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Institutions, Entrepreneurship, and Economic Growth: What

Do We Know and What Do We Still Need to Know? Christian Bjørnskov and Nicolai Juul Foss

Journal article (Final published version)

CITE: Institutions, Entrepreneurship, and Economic Growth : What Do We Know and What Do We

Still Need to Know? / Foss, Nicolai Juul; Bjørnskov, Christian. In: The Academy of Management Perspectives, Vol. 30, No. 4, 08.2016, p. 292-315.

DOI: 10.5465/amp.2015.0135

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r Academy of Management Perspectives2016, Vol. 30, No. 3, 292–315.http://dx.doi.org/10.5465/amp.2015.0135

S Y M P O S I U M

INSTITUTIONS, ENTREPRENEURSHIP, AND ECONOMICGROWTH: WHAT DO WE KNOW AND WHAT DO WE STILL

NEED TO KNOW?

CHRISTIAN BJØRNSKOVAarhus University

NICOLAI J. FOSSBocconi University

We review the literature that links institutions, entrepreneurship, and economic growthoutcomes, focusing in particular on empirical research. Most of the literature has aneconomics orientation, but we also review relevant literature from other social sciences,including management research. The review helps identify a number of conceptual,theoretical, and empirical gaps, calling for further research. For example, the literaturenarrowly identifies entrepreneurship with start-ups and self-employment; does nottheorize many potentially relevant inter-level links and mechanisms; and suffers fromsample limitations, omitted variable biases, causality issues, and response heterogene-ity. We argue that theories in management research, such as the resource-based view,transaction cost economics, and strategic entrepreneurship theory, can fill some of theconceptual and theoretical gaps.

Much of the recent social science interest in en-trepreneurship, including management research, isarguably derived from the importance of entrepre-neurshipoutcomes at higher levels—that is, resourceallocation, economic growth, and social changemore generally. “The entrepreneur,” declared IsraelKirzner (1980, p. 5), “is theprimemover of progress.”“Progress” here means industry dynamism, start-upactivity, corporate renewal, and the creation andrenewal of competitive advantages, as well as tech-nological progress and economic growth. Because ofthe obvious importance of such progress, under-standing the multilevel antecedents of entrepre-neurship and how the entrepreneurship of firms andindividuals aggregates up to economy-wide conse-quences should be of very high importance to socialscientists (cf. Shepherd, 2011). And yet, there issurprisingly little work that systematically links theantecedents of entrepreneurship, entrepreneurialactivity itself, and the aggregate consequencesthereof in a unified framework (Bjørnskov & Foss,2008, 2013; Holcombe, 1998). The reason is thatmultilevel inquiry into the causes and consequences

of entrepreneurial decisions and actions is a highlycomplex undertaking that has been hampered bythe absence of unified theorizing and useful datasources.

Economics interest in entrepreneurship as a causeof growth is rather recent; not much work exists onthe subject, and most of it is theoretical (e.g., Aghion &Howitt, 1992; Baumol, 1990; Blanchflower, 2000; Blau,1987; Parker, 2005; Wennekers & Thurik, 1999). In-terest in entrepreneurship in the other social sciences,such as sociology (Thornton, 1999), anthropology(Oxfeld, 1992), political science (Klein, Mahoney,McGahan, & Pitelis, 2010), and economic and busi-ness history (Landes, Mokyr, & Baumol, 2010), is alsoa relatively recent phenomenon. In any case, specificinterest in the institutions–entrepreneurship–aggregateperformance nexus is largely confined to economics.Therefore, our review and discussion mainly relatesto economics, although we also discuss the potentialof sociology to expand our understanding of theconsequences of institutions on entrepreneurship,and the ability of management research to deal withthe micro-mechanisms of entrepreneurship.

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Specifically, we first review most of the extantempirical research that deals with the institutionaldrivers and the aggregate consequences of entrepre-neurship. We identify several closely related con-ceptual, theoretical, and empirical gaps in thisliterature and discuss how these gaps may be filled.Some of the gaps have to do with conceptualizing(and, in turn, operationalizing and measuring) thekey variables. For example, most of the literature ineconomics and sociology takes entrepreneurship tobe exclusively about start-ups and/or self-employment(Foss & Klein, 2012; Foss & Lyngsie, 2012; Parker,2005, 2011). However, management research pointsto entrepreneurship by established firms as a veryreal factor of substantive importance. As anotherexample, most work on the impact of institutionson entrepreneurs takes an economics perspective onthe relationship between entrepreneurship and in-stitutions. While economists traditionally emphasizerelatively “hard” economic constraints implied byinstitutions, such as the stability and enforceabilityof property rights and non-confiscatory tax regimesand regulations (e.g., North, 1990), sociologistsmay put more emphasis on “soft” aspects, such asshared values and cognition (Scott, 1995), that areembodied in institutions and may also matter toentrepreneurship.1

We also point to several gaps in the understandingof the mechanisms that link institutions, entrepre-neurship, and growth, or, in sociology parlance, thestructure–agency relation(s). Thus, our literaturereview reveals that most studies tend to overlookmany theoretically relevantways inwhich firms andentrepreneurs mediate the relationship betweeninstitutions/policies and aggregate outcomes. Em-pirically, the reviewed literature suffers from samplelimitations, omitted variable biases, causality issues,and response heterogeneity. Some of the latter prob-lems turn on statistical issues that are independent ofconceptual and theoretical considerations. However,some are heavily dependent on, for example, how

entrepreneurship is conceptualized or how the “trans-mission mechanism” from institutions/policies to ag-gregate outcomes in terms of economic performance istheorized.

Filling such gaps calls for drawing on prominenttheories inmanagement research, such as the resource-based view, strategic entrepreneurship theory, andwork on institutional entrepreneurship (Pacheco,York, Dean, & Sarasvathy, 2010), as well as, for ex-ample, transaction cost economics (Williamson,1996), new institutional economics (North, 1990),and institutionalism in sociology (Scott, 1995). Weoffer a sketch of what such multilevel/multifieldtheorizing may look like. The specific argument weoutline is that institutions and policies at the level ofthe regional or national economy influence the ag-gregate elasticity of substitution, an important mech-anism in the growth process, which may be looselydefined as the flexibility withwhich resources can becombined. Institutions and policies exert this influ-ence because they influence the (transaction) costs ofentrepreneurs searching for, combining, andadaptingheterogeneous resources in thepursuit of profit underuncertainty. Entrepreneurial activity depends sys-tematically on such costs, and hence the impactof entrepreneurship on aggregate economic perfor-mance, notably growth, is thus dependent on thetransaction costs of resource assembly and their an-tecedents. In sum, key ideas inmanagement researchcan help us understand the economic mechanismsthat mediate the institutional influence on aggregateeconomic performance.

BACKGROUND AND LITERATURE REVIEW

Background: The Entrepreneur in the SocialSciences

The social science concern with the entrepre-neurial function predates even Adam Smith’sWealth of Nations, often taken to be the birth certif-icate of economics: Richard Cantillon’s Essai sur laNature du Commerce en General was published 21years prior to Smith’s classic. Economists, in par-ticular, have contributed to conceptualizing the en-trepreneur anddefining a role for the entrepreneurialfunction in the broader economic system (Baumol,1990, 1993; Kirzner, 1980, 1997; Knight, 1921; vonMises, 1949; Schumpeter, 1911). Other social sci-entists as well as management scholars contributedmuch later to the entrepreneurship field.

However, in spite of originating the social scienceinquiry into the entrepreneur, the economics and

1 Scott (1995) broadened the entrepreneurship conceptto include institutional entrepreneurship, commonly de-scribed as “activities of actors who have an interest inparticular institutional arrangements and who leverageresources to createnew institutions or to transformexistingones” (Garud, Hardy, & Maguire, 2007, p. 958). While thetopic of institutional entrepreneurship is of clear interest, itis also quite close to the concepts of political rent-seeking,as analyzed in public choice and political economy (cf.Munger, 2008), andBaumol’s (1990) concept of destructiveentrepreneurship.

2016 293Bjørnskov and Foss

management research literatures have until recentlybeen almost silent on the institutional and policyantecedents of the incidence and nature (i.e., whetherproductive or destructive) of entrepreneurship(Baumol, 1990; Foss & Klein, 2012; Zahra & Wright,2011).Forexample,BjørnskovandFoss (2008)pointedout that the classical writers on entrepreneurship ineconomics—notably, Schumpeter (1911), Knight(1921), von Mises (1949), Kirzner (1973), and Casson(1982)—discussed the antecedents of entrepreneur-ship rather sparsely, concentrating on personal char-acteristics or simply the lure of profit.2 The mainreason is arguably that the classical entrepreneurshipscholars—who were virtually all economists—weretakenupwithdefining the entrepreneurship constructand its role in economic theory. This has not alwaysbeenappreciated inmanagement research. Thus, Fossand Klein (2012, in press) pointed to the irony in thefact that although the work of Kirzner (1973, 1997) isheavily cited in recent management research onentrepreneurship, and ostensibly supplies key in-sights for an important contemporary approach toentrepreneurship inmanagement (Shane, 2000), hedeliberately does not offer any psychological orother detail on the entrepreneur. Instead, Kirzner’sinterest is almost solely in the consequences ofentrepreneurship—the process of market equilibra-tion. Since disequilibrium means that unexploitedprofit opportunities exist, positing the existence ofa particular human faculty (albeit one that may beasymmetrically distributed)—namely, the “alertness”to hitherto unrecognized opportunities for profit—provides the basis for a theory of equilibration.

