European Journal of Political Economy 66 (2021) 101954

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European Journal of Political Economy

journal homepage: www.elsevier.com/locate/ejpe

Entrepreneurship, fear of failure, and economic policy☆

Nabamita Dutta a,*, Russell S. Sobel b

aDepartment of Economics, College of Business Administration, University of Wisconsin, La Crosse, USAbBaker School of Business, The Citadel, Charleston, SC, 29409, USA

A R T I C L E I N F O

JEL classification:O11L26E6O1E02

Keywords:EntrepreneurshipFear of failureEconomic policyEconomic freedomBusiness climate

☆ The authors would like to thank the editorat the 2019 European Public Choice Society (EPC* Corresponding author.E-mail addresses: ndutta@uwlax.edu (N. Dutta

1 See, among others, Khyareh and Mazhari (20Wagner (2007); and Caliendo et al. (2009). Cultufactors, can impact the relationship, however, acc2 See Sobel (2015) for evidence and a summary

https://doi.org/10.1016/j.ejpoleco.2020.101954Received 3 June 2019; Received in revised formAvailable online 17 September 20200176-2680/Published by Elsevier B.V.

A B S T R A C T

The previous literature finds that self-reported ‘fear of failure’ has a significant negative effect onindividuals’ choice to become entrepreneurs. We hypothesize this effect is lessened in economieswith a larger number of additional, alternative, entrepreneurial opportunities to pursue if a failureoccurs. Prior literature also concludes the number of entrepreneurial opportunities is enhancedsignificantly by having policies and institutions consistent with higher levels of economic freedom.We therefore test and confirm that fear of failure hurts the entrepreneurial process less when levelsof economic freedom are higher as there are more additional chances for failed entrepreneurs topursue.

1. Introduction

The process of ‘creative destruction’ whereby entrepreneurs create new goods and services that replace old ones is both importantand disruptive [Schumpeter (1934 [1911]; 1942)]. The business failures that occur as part of this churning provide the feedback andknowledge necessary to help guide the process [Sobel et al. (2019)]. Most entrepreneurs eventually fail and the rate is highest amongfirst-time entrepreneurs. There is now a robust empirical literature showing self-reported ‘fear of failure’ is a significant barrier toentrepreneurship using data from the Global Entrepreneurship Monitor (GEM) database.1

It is also firmly established in the empirical literature that the quality of a country’s economic policies and institutions have a largepositive impact on the total number of entrepreneurial ‘opportunities’ individuals in a country as measured by GEM survey data, as wellas on the total rates of measured entrepreneurship, new business formation, and new venture activity in both the World Bank and GEMdata.2 Our novel hypothesis is that these policies may also lessen the negative impact of the fear of failure on entrepreneurship ratesbecause they provide more alternative opportunities for failed entrepreneurs. In areas with a greater economic freedom, and thus agreater number of entrepreneurial opportunities, the cost of failure is lower as there are more alternative chances to redeploy and recoup

and the referees for their invaluable comments and suggestions. We also thank session participantsS) meetings and the 2018 Southern Economic Association (SEA) meetings for their feedback.

), russell.sobel@citadel.edu (R.S. Sobel).16); Arenius and Minniti (2005); Minniti and Nardone (2007); Langowitz and Minniti (2007);ral practices of institutional collectivism and uncertainty avoidance, as well as other ‘meso’-levelording to Wennberg et al. (2013) and Kim et al. (2016).of this literature.

20 August 2020; Accepted 22 August 2020

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

investments in entrepreneurial expertise and capital. We test and confirm our hypothesis using measures of economic institutions andthe GEM ‘fear of failure’ data. Our results seem to explain why Khyareh andMazhari (2016) find such a strong impact of fear of failure onentrepreneurship rates specifically for Iran, a country with low economic freedom.

The logic of our argument is based in the economics of the entrepreneur’s decision making. Starting a business has two possibleoutcomes, success or failure. Because the cost (or value) of the failure outcome depends upon what the future holds after the failure,alternative post-failure opportunities impact the initial decision-making calculus. In an economy with few entrepreneurial opportu-nities, investing time and effort in acquiring the skills, capital, and permissions to start a business is a high-risk endeavor. Should the firstattempt fail, the human capital and networks established will have little ‘scrap’ value in reuse. However, in areas with high economicfreedom and thus a substantial number of other entrepreneurial opportunities, these skills and resources may be redeployed morerapidly and at a lower loss in value. That is, the cost of failure is reduced when entrepreneurs can more easily pursue second, third, orfourth, chance business opportunities should the current one fail.

An interesting implication of our theory is that in areas with higher economic freedom, even individuals with lower risk toleranceswill chose to become entrepreneurs. Thus, the risk preference of individuals should play a weaker role in economies with moreentrepreneurial opportunities. Our argument also implies that higher economic freedom has an indirect effect that increases entre-preneurship as it leads more risk-averse people to try entrepreneurship despite having fear of failure. This is an independent impact ofeconomic freedom over and above the impact on business profitability or ease of entry and red tape.

2. Entrepreneurship, failure risk, and economic policy

Our hypothesis is based on a novel combination of two different strands of literature. The first examines how economic policies andinstitutions impact entrepreneurship rates, and the other explores how the fear of failure influences the choice to become anentrepreneur.

The first strand of literature generally focuses on the role of institutions, or ‘rules of the game,’ that determine the incentive structurefaced by entrepreneurial individuals.3 While some are informal (i.e., cultural) and are therefore difficult to change, such as religion[Baumol and Strom (2010); Nunziata and Rocco (2018)], others such as government policies can be changed with significant positiveeffects. Examples include those policy areas measured in business climate indices such as the World Bank’s Doing Business report or theFraser Institute’s widely used Economic Freedom of the World (EFW) index by Gwartney et al. (2015).

