Types of institutions and well-being of self-employed and paid employees in Europe

The institutional framework of a country and its entrepreneurship-facilitating or entrepreneurship-inhibiting character can have strong effects on the incentives to become and to remain self-employed (e.g., Baumol 1990, 1993; Elert et al. 2017). Since entrepreneurship can be an important driver of economic growth (Fritsch 2013), more entrepreneurship-facilitating institutions may lead to higher levels of economic welfare and the general well-being of a society’s members.

There seems to be a wide consensus that high degrees of economic freedom (e.g., low barriers to entry and exit, open markets, low taxes on profits), the opportunity of gaining private property on the means of production, reliable legal framework conditions (e.g., enforceability of contracts, low levels of corruption), availability of necessary resources (e.g., finance, qualified labor), and a good quality of government are conducive for entrepreneurship (see for example Boettke and Coyne 2009; Elert et al. 2017; Parker 2018). The most prominent institutional frameworks that have been investigated empirically with regard to their importance for entrepreneurship are the regulation of entry and exit,³ the quality of legal institutions (e.g., protection of property rights), the regulation of employment protection, and the institutional framework of credit markets.

While there are a number of studies focusing on the role of institutions on entry and welfare at the country level, almost nothing is known about the role of institutions for the well-being of entrepreneurs as compared with paid employees. It is also unclear whether institutional reform in favor of entrepreneurship comes at the expense of the well-being of paid employees. Conflicts between the self-employed and paid employees may, for example, arise if labor market regulations offer a lower level of employment protection increasing the well-being of the self-employed, at the expense of paid employees who face a greater risk of being laid off.

Our attempt to overcome these shortcomings is twofold. First, we use an individual’s subjective well-being that we operationalize by his or her level of job and life satisfaction as an outcome for the effect of institutions. In addition, we distinguish between self-employed and paid employed individuals to assess whether institutions affect persons in these two types of occupation states differently. Second, we distinguish between several categories of institutions to compare their impact on individual well-being and to identify those types of institutions that have the most impact on the two occupational groups.

Many studies find that self-employed people enjoy higher levels of job and life satisfaction than paid employees.⁴ A main reason for this result discussed in the literature is higher procedural utility that self-employed people draw from the actual work process itself (Frey et al. 2004). This includes higher levels of autonomy and flexibility, as well as a stronger feeling of pursuing one’s own goals through self-employment that stimulates a feeling of self-determination and self-efficacy (for a detailed exposition, see Shir 2016). Higher levels of well-being could explain why people opt for self-employment despite less economic security and often lower incomes than available in paid employment (Benz and Frey 2008).

Fritsch et al. (2019), in an analysis based on the EU-SILC (which is also used for the present study), discover that self-employed individuals tend to enjoy higher levels of job and life satisfaction only in those countries where the entrepreneurship-facilitating quality of the institutional environment has a certain minimum level. In countries where the quality of institutional conditions for entrepreneurship is below this critical level, paid employees reported, on average, higher levels of well-being. This result clearly indicates the important role of institutions for the attractiveness of entrepreneurship. The study by Fritsch et al. (2019) did not, however, investigate which types of institutions are most important in this respect.

The ‘Varieties of Capitalism’ (VoC) approach (Hall and Soskice 2001) is a good starting point for discussing how institutions might affect the well-being of self-employed and paid employees. This approach accounts for complementarities between different categories of institutions and distinguishes several types of institutional frameworks such as the ‘liberal market economy’ and the ‘managed market economy’. Dilli et al. (2018) classify countries according to the VoC approach taking into account variations in financial institutions, labor market institutions, institutions related to education, and institutions governing inter-firm relations. They then explore how entrepreneurship-related outcomes vary across these groups of countries. This approach does not, however, allow for the identification of the relative strength of the relationships between certain types of institutions and entrepreneurship. In addition, it does not take into account that the quality of institutions may considerably vary within the country groups. Nevertheless, these authors conclude that labor market regulation (especially employment protection) and regulation of financial markets are particularly important for entrepreneurship outcomes.