Some Recent Economics Work onEntrepreneurship

Kirzner and the rest of the classical entrepreneur-ship scholars concentrate on the functions of entre-preneurship (Klein, 2008), whether equilibrating(Kirzner, 1973), disrupting (Schumpeter, 1911), orcreating (Casson, 1982) markets or forming newfirms (Knight, 1921). Klein (2008) contrasted thiswith the tendency in recent labor economics and theeconomics of entrepreneurship to conceptualize en-trepreneurship as an occupational choice—that is,a choice between being an employee and being self-employed (Kihlstrom & Laffont, 1979; Lucas, 1978;

Parker, 2005).3 Both views, however, imply thatthere are aggregate consequences of entrepreneursstarting firms to help them realize the opportunitiesthey perceive (Holmes & Schmitz, 1990), and that indoing this entrepreneurs appear as “portfolio” or“serial” entrepreneurs (Parker, 2005; Westhead &Wright, 1998). And yet, it is only rather recently thateconomists have begun to systematically model andmeasure the economy-level consequences of entre-preneurship (cf. Bjørnskov & Foss, 2013), beginningwith Baumol (1990, 1993), Schmitz (1989), andAghion and Howitt (1992). Baumol (1990) basicallyassumed that the supply of entrepreneurship in aneconomy is a constant, but then argued that out-comes may differ dramatically depending on whichkind of entrepreneurial activity—productive, un-productive, or destructive—the institutional matrixincentivizes. In Baumol’s conceptualization of therole of entrepreneurs, institutions and policies thusaffect the productivity of entrepreneurial activityinstead of the supply of it.

Schmitz (1989) and Aghion and Howitt (1992)built on Schumpeterian foundations and in differentways endogenized entrepreneurial activity, whichthen aggregates up to economy-wide growth. Entre-preneurship has also been included in models thatare based on the so-called “endogenous” or “new”

growth theory (e.g., Erken et al., 2008; Romer, 1990).4

In general, economists have increasingly modeledentrepreneurship as the mechanism that picks andimplements process innovations (that improve pro-ductivity) and product innovations (that expandchoice options) that may drive growth (e.g., Acs,Braunerhjelm, Audretsch, & Carlsson, 2009; Agarwal,Audretsch, & Sarkar, 2010; Aghion & Howitt, 1998a;Wennekers & Thurik, 1999). For example, akin toSchumpeter (1911), Audretsch et al. (2006) and Acset al. (2004) suggested that entrepreneurs searchwithin the available knowledge stock and pick“knowledge bits” that may give rise to new productsor processes. This conceptualization links directly to

2 Schumpeter (1911) does point to technology and fi-nance as antecedents, and von Mises (1949) discusses therole of regulation as a factor that hampers the exercise ofthe entrepreneurial function.

3 The occupational choice conceptualization of entre-preneurship associates it strictly with self-employmentand the formation of new firms. Given the importance ofstart-ups for the overall dynamism of the economy (Storey,1994), this is a conceptualization that makes sense. How-ever, it neglects wider views of entrepreneurship, such asthe view that it is a type of behavior that can also be exer-cised by established firms (Foss & Lyngsie, 2014).

4 Still, “entrepreneur” does not appear in the index ofAghion and Howitt’s (1998b) magisterial textbook on en-dogenous growth theory.

294 AugustAcademy of Management Perspectives

institutional and policy concerns, as it supplies a ra-tionale for public R&D policies (see also Zahra &Wright, 2011).

In all, however, it would be an exaggeration to saythat entrepreneurship features prominently in theliterature on the economics of growth. For example,Temple’s (1999)overviewof empiricalworkongrowthdoes not mention entrepreneurship at all. A numberof more recent publications do in fact empiricallylink entrepreneurship and growth (e.g., Audretsch,Keilbach, & Lehmann, 2006; van Stel, 2006), but it re-mains fair to say that, first, such recentwork is not partof mainstream growth economics, and second,this mainstream still—even in spite of Aghion andHowitt’s (1992)work—remains relativelyuninterestedin exploring the role of entrepreneurship in the growthprocess.5 Thus, interest in mainstream economics inlinking growth and entrepreneurship seems to be lo-catedeither in avery specific,mainly formal, approachthat is based on Aghion and Howitt (1992, 1998a) andAghion, Akcigit, and Howitt (2014), in Baumol’s work(1990, 1993), or inmore popular writing (e.g., Baumol,Litan, & Schramm, 2007).

The Entrepreneurship Research Gap: Lack of Dataand Constraining Assumptions

There are many reasons for the relative lack ofsystematic inquiry into the (multilevel) antecedentsand consequences of entrepreneurship in social sci-ence and management research. The first is a basicissue of data availability: Successfully conductingmultilevel quantitative empirics requires qualitydata that allow for sufficient variation at the relevantlevels. Such data have only very recently been madeavailable: The first dataset (the Global Entrepre-neurship Monitor dataset) that allows for suchcomparisonswas first made available as late as 1999.Data that allow for measuring institutional and eco-nomic policy go somewhat further back in time;serious empirical cross-country work that linkseconomic growth to such data can be dated to Barro(1991).

The second reason is that most social sciencefor a long time simply took little interest in the

entrepreneur. Beginningwith Hayek (1945) and vonMises (1949), many have argued that the advent anddominance of the formal/mathematical mode of dis-course in economics, with its attendant assumptionsabout information and equilibrium that becamedominant after World War II, meant that the entre-preneur got squeezed out of economics entirely(e.g., Bianchi &Henrekson, 2005; Kirzner, 1973). Theargument is that the use of functional forms, mathe-matical constants, full information assumptions, andequilibrium reasoning leaves little room for creativeentrepreneurial acts that transcend given means–ends frameworks and exploit pockets of ignorancein the market (Cosgel, 1996; Kirzner, 1973, 1997).However, the problem is not mathematization/formalization as such—a number of formalmodels ofthe entrepreneurial function and its market-leveloutcomes indeed exist (e.g., Kaul, 2013; Littlechild &Owen, 1980; Yates, 2000)—but rather the assump-tions about information and/or risk and uncertaintythat are fed into formal economics models. In par-ticular, Knightian uncertainty—situations central toentrepreneurial activitywhere agentsmakedecisionsbased on an unknown distribution of the outcomes(Foss & Klein, 2012)—is very difficult to incorporateinto the type of formalmathematicalmodeling that iscentral to modern growth theory.

Other social sciences, such as sociology and polit-ical science, have traditionally taken even less in-terest than economics in the entrepreneur.6 However,within the past decade or so, scholars working froma sociology perspective have increasingly taken aninterest in the entrepreneur and the consequences ofentrepreneurship, as witnessed by recent work thathas built on the concept of “institutional entrepre-neurship” (DiMaggio, 1988; cf. Pacheco et al., 2010).This literature focuses on how actors can deployresources, investments, and actions to deliberatelycreate or change institutional arrangements in sucha way that their interests are furthered, for example,by engaging in creative ways of rent-seeking (e.g.,lobbying) relative to political decision makers. Assuch, it is closely related to the concepts of rent-seeking and lobbyism as analyzed in public choiceand political economy for half a century (cf. Baumol,1990; Munger, 2008; Henrekson & Sanandaji, 2010).

Of course, management research has been explic-itly concerned with entrepreneurship for more thanseveral decades. Yet for a long time the study of

5 In the most recent Handbook of Economic Growth(Aghion & Durlauf, 2015), unlike previous handbooks, en-trepreneurship is covered in two chapters. However, bothchapters develop formal mathematical growth models onSchumpeterian foundations. These models and the ante-cedent chapters in the handbook are conceptually entirelyunconnected to the chapters on institutions.

6 The work of Howard Aldrich stands out as an excep-tion; see Aldrich (2011) for a collection of his sociology onentrepreneurship over several decades.


entrepreneurship remained synonymous with thestudy of small firms and even family firms (Foss &Klein, 2012), and the idea of the study of entrepre-neurship as sui generis arguably did not gain wide-spread acceptanceuntil the 1980s (e.g., the Journal ofBusiness Venturing was founded in 1985). Clearlyarticulated research programs have appeared ratherrecently. The opportunity discovery stream of re-search associated most directly with Scott Shane(Shane, 2000; Shane & Venkataraman, 2000) repre-sents perhaps the first major distinct program inmanagement entrepreneurship research. To date,most management entrepreneurship research hasalso been largely micro in the sense that macro an-tecedents and outcomes of entrepreneurship havenot been examined.7

Institutions, Entrepreneurship, and EconomicOutcomes: Levels and Links

In spite of the above challenges related to dataavailability and theoretical assumptions, a smallliterature has begun to explore the links betweeninstitutions and economic policy (antecedents), en-trepreneurship, and aggregate outcomes, notablyeconomic growth. To get a basic grip on those links,consider the so-called “Coleman bathtub” (Coleman,1990) depicted in Figure 1. This will also serve asa diagnostic tool that will help us identify a numberof key gaps in the extant literature.

