Globalization and Informal Entrepreneurship: A Cross-Country Analysis.
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.
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.
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.
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.
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.
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.
Autio, E., & Fu, K. (2015). Economic and political institutions and entry into formal and informal entrepreneurship. Asia Pacific Journal of Management, 32(1), 67-94.
Baum, C. F., Schaffer, M. E., & Stillman, S. (2007). Enhanced routines for instrumental variables/GMM estimation and testing. The Stata Journal, 7(4), 465-506.
Berdiev, A. N., & Saunoris, J. W. (2016). Financial development and the shadow economy: A panel VAR analysis. Economic Modelling, 57, 197-207.
Berdiev, A. N., & Saunoris, J. W. (2018a). Docs globalisation affect the shadow economy? The World Economy, 41(1), 222-241.
Berdiev, A. N., & Saunoris, J. W. (2018b). Corruption and entrepreneurship: Cross-country evidence from formal and informal sectors. Southern Economic Journal, 840), 831-848.
Berdiev, A. N., Saunoris, J. W" & Schneider. F. (2018a). Give me liberty, or I will produce underground: Effects of economic freedom on the shadow economy. Southern Economic Journal, 85(2), 537-562.
Berdiev, A. N., Goel, R. K., & Saunoris, J. W. (2018b). Corruption and the shadow economy: One-way or two-way street? The World Economy, 41(11), 3221-3241.
Berggren, N., & Nilsson, T. (2015). Globalization and the transmission of social values: The case of tolerance. Journal of Comparative Economics, 43(2), 371-389.
Bergh, A., & Nilsson, T. (2010). Do liberalization and globalization increase income inequality? European Journal of Political Economy, 26(4), 488-505.
Buehn, A., & Farzanegan, M. R. (2012). Smuggling around the world: Evidence from a structural equation model. Applied Economics, 44(23), 3047-3064.
Cameron, A. C., & Trivedi, P. K. (1990). The information matrix test and its applied alternative hypotheses. Working Paper 372, University of California-Davis, Institute of Governmental Affairs. http://cameron. econ.ucdavis.edu/research/imtest_impliedaltemativcs_ucdwp372.pdf.
Capasso, S., & Jappelli, T. (2013). Financial development and the underground economy. Journal of Development Economics, 101, 167-178.
Chong, A., & Gradstein, M. (2007). Inequality and informality. Journal of Public Economics, 97(1-2), 159-179.
Clark, W. C. (2000). Environmental Globalization. In J. S. Nye & J. D. Donahue (Eds.), Governance in a globalizing world (pp. 86-108). Washington, DC: Brookings Institution Press.
Cordova, D. (2014). Enhancing formal and informal entrepreneurship in developing countries. In Creating the environment for entrepreneurial success (pp. 31-32). Washington, DC: Center for International Private Enterprise.
Dau, L. A., & Cuervo-Cazurra, A. (2014). To formalize or not to formalize: Entrepreneurship and pro-market institutions. Journal of Business Venturing, 29(5), 668-686.
De Soysa, I., & Vadlamannati, K. C. (2011). Does being bound together suffocate, or liberate? The effects of economics, social, and political globalization on human rights, 1981-2005. Kyldos, 64(1), 20-53.
Dong. B" Dulleck, U., & Torgler, B. (2012). Conditional corruption. Journal of Economic Psychology, 33(3), 609-627.
Dreher, A. (2006). Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 3"(10), 1091-1110.
Dreher. A., & Gaston, N. (2008). Has globalization increased inequality? Review of International Economics, 160), 516-536.
Dreher. A., Gaston, N" & Martens, P. (2008). Measuring globalisation--Gauging its consequences. New York: Springer.
Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn world table. American Economic Review, 705(10), 3150-3182.
Friedman, E" Johnson, S., Kaufmann, D" & Zoido-Lobaton, P. (2000). Dodging the grabbing hand: The determinants of unofficial activity in 69 countries. Journal of Public Economics, 76(3), 459-493.
Gerxhani, K. (2004). The informal sector in developed and less developed countries. A literature survey. Public Choice, 720(3-4), 267-300.
Ghemawat, P.. & Altman. S. A. (2014). DHL global connectedness index 2014. Deutsche post DHL, November 2014. http://www.dhl.com/content/dam/Campaigns/gci2014/downloads/dhl^gci_2014_study_high.pdf
Goel, R. K, & Saunoris, J. W. (2016). Government decentralization and the prevalence of the shadow economy. Public Finance Review, 44(2), 263-288.