The empirical literature has consistently found that higher levels of economic freedom lead to more entrepreneurial opportunitiesand higher rates of entrepreneurship [Hall and Lawson (2014); Sobel (2008, 2015); Kreft and Sobel (2005); Hall and Sobel (2008); andHolcombe (1998)].4 These policies include secure private-property rights, a non-corrupt and independent judicial system, contractenforcement, free trade, monetary stability, and effective limits on government taxation and regulation. Even the subcategories offreedom to trade internationally and domestic regulatory barriers to entry are closely related to the level of entrepreneurship [Sobelet al. (2007)].5

The second strand of literature of relevance uses the new GEM individual survey-level data and consistently finds that self-reported‘fear of failure’ is a significant barrier to entrepreneurship.6 Using this data for Iran, for example, Khyareh and Mazhari (2016) find thatan individual’s self-reported level of ‘fear of failure’ as an entrepreneur causes a 3 to 6 percentage point reduction in the probability ofbeing an entrepreneur. The foundational idea behind this result is that attitudes toward risk effect whether individuals choose to becomean entrepreneur instead of seeking steady employment in the general labor force [Weller and Wenger (2017); Werner et al. (2009)].

To see the linkage between these two strands of literature embedded in our hypothesis, let us definemore precisely how the decision-making calculus of a potential entrepreneur is influenced by risk and second (or third, etc.) chances. At the first decision node, anindividual must decide between a more secure standard career path as an employee working in a job for someone else versus the moreuncertain option of becoming an entrepreneur. If the expected utility from becoming an entrepreneur, E[UENT], exceeds the expectedutility from job employment, E[UJOB], the individual will decide to become an entrepreneur. Further dissecting these expected utilities,the expected utility from becoming an entrepreneur, E[UENT], is the expected value of the weighted outcomes of the two possibleoutcomes—entrepreneurial success and entrepreneurial failure. These two outcomes are weighted by the relevant probabilities. Thus, E[UENT] ¼ P*[UENT(success)]þ(1-P)*[UENT(failure)], where P is the probability of entrepreneurial success, (1-P) is the probability ofentrepreneurial failure. According to theory, this decision will be influenced by three main factors: (1) the probability of success (versusfailure); (2) the differential in the utilities between the success and failure outcomes; and (3) the individual’s relative level of riskaversion embedded in the specific utility function. The following four comparative statics from utility maximization thus immediatelyfollow:

(I) As relative risk aversion increases, the likelihood of becoming an entrepreneur falls.(II) As the reward from entrepreneurial success increases, the likelihood of becoming an entrepreneur rises.(III) As the utility under the outcome of entrepreneurial failure gets worse, the likelihood of becoming an entrepreneur falls.

3 As defined by North (1990, 1991), institutions are the formal and informal rules governing human interactions.4 See also Gwartney and Lawson (2003), De Haan, Lundstrom, and Sturm (2006), and Berggren (2003).5 Other factors also impact entrepreneurship rates, including disasters [Brück et al. (2011)] and self-reliance [Bauernschuster et al. (2012)].6 For example see, Khyareh and Mazhari (2016); Arenius and Minniti (2005); Minniti and Nardone (2007); Langowitz and Minniti (2007); Wagner

(2007); Morales-Gualdron and Roig-Dobon (2005); Caliendo et al. (2009); and Wennberg et al. (2013).

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N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

(IV) As the probability of a successful outcome rises, the likelihood of becoming an entrepreneur rises.

What we hypothesize is that in economies with more entrepreneurial opportunities due to higher economic freedom levels, thedisutility from failure is lessened in that (III) above improves. The ‘value’ attached to the outcome of entrepreneurial failure is simply theexpected outcome that would happen if the venture fails. With career-specific human, physical, and financial capital needing to beacquired and invested in the entrepreneurial process, the more ways to deploy these assets in case of one failure, the less poor is theworst-case outcome of business venture failure.

Interestingly, this theory predicts that in that areas with more possible alternative entrepreneurial opportunities, the marginalentrepreneur will be one with a lower tolerance for risk.7 More precisely, the equilibriummarginal level of risk preference that cuts off ayes/no decision to become an entrepreneur will fall the more alternative opportunities that are present. We believe this is a novel insightthat has been overlooked entirely in the entrepreneurship literature. Testing it would require micro-level analysis of risk preferencecomparisons across economies, which is outside the scope of our current study, but we suggest it as a fruitful avenue for futureentrepreneurship field survey research. With our theoretical framework in place we now turn to a description of our data and ourempirical tests regarding whether higher levels of economic freedom can mitigate the negative impact of fear of failure on entrepre-neurship rates.

3. Data

In line with the prior literature, our dependent variable is country-level total early-stage entrepreneurial activity (TEA) from theGlobal Entrepreneurship Monitor (GEM) database, for the years 2001 through 2017. TEA is defined as the percentage of the populationaged 18 to 64 that are either a nascent entrepreneur or owner-manager of a new business up to 3.5 years old (GEM, 2019). Table 1presents summary descriptive statistics for our main variables, and the mean TEA in our sample is about 11.2 percent with a minimumand maximum ranging from 1.4 to 52.1 indicating a wide empirical variation.

Our first independent variable of interest is the ‘fear of failure’ measure used in prior studies, also from the GEM database. It isdefined as the percentage of individuals aged 18 to 64 who desire to start a business in the country but self-report that ‘fear of failure’prevents them from doing so. The mean for our sample is about 34 percent indicating a generally high fear of failure rate for our sample.Countries such as Japan, Latvia, Greece, Russia, Spain and Vietnam have high fear of failure rates in the data.

Our other main independent variable of interest is the Fraser Institute’s Economic Freedom of the World (EFW) index, which is themost widely-usedmeasure of market-oriented economic institutions and policies [Gwartney et al. (2015); Gwartney and Lawson (2003);Hall and Lawson (2014)]. The index scores countries on a scale of 0–10 based on dozens of sub-indicators measuring the size and scopeof government spending and taxation, the quality of the legal system and enforcement of property rights and contracts, a stable currency,freedom to trade internationally, and business and labor regulation.

We include other control variables commonly employed in empirical studies related to cross-country entrepreneurship rates from theWorld Bank’s World Development Indicators (WDI), 2018 database. These include the growth of GDP per capita, domestic creditavailability, share of population in urban areas, and the labor force participation rate. Higher GDP per capita growth implies risingopportunities for new business [Dutta and Sobel (2016); Audretsch and Keilbach (2007)]. Labor force participation rates are importantdeterminants of entrepreneurship rates [Dutta and Mallick (2018) and Audretsch et al. (2002)]. Urbanization may result in spilloverexternalities on entrepreneurship [Brüderl and Preisendorfer (1998); Reynolds et al. (1995); Storey (1994)]. Finally, domestic credit toprivate money banks as a percentage of GDP is a measure of financial development to control for differences in credit availability forentrepreneurs across countries with differing capital markets.

