Regulatory efficiency, formal education, and opportunity-motivated versus necessity-motivated entrepreneurship

It is well established that formal education enhances human capital, equipping entrepreneurs with competencies in communication, decision-making and opportunity evaluation that are instrumental to entrepreneurial decisions (Becker, 1964; Bosma et al., 2004; Unger et al., 2011). Education also bolsters self-confidence and ambition, which are drivers of entrepreneurship (Jiménez et al., 2020; Hessels et al., 2020; Ahn and Winters, 2022). Yet, despite the extensive research on these effects, far less attention has been paid to how formal education moderates the influence of institutional environments on entrepreneurial motivations (Amorós and Bosma, 2014; Angulo-Guerrero et al., 2017). Moreover, past research demonstrates tensions and inconsistencies related to this moderating influence, as some studies suggest education amplifies the positive effects of supportive institutions on opportunity-motivated entrepreneurship while reducing necessity-driven entry, whereas others report limited, context-specific, or conflicting moderation effects that remain insufficiently explored, reflecting the broader conceptual diversity of entrepreneurship research (Murphy et al., 2006).

In this paper, we address this theoretical puzzle by examining how institutional contexts and formal education jointly influence why entrepreneurs pursue opportunity-motivated versus necessity-motivated ventures. Opportunity-motivated entrepreneurship is driven by perceived profit and innovation prospects, whereas necessity-motivated entrepreneurship stems from limited acceptable employment alternatives (Block and Wagner, 2010; Hessels et al., 2008; Reynolds et al., 2005). The literature shows that stronger formal institutions seem to favor opportunity-motivated over necessity-motivated entrepreneurship, with this association moderated by the distribution of formal education (O'Donnell et al., 2024). Complementary research links education systems (e.g. university-industry partnerships, policy architectures) to entrepreneurial capability formation (Amorós et al., 2019; Capelleras et al., 2019; Gibson, 2014). Moreover, multiple studies highlight how specific regulatory settings and social protections differentially influence the opportunity or necessity motives of entrepreneurs (Stenholm et al., 2013; Block and Wagner, 2010).

Altogether, these research streams show that institutional conditions and formal education jointly relate to the composition of entrepreneurial motivations. However, the precise mechanisms of how the specific regulatory dimensions of institutional conditions interact with education-based human capital are theoretically underdeveloped and empirically unexplored. We address this shortfall by examining these interactions and delineating the implications for opportunity-driven versus necessity-driven entrepreneurial motivations.

Prior research has established that formal institutions influence entrepreneurial motivations (Autio et al., 2013; Chowdhury et al., 2019), that human capital is linked to opportunity-motivated entrepreneurship (van der Zwan et al., 2016) and that individual characteristics relate to entrepreneurial motivations and decisions (Boudreaux and Nikolaev, 2019; Parker, 2018). However, these studies rely predominantly on composite institutional indices that obscure variation across specific regulatory dimensions, while treating formal education as a control variable rather than a potential moderator (Estrin et al., 2013; Valdez and Richardson, 2013; Aparicio et al., 2016). Consequently, the literature has yet to systematically examine how distinct dimensions of regulatory efficiency, such as business, labor and monetary freedom, relate differently to opportunity-motivated versus necessity-motivated entrepreneurship and whether formal education level influences those relations. Thus, we ask: Do business, labor and monetary freedom relate differently to opportunity-motivated and necessity-motivated entrepreneurship and does formal education level moderate these effects?

Adopting a microfoundations perspective, we posit that formal education and institutions relate to entrepreneurial motivations through cognitive and perceptual processing, resource access and opportunity matching/selection processes (Felin et al., 2015; Shepherd et al., 2015). While these underlying mechanisms are not directly tested in our empirical design or in past research, they are a guiding logic for hypothesis development. Building on prior multi-level entrepreneurship research that links individual-level motivations with country-level institutional conditions (Chowdhury et al., 2019; Ferreira et al., 2023), we extend this work by theorizing and testing interaction effects between specific dimensions of regulatory efficiency and formal education jointly shaping entrepreneurial motivations, rather than these as separate, additive influences. To do so, we draw on Global Entrepreneurship Monitor (GEM) individual-level microdata from 2006 to 2017 across 86 countries, merged with country-year indicators from complementary sources. We replicate our findings using two additional datasets to enhance robustness. This multi-level approach enables us to estimate cross-country associations while clearly distinguishing individual-level entrepreneurial motivations from macro-level institutional conditions.

Entrepreneurship research indicates that formal education shapes how individuals perceive and respond to institutional signals, access resources (e.g. finance and talent) and engage in matching/selection processes under varying regulatory conditions (Kuckertz et al., 2016; Wright et al., 2007; van Praag et al., 2013). Drawing on institutional theory (North, 1990; Williamson, 1996) and entrepreneurship research (Boudreaux and Nikolaev, 2019), we conceptualize regulatory efficiency in this paper along three theoretically central dimensions: business freedom (reducing entry, operating and exit frictions), labor freedom (enhancing hiring/firing flexibility and wage/contract autonomy) and monetary freedom (ensuring price stability and low inflation). These three dimensions are vital to entrepreneurship because they directly govern the most proximate constraints entrepreneurs face: product-market barriers (business freedom), talent mobilization (labor freedom) and financing/predictability risks (monetary freedom) (McMullen et al., 2008; Darnihamedani and Terjesen, 2022). As such, they are likely to exert differential effects on opportunity-motivated and necessity-motivated entrepreneurship. In contrast, broader institutional constructs such as rule of law, corruption or welfare regimes serve as important background conditions but remain more distal to core entrepreneurial decisions like hiring, pricing and market entry (Williamson, 1996; Boudreaux and Nikolaev, 2019). In the following section, we review the theory and evidence on these constructs and develop hypotheses regarding their interactions with formal education in entrepreneurial contexts.

Entrepreneurship research has extensively examined the dimensions of regulatory efficiency in shaping entrepreneurial activity primarily through its effects on transaction costs and market uncertainty (Boudreaux and Nikolaev, 2019; Chowdhury et al., 2019; Shleifer, 2010; Kuckertz et al., 2016). The specific dimensions of regulatory efficiency most commonly examined in this literature include business freedom, labor freedom and monetary freedom (Bennett, 2021; Boudreaux et al., 2019; Stenholm et al., 2013). Institutional theory provides a key lens for understanding these dimensional influences, conceptualizing formal regulations as governance structures that reduce opportunism and align incentives in economic exchange (North, 1990; Williamson, 2000). Specifically, research highlights how business freedom reduces frictions in product-market entry, operation and exit. Moreover, labor freedom eases constraints on hiring, firing, contracts and wages. Finally, monetary freedom promotes price stability and predictable financing (Darnihamedani and Terjesen, 2022). This approach contrasts with studies relying on aggregate institutional indices, which often obscure more specific variances and their differential implications for entrepreneurial decisions and activities (Estrin et al., 2013).

