Abstract
Purpose
The study responds to the calls for multilevel approaches in entrepreneurship research and seeks to answer whether the relationships between personal values and entrepreneurship remain stable across different economic conditions, using the unemployment rate as a moderator. It pays attention to the solo self-employed and women, as these groups are particularly vulnerable when crises occur.
Design/methodology/approach
We use Schwartz's theory of human values, which has been understudied in entrepreneurship and follow a correlational research design with micro and macro variables. Multilevel logistic regression is applied to the data from the large sample of 151,032 individuals participating in six waves of the European Social Survey. Solo self-employed are distinguished from those employing others, and analyses are run separately for men and women to understand gender differences.
Findings
The findings show that self-direction and achievement are positively, and benevolence and security negatively related to entrepreneurship. The high unemployment rate lowers the positive relationships with self-direction and achievement and mitigates the negative relationship with security, but only for the solo self-employed and not for employers. Results mostly hold for both genders.
Research limitations/implications
The study suggests that security-related values should not be omitted from entrepreneurship research focused on entrepreneurs' values. It also emphasizes the need to distinguish between various subgroups of entrepreneurs and their motivation, which is important for efficient active labor market policies.
Originality/value
The study utilizes multilevel analyses that account for individual- and country-level influences on entrepreneurial activity. It contributes to understanding how economic context influences value salience and supports the applicability of Schwartz's theory of human values in entrepreneurship.
Keywords
Citation
Lukes, M. and Feldmann, M. (2024), "Personal values and entrepreneurship: does the unemployment rate matter?", Journal of Small Business and Enterprise Development, Vol. 31 No. 8, pp. 125-147. https://doi.org/10.1108/JSBED-04-2023-0150
Publisher
:Emerald Publishing Limited
Copyright © 2024, Martin Lukes and Manuel Feldmann
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Entrepreneurship is driven, initiated, and sustained by values (Holland and Shepherd, 2013; Shir and Ryff, 2022). In line with Gorgievski et al. (2011, p. 2), this study defines values as “abstract and important goals people strive to achieve in life.” Values influence the perception and desirability of alternative actions (Holland and Shepherd, 2013) and are thus useful for explaining what predicts entrepreneurial behavior (Noseleit, 2010). Several scholars have already investigated which values are linked to being an entrepreneur or being in dependent employment. They mostly found that achievement and self-direction values, such as autonomy, are positively, whereas security values are negatively related to being an entrepreneur (e.g. Noseleit, 2010; Gorgievski et al., 2011; Lukeš et al., 2019).
Whether a person is self-employed depends on individual factors and the context in which people operate (Lim et al., 2016). Thus, there is a long-term call in the entrepreneurship literature for studies that combine the person and situation (McMullen and Shepherd, 2006). Researchers call for the investigation of situational factors that may impede the intrinsic motivation of entrepreneurial behavior (Kirkley, 2016). For instance, studies conducted during the time of COVID-19 pandemic showed that people valued conservation values (such as stability) more than usual and self-enhancement (such as achievement) and openness-to-change values (such as self-direction) less than usual (Daniel et al., 2022; Bonetto et al., 2021). Moreover, the COVID-19 pandemic emphasized the vulnerability of the solo self-employed. Their contracts typically allowed for quick termination of the business relationship compared to employees. Consequently, solo self-employed were the first victims of economic slowdown and pandemic-related closures and restrictions (Cieślik and van Stel, 2023; Blackburn et al., 2021). Compared to waged employees, they reported higher reductions in working hours and income, and their well-being felt (Yue and Cowling, 2021). Recent studies also suggested that the struggles are accentuated even more for self-employed women (Caliendo et al., 2023).
Thus, we seek to answer whether the relationships between values and entrepreneurship remain stable (1) across different economic conditions, (2) for the self-employed with and without employees, and (3) what the differences between genders are. Values serve as drivers of entrepreneurial behavior. Therefore, it is important to know whether they change, and for whom. Ignoring the above-mentioned differences and just assuming that entrepreneurs value achievement and self-direction and not security, would lead to wrong theoretical conclusions and lower effectiveness of entrepreneurship focused programs and trainings.
This study applies multilevel logistic regression to the data from the large sample of 151,032 individuals participating in six waves of the European Social Survey. The findings show that self-direction and achievement are positively, and benevolence and security negatively related to entrepreneurship. The high unemployment rate lowers the positive relationships with self-direction and achievement and mitigates the negative relationship with security, but only for the solo self-employed and not for employers. Results mostly hold for both genders. They suggest that security-related values should not be omitted from entrepreneurship research and emphasize the need to distinguish between various subgroups of entrepreneurs and their motivations. This distinction is important for efficient active labor market policies and entrepreneurship training programs under different economic conditions.
This study aims to contribute to entrepreneurship literature in several ways. First, as a key theory of human motivation, Schwartz's theory of human values (Schwartz, 1992, 1994) is understudied in the context of entrepreneurship. Most existing studies using this theory focused on personal values as antecedents of entrepreneurial intentions, utilizing primarily student samples (Santos et al., 2021). However, only a relatively small proportion of those who intend to start a business do so (Parker and Belghitar, 2006). Studies exploring how personal values influence actual self-employment, understood as labor market status, are rare and, except for Noseleit (2010) and Lukeš et al. (2019), were done on small samples of entrepreneurs, comparing them with employees (e.g. Gorgievski et al., 2011) or non-owning managers (Fagenson, 1993). The European Social Survey provides a unique and robust setting to study relationships between personal values and entrepreneurship and enables generalizing findings to the European population (Annink et al., 2016; Bujacz et al., 2020).
Second, entrepreneurship is of a multilevel nature, and thus, researching it calls for multilevel approaches (Hundt and Sternberg, 2016; Lim et al., 2016; Terjesen et al., 2016). Cross-level moderation effects promise new insights because the impact of individual characteristics on entrepreneurship differs in various contexts (Hundt and Sternberg, 2016).
Third, many studies on entrepreneurship do not empirically distinguish different forms of entrepreneurial activity (Lindquist et al., 2015; Liebregts and Stam, 2019). We acknowledge that entrepreneurs differ (Noseleit, 2010; Román et al., 2011), and their motivation differs as well. This study, in line with Cieślik and van Stel (2023), distinguishes solo self-employed from employers and argues that they should be understood as a stand-alone labor market segment qualitatively distinct from employees and employers. The European Social Survey dataset enables this distinction.