3.1. Empirical specification and interpretation of interaction terms

The empirical analysis of our paper aims at testing whether higher levels of economic freedom (EFW) help to offset the negativerelationship between the GEM ‘fear of failure’ (FF) measure and the GEM entrepreneurship rate (TEA) found in the prior literature. Toanswer this question, we estimate the following specification that includes interaction terms between these variables as follows:

TEAit ¼ β1 þ β2FFit�1 þ β3*EFWit þ β4ðFFit�1*EFWitÞ þXJ

j¼1

αjXjit þ γi þ θt þ εit (1)

where TEAit is the GEM entrepreneurship (TEA) rate for country i at time t. FFit�1 refers to the fear of failure rate for country i at time t�1. The impact of fear of failure is lagged because it takes time for perceptions to turn into actions.8 EFWit is overall economic freedomlevel for country i in time t. Xijt represents a matrix of j different control variables. γi is the country specific fixed effect, θt is the timespecific fixed effect, and εit is the random error term. The interaction term FFit�1*EFWit is of primary interest in testing our mainhypothesis.

7 The fact that things that can lessen the disutility of entrepreneurial failure may lead to more entrepreneurship is why some have counterintu-itively argued that more robust welfare systems and social safety nets can lead to higher levels of entrepreneurship, especially among immigrants,despite requiring higher levels of taxation and government spending [Olds (2014); Frick (2015); Kim et al. (2012)].8 As we discuss later, our results are not sensitive to using a contemporaneous or lagged value for this variable.

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Table 1Summary statistics of key variables.

Variable Obs. Mean Std. Dev. Min. Max.

Entrepreneurship Rate (TEA) 454 11.15 7.17 1.48 39.91Fear of Failure (FF) 454 34.60 8.80 12.55 75.42Economic Freedom Index (EFW) 454 6.61 0.92 3.79 8.88GDP Per Capita Growth 454 2.01 3.53 �36.20 13.64Domestic Credit 454 51.10 42.79 2.07 223.13Labor Force Participation Rate (LFPR) 454 67.90 9.33 42.29 85.56Urban Population (in percent) 454 56.90 22.60 8.60 100.00

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

Our coefficients of interest are β2 and β4: While β2 captures the direct impact of the fear of failure variable on entrepreneurship, β4captures the interaction effect through the economic freedom measure. The partial derivative showing the marginal impact of fear offailure rate on entrepreneurship given by ∂TEAit

∂FFit�1¼ β2 þ β4*EFWit. This partial derivative, ∂TEAit

∂FFit�1, will be >;¼; or < 0 depending the sign

and magnitude of β2, β4 as well as the underlying value of the economic freedom variable. Based on prior literature we expect the directeffect of fear of failure to be negative, and thus β2 < 0: Our hypothesis is that a more economically free country will have moreentrepreneurial opportunities and therefore will lessen the harmful effect of fear of failure. Thus, we expect the coefficient on theinteraction term to be positive, and thus β4 > 0, showing that with higher levels of economic freedom, fear of failure hurts entrepre-neurship less. One might expect that the likely estimate will be that at low levels of EFW the overall derivative will be negative showingthat fear of failure hampers entrepreneurship, and that this will either become less negative or become insignificantly different from zeroat higher levels of economic freedom.9

It is important to note that with an interaction term, both the interpretations and statistical significance levels are no longer asstraightforward. Any variable appearing in the interaction term and as a stand-alone variable produces a combined effect that couldhave a different level of significance than either of the variables individually, and this significance may also depend on the level of theunderlying conditioning variable [Berry et al. (2012); Brambor et al. (2006); Braumoeller (2004); Hainmueller et al. (2019)]. While wepresent the overall partial derivative of fear of failure on entrepreneurship rates at the minimum, mean, and maximum sample values ofeconomic freedom in table format, we also present this information graphically for the entire range of EFW values.

3.2. Identification

One of the challenges of panel cross-country studies is that of identification with respect to the independent variables of interest – forus, fear of failure and economic freedom. Endogeneity can arise because of reverse causality, omitted variables bias or both. We establishidentification by resorting to empirical models that consider both internal and external instruments. Our benchmark specificationsconsist of an ordinary least squares (OLS) estimation, a System Generalized Method of Moments (SGMM) estimation, and an Instru-mental Variable (IV) estimation. While employing external instruments to eliminate endogeneity is perhaps the best option, finding pureexogenous instruments that vary across countries and over time can be extremely challenging [Farhadi et al. (2015); Persson andTabellini (2006)]. Inefficient instruments can exacerbate the problems resulting in inconsistent estimates and possibly greater biascompared to ordinary least squares (OLS) estimates [Murray (2006)]. A System GMM estimator can account for reverse causality bygenerating instruments under the assumption that the past values of the regressors are not affected by current period shocks in the errorterm, and that they do not directly affect current values of the dependent variable [Hauk and Wacziarg (2009)].10 There should be nosecond-order serial correlation in the error term as indicated by the p values. The over-identifying restrictions employing the Hansen testprovides validity of the instruments. As a rule of thumb, the number of instruments should stay below the number of countries sinceinstrument proliferation can weaken the instruments [Staiger and Stock (1997)] and, thus, we employ the ‘collapse’ command in STATAto reduce the number of instruments. The two-step GMM estimator is used since it produces more efficient estimators, and to make surethat the standard errors are not biased downward in our finite sample [Bond et al. (2001)] we employ the Windmeijer (2005)finite-sample correction.