Evidence suggests that these macro-regulatory dimensions operate via distinct channels with implications for motivational types. Namely, business freedom applies to market entry and competition, labor freedom pertains to resource and talent mobilization and monetary freedom is linked to economic predictability (McMullen et al., 2008). These regulatory dimensions can thus shape entrepreneurial motivation in different ways (Boudreaux and Nikolaev, 2019). For example, stronger regulatory efficiency in these areas tends to favor opportunity-motivated entrepreneurship, as reduced constraints enable productive rather than subsistence-oriented actions (Angulo-Guererro et al., 2017).

At the micro level, formal education is a factor of general human capital that provides broad, transferable skills and knowledge in a range of pursuits. Specific human capital, in contrast, is more limited to one particular firm, task or context (Becker, 1964). Like an entrepreneur's past experiences, formal education moderates institutional effects on one's entrepreneurial activities over time (Autio et al., 2013; van der Zwan et al., 2016). Accordingly, microfoundations research indicates that formal education enhances cognitive processes for opportunity recognition and resource acquisition in institutional environments (Shepherd et al., 2015; Felin et al., 2015). Formal education aids the translation of regulatory efficiency into opportunity-motivated entrepreneurship (Boudreaux and Nikolaev, 2019), processing information and making complex operational decisions (Parker, 2018), personal imagination and strategic foresight (Caliendo and Kritikos, 2008), calculated risk-taking (Van Praag et al., 2013) and self-confidence (Bhandari and Deaves, 2006; Koellinger et al., 2007). The general human capital capabilities afforded by formal education become instrumental when entrepreneurs undertake external activities such as interpreting institutional signals or navigating compliance and regulatory demands. However, multilevel examinations of how more specific dimensions of the regulatory environment interact with formal education tend to treat formal education as a control variable or focus on its direct effects, rather than hypothesizing its moderated effects.

Research consistently shows that formal education exerts effects beyond the individual level. It influences relational and structural aspects of entrepreneurial activity through enhanced networks, access to financial resources and wealth accumulation (Wright et al., 2007; Cassar, 2006; Van Praag et al., 2013; Ratzinger et al., 2018; Gerber and Cheung, 2008; Stevens et al., 2008). These mechanisms align more strongly with opportunity-motivated entrepreneurship, driven by profit and innovation prospects, than with necessity-motivated entrepreneurship, which is typically constrained by labor-market limitations and lack of viable employment alternatives (Block and Wagner, 2010; Hessels et al., 2008; Parker, 2018). Consequently, formal education not only predicts entry into entrepreneurship but also shapes the underlying motivation for doing so.

Institutional conditions, particularly regulatory efficiency, are associated with entrepreneurial motivation, and these associations appear to differ by formal education. Studies indicate that environments with lower entry barriers, greater labor market flexibility and monetary stability are linked to a stronger association between formal education and opportunity-motivated entrepreneurship (Stenholm et al., 2013; Valdez and Richardson, 2013). This pattern tends to be more pronounced in higher-income countries with robust formal institutions, where market access is greater, and resource channels are more available. In contrast, weaker institutional settings make it more likely that educated individuals engage in informal or subsistence activities, sustaining necessity-driven entrepreneurship amid structural constraints (Autio et al., 2013; Amorós and Bosma, 2014; Chowdhury et al., 2019).

Past research also indicates that the effects of regulatory-efficiency dimensions are heterogeneous rather than uniform, even under broader institutional composites. For instance, greater business freedom is associated with lower entry barriers and stronger competitive pressures, patterns that can disproportionately favor entrepreneurs with more human capital (Djankov et al., 2002; Branstetter et al., 2014; Stenholm et al., 2013). Similarly, labor freedom is associated with conditions that make team assembly and reconfiguration easier, complementing education-derived networks, managerial skills and adaptive capabilities that support opportunity-motivated entrepreneurship (Coad et al., 2017; Block et al., 2019). Monetary freedom, in turn, is associated with lower valuation uncertainty, greater credit access and stronger financial planning conditions (McMullen et al., 2008; Kuckertz et al., 2016). Collectively, these findings suggest that regulatory efficiency and formal education operate through independent mechanisms in shaping the composition of entrepreneurial motivations, not as independent factors. Whereas research has begun to explore these contingencies, the literature has yet to fully integrate them by disaggregating regulatory efficiency into its specific components and examining their joint influence with education levels. Recent work has explicitly called for such nuanced, multilevel approaches to better capture the interactive dynamics (Darnihamedani and Murphy, 2025; van der Zwan et al., 2016).

Microfoundations perspectives in entrepreneurship research provide a multilevel lens for understanding how individual attributes, such as formal education, interact with macro-level institutional conditions to shape entrepreneurial action. Rather than treating education solely as a driver of entrepreneurial action, research emphasizes its role in conditioning responses to regulatory environments through enhanced cognitive and resource-related processes (Felin et al., 2015; Shepherd et al., 2015). The research has explained these effects in terms of two principal mechanisms.

First, formal education strengthens information processing and opportunity evaluation. More educated entrepreneurs detect and interpret regulatory signals such as price stability, licensing requirements or hiring flexibility more accurately. They form precise expectations regarding returns and risks and adapt their beliefs efficiently. These capabilities are salient under conditions of monetary freedom, where stable prices and low inflation enable clearer projections of resource commitments and venture viability (Shane and Venkataraman, 2000; Parker, 2018; Kuckertz et al., 2016). Such forward-looking evaluations align with opportunity-motivated entrepreneurship, which relies on expected returns instead of immediate subsistence pressures (Block and Wagner, 2010; Hessels et al., 2008). Second, formal education enhances financial literacy, credibility with financiers and familiarity with funding instruments (Cassar, 2004; Van Praag et al., 2013; Ratzinger et al., 2018). Overall, regulatory efficiency dimensions do not exert uniform effects on entrepreneurial motivation, but they moderate the linkages by amplifying the benefits of institutional freedoms for opportunity-driven entrepreneurial activities while mitigating barriers for entrepreneurs with greater human capital.

