Income Inequality,Entrepreneurial Activity,and National Business Systems: A Configurational Analysis

Abstract

This article explores how and why high levels of income inequality result from configurations of different types of entrepreneurial activities and elements of the institutional context in a multicountry sample. A configurational approach is used to unpack the complexities associated with how income inequality arises from different types of entrepreneurial activities embedded in different institutional contexts associated with Whitley’s national business systems dimensions. The findings from fuzzy-set qualitative comparative analysis reveal that high levels of both high-growth and necessity entrepreneurial activity are associated with income inequality in certain contexts that are characterized by distinct institutional complementarities.

Keywords

entrepreneurial activity fuzzy-set qualitative comparative analysis (fsQCA)income inequality national business systems

. . . inequality is the other side of successful entrepreneurship.

—Former U.S. Treasury Secretary Larry Summers

Over the past two decades, there has been a dramatic rise in income inequality around the world (Jaumotte, Lall, & Papageorgiou, 2013), and at the same time there have been increasing efforts by national governments to enact policies that stimulate and support entrepreneurial activity (EA; Anokhin & Wincent, 2012). Despite these trends, there is a lack of theoretical and empirical insights regarding the causal relationships between EA and income inequality (Xavier-Oliveira, Laplume, & Pathak, 2015). As the opening quote suggests, EA may be contributing to the upward trajectory of income inequality. It is this issue which the current article explores.

There are two fundamental ways that EA may widen the gap between high- and low-income individuals, thus leading to high levels of income inequality: One is through productive or what is considered high job-producing EA, whereby some individuals within a society (founders/investors) earn vast economic rewards, further concentrating wealth at the upper levels (Goedhuys & Sleuwaegen, 2010). The second is through necessity entrepreneurship, which provides no upward mobility or even reduces income in comparison with that which could have been earned as an employee (Blanchflower & Shadforth, 2007; Shane, 2008).

Empirical research focused on the United States has found positive relationships between EA and wealth inequality (Cagetti & De Nardi, 2006; Gentry & Hubbard, 2004; Quadrini, 1997). From the non-U.S. perspective, Koveos and Zhang (2012) highlight the example of China, where governmental policies have helped to increase the level of EA, yet issues of income inequality still persist throughout Chinese society.

Scholars have also suggested the reverse relationship, whereby rather than being an outcome of EA, income inequality may be a driving force for entrepreneurship (Lippmann, Davis, & Aldrich, 2005; Xavier-Oliveira et al., 2015). Yet, these same scholars acknowledge that “entrepreneurship may unintentionally lead to higher levels of economic inequality” (Lippmann et al., 2005, p. 27) underscoring the complexities associated with the relationship. In the income inequality and institutional literature, Beramendi and Rueda (2014) highlight the theoretical and methodological challenges associated with understanding how income inequality and national institutions coevolve, stressing the need to develop further insights into these mechanisms. These assessments suggest that examining how EA affects income inequality across countries is an important research topic at the intersection of business and societal outcomes (Bapuji & Neville, 2015).

In this study to account for the “complex interconnectedness of multiple elements, non-linearities, and discontinuities” (Park & El Sawy, 2013, p. 208) that characterizes how in a given institutional context income inequality may indeed be an outcome of different types of EAs, I use a configurational perspective with fuzzy-set qualitative comparative analysis (fsQCA). With such an approach, rather than evaluating institutional effects in isolation, they are considered in a holistic fashion as a system of interdependent institutions that may complement and/or compensate for one another (Maggetti, 2014). To conceptualize the institutional context in this systemic manner, Whitley’s (1999, 2000) national business systems’ (NBS) framework is used. In this way, the outcome of income inequality is viewed as one that is interdependent with the institutional context and the EA that is occurring within that context, taking into consideration the observation by Lippmann and colleagues (2005) that institutional variables “have similar effects on inequality and entrepreneurship” (p. 27). Thus, a configurational approach using fsQCA, which is “well-suited to the realities of mutual causality,” provides a means for capturing multifaceted empirical patterns that facilitate important theoretical insights on complex phenomena (Park & El Sawy, 2013, p. 208). Consequently, this study contributes several unique insights to business and society scholarship, in particular that concerned with income inequality around the world.

Specifically, although existing research has provided important insights into income inequality, it has been undertaken with the intent of isolating the net effect of income inequality on EA while holding all other variables constant (Lippmann et al., 2005; Xavier-Oliveira et al., 2015). In contrast, this study is able to conceptually and empirically model the complexity that is associated with the simultaneous interdependencies between income inequality, EA, and institutional dimensions by adopting a configurational perspective. This complexity is compatible with the underlying arguments of those who suggest that income inequality motivates EA; for example, as noted by Xavier-Oliveira and colleagues (2015, p. 1185), those at upper income levels “have strong incentives to preserve inequalities.” This study shows that one means by which this may occur is through productive EA. These findings are important as they highlight that even though EA may produce outcomes that are beneficial for countries in the aggregate, such as economic growth (Reynolds, Bosma, & Autio, 2005), in some countries not all members of society benefit equally (Marcus & Anderson, 2013; Stinchfield & Silverberg, 2016).

Relatedly, despite long recognition by institutional scholars (North, 1990) that institutions are often long lasting despite having inefficient economic outcomes, there is a distinct “lack of research on institutional configurations, which impede rather than promote economic success” (Wood & Frynas, 2006, p. 239). This study contributes new understanding of how institutional complementarities and certain types of business activities (e.g., entrepreneurship) may indeed lead to unintended negative societal consequences.

In the following sections, the relationship between EA and income inequality is first discussed and grounded in the extant literature. The subsequent section provides rationale for taking a configurational approach to unpack the complexities associated with the outcome of income inequality. After describing the methodology, the fsQCA findings are reported. Based on these findings, propositions are developed concerning underlying mechanisms of how high levels of income inequality result from configurations of different types of EAs and elements of the institutional context. The article concludes with a discussion of the unique insights arising from the findings along with their implications for income inequality research and policies.

Income Inequality and EA

There are a variety of views about the relationship between income inequality and EA. One is that income inequality may be diminished by high EA as it serves as a source of upward economic mobility for those at the lower end of the income spectrum (Quadrini, 1999). Aligned with this argument is that EA produces new jobs, as suggested by findings from Stangler and Litan (2009) who show that positive job growth in the United States between 1980 and 2005 was largely attributed to new and young entrepreneurial firms. Job-producing EA provides opportunities for unemployed and/or lower wage earners to increase their income. It is these arguments that motivate policy makers to push for initiatives that will help facilitate EA, as evidenced by these remarks about the U.S. 2012 Jumpstart Our Business Startups (JOBS) Act:

As the President said at today’s signing, “this bill is a potential game changer” for America’s entrepreneurs. For the first time, Americans will be able to go online and invest in small businesses and entrepreneurs. Not only will this help small businesses and high-growth enterprises raise capital more efficiently, but it will also allow small and young firms to expand and hire faster. (Van Roekel, 2012)

Scholars have also argued that high-income inequality leads to higher EA (Bapuji, 2015; Lippmann et al., 2005). The logic for this perspective is based on the assumption that those segments of society with greater wealth will possess the financial means to invest in entrepreneurial ventures, thus increasing the rate of EA driven by market opportunities (Lippmann et al., 2005). Another argument is that high-income inequality suggests that a greater proportion of the population resides in the lower income segment, and therefore necessity EA becomes the most expedient way for low-income individuals to support themselves (Lippmann et al., 2005).

Yet arguably, these same explanations also apply to how EA promotes income inequality. For instance, the positive returns high-income individuals receive from their investments in EA will only further increase disparities between high- and low-income segments of society. Indeed, research has shown that EA provides significant increases in income for those involved (e.g., founders, investors, and/or other employees), which leads to a growing concentration at the top of the income spectrum (Piketty & Saez, 2006). This line of reasoning is also supported by U.S. data, showing that entrepreneurs (self-employed and/or business owners) have greater earning power than nonentrepreneurs accounting for 80% of the top 1% wealthiest households (Carter, 2011). Some researchers (Kimhi, 2010; Rapoport, 2002) have suggested that motivations for increased income may motivate entrepreneurship in developing countries as well. At the organizational level, Wang, Zhao, and Thornhill (2015) drawing from social comparison theory show that up to a certain level pay differences motivate corporate entrepreneurship/innovation as employees are more likely to increase their level of organizational engagement to improve their opportunities to increase their pay. This supports arguments that it is often the hope of achieving great wealth that makes would-be entrepreneurs more willing to pursue risky ventures (Lippmann et al., 2005).

With regard to income inequality being generated by high levels of necessity EA, a major argument is that it increases the proportion of those in the lower end of the spectrum through the reduction in incomes of those who leave paid employment to pursue entrepreneurial ventures. Research has shown that compared with paid employees, median incomes are lower for individuals who pursue EA out of necessity (Hamilton, 2000). In developed countries, necessity EA is most often related to corporate and public sector downsizing (Filion, 2004). Research has shown that in these situations, individuals who leave jobs to become entrepreneurs suffer significant income losses that persist over time (Blanchflower & Shadforth, 2007). Furthermore, necessity entrepreneurship is often reflective of self-employment and/or activity in informal employment sectors (Kautonen et al., 2010; Kautonen & Palmroos, 2010). Goedhuys and Sleuwaegen (2010) find that self-employed entrepreneurs earn slightly less than employed workers and significantly less than entrepreneurs who have employees.