Finally, we consider IV estimates. For the instruments, we employ some from the growth literature following Faria and Montesinos(2009). We employ latitude [Easterly and Levine (2003)] and use indicators for socialist legal origins [Acemoglu and Johnson (2005);Beck et al. (2003)]. In general instruments related to geography are well-established in the literature. Redding and Venables (2004)point out that geographic location factors can affect the flow of goods, factors of production, transport costs, and ideas, so we employmean and centroid distance to coast. Sachs (2000), Gallup et al. (1999), and Bloom and Sachs (1998) have stressed that tropical climates

9 To be clear, our main hypothesis is that the marginal effect of fear of failure is reduced by higher levels of economic freedom, which is implied bythe coefficient on the interaction term, β4 being positive and significant. For our purposes whether the final partial derivative of entrepreneurshiprates with respect to fear of failure goes from negative to zero, or from a big negative effect to a smaller negative effect does not matter, both confirmour hypothesis.10 Dynamic panel estimators like System and Difference GMM have become popular in recent times due to their capability of dealing with multiplepanel data challenges including endogeneity concerns [Dutta and Sobel (2018); Dutta and Sobel (2016); Farhadi et al. (2015); Klomp and de Haan(2015); Asiedu and Lien (2011); Gehlbach and Malesky (2010)). GMM estimators are built to take into account country fixed effects and are con-structed to handle the presence of heteroskedasticity and autocorrelation within countries.

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Table 2Entrepreneurship (TEA), fear of failure (FF), and economic freedom (EFW) – No interaction terms.

Dependent Variable: TEA (1) (2) (3)

Independent Variables: OLS SGMM IV

TEAt-1 – 0.222 (0.156) –

Economic Freedom (EFW) 0.309 (0.335) 0.214 (2.649) �0.289 (2.342)Fear of Failure (FF)t-1 �0.257*** (0.0430) �0.297** (0.122) �0.813*** (0.152)GDP Per Capita Growth �0.0151 (0.0822) �0.181 (0.126) 0.066 (0.124)Urban Population 0.062*** (0.015) 11.22 (11.15) 0.037** (0.0184)Domestic Credit 0.002 (0.009) 0.0728 (0.153) �0.002 (0.009)LFPR 0.022* (0.030) �0.299 (0.661) 0.026 (0.038)Constant 12.88*** (3.343) 7.782 (42.84) 31.75** (16.18)

Observations 454 444 454R-squared 0.25 – 0.64Number of countries 71 71 71Number of instruments – 48 6Hansen J – 0.66 0.17Arellano-Bond test for AR(2) – 0.23 –

Klei.-Paap Wald rk F statistic – – 9.5**F for excluded instruments – – 15.33

Note: Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%, respectively. Instruments (external) employed in IVspecification are: legal origins (socialist), mean distance to coast, percent of population in tropics, percent of land area in tropics, latitude (centroid),distance to coast (centroid). Dependent variable is the TEA entrepreneurship rate (from GEM) defined as the percentage of 18–64 population who areeither a nascent entrepreneur or owner of a new business. Fear of Failure (FF) from GEM is the percentage of 18–64 population perceiving goodopportunities to start a business who indicate that fear of failure prevents them from doing so. Economic freedom (EFW) is the overall summary indexscore from the Fraser Institute’s Economic Freedom of the World database. The controls included are GDP per capita growth, domestic credit, urbanpopulation as share of total population, labor force participation rate.

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

can affect economic development through effects on human health and food productivity so we accordingly employ the percent of the1995 population in the tropics, and percent of land area in the tropics. We instrument for both fear of failure (lagged one year) andeconomic freedom. We present all the diagnostic tests to make sure the instruments are efficient and meet the overidentification re-strictions. We show our results are robust to other combinations of instruments as well.

4. Empirical results

Table 2 presents our first set of empirical results employing OLS, SGMM and IV estimatorswithout our key interaction term simply forcomparison to the prior empirical literature. For the external instruments under IV, as reported in the table, the rejection of the null forthe Kleibergen and Paap (2005) rk LM statistic indicates that the instruments adequately identify the model. The instruments also passthe critical Stock and Yogo (2005) F values. Finally, the Hansen J shows that overidentification conditions are met. This is true forSGMM estimators as well. The second order p values not being rejected for SGMM estimators also satisfy the second order autocor-relation condition.11

Like prior studies we find that the coefficient of fear of failure is negative and significant in the all specifications. The impact is thestrongest for the IV estimates showing that in terms of absolute magnitude, a one percentage point increase in the fear of failure measureresults in a 0.813 percentage point reduction in the TEA entrepreneurship rate. The OLS and SGMM estimates are 0.257 and 0.297,respectively. These are similar to the estimates of Khyareh and Mazhari (2016) once converted into overall percentages of the un-derlying variable values. Given the mean value of TEA is 11.15 in our sample, a change of 0.813 is a meaningful 7.3 percent reduction.The similar conversions for OLS and SGMM are 2.3 percent and 2.7 percent accordingly. Khyareh and Mazhari (2016) found estimatesranging from 3 to 6 percent. Thus, as other studies have also found, fear of failure has a substantial negative impact on entrepreneurshiprates. We now turn to our main question on whether higher levels of economic freedom lessen this negative impact.

In Table 3, we include our key interaction term between fear of failure (FF) and economic freedom (EFW). The interaction term itselfis positive in all specifications and significant in the SGMM and IV specifications, while the fear of failure coefficient is negative andsignificant in all specifications, although as we indicated earlier the true magnitudes and significance levels of the partial derivativesneed to be computed based on a combination of the variables, their coefficients, and the associated errors (which we do momentarily).For our initial purposes, however, our hypothesis is confirmed simply by the positive and significant coefficient on this interaction term.This positive coefficient on the interaction term suggests that higher levels of economic freedom meaningfully lessen the negative

11 In the context of dynamic panel data models with large ‘N’ and small ‘T’, the demeaning process for one-way fixed effect models creates acorrelation between the regressor and error. This is known as Nickell Bias. The bias implies that the regressor cannot be distributed independently ofthe error term. SGMM estimators are constructed to handle the bias. Thus, the lagged dependent variable is only present for the SGMM estimators.

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Table 3Entrepreneurship (TEA), fear of failure (FF), and economic freedom (EFW) – with interaction terms.