Research also highlights how formal education expands networks and access to talent, which interact with labor freedom to facilitate recruiting, contracting and entrepreneurial team building (Gerber and Cheung, 2008; Parker, 2018). In less rigid labor markets, educated founders leverage stronger managerial capabilities and complementary human capital and pivot toward higher-value opportunities, behaviors more characteristic of opportunity-motivated entrepreneurship (Stevens et al., 2008; Coad et al., 2017; Block et al., 2019). Similarly, education aids navigation of regulatory environments by improving compliance, administrative handling and strategic adaptation to barriers (Kaplan et al., 2011; Branstetter et al., 2014). These processes underscore heterogeneity across regulatory dimensions: labor freedom influences team formation and organizational flexibility, whereas business freedom affects entry frictions, compliance burdens and competitive performance. Overall, the literature points to education-conditioned differences in how entrepreneurs interpret and respond to regulatory conditions, leading to differential patterns in their motives in terms of opportunity and necessity.

The regulatory efficiency dimensions of business freedom, labor freedom and monetary freedom create institutional conditions associated with the feasibility of opportunity-motivated entrepreneurship by reducing operational, hiring and planning frictions (Baumol et al., 2007; Shleifer, 2010). These dimensions collectively define a regulatory profile that can influence the composition of entrepreneurial activity within ecosystems (Hart, 2009). However, research indicates that these institutional benefits do not accrue uniformly across potential entrepreneurs. In particular, lower formal education can limit the extent to which regulatory efficiency is associated with opportunity-motivated entry, as less educated individuals face greater challenges in navigating costs, fees, administrative barriers, technology access and competitive dynamics (Belitski et al., 2016; Branstetter et al., 2014; Darnihamedani et al., 2018). Conversely, higher formal education produces capabilities for adaptation and pivots, workaround strategies and the navigation of regulatory frameworks. For those entrepreneurs, these capabilities can strengthen the positive impact of efficient regulations and opportunity-motivated entrepreneurship (Branstetter et al., 2014; Darnihamedani et al., 2018).

Variance in the institutional environment yields different patterns of indirect effects on entrepreneurial activity. In weak or inefficient regulatory environments, for instance, educated entrepreneurs with stronger networks and capabilities are better positioned to overcome regulatory frictions and act based on opportunity but not necessity-based motivations (Boudreaux and Nikolaev, 2019; van der Zwan et al., 2016). Therefore, the literature shows that regulatory efficiency affects an entrepreneur's opportunity or necessity-based motivations, but that formal education does not benefit all entrepreneurs equally. These implications call for research on the moderating effects of formal education.

As a dimension of regulatory efficiency, business freedom reflects the ease with which entrepreneurs can start, operate or terminate a business venture (McMullen et al., 2008). Greater business freedom reduces administrative and entry-related costs for entrepreneurs as they enter a market or community (Kaplan et al., 2011; Branstetter et al., 2014). It also increases competition by lowering barriers to market entry for entrepreneurs in general (Branstetter et al., 2014; Darnihamedani and Terjesen, 2022). Research shows that these effects do not map uniformly onto entrepreneurial motivations. For opportunity-motivated entrepreneurship in particular, lower barriers for competition lead to market saturation as new firms emerge (Djankov et al., 2002; Minniti, 2008; Kaplan et al., 2011). In microfoundational terms, intensified entry raises the information-processing and strategic-positioning burden on founders because more actors are competing to identify, evaluate and appropriate the same opportunities, which dissipates expected rents and discourages opportunity-motivated entrepreneurship (Shane and Venkataraman, 2000; McMullen et al., 2008; Kuckertz et al., 2016).

As more entrepreneurial firms emerge and pursue opportunities, the potential for any of them to gain a competitive advantage declines, which increases uncertainty around differentiation and expected returns (Darnihamedani and Terjesen, 2022). In this way, business freedom simultaneously lowers entry frictions while raising post-entry competitive pressure, increasing uncertainty in entrepreneurial activities and decisions (Kaplan et al., 2011; Darnihamedani et al., 2018). Such environments also intensify risk-taking demands, which raises costs by compelling entrepreneurs to be more intentional about the time and resources they invest in order to achieve growth. Because opportunity-motivated entrepreneurs are more dependent on scalable differentiation and expected economic returns, they are more exposed to these competitive pressures than necessity-motivated entrepreneurs, whose entry is more often driven by subsistence constraints than by opportunity exploitation (Block and Wagner, 2010; Hessels et al., 2008; Parker, 2018).

When entrepreneurs have higher formal education, they can assess opportunities more strategically (Unger et al., 2011; Parker, 2018). They are generally better equipped to compare competing opportunities, evaluate market saturation and anticipate how intensified entry affects expected returns. Entrepreneurs with lower levels of formal education, in contrast, tend to engage in opportunity-motivated entrepreneurship less frequently because they have fewer resources and network connections, lower skill levels, smaller growth aspirations and less access to capital (Cassar, 2004; Ratzinger et al., 2018; van Praag et al., 2013). These constraints make it harder to absorb the competitive pressures that accompany high business freedom, especially when opportunity pursuit requires differentiation and sustained strategic positioning. In addition, weak institutions effectively lower business freedom by providing fewer subsidies to entrepreneurs, which hinders the growth prospects of entrepreneurs with lower levels of formal education (Block and Landgraf, 2016).

Entrepreneurs with more formal education have greater risk tolerance, which builds their capacity to perform in weak institutional environments (Van Praag et al., 2013). They also possess greater social and financial capital to mitigate regulatory inefficiencies when pursuing opportunity-motivated entrepreneurship (Cassar, 2006; Boudreaux and Nikolaev, 2019). By contrast, lower-educated entrepreneurs are more likely to shift toward necessity-motivated entry when business conditions become difficult because necessity-based venturing requires fewer strategic investments and is less dependent on capturing differentiated market opportunities (Branstetter et al., 2014; Rostam-Afschar, 2014). However, because they pursue subsistence rather than growth, operate with fewer resources and enter smaller markets, they are less vulnerable to the threats of weak institutional environments (Autio and Acs, 2010). Taken together, these arguments suggest that the negative association between business freedom and opportunity-motivated entrepreneurship should be more pronounced among entrepreneurs with lower levels of formal education, whose resource and strategic constraints make competitive entry environments harder to navigate.

Labor freedom pertains to the regulatory aspects of a market environment that govern minimum wage laws, restrictions on hiring and firing and severance requirements (Heritage Foundation, 2008; Holmlund, 2014). When these regulations are stringent, entrepreneurs incur higher operational costs as they pursue entrepreneurial opportunities (Heirman and Clarysse, 2004; Estrin et al., 2016). Research shows that such regulations require operational rigidities in entrepreneurial ventures, which hinder the capacity to adapt to the environmental changes that give rise to entrepreneurial opportunities (Darnihamedani and Terjesen, 2022). Accordingly, low labor freedom can constrain opportunity-motivated entrepreneurship by increasing the costs and risks of adjusting a workforce in response to emerging opportunities. Conceptually, labor freedom lowers adjustment costs for assembling and reconfiguring teams, thereby complementing founders' matching and coordination capabilities that are central to seizing opportunities (Teece, 2007; Coad et al., 2017; Block et al., 2019).