Labor market regulations are of key importance for entrepreneurship. These regulations determine the availability of personnel and employment conditions, such as rules for hiring and dismissing employees, as well as employee benefits such as maternity leave (Herrmann 2019). An obvious expectation in this respect is that the more freedom an entrepreneur has in his employment decisions, the greater his or her well-being will be. At the same time, greater flexibility comes at the expense of paid employees who might face a higher risk of being laid off, or lower levels of compensation. Therefore, while one can expect that more flexible labor market agreements will have a positive effect on the well-being of self-employed, they may have a detrimental effect on paid employees.

Besides labor market institutions, institutions designed to facilitate business activities should have a positive influence on the well-being of the self-employed. When bureaucracies are streamlined and administrative burdens are lightened, business decision-making can be carried out with less effort and frustration. Ease of engaging in business activities comprises not only the effort that is necessary for starting and maintaining a business but also basic infrastructure factors such as a reliable electrical supply. Inefficient regulations, bureaucracies, and infrastructures can cause delays in venture creation and frustration for an entrepreneur. Similarly, high costs of contract enforcements and a high level of corporate taxes reduce start-up opportunities and make business management less enjoyable, ultimately reducing well-being.

The same can be assumed for the level of corruption in a country and the general quality of the government (Dixit 2009). In terms of business performance, favorable regulations regarding trade across borders and ease of getting credit should be conducive to business growth and, therefore, they can be expected to feedback into the satisfaction and well-being of entrepreneurs. Moderate insolvency regulations should also have a positive effect on the well-being of self-employed people, since it reduces fear of failure.

Paid employees may also be affected by high levels of corruption, low quality of government, and weak contract enforcement. However, it can be assumed that they are less directly affected by institutions designed to facilitate business activities than self-employed individuals. Hence, the relationship between these types of institutions and well-being of paid employees should be less pronounced. This can be especially expected for those institutions that are related to starting a business, dealing with construction permits, registering property, getting credit, protecting minority investors, trading across borders, and resolving insolvency.

We expect that the relationship between the level of taxation and well-being is more pronounced for individuals with higher income who pay higher taxes, than for low-income groups (Table 10 in the Appendix). Although studies show that self-employed individuals do not generally earn more than paid employees (Sorgner et al. 2017), the effect of taxation on these two occupational groups is undetermined.

Our data source for job and life satisfaction is the EU Statistics on Income and Living Conditions (EU-SILC). These data are the EU reference source for comparative statistics on income distribution and social exclusion at the European level.⁵ The EU-SILC provides comparable and high quality cross-sectional data for 32 European countries including Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, The Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the UK. The reference population of the EU-SILC is all private households and their current members residing in the territory of the countries at the time of data collection. Persons living in institutional households (e.g., hospitals, nursing homes, religious institutions) are generally excluded from the target population. Each year EU-SILC includes an ad hoc module in its survey program that provides additional information in a selected realm. For this study, we use the 2013 data that includes an ad hoc module on individual well-being.

We use two indicators of individual well-being that are available in the EU-SILC, namely, the assessment of current overall job satisfaction and the respondent’s satisfaction with his or her life as a whole. Life satisfaction is intended to represent a broad, reflective appraisal a person makes of his or her life. It is the by far most frequently used concept of measuring well-being and has a high level of validation (Pavot and Diener 2008). The variable refers to the respondent’s feeling about the degree of satisfaction with his or her life in “these days” rather than specifying a longer or shorter time period. Although the measure of life satisfaction is related to happiness, it differs in the sense that responses to the question regarding a person’s life satisfaction tend to be considerably more stable over time and less influenced by momentary incidences (Lucas et al. 1996; Diener et al. 2013).