Dependent Variable: TEA (1) (2) (3)

Independent Variables: OLS SGMM IV

TEAt-1 – 0.016 (0.072) –

Economic Freedom (EFW) �1.638 (1.668) �4.979** (2.330) �10.81* (5.780)Fear of Failure (FF)t-1 �0.658* (0.353) �1.389*** (0.310) �2.929** (1.367)EFW*FFt-1 0.058 (0.050) 0.193*** (0.0454) 0.327* (0.184)GDP Per Capita Growth �0.012 (0.082) 0.093** (0.037) 0.003* (0.121)Urban Population 0.061*** (0.016) 0.236 (3.847) 0.040** (0.018)Domestic Credit 0.003 (0.001) �0.057 (0.051) 0.005 (0.009)LFPR 0.0305 (0.0323) 0.751** (0.322) 0.071 (0.051)Constant 25.60** (11.19) 44.13* (22.79) 71.65 (47.52)

Observations 454 444 454R-squared 0.19 – 0.65Number of countries 71 71 71Number of instruments – 64 6Hansen J – 0.41 0.54Arellano-Bond test for AR(2) – 0.69 –

Klei.-Paap Wald rk F statistic 11.6**F for excluded instruments – – 15.1

Note: Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%, respectively. Instruments (external) employed in IVspecification are: legal origins (socialist), mean distance to coast, percent of population in tropics, percent of land area in tropics, latitude (centroid),distance to coast (centroid). Dependent variable is the TEA entrepreneurship rate (from GEM) defined as the percentage of 18–64 population who areeither a nascent entrepreneur or owner of a new business. Fear of Failure (FF) from GEM is the percentage of 18–64 population perceiving goodopportunities to start a business who indicate that fear of failure prevents them from doing so. Economic freedom (EFW) is the overall summary indexscore from the Fraser Institute’s Economic Freedom of the World database. The controls included are GDP per capita growth, domestic credit, urbanpopulation as share of total population, labor force participation rate.

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

impact of fear of failure on entrepreneurship rates.12

Table 4 shows the computed overall partial derivative of entrepreneurship rates (TEA) with respect to fear of failure (FF), withassociated standard errors and significance levels, for the mean level of economic freedom (EFW), along with the maximum andminimum values within sample for all three specifications –OLS, SGMM and IV. This ensures the entire breadth of the sample values arereflected as it gives the two endpoints, along with the mean value along the line representing the partial derivative. Again, for ourhypothesis, we care that the impact becomes less negative as the level of economic freedom increases. As described earlier when weoutlined our specification, the marginal effects are given by ∂TEAit

∂FFit�1¼ β2 þ β4*EFWit , and we have substituted the corresponding co-

efficient estimates and indicated values for the EFW variable.The estimated marginal effects are exactly as expected. At low values for economic freedom, the negative impact of fear of failure is

greatest and is statistically significant. In terms of interpretation, the magnitudes at the minimum values for EFW are almost twice thesize of the previously discussed levels. In the countries with the lowest economic freedom levels in the sample, the marginal effect of aone unit increase in the fear of failure variable is a reduction in TEA entrepreneurship rates of 0.453, 0.581, or 1.785 percentage pointsdepending on the model specification. As a percent of the underlying variable, these are reductions in entrepreneurship rates of 4.1, 5.2,and 16.0 percent, somewhat comparable (or bigger) than the estimates in Khyareh andMazhari (2016), which indeed were for a countrywith a low level of economic freedom.

As economic freedom scores improve to the mean levels, these are reduced, roughly in half. Even at the mean levels for economicfreedom the effect of fear of failure on entrepreneurship is still significant and negative, just smaller. At the highest levels of economicfreedom, the value continues to be reduced confirming our hypothesis, and according to the SGMM or IV estimates, actually becomesstatistically insignificant (and only barely significant at the 10% level in the OLS version). In other words, in countries with the highestlevels of economic freedom, our estimates suggest the negative effect of fear of failure on entrepreneurship disappears, or at least is sosmall of a negative it ceases to matter statistically. High levels of economic freedom can completely counteract the impact of the fear offailure on entrepreneurship rates. This also helps to explain why prior studies using different samples obtained different estimatedeffects, and why it was so high for Iran in Khyareh and Mazhari (2016).

Another way to present these partial derivatives is to show them for all underlying values of the economic freedom variable ingraphical form. The three panels in Fig. 1 show these plots based on the OLS, SGMM, and IV estimates with associated confidenceintervals. Additionally, the histogram bars (using the other y-axis) show a frequency distribution of the underlying data values for EFWso one can examine the relevance of each value in the sample and the amount of data upon which the estimate is based. They show thesame conclusion as the rows in the table, but in a more continuous manner. At low levels of economic freedom, the estimated marginal

12 As before, the p value of Kleibergen and Paap (2005) rk LM statistic shows that the null can be rejected and thus the instruments are not weaklyidentified. The Stock and Yogo (2005) F values are above the threshold values. The Hansen J values also show that the overidentification restrictionsare met for both IV and SGMM estimates.

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Table 4Marginal effects of fear of failure (FF) on entrepreneurship (TEA) conditional on levels of economic freedom (EFW).

Marginal effect assessed at: OLS SGMM IV

Min. EFW value �0.453*** (0.179) �0.581** (0.265) �1.785*** (0.734)Mean EFW value �0.277** (0.049) �0.140** (0.062) �0.805*** (0.244)Max. EFW value �0.160* (0.088) 0.153 (0.118) �0.151 (0.279)

Note: Marginal effects of Fear of Failure (FF) on TEA entrepreneurship rates conditional on Economic Freedom of the World (EFW) country means and

Fig. 1. Graphical Representation of Partial Derivatives. Note: The graphics illustrate the partial derivative, or marginal effects, given by ∂TEAit∂FFit�1

¼β2 þ β4*EFWit , using corresponding coefficient estimates from the OLS, SGMM, and IV specifications, illustrated for the entire range of values for theEFW variable denoted on the x-axis. Dots illustrate the point estimate for the effect, and the 95 percent confidence intervals for each point calculatedwith the Benjamini and Hochberg (1995) adjustment are shown by the high-low markers. If this confidence interval lies entirely below the value ofzero on the “Partial Derivative” axis, then the estimated marginal effect at that level of economic freedom is negative and significant, while it isinsignificant if the interval includes the value of zero. The histograms of “Density,” plotted using the other y-axis, show the relative frequencydistribution of the underlying EFW variable values to illustrate the relative importance for each value of the corresponding partial derivative.

