WPS4888 P olicy R eseaRch W oRking P aPeR 4888 Informality in Latin America and the Caribbean Norman V. Loayza Luis Servén Naotaka Sugawara The World Bank Development Research Group Macroeconomics and Growth Team March 2009 Policy ReseaRch WoRking PaPeR 4888 Abstract This paper studies the causes and consequences of state. This combination is especially explosive when the informality and applies the analysis to countries in Latin country suffers from low educational achievement and America and the Caribbean. It starts with a discussion features demographic pressures and primary production on the definition and measures of informality, as well structures. Using cross-country regression analysis, as on the reasons why widespread informality should the paper evaluates the empirical relevance of each be of great concern. The paper analyzes informality's determinant of informality. It then applies the estimated main determinants, arguing that informality is not relationships to most countries in Latin America and single-caused but results from the combination of poor the Caribbean in order to assess the country-specific public services, a burdensome regulatory regime, and relevance of each proposed mechanism weak monitoring and enforcement capacity by the This paper--a product of the Growth and the Macroeconomics Team, Development Research Group--is part of a larger effort in the group to understand the relationship between business regulations and economic performance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nloayza@ worldbank.org, lserven@worldbank.org, and nsugawara@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team INFORMALITY IN LATIN AMERICA AND THE CARIBBEAN * Norman V. Loayza, Luis Servén, and Naotaka Sugawara The World Bank JEL classification: K20, K30, H11, O40, O17. Keywords: Regulation, government performance, economic growth, informal economy. * For valuable comments and suggestions, we are grateful to Ibrahim Elbadawi, Pablo Fajnzylber, Fausto Hernández, Ana María Oviedo, Jamele Rigolini, Jaime Saavedra, and Klaus Schmidt-Hebbel. This paper draws from Loayza and Sugawara (2009), "The Informal Sector in Mexico: Basic Facts and Explanations," El Trimestre Económico; and Elbadawi and Loayza (2008), "Informality, Employment, and Economic Development in the Arab World," Journal of Development and Economic Policies. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, their Boards of Directors, or the countries they represent. Introduction Informality is the collection of firms, workers, and activities that operate outside the legal and regulatory frameworks. 1 It entails avoiding the burden of taxation and regulation but, at the same time, not fully enjoying the protection and services that the law and the state can provide. Informality is sometimes the result of agents "exiting" the formal sector as consequence of cost-benefit considerations; other times, it is the outcome of agents being "excluded" from formality as this becomes restrictive and the economy segmented. In all cases, informality is a fundamental characteristic of underdevelopment and is best understood as a complex, multi-faceted phenomenon. It is determined both by the modes of socio-economic organization inherent to economies in the transition to modernity and by the relationship that the state establishes with private agents through regulation, monitoring, and the provision of public services. Informality is not only a reflection of underdevelopment, but may also be the source of further economic retardation. It implies misallocation of resources and entails losing the advantages of legality, such as police and judicial protection, access to formal credit institutions, and participation in international markets. According to the estimates presented below, there is large heterogeneity in the extent of informality across countries in Latin America. In all of them, however, informality is much more widespread than in the USA, and some countries in the region are among the most informal economies in the world. The typical country in Latin America produces about 40% of GDP and employs 70% of the labor force informally. These are astounding statistics, which indicate that informality is a substantive and pervasive phenomenon that must be explained and grappled with, particularly in the design of development policies. 1 This definition, introduced by De Soto (1989) in his classic study of informality, has gained remarkable popularity due to its conceptual strength, which allows it to focus on the root causes of informality rather than merely its symptoms. For an excellent review of the causes and consequences of the informal sector, see Schneider and Enste (2000). Drawing from a public-choice approach, Gerxhani (2004) provides an interesting discussion of the differences of the informal sector in developed and developing countries. The World Bank report by Perry et al. (2007) is the most comprehensive and in-depth study on informality in the Latin America region. 2 This chapter studies informality in Latin America from a macroeconomic and international perspective. It uses the cross-country variation on informality measures and potentially related variables to study its causes and consequences. It then examines Latin American countries against this broad international context. The paper is organized as follows. Section I presents and discusses various measures of informality. Section II assesses the impact of informality on economic growth and poverty. Section III analyzes the main causes of informality. Section IV evaluates the empirical relevance of each determinant of informality to every Latin American country in the sample. Finally, we offer some concluding remarks. I. Measuring Informality in Latin America and around the World Although the definition of informality can be simple and precise, its measurement is not. Given that it is identified with working outside the legal and regulatory frameworks, informality is best described as a latent, unobserved variable. That is, a variable for which an accurate and complete measurement is not feasible but for which an approximation is possible through indicators reflecting its various aspects. Here we consider four such indicators, available for a relatively large collection of countries. Two of them refer to overall informal activity in the country, and the other two relate in particular to informal employment. Each indicator on its own has conceptual and statistical shortcomings as a proxy for informality; taken together, however, they may provide a robust approximation to the subject. The indicators related to overall informal activity are the Schneider index of the shadow economy and the Heritage Foundation index of informal markets. 