In two studies conducted within developing countries, the level of income inequality was found to be significantly influenced by EA, though in different directions. Yanya, Abdul-Hakim, and Abdul-Razak (2011) found that higher levels of entrepreneurship were associated with greater income inequality in Thailand, whereas research based on entrepreneurs in Ethiopia found that income inequality was reduced when entrepreneurs were from low-income households (Kimhi, 2010). In one of the few multicountry studies, Beck, Demirguc-Kunt, and Levine (2005) using a sample of 45 countries concluded that there was a lack of evidence to support a relationship between the growth of small- and medium-sized businesses (a possible proxy for EA) and income inequality. In sum, although there is widespread agreement that income inequality continues to be problematic and troublesome for many economies “explanations for this rise in inequality continue to be contested” (Neckerman & Torche, 2007, p. 337).

Part of the issue may be that the previous research has been undertaken from a net-effects perspective, which assumes that a given variable, such as EA or a national contextual variable, is capable on its own, with all other factors being held constant, to influence income inequality. Although this previous work has provided important insights, its focus on how EA and institutional factors in isolation affect levels of income inequality limits our understanding as it overlooks that the effects may be conditional on the presence or absence of one another (Makino, Isobe, & Chan, 2004). To explore how causal elements “combine rather than compete” (Maggetti, 2014, p. 802) to produce income inequality, a configurational approach is proposed.

A Configurational Approach to Understanding Income Inequality

The complex interconnected nature of how income inequality is associated with EA and institutional factors has been recognized in the related literature (Bapuji, 2015; Beramendi & Rueda, 2014; Khoury & Prasad, 2016; Lippmann et al., 2005; Xavier-Oliveira et al., 2015). Lippmann and colleagues (2005) highlight that multidisciplinary research has shown that institutional factors affecting levels of income inequality also affect levels and outcomes of EA. In a similar fashion, Beramendi and Rueda (2014) note that income inequality is both a result and a determinant of variation in institutional elements, and should be viewed as coevolving. These observations underscore the need for addressing the complexity of these relationships. Using a configurational approach provides such a means, as the outcome of income inequality is viewed as arising from simultaneous interdependencies of EA and elements of the institutional context in which it occurs.

A configurational approach facilitates a whole-systems perspective by rejecting the notion that a causal effect, such as EA, can be disaggregated from the context by considering its effect only when other factors are held constant (Ragin, 2000). Whitley’s (2000) NBS framework is one that conceptually views the institutional context in a systemic and integrated way. More specifically, institutions are not viewed as single, stand-alone entities, but as elements that cluster into complementary systems, such that their influence cannot be understood in isolation from each other.

A given NBS context is defined by four institutional dimensions: the role of the state, the prevailing financial system, the system for developing and controlling skills, and the level of societal trust (Whitley, 2000). The role of the state refers to the extent to which the government authorities intervene in economic activity, such as providing subsidies or promoting fair competitive practices as well as how tolerant it is of intermediaries’ involvement in economic activity (Whitley, 1999, 2000). Financial systems are classified as either those which rely on credit-based organizations, such as banks, or those that have a greater reliance on external capital markets (Whitley, 1999). Skill-development and control systems are related to the quality and equitable distribution of education within the economy as well as relations between firms and labor unions, including the wage-setting processes associated with centralized/decentralized bargaining (Hotho, 2014; Whitley, 1999). Societal trust is related to the reliability of the different actors involved in economic activities (Whitley, 2000). The ways in which these four dimensions differ across countries reflect the degree of economic control exerted by those involved, the extent of flexibility permitted in labor markets, and capabilities of workers, as well as trust levels between the various participants in economic activities (Whitley, 2000). All of which arguably influence how individuals benefit or not from different types of EAs, and thus levels of income inequality within a society.

A fundamental premise of the NBS framework is that different “systems of institutions develop distinctive kinds of market rules and actors, such that they manifest contrasting patterns of economic organization and generate different outcomes” (Whitley, 2003, p. 3). Systems of institutions are characterized by different types of institutional complementarities that vary across countries (Roland, 2004). Complementarity emerges when “the functionality of an institutional form is conditioned by other institutions” (Höpner, 2005, p. 331). In this way, institutions may be synergistic, mutually reinforcing one another’s effects, or they may compensate for one another’s deficiencies (Crouch, 2005). The concept of complementarity is not just about the interdependencies of contextual features but also about activities resulting from choices by actors embedded in a given context (Lane & Wood, 2009), such as different types of EAs.

Whether complementarities are associated with positive or negative societal outcomes is largely dependent upon the perspective of which parties benefit and which do not (Morgan, 2007). For example, if the state is heavily involved in coordinating competitive activity coupled with weak intermediaries, labor unions that have low bargaining power over their wages, and skill-development systems that are undeveloped, having the resources (e.g., human and social capital) to engage in EA may be largely dictated by who the state powers deem should be involved in such activity, as well as what types of firms should be supported. Individuals lacking adequate skill development and power to negotiate wages will have less access to required resources, resulting in further income reductions while those favored entrepreneurs are more likely to see their incomes increase (Beramendi & Rueda, 2014; Xavier-Oliveira et al., 2015). In sum, with a configurational approach rather than EA having a universal causal effect on levels of income inequality, there are likely to be multiple institutional combinations which together with different types of EAs are sufficient for high-income inequality to occur.

Method

Sample and Data

The country-level dataset is generated using multiple sources, starting with the 2011 Gini index values that were available from either the International Institute for Management Development’s (IMD) World Competitiveness Yearbook (IMD, 2011) or the World Bank Poverty and Inequality database (World Bank, 2011). The 2011 values are the most recent data available via IMD and while the World Bank did have more recent values for some countries, these were very limited (e.g., the 2012 database had 26 countries, and the 2013 database had only one). Values for 58 countries were obtained from IMD, and an additional 23 were collected from the World Bank source. These 81 values were then matched with the country-level data available in the Global Entrepreneurship Monitor (GEM) Global Reports, resulting in a final sample of 38 countries. According to GEM (Bosma, Coduras, Litovsky, & Seaman, 2012), the annual national datasets are collected between February and July, thus the use of the 2010 data represents 1 year prior to the Gini inequality measure.

Measurement of Outcome and Causal Variables

The Gini index, one of the most widely accepted measures of income inequality (Cowell, 2011; Lippmann et al., 2005; Xavier-Oliveira et al., 2015), is used to measure income inequality. The index is based on comparing the income distribution of a country’s population with a completely equal distribution (i.e., where every member of the population has equal income).¹ The index ranges from 0 to 100 representing total equality to total inequality, respectively; thus, the higher the number the greater the level of income inequality.

Two measures, the GEM high-growth EA and necessity EA, are used with the intent of distinguishing between what is considered productive/high job-producing and necessity EA, respectively. The GEM data collection methodology allows for capturing formal and informal EA, both of which could affect income inequality, unlike other measures (e.g., World Bank Group Entrepreneurship Survey) which only reflects EAs that legally incorporate (Klapper, Amit, Guillén, & Quesada, 2007).

The GEM high-growth EA measure is the percentage of a country’s population in the 18 to 64 age group who are engaged in early-stage entrepreneurship (i.e., start-up phase or owner-manager of a business less than 42 months), and who expect to have at least 20 employees within 5 years (Kelley, Bosma, & Amorós, 2010). As previous research has shown that individuals who employ others have the highest incomes of all entrepreneurs, the high-growth EA measure is conceptually aligned with the idea of productive EA leading to increases in income.

It is acknowledged that the measure reports expected job creation/growth, which is not the same as actual or realized growth. However, previous research indicates strong associations between expected and actual job growth, providing support for the idea that job creation by entrepreneurs does not happen by accident but as a result of aspirations for such growth (Autio, 2007; Wiklund & Shepherd, 2003). Although GEM does not typically collect data on actual high-growth entrepreneurship, in 2007 such data were collected (Autio, 2007). In this special report, actual job growth data averaged over the years 2000 to 2006 were compared against the high-growth expectations measure. As noted in the report, “The correspondence between high expectations by nascent and new entrepreneurs and actual high-growth by entrepreneurs is quite good” (Autio, 2007, p. 23). Specifically, the correlation between the high expectations measures and actual high growth was .67 (statistically significant at the level of p < .001).

The GEM necessity EA measure refers to individuals who “are pushed into entrepreneurship because all other options for work are either absent or unsatisfactory” (Bosma & Harding, 2006, p. 15). It is measured as the percentage of adults aged 18 to 64, engaged in early-stage entrepreneurship whose involvement is driven by necessity (e.g., lack of alternatives).