Fourth, the current literature, with few exceptions (e.g. Hueso et al., 2020), neglects the importance of conservation values for entrepreneurship. Bolzani and Foo (2018) found that different groups of entrepreneurs differ in how they value security and recommend future research to focus on it. This study posits that security values get more salient for vulnerable solo self-employed individuals than for employers in times of high unemployment.
Finally, we also aim to contribute to practice. Tough economic times impact the value systems of entrepreneurs and those considering entrepreneurship as a potential career choice. We suggest that entrepreneurship programs and trainings should reflect economic conditions in which they occur to align with individual values that drive entrepreneurial behavior.
The article is organized in the following manner. Section 2 starts by describing the relationships between values and entrepreneurship and how these relationships change in changing economic conditions. The solo self-employed and employers are distinguished regarding the salience of their values. Section 3 describes the dataset and operationalization of variables and introduces the modeling approach. Section 4 presents the results, which are further discussed in Section 5. Finally, concluding remarks are provided in Section 6.
2. Theoretical background
2.1 Values and entrepreneurship
Schwartz and colleagues (e.g. Schwartz, 1992; Schwartz, 1994) developed a comprehensive theory of basic human values that distinguishes ten basic types of values by their motivational goals that can be further clustered into four higher-order value types. This theory was empirically tested using various statistical procedures (Bilsky et al., 2010; Bardi et al., 2009). Studies using data from many countries worldwide confirmed systematic relationships of various behavioral, attitudinal, and personality variables with these values (e.g. Schwartz, 1992, 1994).
Four values play a key role in differentiating entrepreneurs from non-entrepreneurs. The first essential value is self-direction, a part of higher-order openness to change values that capture intrinsic motivation in novelty and mastery. Entrepreneurs have decision rights on work tasks performed in their business, i.e. what work to do, when, and how. They take no orders from a boss; they set up goals and strategies for their business, schedule their time, and choose work methods (Gelderen, 2016). In previous studies, entrepreneurs rated self-direction and independence as more important than non-entrepreneurs (Fagenson, 1993; Gorgievski et al., 2011; Noseleit, 2010; Lukeš et al., 2019).
The second crucial value is achievement, a part of higher-order self-enhancement values that motivate self-interested behavior. Individuals with a high need for achievement are likelier to choose careers that link individual work behavior and performance outcomes (Collins et al., 2004), such as entrepreneurship. The need for achievement differentiates entrepreneurs from non-entrepreneurs (Noseleit, 2010; Fagenson, 1993; Collins et al., 2004).
Whereas self-direction and achievement are positively related to entrepreneurship, the opposing values in Schwartz's (1992, 1994) theory, i.e. security and benevolence, can be expected to influence entrepreneurship negatively. Security is a part of higher-order conservation values that aim to protect harmony and the status quo that provides certainty in life. If one highly values security, he/she will likely feel uneasy in uncertain situations that characterize an entrepreneur's daily life. Moreover, employment protection legislation provides waged employees much more security than the self-employed (Liebregts and Stam, 2019). Security that involves the need for safety and stability was found in previous studies to be less important for entrepreneurs than for employees (Noseleit, 2010; Gorgievski et al., 2011; Lukeš et al., 2019) and was also found to inhibit the entrepreneurial activity of those who intended to start up (Delanoë-Gueguen and Liñán, 2019).
Finally, entrepreneurs often work longer hours than employees (e.g. Carrington et al., 1996), and their endeavor requires a lot of resources and effort spent at work, which may limit the resources, time, and energy devoted to family and other close friends. Such a way of life contrasts with benevolence values, characterized by caring for close others. Benevolence is a part of higher-order self-transcendence values related to whether people engage in prosocial behaviors. Gorgievski et al. (2011) and Fagenson (1993) found that entrepreneurs are less benevolent than other groups.
We also note that it is impossible to use all ten Schwartz’s values in the empirical model because, as Schwartz's theory (Schwartz, 1992; Bilsky et al., 2010) suggests, adjacent values are interrelated and would induce multicollinearity issues in the regression models. Thus, we choose those values that represent all four higher-order value dimensions and that are, at the same time, significantly related to entrepreneurship. Accordingly, we hypothesize:
Compared to waged employees, the self-employed rank higher in self-direction.
Compared to waged employees, the self-employed rank higher in achievement.
Compared to waged employees, the self-employed rank lower in security.
Compared to waged employees, the self-employed rank lower in benevolence.
2.2 Values and their relationships with entrepreneurship under changing economic conditions
The focus of this study is not on the value differences between entrepreneurs and non-entrepreneurs but on whether the relationship between specific values and entrepreneurship is stable or whether its strength changes with changing economic conditions, such as the unemployment rate. Inglehart (1977) theorized that people value the most their most pressing needs. These values then influence an individual course of action (Schwartz, 1992, 1994). In the conditions of economic prosperity and an individual's positive socioeconomic environment, postmaterialist values, such as autonomy, which is also associated with entrepreneurship (Gelderen, 2016; Lukeš et al., 2019), are emphasized. On the other hand, periods of scarcity lead to low risk tolerance (Welsh et al., 2022) and an emphasis on materialistic values related to individual security. Nový et al. (2017) support this scarcity hypothesis (Inglehart, 1977) by indicating that GDP per capita positively influences the level of postmaterialism and found strong effects of individuals’ financial security on postmaterialistic values. In the same line of reasoning, Clarke and Dutt (1991) analyze short-term shifts in the economy, measured by changes in the unemployment rate, and found that postmaterialistic values are sensitive to unemployment.
Individuals change priorities for different values through adaptation to changing life and work circumstances (Jin and Rounds, 2012). Daniel et al. (2022) found that conservation values (such as security) became more important during the COVID-19 pandemic, whereas openness to change values (such as self-direction) decreased. They concluded that values are usually stable but also adapt to significant changes in environmental conditions. The same results were reported in a COVID-19-related study by Bonetto et al. (2021) who also found that self-enhancement values (such as achievement) decreased in importance. The process through which values change their importance may be further explained by acclimation and compensation mechanisms (Schwartz and Bardi, 1997). Acclimation means that individuals lower the significance of values that cannot be attained. In challenging economic times, entrepreneurs will more likely perform those activities necessary for the survival of their business and, in line with the decreasing importance of achievement and self-direction values, reduce actions oriented towards growth, innovation, and pursuit of new opportunities, because these are usually related to increased risk and thus violating salient security values. The positive relationship between self-direction and achievement values and entrepreneurship may be reduced due to this acclimation mechanism.
