approach, Kuckertz et al. (2015) consider freedom to be a composite index describing the environment for economic growth that encompasses limited government actions, regulatory efficiency, rule of law and access to markets in a country’s economy (Miller et al., 2013) According to Berggren (2003), indices of economic freedom are composite indicators that attempt to measure the degree to which an economy is a market economy, which implies having a stable predictable rule of law context that makes it possible to sign voluntary contracts with a limited degree of interventionism in the form of government ownership, regulations and taxation.

On the one hand, the government’s restraint (limited government) measure reflects the degree to which the government intervenes in market mechanisms through either taxation (fiscal freedom and tax burden) or consumption and redistribution (public spending). Economists often associate a high level of fiscal freedom, along with low government revenues and low consumption, with a high level of economic development (Bjørnskov & Foss, 2008). On the other hand, labour freedom and labour market regulation also have a strong effect (Chen et al., 2022; Blanchard & Giavazzi, 2003), and can influence not only income and rent distribution, but also well-being (De Jonge et al., 2000). Thus, both business freedom and labour freedom represent regulatory efficiency. Tax burden represents government size, while government integrity represents rule of law

Choosing a measure of economic freedom is one of the main problems in this area because a single measure does not reflect the complex economic environment, while a highly aggregate index makes it difficult to draw policy conclusions. In this article, we investigate which specific types of economic freedom are the most important for growth, but considering all the dimensions of economic freedom and their interplay at the same time is a very complex problem. To tackle this problem, we chose a homogenous context, namely the Eurozone, where some dimensions, such as open markets, remain constant and have a clearly high level. This allows us to take some dimensions for granted and to not include them in the theoretical model for the purpose of considering the effect of the remaining components. Besides, a similar development context can avoid a possible decline in marginal returns to economic growth when comparing countries to different economic developments (Ciftci & Durusu-Ciftci, 2022). The economic freedom components herein analysed, which show significant variation among Eurozone countries, are government integrity, labour freedom, business freedom and tax burden. All these economic freedom aspects have already shown an individual relation with economic growth, and this article simultaneously analyses the influence of bundles of these four factors. To do so, first, the main factors of economic freedom and its effects on economic growth and unemployment are described before the basic study assumptions are stated. Second, this research adopts a configurational approach, which is a fuzzy-set qualitative comparative analysis (fsQCA) (Ragin, 1987, 2000), to explore the set-subset relation between economic growth and economic freedom. It is an empirical method based on Boolean algebra that allows the configurational examination of the causal relation among a group of antecedent conditions and a related outcome. This methodology offers a set-theoretic approach to causality analyses in relation to conditions, and an outcome (Ragin, 2008). Hence this configurational approach allows case-knowledge to be applied to address the small sample size issue, and it assumes conjunctural causation, which implies that not only one condition of economic freedom (economic growth), but also the combination of several conditions, jointly explain the outcome. Kuckertz et al. (2016) report how economic freedom will have very different consequences in the developed world and in less developed countries. This means we must study the effect of the economic freedom aspects selected in homogeneous countries in terms of development and other economic freedom factors. Thus our sample comprised the 19 Eurozone countries, plus four countries from the Eurozone enlargement.

This article is structured as follows. Section 2 presents the theoretical background and hypotheses, including a conceptual explanation of each variable. Section 3 offers a description of the methodologies followed to test the hypotheses. Section 4 provides a detailed description of the main results obtained with the data analysis. Finally, Section 5 presents the conclusions, implications, study limitations and proposed future research lines.

2. Theoretical framework

2.1. Business freedom

Entrepreneurship has emerged as a solution to the unemployment and economic growth problems generated by the global economic crisis (Xu et al., 2021). Many European countries are promoting entrepreneurship as a key policy strategy to ease crisis-affected economies. However, the process of starting a new business is sometimes very complex.