The choice of institutional causal conditions was based on earlier research focused on income inequality, entrepreneurship, and institutions that aligned with Whitley’s (2000) NBS theoretical framework. The framework’s four dimensions are comprised of six elements. The role of the state consists of two elements: the level of state coordination and the strength of intermediaries. The system for developing and controlling skills is also represented by two elements: the strength of education system and centralized bargaining by unions (Whitley, 2000, p. 859). The remaining dimensions, the prevailing financial system and societal trust, consist of one element each (Whitley, 2000). The actual variables chosen have been used in recent NBS research (Judge, Fainshmidt, & Brown, 2014; Witt & Redding, 2013), and are likely to be associated with both EA and income inequality. The measures are also expected to have interdependencies with one another, which is compatible with theory underlying the use of a configurational approach.

For state coordination, measures that are likely to be particularly influential for income inequality and EA are the extent to which the government shows favoritism toward special interest groups and the level of involvement the government takes in economic development and competition (Kritikos, 2014). These two measures represent an informal and formal means by which governmental/state actors attempt to coordinate or intervene in economic activity. Two indicators from the 2010-2011 World Economic Forum (WEF) Global Competitiveness Report are used²: (a) “To what extent do government officials in your country show favoritism to well-connected firms and individuals when deciding upon policies and contracts?” (Item 1.07), and (b) “To what extent does anti-monopoly policy promote competition in your country?” (Item 6.03). The two items are averaged together and inverted, such that higher values represent greater state coordination. For strength of intermediaries, Item 11.03 from the WEF Global Competitiveness Report is used. It asks, “In your country’s economy, how prevalent are well-developed and deep clusters?” (Hotho, 2014).

The financial system measure is calculated as the market capitalization of listed companies as a percentage of gross domestic product (GDP) reported in the World Bank World Development Indicators (WDI) database divided by the private credit provided by deposit money banks as a percentage of GDP, which is reported in the World Bank GDP database (Hotho, 2014). The financial system measure indicates the prevalence of external capital markets relative to credit-based financial sources in a country. Higher values of the measure, capital market-based financial system, imply greater reliance on raising funds via the capital market, and lower values indicate greater dependence on bank-type lending.

The skill-development and control system category includes the education system quality, as well as the role and strength of labor unions (Whitley, 2000). Following Witt and Redding (2013), the 2010 education subindex of the Human Development Index (HDI) of the United Nations Development Programme (UNDP) is used to capture the strength of the education system. The education subindex is made up of two equally weighted indicators: the mean years of schooling for adults 25 years and older, and the expected number of years of schooling for children of school-entering age. This measure reflects how open the education system is to all citizens as well as the quality of outcomes achieved (Witt & Redding, 2013). High-quality educational outcomes should reflect enhanced skills and capabilities that may empower individuals to engage in productive rather than necessity EA, and give them the personal resources to recognize opportunities (Bowen & De Clercq, 2008). Furthermore, the measure captures how effective a country’s education system is in ensuring an equitable distribution of learning opportunities.

To capture the strength of unions, Item 7.03 from the WEF Global Competitiveness Report is used. The item describes wage-setting processes, specifically whether there is centralized bargaining by unions or decentralized where individual firms determine the wages. As noted by Whitley (2006), decentralized bargaining processes are often indicative of unions having less power in a given economy. Other studies (Hotho, 2014; Judge et al., 2014) have also used collective bargaining indicators to capture the strength of labor unions. In countries where centralized bargaining is the norm, employees will generally have higher wage-setting power and job security, while employers (e.g., founders/managers of entrepreneurial firms) may be subject to greater costs and less productivity (Ortigueira, 2013). All of which may influence EA processes and income inequality outcomes.

For societal trust, following Hotho (2014), an indicator, trust in formal institutions, is used and is based on Item 1.04 from the WEF Global Competitiveness Report which asks, “How would you rate the level of public trust in the ethical standards of politicians in your country?” This measure arguably affects how different types of EAs manifest as well as who garners the rewards from EA.

Calibration of Variables

With fsQCA, outcome and causal variables must be calibrated, such that they have values ranging from 0 to 1, representing the degree of membership of each case (country) in a given variable (Ragin, 2008). Ideally, calibration would be based on extant theoretical knowledge, but as the literature does not provide guidance, the 85th and 15th percentiles of the sample data are used to represent these designations, respectively.³ The median value is used as the crossover point with the exception of the financial system. For that dimension, 1.0 is used as the crossover point, as this value represents financial systems where market capitalization of listed companies (capital based) equals the domestic credit provided by financial sector (credit based). The calibrate routine in the fsQCA 2.0 software is used, such that 0.95 represents full membership and 0.05 represents full nonmembership. The value of 0.5 represents the crossover point. Therefore, values close to 1 (e.g., 0.8 or 0.9) while indicating the case is not fully in, still are considered to be a “strong” member of the set or to represent “high levels.” Likewise, cases with values close to 0 (e.g., 0.2 and 0.3) are not fully out but are considered to be weak members of the set or “low levels.” Table 1 shows the calibrated fuzzy-set membership scores for each country.

Table 1.

Calibration Table for fsQCA Membership.

Country	Income inequality	High-growth EA	Necessity EA	State coordination	Strength of intermediaries	Capital-based financial system	Strength of education system	Centralized bargaining by unions	Trust in formal institutions
Argentina	0.89	0.52	0.94	0.99	0.21	0.59	0.92	0.97	0.01
Australia	0.41	0.61	0.25	0.04	0.56	0.54	0.99	0.71	0.99
Belgium	0.21	0.06	0.02	0.10	0.73	0.19	0.87	0.92	0.76
Bolivia	0.92	0.95	0.98	0.99	0.02	0.14	0.07	0.61	0.05
Brazil	0.99	0.87	0.95	0.65	0.81	0.83	0.73	0.76	0.02
Chile	0.98	0.99	0.93	0.09	0.54	0.99	0.65	0.03	0.9
China	0.83	0.99	0.97	0.34	0.9	0.22	0.02	0.15	0.97
Colombia	1.00	1.00	0.99	0.95	0.52	0.99	0.13	0.13	0.08
Costa Rica	0.95	0.13	0.88	0.37	0.32	0.03	0.08	0.39	0.86
Croatia	0.26	0.56	0.44	0.93	0.03	0.19	0.50	0.35	0.08
Ecuador	0.92	0.44	0.97	0.99	0.02	0.06	0.01	0.74	0.02
Finland	0.02	0.13	0.10	0.01	0.96	0.15	0.95	0.96	1.00
France	0.21	0.42	0.26	0.09	0.67	0.22	0.86	0.14	0.93
Germany	0.03	0.15	0.11	0.02	0.95	0.09	0.89	0.97	0.94
Greece	0.32	0.04	0.29	0.9	0.03	0.05	0.92	0.96	0.08
Guatemala	0.98	0.16	0.62	0.97	0.51	0.93	0	0.15	0.02
Ireland	0.32	0.76	0.54	0.26	0.6	0.1	0.99	0.93	0.56
Israel	0.68	0.72	0.15	0.28	0.17	0.07	0.82	0.04	0.88
Italy	0.5	0.01	0.02	0.95	0.99	0.04	0.88	0.95	0.05
Japan	0.01	0.15	0.15	0.03	0.99	0.26	0.7	0.02	0.65
Korea	0.15	0.69	0.63	0.73	0.75	0.5	0.95	0.06	0.07
Malaysia	0.92	0.02	0.04	0.3	0.92	0.86	0.03	0.08	0.89
Mexico	0.95	0.02	0.52	0.96	0.33	0.99	0.03	0.58	0.08
Montenegro	0.09	0.61	0.96	0.55	0.02	0.69	0.72	0.36	0.87
The Netherlands	0.1	0.77	0.04	0.01	0.89	0.1	0.96	0.94	0.99
Pakistan	0.06	0.02	0.82	0.88	0.46	0.52	0	0.66	0.11
Peru	0.95	0.95	0.96	0.84	0.11	1	0.06	0.05	0.03
Portugal	0.67	0.09	0.09	0.63	0.29	0.04	0.91	0.79	0.66
Romania	0.11	0.27	0.17	0.95	0.02	0.14	0.4	0.41	0.04
Russia	0.82	0.18	0.17	0.96	0.07	0.87	0.3	0.38	0.5
Slovenia	0.11	0.42	0.05	0.63	0.34	0.05	0.96	0.77	0.49
South Africa	0.99	0.95	0.75	0.46	0.53	0.99	0.06	0.96	0.14
Spain	0.36	0.03	0.11	0.48	0.56	0.09	0.95	0.9	0.3
Switzerland	0.26	0.16	0.05	0.04	0.97	0.75	0.81	0.03	1
Turkey	0.67	0.96	0.75	0.7	0.21	0.58	0.26	0.15	0.21
The United Kingdom	0.5	0.53	0.04	0.05	0.96	0.22	0.95	0.02	0.7
The United States	0.76	0.88	0.49	0.27	0.97	0.97	0.92	0.05	0.72
Uruguay	0.86	0.95	0.72	0.54	0.07	0.02	0.79	0.99	0.96

Note. fsQCA = fuzzy-set qualitative comparative analysis; EA = entrepreneurial activity.

