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Globalization and Informal Entrepreneurship: A Cross-Country Analysis.

Introduction

The informal, or shadow, economy has attracted much attention given its widespread prevalence across many nations (Schneider and Enste 2000; Gerxhani 2004; Schneider 2011; Tanzi 1982; Johnson et al. 1997; Schneider 2005). (1) Recent estimates by Medina and Schneider (2017) showed that the average size of the informal economy worldwide is about 30% of gross domestic product (GDP). Whereas the informal economy is present in both developed and developing countries (Schneider 2005; Gerxhani 2004; Schneider and Enste 2000), Medina and Schneider (2017) showed that the size of the informal sector varies widely across nations with a high of more than 60% of GDP (e.g., Georgia) and a low of less than 10% of GDP (e.g., Switzerland). What explains this cross-national variation in the size of the informal sector?

Besides understanding the drivers of the informal economy to come up with policy recommendations to discourage economic agents from relocating to the shadow sector or encourage shadow actors to move to the formal sector, the consequences of globalization have been a topic of great interest. Globalization is defined by Clark (2000, p. 86) as "networks of connections among actors (state and nonstate) at multicontinental distances, mediated through an open-ended variety of flows including people, information and ideas, force, capital, goods, and materials". Nations in particular are becoming more globally integrated through, such things as, removing trade restrictions, improving access to information, and participating in international treaties and organizations (Dreher 2006; Dreher et al. 2008). However, as countries continue to open their borders through economic, social and political integration, researchers and policymakers alike are looking to better understand the effects of globalization. (2)

Surprisingly, the interaction between globalization and the spread of shadow economies and, in particular, informal entrepreneurship, has received little attention. While Berdiev and Saunoris (2018a) examined the relationship between globalization and the shadow economy, this study analyzes the impact of globalization on informal entrepreneurship. Informal entrepreneurship is an important subgroup of the informal economy; namely, those economic agents who are active in starting unregistered businesses in the informal sector (Berdiev and Saunoris 2018b). Additionally, whereas the determinants of formal entrepreneurship are widely known (Tetjcsen et al. 2016), the causes of informal entrepreneurship are less forthcoming (Williams and Nadin 2010). Accordingly, this study analyzes what drives entrepreneurs to start their businesses in the shadow sector, and, in particular, whether and to what extent globalization influences the spread of underground entrepreneurship.

Theoretically, the impact of globalization on shadow entrepreneurship is not clear a priori. For example, to the extent that globalization through greater trade openness boosts competition, entrepreneurs move to the shadow sector to decrease the costs of operations to stay competitive (Goel et al. 2019). On the other hand, globalization through such things as eradicating burdensome trade restrictions constrains opportunities for underground participants, thereby supporting shadow entrepreneurs to transition to legitimacy (Schneider and Enste 2000). Furthermore, globalization may reduce the prevalence of informal operations by improving the quality of institutions (Berdiev and Saunoris 2018a). Also, because globalization promotes the growth of official output (e.g., Dreher 2006), it may raise the opportunity cost of operating in the shadow by presenting more prospects for entrepreneurs to earn higher wages in the official economy. Yet, to the extent that globalization raises income inequality (e.g., Bergh and Nilsson 2010), it may drive entrepreneurs underground due to insufficient resources available to those at the lower end of the income distribution to produce formally.

Therefore, this study attempts to settle this theoretical ambiguity by testing the impact of globalization on informal entrepreneurship using cross-national data. Additionally, the effectiveness of globalization dependent on the prevalence of informal entrepreneurship is examined using quantile regression analysis. Countries with widespread informality signal general problems with such things as economic and government institutions, thus targeted policies are less effective compared to a more significant change brought about by, for example, globalization. Overall, the results show that globalization reduces informal entrepreneurship. This finding is robust to various sensitivity analyses including an alternate measure of globalization, alternate measure of informal entrepreneurship, controlling for additional covariates, accounting for endogeneity of globalization, correcting for outliers, and considering nonlinearities. Furthermore, employing quantile regression analysis, the results indicate that globalization is most effective at reducing informal entrepreneurship when it is most prevalent.