The precise formulation of the question about life satisfaction in the questionnaire is as follows: “Overall, how satisfied are you with life as a whole these days?” (OECD 2013). The level of life satisfaction is measured on an 11-point Likert scale, with the lowest value of 0 being “not at all satisfied” and the highest value of 10 being “completely satisfied”. This type of question is well established in empirical research on well-being and it has been shown that responses have a high level of validity (see Diener et al. 2013). The second variable of interest is a person’s assessment of his or her level of job satisfaction, which is also measured at an 11-point Likert scale. The question is: “How satisfied are you with your job?” (OECD 2013), and refers to the respondent’s opinion about the current degree of satisfaction with his or her work for money, not the work someone does in the household or for recreation. If the respondent has several jobs, the answer about the level of job satisfaction refers to the primary job.⁶

While life satisfaction is a rather broad concept, job satisfaction pertains only to issues that are related to a person’s work. Since satisfaction with work is a key element of someone’s life satisfaction, there should be a positive correlation between the two types of assessment. This could be the case if a poor work environment that offers little satisfaction leads an individual to report lower levels of life satisfaction. There may, however, also be a negative effect of job satisfaction on life satisfaction. For example, a satisfying job with high emotional engagement and long working hours could crowd out other activities that are important for life satisfaction, such as satisfying social relationships and good health. For this reason, the correlation between the two concepts may be quite low or even negative.

Self-employed individuals are identified in the EU-SILC based on their self-reported current labor market status. An individual is considered self-employed if he or she works full-time or part-time in self-employment to earn a profit. Paid employees are defined as persons who work for an employer and who receive compensation, for instance, in the form of wages or salaries. We construct a binary variable that equals 1 if a person is self-employed, and 0 if a respondent is a paid employee. While we are well aware that self-employment and entrepreneurship are different but overlapping concepts (Henrekson and Sanandaji 2014), we choose to focus on the broader concept of self-employment because we are interested in the effect of institutions on the well-being of individuals that have made a certain occupational choice, i.e., being self-employed or a paid employee. This operationalization of entrepreneurship is in line with previous literature on entrepreneurship and well-being. In addition, we investigate different categories of self-employed individuals, such as income levels, to account for heterogeneity within this group.

It has been shown that the levels of job and life satisfaction someone experiences in self-employment or paid employment varies based on her or his individual characteristics, as well as job-specific characteristics. Education and income levels, personality, motivation and preferences, and the tasks performed at one’s job all come into the equation (see Shir 2016, for an overview). To account for these characteristics, our analysis uses the set of socio-demographic variables included in the EU-SILC as control variables, such as age, gender, and marital status. We also use the information about education levels (defined according to the ISCED classification),⁷ occupation (defined at a 2-digit level of ISCO-08),⁸ industry sector (according to the NACE rev.2),⁹ the number of hours usually worked per week in the main occupation, and information on change of job in the previous year.

We also account for a person’s financial situation, because this may significantly affect the level of individual well-being. The EU-SILC contains information on gross monetary income of paid employees and gross monetary income or losses of self-employed persons during a previous 12-month period (such as the previous calendar or tax year) in national currency.¹⁰ We construct country-specific income quartiles to make the income measure comparable between countries.¹¹ Since health status is an important determinant of the overall life satisfaction (van Praag et al. 2003; Binder and Coad 2013), we include self-reported information on a person’s current health condition provided by the EU-SILC that is measured on a 5-points ordinal scale ranging from 1 (very bad) to 5 (very good).

The final sample contains 161,127 observations. It does not include unemployed or otherwise economically inactive persons, respondents currently in full-time education, those in compulsory military community or service. We also do not consider home workers in our analysis.

We use several data sources for measuring the quality of different types of institutions in a country. One of these data sources is the Doing of Business Index provided by the World Bank for the year 2013. The Doing of Business score assesses the regulatory performance of more than 180 countries in terms of general business-friendliness. It covers various areas that are relevant for self-employment such as the ease of starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts, and resolving insolvency (see Table 1). One may expect that some of the abovementioned facets of institutions like getting electricity should not be an issue for entrepreneurship in well-developed, high-income countries. However, they may be relevant for low-income European countries. We use the overall Doing of Business score as a general measure of the entrepreneurship-facilitating quality of a country’s institutions, and we analyze the sub-indices of the Doing of Business Index as separate indicators of the quality of different types of institutions.