2 The Schneider index combines the DYMIMIC (dynamic multiple-indicator-multiple-cause) method, the physical input (electricity) method, and the excess currency-demand approach for the estimation of the share of production that is not declared to tax and regulatory authorities. The Heritage Foundation index is based on subjective perceptions of general compliance with the law, with particular emphasis on the role played by official corruption. 2 Details on definitions, sources, and samples for these and other variables used in this chapter are provided in Appendix 2. 3 The indicators that focus on the labor aspect of informality are the prevalence of self-employment and the lack of pension coverage. The former is given by the ratio of self to total employment, as reported by the International Labor Organization. The latter is given by the fraction of the labor force that does not contribute to a retirement pension scheme, as given in the World Bank's World Development Indicators. Appendix 3 presents some descriptive statistics on the four informality indicators. In particular, it shows that, as expected, they are significantly positively correlated, with correlation coefficients ranging from 0.59 to 0.90 ­high enough to represent the same phenomenon but not so high as to make them mutually redundant. Using data on these four indicators, we can assess the prevalence of informality across Latin America. For comparison purposes, Figure 1 presents data on the four informality indicators for individual countries in Latin America and the Caribbean (LAC). The USA and Chile are used as benchmark countries. The USA is the developed country to which the region is most closely related. Chile is the Latin American country often taken as a model for economic reforms and sustained growth in the region. 3 It is clear from the figure that there is considerable variation in informality across countries in Latin America. However, in all of them, the degree of informality is much higher than in the USA; and for some countries (e.g., Bolivia and Haiti) it is comparable to the most informal countries in the world. For the median country in Latin America, about 40% of GDP is produced informally. Informal employment is more difficult to ascertain. Using the measure based on pension contributions, about 70% of the labor force is informal in the median country in Latin America. 4 II. The Cost of Informality Informality is a distorted, second-best response of an excessively regulated economy to the shocks it faces and its potential for growth. It is a distorted response 3 The LAC countries under consideration are those included in any of the four regressions where informality is a dependent variable (Table 3). They are 20 countries plus Chile, which functions as a comparator country, unless otherwise noted. Trinidad and Tobago is also excluded since the World Bank classification (as of July 2007) considers the country as a high-income country. See Appendix 1 for sample of countries in each regression. 4 Self-employment is arguably a lower bound for the measure of informal labor given that tax and regulation evasion occurs massively in all types of firms. 4 because it implies misallocation of resources and entails losing, at least partially, the advantages of legality, such as police and judicial protection, access to formal credit institutions, and participation in international markets. Trying to escape the control of the state induces many informal firms to remain sub-optimally small, use irregular procurement and distribution channels, and constantly divert resources to mask their activities or bribe officials. Conversely, formal firms are induced to use more intensively the resources that are less burdened by the regulatory regime; in particular for developing countries, this means that formal firms are less labor intensive than they should be according to the countries' endowments. In addition, the informal sector generates a negative externality that compounds its adverse effect on efficiency: informal activities use and congest public infrastructure without contributing the tax revenue to replenish it. Since public infrastructure complements private capital in the process of production, a larger informal sector implies slower productivity growth. 5 Compared with a first-best response, the expansion of the informal sector often represents distorted and deficient economic growth. 6 This statement merits further clarification: informality is sub-optimal with respect to the first-best scenario that occurs in an economy without excessive regulations and with adequate provision of public services. Nevertheless, informality is indeed preferable to a fully formal but sclerotic economy that is unable to circumvent its regulation-induced rigidities. This brings to bear an important policy implication: the mechanism of formalization matters enormously for its consequences on employment, efficiency, and growth. If formalization is purely based on enforcement, it will likely lead to unemployment and low growth. If, on the other hand, it is based on improvements in both the regulatory framework and the quality/availability of public services, it will bring about more efficient use of resources and higher growth. 5 See Loayza (1996) for an endogenous-growth model highlighting the negative effect of informality through the congestion of public services. 6 This does not necessarily mean that informal firms are not dynamic or lagging behind their formal counterparts. In fact, in equilibrium the risk-adjusted returns in both sectors should be equalized at the margin. See Maloney (2004) for evidence on the dynamism of Latin American informal firms. The arguments presented in the text apply to the comparison between an excessively regulated economy and one that is not. 5 From an empirical perspective, the ambiguous impact of formalization highlights an important difficulty in assessing the impact of informality on economic growth: two countries can have the same level of informality, but if it has been achieved in different ways, the countries' growth rates may also be markedly different. Countries where informality is kept at bay by drastic enforcement will fare worse than countries where informality is low because of light regulations and appropriate public services. We now present a simple regression analysis of the effect of informality on growth. As suggested above, this analysis must control for enforcement; and a straightforward, albeit debatable, way to do so is by including a proxy for the overall capacity of the state as a control variable in the regression. For this purpose, we try two proxies: the level of GDP per capita, and the ratio of government expenditures to GDP. The former has the advantage of also accounting for conditional convergence, and the latter has the advantage of more closely reflecting the size of the state. 