Literature Review

The literature has provided various theories to explain the existence of underground entrepreneurship (see, for a discussion, Williams and Nadin 2010, 2011; and Webb et al. 2013). Williams and Nadin (2011) provided four views to describe what drives entrepreneurs to the shadow sector: (1) The modernization view suggests that the prevalence of underground entrepreneurship is a remnant from a prior method of production; (2) The structuralist view describes the development of informality as a result of entrepreneurs' incapability to acquire employment in the official economy; (3) The neo-liberal view hypothesizes that entrepreneurs find refuge in the shadow economy to circumvent onerous regulations and bureaucratic government officials; and (4) The post-structuralist view notes that entrepreneurs retreat to the shadow economy to pursue "social, redistributive, resistance or identity" motives. Using data for English localities, Williams and Nadin (2011) found that a mixture of these theories are essential to describing the prevalence of shadow entrepreneurship. (3)

The relationship between globalization and underground entrepreneurship can be explained by several of the aforementioned views. For example, related to the structuralist view, whereas research has shown that globalization promotes growth (e.g., Dreher 2006), Stiglitz (2004, p. 469) emphasized that globalization may hinder growth "when not managed well". According to Williams et al. (2016, p. 92), the procedures related to economic integration through "a dangerous cocktail of deregulation and increasing global competition" contribute to the development of the informal economy. Because globalization promotes competition, entrepreneurs are more likely to retreat to the shadow sector to reduce operational costs to stay competitive (Goel et al. 2019). Moreover, Williams et al. (2012, p. 530) explained that "informal entrepreneurs are the unwilling and unfortunate pawns within an exploitative global economic system in which work is becoming ever more precarious and poorly paid." Consequently, entrepreneurs may migrate to the shadow sector because of limited opportunities in the formal sector, thereby fostering informal entrepreneurship.

On the other hand, the interaction between globalization and shadow entrepreneurship may be connected to the post-structuralist and neo-liberal views. Specifically, the poststructuralist view explains the prevalence of shadow entrepreneurship as a "resistance" motive to the rent-seeking and corrupt activities that are taking place in the official economy, whereas the neo-liberal view proposes that the informal sector provides an alternative for entrepreneurs to escape government distortions associated with such things as burdensome regulations and bureaucratic public officials (Williams and Nadin 2010, 2011). Empirical literature has provided evidence that corruption increases participation in the underground sector (e.g., Johnson et al. 1997; Friedman et al. 2000). (4) Recently, Berdiev and Saunoris (2018b) found that entrepreneurs migrate to the shadow sector in response to corrupt governments. Thus, to the extent that globalization improves institutional quality by lowering corruption (e.g., Potrafke 2012; Dong et al. 2012), it may encourage underground entrepreneurs to transition to legitimacy.

Globalization may also raise the opportunity costs of informal startups by reducing trade barriers, increasing access to capital, and lowering transaction costs (World Bank 2002). In particular, prior to globalization, high trade barriers (e.g., tariffs, quotas, embargoes) empower informal entrepreneurs to satisfy demand and provide these restricted products from other countries (Buehn and Farzanegan 2012; Mishkin 2009; Saunoris and Sajny 2017; Berdiev et al. 2018a). Indeed, using a multiple indicators multiple causes model, Buehn and Farzanegan (2012) showed that higher tariffs contribute to smuggling. Globalization through, for example, removing trade barriers effectively limits these opportunities for informal entrepreneurs (Schneider and Enste 2000; Berdiev and Saunoris 2018a; Berdiev et al. 2018a).

Moreover, globalization contributes to the transmission of ideas, information and knowledge across countries. For example, policy diffusion through emulation of successful policies adopted in other countries can be useful in deterring entrepreneurs from moving underground or encouraging their move to legitimacy (Berdiev and Saunoris 2018a). Starr (1991), for example, showed the diffusion of democracy across countries, which may avert informality by giving voice to potential informal participants in the political arena, thereby giving them partial control over government policies. Furthermore, Europe has been proactive in policy experimentation (supply and demand side initiatives) to reduce the cost of formalizing by making it easier for businesses to register and transition to legitimacy, and imitation of these successful policies can be spread by globalization (Williams 2006).

Likewise, globalization conceivably promotes the spread of microfinance, which may prevent informality by allowing access to capital (e.g., Cordova 2014). Studies have shown that financial development reduces the spread of shadow activity by providing entrepreneurs with such things as access to credit (e.g., Straub 2005; Capasso and Jappelli 2013; Berdiev and Saunoris 2016). Furthermore, to the extent that globalization fosters growth of output in the formal sector (e.g., Dreher 2006), it may increase the opportunity cost of participating in the informal sector by providing better opportunities for entrepreneurs to earn higher income in the formal sector. Conceivably, higher formal sector growth may also promote the development of the shadow economy through stronger demand for products that are delivered by underground entrepreneurs (Goel et al. 2019).

Research has also documented that globalization is positively associated with income inequality (e.g., Dreher and Gaston 2008; Bergh and Nilsson 2010), which may push entrepreneurs to the shadow economy because of limited resources available to those at the bottom end of the income spectrum to participate in the formal economy. Indeed, the positive relationship between income inequality and the informal economy has been well documented in the literature (e.g., Rosser et al. 2000; Chong and Gradstein 2007). Lastly, it is possible that the linkages between globalization and informal entrepreneurship may vary over time. Goel et al. (2019), for example, argued that in the short run greater trade openness invites more competition thereby leading entrepreneurs to move to the informal sector to reduce the costs of operations in order to remain competitive; whereas in the long run, more trade openness enhances institutional quality which increases the opportunity cost of operating informally.