The compensation mechanism occurs for security values concerned with material well-being when their attainment is beyond personal control. Deprivation increases the importance of these needs (Schwartz and Bardi, 1997). During severe economic conditions, people have concerns about being able to provide financial security to themselves and their families. They modify their behaviors and strategies to cope with the risks (Marcazzan et al., 2022) and satisfy actual needs, even if it does not fit their primary personal values. High levels of unemployment are stimuli showing that a national economy is in crisis and would likely lower the motivation of individuals to be entrepreneurs (Hundt and Sternberg, 2016). Carrington et al. (1996) found that hourly wages and annual earnings of the self-employed, compared to wage employees, are more related to the economic cycle. As unemployment rises, harsh economic conditions may increase the risk of bankruptcy, and families of the self-employed may get into financial trouble.
Furthermore, the economics of entrepreneurship literature (Thurik et al., 2008; Parker et al., 2012) analyzed the relationships between unemployment and entrepreneurship and found two different effects. The first is called unemployment or push effect, which means people enter self-employment out of necessity to escape unemployment or mediocre jobs. The other effect, the Schumpeterian or pull effect, is oriented on opportunity-based entrepreneurship that is more likely related to growth and job creation. It makes sense to distinguish solo self-employed and those who created businesses employing others. This approach has been increasingly applied in various studies related to entrepreneurship (Cieślik and van Stel, 2023; Hessels et al., 2017) as there is a strong reasoning behind it. The solo self-employed belong to the most vulnerable groups in the labor market (Román et al., 2011). They often started out of necessity, and their work may be largely precarious (e.g. Vosko and Zukewich, 2006). Most do not plan to employ others (Cieślik and Dvouletý, 2019). They are the least protected by labor market regulations, and their economic power is, on average, much smaller than it is in the case of employers. Employers have various other means to protect themselves as individuals against adverse economic conditions. They can lay off people, postpone investments, or sell company assets. High unemployment also enables them to spend less on wages and choose from a wider pool of suitable job candidates. A high unemployment rate does not mean an immediate personal danger for them. Thus, it can be expected that the change in the salience of their values will be much lower when compared with the solo self-employed, for whom the economic downturn and related high unemployment rates are especially dangerous period, with a higher likelihood of substantial and negative life changes, such as their unemployment or substantial decrease of their income. Therefore, in line with Bardi et al. (2009), this study expects that the relationship between personal values and solo self-employment will be less stable during the changing situation in the labor market than in the case of employers.
Demographic differences may also play a role in the suggested relationships. Especially gender has a consistently significant influence on personal values. A large study by Schwartz and Rubel-Lifschitz (2009), based on representative population samples in 25 countries and 68 student samples, provided convincing evidence that women, compared to men, value self-enhancement and achievement less, and security and benevolence more, i.e. showing the very same value preferences as employees vs self-employed. Moreover, GEM-based studies consistently show that women fear failure more than men (Cacciotti and Hayton, 2015), with an obvious link to security values. Similar findings can be found concerning the influence of age on personal values. Older people value security and benevolence more and self-enhancement and achievement less than younger ones (Robinson, 2013).
Overall, despite the relative stability of individual values (Schwartz, 1992, 1994) that are to a large extent established in the process of socialization (Inglehart, 1977), the authors posit that in times of high unemployment and for the solo self-employed rather than for employers, security values become salient, and on the other hand achievement values and self-direction values become less critical. As there is no sufficient theoretical reasoning in the literature to support a change in the importance of benevolence in a time of high unemployment, the authors refrain from hypothesizing any effect on benevolence.
High unemployment rates lessen the positive relationship between self-direction and solo self-employment.
High unemployment rates lessen the positive relationship between achievement and solo self-employment.
High unemployment rates lessen the negative relationship between security and solo self-employment.
High unemployment rates do not moderate the relationship between being an employer and self-direction.
High unemployment rates do not moderate the relationship between being an employer and achievement.
High unemployment rates do not moderate the relationship between being an employer and security.
We present our theoretical model in Figure 1. The model describes the hypothesized relationships between our independent, moderating, and dependent variables.
3. Methods
In this section, we introduce our data set and the sample we took and argue why the data from the European Social Survey comprehends the necessary information for our research (3.1). Then, we operationalize variables (3.2), describe the data analyses used to test the hypotheses, and argue why this multilevel research design is suitable for answering our research questions (3.3). Generally, we follow a correlational research design that investigates associations between both micro and macro variables. We chose this research design because it allows to compare a large number of individuals from wider geographic areas along a large set of variables. Moreover, the structure of the data permits to conduct a multi-level analysis, as we lay out in section 3.3.
3.1 Data and sample
The analyses were based on publicly available data from several European Social Survey (ESS) rounds. ESS datasets have been broadly used in social science research. In several cases, it was utilized also in entrepreneurship research (Annink et al., 2016; Bujacz et al., 2020) focused on the well-being of the self-employed. Our analysis uses data from rounds 3 to 8 from 2006 to 2016. We used observations from countries that provided data in at least two rounds. In the ESS sampling process, respondents are drawn randomly and interviewed face-to-face. Within this process, the ESS guidelines aim for a response rate of at least 70% to mitigate non-response bias. ESS aims to adhere to very high standards (see, e.g. Koch et al., 2009). By eliminating countries that participated in less than two rounds, all data that was not reliably replicated with representative samples in later rounds were dropped [1]. Moreover, all observations with missing data were deleted according to listwise deletion procedures. Given the focus on career choice between entrepreneurship and paid employment we also excluded the respondents who were unemployed, in full-time education, doing training, household work, full-time caring, obligatory military service, or other, as well as those outside the working-age range of 18–65. As all the samples in all the countries are representative, and all respondents active in the labor market were included, sample selection bias is minimized. After all adjustments, the final sample contains 151,032 respondents from 25 countries [2]. These 25 countries are all European; thus, their data from 2006 to 2016 allow for a more in-depth assessment of effects before, during, and after this century's first global financial crisis. The sample includes 12,598 solo self-employed individuals, 4,419 employers and 134,015 individuals in waged employment.