7 Another important consideration for this empirical analysis is that informality may not only affect but also be affected by economic growth. For example, faster growth could raise the profitability of production and the real wage, relative to the perceived costs of formality, thus encouraging more firms and workers to shift out of the informal sector. In order to ascertain the impact of informality on growth, we need to isolate the exogenous variation in informality. We do this through an instrumental-variable approach, where the instruments are selected among the variables that are postulated as determinants of informality ­indicators of law and order, business regulatory freedom, secondary schooling, and socio-demographic factors. Since some of them have a relationship with economic growth that is independent of informality, we only use as instruments the sets of variables that comply with the exclusion restrictions, as diagnosed by the Hansen test of orthogonality between the instruments and the regression residuals (see notes on Table 1a and 1b). Table 1 presents the regression results. The dependent variable is the average growth of per capita GDP over 1985-2005. We choose a period of about 20 years for the measure of average growth in order to achieve a compromise between merely cyclical, 7 We also considered as proxy the ratio of tax revenues to GDP. Even though the number of observations drops considerably, the results were similar regarding the negative effect of informality on growth. 6 short-run growth (which would be unaffected by informality) and very long-run growth (which could be confused with the sources, rather than consequences, of informality). We consider two alternative control variables: Initial GDP per capita (Table 1a) or initial ratio of government expenditures to GDP (Table 1b). The explanatory variables of interest are the four informality indicators, considered one at a time. The table first presents the ordinary least-square (OLS) results and then the instrumental-variable (IV) results. The OLS and IV regression results are basically the same regarding the sign and significance of the coefficients on the informality indicators. If anything, the IV coefficient estimates are somewhat larger in magnitude than their OLS counterparts. They clearly indicate that an increase in informality leads to a decrease in economic growth. All four informality indicators carry negative and highly significant regression coefficients. The harmful effect of informality on growth is not only robust and significant, but its magnitude makes it also economically meaningful: Using the estimates from the IV regressions controlling for initial government expenditures/GDP, an increase of one standard deviation in any of the informality indicators leads to a decline of 0.7 ­ 1 percentage points in the rate of per capita GDP growth. 8 These are conservative estimates when compared to those from the regression that controls for GDP per capita ­there, the growth effects of a reduction in informality are about twice as large. There is also a close connection between poverty and informality, reflecting at least in part the negative relationship between economic growth and informality. Table 2 presents cross-country regression analysis with the headcount poverty index as dependent variable and, in turn, the four measures of informality as explanatory variables. In order to have a close chronological match between dependent and explanatory variables, the headcount poverty index corresponds to the latest available measure per country. As in the growth regressions, the level of GDP per capita (Table 2a) or the ratio of government expenditures to GDP (Table 2b) are included as control variables. Also as in previous 8 To be precise, a one-standard-deviation increase of, in turn, the Schneider index, the Heritage Foundation index, the share of self-employment, and the labor force lacking pension coverage leads to a decline of, respectively, 1.1, 0.8, 0.8, and 0.7 percentage points of per capita GDP growth. 7 regressions, we present both OLS and IV estimates, the latter to account for the likely endogeneity of informality with respect to poverty. The regression results reveal a positive relationship between the prevalence of informality and the incidence of poverty. When government expenditure is controlled for, the four measures of informality carry positive and significant coefficients in the IV regressions. Similarly, when the level of GDP per capita is controlled for, three of the four informality indicators carry positive and significant coefficients (self-employment is the exception). The significant relationship between informality, on the one hand, and economic growth and poverty, on the other, is remarkable: it underscores the importance of the issue and urges for the analysis on the complex sources of informality. To this, we turn next. III. The Causes of Informality Informality is a fundamental characteristic of underdevelopment, shaped both by the modes of socio-economic organization inherent to economies in the transition to modernity and by the relationship that the state establishes with private agents through regulation, monitoring, and the provision of public services. As such, informality is best understood as a complex, multi-faceted phenomenon. Informality arises when the costs of belonging to the country's legal and regulatory framework exceed its benefits. Formality entails costs of entry --in the form of lengthy, expensive, and complicated registration procedures-- and costs of permanence --including payment of taxes, compliance with mandated labor benefits and remunerations, and observance of environmental, health, and other regulations. The benefits of formality potentially consist of police protection against crime and abuse, recourse to the judicial system for conflict resolution and