Labor freedom enables entrepreneurs to manage many different aspects of human capital more readily (Coad et al., 2017; Block et al., 2019). This capacity allows entrepreneurs to hire for the specific skills and expertise needed for pursuing venture opportunities (Teece, 2007). As such, when labor freedom is low, rigid compliance structures such as collective bargaining and wage negotiations tend to complicate hiring practices and slow workforce adjustment, which can reduce a venture's ability to realign resources as opportunities emerge (McMullen et al., 2008; Block et al., 2019). Therefore, opportunity-motivated entrepreneurship depends on labor freedom in order to respond to changing circumstances (Mueller and Thomas, 2001). In mechanism terms, greater contracting flexibility increases the speed and precision with which founders can select and redeploy talent, making opportunity-oriented entry more feasible when opportunities require rapid team formation and adaptation (Teece, 2007; Coad et al., 2017).

Low levels of labor freedom hinder an entrepreneurial venture's capacity to adapt to environmental change (Nickell, 1997; Acemoglu, 2001). Compliance costs and legal risks can prevent the recasting of workforces when responding to opportunities and threats (Bhandari and Deaves, 2006; van Praag et al., 2013). Formal education is instrumental to opportunity-motivated entrepreneurship because it is associated with a stronger ability to interpret and navigate these costs and risks when making entry and early staffing decisions (Cassar, 2006; van Praag et al., 2013). Moreover, entrepreneurs with greater formal education tend to pursue opportunities that require specialized human capital, increasing the importance of being able to assemble and reconfigure teams efficiently (Siebert, 1997).

Entrepreneurs with higher formal education formulate stronger venture strategies (Holmlund, 2014). Therefore, when labor freedom is high, they can more readily mobilize and redeploy talent to align a workforce with changing opportunity conditions. Put differently, labor freedom increases the value of education-linked capabilities, such as broader networks, managerial expertise and screening capacity, because it reduces the frictions of hiring, contracting and separation when opportunity pursuit requires rapid adjustment (Gerber and Cheung, 2008; van Praag et al., 2013; Block et al., 2019). As a result, the positive association between labor freedom and opportunity-motivated entrepreneurship should be stronger among entrepreneurs with higher formal education, who are better positioned to exploit flexibility in labor markets when assembling teams for opportunity-driven entry.

Monetary freedom refers to less price control, lower inflation and more stable economic conditions (Friedman, 1962). It influences the kinds of human, technological and financial resources that are essential for opportunity-motivated entrepreneurship (Heritage Foundation, 2008; McMullen et al., 2008). Because monetary freedom strengthens price stability and reduces inflationary uncertainty, it facilitates financial planning and longer-horizon risk assessments, capacities that are especially relevant for opportunity-motivated entry, where founders rely on forward-looking judgments about expected returns (Friedman, 1962; McMullen et al., 2008).

Monetary freedom provides a foundation for more reliable resource valuation and capital allocation. It derives from low inflation and limited price controls, which make it easier for entrepreneurs to evaluate investment risks and expected returns (Bjornskov and Foss, 2008; Díaz-Casero et al., 2012). Low monetary freedom complicates even simple metrics like profitability because it renders revenues and costs less predictable. Thus, it discourages opportunity-motivated entrepreneurship, where calculated risks are necessary. Conceptually, clearer price signals reduce noise in opportunity evaluation and intertemporal judgment, making it easier for founders to form expectations and act on uncertain projects (McMullen et al., 2008; Kuckertz et al., 2016).

High monetary freedom is also associated with improved access to financial capital and more credible valuation conditions (Cassar, 2004; Bjornskov and Foss, 2008). Because opportunity-motivated entrepreneurs often require external financing for opportunity pursuit and early resource mobilization, stable monetary conditions can lower information frictions for both founders and financiers (Heritage Foundation, 2008; Kuckertz et al., 2016). In this sense, monetary freedom should be positively associated with opportunity-motivated entrepreneurship.

Monetary freedom and formal education interact with each other with respect to engagement with opportunity-motivated entrepreneurship. Entrepreneurs with higher formal education have greater financial literacy regarding investments and resource allocations when pursuing entrepreneurial opportunities (Wright et al., 2007; Bjornskov and Foss, 2008; Ratzinger et al., 2018). Entrepreneurs with lower formal education, in comparison, are less likely to identify and act on financial opportunities that depend on formal market signals and institutional financing. When monetary freedom is low, formal education can also heighten awareness of financial uncertainty and downside risk, which can make opportunity-motivated entrepreneurship less attractive under unstable price conditions. Again, entrepreneurs with lower formal education are less aware of these factors in general due to limited financial knowledge, smaller networks and a tendency to focus on necessity-motivated entrepreneurship (Parker, 2018; Hessels et al., 2008). They also operate more frequently in informal economic environments without institutional financing (Minniti, 2008; Autio and Acs, 2010).

Accordingly, education conditions the monetary-freedom effect: more educated founders can translate stable prices and accessible credit into more accurate cash-flow projections, capital-structure decisions and risk–return assessments, whereas less educated founders lack the financial literacy and network depth to fully exploit these conditions (Cassar, 2004; van Praag et al., 2013; Ratzinger et al., 2018). In mechanism terms, monetary stability reduces valuation noise, but education determines who can convert that stability into opportunity-oriented entry through credible planning and resource mobilization (McMullen et al., 2008; Kuckertz et al., 2016). Whereas monetary freedom generally promotes opportunity-motivated entrepreneurship, this effect is liable to be stronger for entrepreneurs with higher formal education.

We utilized the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) microdata for 2006–2017 in order to analyze entrepreneurial motivations among entrepreneurs at the individual level. The GEM is the world's largest cross-country entrepreneurship study and provides harmonized survey measures of entrepreneurial activity, human capital, attitudes and motivations (Reynolds et al., 2005). For data pertaining to institutional effects, we utilized the Heritage Foundation's Economic Freedom dataset, focusing on the regulatory-efficiency sub-indices for business freedom, labor freedom and monetary freedom (Kuckertz et al., 2016; Darnihamedani and Terjesen, 2022). Finally, we accessed World Bank Development Indicators (WBDI) for certain control variables, as we detail in the following sections. Each of these sources has been used in prior cross-country entrepreneurship research to operationalize institutional and macroeconomic conditions alongside GEM microdata (McMullen et al., 2008; Kuckertz et al., 2016; Bradley and Klein, 2016).