Business freedom represents an overall indicator of the efficiency of the government regulation of entrepreneurship. Its quantitative score derives from a series of measurements that assume the difficulty of starting, operating and closing a business. Business freedom, however, affects all businesses in a dynamic environment. Indeed as Guo et al. (2022) state, businesses are under pressure to transform their business models and to be competitive in the new digital economy (Nambisan et al., 2019). According to Müller et al. (2018), business models show how organisations design and conduct activities in their environment. Entrepreneurship is a synonym of adaptation to new market requirements and to technological advances. To hinder entrepreneurship is to hinder better meeting future requirements and allocating to society resources in the most adequate way and, hence, impedes economic growth.

Thus business freedom might be considered an important mechanism for economic development and the prosperity of nations through entrepreneurship, employment, innovation and welfare effects (Block et al., 2017; Koellinger, 2008).

Many nations have adopted policies to stimulate aspects related to business freedom in the hope of facilitating economic growth and employment (Autio et al., 2014; Wong et al., 2005).

So we can state that a high level of business freedom leads to increased economic growth and more employment.

Proposition 1. A country’s economic growth and unemployment are related to its business freedom.

2.2. Labour freedom

Labour freedom and labour market regulations also strongly influence economic development (Chen et al., 2022; Blanchard & Giavazzi. 2003). The labour freedom component of economic freedom represents several aspects related to the legal and regulatory frameworks of a country’s labour market, including regulations on minimum wages, laws that soften layoffs, dismissal requirements, and measurable regulations that affect restrictions to hiring and worked hours. According to Blanchard and Giavazzi (2003), labour market deregulation reduces and redistributes rents. The labour force participation rate is also considered an indication of the measure of employment opportunities on the labour market (Foss & Garzarelli, 2007; Garzarelli et al., 2008).

Agreement about the negative relation between the level of employment protection laws and higher unemployment rates is a widespread (Bruno & Rovelli, 2010). The workforce is the preeminent production factor in society. With its marked heterogeneity and specificity according to workers’ training, experience and skills, the non-specific production factor is needed in every production process. As a resource, any attempt to restrict or limit by compulsion workforce supply will result in its underutilisation and unemployment for concrete market conditions and production structures (Mises, 1998, pp. 599). Thus, restrictions in the most important economic factor, labour, will first result in less production, and then in less economic development.

Some studies demonstrate how labour freedom creates employment opportunities and economic growth (Nickell, 1997; Audretsch & Thurik, 2001; Agnello et al., 2014). Nevertheless, others have drawn contradictory conclusions (Sarkar, 2013). These contradictory results can stem from the numerous factors that affect growth and the difficulty of isolating only the labour freedom effect. Thus from our theoretical framework, we can propose the following hypothesis:

Proposition 2. A country’s economic growth and/or unemployment is/are related to its labour freedom.

2.3. Tax burden

Tax burden is a measure of the total tax burden applied by the government (Bjørnskov & Foss, 2008). It includes, but is not limited to, direct taxes in terms of higher marginal tax rates on individual and corporate incomes, and also to general taxes, including all forms of direct and indirect taxes that apply to all government levels, as a percentage of the GDP. In this way, tax freedom is composed of three quantitative factors: (a) the top marginal tax rate on individual income; (b) the top marginal tax rate on corporate income; (c) the total tax burden as a percentage of the GDP.

The effect of taxes on a country or region’s economic development is well-studied (Phillips & Goss, 1995; Gechert & Heimberger, 2022). Much debate on tax burden has looked at its influence on a country’s economic growth and employment (Lee & Gordon, 2005; Paientko & Oparin, 2020). Although most studies find a strong robust negative relation between tax rates and future macroeconomic growth (Lee & Gordon, 2005; Mertens & Ravn, 2013; Ozpence & Mercan, 2020), discrepancies about the effect of taxes on growth in other empirical studies (Gale et al., 2015,TenKate & Milionis, 2019) might be due to tax exemptions, tax deductions, tax enforcement and firms’ tax planning not being included in models (Shevlin et al., 2019). So, our third proposition is:

Proposition 3: A country’s economic growth and/or unemployment is/are related to its tax burden

2.4. Government integrity

Government integrity and controlling corruption are criteria that strongly influence the movement of economic growth. According to Škare et al. (2021), the importance of government effectiveness is seen through economic growth, especially in the long term. Moreover, corruption erodes economic freedom by introducing insecurity and uncertainty into economic relations (Haggard & Tiede, 2011). Therefore, government quality will affect a country’s economic growth and social cohesion (Kyriacou et al., 2017, Uzelac et al., 2020).