contract enforcement, access to legal financial institutions for credit provision and risk diversification, and, more generally, the possibility of expanding markets both domestically and internationally. At least in principle, formality also voids the need to pay bribes and prevents penalties and fees, to which informal firms are continuously subject to. Therefore, informality is more prevalent when the regulatory framework is burdensome, the quality of government 8 services to formal firms is low, and the state's monitoring and enforcement power is weak. These cost and benefit considerations are affected by the structural characteristics of underdevelopment, dealing in particular with educational achievement, production structure, and demographic trends. Other things equal, a higher level of education reduces informality by increasing labor productivity and, therefore, making labor regulations less onerous and formal returns potentially larger. Likewise, a production structure tilted towards primary sectors like agriculture, rather than to the more complex processes of industry, favors informality by making legal protection and contract enforcement less relevant and valuable. Finally, a demographic composition with larger shares of youth or rural populations is likely to increase informality by making monitoring more difficult and expensive, by placing bigger demands on resources for training and acquisition of abilities, by creating bottlenecks in the initial school-to-work transition, and by making more problematic the expansion of formal public services (see Fields, 1990; Schneider and Enste, 2000; ILO, 2004). Popular and even academic discussions often focus on particular sources of informality, rather than taking this comprehensive approach. Thus, some observers stress insufficient enforcement and related government weaknesses such as corruption; others prefer to emphasize the burden of taxes and regulations; yet others concentrate on explanations dealing with social and demographic characteristics. As suggested above, all these possibilities make sense, and there is some evidence to support each of them. To illustrate this, Figure 2 presents cross-country scatter plots of each of the four measures of informality versus proxies for the major proposed determinants of informality. The sample observations include all countries with available data, and, for illustration purposes, countries in Latin America and the Caribbean are highlighted in the figures. The proxies for the determinants of informality are as 9 follows. An index on the prevalence of law and order --obtained from The International Country Risk Guide-- to proxy for both the quality of formal public services and government's enforcement strength. An index of business regulatory freedom --taken from Fraser Foundation's Economic Freedom of the World Report-- to represent the ease 9 Again, details on definitions and sources of all variables are presented in Appendix 2. 9 of restrictions imposed by the legal and regulatory frameworks. The average years of secondary schooling of the adult population --taken from Barro and Lee (2001)-- to represent educational and skill achievement of the working force. And an index of socio- demographic factors --constructed from the World Bank's World Development Indicators and other databases-- which includes the share of youth in the population, the share of rural population, and the share of agriculture in GDP. 10 Remarkably, all 16 correlation coefficients (4 informality measures times 4 determinants) are highly statistically significant, with p-values below 1%, and of large magnitude, ranging approximately between 0.54 and 0.87. All informality measures present the same pattern of correlations: informality is negatively related to law and order, regulatory freedom, and schooling achievement; and it is positively related to factors that denote the early stages of socio-demographic transformation. Therefore, all these explanations may hold some truth in them. What we need to determine now is whether each of them has independent explanatory power with respect to informality. Or, more specifically, we need to assess to what extent each of them is relevant both in general for the cross-section of countries and in particular for a given country. To this purpose we turn next. In what follows, we use cross-country regression analysis to evaluate the general significance of each explanation on the origins of informality. Each of the four informality measures presented earlier serves as the dependent variable of its respective regression model. The set of explanatory variables is common to all informality measures and represents the major determinants of informality. They are the same variables used in the simple correlation analysis, introduced above. Then, we apply these estimated relationships to the case of the Latin American and Caribbean countries with available data in order to evaluate the country-specific relevance of each proposed mechanism. We can do this for those countries that possess complete information on dependent and explanatory variables, or at least information on the latter, with which we can obtain predicted values of the dependent variable. There are 20 countries in the Latin 10 This is constructed by first standardizing each component (to a mean of zero and a standard deviation of 1) and then taking a simple arithmetic average. We use a composite index, rather than the components separately, given the very high correlation among them. 10 American and Caribbean region that possess complete information on all explanatory variables, but comparable data on self-employment and pension coverage are not available for Haiti. Likewise, Nicaragua and Paraguay do not have data on self- employment, and Guyana has data on the Heritage index only. (In both cases, however, we can construct for them a predicted value based on the regression analysis using the sample of all other countries.) The regression results are presented in Table 3. They are remarkably robust across informality measures. Moreover, all regression coefficients have the expected sign and are highly significant. Informality decreases when law and order, business regulatory freedom, or schooling achievement rise. Similarly, informality decreases when the production structure shifts away from agriculture and demographic pressures from youth and rural populations decline. The fact that each explanatory variable retains its sign and significance after controlling