3.2 Operationalization and variables
The dependent variable in this study, labor market status, is a categorical indicator taking a value 1 if the respondent is self-employed without employees, 2 if the respondent is self-employed with employees and 0 if in paid employment The independent variables follow in groups: The first group covers demographic and socioeconomic controls, whose relationship with entrepreneurship has been addressed in the literature. The second group introduces four personal values that have been shown to impact entrepreneurial activity.
The individual controls are generally known variables that affect the likelihood of whether an individual is or is not an entrepreneur. These variables include age, education, caring responsibilities, and self-employed parents where the authors expect a positive relationship between these variables and entrepreneurship (Global Entrepreneurship Research Association, 2018; Hundt and Sternberg, 2016; Noseleit, 2014; Lindquist et al., 2015) and gender, where they expect that males will be more likely entrepreneurs (e.g. Global Entrepreneurship Research Association, 2018; Cieślik and Dvouletý, 2019). The control variables thus describe age (a continuous numerical variable ranging from 18 to 65, centered around the median age of 42), gender (a dummy variable for female respondents), and education (an ordinal variable based on the ISCED 2011 classification, recoded in 3 categories, and centered around middle education) [3]. Further, a dummy variable indicates whether the respondents have caring responsibilities in their current household. Self-employed parents are addressed by a variable that takes a value of 1 if one parent was self-employed when the respondent was 14, 2 if both, and 0 if there was no entrepreneurial role model. This age was chosen due to the importance of parents in establishing work values and entrepreneurial intentions during adolescence (e.g. Laspita et al., 2012).
The second group captures respondents' values known to be relevant for self-employment entrepreneurship, according to Schwartz's (1992, 1994) theory of values. It includes a selection of personal values from the Schwartz Portrait Values Questionnaire battery used extensively in the frame of ESS. Beckers et al. (2012) confirmed that this measure is a more comprehensive tool for capturing values than the measures of postmaterialism and self-expression values used in World Values Survey. The value constructs are measured with two items each. We report Spearman-Brown for the two-item scale reliabilities, in line with Eisinga et al. (2013). These are given in parentheses: Benevolence (r = 0.63), Self-direction (r = 0.46), achievement (r = 0.71), and security (r = 0.62). All are coded from 1 (“not like me at all”) to 6 (“very much like me”) and subsequently grand-mean centered and standardized; for the specific reasoning behind using this questionnaire see Davidov et al. (2008) and Schwartz et al. (2012).
The third group includes the indicator for the unemployment rate, measured in percent, which was retrieved from the Eurostat database, centered around its grand mean (munemployment = 8.43%), and standardized. It addresses the labor market conditions in each country. As such, it covers geographic variations, for example, in 2010 from 5% in the Netherlands to 17.8% in Lithuania, and timely variations, for example in Greece from 7.8% in 2008 to 26.5% in 2014.
Lastly, the fourth group interacts the personal values with the macro factor of unemployment. All items with Likert-type response formats are treated as quasi-continuous interval data that allow parametric statistical analysis in line with the recommendations of Carifio and Perla (2007). Table 1 reports standard descriptive statistics and correlations between variables.
3.3 Data analysis
Multinomial logistic regression models are applied to test the hypotheses, as the dependent variable is a categorical indicator. Moreover, our research questions address the interaction of variables both on the individual and on the macro level. While cross-level interactions can be assessed with standard regression techniques, they do not enable a combination of direct macro effects and cross-level interactions, which is pursued in this paper. Thus, multilevel analysis techniques are needed to assess the relationship between micro-level factors and entrepreneurship and how macro-factors from the one's environment moderate this relationship. While this approach is recommended for this type of research questions, it also carries some risks on the country level effects for smaller numbers of countries (Bryan and Jenkins, 2016). However, we are confident to mitigate these risks by including a large number of countries and several waves of the ESS, leading to n = 120 on the macro level (Schmidt-Catran et al., 2019).
In line with the reasoning described above and other research calling for multilevel approaches in entrepreneurship (e.g. Hundt and Sternberg, 2016; Lim et al., 2016; Terjesen et al., 2016), we apply multilevel modeling to build statistical models and test our hypotheses. The models were tested with a multilevel analysis in which individuals (Level 1) are clustered within several country-samples from different years (Level 2); thus, the upper level consists of countries and years (Schmidt-Catran et al., 2019). All the models were fitted with Stata 18 and its multilevel package.
The models are split into three columns: Column (1) compares employees with the solo self-employed, column (2) compares employees with employers, and column (3) compares the solo self-employed with employers. First, the three groups of predictors are modeled. Second, interactions between one Schwartz value and the macro factor are added step-by-step in models (2) to (4). In line with the suggestions by Angrist and Pischke (2008), weights are not applied to the model estimation. Variance Inflation Factors (VIF) were calculated to control for multicollinearity. While the mean VIF for all controls and predictors is 1.13, no single value exceeds the threshold of 10, not even the stricter cut-off at 3 (cf. Hair et al., 1998). AIC and Likelihood ratios are reported to demonstrate that the models improve the fit.
Additional robustness tests are conducted by splitting the sample along reported gender lines and running the models separately along this demographic characteristic. Thus, we aim to show the robustness of our results and explore potential differences. These tables (A1-A4) are reported in the appendix.
4. Results
In this section, we provide the results of our hypotheses tests. First, we show that control effects align with prior research, then list the simple effects and results for hypotheses 1a–d. Second, we present the interaction effects testing hypotheses 2a–c and 3a–c. Lastly, we explore the nature of the complex interaction effects in more detail by visualizing the effects.
Table 2 shows regression results for the standard model with all control variables and the simple effects of our predictors. The odds ratios (OR) display how a unit change in the independent variables affects the log of the odds to be in the target category vs the reference category.
The control effects are in line with prior research: Odds ratios are above 1 for age (Simoes et al., 2016), caring responsibilities (cf. Noseleit, 2014), and self-employed parents (cf. Lindquist et al., 2015), and smaller than 1 for females versus males (cf. Global Entrepreneurship Research Association, 2018). Employers are older, less often female, better educated, and more often with caring obligations than solo self-employed. Entrepreneurship must pay off for those with higher education levels, which is mostly the case in businesses that employ other employees. On the other hand, solo self-employed have lower education than both employees and employers. Higher unemployment rates are significantly associated with a higher propensity to be solo self-employed, which may relate to some employers laying off their workforce in times of economic hardship and consequent necessity entrepreneurship of these laid off workers (Mühlböck et al., 2018).