Our study required an intentional linking of country-level data regarding institutions with individual-level scores. We constructed a multilevel dataset structure by matching individual responses to country-level indicators using country and survey year as keys. Specifically, each individual GEM observation was assigned the corresponding country-year values of business, labor and monetary freedom (lagged to t–1), as well as the country-year macroeconomic controls from WBDI (also lagged to t–1). This follows prior GEM-based cross-country studies that lag institutional and macroeconomic variables to ensure that country conditions temporally precede the measured entrepreneurial outcomes and to mitigate simultaneity concerns (McMullen et al., 2008; Bradley and Klein, 2016; Darnihamedani and Terjesen, 2022). This structure allowed us to examine covariation between individual-level factors, such as formal education and entrepreneurial motivation (opportunity versus necessity) and country-specific regulatory conditions, which were instrumental to our main analyses.

To examine our hypotheses, we indexed scores for a range of demographic variables, entrepreneurial motivation (opportunity, necessity), formal education level and institutional indicators (business, labor, monetary freedom). Consistent with our research question, we restricted the estimation sample to respondents classified as entrepreneurs in GEM who reported a non-missing motivation response (opportunity versus necessity). Cases that did not meet these criteria due to missing individual scores or institutional data were excluded. This process yielded 128,253 useable cases across 86 countries. Table A1 (see Appendix) illustrates this linking process and includes representative references from the literature.

We applied GEM's individual-level case weights in accordance with GEM guidelines for descriptive statistics and model estimation (Reynolds et al., 2005). For the dependent variable, opportunity-motivated entrepreneurship, we use the GEM item that asks whether the venture was started to pursue an opportunity or because of no better options, which follows past research in cross-national entrepreneurship research using the GEM (Reynolds et al., 2005; McMullen et al., 2008). Consistent with our research design, this item is operationalized among respondents classified as entrepreneurs in GEM with non-missing motivation. We code 1 = opportunity-motivated and 0 = necessity-motivated, in line with prior entrepreneurship studies (e.g. Autio et al., 2013; Chowdhury et al., 2019). This binary specification reflects the opportunity/necessity distinction in entrepreneurial motivation but does not capture mixed or more nuanced motivational profiles. Accordingly, observations with missing values on motivation are excluded following a complete-case approach. See Table A2 (Appendix) for items, codings and transformations.

As an additional validity assessment, we examined whether the GEM motivation measure displayed theoretically expected associations with related variables. Opportunity-motivated entrepreneurship was more prevalent among respondents with higher perceived skills, stronger opportunity recognition and higher formal education, which is consistent with prior entrepreneurship research. For the country-level independent variables, we used Heritage Foundation sub-indices (0–100). Business freedom captures regulatory burdens related to entry, operation and closure (e.g. start-up procedures/costs and administrative frictions), which has been used as an indicator of entry regulation and administrative burdens relevant to entrepreneurship (Djankov et al., 2002; Branstetter et al., 2014). Labor freedom captures hiring and firing flexibility and wage regulation, consistent with studies linking labor-market institutions to entrepreneurial adjustment capacity and incentives (Siebert, 1997; Holmlund, 2014). Monetary freedom captures price stability and the extent of price controls, which have been applied in institutional entrepreneurship research to reflect inflation-related uncertainty and planning conditions (Friedman, 1962; Kuckertz et al., 2016). For all three indicators, we matched the score by country-year and lagged them by one year (t−1) so that institutional conditions temporally precede the reported entrepreneurial activity and to reduce concerns about simultaneity. Table A7 (Appendix) summarizes these findings.

To index formal education level, we utilized the GEM item on the highest schooling attainment (ISCED-aligned categories). Our main specification codes tertiary education = 1 and upper secondary or less = 0, with “DK/Refused” set to missing under the complete-case rule. This operationalization models prior entrepreneurship research using GEM education measures as a proxy for one aspect of general human capital in cross-country analyses (Unger et al., 2011; van Praag et al., 2013; Darnihamedani et al., 2018). Given cross-country comparability constraints in GEM, we treat this measure as one aspect of general human capital rather than a fine-grained indicator of education type or quality. The measure provides a stable and consistent distinction across countries while preserving statistical power for interaction tests. In robustness checks, we also evaluate alternative education codings to assess sensitivity to this binary threshold (Table A6, Appendix).

We included a series of control variables to limit alternative explanations of the findings of our main analyses. For age, we followed the GEM convention (18–64) and excluded cases with out-of-range values (Reynolds et al., 2005; Estrin et al., 2013). We also included a squared age term to account for the well-established nonlinear relationship between age and entrepreneurship. We indexed gender based on standard GEM-based operationalization (Reynolds et al., 2005; Hessels et al., 2008). Gender was coded female = 0, male = 1, with “other/Refused” set to missing under our complete-case rule.

To index household resources, we used within-country income terciles and corresponding dummy indicators to index average household income, a common proxy for resource access and constraints in GEM studies (Hessels et al., 2008; Estrin et al., 2013). Fear of failure was measured using the GEM item “Would fear of failure prevent you from starting a business?”, coded yes = 1 and no = 0, setting “DK/Refused” to missing (Hessels et al., 2008; McMullen et al., 2008). We measured opportunity recognition based on the GEM item, “In the next six months, will there be good opportunities to start a business?”, coded yes = 1 and no = 0. These scores reflect the well-known opportunity construct in the entrepreneurship literature (Shane and Venkataraman, 2000; Hessels et al., 2008). We measured entrepreneurial networks using the GEM item, “Do you know someone personally who started a business in the past two years?” coded yes = 1, no = 0, consistent with prior work emphasizing social capital and network exposure (Gerber and Cheung, 2008; Stevens et al., 2008).

Entrepreneurial self-efficacy was included in our study via the GEM item “Do you have the knowledge, skill and experience required to start a new business?” coded yes = 1, no = 0. This indicator of perceived capability is a robust correlate of entrepreneurial motivations in GEM-based research (Hessels et al., 2008; Parker, 2018). Finally, we included a range of macro-level controls for GDP per capita (log transformed), GDP growth and unemployment from the WBDI dataset. We lagged these scores (t−1) so that macroeconomic conditions temporally precede entrepreneurial activities and to reduce concerns about simultaneity between country-level dynamics and individual decisions (Darnihamedani and Terjesen, 2022). Table 1 presents definitions, measurement information, sources and brief justifications for these variables.

To test our hypotheses, we utilized multi-level logistic regression with random intercepts at the country level (Table A3, Appendix). This analysis approach is common when conducting empirical research across levels of analysis, and it has been used widely in entrepreneurship research (Peterson et al., 2012; Boudreaux et al., 2019; Stephan et al., 2015). We deemed the approach to be suitable as it accommodates predictors at multiple levels and accounts for dependence among observations within countries (Hofmann et al., 2000). Consistent with GEM guidelines, we applied the survey case weights in descriptive statistics and model estimation (Reynolds et al., 2005). We also accounted for sample size differences across countries and individuals to generate reliable coefficient estimations.