for the rest indicates that no single determinant is sufficient to explain informality. All of them should be taken into account for a complete understanding of informality. The four explanatory variables account jointly for a large share of the cross- country variation in informality: the R-squared coefficients are 0.57 for the Schneider shadow economy index, 0.89 for the Heritage Foundation informal market index, 0.78 for the share of self-employment, and 0.88 for the share of the labor force not contributing to a pension program. IV. Explaining Informality in Latin American Countries The cross-country regression analysis presented above can be used to assess the determinants of informality that are most relevant to each Latin American country. The first issue to explore is whether these countries are outliers or follow the general trend established by the cross-country regressions. Figure 3 presents a scatter plot of the actual vs. predicted values of each informality measure. (For illustrative purposes, observations corresponding to Latin American countries are highlighted in the figure). The majority of countries in the world have small residuals (i.e., the unpredicted portion of informality), a fact which is consistent with the large R-squared coefficients obtained in the regressions. 11 Is this also the case of the Latin American and Caribbean countries under consideration? The answer is not simple and must be nuanced by the heterogeneity of the countries in the region. Some LAC countries are located around the 45-degree line, but some are quite far from it. In fact, when we include a "Latin American and Caribbean country" dummy in the regressions, its coefficient turns out to be positive in all cases and significant in three of them (the exception is self-employment). 11 The significance of the regional dummy indicates that the actual values of informality are larger than the predicted values for the majority of countries in the region. This is so for the Heritage index and the pension coverage measure. For the Schneider index, not only the majority of countries have positive residuals but also some of them could be considered as outliers. In terms of specific countries, the following points seem noteworthy. For Brazil, Costa Rica, Honduras and Jamaica, the predicted values of informality are similar to their actual counterparts in all of the four informality measures. Five more countries -- Argentina, Guatemala, Nicaragua, Panama and Uruguay-- join this group in all but the Schneider index. In Colombia and Dominican Republic, while predicted values are much smaller than actual ones regarding labor informality (the last two indices), the actual and predicted values of production informality (that is, the first two indices) are quite close. Contrary to this, as clearly shown in the figure, actual values of the Schneider index are much larger than predicted ones for Bolivia, Panama, Peru and Uruguay, which in part explains why the R-squared coefficient for this regression is smaller than those of the other informality measures. Focusing now on the portion of informality explained by the cross-country regression model, we can evaluate the importance of each explanatory variable for the case of the 20 Latin American and Caribbean countries with sufficient available data. In particular, we can assess how each determinant contributes to the difference in informality between individual countries and a comparator one, for which we choose Chile given its widely-recognized status of reform leader in the region. The contribution 11 Regression results with "LAC country" dummy are not presented but available upon request. For the Schneider index, the Heritage index, self employment, and pension coverage, t-statistics of the dummy variable are 2.91, 2.46, 1.20, and 2.36, respectively. 12 of each explanatory variable is obtained by multiplying the corresponding regression coefficient (from Table 3) times the difference in the value of this explanatory variable between each Latin American and Caribbean country and the comparator country. The importance of a particular explanatory variable would, therefore, depend on the size of its effect on informality in the cross-section of countries and how far apart the two countries are with respect to the explanatory variable in question. Naturally, the sum of the contributions equals the total difference in predicted informality between each individual country and Chile. This difference is plotted in Figure 4. As expected, it shows that all the countries have larger (predicted) informality levels than Chile. Haiti, Honduras and Guatemala are predicted to be the most informal (and in general show the largest difference with respect to Chile). On the other hand, Uruguay, Argentina and Costa Rica are predicted to be the least informal among the Latin American and Caribbean countries, though they still show larger informality levels than Chile. Figure 5 presents the decomposition of the difference of (predicted) informality between each of the 20 countries under analysis and Chile. The figure has four panels, corresponding to each of the four informality indicators. The most remarkable observations are the following. Policy and institutional variables related to the quality of the state are the most important factors explaining the differences in informality. Restricted regulatory freedom tends to contribute to larger informality in all Latin American and Caribbean countries for the Heritage index, self employment and pension coverage, while deficient law and order explains the bulk of informality for the Schneider index. Education, measured by average years of secondary schooling, does not play a major role in explaining differences in informality with respect to Chile for any of four informality measures, even in the cases of Haiti and Honduras. Socio-demographic factors are particularly important in explaining the differences regarding labor informality, and less so regarding production informality. Moreover, in the case of labor informality, the larger the differences in informality with respect to Chile, the larger the importance of socio-demographic factors. This is the case of, for instance, Haiti and Honduras, where all determinants of informality (excluding educational level) are about as important. On the other hand, there is not such trend regarding the two production 13 informality measures ­for them, the variables dealing with the quality of the state are always more important, especially