The diagram is a basic two-level (micro–macro)structure that depicts inter-level relations as causal,and asserts that all macro-to-macro links are in ac-tualitymediated by links that involve themicro level(the underlying argument is that no causal mecha-nisms unfold entirely on themacro level). Thus, thereis a causal link from the matrix of incentives repre-sented by institutions and economic policies repre-sented by the node in the upper left corner of thediagram (macro) to the conditions of entrepreneur-ship, represented by the lower left node (micro). Suchconditions include typical economic constraintsand enablers, such as prices and personal wealth(Henrekson, 2005), that directly influence entrepre-neurial decision making (i.e., arrow 2 in Figure 1).

However, as sociological institutionalism sug-gests, institutions are not just more or less explicitlydesigned external constraints on actions, as inNorth’s (1990, p. 3) definition of institutions as the“humanly devised constraints” on action. Institu-tions are also emergent (Hayek, 1973). They transmitvalues and influence cognition by providing sharedcognitive categories (e.g., how risk is perceived andevaluated) and norms (e.g., how acceptable it is tobreak norms, be competitive, etc.) that are in-ternalized by actors (Denzau & North, 1994; Scott,1995) and that may influence the conditions of en-trepreneurial action, as the actors see these (Pachecoet al., 2010).8 As Shepherd (2011) suggested, a pos-sible bridge between the institutional and the indi-vidual level (i.e., arrow 1) is represented by work onidentity and categorization (e.g., Tajfel & Turner,1979), institutions supplying basic identities andcategories that, when internalized by entrepreneur-ial decision makers, influence entrepreneurial de-cisions (arrow 2).

Thus, the conditions depicted in the lower leftnode of Figure 1 give rise to entrepreneurial actions(the lower right node), such as the establishment ofnew firms or the recognition and exploitation of op-portunities by existing firms (micro) that aggregateup to economy-wide outcomes, such as “churn” oreconomy-wide changes in productivity (macro)(i.e., arrows 1, 2, and 3). Fine-grained analysis of ar-row 2 makes it clear that entrepreneurs may react

FIGURE 1Illustrating Links Between Institutions/Policies,

Entrepreneurship, and Outcomes

Entrepreneurial action

Conditions of entrepreneurial action

Institutions/economic policy 4

1 3

2

Entrepreneurial outcomes

7 This depends somewhat on where one locates“macro.” If networks, organizational structures, etc., areseen as “macro,” then a considerable amount of work hasbeen done on this. However, if “macro” is considered thenational or state level (as in this paper), the claim holdstrue.

8 In a relevant study of the interplay of institutions andventure capitalist behaviors in three countries, Zacharakis,McMullen, and Shepherd (2007) examined how percep-tions of institutions (notably the law) influence the in-formation venture capitalists attend to when makinginvestment decisions, and documented the relevant cross-country variation.


differently to theseconditions (McMullen&Shepherd,2006) depending on their level and kind of motiva-tion (e.g., the relative roles of intrinsic and extrinsicmotivation), attitude toward errors, level of priorknowledge (Shane, 2000), commitment to theirventure, identity, etc. (Shepherd, 2011). These dif-ferent reactions cause response heterogeneity. Inturn, these reactions complicate the analysis of ar-row 3 (i.e., aggregation from entrepreneurial de-cisions and actions to aggregate outcomes) relative toa situation where entrepreneurs can be taken to behomogeneous. For example, heterogeneitymay existwith respect to persistence and commitment tofailing enterprises, which introduces evolutionary(selection) considerations in the analysis of theaggregation of entrepreneurial actions (Nelson &Winter, 1982).

In addition, the basic two-level diagram can bemade more complicated in various ways. For exam-ple, additional levels can be added to it (e.g., the levelof industries, networks, etc.), in effect stacking“bathtubs” vertically, or, recognizing that institu-tions and policies are themselves endogenous,“bathtubs” can be arranged horizontally. For exam-ple, DiMaggio (1988) and Baumol (1990) argued thatentrepreneurial effort may be directed to influencinginstitutions and policies, and the recent literature on“institutional entrepreneurship” (Garud, Hardy, &Maguire, 2007; Pacheco et al., 2010) amounts to thesame basic idea. As noted above, this literaturetherefore also suggests that entrepreneurial activityand entrepreneurial problems can affect institu-tional choices and policy. To the extent that thisoccurs and represents the opposite causal influencethan that shown in the figure, it creates a potentialendogeneity problem.

In the next section, we use the diagram to reviewthe existing empirical literature on the links amonginstitutions, entrepreneurship, and growth.Wedo sobecause this literature encapsulates previous argu-ments based on theoretical developments. However,our gap-finding exercise has implications for boththe empirical and the theoretical literature.

EMPIRICAL WORK ON INSTITUTIONS,ENTREPRENEURSHIP, AND GROWTH

To identify relevant empirical work on the in-stitutions, entrepreneurship, and growth nexus, wedid a thorough literature search based on relevantkeywords in the Scopus and Web of Science data-bases. Our criteria for selecting studies to be coveredwere that papers should (1) focus on institutional

characteristics, (2) include formal empirical analysisbeyond simple correlations, and (3) focus on a di-rectly demonstrated association to entrepreneurialactivity and not only an interpretation consistentwith effects of entrepreneurship. The third re-quirement means that we excluded several other-wise highly profiled papers. A recent example isAcemoglu, Akcigit, and Celik (2014), who built atheoretical model of innovation within firms andfocused their empirical analysis on CEO age andcharacteristics. Their interpretation revolves aroundentrepreneurial activity but without including anydirect indicators, which is a typical feature of severalrecent studies in both economics and business re-search.9 This resulted in the identification of only 28studies, including book chapters, that directly ad-dress the aim of this paper. We summarize thesestudies in Appendix Table 1 and discuss the keycontributions below.

Pioneering Studies

Audretsch and Acs (1994) and Audretsch andFritsch (1994) conducted pioneering empiricalstudies in the stream of entrepreneurship researchaddressed here. Focusing on start-ups in WestGerman regions and across 177 industries, the stud-ies dealt primarily with convexities in production,unemployment, and capital costs. While Audretschand Acs (1994) also found evidence that start-upactivity was positively influenced by macroeco-nomic growth (i.e., the macro-to-micro link inFigure 1), these first studies did not focus on spe-cific institutions or policies or assess the economicimpact of entrepreneurial activity. They have,however, been followed by a long string of studieson the particular characteristics of entrepreneursand start-up firms that more explicitly link tomacro-level determinants (see, for example, the over-views in Freytag and Thurik, 2007, and Shah, Smith,and Reedy, 2012).

Other studies on the relationships amonginstitutions/economic policy, entrepreneurship, andmacroeconomic performance have focused onmacrovariables only. Such studies are typically cross-countryor use cross-state variation within the United States.The first empirical studies of how institutions and

9 Note that we have also performed searches based onmore recent references to seminal papers in the entrepre-neurship literature. We have thus found additional papersby startingwithScott (1995) andKreft andSobel (2005) andworking our way forward.


economic policy influence the country- or state-levelincidence of entrepreneurship are Kreft and Sobel(2005) for the United States, Ovaska and Sobel (2005)for transition countries, Bjørnskov andFoss (2008) fora larger (though still small) cross-country context (27countries), andMcMullen,Bagby, andPalich (2008) fora somewhat larger context (37 countries). For concep-tualizingandmeasuring the institutional andeconomicpolicy dimension, all four studies focused strongly onthe construct of economic freedom. In this case, “eco-nomic freedom” is a situation where “individuals arepermitted to choose for themselves and engage in vol-untary transactions as long as they do not harm theperson or property of others” (Gwartney, Hall, &Lawson, 2014, p. 11). Accordingly, the focus in thisliterature ismainlyontheprotectionofprivatepropertyrights, the absence of intrusive regulations, and tax andtransfer differences across countries, although theconcept also includes monetary transparency and in-dicators of investment and trade policy.

The literature on economic and social conse-quences of economic freedom is rather large andgrowing rapidly. As the comprehensive survey inHall and Lawson (2014) showed, almost all papersshowed positive effects on growth, productivity,employment, and other macroeconomic variables ofeconomic freedom. However, only four of the 402studies surveyed by Hall and Lawson (2014) directlyexplored any relationship between economic free-dom and entrepreneurship. These four studies in-clude Kreft and Sobel (2005), Ovaska and Sobel(2005), Freytag and Thurik (2007), and Bjørnskov andFoss (2008). Incidentally, these studies arrived atsomewhat opposite conclusions. While two foundthat low tax levels are likely to reduce entrepre-neurial activity, Kreft and Sobel concluded that rel-ative freedom from restrictive labor regulations ispositively associated with entrepreneurial activityacross the United States, while Bjørnskov and Fossfound that the provision of sound money—low andstable inflation rates and predictable monetarypolicy—is positively correlated with entrepreneur-ial activity.10

Later Work on Institutions, Policies, andEntrepreneurship

A subsequent although still quite small literaturedevelops these themes inmoredetail. Thus,Nystrom(2008) reexamined the cross-country evidence inBjørnskov andFoss (2008) in amuch larger sample ofcountries, using panel data and consistently com-parable self-employment rates instead of survey datafrom the Global EntrepreneurshipMonitor database.McMullen et al. (2008) used Heritage Foundation/Wall Street Journal economic freedom data andGEM data, regressed “opportunity” and “necessity”entrepreneurship against measures of economic free-dom, and found that different freedoms affect entre-preneurship differently. A series of studies likewisereexamine themes introduced by the first studies,mostof which are surveyed in Alhorr, Moore, and Payne(2008), Terjesen, Hessels, and Li (2016), and Arin,Huang, Minniti, Nandialath, and Reich (2015).