Analytical Technique of fsQCA

With fsQCA, cases (countries) are compared on a set of causal variables or conditions (EA and the NBS dimensions) to see if their presence (high level) or absence (low level) is sufficient for the occurrence of a particular outcome (high- or low-income inequality). The test of sufficiency fails if the cause occurs but the outcome does not (Rihoux & Ragin, 2009). Even though the causal variables may not be sufficient on their own, fsQCA allows consideration of how these variables may still be causally relevant when part of a specific combination.

There are three key aspects of fsQCA which make it particularly appropriate for this study. One, it permits conjunctural causation, which suggests that rather than institutional factors acting independently, they act interdependently in combinations to produce a given outcome (Schneider & Wagemann, 2012). Two, it allows for equifinality, meaning that different combinations may lead to the same outcome (Ragin, 2000). Three, causality may be asymmetrical, such that the presence and absence of an outcome may require different causal explanations (Schneider & Wagemann, 2012). These dimensions of causal complexity are not readily detected with conventional techniques such as regression analysis, whereby to understand the causal effects of a given factor, all other effects must be assumed to be constant (Lacey & Fiss, 2009). Another advantage of fsQCA for the current study is its accommodation of relatively small samples (Fiss, 2011; Ragin, 2008).

To identify if any causal variables or configurations of variables are sufficient for the occurrence of high-income inequality, the fsQCA 2.0 software based on Boolean algebra first creates a truth table of the 2^k (k = number of variables) possible combinations. Combinations not meeting specified frequency and consistency thresholds are eliminated. The consistency threshold used is 0.80 based on the recommendations from Rihoux and Ragin (2009). A frequency of 2 is chosen to avoid any single-country configurations. The fsQCA software uses the Quine-McCluskey algorithm to simplify the causal combinations. Ragin (2008) provides details of the procedure.

Results of the Empirical Analysis

Table 2 provides descriptive statistics and correlations for the sample. Similar to other studies, the correlation between the two measures of EA is relatively high at .67 (Reynolds et al., 2005; Wong, Ho, & Autio, 2005). Correlations between income inequality, EA, and four of the institutional measures are statistically significant at the .05 level. Yet unlike methods based on the use of correlations (e.g., regression) with fsQCA, multicollinearity is not problematic.

Table 2.

Descriptive Statistics and Correlations for Sample.

Conditions/variables	M	SD	1	2	3	4	5	6	7	8
1. Income inequality—Gini	39.43	8.80
2. High-growth EA	0.75	0.55	.50*
3. Necessity EA	2.67	2.10	.61*	.67*
4. State coordination	3.12	0.76	.44*	.02	.45*
5. Strength of intermediaries	4.03	0.79	−.21	−.12	−.38*	−.63*
6. Financial system	1.00	0.79	.60*	.37*	.37*	.15	.04
7. Strength of education system	0.83	0.12	−.41*	−.05	−.44*	−.46*	.25	−.35*
8. Centralized bargaining by unions	2.47	0.98	−.13	−.19	−.16	−.00	−.05	−.40*	.30
9. Trust in formal institutions	3.03	1.03	−.37*	.01	−.36*	−.84*	.42*	−.22	.37*	.02

Note. EA = entrepreneurial activity.

p < 0.05 or lower.

Configurations Sufficient for High-Income Inequality

Tables 3 and 4 present the fsQCA results for the institutional conditions with high-growth and necessity EA, respectively. The notation format suggested by Ragin (2008) is used for distinguishing a causal variable’s presence/high level (black circle) or absence/low level (circle with a crossout). Where blanks exist, the condition may be present or absent in the configuration. The complex solution is reported, as this does not require that any counterfactuals or a priori “theory-guided hunches” about effects of the variables on the outcome (Schneider & Wagemann, 2012, p. 168).

Table 3.

Configurations for Income Inequality With High-Growth EA.

	Income inequality
Consistency threshold	0.80
C	C1	C2	C3
High-growth EA	●	●	⨂
Role of the state
State coordination	●	⨂	●
Strength of intermediaries	⨂	●	⨂
Financial system
Capital market-based	●	●	●
Skill-development and control system
Strength of education system	⨂	●	⨂
Centralized bargaining by unions	⨂	⨂	●
Societal trust
Trust in formal institutions	⨂	●	⨂
Consistency	0.98	0.95	0.84
Raw coverage	0.19	0.16	0.13
Unique coverage	0.08	0.10	0.05
Representative national economies with high membership in the configuration	PeruTurkey	ChileThe United States	BrazilMexico
Overall consistency	0.91
Overall coverage	0.33

Note. ● Presence (high level) of condition; ⨂ absence (low level) of condition; blank cells indicate that the condition can be either present or absent in that configuration. EA = entrepreneurial activity; C = configuration.

Table 4.

Configurations for Income Inequality With Necessity EA.

	Income inequality
Consistency threshold	0.80
C	C4	C5
Necessity EA	●	●
Role of the state
State coordination	●	●
Strength of intermediaries		⨂
Financial system
Capital market-based	●	●
Skill-development and control system
Strength of education system	⨂	⨂
Centralized bargaining by unions	⨂	●
Societal trust
Trust in formal institutions	⨂	⨂
Consistency	0.93	0.87
Raw coverage	0.25	0.22
Unique coverage	0.13	0.10
Representative national economies with high membership in the configuration	ColombiaGuatemala	BoliviaEcuador
Overall consistency	0.91
Overall coverage	0.35

Note. ● Presence (high level) of condition; ⨂ Absence (low level) of condition; Blank cells indicate the condition can be either present or absent in that configuration. EA = entrepreneurial activity; C = configuration.

The results indicate that none of the conditions on their own are sufficient for high-income inequality to occur (i.e., no single-condition configurations), including the EA measures. In other words, per configurational theory, high-growth and necessity EA are only sufficient conditions for high-income inequality when in combination with certain institutional conditions.

There are five configurations sufficient for the occurrence of high-income inequality, demonstrating equifinality. Configuration 1 (C1) and Configuration 2 (C2) include the presence of high-growth EA, while Configuration 3 (C3) indicates it is absent. C1 and C3 have similar institutional contexts, with the only difference being C3 has the presence of high centralized bargaining by unions and C1 has the absence of this condition. C2, which is representative of the United States and Chile, differs from the other configurations in four of the institutional dimensions, as it is characterized by having low state coordination, high strength of intermediaries, a strong education system, and high trust in formal institutions.

Comparing high-growth EA (C1-C3) and necessity EA (C4 and C5) configurations, the institutional contexts of C1 and C4 differ only with respect to the strength of intermediaries, as it is absent in C1 and can be either present or absent in C4. C3 and C5 have the same institutional conditions but differ with respect to the presence of their respective EA measures. How the institutional complementarities associated with each of these configurations shape the EA—income inequality relationship is elaborated upon in the discussion of the results section.

The analysis also reports consistency and coverage values for each configuration and the overall solution. Together these values “allow for the use of set theory and formal logic to find patterns in noisy social science data,” and are considered the main parameters of fit for making meaningful interpretations about the underlying empirical data (Schneider & Wagemann, 2012, p. 148). Consistency reflects the degree to which the cases (countries) sharing a combination of variables agree in the occurrence of a given outcome (Ragin, 2008). The coverage value indicates the empirical importance of a combination, as it shows how much of the outcome is explained by the combination (Ragin, 2008). For example, for C1 to C3 with the high-growth EA measure, the overall solution consistency of 0.91 and coverage of 0.33 indicate that the solution brings about high-income inequality 91% of the time, and accounts for 33% of membership in the outcome. The coverage values indicate that these configurations play an important role in explaining the occurrence of high-income inequality and are in line with previous studies using fsQCA to examine country-level outcomes (Hotho, 2014; Judge et al., 2014).

For each individual configuration, raw and unique coverages are also reported. Raw coverage measures the extent to which membership in the outcome condition is covered by membership in a single path, and unique coverage gives a measure of the proportion of outcome cases that are covered by a single path (Rihoux & Ragin, 2009). All configurations have unique coverage above 0 (0.04-0.20), indicating that each configuration makes a unique contribution to covering the outcome (Schneider & Wagemann, 2012).

Configurations Sufficient for High-Income Equality

As noted above, with fsQCA, causal asymmetry may exist, such that conditions leading to high-income equality are not necessarily the opposite to those for income inequality (Ragin, 2008). Therefore, identifying configurations sufficient for the inverse outcome, high-income equality, may provide further insights into the relationship between EA and income inequality.

Using the same guidelines for frequency and consistency thresholds, fsQCA results for the income equality outcome are shown in Tables 5 and 6. There are six configurations sufficient for producing the high-income equality outcome. Similar to the results for income inequality, the overall solution consistency (0.94 for both) and coverage (0.59 and 0.63) values for the configurations are also relatively high. None of these configurations indicate the presence of high level of either high-growth or necessity EA, but rather five of the six configurations have low levels of EA. Configurations 6 and 7 (C6 and C7) have similar institutional contexts with the only difference being in C6 the condition of high centralized bargaining by unions may be either present or absent, whereas in C7 it is present. Configuration 8 (C8), which is representative of Greece and Slovenia, differs from the other two configurations in three of the institutional dimensions, as it is characterized by having high state coordination, low strength of intermediaries, and low trust in formal institutions. Comparing the high-growth EA (Table 5) and necessity EA (Table 6) configurations, C6 and Configuration 9 (C9) are identical as are C8 and Configuration 10 (C10).