law and order for the Schneider index and regulatory freedom for the Heritage index. V. Conclusion By any measure, informality is quite prevalent in the countries of Latin America and the Caribbean. This is worrisome because it denotes misallocation of resources (labor in particular) and inefficient utilization of government services, which can jeopardize the countries' growth and poverty-alleviation prospects. The evidence presented in this chapter shows that informality has a statistically and economically significant negative impact on growth ­ and an equally significant positive impact on the incidence of poverty across countries. Informality arises when the costs of belonging to the economy's legal and regulatory framework exceeds the benefits. Thus informality is more prevalent where the regulatory framework is burdensome, the quality of government services is low, and the state's monitoring and enforcement capacity is weak. But these cost-benefit calculations are also affected by key structural characteristics of the economy ­ such as its productive and demographic structure and the availability of skilled labor. This chapter has argued that it is important to take into account all these factors when trying to ascertain the causes of informality. In the case of Latin America, this chapter has shown that informality is primarily the outcome of a combination of poor public services and a burdensome regulatory framework. Low levels of education, as measured by secondary schooling, are less important in this respect. In lower income countries, informality (particularly regarding labor markets) is exacerbated when the production structure is heavily based on agriculture and other rural activities and when the labor participation of young people, resulting from recent demographic transition, is large. Informality is a complex phenomenon that is best understood from several angles: considering different indicators that reflect its various aspects and treating it as both cause and consequence of underdevelopment. This chapter is a modest contribution in this direction. 14 References [1] Barro, Robert and Jong-Wha Lee, "International comparisons of educational attainment," Journal of Monetary Economics, 32(3), 363-94 (1993). [2] Barro, Robert and Jong-Wha Lee, "International data on educational attainment: updates and implications," Oxford Economic Papers, 53(3), 541-63 (2001). [3] De Soto, Hernando, The Other Path: The Invisible Revolution in the Third World, HarperCollins (1989). [4] Fields, Gary. 1990. "Labour Market Modelling and the Urban Informal Sector: Theory and Evidence." In David Turnham, Bernard Salomé and Antoine Schwarz, eds, The Informal Sector Revisited, 49-69. Paris: OECD. [5] Gerxhani, Klarita, "The Informal Sector in Developed and Less Developed Countries: A Literature Survey," Public Choice, 120(3/4), 267-300 (2004). [6] Gwartney, James, Robert Lawson, Russell Sobel and Peter Leeson, Economic Freedom of the World: 2007 Annual Report, The Fraser Institute (2007). www.freetheworld.com. [7] International Labour Organization (ILO), Bureau of Statistics, LABORSTA Internet. laborsta.ilo.org. [8] International Labour Organization (ILO), Global Employment Trends for Youth, Geneva (2004). [9] Loayza, Norman, "The Economics of the Informal Sector: A Simple Model and Some Empirical Evidence from Latin America," Carnegie-Rochester Conference Series on Public Policy, 45, 129-62 (1996). [10] Loayza, Norman and Claudio Raddatz, "The Composition of Growth Matters for Poverty Alleviation," World Bank Policy Research Working Paper No. 4077 (2006). [11] Loayza, Norman and Jamele Rigolini, "Informality Trends and Cycles," World Bank Policy Research Working Paper No. 4078 (2006). [12] Maloney, William, "Informality Revisited," World Development, 32(7), 1159-78 (2004). [13] Miles, Marc, Edwin Feulner and Mary O'Grady, 2005 Index of Economic Freedom, The Heritage Foundation and The Wall Street Journal (2005). [14] Perry, Guillermo, William Maloney, Omar Arias, Pablo Fajnzylber, Andrew Mason and Jaime Saavedra-Chanduvi, Informality: Exit and Exclusion, The World Bank (2007). [15] The PRS Group, International Country Risk Guide (ICRG). www.icrgonline.com. [16] Schneider, Friedrich, "The Size of the Shadow Economies of 145 Countries all over the World: First Results over the Period 1999 to 2003," IZA DP No. 1431 (2004). [17] Schneider, Friedrich and Dominik Enste, "Shadow Economies: Size, Causes, and Consequences," Journal of Economic Literature, 38(1), 77-114 (2000). 15 [18] United Nations (UN), Population Division, World Population Prospects: The 2004 Revision, UN (2005) [19] The World Bank, World Development Indicators, The World Bank, various years. 16 Table 1a. The Effect of Informality on Economic Growth, controlling for GDP per capita Dependent variable: Per capita GDP Growth, 1985-2005, country average OLS IV [1] [2] [3] [4] [5] [6] [7] [8] Initial GDP per capita -0.1966 -0.3519 -0.3498* -0.6910* -0.6976*** -0.7684*** -1.2819*** -1.7200*** (2000 US$, 1985, in logs) 1.29 1.54 1.88 1.98 3.06 2.83 2.69 2.95 Schneider Shadow Economy -0.0747*** -0.1479*** (% of GDP) 3.87 4.39 Heritage Foundation Informal Market -0.8009** -1.3294*** (ranging 1-5: higher, more informality) 2.41 4.05 Self Employment -0.0657*** -0.1775*** (% of total employment) 3.11 3.21 Non-contributor to Pension Scheme -0.0423*** -0.0872*** (% of labor force) 2.80 3.39 Constant 5.4231*** 6.9131** 6.6475*** 9.2161** 11.8634*** 11.7604*** 17.1971*** 19.8890*** 3.15 2.57 3.35 2.59 4.29 3.80 3.18 3.33 No. of observations 119 127 72 91 84 87 59 68 R-squared 0.20 0.08 0.13 0.11 - - - - Hansen J Statistic (P-value) - - - - 0.48 0.21 0.30 0.70 Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively. 3. For IV regressions [5] to [8], Endogenous variable: each of four informality measures. Instruments: Law and order; Business regulatory freedom; Average Years of Secondary Schooling. Sociodemographic factors is not included as an instrument because it does not pass the exogeneity test using the C statistic (Difference-in-Sargan statistic). 