Based on the above discussion, the relationship between globalization and underground entrepreneurship is ambiguous and thus an empirical analysis is used to disentangle these opposing views and provide some clarity as to the underlying relationship between globalization and informal entrepreneurship. The following section describes the data and the empirical methodology used to settle this theoretical ambiguity by examining the influence of globalization on informal entrepreneurship.

Data and Empirical Model

To examine the impact of globalization on informal entrepreneurship, the following equation was estimated:

Informal-[Entrepreneurship.sub.i] = [[beta].sub.0] + [[beta].sub.1] Globalization, + [gamma][X.sub.i] + [[epsilon].sub.i] (1)

where Informal_Entrepreneurship denotes the prevalence of informal entrepreneurs in country i; the variable Globalization measures the degree of globalization; [X.sub.i] represents the vector of control variables; and e, is the error term.

The data for Informal Entrepreneurship were from Autio and Fu (2015), who estimated the number of informal new business entries per 100 adult-age population by employing the Global Entrepreneurship Monitor Adult Population Survey. The survey contained data on registered and unregistered entrepreneurs as it collected no information on registration intent. The authors thus computed the entry rate of new businesses, which include both registered and unregistered entrepreneurs. This estimate was then subtracted from the World Bank (2018) Enterprise Snapshot measure of the entry rate of registered new businesses, and thus the residual was a proxy for the number of unregistered new business entries. To check the robustness of the results, an alternate measure of informal entrepreneurship from Dau and Cuervo-Cazurra (2014) was used.

Next, globalization was a multidimensional concept that consisted of economic, political and social facets (Dreher 2006; Potrafke 2015). Therefore, the index of globalization developed by Dreher (2006) and Dreher et al. (2008) was employed, which encompassed economic, political and social dimensions of globalization. For example, the globalization index included data on foreign direct investments, taxes on international trade, international tourism, internet users, embassies in countries, and memberships in international organizations (Dreher 2006; Dreher et al. 2008). The globalization index ranges between 1 and 100, where higher values represented a greater degree of globalization. Notice that an alternate measure of globalization from Ghemawat and Altman (2014) was used to check the sensitivity of the findings.

In choosing control variables, the extant literature on the informal economy was informative (Schneider and Enste 2000; Goel et al. 2015, 2016; Berdiev and Saunoris 2018b). The log of real GDP from the Penn World Table 8.0 (Feenstra et al. 2015) was employed to account for the level of economic development as more prosperous countries raised the opportunity costs of participating in informal activities and offered superior checks on informal entrepreneurs. In addition, the impact of political institutions was considered because democracies were more likely to have better institutions and provide greater public resources that might encourage entrepreneurs to migrate from the informal economy (Teobaldelli and Schneider 2013). The democracy index was from Polity IV (Marshall et al. 2016) and ranged between 0 and 10, where higher values signified higher degrees of democracy.

Moreover, high taxes and regulatory burdens increased the potential payoffs of hidden startups, which generated incentives for entrepreneurs to function informally (Schneider and Enste 2000). Thus, indexes of minimum wage regulations and top marginal tax rates (Gwartney et al. 2017) were included, which ranged between 0 and 10 where higher scores indicate more favorable (i.e., less regulations and taxes) conditions. Finally, Goel et al. (2016, p. 4) indicated that transition economies "possess unique incentives for entrepreneurs to start businesses formally or informally" due to institutional transitions and evolutions throughout the transition process. As a result, a dummy variable was included for transition countries following the classifications in United Nations (2014). Cross-national data for 66 countries averaged from 2001 to 2010 were utilized (see Table 1 for a list of countries). Table 2 provides variable details including summary statistics and data sources.

Equation (1) was estimated using ordinary least squares (OLS) methodology and several diagnostic tests were reported. First, the Ramsey Reset test for model misspecification under the null reported that the model was correctly specified. To check for heteroscedasticity and non-normal errors, the Cameron and Trivedi's (1990) decomposition information matrix tests based on tests of heteroscedasticity, skewness, and kurtosis under the null reported that the errors were normal and homoscedastic. Finally, variance inflation factors (VIF) were used to check for multicollinearity where a VIF > 10 signals potential problems with multicollinearity.

Results

Baseline Results

Table 3 includes the baseline results. The diagnostic tests showed evidence of model misspecification with the statistical significance of the RESET test statistic, some evidence of heteroscedasticity and non-normality of the residuals, and no evidence of multicollinearity problems. To deal with heteroscedasticity, robust standard errors are reported and model misspecification and residual non-normality are dealt with in the next section.