To assess error effects in our analyses, we included survey-year fixed effects to account for unobserved heterogeneity across time. We lagged the country-year institutional and macroeconomic indicators by one year (t–1) so that these conditions temporally precede the measured entrepreneurial motivation and to reduce concerns about simultaneity. We estimated cluster-robust standard errors at the country-year level to account for correlated residuals within country-year clusters. We mean-centered the institutional variables to ease the interpretation of interaction terms. Finally, we report odds ratios with 95% confidence intervals and complement them with average marginal effects indices for the interaction terms.

Consistent with past research involving multiple levels of analysis, we calculated intra-class correlations (ICC) to examine if individual observations differed from group observations (Peterson et al., 2012). The research literature observes ICC cut-off points ranging from 15% (Stephan et al., 2015) to 9.3% (Boudreaux et al., 2019). In our study, we observed Heck et al. (2010, p. 74) and observed 8.3% (Model I), 8.0% (Model II) and 7.8% (Model III), which are appropriate for this multi-level empirical analysis. Overall, these modeling choices align with our research design and enable a transparent assessment of the hypothesized associations and education-conditioned interaction patterns.

Table 2 reports the GEM-weighted descriptive statistics (means, standard deviations and Ns) and correlations for all study variables. In the sample of entrepreneurs, 70% of respondents are coded as opportunity-motivated entrepreneurs. The sample was 57% male and 43% female. Approximately 42% of cases reported tertiary education, and 26% reported a fear of failure. At the country level, the average scores for business freedom, labor freedom and monetary freedom were 67.4, 61.3 and 72.7, respectively. To address skewness in the distributions of scores, as noted above, we log-transformed GDP per capita.

Pairwise associations at the individual level (<0.20) and the country-level correlations among institutional predictors were modest. To assess multicollinearity, we computed variance inflation factors (VIF: each coefficient was <5) and found satisfactory estimates. As noted above, we mean-centered the institutional indices and clustered standard errors for all models at country-year levels.

In Table 3 (Model I), university education, perceived entrepreneurial skills and gender (male) were positively and significantly associated with opportunity-motivated entrepreneurship. Fear of failure was negatively and significantly related. The odds-ratio of tertiary education (OR ≈ 1.34, 95% CI 1.32–1.36), perceived skills (OR ≈ 5.72, 5.68–5.8) and male gender (OR ≈ 1.40, 1.39–1.42) increased the odds of reporting opportunity motivation. Meanwhile, the fear of failure reduced that effect (OR ≈ 0.62, 0.61–0.63). The age variable displayed a nonlinear functional form (i.e. quadratic; inverted U), indicating that age has a positive relation, but age-squared has a negative relation, with opportunity-motivated entrepreneurship.

In Table 3 (Model II), we introduced lagged institutional scores without calculating the interaction effects. Business freedom showed a negative and significant association (OR ≈ 0.994 per index point, 0.992–0.996), whereas monetary freedom showed a positive and significant association with opportunity-motivated entrepreneurship (OR ≈ 1.017, 1.010–1.022). These results support H1a and H3a. Labor freedom had no relation to opportunity-motivated entrepreneurship, which does not support H2a.

To test for a moderation effect for education, we observed average marginal effects (Table 4) and predicted probabilities (Table 5). A 10-point increase in business freedom was associated with a −0.80 percentage-point (pp) change in the predicted probability of opportunity-motivated entrepreneurship among those with higher formal education (95% CI −1.11, −0.52; p < 0.01), whereas the corresponding AME among those with lower formal education was −0.10 pp (95% CI −0.30, +0.12). The difference in these slopes was −0.70 pp (p < 0.01), which supports H1b.

For labor freedom, the average marginal effect (AME) is +0.60 pp (+0.30, +0.91, p < 0.01) for higher formal education versus +0.10 pp (−0.10, +0.30, n.s.) for lower formal education. This statistical difference (p < 0.01) supports H2b. For monetary freedom, we found positive AMEs for both education groups, but stronger for higher formal education: +2.00 pp (95% CI + 1.50, +2.50; p < 0.01) versus +0.80 pp (95% CI + 0.50, +1.10; p < 0.01). The difference in slopes was +1.20 pp (p < 0.01), supporting H3b.

Table 5 reports predicted probabilities at representative values (−1 SD, mean, +1 SD). For higher formal education, the predicted probability declines from 0.730 to 0.670 across business freedom (−1 SD to +1 SD), increases from 0.680 to 0.720 across labor freedom and increases from 0.660 to 0.738 across monetary freedom. For lower formal education, the corresponding probabilities are comparatively flatter: 0.690 to 0.690 for business freedom, 0.688 to 0.700 for labor freedom and 0.680 to 0.721 for monetary freedom. Altogether, the results indicate that formal education conditions the direction and magnitude of the associations between regulatory efficiency and opportunity-motivated entrepreneurship. Tables 3–5 report the results of our empirical study.

A closer examination of our margins plots (Figure A1) shows that for business freedom, the lines diverge at higher values and higher formal education cases show a steep negative slope in opportunity motivation, which supports H1b. For labor freedom, the formal education effect becomes significant and positive at high levels, which supports H2b. For monetary freedom, both groups slope upward, but the higher formal education cases increase sharply, which supports H3b.

We conducted multiple supplementary analyses to assess the robustness of our findings. First, to address missing data concerns, we re-estimated our interaction model while adding specific country-level controls one at a time. The controls for these analyses included population growth, government consumption, trade openness, investment freedom, corruption perceptions and tertiary attainment (Table A4, Appendix). Across all specifications, the main effects and interaction effects remained directionally and statistically unchanged and the magnitudes remained within baseline confidence intervals.

Second, to verify that our results were not driven by the use of a single institutional data source, we replaced the Heritage sub-indices with the Fraser Economic Freedom components, using measures that capture closely comparable constructs: Labor-market regulations (for Labor Freedom) and Credit/financial-market regulations (for Monetary Freedom). Similarly to the main analyses, we lagged these scores. These results reproduced the same patterns as our main analyses and are summarized in Table A5 (Appendix).

Third, we tested robustness using alternative estimators, clustering choices and model specifications. We utilized probit and Linear Probability Models with country fixed effects, alternative clustering at the country level (instead of country-year), leave-one-country-out re-estimations and alternative codings of formal education, including broader categorical levels rather than the main binary specification (Table A6, Appendix). In all of these cases, the direction and magnitude of the institutional effects and their interactions with formal education remained stable.