4. See Appendix 1 for definitions and sources of variables. Source: Authors' estimation 17 Table 1b. The Effect of Informality on Economic Growth, controlling for government expenditure/GDP Dependent variable: Per capita GDP Growth, 1985-2005, country average OLS IV [1] [2] [3] [4] [5] [6] [7] [8] Initial Government Expenditure -0.0340* -0.0513** -0.0681*** -0.0588** -0.0593** -0.0717*** -0.1008** -0.0776*** (% of GDP, 1985) 1.96 2.60 2.82 2.59 2.14 2.94 2.55 3.34 Schneider Shadow Economy -0.0622*** -0.0789*** (% of GDP) 4.76 4.31 Heritage Foundation Informal Market -0.6724*** -0.6085*** (ranging 1-5: higher, more informality) 5.52 4.18 Self Employment -0.0557*** -0.0596*** (% of total employment) 3.84 2.85 Non-contributor to Pension Scheme -0.0183*** -0.0203*** (% of labor force) 3.58 3.51 Constant 4.1214*** 4.5441*** 4.6023*** 3.5267*** 5.0933*** 4.6934*** 5.0909*** 4.1156*** 6.71 7.98 6.69 6.19 6.18 6.72 4.50 6.70 No. of observations 112 118 69 85 88 91 59 72 R-squared 0.20 0.18 0.18 0.11 - - - - Hansen J Statistic (P-value) - - - - 0.53 0.89 0.62 0.72 Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively. 3. For IV regressions [5] to [8], Endogenous variable: each of four informality measures. Instruments: Business regulatory freedom; Average Years of Secondary Schooling; Sociodemographic factors Law and order is not included as an instrument because it does not pass the exogeneity test using the C statistic (Difference-in-Sargan statistic). 4. See Appendix 1 for definitions and sources of variables. Source: Authors' estimation 18 Table 2a. The Effect of Informality on Poverty, controlling for GDP per capita Dependent variable: Poverty Headcount index, latest year OLS IV [1] [2] [3] [4] [5] [6] [7] [8] Initial GDP per capita -0.1331*** -0.1028*** -0.0995*** -0.0656** -0.1129*** -0.0800*** -0.0796 -0.0346 (2000 US$, 1985, in logs) 6.18 4.07 3.02 2.33 3.48 3.10 1.26 0.94 Schneider Shadow Economy 0.0067** 0.0104* (% of GDP) 2.34 1.71 Heritage Foundation Informal Market 0.0841** 0.1229* (ranging 1-5: higher, more informality) 2.38 1.89 Self Employment 0.0004 -0.0017 (% of total employment) 0.22 0.24 Non-contributor to Pension Scheme 0.0031** 0.0051** (% of labor force) 2.34 2.08 Constant 0.8607*** 0.6053** 0.8476*** 0.4127 0.5717 0.3001 0.7636 0.0436 4.54 2.30 3.48 1.55 1.46 0.83 1.09 0.11 No. of observations 51 51 34 46 41 42 30 38 R-squared 0.51 0.42 0.34 0.35 - - - - Hansen J Statistic (P-value) - - - - 0.47 0.33 0.11 0.69 Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively. 3. For IV regressions [5] to [8], Endogenous variable: each of four informality measures. Instruments: four determinants of informality (Law and order; Business regulatory freedom; Average Years of Secondary Schooling; Sociodemographic factors). 4. See Appendix 1 for definitions and sources of variables. Source: Authors' estimation 19 Table 2b. The Effect of Informality on Poverty, controlling for government expenditure/GDP Dependent variable: Poverty Headcount index, latest year OLS IV [1] [2] [3] [4] [5] [6] [7] [8] Initial Government Expenditure 0.0031 0.0096 0.0114 0.0063 0.0033 0.0157* 0.0224*** 0.0123 (% of GDP, 1985) 0.51 1.58 1.09 1.01 0.37 1.86 3.44 1.54 Schneider Shadow Economy 0.0075* 0.0240*** (% of GDP) 1.95 2.97 Heritage Foundation Informal Market 0.2135*** 0.2470*** (ranging 1-5: higher, more informality) 4.41 3.47 Self Employment 0.0091 0.0230*** (% of total employment) 1.51 3.08 Non-contributor to Pension Scheme 0.0064*** 0.0076*** (% of labor force) 4.95 3.41 Constant -0.1130 -0.7019*** -0.2911 -0.3624*** -0.7887** -0.9201*** -0.9325*** -0.5467** 0.59 3.33 0.99 2.86 2.45 2.77 3.34 2.56 No. of observations 48 48 32 43 40 41 29 37 R-squared 0.12 0.33 0.13 0.36 - - - - Hansen J Statistic (P-value) - - - - 0.25 0.14 0.52 0.61 Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively. 3. For IV regressions [5] to [8], Endogenous variable: each of four informality measures. Instruments: four determinants of informality (Law and order; Business regulatory freedom; Average Years of Secondary Schooling; Sociodemographic factors). 4. See Appendix 1 for definitions and sources of variables. Source: Authors' estimation 20 Table 3. Determinants of Informality Method of estimation: Ordinary Least Squares with Robust Standard Errors Dependent variable: Four types of informality measures, country average Informality measures Schneider Shadow Heritage Foundation Self Non-contributor to Economy index Informal Market index Employment Pension Scheme Explanatory variables: (% of GDP) (1-5: higher, more) (% of total employment) (% of labor force) Average of 2000-2005 by country [1] [2] [3] [4] Law and Order -3.2360** -0.0969* -1.6925* -2.9764* (ICRG, index ranging 0-6: higher, better) -2.57 -1.76 -1.84 -1.67 Business Regulatory Freedom -2.0074* -0.5333*** -2.5196** -5.8675** (The Fraser Institute, index ranging 0-10: higher, -1.80 -9.95 -2.17 -2.28 less regulated) Average Years of Secondary Schooling -1.9684* -0.1152** -2.1527** -5.8114*** (Barro and Lee 2001) -1.70 -2.00 -2.25 -3.27 Sociodemographic Factors 3.8438** 0.5027*** 5.9743*** 21.6130*** (average of share of youth population, share of 2.00 4.99 3.77 7.31 rural population, and share of agriculture in GDP) Constant 60.3429*** 6.6326*** 54.7254*** 113.3110*** 10.48 31.72 14.06 11.40 No. of observations 84 86 57 70 Adjusted R-squared 0.57 0.89 0.78 0.88 Notes: 1. t-statistics are presented below the corresponding coefficients. 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively. 3. See Appendix 1 for countries included in each regression and Appendix 2 for definitions and sources of variables and periods used to compute country averages of informality measures. Source: Authors' estimation 21 Figure 1. Size of Informality, Various Measures A. Schneider Shadow Economy index 80 (% of GDP) 60 LAC Countries 40 Comparator Countries 20 0 USA CHL CRI ARG PRY MEX DOM ECU VEN JAM BRA COL NIC SLV HND URY GTM HTI PER PAN BOL B. Heritage Foundation Informal Market index 5 (Higher, more informality) 4 LAC Countries 3 Comparator Countries 2 1 USA CHL URY CRI JAM MEX SLV PAN ARG BRA DOM PER COL GTM ECU GUY HND BOL NIC VEN HTI PRY C. Self Employment (% of total employment) 40 Comparator LAC Countries 30 Countries 20 10 0 USA CHL ARG CRI URY MEX BRA PAN SLV VEN ECU JAM GTM PER HND COL BOL DOM D. Non-contributor to Pension Scheme (% of labor force) 80 LAC Countries 60 Comparator Countries 40 20 0 USA CHL URY CRI PAN JAM BRA ARG MEX ECU VEN DOM PER SLV GTM HND PRY COL NIC BOL 22 Figure 2. Informality and Basic Determinants A. Schneider Shadow Economy index (% of GDP) 80 (% of GDP) 80 (% of GDP) correlation: -0.62*** correlation: -0.60*** N = 118 N = 125 BOL BOL GEO PAN PAN ZWE AZE ZWE AZE TZA 60 NGA PER TZA 60 NGA PER HTI HTI UKR THA UKR THA ZAR GTM HNDCOG URY ZMB BLR COG ZAR GTM HND ZMB URY SLV ARM SEN LKA RUS NIC MDA TCD BEN RUS SENNICARM MDASLV LKA CAFPHL UGA NERPHL CIV AGO MLI SLE UGA KAZ AGO MLI GHA SLE CIV NER