Table 5.

Configurations for Income Equality With High-Growth EA.

	Income equality
Consistency threshold	0.80
C	C6	C7	C8
High-growth EA	⨂		⨂
Role of the state
State coordination	⨂	⨂	●
Strength of intermediaries	●	●	⨂
Financial system
Capital market-based	⨂	⨂	⨂
Skill-development and control system
Strength of education system	●	●	●
Centralized bargaining by unions		●	●
Societal trust
Trust in formal institutions	●	●	⨂
Consistency	0.99	0.99	0.85
Raw coverage	0.40	0.36	0.23
Unique coverage	0.11	0.07	0.12
Representative national economies with high membership in the configuration	FranceJapan	FinlandGermany	GreeceSlovenia
Overall consistency	0.94
Overall coverage	0.59

Table 6.

Sufficient Configurations for Income Equality With Necessity EA.

	Income equality
Consistency threshold	0.80
C	C9	C10	C11
Necessity EA	⨂	⨂	⨂
Role of the state
State coordination	⨂	●	⨂
Strength of intermediaries	●	⨂	●
Financial system
Capital market-based	⨂	⨂
Skill-development and control system
Strength of education system	●	●	●
Centralized bargaining by unions		●	⨂
Societal trust
Trust in formal institutions	●	⨂	●
Consistency	0.97	0.97	0.88
Raw coverage	0.48	0.23	0.27
Unique coverage	0.20	0.11	0.04
Representative national economies with high membership in the configuration	BelgiumThe Netherlands	Greece Slovenia	SwitzerlandThe United Kingdom
Overall consistency	0.94
Overall coverage	0.63

Analysis of Necessary Conditions

FsQCA can also determine if EA or any of the institutional dimensions are necessary for high-income inequality or equality to occur. If the occurrence of income inequality or equality always involves a specific condition, then it is considered necessary. As the presence of a capital-based financial system occurs across all of the income inequality configurations (C1-C5), it appears that it may be a necessary condition for this outcome. Similarly, for income equality, a strong education system is present in all of the configurations. However, as there are configurations leading to income inequality and equality that were below the specified consistency and frequency thresholds, it is unlikely that these conditions actually constitute necessary conditions. To verify this, the Necessary Analysis function in the fsQCA 2.0 software is used. If the presence or absence of any given condition has a consistency value greater than 0.90, it is considered almost always necessary, as a value of 1.0 represents always necessary (Ragin, 2008). In contrast to consistency measures, there are not recommended minimums for coverage values; however, variables or combinations with coverage values close to 0 would be considered trivial (Schneider & Wagemann, 2012).

As shown in Table 7, none of the measures meet the criteria for being necessary for either income inequality or equality. For income inequality, the presence of necessity EA and high state coordination has the highest consistency values at 0.70. For income equality, a strong education system does have the highest consistency at 0.85. A credit-based financial system and the absence of necessity EA also have relatively high consistencies at 0.83 and 0.81, respectively.

Table 7.

Necessary Analysis.

	Income inequality	Income equality
Conditions	Consistency/coverage	Consistency/coverage
High-growth EA	0.66/0.75	0.47/0.45
~ High-growth EA	0.52/0.54	0.74/0.64
Necessity EA	0.70/0.82	0.40/0.38
~ Necessity EA	0.46/0.48	0.81/0.69
State coordination	0.70/0.72	0.52/0.45
~ State coordination	0.47/0.53	0.68/0.65
Strength of intermediaries	0.50/0.54	0.69/0.63
~ Strength of intermediaries	0.66/0.71	0.51/0.46
Financial system (capital based)	0.64/0.81	0.37/0.40
~ Financial system (credit based)	0.53/0.49	0.83/0.65
Strength of education system	0.48/0.45	0.85/0.66
~ Strength of education system	0.65/0.83	0.31/0.34
Centralized bargaining by unions	0.52/0.55	0.65/0.58
~ Centralized bargaining by unions	0.52/0.48	0.52/0.48
Trust in formal institutions	0.50/0.55	0.66/0.61
~ Trust in formal institutions	0.65/0.69	0.53/0.47

Note. ~ represents negation of the variable. EA = entrepreneurial activity.

Supplementary Analyses

Several supplementary analyses were conducted to check for the robustness of the findings and to provide additional insights.⁴ As a first check, consistency thresholds were varied. Reducing the consistency threshold to 0.75 does not change any of the income inequality configurations. Increasing the consistency threshold to 0.85, as would be expected, eliminates C3 (consistency of 0.84), while C4 and C5 remain unchanged. The income equality configurations did not change with the varied consistency thresholds.

Second, the data were recalibrated using 75th and 25th percentiles. With the modified calibration settings, the sufficient configurations with the high-growth EA condition were essentially the same, with the only differences being that C1 has the condition of high-growth EA as either present or absent instead of present and C3 has the centralized bargaining by unions condition as either present or absent instead of present. With respect to configurations with the necessity EA measure again, there were minimal differences. C4 has strength of intermediaries absent instead of either present or absent, and the centralized bargaining by unions condition is either present or absent instead of absent. Configuration 11 (C11) has a credit-based financial system instead of having either a capital or credit-based one, and the centralized bargaining by unions condition is either present or absent instead of absent. Consistency and coverage values remain acceptable and nontrivial with the calibration modifications.

As a third robustness check, the actual job growth data from the previously mentioned 2007 GEM special report on high-growth entrepreneurship (Autio, 2007) are used as the measure of high-growth EA. These data were not used for the main analysis as it would have resulted in a sample of only 30 countries and because the data from the WEF database only go back to 2005, thus preventing obtaining institutional data that correspond with the GEM average value for the realized/actual growth from 2000 to 2006. For the robustness check, institutional dimensions are obtained from 2006, and the income inequality values (e.g., Gini index) are obtained from 2007. C3 is replicated when using the same frequency (2) and consistency (0.80) thresholds. As this sample does not include several of the same countries as the original sample (e.g., Chile, Peru, and Turkey), it is not surprising that C1 and C2 are not replicated. However, if the frequency is dropped to 1, C2 is present and a configuration very similar to C1 occurs. The only difference is that the financial system is credit based versus being capital based in the main analysis. Considering that the robustness check is for a time period prior to the global financial crisis, this difference seems understandable, as credit availability was much greater at that time. With respect to the income equality outcome, C6 and C7 are replicated with only minor differences. In the robustness analysis, centralized bargaining is present in C6 instead of being either present or absent, and high-growth EA is present in C7 instead of being either present or absent. If the frequency is dropped to 1, C8 occurs, which is not unexpected as Greece is not part of this sample. These results support those in previous research that show a strong correlation between expected and actual job growth (Autio, 2007).

Fourth, as there is a lack of consensus regarding time lags between EA and income inequality (Carree & Thurik, 2010; Fritsch & Mueller, 2008), I ran the analysis using data for 5 years prior to the 2011 Gini outcome measure. The configurations generally show consistency over time even though the countries in the 2006 sample differ slightly due to the GEM data for that year. For the income inequality outcome, C1 was the same. For C3 and C5 rather than centralized bargaining being present, it was either present or absent. C4 had the strength of intermediaries present instead of either present or absent. C2 only appeared if a frequency of 1 was used in the analysis, probably because Chile was not present in the 2006 data. For income equality, C9 was unchanged. C6 could have either system instead of the presence of a credit-based system. C7 had high-growth EA as absent rather than either present or absent. C11 had centralized bargaining by unions as either present or absent rather than absent. C8 and C10 were not identified as sufficient configurations. Due to institutional instability in both Greece and Slovenia from 2006 to 2010, it is not unexpected to see that these configurations have not been stable. The consistency and coverage values are fairly consistent between the two time periods.

Schneider and Wagemann (2012) suggest that results can be considered robust if changes do not “lead to solution terms that are not in a subset relation to one another . . . or to differences in the parameters of fit that are large enough to warrant a meaningfully different substantive interpretation” (p. 286). In sum, these additional fsQCA analyses support the robustness of the reported findings.

As a final supplementary analysis, regression analyses with both the individual institutional dimensions and configuration scores using procedures suggested by Fiss, Sharapov, and Cronqvist (2013)⁵ are run. As noted by Fiss and colleagues (2013), “The notion of controls is usually not part of the analysis” (p. 195). However, by incorporating identified fsQCA solutions into a regression analysis it provides a way to include the additional control variable of GDP per capita. Although acknowledging that other country-level variables may also be relevant, because of the small sample it is not possible to include other control variables.