The results of empirical studies that have checked the influence of government integrity on economic development can be biased by how corruption is measured in a country. The heaviest weight for scoring this component falls mainly on the 2011 Corruption Perceptions Index (CPI) by Transparency International, which measures the level of corruption in 183 countries. The CPI is based on a 10-point scale for which a score of 10 indicates very little corruption and a score of 0 denotes a very corrupt government.

For the countries not included in the CPI, the corruption freedom score is determined using qualitative information from internationally recognised and reliable sources. In this way, the procedure considers the degree to which corruption prevails in a country.

According to the Heritage Foundation, this index relies on the following sources for information about informal market activities, which come in this order of priority: Transparency International, Corruption Perceptions Index, 2011; U.S. Department of Commerce, Country Commercial Guide, 2009–2012; Economist Intelligence Unit, Country Commerce, 2009–2012; Office of the U.S. Trade Representative, 2012 National Trade Estimate Report on Foreign Trade Barriers; and official government publications from each country. The higher the corruption level, the lower the level of overall economic freedom and the lower the score for a given country.

We conclude that institutions matter for economic growth (Uzelac et al., 2020). Government integrity can exert and influence a country’s economic growth and/or unemployment (Dreher et al., 2012; Škare et al. 2021). Therefore, we propose:

Proposition 4: A country’s economic growth and/or unemployment is/are related to its government integrity.

3. Method

The qualitative comparative analysis (QCA) assumes that rather than isolated levels of individual attributes, the influence of different attributes on a specific outcome depends on the way in which they are combined, (Wu et al., 2021). The QCA uses Boolean algebraic techniques to compare pairwise combinations of antecedents and outcome conditions to identify those that produce an outcome (Nambisan et al., 2017). This research uses the fuzzy-set qualitative comparative analysis (fsQCA) to analyse the configurations of economic conditions that mostly lead to a positive outcome (GDP growth and low unemployment rate). This methodology is primarily useful for studies in which a set of characteristics that reflect alternative configurations must be analysed (Pateli & Giaglis, 2005). Another advantage of the fsQCA is that it allows the best of both quantitative and qualitative approaches establishing multiple and complex causality (Finn, 2022). The fsQCA method assumes complex causality and takes into account asymmetric relations to detect the combinations of conditions (configurations) that are minimally necessary or sufficient to obtain a satisfactory outcome (Woodside, 2013).

The fsQCA is useful when N-samples are small (Fiss, 2011; Ide & Mello, 2022; Medina-Molina & Tienda, 2022), which is our case. To date, different studies that have analysed issues related to entrepreneurship and economic freedom by means of the fsQCA can be found (Kuckertz et al., 2016). In this research work, the fs/QCA software v. 3.0 was used to apply the fsQCA (Thiem & Duşa, 2013). In addition, the fsQCA allows non-dichotomous conditions to be incorporated.

3.1. Sample and data

Date came from a secondary data source, namely the Index of Economic Freedom provided by the Heritage Foundation (Miller et al., 2013). This database is public. The Index of Economic Freedom assesses all the countries on several economic dimensions related to economic freedom, such as business, labour, monetary, trade, investment and financial freedom. The Economic freedom index combines qualitative and quantitative data in a set of composite indicators for each country.