First, the study finds full support for all Hypotheses 1a to 1d: All personal values introduced into the model have highly significant effects (p < 0.001) on both categories on self-employment: Self-direction (H1a, OR: 1.559/1.753) and achievement (H1b, 1.086/1.206) increase the odds of both categories of self-employment, whereas security (H1c, OR: 0.842/0.883) and benevolence (H1d, OR: 0.916/0.915) decrease the odds. Thus, all four Schwartz values used in the analysis strongly affect the likelihood of self-employment – both with and without employees. Moreover, employers are significantly more likely to value self-direction (OR: 1.051), achievement (OR: 1.124), and security (OR: 1.046) than solo-entrepreneurs. Robustness tests with sub-samples split by reported gender confirm the main results. They also show that solo self-employed men, not women, have lower education than employees.
Table 3 shows the results of the interaction models (2) to (4). All simple non-moderated effects remain stable and thus are not reported again to improve the table's readability. First, the results support Hypothesis 2 because each personal value is significantly moderated by the unemployment rate among solo self-employed: A strong significant interaction is found between the unemployment rate and self-direction (H2a, OR: 0.986, p < 0.001), achievement (H2b, OR: 0.976, p < 0.01) and security (H2c, OR: 1.024, p < 0.05). Hence, there are meaningful interactions of all selected personal values with the unemployment rate. Second, Hypothesis 3 is mostly supported because, for employers, there are no significant interactions between unemployment rate and two personal values, achievement (3b) and security (3c); yet there is a significant negative effect for self-direction (3a). Concluding, there is strong support for Hypothesis 2 and additional support for Hypothesis 3b and 3c. We reject H3a. Overall, the unemployment rate moderates the relationships between values and solo self-employment; it lowers the positive effects of self-direction and achievement and simultaneously lessens the negative effect of security on solo self-employment.
To investigate significant interactions further, Figure 1 represents the results in contour plots that demonstrate the impact of the three personal values and unemployment on the probability of being solo self-employed.
The graphs in Figure 2 depict how different combinations of values and economic circumstances predict solo self-employment. If there were no interactions, the predicted levels of entrepreneurship would show straight horizontal lines for different levels of personal values. Both X- and Y-axis variables are centered around their mean. Thus, the value of 0 on the X-axis corresponds to 8.43% unemployment rate. Also, they are standardized, and the axis covers values from 1 standard deviation below the mean to one standard deviation above. The shades of gray indicate the predicted probability of entrepreneurship from light gray (low) over gray (medium) to black (high). The first graph shows how unemployment and self-direction interact: Generally, being highly self-directed is more important to predict solo self-employment when unemployment is low. In other words, the same level of self-direction predicts a lower level of self-employment when unemployment is one standard deviation above the mean. Valuing self-direction is more related to solo self-employment under favorable economic conditions. The second contour graph interacts the unemployment rate and the need for achievement. Achievement becomes less important for predicting solo entrepreneurship when unemployment is high, i.e. the achievement is more related to solo self-employment when economic situations are better. The third graph shows that the negative correlation with the need for security decreases when unemployment rates are higher, i.e. when unemployment is high, even people with a high need for security have a higher probability of being self-employed.
Robustness tests with sub-samples split according to gender support the main results, all the directions of moderation effects hold. However, the gender split also explains which gender is more strongly affected by the moderation effect of the unemployment rate. First, the unemployment rate lowers the negative effect of security on solo self-employment only for men but has no effect on the women subsample. The interpretation may derive from the fact that women value security anyhow, and changing economic conditions do not substantially change this, or that women, in general, are more vulnerable in the labor market, thus higher in their needs for security. Second, the unemployment rate lowers the positive effect of achievement on solo self-employment only for women but has no effect in men subsample. This largely derives from the higher level of valuing achievement in men that allows for much smaller variance and effect size between the employed and, e.g. solo self-employed. Third, the unemployment rate lowers the positive effect of self-direction on being an employer only for men but has no significant effect in the women subsample; here, we refrain from interpreting it due to the much smaller sample size for female employers.
5. Discussion and implications
This study seeks to answer whether the relationships between personal values and entrepreneurship remain stable across different economic conditions. It also explores how the results differ for the self-employed with and without employees, and for women and men. We find that in situations of high unemployment rates, the positive relationship of self-direction and achievement values with solo self-employment decreases, and the negative relationship of security and solo self-employment tend to be reduced. We further find notable differences based on economic conditions and the type of self-employment. For employers, the significant moderation effect of high unemployment rates occurs only in the case of self-direction.
People place the highest value on their most pressing needs (Inglehart, 1977) and adapt to changing life and work circumstances (Jin and Rounds, 2012). Periods characterized by uncertainty, such as those with a high unemployment rate, lead to the emphasis on materialistic values related to individual security (Schwartz and Bardi, 1997) and decrease the importance of postmaterialistic values (Clarke and Dutt, 1991), such as self-direction. This study supports the acclimation mechanism that Schwartz and Bardi (1997) theorized and puts it in the context of entrepreneurial activity, specifically for solo-entrepreneurs. When the unemployment rate is high, the importance of values that cannot be easily attained in a bad economic situation, such as achievement and self-direction, decreases, and they predict solo self-employment to a lesser degree.
On the other hand, high unemployment rates create a push effect (Thurik et al., 2008; Parker et al., 2012). Even for those individuals with a relatively high need for security, entrepreneurship starts to present itself as a viable option in times of dire prospects of finding paid employment. It can be illustrated in the situation of a high unemployment rate when the likelihood of solo self-employment is relatively high even when one holds relatively high security values (see Figure 2). Given the risks and vulnerability inherent in entrepreneurship (Román et al., 2011), this can lead to the creation of precarious businesses (MacDonald and Giazitzoglu, 2019).
5.1 Theoretical contributions
The novel result of this study is that the relationships between personal values and entrepreneurship differ across different economic conditions. Thus, we challenge the notion that entrepreneurs as a group are uniformly motivated by self-direction and achievement and do not value security (Noseleit, 2010; Gorgievski et al., 2011; Lukeš et al., 2019). Researchers should recognize that economic context significantly influences the alignment of personal values with entrepreneurial choices. The results suggest that employers’ value orientations may be less susceptible to economic fluctuations. Solo-entrepreneurs, however, adapt their value orientations based on prevailing economic conditions, emphasizing practical concerns over idealistic pursuits. Despite the relative stability of values in human lives (Schwartz, 1992), economic conditions matter and change the importance of values. Thus, economic conditions should not be ignored by scholars interested in the interplay between values and entrepreneurship.