Goel. R. K., Saunoris, J. W" & Zhang, X. (2015). Innovation and underground entrepreneurship. The Journal of Technology Transfer, 40(5), 800-820.
Goel, R. K., Saunoris, J. W" & Zhang. X. (2016). Intranational and international knowledge flows: Effects on the formal and informal sectors. Contemporary Economic Policy, 34(2), 297-311.
Goel, R. K" Saunoris, J. W.. & Schneider, F. (2019). Drivers of the underground economy for over a century: A long term look for the United States. The Quarterly Review of Economics and Finance, 71, 95-106.
Gwartney, J., Lawson, R, & Hall, J. (2017). Economic freedom of the world: 2017 annual report. Vancouver, BC: The Frascr Institute. https://www.ftaserinstitute.org/sitcs/default/files/economy
Johnson, S., Kaufmann, D., & Shleifer, A. (1997). The unofficial economy in transition. Brookings Papers on Economic Activity, (2), 159-239.
Kaufmann, K, Kraay, A., & Mastruzzi, M. (2010). The worldwide governance indicators: A summary of methodology, data and analytical issues. World Bank Policy Research Working Paper No.. 5430 http://info.worldbank.org/governance/wgi/pdf/wgi.pdf.
Koenker, R.. & Bassett. G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Li, G. (1985). Robust regression. In D. C. Hoaglin, F. Mosteller, & J. W. Tukey (Eds.), Exploring data tables, trends, and shapes (pp. 281-304). New York: Wiley.
Marshall, M. G., Gurr, T. R., & Jaggers. K. (2016). Polity IV project: Political regime authority characteristics and transitions 1800-2015. http://www.systemicpeace.org/polity/polity4.htm.
Medina, L" & Schneider, F. (2017). Shadow economies around the world: New results for 158 countries over 1991-2015. CESifo Working Paper Series No. 6430. https://www.cesifo-group.de/DocDL/cesifol_ wp6430.pdf
Mishkin. F. S. (2009). Globalization and financial development. Journal of Development Economics. 89(2). 164-169.
Potrafke, N. (2012). Intelligence and corruption. Economics Letters, 114(1), 109-112.
Potrafke, N. (2015). The evidence on globalisation. The World Economy, 38(3), 509-552.
Rosser, J. B., Rosser, M. V., & Ahmed, E. (2000). Income inequality and the informal economy in transition economies. Journal of Comparative Economics, 28, 156-171.
Saunoris. J. W., & Sajny, A. (2017). Entrepreneurship and economic freedom. Cross-country evidence from formal and informal sectors. Entrepreneurship and Regional Development, 29(3-4), 292-316.
Schneider, F. (2005). Shadow economies around the world: What do we really know? European Journal of Political Economy, 21(3), 598-642.
Schneider, F. (2011). Handbook on the shadow economy. Cheltenham: Edward Elgar.
Schneider, F., & Enste, D. H. (2000). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, 3S(1), 77-114.
Starr, H. (1991). Democratic dominoes: Diffusion approaches to the spread of democracy in the international system. Journal of Conflict Resolution, 35(2), 356-381.
Stiglitz. J. E. (2004). Globalization and growth in emerging markets. Journal of Policy Modeling, 26(4), 465-484.
Straub, S. (2005). Informal sector: The credit market channel. Journal of Development Economics, 78(2), 299-301.
Tanzi, V. (1982). The underground economy in the United States and abroad. Lanham: Lexington Books.
Teobaldelli, D., & Schneider. F. (2013). The influence of direct democracy on the shadow economy. Public Choice, 1570-4), 543-567.
Terjesen. S.. Hessels, J., & Li, D. (2016). Comparative international entrepreneurship: A review and research agenda. Journal of Management, 42(1), 299-344.
United Nations (2014). Country classifications. United Nations, http://www.un.org/en/development/desa/ policy/wesp/wesp_current/2014wesp_country_classification.pdf.
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  * James W. Saunoris 
Published online: 29 March 2019
[mail] Aziz N. Berdiev
 Department of Economics, Bryant University, Smithfield, RI 02917, USA
 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|
|Date:||Mar 1, 2019|
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