The Doing of Business Index and its separate pillars measure the distance each country is from the ‘frontier’. The frontier is a value that represents the highest level of performance observed across all countries in the sample in the respective year. A country’s distance to the frontier is reflected on a scale from 0 to 100, where 0 signifies the lowest performance and 100 represents the frontier. For example, a country score of 75 means that the country was 25 percentage points away from the frontier.¹²

We employ two OECD indicators of employment protection as measures of a country’s labor market regulation. For each country, employment protection legislation is described by (i) employment protection of regular workers against dismissal, and (ii) regulation of temporary forms of employment. The indicator for protection of workers against individual and collective dismissals measures costs and procedures involved in dismissing workers with regular contracts. The indicator for temporary contracts refers to restrictions on the use of fixed-term contracts, such as the number of renewals and maximum cumulated duration of successive fixed-term contracts, among others.¹³ Thus, higher values of these indicators reflect stricter levels of employment protection.

We use two indicators to assess the general quality of government in a country. First, the Corruption Perception Index provided by Transparency International ranks countries based on a score indicating the perception of how corrupt a country’s public sector is. The Corruption Perception Index is a widely used indicator that draws on data sources from independent institutions specialized in governance and business climate analysis. A higher score of the Corruption Perception Index indicates a lower level of perceived corruption in a country’s public sector.¹⁴ The second indicator, the European Quality of Government Index, focuses on both perceptions and experiences with public sector corruption, along with the extent to which citizens believe various public sector services are impartially allocated and of good quality.¹⁵ Table 9 in the Appendix provides descriptive statistics of all variables used in the empirical analysis.

In order to estimate the impact of the different measures of the quality of entrepreneurship-facilitating institutions on individual job and life satisfaction, we apply ordered logit analysis. This method is appropriate, because it accounts for the ordinal nature of our dependent variables. Differences in the effects of institutions on well-being of self-employed and paid employed individuals are captured by means of interactions between each institutional measure and the dummy variable that indicates an individual’s current employment status: paid employment (base category) or self-employment.

Furthermore, we include the following control variables introduced in Section 3.1: gender, age, marital status, highest achieved level of formal education (three categories), job-specific variables (number of working hours, job change since previous year), financial situation (country-specific gross income quartiles), health condition (for life satisfaction models), industry (13 industries according to the NACE rev.2), and occupation (50 occupations defined at a 2-digit level of ISCO-08). Since the dependent variables are defined at the level of individuals across countries, observations within countries might be correlated. Hence, we report standard errors clustered at the country level in all regressions. In order to facilitate the interpretation of the results of ordered logit regressions, we use the estimated coefficients to calculate predicted probabilities of being completely satisfied with one’s job and life for both employment states at the different levels of the institutional quality measures and the mean values of the control variables.

Table 2 shows the distribution of scores on both satisfaction scales by employment status. We observe that a higher percentage of self-employed individuals (13.58%) are completely satisfied with their jobs, as compared with paid employed individuals (11.82%). At the same time, in our sample, there are more self-employed persons (1.82%) who are completely unsatisfied with their jobs than paid employed persons (0.75%). Our analysis shows that when compared with self-employed individuals, a slightly higher percentage of paid employees are strongly satisfied with their lives in general. On average, the self-employed report a significantly lower satisfaction score on both scales compared with paid employees, and there is a stronger variation in the satisfaction scores among the self-employed than among paid employed individuals.

Table 3 shows the correlations between the well-being variables and institutional indicators. Individual life satisfaction has the strongest positive correlation with the Doing of Business index (r = 0.213) and specifically with three of its pillars “resolving insolvency” (r = 0.201), “trading across borders” (r = 0.192), and “paying taxes” (r = 0.178). We also observe a strong positive correlation between individual life satisfaction and both of our indicators of the quality of governance, the Corruption Perception Index (r = 0.253) and the Quality of Government Index (r = 0.217). Similar results are observed for individual job satisfaction, although the correlation coefficients are slightly lower in comparison with the results for life satisfaction. Moreover, both the Corruption Perception Index and the Quality of Government Index show rather strong correlations with the Doing of Business Index. Indeed, the Corruption Perception Index and the Quality of Government Index are statistically closely related (r = 0.943), and there are relatively high correlations between the Quality of Government Index and the following pillars of the Doing of Business Index: “dealing with construction permits,” “getting electricity,” “paying taxes,” “trading across borders,” “enforcing contracts,” and “resolving insolvency”.