Model 3.1 of Table 3 provides estimates of the impact of globalization on informal entrepreneurship with no control variables. The R-squared is quite high showing that globalization explains approximately 44% of the variation in informal entrepreneurship, and the coefficient on globalization is negative and statistically significant at the 1% level suggesting that globalization is associated with lower informal entrepreneurship. The magnitude of the effect is quite large suggesting a 4.1% drop in informal entrepreneurship with a 1% increase in globalization.

In Model 3.2 of Table 3, a set of control variables is included as discussed in the previous section. The coefficient on globalization remains negative and statistically significant at the 1 % level, thereby indicating that globalization is significantly associated with lower informal entrepreneurship even after controlling for other relevant factors. Countries opening their borders through economic, social and political integration promote economic freedom and open new opportunities for entrepreneurs to bring their products and services to a much larger market. Over the long run, countries with open borders that embrace globalization experience benefits in transformed institutions and reduced risk of international conflicts, which all contribute to reducing the benefits of participating in the informal sector.

Regarding the control variables, higher GDP and transition countries are significantly associated with lower informal entrepreneurship. Countries going through transition by improving their institutions help to discourage informality. Curiously, more regulations associated with hiring and minimum wages reduce informal entrepreneurship. Conceivably, individuals shut out of the formal sector by labor regulations move to the shadow economy as informal workers rather than entrepreneurs. The variables democracy and top marginal tax rates have no significant influence on informal entrepreneurship at conventional levels. The findings for the control variables are broadly consistent with those of Goel et al. (2015, 2016) and Berdiev and Saunoris (2018b).

The following section conducts a series of robustness tests to check the validity of our baseline results. First, a set of additional control variables that have been suggested in the literature are included. Second, we account for the possible endogeneity and reverse causality using an instrumental variables technique. Third, we check for non-linear and diminishing returns to globalization. Fourth, an alternate measure of globalization is considered. Fifth, we correct for the influence of outliers. Sixth, an alternate measure of informal entrepreneurship is considered. Lastly, we correct the shortcomings of OLS by using quantile regression analysis. The robustness results are reported in Table 3 (Models 3.3-3.5), Table 4, and the quantile regression results are displayed in Table 5.

Robustness Checks

To begin, the sensitivity of the results was checked after controlling for additional variables that might influence informal entrepreneurship (e.g., Goel et al. 2015, 2016). Specifically, regulatory quality, government effectiveness, and the rule of law indexes, which come from Kaufmann et al. (2010), were included in the analysis. Greater rule of law, more effective governments and better quality of regulations raise the opportunity cost of informality. The results after adding these variables are included in Models 3.3-3.5 of Table 3. While the coefficient on each of these covariates is negative which is consistent with reducing informal entrepreneurship, they are statistically insignificant at conventional levels. Nonetheless, the coefficient on globalization maintains its sign and significance, thus confirming our baseline results.

Second, the possible endogeneity of globalization was accounted for. In particular, endogeneity is a result of reverse causality as countries with lower informal entrepreneurship are conceivably more likely to develop policies that promote globalization. To address the possible endogeneity of globalization, two-stage least squares (2SLS) regression and an instrument for globalization using the lagged (averaged from 1990 to 2000) values of neighboring overall globalization was employed, following Berggren and Nilsson (2015). According to Berggren and Nilsson (2015), a country's decision to become more open will be influenced by "peer effects" as their neighbors begin opening their borders; however, it seems unlikely that the level of globalization in neighboring countries in the previous decade would have a direct impact on own-country's prevalence of informal entrepreneurs. Due to the difficulty in finding truly exogenous instruments, an additional robustness check was considered by using geographic area measured by the log of kilometers squared (de Soysa and Vadlamannati 2011). These results are reported in Models 4.1 and 4.2, respectively, of Table 4. The coefficient on the variable globalization remains negative and statistically significant at the 1% level, thereby suggesting that globalization decreases informal entrepreneurship. The diagnostic tests, which are reported at the bottom of Table 4, confirm the relevancy of the instrument given by the significance of the Kleibergen-Paap test statistics; however, the endogeneity tests show mixed results (see, for details, Baum et al. 2007).

Third, in response to the significance of the Ramsey RESET tests suggesting evidence of non-linearities, Eq. (1) is re-estimated by including a quadratic variable of globalization (globalization squared). Conceivably, globalization's effect on informal entrepreneurship exhibits diminishing returns. These results are reported in Model 4.3 of Table 4. While the coefficient on the linear globalization term is negative and statistically significant, consistent with the baseline results, the coefficient on the quadratic term is positive and statistically significant suggesting evidence of diminishing returns. However, the small magnitude of the coefficient on the quadratic term shows little curvature. The control variables are consistent with the baseline models with the exception that the coefficient on minimum wage regulations is now statistically insignificant.