5.2 Implications for practice
This study explains why policymakers find it hard to stimulate job creation through new entrepreneurial activity in times of a high unemployment rate. Self-direction and achievement values, linked to innovative and growth-oriented businesses, are more salient among entrepreneurs during economic growth phases – but less during economic downturns. Thus, it may be hard to promote growth-oriented behavior as a means to mitigate negative economic effects because, in bad economic conditions, entrepreneurs, especially the solo self-employed, shift their value system towards security values (cf. Gorgievski et al., 2011).
Concerning policy implications, relevant policies fit under Active Labor Market Programs (ALMP) that stimulate primarily solo self-employment as a way out of unemployment (Dvouletý and Lukeš, 2016; Dvouletý, 2022), which is especially important in times of high unemployment rates. These policies aim to help economically inactive individuals create income for themselves and their families through self-employment. Studies show that during weak economic conditions, the share of the self-employed grows (Thurik et al., 2008; Parker et al., 2012). The entry is caused primarily by different motives than when the economy performs well. The previous studies suggested that self-employment can be an effective way out of unemployment (Dvouletý and Lukeš, 2016) despite the effects being time-limited and mostly unrelated to additional job creation (Dvouletý, 2022). Self-employment can also be attractive for those women who try to combine work with childcare and family responsibilities (Cieślik and van Stel, 2023).
The effectiveness of these policies is not related only to financial support but also to the training the unemployed receive. This training should reflect realistic expectations from policymakers and the unemployed as well. Cieślik and Dvouletý (2019) found that most new self-employed individuals do not expect to employ anybody else. Thus, the training should mostly focus on the viability and sustainability of the business idea, supported by the research of customer needs, market demand and differentiation from existing competition, and meaningful business model that matches the human, social, and financial capital of the individual entering the self-employment. Also, trainers and mentors working with to-be entrepreneurs should be aware of their clients’ motivation and consider the match between their values and the specifics of the intended venture to efficiently guide them through the process of preparing them to enter self-employment (Santos et al., 2021).
For policies focused on existing entrepreneurs, it may be reasonable to support them with programs that reflect their security needs more explicitly, for example, by providing guarantees and means to mitigate personal risks associated with self-employment (cf. Dvouletý and Lukeš, 2016). The COVID-19 pandemic emphasized the need for quick financial support with a low administrative burden targeted at the self-employed. The COVID-19 pandemic further enhanced the importance of this research because, as our study shows, times of crisis impact the value systems of entrepreneurs and those considering self-employment as a potential career choice.
5.3 Limitations and future research directions
This study also has several limitations. First, it uses the data gathered from different individuals in the subsequent waves of ESS and does not use a longitudinal design. Thus, claims regarding the direction of causality cannot be made. In reality, there might be several feedback loops: personal values influence what happens in one's entrepreneurial efforts, and what happens in entrepreneurship influences values. These effects, in turn, are moderated by economic conditions that affect not only actual business outcomes but also individual cognitive processes. Second, changes in entrepreneurs' values may also be caused by factors that could not be acknowledged in this study, such as firm age or the cyclicality of the particular industry. Third, the available data did not enable us to fully distinguish between necessity and opportunity entrepreneurs, two forms that entail different value perspectives. However, robustness tests with household income as a proxy for necessity entrepreneurship show similar results. Fourth, short two-item scales for measuring personal values were used, which may, despite their widespread use in previous research (Davidov et al., 2008; Schwartz et al., 2012), pose some limitations regarding the correct capturing of the respective value constructs. Especially for self-direction, a relatively low reliability coefficient may suggest that contrary to Schwartz's value theory (1992, 1994), creativity and independence should be considered as two adjacent but distinct values (cf. Lukeš et al., 2019). It should also be noted that this study involves European countries only, so its findings may be less applicable to developing countries.
Given the nature of the available data, the authors of this study could not distinguish in more detail the underlying mechanisms causing the observed moderation effects. The longitudinal research design is needed to determine whether the value change occurs depending on changes in economic conditions or whether individual values remain rather stable and only behavior changes to adapt to current conditions. Future research should also distinguish the value changes of those entrepreneurs who stay in business despite the changing economic situations from value changes of those who transit from and into self-employment due to changes in economic conditions. Finally, it can be recommended to look at the link between individual values and surrounding conditions and how it affects entrepreneurial behavior. Qualitative research might be useful to understand these motivational antecedents of entrepreneurial behavior better.
6. Conclusion
This study provides a better understanding of the relationships between personal values and individual entrepreneurship, particularly how the country-level economic context moderates this relationship. Schwartz's theory of human values (Schwartz, 1992, 1994) has been understudied in entrepreneurship, and this study helps to close this gap. As expected, the direct effects of personal values on entrepreneurship were found. Higher benevolence and security values relate to a lower likelihood of solo self-employment and entrepreneurship with employees, whereas higher self-direction and achievement values are connected with a higher likelihood of participation in both forms of entrepreneurship. These results fully hold for men and women separately.
No study so far has analyzed the moderating effect of economic conditions on the relationship between personal values and entrepreneurship. Responding to the calls for multilevel approaches in entrepreneurship research (e.g. Hundt and Sternberg, 2016; Lim et al., 2016; Terjesen et al., 2016), this study shows that self-direction and achievement are less related to solo self-employment during economic downturns than they are when the labor markets are in a more positive state, whereas the opposite effect holds for security. We also find the moderating effects of a high unemployment rate being more accentuated for one of the genders, men in the case of security and women in the case of achievement. This study is the first to analyze the underlying moderating mechanisms across a large multi-country sample and the time frame of the last economic cycle involving the recession in 2008 and after. Two different forms of entrepreneurial activity are also distinguished: solo self-employed and employers. Significant moderation effects were found between values and the unemployment rate for the solo self-employed, but no significant effects for employers. Thus, the results empirically support the notion that entrepreneurship research should distinguish the solo self-employed from those employing others. This study gives ample support for why this affects self-employed individuals more than entrepreneurs with employees. It shows that the solo self-employed are vulnerable to economic conditions not only in terms of their income (Carrington et al., 1996) but also in terms of pressure on their value systems. Finally, even though entrepreneurs are mostly portrayed as accepting risks and with no need for security, this study suggests that security and safety values cannot be ignored in entrepreneurship research. They get salient for the solo self-employed when labor market conditions worsen. Relatedly, there are practical implications for entrepreneurship education that needs to reflect the participants' personal values (Loi et al., 2022) and consider that these can change together with changing economic conditions.