This work considers four main economic freedom indicators (conditions) that are relevant for GDP growth and unemployment (outcomes, and can vary according to the policies of each Eurozone country. The homogeneity of the Eurozone and its enlargement with some other relevant conditions, such as monetary and financial freedom, allow us to evaluate the effects of different national policies on a limited number of variables, such as business freedom, labour freedom, government integrity and tax burden, while considering a reasonable ceteris paribus in other relevant economic policies. The possible successful combinations of these conditions (business freedom, labour freedom, government integrity and tax burden) were evaluated on two outcomes; the 5-year mean GDP growth rate and the unemployment rate. To mitigate conjunctural variations in indicators, the final conditions and outcomes were calculated as the 5-year mean (from 2015 to 2019) of the original indicators. The indicators selected from the Index of Economic Freedom database are shown in Table 1. The study analyses the 19 Eurozone countries (Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Italy, Ireland, Latvia, Lithuania, Luxembourg, Netherlands, Malta, Portugal, Slovakia, Slovenia, Spain), and four countries from the Eurozone enlargement agenda (Croatia, Czech Republic, Poland and Sweden). Thus the final sample was composed of 23 European countries. Each variable considered in this study was extracted directly from the Index of Economic Freedom, which assesses every country according to a set of composite indicators (Dialga & Vallee, 2021) for each year and country. Instead of considering the indicators for each year separately, we used the 5-year mean (from 2015 to 2019) for each indicator and country.

Table 1. Definition of variables, calibration values and descriptive statistics.

Outcomes and conditions^1	Calibration values^2	Abbrev.	Mean	S.D.	Min.	Max.
5-year GDP Growth Rate (%)	(4; 1; 0)	GDPGro	0.82	0.15	0.43	1.00
Unemployment (%)	(16; 7; 4)	Unempl	0.31	0.28	0.01	0.97
Government Integrity	(86.0; 43.7; 19.6)	GovInt	0.71	0.24	0.08	0.97
Tax Burden	(94.8; 77.6; 50.0)	TaxBur	0.29	0.22	0.03	0.69
Business Freedom	(89.5; 64.5; 31.4)	BusFree	0.69	0.23	0.03	0.94
Labour Freedom	(85.3; 59.6; 30.7)	LabFree	0.44	0.28	0.01	0.97

¹ Data source: Index of Economic Freedom (the original name of the indicators remains).

² (full membership; crossover; non-full membership); N = 23; all outcomes and conditions are calculated as the 5-year mean (from 2015 to 2019) of the indicators in the original database.

Source: Authors’ own elaboration.

3.2. Calibration of outcomes and conditions

The calibration of the original data is the first step in the fsQCA method (Rihoux & Ragin, 2009). All the values must range from 0 to 1, and it is necessary to establish three cut-off or observation points to carry out this calibration: the full membership threshold; the point of indifference or the crossover point; the full non-membership threshold (Ragin, 2008). Calibrating the original data with these three cut-off points must be based on the theory and external knowledge of each outcome or condition. To calibrate outcomes, we considered a GDP growth rate of 4% for full membership, 1% GDP growth for the crossover, and 0% for the full non-membership point (Singer, 2013). These values were selected after evaluating the GDP growth rate of the European countries over the last 10 years (from 2010 to 2019). For unemployment, the three values for full membership, crossover and full non-membership were taken as an unemployment rate of 16%, 7% and 4%, respectively (Daly et al., 2012).

These conditions are abstract concepts measured by a combination of indicators. As it is more difficult to theoretically define cut-off points, we established the crossover point as being the mean of all the values (the 5-year mean of the corresponding indicator) from the countries in the database for that indicator (not only the Eurozone). Full membership was calculated as the maximum value of all the countries, minus the 10% of the range of the indicator in the database, with the full non-membership point as the minimum value of all the countries, plus the 10% of the range. As cut-off points are calculated by taking into account all the countries in the world, excessive sensitivity for common high scores in all the Eurozone countries is avoided. Table 1 details the cut-off points used for each variable type and the abbreviations employed for the outcomes and conditions in the other tables. The values of the mean, standard deviation, minimum and maximum for each variable (conditions and outcomes; once calibrated) are provided in Table 1.

4. Results and discussion

4.1. Necessary conditions analysis

The Necessary Conditions Analysis assesses the individual conditions that must be present to obtain high values for a given outcome. Table 2 shows the necessary conditions to achieve a high GDP growth rate and a high employment (no unemployment). A condition is considered necessary when its consistency is above the threshold of 0.9 (Schneider et al., 2010). Although the consistencies of all the conditions are relatively high (around 0.8 in most cases; see Table 2), they are below 0.9 for both outcomes and, thus, no condition is strictly necessary to attain a good GDP growth rate or employment. Nevertheless, except for the labour freedom condition (with a low consistency of 0.53 and 0.56 for the GDP growth rate and employment, respectively; see Table 2), we consider that government integrity, business freedom and labour freedom are conditions that must take a relatively high value in most cases to obtain a good outcome.