Fourth, because both the dependent variable and the education variable involve simplifying operational choices, we conducted supplementary analyses to assess whether the findings are sensitive to possible measurement limitations. These analyses included a validity assessment of the GEM binary motivation measure using theoretically related variables, as well as supplementary model specifications in which formal education was operationalized more finely. Opportunity-motivated entrepreneurship was more prevalent among respondents with higher perceived skills, stronger opportunity recognition and higher formal education, patterns consistent with prior entrepreneurship research. Alternative codings of formal education are reported in Table A6, while Table A7 reports a supplementary validity assessment of the GEM binary motivation measure.

These comprehensive supplementary analyses indicate that our results were not contaminated by omitted country-level controls, data source, estimator and clustering decisions or variable operationalization. Whereas these checks do not completely eliminate the inherent limitations of any survey-based measure, they do provide confidence that our main results were not artifacts of modeling or measurement.

This paper reports empirical research findings regarding how regulatory efficiency is associated with formal education level in shaping whether entrepreneurship is opportunity-motivated or necessity-motivated. We operationalized business freedom, labor freedom and monetary freedom using multiple large-scale data sources and conducted supplementary robustness and sensitivity analyses. Our results contribute to institutional research in entrepreneurship (North, 1990; Williamson, 1996) by showing that the association between regulatory efficiency and entrepreneurial motivation varies systematically with formal education level. Our study builds on microfoundational research in entrepreneurship (Felin et al., 2015; Shepherd et al., 2015) by linking macro-level institutional conditions to differences in individual-level entrepreneurial motivation via formal education. We found evidence that macro-level institutional factors are associated with micro-level entrepreneurial behaviors through their relationship with regulatory efficiency, opportunity-motivated entrepreneurship and formal education. Whereas prior research shows that institutions are linked to entrepreneurial activity (Boudreaux and Nikolaev, 2019), our study provides cross-national micro-level evidence that the association between regulatory efficiency and entrepreneurial motivation differs by formal education level.

Previous research shows that educated entrepreneurs tend to benefit from high labor freedom and monetary freedom (Wright et al., 2007; van Praag et al., 2013; Boudreaux and Nikolaev, 2019). We build on these findings by showing that the positive association of labor freedom and monetary freedom with opportunity-motivated entrepreneurship is stronger among entrepreneurs with higher formal education. We also show evidence that these associations are weaker or absent among entrepreneurs with lower levels of formal education. This moderation effect suggests that policymakers and entrepreneurial ecosystem builders may consider differences in formal education when designing programs intended to support opportunity-oriented entrepreneurship under differing institutional conditions (Urbano et al., 2019).

Our study has implications for research on labor freedom and entrepreneurial motivation. We found that labor freedom is associated with a higher prevalence of opportunity-motivated entrepreneurship among individuals with greater formal education. We further showed that entrepreneurs with lower levels of education are more likely to engage in necessity-motivated entrepreneurship when labor freedom is low, which is consistent with the idea that tight labor markets may create greater constraints for those with fewer skills and weaker access to human capital. This relationship underscores heterogeneity across education levels. It implies that labor-market reforms are not uniformly associated with entrepreneurial motivation, but instead appear to operate differently depending on entrepreneurs' formal education. Prior work would suggest that upskilling initiatives can help entrepreneurs to leverage regulatory advantages (Bruton et al., 2013). Our findings suggest that such policies may be more effective if paired with skills development programs for entrepreneurs with lower formal education.

Our findings also contribute to what is known about the effects of monetary freedom on opportunity-motivated entrepreneurship. Prior research has demonstrated that monetary freedom aligns with entrepreneurial decision-making (Kuckertz et al., 2016). Our results are consistent with this view. However, we additionally find that entrepreneurs with lower formal education have a weaker association with monetary freedom. One plausible explanation is that necessity-motivated entrepreneurs rely more on localized networks and subsistence-based strategies instead of formal financing. Although we cannot test this mechanism directly, monetary freedom appears to be unevenly associated with opportunity motivation across entrepreneurs with different levels of formal education.

Finally, we observed a counterintuitive pattern regarding business freedom and opportunity-motivated entrepreneurship. Opportunity-motivated entrepreneurs, especially those with higher formal education, had a negative association with business freedom. This pattern may be consistent with higher competitive pressures and a greater need for differentiation in environments with fewer business-entry constraints. In contrast, necessity-motivated entrepreneurs appear less sensitive to these competitive dynamics. Here again, our findings suggest that business freedom is not uniformly associated with different kinds of entrepreneurial motivations (Boudreaux et al., 2019). These results suggest, somewhat unexpectedly, that higher business freedom may be less positively associated with opportunity-motivated entrepreneurship among entrepreneurs with higher formal education than our initial expectations implied.

Our overall findings underscore the multifaceted nature of regulatory efficiency for different types of entrepreneurs. Regulatory reforms may be more effective when they balance competitive intensity with targeted support. Entrepreneurs with higher formal education tend to show stronger positive associations with labor and monetary freedoms. Meanwhile, those with lower formal education show weaker associations and appear more concentrated in necessity-motivated entrepreneurship under less favorable institutional conditions. The broader evidence points to formal education as a meaningful boundary condition in the association between regulatory efficiency and entrepreneurial motivation. As such, policy design and ecosystem programming may benefit from closer alignment with human capital and skills-development efforts, so that more entrepreneurs can benefit from regulatory efficiency.

Our study offers implications for research on institutions, human capital and entrepreneurial motivation. First, we show that formal education is associated with how entrepreneurs differ in their motivation across regulatory contexts. In particular, under greater monetary and labor freedom, entrepreneurs with higher formal education are more likely to be opportunity-motivated. Second, formal education may be linked to financial literacy, broader networks and better connections with capital providers, although these mechanisms are not directly tested in our data.

Environments with labor freedom may entail conditions under which more highly educated entrepreneurs are better positioned to access and assemble human capital (Wright et al., 2007; van Praag et al., 2013). Along these lines, our research shows that when business freedom intensifies entry and competition, more highly educated founders may face stronger differentiation pressures, which can temper their opportunity orientation unless distinctive assets are secured (Boudreaux et al., 2019). Another implication relates to the impact of institutional forces across different kinds of entrepreneurs. We view formal education as an important empirical boundary condition in the relationship between regulatory efficiency and entrepreneurial motivation (North, 1990; Williamson, 2000). The interaction helps clarify the conditions under which regulatory efficiency is more strongly associated with opportunity-oriented entrepreneurship. In this study, we found that labor and monetary freedoms are more positively associated with opportunity orientation among the more educated. Moreover, business freedom shows a weaker or negative association with opportunity-motivated entrepreneurship. This paper thus helps contextualize the importance of institutional and education-based heterogeneity perspectives in entrepreneurship research.