Figures
Figure 1
Theoretical model
Figure 2
Predicted probability of solo self-employment
Model (1) with controls, values and unemployment variables, men only
| (a) | (b) | (c) | |
|---|---|---|---|
| Solo S-E/Employees | Employers/Employees | Employers/Solo S-E | |
| Age | 1.025*** (23.34) | 1.033*** (20.06) | 1.011*** (5.58) |
| Education | 0.941*** (−3.34) | 1.192*** (6.37) | 1.286*** (7.74) |
| Caring responsibilities | 1.290*** (9.91) | 1.935*** (17.46) | 1.530*** (9.58) |
| Parents self-employed | 1.964*** (33.63) | 1.838*** (20.78) | 0.979 (−0.61) |
| Benevolence | 0.906*** (−6.75) | 0.938** (−2.90) | 1.046+ (1.73) |
| Self-direction | 1.628*** (29.96) | 1.848*** (24.32) | 1.075* (2.55) |
| Achievement | 1.038* (2.48) | 1.169*** (7.00) | 1.125*** (4.56) |
| Security | 0.852*** (−11.16) | 0.881*** (−6.08) | 1.030 (1.22) |
| Unemployment | 1.073* (2.04) | 1.032 (0.94) | 0.960 (−0.91) |
| Constant | 0.069*** | 0.022*** | 0.303*** |
| Country level variance | 0.124*** | 0.087*** | 0.172*** |
| Log lik | −22020.3 | ||
| Chi-squared | 2672.9 | ||
| AIC | 44062.6 |
Note(s): N = 72,307; exponentiated coefficients; t statistics in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001
Source(s): Created by authors
Sociodemographic interaction: Models (2) to (4) with interaction effects, all standard effects omitted, women only
| (2a) | (2b) | (2c) | (3a) | (3a) | (3c) | (4a) | (4a) | (4c) | |
|---|---|---|---|---|---|---|---|---|---|
| Unemployment × self-direction | 0.982*** (−4.63) | 0.981 (−0.59) | 1.056 (1.61) | ||||||
| Unemployment × achievement | 0.965* (−2.49) | 0.982 (−0.64) | 1.026 (0.78) | ||||||
| Unemployment × security | 0.999 (−0.08) | 0.997 (−0.12) | 1.003 (0.10) | ||||||
| Constant | 0.042*** | 0.008*** | 0.177*** | 0.042*** | 0.008*** | 0.176*** | 0.042*** | 0.008*** | 0.176*** |
| Country level variance comp | 0.180*** | 0.136*** | 0.176*** | 0.177*** | 0.136*** | 0.174*** | 0.178*** | 0.136*** | 0.174*** |
| Log lik | −17688.7 | −17696.3 | −17699.3 | ||||||
| Chi-squared | 1370.4 | 1361.4 | 1355.6 | ||||||
| AIC | 35401.5 | 35416.5 | 35422.5 |
Note(s): N = 78,725; exponentiated coefficients; t statistics in parentheses; all covariates centered; mixed effects logistic regressions with 120 random intercepts for participating countries and ESS waves; *p < 0.05, **p < 0.01, ***p < 0.001
Source(s): Created by authors
Sociodemographic interaction: Models (2) to (4) with interaction effects, all standard effects omitted, men only
| (2a) | (2b) | (2c) | (3a) | (3a) | (3c) | (4a) | (4a) | (4c) | |
|---|---|---|---|---|---|---|---|---|---|
| Unemployment × self-direction | 0.990** (−2.83) | 0.954* (−2.12) | 0.991 (−0.37) | ||||||
| Unemployment × achievement | 0.983 (−1.43) | 0.977 (−1.24) | 0.994 (−0.28) | ||||||
| Unemployment × security | 1.041** (3.17) | 0.986 (−0.73) | 0.954* (−2.11) | ||||||
| Constant | 0.069*** | 0.022*** | 0.303*** | 0.069*** | 0.022*** | 0.303*** | 0.069*** | 0.022*** | 0.305*** |
| Country level variance comp | 0.125*** | 0.087*** | 0.173*** | 0.124*** | 0.087*** | 0.173*** | 0.126*** | 0.086*** | 0.173*** |
| Log lik | −22557.7 | −22560.3 | −22555.9 | ||||||
| Chi-squared | 2389.4 | 2386.3 | 2394.9 | ||||||
| AIC | 45139.5 | 45144.7 | 45135.9 |
Note(s): N = 72,307; exponentiated coefficients; t statistics in parentheses; all covariates centered; mixed effects logistic regressions with 120 random intercepts for participating countries and ESS waves; *p < 0.05, **p < 0.01, ***p < 0.001
Source(s): Created by authors
Notes
See ESS sampling guidelines for further information on the representativeness of country samples: https://www.europeansocialsurvey.org/docs/round9/methods/ESS9_sampling_guidelines.pdf
Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, Great Britain, Greece, Croatia, Hungary, Ireland, Iceland, Lithuania, Netherlands, Norway, Poland, Portugal, Sweden, Slovenia, and Slovakia. For the entire list before adjustments, see: http://www.europeansocialsurvey.org/about/participating_countries.html
The highest achieved level was used for the respondents still in part-time education. For easier interpretation, the 7 ISCED levels were converted into a three-level index with ISCED levels 1 and 2 grouped (coded as −1), 3 to 5 grouped and coded as 0, and levels 6 and 7 grouped and coded as +1.