Table 2. Necessary conditions analysis.

Outcome	GDPGro		∼ Unempl
Conditions	Consistency	Coverage	Consistency	Coverage
GovInt	0.79	0.90	0.83	0.80
∼ TaxBur	0.75	0.86	0.81	0.79
BusFree	0.76	0.90	0.78	0.78
LabFreed	0.53	0.998	0.56	0.90

File: authentic europe 19 ampliada sin ruma hung bulg.csv.

Source: Authors’ own elaboration.

4.2. Sufficiency analysis

The Sufficiency Analysis enables the possible combinations of the sufficient causal conditions (configurations or paths) to be evaluated to attain a desired result (outcome). The Sufficiency Analysis is performed in two steps. First, the truth table with all the possible causal combinations is built (Ragin, 2008). As our model has four conditions, the truth table presents 16 combinations. The configurations with no cases must be dropped and, as our sample size is relatively small, those configurations with a single case must also be eliminated. Second, the truth table with all possible combinations of configurations must be reduced to the relevant and consistent causal conditions.

Reducing the rows in the truth table with the Quine-McCluskey algorithm is presented in Table 3 (the GDP growth rate as an outcome) and Table 4 (employment as an outcome). The truth table analysis provides three outputs: a complex, an intermediate and a parsimonious solution. As the intermediate solution does not enable the necessary conditions to be suppressed, it is considered the best output of the analysis (Rihoux & Ragin, 2009). There are two main metrics that evaluate the strength and importance of the relations between conditions and outcome: consistency and coverage. Coverage indicates the empirical relevance (presence) of the path in the sample. Consistency quantifies the degree to which the cases that share the same configuration also share the same outcome (Ragin & Fiss, 2008). Tables 3 and 4 include raw coverage (it reflects the share of outcome explained by a path), and unique coverage (it reflects the share of outcome exclusively explained by a path) (Ragin, 2006). Consistency measures the extent to which a causal combination produces an outcome. If a configuration has low consistency, it is not supported by empirical evidence.

Table 3. Intermediate fsQCA solution: configurations leading to the GDP growth rate.

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	GovInt * ∼TaxBur* BusFree → GDPGro	0.65	0.19	0.92
2	GovtInt * BusFree * LabFree → GDPGro	0.51	0.05	0.998

Solution coverage: 0.70; Solution consistency: 0.93; ∼ = set negated.

Source: Authors’ own elaboration.

Table 4. Intermediate fsQCA solution: configurations leading to full employment.

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	GovInt * ∼TaxBur* BusFree → ∼Unempl	0.69	0.19	0.83
2	GovInt * BusFree * LabFree → ∼Unempl	0.55	0.05	0.92

Solution coverage: 0.74; Solution consistency: 0.84; ∼ = set negated.

Source: Authors’ own elaboration.

A consistency threshold allows the causal combinations that are the subsets of the outcome to be distinguished. In general, values below 0.75 indicate substantial inconsistency. Nevertheless, Rubinson (2013) warns that establishing a standard consistency threshold can lead to incorrect conclusions. So we considered a restrictive consistency reference level of 0.9, which ensures no false-positives.

Table 3 shows the configuration of causal conditions that leads to GDP growth. The configuration corresponding to path 1 (GovInt*∼TaxBur*BusFree → GDPGro) suggests that, in the Eurozone economic context, the countries with better government integrity and business freedom, coupled with a low tax burden, will have a better GDP growth rate. This path has a consistency of 0.92, and the number of cases with this configuration in the sample is large (raw coverage of 0.65). Unique coverage is also high (0.19) if the complexity of the analysed outcome is taken into account. Path 2 has very high consistency (0.998), although its low unique coverage (0.05) shows that the path little explanatory power on its own.