Fourth, given the difficulty in measuring both informal entrepreneurship and globalization another robustness check was conducted by replacing globalization with an alternate measure of globalization (globalization (DH)) from Ghemawat and Altman (2014) (Model 4.4) and informal entrepreneurship with an alternate measure (Informal_Entrepreneurship (DC)) from Dau and Cuervo-Cazurra (2014) (Model 4.5). Using these alternate measures shows that, regardless of how globalization or informal entrepreneurship is measured, globalization continues to have a negative and statistically significant effect on informal entrepreneurship. Furthermore, except for minimum wage regulations, the results for the control variables are consistent with the baseline model.

Fifth, the robustness of the baseline results to the influence of outliers was checked using robust regression. Robust regression first eliminates outliers using a Cook's distance of less than one and then applies Huber iterations and then biweight iterations (Li 1985). The results posted as Model 4.6 show that the coefficient on globalization, although smaller, is negative and statistically significant at the 1% level. Thus, the impact of globalization on informal entrepreneurship is immune to the influence of outliers.

Finally, one problem with OLS estimation is that it only provides a partial explanation of the effect of globalization on informal entrepreneurship by estimating the conditional distribution at the mean. Furthermore, the Cameron and Trivedi's (1990) decomposition information matrix test statistics reported at the bottom of Table 3 suggest that the residuals are non-normal casting doubt on the reliability of the OLS estimates. To address these shortcomings with respect to OLS, the impact of globalization is estimated over the entire conditional distribution of informal entrepreneurship using quantile regression analysis (Koenker and Bassett 1978). This is important as the causes of informal activities may be conditional on the spread of informal activities (e.g., Goel and Saunoris 2016). Also, quantile regression is semi-parametric and thus does not require that the errors be normally distributed.

The results are displayed in Table 5 for the following percentiles: 10%, 25%, 50%, 75%, and 90%. The coefficient on globalization is negative and statistically significant at least at the 5% level at all quantiles (except the tenth quantile). In fact, the effectiveness of globalization at reducing informal entrepreneurship is increasingly more effective the more prevalent is informal entrepreneurship.

Indeed, the F statistic (2.33), which tests the null hypothesis that the effect of globalization on informal entrepreneurship is equalized across quantiles is rejected at the 10% level (p-value = 0.068). Hence, based on the quantile estimates, the results confirm the baseline estimates while also suggesting that globalization is most effective when informal entrepreneurship is most prevalent. Conceivably, widespread informality results from systematic failure in official institutions and thereby necessitates a more global approach, such as opening borders, to combat informality. The pseudo R-squared (reported toward the bottom of Table 5) also shows increasing explanatory power at higher quantiles. These results confirm the baseline findings while offering deeper insight into the effectiveness of globalization across the prevalence of informal entrepreneurship.

Conclusion

This paper investigated the impact of globalization on informal entrepreneurship using cross-country data. Employing OLS and 2SLS techniques, the results showed that globalization decreased informal entrepreneurship. In particular, the evidence suggested that globalization, though removing barriers on trade, improving the transmission of information and promoting involvement in international organizations, deterred the spread of shadow entrepreneurship. The opportunities that unveil themselves as countries embrace globalization and its positive influence on institutions and policies help reduce the incentive for retreating underground to start businesses.

This finding was robust to various sensitivity analyses including an alternate measure of globalization, an alternate measure of informal entrepreneurship, controlling for additional covariates, accounting for endogeneity of globalization, correcting for outliers, and considering nonlinearities. The results from examining the influence of globalization across the prevalence of informal entrepreneurship using quantile regression suggest that countries with higher prevalence rates of informal entrepreneurship (e.g., developing countries) would benefit most from globalization.

Overall, these results suggest that countries more favorable to open borders are, ceteris paribus, more likely to see benefits in terms of lower informal entrepreneurship and, therefore, less tax evasion. Yet, it is important to note that although the formal sector is preferred, the informal sector can be viewed as a second best option particularly when institutions are impaired by such things as corruption. However, this study is not without its limitations. For instance, the use of aggregated data precludes any analysis of the disparate effects globalization might have on different industries. Additionally, the lack of long time series data precludes any investigation of the short-run and long-run effects of globalization on informal entrepreneurship. With the advent of new data at finer levels we will be able to address these interesting questions.

https://doi.org/10.1007/s11293-019-09612x

Acknowledgements We thank the editor, anonymous referees and participants at the 2016 American Public Choice conference in Fort Lauderdale and the 2017 Eastern Economic Association conference in New York City for valuable suggestions.

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Webb, J. W., Bruton, G. D., Tihanyi, L., & Ireland, R. D. (2013). Research on entrepreneurship in the informal economy: Framing a research agenda. Journal of Business Venturing, 28(5), 598-614.

Williams, C. C. (2006). The Hidden Enterprise Culture: Entrepreneurship in the Underground Economy. Cheltenham: Edward Elgar.