Variables and correlations
| Variable | Mean | S.D. | Min | Max | VIF | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Labor market status | 0.14 | 0.42 | 0.00 | 2.00 | 1 | |||||||||||
| 2 | Age | 1.54 | 12.90 | −24.00 | 23.00 | 1.07 | 0.078* | 1 | |||||||||
| 3 | Gender | 0.52 | 0.50 | 0.00 | 1.00 | 1.04 | −0.115* | 0.011* | 1 | ||||||||
| 4 | Education | 0.12 | 0.72 | −1.00 | 1.00 | 1.06 | 0.016* | −0.094* | 0.04* | 1 | |||||||
| 5 | Caring | 0.47 | 0.50 | 0.00 | 1.00 | 1.02 | 0.041* | −0.025* | 0.085* | 0.035* | 1 | ||||||
| 6 | Parental SE | 0.26 | 0.54 | 0.00 | 2.00 | 1.01 | 0.128* | 0.040* | −0.004 | −0.008* | −0.001 | 1 | |||||
| 7 | Benevolence | 0.00 | 1.00 | −5.00 | 1.33 | 1.24 | 0.007* | −0.022* | 0.111* | 0.043* | 0.023* | 0.031* | 1 | ||||
| 8 | Self-direction | 0.00 | 1.00 | −4.00 | 1.48 | 1.26 | 0.119* | −0.084* | −0.033* | 0.139* | −0.042* | 0.035* | 0.335* | 1 | |||
| 9 | Achievement | 0.00 | 1.00 | −2.47 | 1.82 | 1.29 | 0.049* | −0.209* | −0.058* | 0.069* | 0.0022 | −0.017* | 0.197* | 0.341* | 1 | ||
| 10 | Security | 0.00 | 1.00 | −3.53 | 1.35 | 1.27 | −0.009* | 0.064* | 0.087* | −0.101* | 0.048* | −0.024* | 0.311* | 0.165* | 0.303* | 1 | |
| 11 | Unemployment | 0.00 | 1.00 | −1.47 | 4.22 | 1.03 | 0.012* | 0.008* | 0.015* | −0.098* | 0.036* | 0.006* | −0.006* | −0.012* | 0.045* | 0.142* | 1 |
Note(s): * for 0.05 significance
Model (1) with controls, values, and unemployment variables
| (a) | (b) | (c) | |
|---|---|---|---|
| Solo S-E/Employees | Employers/Employees | Employers/Solo S-E | |
| Age | 1.024*** (29.20) | 1.034*** (24.26) | 1.013*** (7.76) |
| Female | 0.577*** (−27.70) | 0.322*** (−32.29) | 0.543*** (−15.26) |
| Education | 0.970* (−2.16) | 1.210*** (8.25) | 1.292*** (9.42) |
| Caring responsibilities | 1.312*** (13.92) | 1.850*** (19.22) | 1.450*** (9.99) |
| Parents self-employed | 1.801*** (38.74) | 1.785*** (23.53) | 1.005 (0.17) |
| Benevolence | 0.916*** (−7.79) | 0.915*** (−4.77) | 1.011 (0.50) |
| Self-direction | 1.559*** (36.64) | 1.753*** (27.01) | 1.051* (2.20) |
| Achievement | 1.086*** (7.27) | 1.206*** (9.98) | 1.124*** (5.49) |
| Security | 0.842*** (−15.50) | 0.883*** (−6.99) | 1.046* (2.21) |
| Unemployment | 1.073* (2.06) | 1.034 (1.01) | 0.967 (−0.79) |
| Constant | 0.071*** | 0.023*** | 0.310*** |
| Country level variance | 0.130*** | 0.100*** | 0.168*** |
| Observations | 151,032 | ||
| Log lik | −39653.4 | ||
| Chi-squared | 4776.6 | ||
| AIC | 79330.8 |
Note(s): Exponentiated coefficients; t statistics in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001
Models (2) to (4) with interaction effects, all standard effects omitted
| (2a) | (2b) | (2c) | (3a) | (3a) | (3c) | (4a) | (4a) | (4c) | |
|---|---|---|---|---|---|---|---|---|---|
| Unemployment × self-direction | 0.986*** (−5.36) | 0.961* (−2.16) | 1.014 (0.69) | ||||||
| Unemployment × achievement | 0.976** (−2.65) | 0.979 (−1.31) | 1.003 (0.14) | ||||||
| Unemployment × security | 1.024* (2.43) | 0.991 (−0.56) | 0.968+ (−1.74) | ||||||
| Constant | 0.071*** | 0.023*** | 0.310*** | 0.072*** | 0.023*** | 0.310*** | 0.071*** | 0.023*** | 0.311*** |
| Country level variance comp | 0.132*** | 0.100*** | 0.168*** | 0.130*** | 0.100*** | 0.168*** | 0.131*** | 0.100*** | 0.168*** |
| Log lik | −39639.3 | −39649.9 | −39650.4 | ||||||
| Chi-squared | 4794.8 | 4782.4 | 4783.0 | ||||||
| AIC | 79304.6 | 79325.8 | 79326.8 |
Note(s): N = 151,032; exponentiated coefficients; t statistics in parentheses; all covariates centered; mixed effects logistic regressions with 120 random intercepts for participating countries and ESS waves; *p < 0.05, **p < 0.01, ***p < 0.001
Model (1) with controls, values and unemployment variables, women only
| (a) | (b) | (c) | |
|---|---|---|---|
| Solo S-E/Employees | Employers/Employees | Employers/Solo S-E | |
| Age | 1.023*** (17.63) | 1.035*** (13.11) | 1.017*** (5.25) |
| Education | 1.010 (0.45) | 1.240*** (5.04) | 1.312*** (5.43) |
| Caring responsibilities | 1.348*** (9.80) | 1.675*** (8.49) | 1.292*** (3.67) |
| Parents self-employed | 1.625*** (20.75) | 1.690*** (11.78) | 1.049 (0.94) |
| Benevolence | 0.935*** (−3.75) | 0.864*** (−4.28) | 0.937+ (−1.69) |
| Self-direction | 1.489*** (21.75) | 1.569*** (12.33) | 1.014 (0.36) |
| Achievement | 1.154*** (8.21) | 1.284*** (7.31) | 1.113** (2.85) |
| Security | 0.835*** (−10.30) | 0.902** (−3.02) | 1.079* (2.03) |
| Unemployment | 1.077+ (1.79) | 1.044 (0.97) | 0.990 (−0.19) |
| Constant | 0.042*** | 0.008*** | 0.176*** |
| Country level variance | 0.178*** | 0.136*** | 0.174*** |
| Log lik | −17578.3 | ||
| Chi-squared | 1418.7 | ||
| AIC | 35178.7 |
Note(s): N = 78,725; exponentiated coefficients; t statistics in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001
Source(s): Created by authors
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Acknowledgements
This study was supported by the Internal Grant Agency of the Faculty of Business Administration, Prague University of Economics and Business (IP300040).