Recent studies have also explored more specificinstitutions and policies. For example, Djankov et al.(2010) explored the effects of effective corporate taxrates on investment, FDI, and entrepreneurial activ-ity. They found that the corporate tax rate is partic-ularly harmful to business entry, while other taxesalso affect domestic and foreign investments inestablished companies. Ardagna and Lusardi (2009)instead focused on how regulations affect the selec-tion into entrepreneurship, finding a consistentlynegative effect of regulations. They neverthelessalso found that tighter regulations imply that femaleentrepreneurs start their own businesses (as neces-sity entrepreneurship) mainly because better em-ployment opportunities do not exist. Moreover,individuals with good business skills and entre-preneurial networks are substantially less likely tobecome entrepreneurs when facing stronger regula-tions. Consistent with Baumol’s (1990) consider-ations, these results suggest that regulations affectthe productivity of entrepreneurship.

Outcomes of Entrepreneurship

While empirical studies have explored the effectof different institutions and policies on start-uppropensity, very few studies have sought to directlyestimate or even discuss the aggregate conse-quences, such as productivity increases, of entre-preneurship measured as a start-up activity. Part ofthe issue probably derives from the well-knownproblem of how to measure productivity at the firm,regional, or national level (Caselli, 2005; Hulten,

10 While not specifically addressing entrepreneurship,Ciccone and Papaioannou (2007) belong in the group ofpioneering studies. They explored the determinants ofestablishment growth, specifically the success of start-ups.Klapper et al. (2006) likewise focused narrowly on thecreation of limited liability firms. Their main result is thatregulations affecting the time taken to register new busi-nesses negatively affect entry in industries experiencingexpansionary global demand.


2001). Productivity, or economic efficiency, is inprinciple easy to conceptualize by using a Solowresidual (i.e., the unexplained part of economicperformance when the effects of capital, labor, andother inputs have been accounted for). In practice,however, any assessment must rest on strong as-sumptions about, for example, how to measure ed-ucation properly, whether or not to account forquality differences in capital, which functional formto employ, and how to take externalities into accountwhen moving from the firm level to a regional ornational level. Yet another reason that few studiesseek to directly estimate or even discuss the aggre-gate consequences of entrepreneurship may be thatmost studies so far have simply taken the employ-ment, productivity, or innovation effects as given,focusing on how to foster entrepreneurship (e.g.,Djankov et al., 2010); in other words, their aim isnot to cover all the mechanisms in Figure 1, but onlythe top-down ones (i.e., arrow 1).

Anokhin and Wincent (2012) produced one of thefew attempts in the literature to bridge this gap. Usingthe GEM data for a sample of 35 countries, theyassessed the effectiveness ofwhat they termed “broad-strokes policies” aimed at promoting entrepreneur-ship that affects country innovativeness.Theyproxieddifferences in broad policies by the Heritage Founda-tion Index of Economic Freedom and a dummy forcorporatist “coordinated market” economies. Theauthors found that policies explicitly aimed at pro-moting entrepreneurship are likely to be misguidedbecause they suffer from what they call a “Hayekiandesign problem”: It is inherently impossible for policymakers to know ex ante which start-up firms willsucceed and which will not, and broad-strokes poli-cies will therefore be designed in ways that benefitexisting firms instead of potential start-ups (Hayek,1948; Munger, 2008). In their search for policy impli-cations, Anokhin and Wincent (2012) instead recom-mendedmore “contingent” public efforts.While suchefforts that are designed to apply only to firms undervery specific circumstances probably alleviate mostrent-seeking problems associated with broad-strokespolicies, they nevertheless suffer from severe in-formation problems, as most simple, operationalcontingencies are likely to be strongly misleading(cf. Daunfeldt & Halvorsson, 2014).

Among the few studies that have taken on this is-suedirectly, Bjørnskov andFoss (2012, 2013) insteadfocused on the total factor productivity effects ofentrepreneurial activity. Both papers first estimatedthe institutional effects on entrepreneurial activityand found similar results to other papers that

regressed entrepreneurship data against measures ofeconomic freedom, notably that a big public sector isharmful to the incidence of entrepreneurship. Sub-sequently, they estimated the average productivityconsequences of entrepreneurship, finding sub-stantial productivity increases arising from entre-preneurship. Interestingly, Bjørnskov and Foss (2013)documented that the marginal productivity effects ofentrepreneurship are significantly larger in countrieswith large public sectors andhigh taxes.Although thismay suggest that the public sectors in large welfarestates provide services that are in some way comple-mentary to entrepreneurship, the authors noted thatthe same features directly reduce entrepreneurial ac-tivity. They therefore preferred the interpretation thatthe higher marginal effect is due to the smaller supplyof the activity—that is, a situation consistent withdecreasing marginal productivity gains from entre-preneurial activity.

SHORTCOMINGS AND OPEN ISSUES I: DATAAND MEASUREMENT

In an ideal world, scholars would have completelyidentifiedall thevariables andmechanisms implied inFigure 1. While many advances have been made, weare quite far from this ideal, as indicated by the rela-tively small amount of empirical research work on anissue that has often been highlighted by policymakersas crucially important (OECD, 2015). The shortcom-ings and open issues are conceptual, theoretical,methodological, and empirical. However, they areclosely linked, as, for example, data availability maysteer theorizing inonedirectionrather thananother. Inthis section,we reviewand exemplify these problems.

Dependent Variable: Entrepreneurship

A key problem with taking entrepreneurship asa dependent variable is related to the choice of rele-vantmeasures of entrepreneurial activity/decisions/actions—that is, the lower right node in Figure 1.Entrepreneurship can be measured in a multitude ofways, depending on the theoretical perspective. Forexample, while labor economists may prefer mea-suring potentially productive entrepreneurship interms of self-employment, other economists mayprefer tomeasure it as start-up activity. Managementscholars inspired by Kirzner (1973) tend to highlightthe discovery of opportunities, while others inspiredby a more Knightian approach (e.g., Foss & Klein,2012) may prefer to measure it in terms of the actualinvestments dedicated to the pursuit of imagined


opportunities. Obviously, measures will differ widelybased on these different conceptualizations. In addi-tion, some economists (Baumol, 1993; Schumpeter,1939, 1942) andmanagement scholars (Foss&Lyngsie,2014) have highlighted the ability of established firmsto engage in entrepreneurship andwill rejectmeasuresthat address only start-up activity.11

Actual measurement is usually a compromise be-tween the theoretical perspective and availablemeasures. Most of the studies summarized inAppendix Table 1 used either survey-based mea-sures of intending to or being in the process of start-ing up an enterprise (mostly based on the GEM) ormeasures of self-employment, micro-firms (withfewer than five employees), or start-ups. While suchmeasurement is consistent with a labor economicsapproach that conceptualizes entrepreneurship interms of occupational choice (Parker, 2005), it hasdifficulties capturing at least two other importantviews of entrepreneurship: Kirzner’s (1973, 1997)view that entrepreneurship can be exercised by(“poor and penniless”) individuals by means of ar-bitrage and does not logically require the formationof a new firm, and the view that entrepreneurshipcan be performed by established firms (Baumol,1993; Foss & Lyngsie, 2014; Schumpeter, 1942).

It is far from clear how the Kirznerian view of en-trepreneurship can be operationalized and mea-sured. Some aspects are captured by choosing tobecome self-employed; as such, this is captured bypublic registers in many countries. However, fleet-ing, informal arbitrage activities, perhaps takingplace in the gray or black sectors of the economy, aremore likely to escape seriousmeasurement (Antunes& Cavalcanti, 2007). In addition, Henrekson andSanandaji (2014) argued that self-employment mea-sures also fail to capture the effects of what they term“high-impact Schumpeterian entrepreneurship”—that is, entrepreneurial activity that destroys oldfirms but provides great societal benefits.12

Measuring the entrepreneurial activities of estab-lished firms remains a challenge (e.g., Decker et al.,

2014; Foss & Lyngsie, 2014). Patent activity and re-search and development (R&D) expenditures pro-vide some of the most widely employed measures,but neither patents nor R&D expenditures measureinnovation or entrepreneurship per se. Additionally,as Schumpeter (1911) clarified, entrepreneurshipgoes beyond innovation and includes, for example,discovering new markets, suppliers, market chan-nels, and organization forms.However, the literaturelacks scales that allow for tapping into entrepre-neurial outcomes at the firm level. Foss, Lyngsie, andZahra (2013) applied a count measure based onquestions to CEO respondents regarding the numberof opportunities they successfully realized in theprevious three years. Apart from obvious problemsof recall bias, such a measure lumps together verysmall opportunities with large opportunities. Thus,a firm that has successfully realized five trivial op-portunities appears more entrepreneurial than onethat has realized a truly major opportunity. The de-velopment of valid and reliable measurement scalesof established firm entrepreneurship remains a ma-jor challenge.

Dependent Variable: Aggregate Outcomes

On the economy level, outcomes can be measuredin many different ways, depending on interests,context, and data availability (e.g., job creation, firmgrowth, innovative entry, innovation activity, andproductivity advances) (Arzeni, 1997; Baptista,Escaria, & Madruga, 2008; Capelleras et al. 2007;Erken,Donselaar, &Thurik, 2008; Klein&Luu, 2003).In the labor economics, trade studies, and indus-trial economics fields, large-scale firm-level andindustry-level datasets exist and have been usedextensively by economists and management scholarsalike. However, the use of these data sources in en-trepreneurship studies remains in its infancy becausenew firms often enter datasets only after some periodof time.Notably, failing start-upsmaygounregistered,and the standard use of small business statistics canhidevery largeandconsequential differencesbetweensmall and young firms (Decker et al., 2014).