Williams, C. C., & Nadin, S. (2010). Entrepreneurship and the informal economy: An overview. Journal of Developmental Entrepreneurship, 15(4), 361-378.

Williams, C. C., & Nadin, S. (2011). Theorising the hidden enterprise culture: The nature of entrepreneurship in the shadow economy. International Journal of Entrepreneurship and Small Business, 14(3), 334-348.

Williams, C. C., Nadin, S., & Rodgers, P. (2012). Evaluating competing theories of informal entrepreneurship: Some lessons from Ukraine. International Journal of Entrepreneurial Behaviour & Research, 18(5), 528-543.

Williams, C. C., Horodnic, I. A., & Windcbank, J. (2016). The participation of the self-employed in the shadow economy in the European Union. In A. Sauka, F. Schneider, & C. C. Williams (Eds.), Entrepreneurship and the shadow economy (pp. 89-116). Cheltenham: Edward Elgar Publishing Limited.

World Bank (2002). Globalization, growth and poverty: Building an inclusive world economy. World Bank and Oxford University Press.

World Bank (2018). Enterprise surveys. World Bank Enterprise snapshot. http://www.enterprisesurveys. org/Data.

(1) The informal economy includes economic activity that is unregistered in the formal economy.

(2) Potrafke (2015) provided an excellent literature review on the consequences of globalization.

(3) Williams and Nadin (2011, p. 345) noted that "although each explanation is more valid in relation to some populations than others, it is only by combining and using all of these explanations that a more comprehensive and fuller explanation can be achieved of the complex multifarious rationales for shadow entrepreneurship in these English communities."

(4) Notice however that corruption may also lower the spread of informal activities (see Berdiev et al. (2018b) for a discussion on the relationship between corruption and the shadow economy).

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aziz N. Berdiev [1] * James W. Saunoris [2]

Published online: 29 March 2019

[mail] Aziz N. Berdiev

aberdiev@bryant.edu

[1] Department of Economics, Bryant University, Smithfield, RI 02917, USA

[2] Department of Economics, Eastern Michigan University, Ypsilanti, MI 48197, USA
Table 1 List of Countries in the Analysis

Algeria            France        Macedonia, FYR       Singapore
Argentina          Germany       Malaysia             Slovenia
Australia          Ghana         Mexico               South Africa
Austria            Greece        Montenegro           Spain
Belgium            Guatemala     Morocco              Sweden
Bolivia            Hungary       Netherlands          Switzerland
Bosnia and         Iceland       New Zealand          Thailand
Herzegovina
Brazil             India         Norway               Tunisia
Canada             Indonesia     Pakistan             Turkey
Chile              Ireland       Panama               Uganda
Colombia           Israel        Peru                 United Kingdom
Costa Rica         Italy         Philippines          United States
Croatia            Japan         Poland               Uruguay
Czech Republic     Jordan        Portugal             Vanuatu
Denmark            Kazakhstan    Romania              Zambia
Egypt, Arab Rep.   Korea, Rep.   Russian Federation
Finland            Latvia        Serbia

Table 2 Variable definitions, summary statistics and data sources

Variable                Description [observations;    Source
                        mean; standard deviation]

Informal                Average of the prevalence     Autio and Fu
Entrepreneurship        of informal                   (2015)
                        entrepreneurship. The
                        number of new informal
                        business entries per 100
                        adult-age population. [67;
                        1.60; 2.04]

Informal                Informal entrepreneurship     Dau and
Entrepreneurship (DC)   index measuring the number    Cuervo-Cazurra
                        of new unregistered           (2014)
                        businesses as a percentage
                        of working age population.
                        Data from 2002 to 2009.
                        [59; 4.51; 3.94]

globalization           An index of overall           Dreher (2006)
                        (political, economic and      & Dreher et
                        social) globalization. The    al. (2008)
                        index is scaled from 1 to
                        100 where higher numbers
                        denote more globalization.
                        [66; 69.40; 13.94]

globalization (DH)      An index measuring the        Ghemawat and
                        global connectedness based    Altman (2014)
                        on breadth and depth. The
                        index is measured on a
                        scale from 0 to 100 with
                        higher numbers denoting
                        more global connectedness.
                        Data from 2005 to 2010.
                        [63; 52.90; 16.50]

log GDP                 Natural log of expenditure-   Feenstra et al.
                        side real Gross Domestic      (2015); Penn
                        Product (chained PPP in       World Tables
                        Millions of 2005 U.S.         8.0
                        dollars). [64; 12.31; 1.60]

minimum wage            A sub-index of labor          Gwartney et
regulations             regulations which focuses     al. (2017)
                        on hiring and minimum wage
                        regulations. The index is
                        on a scale of 0 to 10 with
                        higher numbers denoting
                        more freedom from
                        regulations. [65; 6.41;
                        2.51]