Theoretically, our findings suggest that institutional conditions should not be treated as uniformly associated with entrepreneurial motivation across individuals, because the linkages vary systematically by formal education level (North, 1990; Williamson, 1996; Boudreaux and Nikolaev, 2019). In this sense, the results support a more heterogeneous view of how institutional conditions relate to entrepreneurial motivation across actors. Practically, this implies that reforms aimed at improving labor or monetary freedom may be insufficient on their own if entrepreneurs with lower formal education lack the skills, networks or resources needed to benefit from those institutional conditions (Wright et al., 2007; van Praag et al., 2013; Bruton et al., 2013).

We offer specific guidance for policymakers and ecosystem builders regarding regulatory efficiency and formal education. When labor freedom and monetary freedom are high, entrepreneurship education should not only be expanded broadly but connected to concrete conversion mechanisms such as talent pipelines, venture-scouting channels, founder placement into entrepreneurial teams and investment-ready networks, to better serve entrepreneurs with high formal education. By contrast, for entrepreneurs with lower formal education, the more relevant intervention may not be additional deregulation itself, but targeted mechanisms that help them translate favorable institutional conditions into entrepreneurial action, such as skills vouchers, modular applied training courses, regulatory navigation support and brokered access to financing and professional networks.

If business freedom is low, then the implication is not simply to deregulate more broadly, but to identify which frictions are most exclusionary for less-advantaged entrepreneurs and streamline those specific institutional resources instead of enacting broad deregulation. For example, policymakers could reduce costs associated with licensing, simpler registration pathways or provide first-step compliance support, particularly in sectors where such frictions disproportionately affect entrepreneurs with fewer resources. If business freedom is high, our findings suggest that additional deregulation alone may yield limited gains. Policymakers may instead need to add differentiated institutional services based on the needs of the particular ecosystem, especially among entrepreneurs with lower formal education. Such services might include guided onboarding into formal markets, financial-recordkeeping support or matchmaking with sector-specific intermediaries.

Finally, accelerators and incubators can draw operational lessons from these findings, particularly in designing differentiated entrepreneurship support and educational pathways for founders facing distinct institutional conditions (Murphy et al., 2019). One straightforward way is to match the institutional context with the education profiles of entrepreneurs. Entrepreneurs with higher formal education may benefit from recruitment funnels, mentors and investor access. Entrepreneurs with lower formal education may benefit from regulatory navigation assistance, credit access, financial literacy and skill-building programs. More specifically, incubators could segment support tracks at entry: one track oriented toward scaling and investor readiness in institutionally supportive contexts and another focused on capability-building, compliance support and early market access for founders less able to benefit immediately from favorable regulatory conditions.

Of course, certain limitations warrant caution when interpreting the results of our empirical study. The GEM dataset's inclusion of developing countries is notably restricted (Reynolds et al., 2005; Acs et al., 2016). Thus, some variance in institutional contexts is necessarily limited. As a result, we cannot fully unpack how the linkages between formal education and institutions differ across developed, emerging and low-income economies, and we treat this as a circumscription of our findings. More research on emerging ecosystems is needed to better understand the full effects of institutions, particularly in emerging contexts. Prior studies suggest that institutional voids in developing economies shape entrepreneurship differently, making it critical to expand research on these underrepresented environments (Aparicio et al., 2016; Darnihamedani and Murphy, 2025).

The cross-sectional nature of the GEM dataset prevented us from observing the same entrepreneurs over longer periods, which limited our ability to draw causal inferences. Although we lagged the institutional variables by one year (and two years in the robustness checks) to improve temporal ordering modestly, this does not resolve endogeneity concerns, especially given the slow-moving nature of regulatory systems and educational structures. Accordingly, our findings should be interpreted as conditional associations rather than causal effects.

Moreover, the GEM motivation measure captures self-reported motives at the time of the survey, even though entrepreneurial motives can be mixed, fluid and context dependent (Hessels et al., 2008; Block and Wagner, 2010). The opportunity and necessity motivation distinction is analytically useful and widely used in research like ours, but it should be interpreted cautiously as a simplified categorization rather than a definitive representation of entrepreneurial motivation (Reynolds et al., 2005; Stenholm et al., 2013). The motivation measure in our study distinguished only between opportunity-motivated and necessity-motivated entrepreneurs, which discounts more nuanced motivational profiles. Thus, longitudinal studies would allow scholars to investigate these effects as they evolve over time. Along these lines, regulatory reforms may produce nonlinear trajectories and discontinuous variance, which also calls for longitudinal approaches. Future research using panel data could provide deeper insights into how our findings are associated with such shifts. Furthermore, our institutional measures relied primarily on the Heritage sub-indices, which, while standard, can also embed measurement error or value judgments into the scores. Our robustness checks with alternative indices yielded consistent patterns, which helped mitigate this concern.

The kind of motivation that drives an entrepreneur should not be conflated with success or performance, even if such outcomes are more likely in certain external conditions. We focused on how regulatory efficiencies interact with formal education to yield different kinds of entrepreneurial motivation. These relationships may have implications for venture performance, but venture performance was not directly examined in our study. Future research should refine the implications of our study by examining institutional effects across more diverse external conditions. Examining countries with varying levels of economic development, regulatory frameworks or informal institutional norms can enhance generalizability and clarify the boundaries of our findings. Institutions can interact with cultural norms in ways that shape opportunity-motivated entrepreneurship, making it critical to analyze cross-country differences in institutional efficiency. Future research could also examine whether the moderated effects of formal education extend to other aspects of general human capital, such as prior entrepreneurial experience or industry experience. In addition, more granular data would help clarify the mechanisms through which entrepreneurs with different education profiles are more or less able to benefit from labor, monetary and business freedom. With these limitations in mind, future research will offer deeper insights into the interplay between institutional efficiencies, ecosystem development, entrepreneurial motivations and entrepreneurial performance.

Our paper offers a more nuanced view of the linkages between institutions, education levels and entrepreneurial motivations than is found in previous entrepreneurship research. Our empirical study is intensive, and it provides defensible evidence for extending prior theory about these linkages. We show that labor, monetary and business freedom influence opportunity-motivated entrepreneurship in unique ways based on formal education level. Entrepreneurs with higher formal education benefit from labor and monetary freedoms, whereas entrepreneurs with lower formal education generally engage in necessity-motivated entrepreneurship. These implications not only extend existing theory but offers actionable practical implications for scholars, policymakers, entrepreneurs and ecosystem builders. By tailoring progressive institutions to the needs and affordances of different groups in entrepreneurial ecosystems, observing principles we delineate in this paper, scholars and policymakers will foster more robust entrepreneurship activities.

The supplementary material for this article can be found online.