Causality

A distinct challenge in the literature is how toidentify and document causality. This is partly a theo-retical issue and partly an empirical challenge. Forexample, entrepreneurship may be both a cause andaconsequenceof economicgrowth. Indeed,Audretschand Acs (1994) suggested that entrepreneurship reacts

11 We refrain from discussing the additional complica-tions of measuring unproductive and destructive entre-preneurship (Baumol, 1990). Such activities are welldescribed in public choice and political economy tradi-tions, and are mostly equated with a variety of lobby ac-tivities. Young andWiseman (2014)made a recent attemptto separate productive and unproductive entrepreneur-ship in the United States.

12 Their new alternative is to measure the number ofbillionaires in each society (Sanandaji & Leeson, 2014).


positively to economic growth, as a growing economyoffers more opportunity. On the other hand, as high-lighted by theoretical work (Aghion & Howitt, 1992;Schumpeter, 1911), recent empirical studies insteadshow that entrepreneurial activity affects long-rungrowth and productivity—that is, the opposite causalassociation (cf. Bjørnskov & Foss, 2012; Koellinger &Thurik, 2012).

Causality issues are also manifest in the link be-tween institutions and entrepreneurship. Thus,Baumol’s work (1990) and the recent literature oninstitutional entrepreneurship (Li, Feng, & Jiang,2006) suggest that entrepreneurship may not just beendogenous to institutions; institutions may also beendogenous to entrepreneurship (Pacheco et al.,2010). For example, Dean and McMullen (2007) ar-gued that externality problems and other marketfailures may represent entrepreneurial opportuni-ties. Institutional entrepreneurs may seize suchopportunities by enacting institutions that help in-ternalize the relevant externalities (what Dean andMcMullen called “sustainable entrepreneurship”).

A different take on the relationship between en-trepreneurs and institutions is represented by publicchoice theory, which implies that established in-dustries typically form strong and influential lobbygroups that can affect—and in some cases define—the regulatory framework and specific legislation(Olson, 1982; Stigler, 1971). A lack of entrepreneur-ial activity and productivity gains can thereforeeasily lead to persistent lobbying for institutionalchange to prevent the entry of new and more pro-ductive or innovative firms (e.g., Baumol, 1990;Hillman, 1982). Conversely, making a clean breakwith special interests and policies can, in principle,provide substantial impetus for firm creation andentrepreneurial activity. However, the endogeneityproblem creates a need to find either specific cir-cumstances in which institutions are not affected byexisting conditions or where other information al-lows the identification of mono-causal effects.

Omitted-Variable Bias

As in many other fields, such as the literature onregional and national productivity and developmentaccounting (Caselli, 2005; Klenow & Rodriguez-Clare, 1997), the entrepreneurship literature hasnot converged on a consensus onwhat to consider asa standard or even minimalist empirical specifica-tion. As such, all studies risk suffering from omitted-variable bias, which necessitates careful robustnessanalysis (cf. Dreher & Gassebner, 2013). Among the

variables that are characteristically omitted in thestudies surveyed in Appendix Table 1 are culturalfeatures, trade flows and policy, the initial techno-logical level of the country, factor endowments, andproxies for the ease of information flows. The type ofrobustness studies pioneered in growth studies byLevine andRenelt (1992) andSala-i-Martin (1997), inwhich researchers expose their main results andpreferred specifications to extensive sets of addi-tional, potentially influential factors and empiricalalternatives, remain entirely absent in the literatureon entrepreneurship.

Multilevel Designs

Similarly, asnotedabove,manystudiesdonotbringmicro-data into the analysis even though multilevelanalysis may in many cases be both feasible and de-sirable. This lacuna is not merely a source of omitted-variable bias in cross-state and cross-country studies.As represented in the diagram in Figure 1, the re-lationships among institutions, entrepreneurship, andaggregate outcomes are, as a logical matter, multilevel(Shepherd, 2011). Thus, entrepreneurial conditionsand actions are nested in higher-level entities, such asnationally defined institutions and policies as well asinternational business cycle fluctuations. Yet part ofthe challengeof dependablemultilevel analysis lies inthe question of when new firms enter official registersand statistics (Decker et al., 2014); another part restsin the practical difficulties in separating apparentshort-term success from long-term productive contri-butions and sustainable business growth (Daunfeldt &Halvorsson, 2014).

Response Heterogeneity

Regardless of which level of analysis scholars ad-dress, a specific shortcoming in the literature is animportant assumption that mostly goes undiscussed(but see McMullen & Shepherd, 2006; Zacharakiset al., 2007): that studies assume that the responses toinstitutional and policy differences are approximatelyhomogeneous across different types of industries,businesses, and countries and institutional settings.Almost all empirical studies thereforemerely identifyaverage treatment effects that can easily hide verydifferent effects across industries or countries and, incases where actual effects are heterogeneous, createsubstantial measurement error. Related to the entre-preneurship literature, studies for example find thatheavy business and credit regulations and poor pro-tection of private property rights can be a detriment to


entrepreneurial activityand inparticular thegrowthofnew firms (De Soto, 2000), but more so when small-scale business owners cannot bribe their way aroundthese institutional problems (Bologna & Ross, 2015).Similarly, certain policies and institutions may havedifferent effects depending on the quality of otherinstitutions.

Additionally, as shown by Bjørnskov and Foss(2013), which is the only paper in Appendix Table 1that explicitly deals with the heterogeneity problem,productivity inferences can be misleading if re-searchers do not know the effects on both the supplyand characteristics of entrepreneurial activity. Find-ing that certain welfare state policies are positivelyassociated with the marginal productivity effect ofentrepreneurship may therefore not be evidence ofpositive effects of welfare policies, but rather of aninteraction between the effects on the quantity andquality of entrepreneurial activity. Generous bene-fits systems and high and progressive taxationmerely drive out the relatively less productive start-up firms. Such interactions across levels of analysisand different institutional characteristics remain al-most entirely absent from the entrepreneurshipliterature.

Sample Frames and Data Limitations

A final problem that deservesmention derives fromthe fact that despite the impressive growth of data anddata availability, only very few databases providespecialized information on entrepreneurial activity,firm growth, and start-ups. Sampling remains a par-ticular problem of studying new firms and innovativeactivity in small or relatively young firms, as samplingprocedures in most cases exclude the smallest, youn-gest, and hardest-to-find entities. Most studies use ei-ther survey data from GEM or self-employment ratesacross OECD countries, although both survey evi-dence on stated intentions and a focus on small firmsmay be misleading. For example, Decker et al. (2014)showed that productivity differences in the UnitedStates arise fromyoung firms insteadof small firms.Asmost young firms are small, what arguably drives en-trepreneurial productivity effects thus arises froma subset of self-employed individuals or very smallfirms. The real effects of start-ups will therefore beunderestimated when employing the standard ap-proach in the current literature. However, to distin-guish between different types of firms, it is necessaryto observe firms from their infancy.

In the context of data on institutions and in-stitutional characteristics, two problems persist: (1)

the problem of how best to measure institutions, andin particular how to separate de jure features from defacto implementation and execution (Voigt, 2013),and (2) the extent to which institutional details areknown and available to researchers. While data lim-itations are often treated as a rather mundane prob-lem, they must be carefully addressed before any ofthe other shortcomings of the literature can bealleviated.

Summing Up

To some extent, the above gaps and shortcomingsreflect the fact that entrepreneurship research inmanagement has tended to focus on different issuesand different levels of analysis and measurementthan that in economics. Measurement problems andconceptual issues have often been carefully dealtwith in a number of management studies, but per-haps remain somewhat less developed in the eco-nomic part of the field. Conversely, specificationissues and the causality problem are traditionallyparticularly salient among economists and have ar-guably, on average, beenmore thoroughly addressedin that part of the literature. Yet other problems arecommon for all studies regardless of whether theyoriginate in management science or economics.These include a lack of detailed micro-level databeyond case studies, the interaction betweenmacro-level institutions and policies and firm-level re-sponses, and in particular the potentially complexinteractions between different institutions andpolicies. The relative absence of studies of theinteractions between macro-level institutions andpolicies and firm-level responses also prevent moredetailed work in transmission mechanisms (at themicro level of Coleman’s bathtub in Figure 1) con-necting institutions and entrepreneurial outcomes.Any discussion of optimal institutional and policydesign is out of reach before substantially more isknown about these transmission mechanisms. In thefollowing section, we argue that extant managementresearch can contribute important insights to the the-oretical understanding of these mechanisms.