The results of the configurations are consistent with the previous theory, which indicated the importance of business freedom and government integrity, as supported in path 1 and path 2 by low taxation and labour freedom, respectively.

For unemployment (see Table 4), the consistent paths provided by the analysis are similar to the GDP growth in Table 3. Nevertheless, path 1 is not consistent enough to be considered a universal solution. Consistency (0.83) comes close to the threshold of 0.9, but does not completely ensure a true positive combination. Path 2 is an interesting combination supported by the theoretical framework because, besides government integrity and business freedom, it also includes labour freedom. This path has a high consistency of 0.92.

The results provided by our study reveal consistent patterns for government integrity, tax burden, business freedom and labour freedom that obtain better results in growth and employment in a homogeneous zone as regards other fundamental economic freedom aspects, such as monetary and financial freedom. Therefore, despite there is abundant literature on this topic, no studies were found that jointly address the effect of these factors on economic growth, although many studies have dealt with individual dimensions of economic freedom or the whole construct. For instance, a general approach of economic freedom with a positive result on economic growth using a panel data of the Index of Economic Freedom for European Community countries is found in Brkic et al. (2020).

Similar to the results obtained by De La Fuente-Mella et al. (2020), our most remarkable result is that government integrity and business freedom are present in each successful path, and they must be complemented with not only a low tax burden to improve GDP growth, but also with labour freedom to improve employment. These results perfectly match our work assumptions and hypotheses but reveal, at the same time, the necessary complementarity of the different economic freedom aspects herein considered. Besides, outcomes establish a graduation between the economic freedom dimensions and their specific relevance in different economic indicators.

5. Conclusions and implications

The present study sets out to paint a specific picture of the relation between economic freedom and economic growth. To do so, it searches for combinations of business freedom, financial freedom, tax burden and government integrity that enhance a country’s economic growth. The relation between economic freedom and economic growth has been largely discussed, but finding causal proof is difficult for several reasons. First, the very large quantity of variables that affects a country’s economic growth cannot be isolated. Second, there must be several conditions that work in unison to produce the expected outcome. Third, a single factor can enhance economic growth if economic freedom is lacking or can more easily spoil its positive effects. Although this study has a limited context and a restricted number of countries, it shows the consistency of combining business freedom, labour freedom, government integrity and tax burden.

By employing a database comprising information from 23 Member Countries of the enlarged Eurozone from 2015 to 2019 and the fsQCA analysis, this study suggests a number of options for decision makers who wish to shape the regulative frameworks that supports economic growth.

The results show that combinations of business freedom and government integrity, along with high labour freedom levels or low tax burden levels, enhance a country’s economic growth. For full employment, the best combination is business freedom, government integrity and labour freedom. We should always take into account that these results fall in the Eurozone context, where there is a common monetary policy in place and has similar financial freedom levels. The results of this study are relevant because, despite the abundant literature on the topic, no empirical study has been found that jointly addresses the effect of these factors on economic growth with clear results. The study’s results suggest a number of options for decision makers who wish to shape the regulative framework that supports economic growth. The present study illustrates the potential of a configurational perspective on the relation between economic freedom and economic growth. It shows that the degree of economic freedom has more explanatory power for economies. The results prove the advantages of business freedom and government integrity in the Eurozone context, and the partial role that labour freedom and tax burden can play, which are much more controversial issues in the political agenda. The results are similar to those obtained by De La Fuente-Mella et al. (2020), who employ the GDP as output to measure countries’ efficiency and inputs are expressed by labour, savings, capital, among other variables. Their study and the present one clearly have managerial implications for countries to develop governmental policies with which to face unemployment and improve their economic growth.

5.1. Limitations and future research lines

The main limitation of this study is that the results are only circumscribed to the Eurozone and within a specific time lapse. Although this context limits the number of variables that can change significantly from country to country, and allows the use of the fsQCA, the relevance of other important economic indices cannot be analysed. Another limitation is that even with the considered indices, variance among countries is significant and values remain quite favourable for the mean obtained for all the countries. Future studies can be conducted in other world areas, for example, Latin-American countries, and different combinations of variables can also be considered.

Disclosure statement

No conflict of interest has been reported by the authors.