The regression models show that high-growth and necessity EA are both significantly related to income inequality, with p values ranging from .01 to .05. The control variable of GDP per capita was negatively related at the .10 significance level in the model with high-growth EA and configuration scores, while being nonsignificant in the necessity EA model. The only individual institutional dimension that exhibited a significant effect was the financial system ratio. It was positively related to income inequality in both the EA models (p < .01). All four models had relatively high adjusted R² values ranging from .42 to .55. These additional correlation-based analyses provide “broad support” for the theoretical causal relationships between EA and income inequality, and also “show the methodological differences of these approaches” (Fiss, 2011, p. 410). Furthermore, similar to arguments by García-Castro, Aguilera, and Ariño (2013), the absence of significant coefficients for the institutional dimensions when considered independently supports the need to study the influence of institutions using a configurational approach.

Discussion and Implications of the Results

The findings indicate that there are a number of sufficient combinations of different types of EAs and institutional conditions that lead to high levels of income inequality. This is consistent with a configurational approach and the idea of institutional complementarities, as no single measure of EA or the institutional dimensions was sufficient for the occurrence of either high-income inequality or equality. Four of the configurations sufficient for high-income inequality had high levels of either high-growth or necessity EA; while five of the six configurations sufficient for high-income equality had low levels of EA. Overall, there is evidence that neither high-growth nor necessity EA has universal societal benefits, suggesting that high levels of EA facilitate either increases for those at upper levels of the income spectrum, decreases for those at the lower end, or both. By examining patterns across configurations, insights emerge about how and why in certain institutional contexts income inequality arises from different types of EAs.

Among the five configurations sufficient for income inequality, there are three distinct institutional contexts (i.e., different institutional conditions) in which different levels and/or types of EAs occur: one shared by C1 and C4, one for C2, and one shared by C3 and C5. The only institutional condition present in all five high-income inequality configurations is that of a capital-based financial system. In contrast, none of the income equality configurations have this type of financial system, although in C11, the financial system may be either capital- or credit based. Credit-based financial systems are thought to represent “patient” capital (Whitley, 1999), while in capital/equity-based financial systems there is pressure for quick returns from EA. This is likely to amplify large gains for some involved in EA while facilitating losses for others. This reasoning aligns with research, suggesting that entrepreneurship motivated by self-interested desire to maximize private gain may have harmful societal outcomes (Casson & Pavelin, 2016). Indeed, one such outcome may be the promotion of income inequality within the society.

One means by which returns can be enhanced is to drive down labor costs, which would be of particular importance with high-growth EA. Thus, profit maximization pressures present in capital-based systems markets have a complementary relationship with institutional forces that lower labor costs. Indeed, the two income inequality configurations with high levels of high-growth EA (C1 and C2) support this mechanism as they also lack centralized bargaining, which provides opportunities for cost structures that would enhance returns to founders/investors while driving wages down for employees (Beramendi & Rueda, 2014). In addition, in C1, the weak education system provides a less skilled workforce that can be paid low wages providing founders/investors opportunities to further their profitable returns. As noted previously, entrepreneurs employing others have significantly higher incomes than entrepreneurs who do not (Goedhuys & Sleuwaegen, 2010), suggesting that high levels of high-growth EA in a country have the potential of creating more high-income individuals. Hence, these interpretations lead to the following theoretical proposition:

Proposition 1: High-income inequality resulting from increasing incomes at upper levels and decreasing incomes at lower levels is more likely with high levels of high-growth EA occurring in contexts with capital-based financial systems and institutional conditions that promote reductions in wages (C1 and C2).

Configuration C3 shows that having low levels of high-growth EA may hinder upward mobility for low-income individuals. The institutional context of C3 differs from C1 only with respect to having high levels of centralized wage bargaining. Despite the possibility that workers may have greater power to affect their incomes, the lack of job opportunities associated with the lack of high-growth EA may decrease labor mobility (Lippmann et al., 2005), which would reduce the negotiating power of labor groups. Also in C3, there are mutually reinforcing institutional conditions that are likely to deter increases in income for those at the lower end. In this configuration, because business activity is so dependent on the state, those individuals who are already wealthy will be motivated to have close relationships with state actors (Whitley, 2000) enhancing their ability to maintain high levels of wealth. Due to the lack of strong intermediaries, dependence on the state as an economic actor is further enhanced. Those actors favored or selected by the government to gain from state coordination will also benefit from having access to educational systems that others are denied admission to.

In the income equality configurations C8 and C10, there are also low levels of high-growth EA, high state coordination, low strength of intermediaries, high levels of centralized bargaining by unions, and low trust in formal institutions. Yet unlike C3, these configurations have a strong education system and a credit-based financial system. This suggests that these two conditions play an important role in preventing or diminishing income inequality. Indeed, these two conditions based on the necessary analysis come the closest to being considered necessary for explaining income equality. Not being able to readily access the education system may disadvantage lower income actors with respect to being able to obtain and leverage the needed resources for high-growth EA outcomes (Lippmann et al., 2005; Xavier-Oliveira et al., 2015). This will only be reinforced by the lack of trust that their business transactions will be free from opportunism by government agents or firms favored by the state.

The stagnation of those in the lower income brackets is also relevant to C4, where there are high levels of necessity EA. C4 differs institutionally from C3 by having a lack of centralized bargaining, which reinforces the inability to increase one’s income. In this situation, the issues of not having access to educational resources and human capital development, which are key to being employed and higher wages (Xavier-Oliveira et al., 2015) are likely to be particularly salient. Again, with the lack of centralized wage bargaining, those who already have high incomes are likely to be further advantaged while providing no gains to those in the lower income ranks, who have necessity motives for engaging in EA (Beramendi & Rueda, 2014). In countries represented by these configurations, necessity EA may reflect activity in informal sectors, whereby it is providing those involved with day-to-day survival not any appreciable increase in income levels (Lippmann et al., 2005).

Arguably, the same mechanism preventing opportunities for upward income mobility could be at play in C5, which has an institutional context identical to C3, but differs by having high levels of necessity EA. As noted previously, necessity entrepreneurship may indicate that individuals are self-employed, and research has shown that these types of entrepreneurs tend to earn less than employed workers (Goedhuys & Sleuwaegen, 2010). Thus, high-income inequality could be due to those involved in necessity EA having markedly lower incomes compared with what they would earn in paid employment (Hamilton, 2000). Furthermore, C5 has the presence of high centralized bargaining by unions, which would generally lead to higher wages for employees, thus making it plausible that one’s income as a necessity entrepreneur would be less than as an employee. These patterns suggest the following propositions:

Proposition 2a: High-income inequality resulting from decreasing incomes or lack of upward mobility at lower income levels is more likely with low levels of high-growth EA and high levels of necessity EA in contexts with high state coordination, weak intermediaries, capital-based financial systems, weak education systems, high levels of centralized bargaining by unions, and low levels of societal trust (C3 and C5).

Proposition 2b: High-income inequality resulting primarily from lack of upward mobility at lower income levels is more likely with high levels of necessity EA in contexts with high state coordination, weak or strong intermediaries, capital-based financial systems, weak education systems, low levels of centralized bargaining by unions, and low levels of societal trust (C4).

The fsQCA results also highlight instances of institutional complementarities, including those which reinforce as well as compensate or substitute for one another. Across both the income inequality and equality configurations whenever trust in formal institutions and strength of intermediaries are low (high), state coordination is high (low). Thus, trust in formal institutions and strength of intermediaries complement each other, while serving as substitutes for state coordination. When the state is actively involved in coordinating business activities, it is more likely to discourage relationships with intermediaries to maintain its level of control and influence (Whitley, 2000). Also, the state’s powerful position may indeed promote a climate where corruption is viewed as part of the business landscape (Witt & Redding, 2013), and previous research has indicated that societal trust is negatively correlated with corruption (Uslaner, 2004).

There is also evidence suggesting that the predominant type of financial system and the quality of the education system in a country complement or mutually reinforce one another with respect to income inequality and equality. More specifically, capital-based financial systems and weak education systems occur together in four of the five income inequality configurations, whereas five of the six income equality configurations have credit-based financial systems along with strong education systems. These results are compatible with research proposing that wealthier individuals are motivated to sustain inequalities by promoting systems that allow for profit enhancement (Seery & Arendar, 2014), limit access to education (Seery & Arendar, 2014), and make accessing capital more difficult for new entrants (Acemoglu & Johnson, 2005). These consistent patterns of complementarities emphasize that institutional elements are interdependent and work together in complex but systematic ways to influence business activities and the resulting societal outcomes.

Implications of the Findings

The research presented in this article enhances understanding of why income inequality varies across countries and how it is related to different types of EAs. As noted by scholars, “the question about global inequality trends is still open . . . [with] little consensus about the explanations for these patterns” (Neckerman & Torche, 2007, pp. 347-348), and “large gaps exist in our understanding of how business activities contribute to economic inequality” (Bapuji, 2015, p. 1070). This study not only helps address an important shortcoming in the literature but also highlights the salience of using a configurational approach to theorize about the complexity of these relationships.