SHORTCOMINGS AND OPEN ISSUES II:THEORETICALLY UNDERSTANDING

TRANSMISSION MECHANISMS

Factors in the Growth Process

The understanding of the growth process has tra-ditionally proceeded on a high level of aggregation


(i.e., arrow 4 in Figure 1), with little attention beingpaid to the micro-aspects of the complex trans-mission mechanisms between institutions and poli-cies and economic growth. The growth literature ineconomics has traditionally been divided betweenscholars who stress the role of capital accumulationin the growth process (Lucas, 1988) and those whostress “technology” (Solow, 1956, 1957) and themany other factors that cannot be understood interms of the accumulation of capital and improve-ment of, or increases in the supply of, human capital(Bjørnskov&Foss, 2013). The latter groupof scholarsthinks of the growth process in terms of improve-ments in “total factor productivity.” This tradition-ally refers to the output changes that cannot beascribed to changes in traditional production factors(labor, capital, land). There is evidence that themainfactor that accounts for different growth experienceacross countries is the difference in total factor pro-ductivity (Parente & Prescott, 2005).

Entrepreneurial Resource Combination

Discussions of total factor productivity and thegrowth process have often highlighted R&D and theinnovations that emerge from R&D (e.g., Romer,1990). Innovations are, of course, introduced byfirms and also include nonscience-based innova-tions (e.g., many process and organizational in-novations). As Foss and Klein (2012) argued, theseprocesses are entrepreneurial ones; specifically, theyinvolve experimenting with the combination andrecombination of heterogeneous resources in thepursuit of profits under uncertainty (Agarwal,Barney, Foss, & Klein, 2009; Rumelt, 1987). In theaggregate (i.e., arrow 3 in Figure 1), these processeslead to the productivity advances and improvementsin resource utilization that are significant parts ofincreases in total factor productivity. This suggeststhat the resource-based view, which stresses funda-mental resource heterogeneity, has insights to offeron the micro-mechanisms of the growth process.This requires a break with certain aspects of theproduction function framework that has dominatedpostwar economics. Thus, this framework takes re-sources to be homogeneous within categories (land,labor, capital) and assumes that resources are alwayscombined in the best possible manner. In Olson’s(1996) words, the traditional framework explicitlyassumes that big bills are not “left on the sidewalk.”13

This standard framework has the unfortunateconsequence that it becomes hard to define a mean-ingful role for the entrepreneur (cf. Agarwal et al.,2009). In actuality, resources are of course hetero-geneous (Barney, 1991; Lachmann, 1956), and com-bining them in the uncertain pursuit of profitsconstitutes the essence of enterprise (Foss & Klein,2012; Knight, 1921). Because of uncertainty andasymmetric information, the optimal combinationof resources is not a given (as in the economics ofproduction), but is something that at best can beapproached through managerial processes of re-source experimentation. Thus, as argued by FossandKlein (2012), processes ofmergers, divestments,spin-offs, new firm formation, etc., reallocate re-sources across firms in response to price signalsand entrepreneurial behaviors, and make theeconomy track its (moving) production possibilityfrontier (e.g., Foster, Haltiwanger, & Krizan, 2002).

The Role of Institutions

Experimental processes of resource combinationare influenced by institutions and policy (i.e., arrow1 in Figure 1). In general, there ismuch evidence thatinstitutions have a strong impact on growth (see, inparticular, Rodrik, Subramanian, &Trebbi, 2004). AsNorth (1990, p. 6) explained, “The major role of in-stitutions in a society is to reduce uncertainty byestablishing a stable (but not necessarily efficient)structure to human interaction. The overall stabilityof an institutional framework makes complex ex-change possible across both time and space.” Suchhigher certainty translates into lowered transactioncosts. Specifically, higher certainty means that thecosts of contracting and of protecting property arelowered, which in turn means that more entrepre-neurial projects will be undertaken. As Bjørnskovand Foss (2013) argued, higher certainty and in-centives for productive behaviors are particularlystrongly influenced by the extent to which privateproperty rights are protected, including dimensionssuch as generality (i.e., equals are treated equally),transparency and accountability in public decisionmaking, and, importantly, an expectation that prop-erty rights are effectively enforced.

While many social scientists have made similarpoints, the ways in which institutions and entre-preneurial activities are related have seldom beenpinned down with much precision (for some recentattempts, see Henrekson, 2005). If one accepts theargument that economic growth is to a large extentthe result of the introduction of new modes of

13 These words appear in the title of Olson’s 1996 articlein the Journal of Economic Perspectives.


organization, ways of better allocating resources topreferred uses, and so on, the flexibility (i.e., costli-ness) withwhich these changes can bemade becomesa central concern (Bjørnskov & Foss, 2013). Economicproduction theory captures such flexibility with thenotion of the “elasticity of factor substitution” (Klump& de La Grandville, 2000).14 A high elasticity of sub-stitution means high factor productivity, as resourcesare more easily allocated to highly valued uses. Theelasticity of substitution is not a purely technical pa-rameter and is endogenous to institutional variables.Institutions that lower the transaction costs forsearching for contract partners, bargaining, andmonitoring and enforcing contracts, positively in-fluence the ease, speed, and flexibility with whichresources canbe identified, allocated, combined, etc.by these entrepreneurs. In other words, such in-stitutions imply that the elasticity of substitution ishigh, and hence positively influence total factorproductivity. Low transaction costs result fromwell-defined and enforced property rights. Thus, in-stitutional and political features, such as the qualityof regulations and the judicial system, that directlyinfluence property rights influence the relationshipbetween entrepreneurship and total factor produc-tivity, and therefore growth.

Summing Up

Bjørnskov and Foss (2013) made use of argumentssuch as the ones above in linking their institutionalmeasures (mainly “freedom variables” relating toregulatory quality, the size of government, soundmoney, etc., but also measures such as the extent offoreign trade). However, the theorized mechanismsremain unobserved in their work. This is significantbecause it points to both the lack of theorizing of themechanisms that traverse levels and the absence ofappropriately nested data across a sufficient numberof countries (or regions) that can be used to test andotherwise further theorizing in this domain, as wepointed out earlier. We are, however, optimisticconcerning progress in this area. First, data relevantto understanding the mechanics of the growth pro-cess have increasingly become available, and there isno reason to think that relevant multilevel datasetswill remain unavailable. Second, theorizing the rel-evant inter-level mechanisms will, we suggest, re-quire the integration of the economics of institutions

and growth with management research insight intoentrepreneurship and resource allocation in firms.As economists increasingly question the traditionalproduction function view of firms (Williamson,1985) and as management scholars increasinglyadopt rigorous methods, we are confident that sucha combined enterprise will not only be possible butwill be successful as well.

CONCLUSION

The ultimate purpose of a structured survey ofa literature is to provide an overview that allowsreaders to form a sense of the shortcomings of thisliterature and how these may be addressed. In otherwords, the main question of this paper is what theliterature so far has not addressed and how best tomeet the challenge of moving closer to a real un-derstanding of the links between institutions, entre-preneurship, and subsequent economic performance.In this concluding discussion we further reflect onsomeof themajor challenges left in the literatures.Wespecifically identify five related gaps.

First, as Casson andWadeson (2007, pp. 239–240)emphasized, growth is an important political objec-tive, but “entrepreneurship is most often used toexplain differences between countries (and regions)in levels of economic activity.” Thus, there is littleemphasis on growth. This may be a consequenceof the relative absence of theoretical developmentproviding clear guidelines for empirical studies ofthe association between growth and entrepreneurialactivity. The line of research started by Aghion andHowitt (1992) to some extent rises to this challengeby building from the Schumpeterian concept ofcreative destruction (Schumpeter, 1911). However,the difficulty of modeling creative destruction haslimited its direct applicability, and most of the im-plications that empirical researchers have drawnfrom this tradition have focused on institutionalconstraints to growth.

A second but related gap arises as current perfor-mance (in terms of either economic activity or pro-ductivity) can be seen as the compounded influenceof entrepreneurship and other influences. Focusingon current performance instead of growth can there-fore be very misleading, as it is the result of pastinstitutions and policies that may not even existanymore. Relatedly, as stressed by Bjørnskov andFoss (2013), institutions and policies may bothmoderate and mediate the effects of entrepreneur-ship on growth and performance. Several studieshave documented that institutions can affect the

14 This elasticity measures the percentage change infactor proportions due to a change in marginal rate oftechnical substitution.


supply of entrepreneurship, whereas very few stud-ies have tested whether the impact of entrepreneur-ial activity is systematically heterogeneous acrossdifferent institutions, although this point was origi-nally stressed in Baumol’s (1990) seminal work. Yetthese studies suggest that one of the mechanismsconnecting good judicial and market institutions tosubsequent growthmay be theway these institutionsadvance productive entrepreneurship. Theoreti-cally, the understanding of such institutional com-plementarities, though highlighted by economicshistorians for decades (e.g., Mokyr, 2009; North,1990), is in its infancy.

A third gap exists because many of the mecha-nisms hiding in the arrows of Figure 1 are not clearlytheorized. For example, institutions and policiesmay influence the incidence of entrepreneurship inmultiple ways, some direct, some more indirect.Intellectual property rights (IPR) regimes and taxpolicies may have very direct effects; for example,high taxation makes it difficult to accumulate thesavings that may be required for launching a start-up(Henrekson, 2005), while weak IPR regimes may re-duce the expected rents from innovative entrepre-neurship. However, informal institutions such asgeneralized trust or social norms may have moreindirect effects (cf. Scott, 1995). Such institutionsreduce the costs of searching for (trustworthy) con-tracting partners and reduce the costs of bargaining,monitoring, and enforcing contracts (Ikeda, 2008;Williamson, 1996). In turn, this increases the aggre-gate elasticity of substitution of the economy, as weargued.