top marginal tax rate   An index measuring the top    Gwartney et
                        marginal tax rate. The        al. (2017)
                        index is on a scale of 0 to
                        10 with higher numbers
                        denoting more freedom from
                        taxes. [64; 5.59; 2.12]

democracy               An index measuring the        Marshall et
                        degree of democracy. This     al. (2016)
                        measures the general
                        qualities of political
                        institutions and processes.
                        The index is on a scale
                        from 0 to 10 with higher
                        numbers denoting higher
                        degrees of democracy. [61;
                        7.75; 3.05]

transition              Dummy variable equal to one   United Nations
                        if the country is a           (2014)
                        transition country and zero
                        otherwise. [67; 0.16; 0.37]

regulatory quality      A perception-based index      Kaufmann et
                        measuring the degree of       al. (2010)
                        regulatory quality. The
                        index is on a scale of-2.5
                        to +2.5 with higher numbers
                        denoting greater quality.
                        [67; 0.62; 0.82]

government              A perception-based index      Kaufmann et
effectiveness           measuring the quality and     al. (2010)
                        credibility of government.
                        The index is on a scale of
                        -2.5 to +2.5 with higher
                        numbers denoting better
                        outcomes. [67; 0.64; 0.92]

rule of law             A perception-based index      Kaufmann et
                        measuring the strength and    al. (2010)
                        quality of the rule of law.
                        The index is on a scale of-
                        2.5 to +2.5 with higher
                        numbers denoting better
                        outcomes. [67; 0.53; 0.95]

Unless otherwise noted, data are based on annual observations by
country averaged over the period 2001 to 2010

Table 3 Globalization and informal entrepreneurship: OLS
regression estimates

                           (3.1)        (3.2)        (3.3)

Globalization              -0.097 ***   -0.099 ***   -0.081 ***
                           (0.019)      (0.021)      (0.029)
Log GDP                                 -0.324 **    -0.322 **
                                        (0.147)      (0.145)
Democracy                               0.037        0.054
                                        (0.082)      (0.089)
Minimum wage regulations                0.156*       0.174 *
                                        (0.093)      (0.097)
Top marginal tax rates                  -0.070       -0.074
                                        (0.078)      (0.079)
Transition                              -1.594 ***   -1.638 ***
                                        (0.382)      (0.368)
Regulatory quality                                   -0.420 (0.434)
Government effectiveness
Rule of law
Elasticity Estimate
Globalization              -414 ***                  -3.66 ***
                           (0.530)      (0.744)      (1.187)
Diagnostic Tests
Ramsey RESET test          8.961 ***    8.26 ***     922 ***
                           [0.000]      [0.000]      [0.000]
Heteroskedasticity         [0.000]      [0.070]      [0.215]
Skewness                   [0.000]      [0.024]      [0.037]
Kurtosis                   [0.112]      [0.260]      [0.289]
Total                      [0.000]      [0.015]      [0.070]
VIF                        1.00         1.42         2.66
Observations               66           60           60
R-squared                  0.439        0.588        0.594

                           (3.4)            (3.5)

Globalization              -0.076 **        -0.081 ***
                           (0.030)          (0.030)
Log GDP                    -0.311 **        -0.328 **
                           (0.143)          (0.144)
Democracy                  0.044            0.051
                           (0.082)          (0.082)
Minimum wage regulations   0.179 *          0.179 *
                           (0.093)          (0.091)
Top marginal tax rates     -0.080           -0.077
                           (0.079)          (0.078)
Transition                 -1.745 ***       -1.727 ***
                           (0.363)          (0.369)
Regulatory quality
Government effectiveness   -0.452 (0.309)
Rule of law                                 -0.359 (0.298)
Elasticity Estimate
Globalization              -3.46 ***        -3.68 ***
                           (1.201)          (1.168)
Diagnostic Tests
Ramsey RESET test          8.59 ***         9.71 ***
                           [0.000]          [0.000]
Heteroskedasticity         [0.096]          [0.102]
Skewness                   [0.045]          [0.029]
Kurtosis                   [0.287]          [0.316]
Total                      [0.031]          [0.027]
VIF                        2.49             2.34
Observations               60               60
R-squared                  0.597            0.595

Table 2 provides variable definitions and data sources. The
dependent variable is Informal Entrepreneurship. A constant is
included but not reported. Robust standard errors are in
parentheses and probability values are in brackets. Asterisks
denote the following significance levels: *** p<0.01, ** p<0.05, *
p<0.10

Table 4 Globalization and informal entrepreneurship: Robustness
checks

                            (4.1)        (4.2)        (4.3)