The findings offer novel insights to the literature examining institutions and income inequality by revealing different types of interdependencies between institutions representing institutional complementarities. These complementarities represent institutional elements mutually reinforcing one another not only in ways that lead to enhanced functionality but also in ways that lead to ineffective outcomes. More specifically, in certain institutional configurations, high levels of EA (both high growth and necessity) lead to high levels of income inequality, illustrating how certain institutional complementarities may be predatory, allowing those in power to favor some economic agents to the detriment of others (Roland, 2004). Yet other institutional complementarities (e.g., credit-based financial systems and strong education systems) may be developmental, providing a “helping hand” to private actors, so that they have fair and equal access to institutional infrastructure (Roland, 2004). This is important as prior research has predominantly focused on how institutional complementarities are related to economic advantages rather than disadvantages (Wood & Frynas, 2006). By showing that effects of institutional complementarities may produce different outcomes for different economic actors (the haves and have-nots), it is demonstrated very clearly that effects of institutional complementarities are more complex and nuanced than was previously thought. Furthermore, using Whitley’s (2000) NBS framework to characterize institutional contexts highlights how NBS theory can be leveraged to explain variations across countries with respect to the important societal outcome of income inequality.

The findings also have implications for policy makers wishing to generate greater income equality within their countries. First and foremost, it underscores the critical importance of viewing the important economic activity of entrepreneurship as one that is interdependent with institutional complementarities that are present within a given nation state. Indeed, the findings are at odds with the assumption of positive outcomes from many countries’ policy recommendations for promoting EA. Therefore, policy makers in these countries may consider taking actions that develop complementarities more in line with those configurations that lead to income equality. Although none of the income equality configurations exhibited the high-growth EA condition, it is possible that by developing some of the complementarities observed in most of these configurations, countries with high-growth and even necessity EA could promote the type of entrepreneurship that provides greater chances for increasing incomes of those at the lower income spectrum. For example, the presence of a strong education system was close to being a necessary condition for income equality, thus strengthening educational involvement for greater proportions of the population may provide would-be entrepreneurs with greater skills and relationships to not only identify opportunities but also to make them successful for themselves and their community members.

Limitations and Future Research Opportunities

With fsQCA, the number of included variables is limited as each added condition increases the complexity exponentially, and while the chosen institutional conditions have been used in prior research, it is recognized that others may also be relevant. As the institutional conditions were based on Whitley’s (2000) NBS framework, using other models such as Scott’s (2008) regulative, normative, and cultural-cognitive framework may identify other institutional complementarities important for income inequality. Also, national cultural systems, which have been shown to influence national levels of entrepreneurship (Lewellyn & Muller-Kahle, 2016), are likely to play a role in income inequality outcomes, and thus should be considered in future research.

Also, as established businesses tend to have more developed resource endowments and external connections which convey advantages over newer business entities (Lippmann et al., 2005), studying how these firms’ performance may configure with their institutional context is suggested as an interesting avenue for future research. As fsQCA is able to accommodate multilevel analysis (Crilly, 2013), future studies may also benefit from employing it to capture how incomes of individual societal members are affected by EA. Relatedly, it would be worthwhile to conduct in-depth qualitative exploration of how entrepreneurship around the world affects income inequality.

Conclusion

Increasing income inequality around the world has garnered heightened attention from academic scholars, policy makers, and the general public. At the same time, policy makers in numerous countries advocate for increasing EAs among their citizens. The goal of this study was to provide a configurational and therefore more nuanced understanding of how high-growth and necessity EA affect income inequality. This configurational approach with fsQCA fosters examining the simultaneous interdependencies of EA and the institutional context in which those who instigate such actions are embedded. The findings show that high-growth and/or necessity EA when occurring in a given context with particular institutional complementarities is not sufficient to prevent income inequality; rather in some cases, it is helping to drive the widening income gap between different societal actors. Consequently, it is hoped that the conceptual framework and findings provide scholars and policy makers a better understanding of how high levels of income inequality are linked to EA.

Footnotes

Acknowledgements

The author would like to thank Dr. Jane Lu, associate editor, as well as the anonymous reviewers who provided helpful comments throughout the review process. She is also grateful to insightful feedback from participants at 75th Annual Meeting of the Academy of Management in Vancouver, British Columbia, where an earlier version of the article was presented.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Krista B. Lewellyn (PhD, Old Dominion University) is assistant professor of management at University of Wyoming. Her research focuses on how contextual elements at multiple levels of analysis influence strategic-type decisions individuals make for themselves and on behalf of their organizations. She has published her work in Academy of Management Learning & Education, Corporate Governance: An International Review, International Business Review, Journal of International Management, Journal of Management and Governance, Organization Studies, and Strategic Management Journal.

References

Acemoglu

Johnson

(2005). Unbundling institutions. Journal of Political Economy, 113, 949-995.

Anokhin

Wincent

(2012). Start-up rates and innovation: A cross-country examination. Journal of International Business Studies, 43, 41-60.

Autio

(2007). 2007 global report on high-growth entrepreneurship (Global Entrepreneurship Monitor). Wellesley, MA: Babson College. Retrieved from http://www.babson.edu/Academics/centers/blank-center/global-research/gem/Documents/gem-2007-global-high-growth-entrepreneurship.pdf

Bapuji

(2015). Individuals, interactions and institutions: How economic inequality affects organizations. Human Relations, 68, 1059-1083.

Bapuji

Neville

(2015). Income inequality ignored? An agenda for business and strategic organization. Strategic Organization, 13, 233-246.

Beck

Demirguc-Kunt

Levine

(2005). SMEs, growth, and poverty: Cross-country evidence. Journal of Economic Growth, 10, 199-229.

Beramendi

Rueda

(2014). Inequality and institutions: The case of economic coordination. Annual Review of Political Science, 17, 251-271.

Blanchflower

D. G.

Shadforth

(2007). Entrepreneurship in the UK. Foundations and Trends in Entrepreneurship, 3, 257-264.

Bosma

Coduras

Litovsky

Seaman

(2012). GEM manual: A report on the design, data and quality control of the global entrepreneurship monitor (Technical report). Global Entrepreneurship Monitor. Retrieved from http://www.espae.espol.edu.ec/images/stories/GEM_Manual_Quality_Control.pdf

10.

Bosma

Harding

(2006). Global entrepreneurship monitor: GEM 2006 summary results. Wellesley, MA: Babson College.

11.

Bowen

H. P.

De Clercq

(2008). Institutional context and the allocation of entrepreneurial effort. Journal of International Business Studies, 39, 747-767.

12.

Cagetti

De Nardi

(2006). Entrepreneurship, frictions, and wealth. Journal of Political Economy, 114, 835-870.

13.

Carree

M. A.

Thurik

A. R.

(2010). The impact of entrepreneurship on economic growth. In Acs

Z. J.

Audretsch

D. B.

(Eds.), Handbook of entrepreneurship research (pp. 437-471). New York, NY: Springer.

14.

Carter

(2011). The rewards of entrepreneurship: Exploring the incomes, wealth, and economic well-being of entrepreneurial households. Entrepreneurship Theory and Practice, 35, 39-55.

15.

Casson

M. C.

Pavelin

(2016). The social performance and responsibilities of entrepreneurship. Business & Society, 55, 11-13.

16.

Cowell

(2011). Measuring inequality. Oxford, UK: Oxford University Press.

17.

Crilly

(2013). Corporate social responsibility: A multilevel explanation of why managers do good. Configurational Theory and Methods in Organizational Research, 38, 181-204.

18.

Crouch

(2005). Models of capitalism. New Political Economy, 10, 439-456.

19.

Filion

(2004). Two types of self-employed in Canada. In Dana

(Ed.), Handbook of research on international entrepreneurship (pp. 308-329). Cheltenham, UK: Edward Elgar.

20.

Fiss

P. C.

(2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54, 393-420.

21.

Fiss

P. C.

Sharapov

Cronqvist

(2013). Opposites attract? Opportunities and challenges for integrating large-N QCA and econometric analysis. Political Research Quarterly, 66, 191-197.

22.

Fritsch

Mueller

(2008). The effect of new business formation on regional development over time: The case of Germany. Small Business Economics, 30, 15-29.

23.

García-Castro

Aguilera

R. V.

Ariño

M. A.

(2013). Bundles of firm corporate governance practices: A fuzzy set analysis. Corporate Governance: An International Review, 21, 390-407.

24.

García-Castro

Francoeur

(2016). When more is not better: Complementarities, costs and contingencies in stakeholder management. Strategic Management Journal, 37, 406-424. doi:10.1002/smj.2341

25.

Gentry

W. M.

Hubbard

R. G.

(2004). Entrepreneurship and household saving. Advances in Economic Analysis & Policy, 4, 1053. doi:10.2202/1538-0637.1053

26.

Goedhuys

Sleuwaegen

(2010). High-growth entrepreneurial firms in Africa: A quantile regression approach. Small Business Economics, 34, 31-51.

27.

Hamilton

B. H.

(2000). Does entrepreneurship pay? An empirical analysis of the returns to self employment. Journal of Political Economy, 108, 604-631.

28.

Höpner

(2005). What connects industrial relations and corporate governance? Explaining institutional complementarity. Socio-Economic Review, 3, 331-358.

29.

Hotho

(2014). From typology to taxonomy: A configurational analysis of national business systems and their explanatory power. Organization Studies, 35, 671-702.

30.