Fourth, only very few of the papers we surveyeddeal with the causality issue inherent in assessingeffects of institutions and entrepreneurship, andmore than half do not even mention the problem. Tosome extent, this reflects different traditions acrossfields, with economics having developed extremestandards of causal identification in recent decades.But it also reflects that mechanisms are under-theorized, such that an otherwise rich theoreticalliterature provides only weak guidelines for dealingwith the issue.

Finally, the entrepreneurship literature tends tosuffer from the same problem as other fields thatare taken up with the consequences of institutionaldifferences. The concept of institutions and in-stitutional quality is treated very differently, rangingfrom aNorthian understanding inmost of economics(North, 1990) to a substantially broader concept insociology (cf. Scott, 1995) and a generally vagueseparation ofmarket and government institutions, as

well as an unclear separation of formal and informalinstitutions (Voigt, 2013). Scholars who work on in-stitutions andentrepreneurship thus risk engaging inconceptual stretching, which further exacerbates theproblem of under-theorizing when what is meant bythe very term “institutions” remains unclear andpoorly delineated.

Despite the gaps outlined here, the literature onentrepreneurship, institutions, and aggregate eco-nomic performance has made many importantstrides. There is very substantial evidence backingthe claim that entrepreneurial activity has positivelong-run economic consequences in terms ofwealth,productivity, and growth. Institutions advance thelevel of entrepreneurial activity andmay also channelentrepreneurship in productive, rather than unpro-ductive, directions. However, exactly which institu-tional elements are more important for bringing aboutthese beneficial consequences remains an openquestion. As signaled already, we are, nevertheless,optimistic concerning progress in this area, as high-quality data increasingly become available and associal scientists increasingly open up the black boxesof inter-firm mechanisms, likely in joint research en-deavors across disciplines and fields.

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Christian Bjørnskov ([email protected]) is a professor atAarhus University in Denmark. He is also an affiliated re-searcher with the Research Institute of Industrial Eco-nomics in Stockhom, Sweden. His research focuses oneffects of institutional differences, including informal in-stitutions, and their consequences for long-run economicand political development.

Nicolai J. Foss ([email protected]) is a professor of organi-zation theory atBocconiUniversity andholds professorialpositions at theWarwickBusiness School andNorwegianSchool of Economics. His research has been published inthe leading journals in management. He is a member ofAcademia Europaea.


mailto:[email protected]

mailto:[email protected]

APPENDIX

TABLE1

Empirical

Studies

Article

Mea

suremen

tof

entrep

reneu

rship

(dep

enden

t)(Indep

enden

t)Variables

Sam

ple

details

Cau

sality

Finding

Lev

elsof

analysis

sampling

Acs,M

orck

,&Yeu

ng(200

1)

Internationalizationof

smalla

ndmed

ium-size

enterprises(SMEs)

Gov

ernmen

tpolicyan

dinvo

lvem

entinrelation

toex

pan

sion

ofdom

esticfirm

s

Can

adiandom

estic

expan

sion

ofSMEs

Cau

sality

relatedto

gove

rnmen

talloa

nsolution

sin

contrastto

SMEs’accessibilityan

dsu

ccessin

new

marke

ts

Gov

ernmen

tinvo

lvem

ent

may

beco

unter-

productive.

Mac

ro,related

tothe

Can

adiango

vern-

men

t’sinvo

lvem

ent

inex

port

Alhorr,

Moo

re,&

Payn

e(200

8)

Metastudy;

literature

measu

remen

tof

entrep

reneu

rship

Marke

tandcu

rren

cyco

mmon

alityan

dcross-bo

rder

venture

capital

flow

Participatingnationsin

theEU

Linea

rmixed

mod

elassu

mingtime-series

and

cross-sectional

types

ofco

rrelations

Broad

-sca

leec

onom

icintegrationpoliciesdo

influen

cetheex

tento

fforeignve

nture

capital

inve

stmen

tmad

einto

other

mem

ber

countries.

Mac

roleve

l:EU

countries

Anok

hin

&W

ince

nt

(201

2)

Start-upratesan

dinnov

ation

Cou

ntry’sstageof

dev

elop

men

tMulti-sourcedataset

that

cove

rs35

countriesov

ertheperiodfrom

1996

to20

02

Basic

DEA

mod

el,w

here

multi-stageproduction

differencesac

ross

countries

arech

aracterizedas

the

distance

from

thetech

no-

logicalfrontier

Broad

-strok

espolicy

effortsthat

aim

atpromotionof

entrep

reneu

rship

asamea

nsto

boostcou

ntry

innov

ativen

essmay

bemisgu

ided

,andinstea

dsu

ggests

that

gove

rnmen

tadop

taco

ntinge

ncy

approac

h.

Mac

ro

Aparicio

etal.

(inpress)

Self-em

ploym

ent

Corruption

,red

tape

regu

lation

s,priva

tecred

itinstitution

s

Measu

reddataon

entrep

reneu

rship

from

Globa

lEntrep

reneu

rship

Mon

itor

Use

of3S

LSto

control

for

simultan

eity

bias

Opportunity

entrep

reneu

rship

isharmed

byco

rruption

andredtape.

Macro

pan

el

Ardag

na&

Lusardi

(200

9)

Start-ups

Regulation

sGEM

dataan

dFlash

EuroBarom

eter

for

Eastern

Europe

Non

eReg

ulation

sdam

pen

entre-

preneu

rship.P

aper

implies

that

peo

ple

with

better

businessskillsare

less

like

lyto

beco

me

entrep

reneu

rs.

Micro

Arin,H

uan

g,Minniti,

Nan

dialath,

&Reich

(201

5)

Metastudy;

literature

measu

remen

tof

entrep

reneu

rship

Multiple

variab

les;drawn

from

entrep

reneu

rship

studies

Bay

esianmod

elav

eraging

onnational

leve

lNotes

that

causality

isdifficu

ltto

establish,b

utp

rovides

no

form

altest

Highmarginal

taxrates

andva

riab

leinflation

deter

entrep

reneu

rship.

Pap

erim

plies

that

policiesaffecting

entrep

reneu

rship

are

consisten

twith

fundam

entale

conom

icstab

ility.

Macro,p

rimarily


APPENDIX

TABLE1

(Con

tinued

)

Article

Mea

suremen

tof

entrep

reneu

rship

(dep

enden

t)(Indep

enden

t)Variables

Sam

ple

details

Cau

sality

Finding

Lev

elsof

analysis

sampling

Audretsch

&Acs

(199

4)

New

-firm

startup

activity

Macroecon

omic

grow

thrate,c

osto

fcap

ital,a

nd

unem

ploym

entrate

Cross-section

of11

7industries

over

sixtime

periodsbe

twee

n19

76an

d19

86

Non

eStart-upsarepositively

affected

bymac

roec

onom

icgrow

than

dpromoted

bylow-

cost

capital

andahigh

unem

ploym

entrate.

Mesofocu

son

six

differentindustries

Audretsch

&Fritsch

(199

4)

Birth

ofnew

firm

sIncreasingreturnsof

productionswithin

asp

atialu

nitof

observationan

dthe

tenden

cytoward

increasedco

ncentration

ofecon

omic

activity

75WestG

erman

region

sNon

eThereis

eviden

cesu

ggestingthat

birth

ratesaregreaterin

region

sex

hibiting

characteristics

reflectingco

nve

xities

inproduction.

Micro

region

sof

German

y

Avn

imelec

het

al.(20

14)

Self-em

ploym

ent

Corruption

Start-updataba

sedon

Linke

dIn

acco

unts

Non

eCorruption

deters

entrep

reneu

rship,b

ut

mostlyin

dev

elop

edsocieties.

Macro

pan

el

Bjørnskov

&Foss(200

8)Self-em

ploym

ent

Eco

nom

icpolicyan

dinstitution

aldesign

Measu

reddataon

entre-

preneu

rship

from

Globa

lEntrep

reneu

rship

Mon

itor

Nocausality

tests

Sizeof

gove

rnmen

tis

negativelyco

rrelated

andsoundmon

eyis

positivelyco

rrelated

withen

trep

reneu

rial

activity.

Mac

ro

Bjørnskov

&Foss(201

3)Strategic

entrep

reneu

rship

Alloc

ationof

resources,

institution

alecon

omic

free

dom

Uniquepan

eldataset

derived

from

Com

pen

dia,W

orld

Ban

kdata,

andthe

FraserInstitute’s

econ

omic

freedom

data

Eviden

ceforastrongeffect

ofen

trep

reneu

rship

ontotal

factor

productivity,

which

approximatelydou

bles

when

dea

lingwithwhat

appears

tobe

strong

endog

eneity

bias.

Eco

nom

icfree

dom

and

low

tran

sactionco

sts

positivelyinfluen

cetotalfactorp

roductivity.

Macro

pan

el

Bow

en&De

Clercq

(200

8)

High-growth

self-

employm

ent

Regulatory

protectionan

dco

mplexity,c

orruption

Globa

lEntrep

reneu

rship

Mon

itor

for4

0co

untries

Non

eCorruption

detershigh-

grow

then

trep

reneu

rial

dec

isions.

Mac

ro

Ciccone

&Pa

paioan

nou

(200

7)

Estab

lish

men

tgrowth

Tim

eto

comply

with

gove

rnmen

tregulation

sEstab

lish

men

tgrowth

data

for45

countriesduring

the19

80s

Non

eThetimetake

nto

register

new

businesses

negativelyaffectsen

try

inindustries

experiencing

expan

sion

aryglob

aldem

and.