Remarkably, the correlations between our measures of individual well-being and the indicators of employment protection legislation are rather low. The strongest relationship that we find in this category of institutions is between individual life satisfaction and regulation of temporary contracts (r = − 0.097). It is also noticeable that regulations and practices that directly affect starting a business as measured by the Doing of Business pillar “starting a business” are most strongly and positively correlated with the general quality of government. The statistical relationships between the pillar “starting a business” and job and life satisfaction are, however, rather low (r = 0.069 and r = 0.021, respectively).

Table 4 shows correlation coefficients between individual job and life satisfaction and the indicators for institutional quality for self-employed and paid employed individuals separately. Confirming to our expectations (see Section 2.2), we observe a stronger positive relationship between the separate pillars of the Doing of Business Index and the job satisfaction of the self-employed in comparison with that of paid employees. The Corruption Perception Index and the Quality of Government Index are both positively associated with the life satisfaction of individuals regardless of their employment status. However, the relationship between these indices and job satisfaction is stronger for the self-employed than for paid employees.

To summarize, the correlations indicate a moderate relationship between the measures for the different types of institutions used in our analysis and individual well-being. These relationships are stronger for job satisfaction than for life satisfaction and for the self-employed than for paid employees.

This section presents the results of our multivariate analysis. As a first step, we identify the effects of the quality of entrepreneurship-facilitating institutions on an individual’s job and life satisfaction for each institutional measure separately (Section 4.2.1). We then identify the relative importance of institutional factors by estimating our model and including all measures of institutions simultaneously (Section 4.2.2). Section 4.3 performs a robustness check by estimating the model for different income quartiles to account for heterogeneity among self-employed individuals.

As a final step of our analysis, we investigate whether our main results differ depending on the individual income level. Relative income level can be regarded as an indication of entrepreneurial success and more productive entrepreneurship (Sorgner et al. 2017). Fostering successful, productive entrepreneurship is therefore crucial for economic growth (Shane 2009). Thus, we investigate whether the quality of entrepreneurship-facilitating institutions is more relevant for more successful entrepreneurs by estimating our baseline model (as in Table 5) separately for individuals in the lowest (1st) and the highest (4th) quartiles of the country-specific income distribution. The results are shown in Table 8, which only reports the coefficients of models for job satisfaction, since they are more pronounced as compared with the results for life satisfaction.²¹

The results suggest that the ease of doing business is important for the self-employed in both income quartiles, but the effects are stronger for entrepreneurs with high incomes. This indicates that entrepreneurship-fostering institutions might be particularly relevant for successful entrepreneurs. The results are quite different for paid employees, though. We observe that regulations related to the ease of doing business are mainly relevant for the job satisfaction of respondents in the lowest (1st) income quartile while they are almost irrelevant for paid employees in the highest (4th) income quartile. The only exception is the ease of registering property, which is only important for paid employed persons with high incomes.

Figures 6 and 7 demonstrate the differences in the effects of the Doing of Business Index on the predicted probability of being completely satisfied with one’s job for individuals in low- and high-income quartiles. We find that higher values of the Doing of Business Index are related to a significant increase in the probability of being completely satisfied with one’s job for self-employed persons in both income quartiles. For paid employees, such a pattern is only observed for those in the 1st income quartile. The probability of being highly satisfied with one’s job is rather unaffected by variations of the overall Doing of Business Index for individuals in the 4th income quartile.

When we examine the effects of strictness of employment regulation, the results are robust for both income quartiles. The strictness of temporary contract regulation is significantly and negatively related to the well-being of self-employed individuals. In addition, the quality of government and the level of perceived corruption seem to be largely irrelevant for the job satisfaction of paid employees with high incomes. However, we observe significant effects of both measures for self-employed individuals in both income quartiles.