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

effect is negative and significant shown by the confidence intervals falling entirely below the value of zero, while it becomes insig-nificant at higher values of economic freedom as the interval eventually moves up to include the value of zero.

7

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

5. Robustness checks

We have subjected our estimates to a battery of robustness checks, and briefly discuss those in this section. Importantly, our resultsremain consistent with our findings presented above. Appendix 1 includes the results based on different sets of exogenous instrumentsfor the IV specifications. The instruments chosen in different combinations reflect a variety of legal origins (France, German, U.K. andSocialist), latitude, distance from the coast (in mean and centroid units), mean elevation above sea level, percent of land area 100 kmfrom ice-free coast, percent of population in the tropics, and area in square kilometers. We use different combinations of these in-struments for alternate specifications as indicated in the table. The exclusions restrictions are met as implied by the Hansen J values. TheF values meet the threshold Stock and Yogo (2005) level for all the specifications. The results are consistent across the variety of differentinstrument combinations listed in the appendix table. The partial derivatives of TEA with respect to fear of failure, provided in the lowerrows of the table, show in all cases that the large negative impact is present only at low levels of economic freedom, and it becomesinsignificantly different from zero by the highest values of economic freedom. In all cases, economic freedom helps to mitigate thenegative impact of this variable.

Appendix 2 shows the robustness of our results to different subsets of the data, a non-linear form of the interaction term, and abalanced panel to check for issues with panel attrition. As our control variables have the largest number of missing values, we create abalanced panel by omitting our controls and the countries for which our three key variables are not complete. Our results remain robustfor OLS, IV, and System GMM estimates, and the IV results are presented in Column (4).13 To check for possible non-linearities in ourinteraction term, as well as whether the interaction differs among subsamples of data, we employ a modified dual-bin procedure adaptedfrom Hainmueller et al. (2019).14 This procedure allows the coefficient on the interaction term to have a different value for differentsubsets of the data, by including an additional interaction with the interaction term. More precisely, in addition to the full interactionterm, we include a second term allowing for an additional marginal change to the value of the main interaction as indicated. When theadditional term is insignificant, it implies we cannot reject that the interaction term is a single common one across the subsamples.

The results in Column (1) allow for a non-linear (e.g. U-shaped) interaction splitting the interaction based on above versus belowmean values of the conditioning variable EFW. The coefficient showing the marginal change in the interaction at higher EFW values,EFW*FFt-1*(High EFW), is insignificant, thus we cannot reject the linear specification we employed in our main results in favor of a non-linear interaction effect using themodified procedure of Hainmueller et al. (2019). Following a similar procedure, the results in Columns(2) and (3) allow the interaction coefficient to differ based on a country’s level of development using above and below mean GDP percapita (High GDP PC interaction), and pre and post the Great Recession (Year, 2009þ interaction). In all cases, the main interactionremains significant and robust, while the marginal effect for high values (or later years) are statistically insignificant indicating the maininteraction term is descriptive of the entire sample, and the subsets tested do not have different interaction values or slopes.

A variety of other robustness checks were run and we briefly discuss them here to assure readers we have considered these alter-natives (results available upon request). To address possible non-linearity we constructed an indicator variable for TEA above average(and zero otherwise) to replace the continuous TEA measure and the results remained robust. Additionally, while our theory and testingregards the overall EFW level in a country, the index itself has five component areas.15 In specifications including one or more of theareas separately, in no case are the results largely inconsistent with our main findings, with one exception—the size of governmentsubarea when included alone, omitting the other areas, does produce a negative coefficient on the interaction term, and an oddlypositive initial value—seemingly indicating that fear of failure improved entrepreneurship rates when government size was large, andthat the impact of fear became increasingly negative as government size was reduced.16

Our results are also robust to additional controls for educational attainment,17 foreign direct investment as a share of GDP, grosscapital formation as a share of GDP (as a measure of domestic investment), central bank independence following Berggren and Nilsson(2014), and Polity 2 from the Polity IV database (measuring level of democracy in a nation). In addition, in the specifications presentedin the main part of the paper our fear of failure variable is lagged, and our results are robust to using the contemporaneous value.

While our results remain robust across estimations using System GMM (with internal instruments) and IV (with external in-struments), one additional possible concern with our IV specifications is that in searching for a set of instruments that satisfy the critical

13 Our results remain robust as well using our original unbalanced sample but dropping controls, and to using contemporaneous values (not lagging)fear of failure.14 Hainmueller et al. (2019) derive several tests only for OLS regressions, not SGMM or IV as we additionally employ, which prevents directimplementation. Rather than creating mutually exclusive variables for above-mean and below-mean values of the interaction (as they do), and testingfor the statistical difference between the two coefficient estimates, we employ the full interaction along with a second marginal effect for above-meanvalues, the latter of which, if statistically significant would indicate the interaction slope differs within the upper sample. In no case was it statisticallysignificant, suggesting a common interaction in the subsamples is appropriate.15 We have no reason to think that areas would work differently, and prior literature has shown they are mostly important in combination with oneanother [Bolen and Sobel (2020); Nikolaev and Bennett (2016); Farhadi et al. (2015)].16 While this could be a spurious result due to the omission of the other areas, and we do not wish to draw any firm conclusions, it is possible the sizeof government can influence rates of entrepreneurship in complex ways through public service provision, social safety nets, and levels of rent seeking[Carlsson and Lindstr€om (2002); Farhadi et al. (2015)]. De Haan and Sturm (2000) argue that the government size area of these indices is arguably tobe interpreted and treated quite differently than the other components.17 Many studies conclude that human capital is related to entrepreneurial success (Dutta and Sobel, 2018; Cassar, 2006; Van der Sluis et al., 2005;Haber and Reichel, 2007). We consider primary gender parity index which takes into account male and gender schooling enrolment for both publicand private schools.