International Institute for Management Development. (2011). IMD world competitiveness yearbook 2011. Lausanne, Switzerland: Author.

31.

Jaumotte

Lall

Papageorgiou

(2013). Rising income inequality: Technology, or trade and financial globalization. IMF Economic Review, 61, 271-309.

32.

Judge

W. Q.

Fainshmidt

Brown

J. L.

III . (2014). Which model of capitalism best delivers both wealth and equality? Journal of International Business Studies, 45, 363-386.

33.

Kautonen

Down

Welter

Vainio

Palmroos

Althoff

Kolb

(2010). “Involuntary self-employment” as a public policy issue: A cross-country European review. International Journal of Entrepreneurial Behavior & Research, 16, 112-129.

34.

Kautonen

Palmroos

(2010). The impact of a necessity-based start-up on subsequent entrepreneurial satisfaction. International Entrepreneurship and Management Journal, 6, 285-300.

35.

Kelley

Bosma

Amorós

J. E.

(2010). Global entrepreneurship monitor: 2010 global report. Wellesley, MA: Babson College and Universidad del Desarrollo. Retrieved from http://entreprenorskapsforum.se/wp-content/uploads/2011/02/GEM-2010-Global-Report.pdf

36.

Khoury

Prasad

(2016). entrepreneurship amid concurrent institutional constraints in less developed countries. Business & Society, 55, 934-969.

37.

Kimhi

(2010). Entrepreneurship and income inequality in southern Ethiopia. Small Business Economics, 34, 81-91.

38.

Klapper

Amit

Guillén

Quesada

J. M.

(2007). Entrepreneurship and firm formation across countries (World Bank, Policy Research Working Paper Series No. 4313). Retrieved from http://elibrary.worldbank.org/doi/abs/10.1596/1813-9450-4313

39.

Koveos

Zhang

(2012). Regional inequality and poverty in pre- and post-reform China: Can entrepreneurship make a difference? Thunderbird International Business Review, 54, 59-72.

40.

Kritikos

A. S.

(2014). Greece needs a strategy for its transition to an innovation economy. DIW Economic Bulletin, 4(10), 3-10.

41.

Lacey

Fiss

(2009). Comparative organizational analysis across multiple levels: A set-theoretic approach. In King

B. G.

Felin

Whetten

D. A.

(Eds.), Research in the Sociology of Organizations, Vol. 26: Studying differences between organizations: Comparative approaches to organizational research (pp. 91-116). Bingley, UK: Emerald Group.

42.

Lane

Wood

(2009). Capitalist diversity and diversity within capitalism. Economy and Society, 38, 531-551.

43.

Lewellyn

K. B.

Muller-Kahle

(2016). A configurational approach to understanding gender differences in entrepreneurial activity: A fuzzy set analysis of 40 countries. International Entrepreneurship and Management Journal, 12, 765-790.

44.

Lippmann

Davis

Aldrich

H. E.

(2005). Entrepreneurship and inequality. In Keister

L. A.

(Ed.), Research in the Sociology of Work, Vol. 15: Entrepreneurship (pp. 3-31). Bingley, UK: Emerald Group.

45.

Maggetti

(2014). Promoting corporate responsibility in private banking necessary and sufficient conditions for joining the Wolfsberg initiative against money laundering. Business & Society, 53, 787-819.

46.

Makino

Isobe

Chan

C. M.

(2004). Does country matter? Strategic Management Journal, 25, 1027-1043.

47.

Marcus

A. A.

Anderson

M. H.

(2013). Commitment to an emerging organizational field: An enactment theory. Business & Society, 52, 181-212.

48.

Morgan

(2007). National business systems research: Progress and prospects. Scandinavian Journal of Management, 23, 127-145.

49.

Neckerman

K. M.

Torche

(2007). Inequality: Causes and consequences. Annual Review of Sociology, 33, 335-357.

50.

North

(1990). Institutions, institutional change and economic performance. Cambridge, UK: Cambridge University Press.

51.

Ortigueira

(2013). The rise and fall of centralized wage bargaining. Scandinavian Journal of Economics, 115, 825-855.

52.

Park

El Sawy

(2013). The value of configurational approaches for studying digital business strategy. In Cambre

Fiss

Marx

(Eds.), Research in the sociology of organizations: Configurational theory and methods in organizational research (pp. 205-224). Bingley, UK: Emerald.

53.

Piketty

Saez

(2006). The evolution of top incomes: A historical and international perspective. American Economic Association Papers and Proceedings, 96, 200-205.

54.

Quadrini

(1997). Entrepreneurship, saving and social mobility (Discussion Paper No. 116). Minneapolis, MN: Institute of Empirical Macroeconomics, Federal Reserve Bank.

55.

Quadrini

(1999). The importance of entrepreneurship for wealth concentration and mobility. Review of Income and Wealth, 45, 1-19.

56.

Ragin

(2000). Fuzzy set social science. Chicago, IL: University of Chicago Press.

57.

Ragin

(2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago, IL: University of Chicago Press.

58.

Rapoport

(2002). Migration, credit constraints and self-employment: A simple model of occupational choice, inequality and growth. Economics Bulletin, 15(7), 1-5.

59.

Reynolds

Bosma

Autio

(2005). Global Entrepreneurship Monitor: Data collection design and implementation 1998–2003. Small Business Economics, 24, 205-231.

60.

Rihoux

Ragin

(2009). Configurational comparative methods: Qualitative comparative analysis and related techniques. Thousand Oaks, CA: Sage.

61.

Roland

(2004). Understanding institutional change: Fast-moving and slow-moving institutions. Studies in Comparative International Development, 38, 109-131.

62.

Schneider

Wagemann

(2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. New York, NY: Cambridge University Press.

63.

Scott

(2008). Institutions and organizations: Ideas and interests. Thousand Oaks, CA: Sage.

64.

Seery

Arendar

(2014). Even it up: Time to end extreme inequality. Oxford, UK: Oxfam International.

65.

Shane

(2008). The illusions of entrepreneurship: The costly myths that entrepreneurs, investors and policymakers live by. New Haven, CT: Yale University Press.

66.

Stangler

Litan

(2009). Where will the jobs come from? Kansas City, MO: Ewing Marion Kauffman.

67.

Stinchfield

B. T.

Silverberg

R. T.

(2016). Empirical evidence that high levels of entrepreneurial attitudes dampen the level of civil disorder. Business & Society, 55, 676-705.

68.

Uslaner

E. M.

(2004). Trust and corruption. In Lambsdorff

J. G.

Taube

Schramm

(Eds.), The new institutional economics of corruption (pp. 76-86). New York, NY: Routledge.

69.

Van Roekel

. (2012, April 5). The JOBS Act: Encouraging startups, supporting small businesses. The White House. https://obamawhitehouse.archives.gov/blog/2012/04/05/jobs-act-encouraging-startups-supporting-small-businesses

70.

Wang

Zhao

Thornhill

(2015). Pay dispersion and organizational innovation: The mediation effects of employee participation and voluntary turnover. Human Relations, 68, 1155-1181.

71.

Whitley

(1999). Divergent capitalisms: The social structuring and change of business systems. Oxford, UK: Oxford University Press.

72.

Whitley

(2000). The institutional structuring of innovation strategies: Business systems, firm types and patterns of technical change in different market economies. Organization Studies, 21, 855-887.

73.

Whitley

(2003, May 8-11). How national are business systems? The role of different state types and complementary institutions in constructing homogenous systems of economic coordination and control. Proceedings of the Workshop on National Business Systems in the New Global Context, Oslo, Norway.

74.

Whitley

(2006). Understanding differences: Searching for the social processes that construct and reproduce variety in science and economic organization. Organization Studies, 27, 1153-1177.

75.

Wiklund

Shepherd

(2003). Aspiring for, and achieving growth: The moderating role of resources and opportunities. Journal of Management Studies, 40, 1919-1941.

76.

Witt

Redding

(2013). Asian business systems: Institutional comparison, clusters and implications for varieties of capitalism and business systems theory. Socio-Economic Review, 11, 265-300.

77.

Wong

P. K.

Y. P.

Autio

(2005). Entrepreneurship, innovation and economic growth: Evidence from GEM data. Small Business Economics, 24, 335-350.

78.

Wood

Frynas

J. G.

(2006). The institutional basis of economic failure: Anatomy of the segmented business system. Socio-Economic Review, 4, 239-277.

79.

World Bank. (2011). Retrieved from http://databank.worldbank.org/data/reports.aspx?source=2&series=SI.POV.GINI&country=

80.

World Bank. (2015). Measuring inequality. Retrieved from http://go.worldbank.org/3SLYUTVY00

81.

Xavier-Oliveira

Laplume

A. O.

Pathak

(2015). What motivates entrepreneurial entry under economic inequality? The role of human and financial capital. Human Relations, 68, 1183-1207.

82.

Yanya

Abdul-Hakim

Abdul-Razak

(2011). Does higher entrepreneurial activity correlate with higher level of growth? Evidence from Thailand. Paper presented at Society of Interdisciplinary Business Research (SIPR) 2011 Conference on Interdisciplinary Business Research. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1869352##