Globalization               -0.071 ***   -0.222 **    -0.426 **
                            (0.022)      (0.108)      (0.179)
Globalization (DH)
Globalization squared                                 0.002 *
                                                      -0.001
Log GDP                     -0.349 **    -0.223       -0.289 *
                            (0.148)      (0.192)      (0.150)
Democracy                   -0.012       0.258        0.053
                            (0.083)      (0.191)      (0.083)
Minimum wage regulations    0.127        0.269 **     0.105
                            (0.079)      (0.126)      (0.093)
Top marginal tax rates      0.017        -0.464       -0.029
                            (0.099)      (0.378)      (0.066)
Transition                  -1.554 ***   -1.752 ***   -1.355 ***
                            (0.351)      (0.535)      (0.421)
Observations                59           59           60
R-squared                   0.566        0.287        0.619
Estimation method           2SLS         2SLS         OLS
Endogeneity test            1.623        3.344 *
                            [0.203]      [0.067]
Kleibergen-Paap rk Wald F   19.57        2.183
statistic
Kleibergen-Paap rk LM       11.30 ***    2.547
statistic                   [0.001]      [0.110]

                            (4.4)         (4.5)        (4.6)

Globalization                             -0.199 ***   -0.027 ***
                                          (0.043)      (0.006)
Globalization (DH)          -0.059 ***
Globalization squared       -0.011

Log GDP                     -0.265 *      -0.575 *     -0.066
                            OLS           (0.293)      (0.040)
Democracy                   -0.069        -0.111       -0.020
                            (0.078)       (0.141)      (0.023)
Minimum wage regulations    0.144         0.043        0.065 ***
                            (0.095)       (0.171)      (0.023)
Top marginal tax rates      0.003         0.034        0.004
                            (0.082)       (0.202)      (0.033)
Transition                  -1.527 ***    -4.022 ***   -0.416 **
                            (0.320)       (0.878)      (0.167)
Observations                60            55           55
R-squared                   0.480         0.613        0.613
Estimation method           OLS           OLS          Robust
Endogeneity test                                       OLS

Kleibergen-Paap rk Wald F
statistic
Kleibergen-Paap rk LM
statistic

Table 2 provides variable definitions and data sources. The
dependent variable is Informal_Entrepreneurship. A constant is
included but not reported. Robust standard errors are in
parentheses and probability values are in brackets. Model 3.1
employs the lagged values of neighboring overall globalization as
an instrument Model 3.2 uses the log of land area (kilometers
squared) of a country as an instrument. The dependent variable is
Informal Entrepreneurship (DC) in Model 3.5. Asterisks denote the
following significance levels: *** p<0.01, ** p<0.05, *p<0.10

Table 5 Globalization and informal entrepreneurship: Quantile
regression estimates

                           (5.1)      (5.2)       (5.3)
                           Q(10%)     Q(25%)      Q(50%)

Globalization              -0.023     -0.036 **   -0.064 **
                           (0.014)    (0.016)     (0.028)
Log GDP                    -0.075     -0.087      -0.166
                           (0.092)    (0.087)     (0.126)
Democracy                  0.004      -0.027      0.027
                           (0.059)    (0.059)     (0.099)
Minimum wage regulations   0.045      0.025       0.033
                           (0.067)    (0.067)     (0.096)
Top marginal tax rates     0.022      -0.014      0.015
                           (0.056)    (0.053)     (0.087)
Transition                 -0.234     -0.619 *    -1.110 **
                           (0.290)    (0.315)     (0.483)
Constant                   2.724      4.707 **    7.576 ***
                           (1.657)    (1.779)     (2.674)
Observations               60         60          60
Pseudo R-Squared           0.130      0.215       0.317
F-test                     2.333 *
                           [0.068]

                           (5.4)        (5.5)
                           Q(75%)       Q(90%)

Globalization              -0.125 ***   -0.137 ***
                           (0.036)      (0.044)
Log GDP                    -0.300 *     -0.428
                           (0.163)      (0.281)
Democracy                  0.052        0.190
                           (0.162)      (0.215)
Minimum wage regulations   0.146        0.135
                           (0.117)      (0.141)
Top marginal tax rates     -0.023       0.025
                           (0.097)      (0.166)
Transition                 -1.818 ***   -1.474 *
                           (0.616)      (0.764)
Constant                   13.757 ***   15.741 ***
                           (2.958)      (3.662)
Observations               60           60
Pseudo R-Squared           0.481        0.576
F-test

Table 2 provides variable definitions and data sources. The
dependent variable is Informal Entrepreneurship. A constant is
included but not reported. Q (10%) denotes estimates at the 10th
percentile, and so on. Bootstrapped standard errors are in
parentheses (500 iterations used) and probability values are in
brackets. Asterisks denote the following significance levels.' ***
p < 0.01, ** p < 0.05, * p < 0.10
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Author:Berdiev, Aziz N.; Saunoris, James W.
Publication:Atlantic Economic Journal
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Date:Mar 1, 2019
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