8

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

Stock and Yogo (2005) F-values based on the prior cross sectional Faria and Montesinos (2009) paper, we have relied upontime-invariant instruments in our panel IV estimation that give us plausibly exogenous variation in a cross-section, but not over time.The difficulty is in finding time-varying instruments at the country level that explain EFW but are not correlated with entrepreneurship.In searching the literature, we did find a time-varying instrument employed by Dreher et al. (2009), which is a dummy indicating acountry is one of the 10 temporary members of the United Nations Security Council (UNSC) in a particular year (each serves a 2 yearterm). Appendix (3) presents the results in columns (1) to (3) of several alternative groups of our prior instruments now with theadditional inclusion of the UNSC instrument. Our results remain robust.

As a final set of robustness tests, we check our benchmark IV specification with a simpler cross section model including combinationsof instruments. We present the results in Columns (4) and (5) of Appendix 3. The results remain robust in the IV cross section as well.This also implies that our conclusions regarding this relationship between economic freedom and fear of failure not only can beinterpreted as changes within a country over time, but also across countries in a given time period.

6. Conclusion

Previous literature has found that individuals’ fear of failure is a barrier to their choice to become entrepreneurs. In this paper wehypothesize that this relationship is lessened with higher levels of economic freedom because there are more entrepreneurial oppor-tunities. We believe the theoretical linkage is clear. When a person chooses to become an entrepreneur, they make a choice to accept theexpected value of the two possible outcomes—business success or business failure. What alternatives exist if the business fails istherefore relevant in the initial choice. When more second (or third or fourth) chance opportunities are present in an economy, theoutcome in the case of a failure is less negative because the investment in entrepreneurial talent and resources may be re-deployed intoanother business idea with minimal reduction in value.

Our empirical results confirm our hypothesis—higher economic freedom significantly helps to mitigate the negative impact of fear offailure on entrepreneurship rates. At low levels of economic freedom, we find higher fear of failure has a large and meaningful negativeimpact on entrepreneurship. The magnitude of our estimates is similar to the prior findings of Khyareh and Mazhari (2016) for Iran, acountry with weak economic policies. It is precisely in those environments where fear of failure hurts entrepreneurship the most due tothe lack of economic freedom and opportunity. As economic freedom levels increase, the impact becomes less negative, and at very highlevels of economic freedom, the impact of fear of failure becomes insignificant. Thus, our results also explain why some prior studiesusing different samples of countries find different impacts of fear of failure.

Our findings have important implications for the entrepreneurship literature. Because higher economic freedom reduces the impactof fear of failure on individuals’ choice to become entrepreneurs, even individuals with a lower risk tolerance will choose to becomeentrepreneurs in areas with higher economic freedom. Thus, higher economic freedom has an indirect effect that increases entrepre-neurship in that it leads more people to become entrepreneurs despite having a fear of failure. This is in addition to the direct impacteconomic freedom has on business profitability or ease of entry and red tape, yielding yet another reason that it is important foreconomic development, progress, and fostering entrepreneurship—economic freedom lessens the negative impact of risk and the fear offailure.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared toinfluence the work reported in this paper.

Data availability

Data will be made available on request.

9

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

Appendix 1. Entrepreneurship (TEA), Fear of Failure (FF), and Economic Freedom (EFW) – IV Estimates Using AlternativeInstruments

(1) (2) (3) (4)

10

Fear of Failure(FF)t-1

�2.474* (1.284)

�3.766** (1.826) �3.649** (1.803) �6.942*** (2.675)

EconomicFreedom(EFW)

�10.11* (5.520)

�15.38* (8.341) �14.83* (8.293) �36.03** (15.10)

EFW*FFt-1

0.288* (0.175) 0.474* (0.263) 0.455* (0.258) 0.932** (0.407) GDP Per Capita

Growth

0.00796 (0.00936) 0.00800 (0.00968) 0.00836 (0.00954) 0.0104 (0.0126)

UrbanPopulation

0.0408** (0.0174)

0.0445** (0.0190) 0.0421** (0.0175) 0.0170 (0.0257)

DomesticCredit

�0.0368 (0.107)

�0.0212 (0.109) �0.0323 (0.0978) �0.219 (0.151)

LFPR

0.0471 (0.0468) 0.0931 (0.0662) 0.0887 (0.0643) 0.153* (0.0795) Constant 89.39** (38.40) 122.5** (53.97) 119.8** (54.06) 262.4*** (95.42)

Observations

454 454 454 454 R-squared 0.71 0.67 0.67 0.26 Number of

countries

71 71 71 71

Number of

instruments 8 7 8 6Hansen J 0.13 0.15 0.17 0.14 F for excluded instruments 11.93 13.03 11.83 12.36Instruments

(external)

Legal origins (socialist, France,German), mean distance to coast,% tropical pop., latitude, dist. tocoast (centroid), % land area intropics

Legal origins (socialist, France,German), mean dist.to coast,latitude, dist. to coast(centroid), % land area 100 kmfrom ice-free coast

Legal origins (socialist, France,German, U.K.), mean dist.tocoast, latitude, dist. to coast(centroid), % land area 100 kmfrom ice-free coast

Legal origins (socialist), meanelev. above sea level, % pop of ‘95in tropics, latitude % land area100 km from ice-free coast, areain sq. km

∂TEAit

∂FFit�1

evaluatedat:

Min._EFWValue

�1.46** (0.68)

�2.10** (0.91) �2.05** (0.91) �3.27*** (1.26)

Max._EFWValue

�0.03 (0.26)

0.26 (0.44) 0.22 (0.42) 0.98 (0.83)

Note: Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%, respectively. Instruments (external) employed in IVspecification vary by model as indicated. Dependent variable is the TEA entrepreneurship rate (from GEM) defined as the percentage of 18–64population who are either a nascent entrepreneur or owner of a new business. Fear of Failure (FF) from GEM is the percentage of 18–64 populationperceiving good opportunities to start a business who indicate that fear of failure prevents them from doing so. Economic freedom (EFW) is the overallsummary index score from the Fraser Institute’s Economic Freedom of the World database. The controls included are GDP per capita growth, domesticcredit, urban population as share of total population, labor force participation rate.