Industry

Dau

&Cuervo

-Cazurra

(201

4)

Self-em

ploym

ent

HeritageIndex

ofEco

nom

icFreed

omW

orld

Ban

kGroup

Entrep

reneu

rship

Survey

andGloba

lEntrep

reneu

rship

Mon

itor

Non

eEco

nom

iclibe

raliza

tion

furthersall

entrep

reneu

rship,

whilego

odgo

vernan

celimitsinform

alen

trep

reneu

rship.

Macro

pan

el


APPENDIX

TABLE1

(Con

tinued

)

Article

Mea

suremen

tof

entrep

reneu

rship

(dep

enden

t)(Indep

enden

t)Variables

Sam

ple

details

Cau

sality

Finding

Lev

elsof

analysis

sampling

Dov

e(201

5)Kau

fman

nIndex

ofEntrep

reneu

rial

Activity

Judicialindep

enden

cean

dqu

ality

Entrep

reneu

rial

index

for

50U.S.statesin

15-year

pan

el

Non

eJudicialn

ominationan

dindep

enden

cestrengthen

sen

trep

reneu

rship.

U.S.state-lev

elpan

el

Goh

man

n(201

2)Self-em

ploym

ent

Eco

nom

icpolicyan

dinstitution

aldesign

Entrep

reneu

rship

Flash

Euroba

rometer

Survey

s20

01–20

04

Non

eCorruption

detersan

dec

onom

icfree

dom

furthers

entrep

reneu

rship.

Multilev

el

Grilo

&Irigoy

en(200

6)

Laten

tandac

tual

entrep

reneu

rship,

measu

redthrough

self-

employm

ento

ver

employm

ent

Dem

ographic

variab

les,

countryfixe

deffects,

perception

ofad

ministrative

complexities,

availability

offinan

cial

support,an

dmeasu

reof

risk

tolerance

Survey

datafrom

the15

EU

Mem

berstates

andthe

U.S.

Log

isticmod

elfocu

singon

self-employm

entdecision

withco

ntrols(m

en,age

,low

educa

tion

,highed

uca

tion

,lack

offinan

cial

support,

presence

ofad

ministrative

complexities,risktolerance,

countrydummies)

Perce

ption

sof

lack

ofav

ailablefinan

cial

supportd

onot

clea

rly

affect

entre-

preneu

rship.

Mac

ro

Klapper

etal.

(200

6)Lim

ited

-liability

firm

creation

Entryregu

lation

sAmad

eusdatab

ase,

cove

ringEuropean

countries

Instrumen

talv

ariable

regression

alleviates

endog

eneity

bias

Entryregu

lation

sham

per

firm

entryan

dreduce

value-ad

ded

grow

thper

employe

e.Effects

arise

primarilyin

rich

er,less

corruptc

ountries.

Industry

Kreft&Sob

el(200

5)Non

-farm

proprietor

employm

ent

Eco

nom

icfree

dom

,inheritan

cetaxlaw

Bureau

ofEco

nom

icAnalysis

Grangercausality

Low

taxa

tion

andfreedom

from

labo

rregu

lation

sarepositively

associated

with

entrep

rene

uriala

ctivity.

U.S.state-lev

elpan

el

Lev

ie,A

utio,

Acs,&

Hart

(201

4)

Introd

uctionto

theGloba

lEntrep

reneu

rship

Mon

itor

Research

Con

ference

2010

Pap

erspresentedat

the

GEM

NA

NA

Meta

McM

ullen

etal.(20

08)

Self-em

ploym

ent

Heritag

eIndex

ofEco

nom

icFreed

omMeasu

reddataon

entrep

reneu

rship

from

Globa

lEntrep

reneu

rship

Mon

itor

Non

eProperty

righ

tsan

dlabo

rinstitution

saffect

opportunity-ba

sed

activity,w

hilefiscalan

dmon

etaryindices

affect

necessity-ba

sedactivity.

Mac

ro

Nystrom

(200

8)Self-em

ploym

ent

Sizeof

gove

rnmen

t;legal

structure

andsecu

rity

ofproperty

righ

ts;a

ccess

tosoundmon

ey;

freedom

totrad

einternationally;a

ndthe

regu

lation

ofcred

it,

labo

r,an

dbu

siness

Pan

eldatafrom

23OECD

countriesfortheperiod

1972

–20

02

Foc

usingon

totals

elf-

employm

ent,threeou

tof

five

compon

ents

ofec

onom

icfree

dom

hav

estatisticallysign

ifican

tco

efficien

ts:sizeof

gove

rnmen

t;legalstruc

ture

andsecu

rity

ofprop

erty

righ

ts;and

regu

lation

ofcred

it,lab

or,and

busine

ss.

Smallergo

vernmen

tsector;b

etterlega

lstructure

andsecu

rity

ofproperty

righ

ts;a

ndless

regu

lation

ofcred

it,

labo

r,an

dbu

sinesstend

toincrease

entrep

reneu

rship.

Macro

pan

el


APPENDIX

TABLE1

(Con

tinued

)

Article

Mea

suremen

tof

entrep

reneu

rship

(dep

enden

t)(Indep

enden

t)Variables

Sam

ple

details

Cau

sality

Finding

Lev

elsof

analysis

sampling

Ova

ska&

Sob

el(200

5)

New

enterprise

creation

Eco

nom

icfreedom

,co

rruption

,inflation

WIPO

tran

sition

reports

Cau

sality

istricky

.Low

corruption

,credit

availability,a

ndsound

mon

eyarepositively

correlated

with

enterprise

creation

.

Mac

ro

Sob

el&King

(200

8)You

then

trep

reneu

rship

rates

You

then

trep

reneu

rship

U.S.c

ounty-lev

eldataon

youth

self-employm

ent

from

the20

00Cen

sus,

bytheCen

susBureau

They

use

amod

elthat

allows

forsp

atiald

epen

den

ceeither

inthedep

enden

tva

riab

le,c

alledasp

atial

auto-regressivelag,or

inthe

errorco

mpon

ent,term

edasp

atiale

rror

lag.Apositive

andsign

ifican

tvalueforr

wou

ldindicatethat

observationsof

self-

employm

entinco

unty

iare

positivelyco

rrelated

with

observationsof

self-

employm

entinnea

rby

counties.F

ailure

toco

ntrol

forthis

correlationwou

ldlead

tobiased

estimates

oftheex

planatoryva

riab

les.

Vou

cher

program

screate

higher

ratesof

youth

entrep

reneu

rship,

whilech

artersch

oolsdo

not,relativeto

public

schoo

ls.

Micro,related

toyo

uth

andschoo

lchoice

s

Terjesen,

Hessels,&

Li(20

16)

Metaarticle;

exam

ines

comparative

international

entrep

reneu

rship

(CIE)resea

rch

Multi-va

riab

leco

mparison

259articles

published

in21

lead

ingjournalsfrom

1989

to20

10

Nocausality

(metatext)

CIE

literature

ishighly

fragmen

tedwith

substantial

know

ledge

gapsrelatedto

content,

theo

ry,a

nd

method

olog

y.

Meta;

macro

topics

Troilo(201

1)Self-em

ploym

ent

Eco

nom

icpolicyan

dinstitution

aldesign

Measu

reddataon

entrep

reneu

rship

from

Globa

lEntrep

reneu

rship

Mon

itor

Non

eThetimeittake

sto

enforce

contracts

affectshigh-

grow

th-asp

iration

activity;p

roperty

righ

tsan

drule

oflaw

affect

differenttyp

esof

activity.

Macro

pan

el

Wen

nek

ers,

Uhlaner,&

Thurik

(200

2)

Bothex

istingbu

siness

ownersan

dstart-up

rate.

Multidim

ension

alva

riab

lesov

erperiodsof

time

Datafrom

seve

ralm

odern

Western

nations.(U

.S.,

U.K.,theNetherlands)

Sev

eral

causalities

dep

ending

onco

untryan

dtimeperiod

Theex

planationof

the

largeva

riationin

entrep

reneu

rship

rates

(ove

rtimean

dac

ross

countries)

isnot

straightforward.

Mac

ro,o

vertime


APPENDIX

TABLE1

(Con

tinued

)

Article

Mea

suremen

tof

entrep

reneu

rship

(dep

enden

t)(Indep

enden

t)Variables

Sam

ple

details

Cau

sality

Finding

Lev

elsof

analysis

sampling

Wen

nek

ers,

vanStel,

Carree,

&Thurik

(201

0)

Self-em

ploym

entan

dinnov

ative

entrep

reneu

rs

Cross-section

alecon

omic

dev

elop

men

tSev

eral

datastudiesdating

back

to18

00Show

sthat

theself-

employm

entratein

total

employm

entc

anbe

written

asaweigh

tedsu

mof

self-

employm

entrates

per

sector,w

iththesector

employm

ents

hares

intotal

employm

ents

ervingas

weigh

ts.W

eareinterested

inthelong-term

dev

elop

men

tsof

both

self-

employm

entrates

per

sector

andsector

shares

intotal

employm

ent.

Withself-employe

dat

the

lower

endan

dam

bitiou

s/innov

ative

entrep

reneu

rsat

the

upper,itc

reates

areviva

latb

oth

extrem

es.

Mac

ro,o

veralong

timeline


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