Appendix 2. Robustness to Subsamples, Non-linear Interactions and Balanced Panel

Dependent Variable: TEA (1) (2) (3) (4)

Independent Variables:

Economic Freedom (EFW)

�20.60*** (6.917) �22.46*** (7.644) �21.53*** (6.566) �13.27** (6.553) Fear of Failure (FF)t-1 �4.899** (2.398) �6.269*** (2.065) �5.040*** (1.443) �3.213** (1.428) EFW*FFt-1 0.606* (0.361) 0.801*** (0.279) 0.628*** (0.198) 0.377** (0.192) EFW*FFt-1*(High EFW) 0.005 (0.052) – – –

EFW*FFt-1*(High GDP PC)

– – �0.025 (0.051) –

EFW*FFt-1*(Year, 2009þ)

– �0.067 (0.062) – –

GDP Per Capita Growth

�0.078 (0.188) �0.212 (0.220) �0.217 (0.367) –

Urban Population

0.037 (0.041) 0.058 (0.036) 0.094 (0.114) –

Domestic Credit

0.011 (0.030) 0.0176 (0.0181) 0.0236 (0.0338) –

LFPR

0.117* (0.065) 0.147* (0.0846) 0.161 (0.102) –

Constant

166.1*** (48.01) 188.2*** (55.65) 168.1*** (46.16) 120.9** (48.04)

Observations

454 454 454 551 R-squared 0.56 0.20 0.47 0.65 Number of countries 71 71 71 85

(continued on next page)

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

(continued )

Dependent Variable: TEA

(1) (2)

11

(3)

(4)

Independent Variables:

Number of instruments

7 7 7 7 Hansen J 16.7 0.62 0.62 0.34 F for excluded instruments 12.2 18.9 18.9 21.1

Note: Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%, respectively. Instruments (external) employed in IVspecification are: legal origins (socialist), legal origins (France), mean elevation above sea level, percent of population in tropics, percent of land area intropics, latitude (centroid) and distance to coast (centroid). Dependent variable is the TEA entrepreneurship rate (from GEM) defined as the percentageof 18–64 population who are either a nascent entrepreneur or owner of a new business. Fear of Failure (FF) from GEM is the percentage of 18–64population perceiving good opportunities to start a business who indicate that fear of failure prevents them from doing so. Economic freedom (EFW) isthe overall summary index score from the Fraser Institute’s Economic Freedom of the World database. The final column (4) shows the results for abalanced panel achieved by removing control variables and any countries without full data on the three main variables of interest. In the otherspecifications, the controls included are GDP per capita growth, domestic credit, urban population as share of total population, labor force participationrate. Year fixed effects are included in all specifications. The additional interaction terms in the first three columns allow for an additional marginalchange in the slope of the interaction for subsets of the data. EFW*FFt-1*(High EFW) allows for a non-linear interaction in which the slope could changein each half of the range of the interaction term. EFW*FFt-1*(High GDP PC) allows for developed countries (those with above average GDP per capita)to have a different interaction, while EFW*FFt-1*(Year, 2009þ) allows a break before and after the Great Recession. In no case are these marginaleffects significant, implying no non-linearities and that a single linear interaction across the entire sample cannot be rejected.

Appendix 3. Entrepreneurship (TEA), Fear of Failure (FF), and Economic Freedom (EFW) – IV Estimates Using AdditionalAlternative Instruments, and Cross-Sectional Estimates

Panel Cross Section

(1)IV

(2)IV

(3)IV

(4)IV

(5)IV

Fear of Failure(FF)t-1

�14.28*** (4.334)

�4.452** (2.013) �12.89** (5.177) �9.595* (5.674) �8.442* (4.957)

EconomicFreedom(EFW)

�75.71*** (20.04)

�19.15* (10.84) �62.51** (24.53) �44.04 (28.38) �39.27 (25.00)

EFW*FFt-1

2.082*** (0.624) 0.559* (0.291) 1.945** (0.804) 1.235* (0.733) 1.102* (0.645) GDP Per

CapitaGrowth

�0.201 (0.205)

�0.041 (0.136) 0.071 (0.172) 0.339 (0.629) 0.287 (0.559)

Urban

Population 0.005 (0.047) 0.044* (0.024) 0.070* (0.042) 0.007 (0.129 0.034 (0.74)Domestic

Credit

0.0185 (0.020) 0.001 (0.011) 0.021 (0.018) 0.005 (0.049) 0.012 (0.042)

LFPR

0.251* (0.141) 0.110* (0.0604) 0.310* (0.158) 0.030 (0.246) 0.032 (0.220) Constant 507.1*** (134.1) 152.4** (71.85) 395.1*** (146.5) 349.3 (222.1) 308.2 (194.6)

Observations

440 440 440 86 86 R-squared 0.23 0.60 0.33 0.28 0.42 Number of

countries

69 69 69 86 86

Number of

instruments 4 4 4 6 8Hansen J 0.69 0.55 0.47 0.57 0.58 F for excluded instruments 12.9 11.9 10.9 10.7 10.1Instruments

(external)

UNSC dummy, legalorigins (socialist),mean dist.to coast,tropical pop

UNSC dummy,mean dist.to coast,tropical pop, %land area (tropics)

UNSC dummy,legal origins(socialist), meandist.to coast,latitude

Legal origins (France), cent. bankindependence, ‘95 pop. 100 kmfrom ice-free coast, % land area intropical monsoon climate, % landarea in highlands

Legal origins (France, German,socialist), cent. Bank independence,‘95 pop. 100 km from ice-free coast,% land area in tropical monsoonclimate, % land area in highlands

Note: Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%, respectively. Instruments (external) employed in IVspecification vary by model as indicated. Dependent variable is the TEA entrepreneurship rate (from GEM) defined as the percentage of 18–64population who are either a nascent entrepreneur or owner of a new business. Fear of Failure (FF) from GEM is the percentage of 18–64 populationperceiving good opportunities to start a business who indicate that fear of failure prevents them from doing so. Economic freedom (EFW) is the overallsummary index score from the Fraser Institute’s Economic Freedom of the World database. The controls included are GDP per capita growth, domesticcredit, urban population as share of total population, labor force participation rate.

N. Dutta, R.S. Sobel European Journal of Political Economy 66 (2021) 101954

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