MWICOL KAZ GHA 40 COL BRA MDG MOZMWI BFA EST GIN TGO ETH LVA TUN 40 MOZ BRANPL MDG BFA ETH TGO KGZLVA RWA TUN EST JAM PNG VENPAK BGD KENDZA ECU YUG BGR ROM EGY LBN MAR VEN BGD LSOMARJAM PAK BGR MRT BDI BIH DZA ROM BWA EGY MKD ECUHRV TUR KEN PNG ALB CMR DOM BWA TUR HRV CMR ALB FJI MEX MYS LTU NAM MEXDOM MYSNAMLTU YEMPRY ZAF PRY GRCSVN ZAF ARG GRC CRI KORSVN POL ARE ITA IND TWN HUN ARG POLKOR CRI IND ITA TWN HUNARE IDN ISR ISR 20 JOR ESP MNG SVK IRN PRT BEL KWT SYR CHL CZE OMN SAU 20 IDN SYRPRT ESP MNG BEL KWT JOR CZE SVK IRN OMNCHL VNM HKG DEU NOR SWE FIN DNK VNM NORSWE DNK FIN CHN FRA CAN IRL AUS CHN IRLCAN HKG FRA DEU AUS SGP GBR NLD NZL NLDGBR SGP NZL JPN CHE AUT JPN AUT CHE USA USA 0 0 1 2 3 4 5 6 2 3 4 5 6 7 8 Law and Order (index: higher, better) Business Regulatory Freedom (index: higher, less regulated) 80 (% of GDP) 80 (% of GDP) correlation: -0.66*** N = 94 BOL BOL GEO PAN PAN ZWE ZWE 60 TZA PER 60 PER AZE NGA HTI TZA HTI THA UKR THA GTM ZMB ZAR HND COG URY URY BLR GTM COG HND ZMB KHM BEN SLV SEN NIC CAF LKA RUS SLV NIC CIVBEN UGA ARM MDA LKA SEN TCD ZARCAF MLI UGA NER SLE GHA PHL PHL KAZ AGO GHA MLI ETH SLENER BFA RWA 40 MWI BRA MOZ RWA NPL TUNCOL 40 LVABRA COL MOZ GIN KGZ MWI MDG TGO NPL PNG TGO MRT BGD PAK MAR EGYJAM EST BGR ROM TUN JAM EGY BGD TONKEN BDI YUG MAR MRT PAKUZB PNG BWA VEN TUR ECU VEN BIHTUR DZA ALB KEN CMR DOM DZA FJI MKD ECUBWA LBN HRV FJI NAM LSO MEX MYS PRY ARG GRC LTU PRI KORSVN MEX DOM PRY CMRBTN LAO MYS WSM VUT LSO YEM ZAF ZAF YEM ARE CRI POLHUN ITATWN KOR AREPOL ARG GRC ITA HUN CRI IND ISR IND IDN CHL PRT ESP SYR IRN JOR BEL KWT ESP CHL JOR BELKWT CZEPRT SVK IDN 20 NOR SWE 20 OMN SAU IRN SYR MNG FIN DNK HKG DEU SWE HKG NOR FIN IRL DNK DEU VNM CHNSGP FRAIRL AUS CAN CAN FRA AUS SGPNLD NZL CHN NLD GBR NZL JPN GBRJPN AUTCHE CHEAUT USA USA correlation: 0.54*** N = 137 0 0 0 1 2 3 4 5 -2 -1 0 1 2 Average Years of Secondary Schooling Sociodemographic Factors, standardized B. Heritage Foundation Informal Market index (range 1-5: higher, more informality) (Higher, more informality) (Higher, more informality) 5 GNB NER IRQ SUR TGO YUG MMR CUB PRK 5 TCDNER TGO NPL RWA BGD COG PRY YEM HTI MLI SLE LBN GMB LBYIRN SYR COG HTI BGD PRY IRN SLE MLI GEO SYR BIH ALB CMRIDN AZE CMRIDN AZE KEN NGA ALB NGA KEN MKD VENPAK MDG BOL RUS NIC MOZ BFA KAZ BLR INDVNM VEN VNM NIC MDG BOL PAK MOZ BFA IND KAZ 4 HND ZWE PHL CIVGUY ECU GIN ARM UGA UKR ZMB ROM MDA TZA 4 ECU ZMB UKRHNDBEN ARM MDA ZWE MRT PHL ROMGUYKGZ CIV RUS LSO CAF TZA UGA FJI COL GTM MWI ETH GTM MWI COLETH BRA DOMPAN PERARG SEN EGY CHN MLT LVA CHN DOM SEN BRA EGY PER ARG PAN LVA MLT MEX SLV GAB SVK BGRTUR POL HRV CZE MEX TUR POL BGR HRV SVK CZE SLV JAMGHA ZAF DZA LKA THALTU JOR MAR JAM ZAF THA DZAMARLKA JOR LTU GHA 3 GRC CRI MNG PNG 3 PNG URY TTO GRC CRI MNG KOR URY TTO MYS MUSNAM TUN KOR TUN BWASVN HUN ISR MYS BWAHUN SVNSAU ISR NAM ITA CYP EST EST ITA CYP TWN TWN BHR KWT KWT QAT BHR 2 BRB PRT ESP OMN 2 BHS ESP PRT OMN JPN FRA JPN FRA BEL BEL CHL ARE CHL DEUIRLUSA AUT HKG correlation: -0.69*** ARE HKG DEU USA IRL AUT correlation: -0.79*** GBRDNK N = 134 GBR DNK 1 N = 131 NLD NOR SWE NZL ISL AUS CAN LUX CHE SGP FIN 1 CHE SGP NZL NLD SWE NOR LUX ISL CAN AUS FIN 1 2 3 4 5 6 3 4 5 6 7 8 Law and Order (index: higher, better) Business Regulatory Freedom (index: higher, less regulated) (Higher, more informality) (Higher, more informality) correlation: -0.80*** 5 GNB MMRNPL RWA NER TGO IRQ N = 105 5 SUR YUG IRQ GNQ TKM TGONER RWA TCD NPL LAO GNB YEMGMB MLI BGD HTI SLE PRY IRN COG SYR COG PRY YEM TJK LBN BIH GEO IRN GMB BGD SYR HTI MLI MMR CMR IDN AZEALB IDN SLE KEN MKD NGA CMR KEN MOZ NIC PAK BOL IND VEN VEN KAZ BLR BOL NIC PAKUZB BFA MOZ INDVNM 4 UGA ZMB GUY ECU MRT HND FJI ZWE PHL TZABEN SWZ LSO CAF MDG MWI GTM 4 GUY ARMHNDGIN KGZ UGA UKR ECUMDA MRTCIVBENLSO TZA RUS ROM DJI PHL CPV FJI ZMB ZWE SWZ CAF COL COL GTM MWI ETH BRA DOM EGY PER SEN ARG CHN PAN SLV TUR POL MEX PER BRA CHN ARG PANDOM EGY LVA SEN THAMAR JAMJOR LKA ZAF GHA DZA CZE BGRMEX SLV POL SVK TUR HRV GAB 3 PNG CRI GRC LTU JOR JAMMAR LKA GHA KHM ZAF DZA THA TUN MUS URY TTO KOR 3 GRC CRI MNG BLZ PNG MYS BWA HUN ISR URY KOR MUSTUN TTO ITACYP HUN SAU MYS BWA NAM SVN BHR PRT ESP TWN ITA EST 2 BRB KWT KWT CHL BEL FRAJPN 2 ESP PRT BRB OMN WSM ARE IRL HKG AUT USA DEU BEL JPN FRA CHL GBR DNK HKG USA DEU AUT AREIRL correlation: 0.72*** 1 SGP ISL AUS FIN NLD NZL NOR SWE CHE CAN DNK GBR N = 149 1 LUXCANFIN SWE NZL AUS NOR SGPNLD ISL CHE 0 1 2 3 4 5 -2 -1 0 1 2 Average Years of Secondary Schooling Sociodemographic Factors, standardized 23 Figure 2. Informality and Basic Determinants (continued) C. Self employment (% of total employment) (% of total employment) (% of total employment) correlation: -0.72*** correlation: -0.70*** 60 CMR N = 69 60 CMR N = 71 IDN IDN COL HND DOM MDG BOL VNM ETH MDG BOL VNM DOMCOL ETH 40 BGD PER PAK ZMB 40 BGD HND ZMB PAK PER GTM JAM PHL IRN PHL GTM JAM IRN YEM ECU VENLKAGRC SLV THA MNG SYR VEN ECU THA SYR SLV PAN LKA MNG BRADZA EGY KOR CHL DZA PAN GRC EGY BRA URY CHL MEX URY CRI MAR NAM MEX KORNAM MAR CRI ARG TUN PRT ARG PRT TUN FJI TWNITA ITA TWN 20 TTO MYS CYP 20 TTO MYS CYP ZAF NZL MUS ZAFESP NZL ESP IRL ISL IRL BEL CHE CAN MLT SGP AUS MLT BRB BEL CAN CHE AUS SGP ISL ISR HKGOMN JPN GBR AUT NLD JPN DEUGBR ISRNLD OMN AUT HKG FRADEU FRA USA DNK NOR DNK USA NOR LSO 0 QAT 0 1 2 3 4 5 6 3 4 5 6 7 8 Law and Order (index: higher, better) Business Regulatory Freedom (index: higher, less regulated) (% of total employment) correlation: -0.67*** correlation: 0.71*** 60 CMR N = 65 60 N = 74 CMR (% of total employment) IDN IDN BOLDOM COL DOM COLBOL MDG VNM ETH BGD HND KHM 40 GTM ZMB PAK PER 40 PER PHLHNDPAKBGD ZMB JAM IRNPHL JAM IRNGTM YEM SLV THA SYR ECU VEN VEN ECU LCA SYR THA MNGYEM BRA DZA PAN LKA GRC GRCBRA SLVCPV LKA PANWBGDZA BLZ CRI EGY URY MAR FJI CHL MEX ARG KOR CHL URY MEXCRI MAREGY KOR ARG FJI NAM TUN PRT PRT TUN ITATWN ITA 20 MYS TTO CYP 20 MYS TTO ZAF MUS NZL NZL ZAF MUS ESP ISL IRL ESP IRL SGP BEL BRB AUS CAN CHE PRI ISL CAN BEL CHE SGP AUS BRB ISR GBR NLDJPN HKG AUT GBRJPN HKG NLDAUT OMN FRA DEU DEUFRA DNK USA NOR DNK USA NOR LSO LSO 0 0 0 1 2 3 4 5 -2 -1 0 1 2 Average Years of Secondary Schooling Sociodemographic Factors, standardized D. Non-contributor to pension scheme (% of labor force) (% of labor force) (% of labor force) 100 GIN correlation: -0.72*** 100 BGDNGA BDI TZA NPL MOZ correlation: -0.70*** BGDNGA MDG MOZ SLE SEN PAK BFA UGA TZA MRTSENBFA MDG BEN PAK UGA SLE IDN GHASDN CIV ZMB VNM N = 99 IDNVNM INDGHA ZMB RWA CIV N = 101 IND ZWE KEN CMR YEM BOL GAB ZWE CMR BOL KEN NIC THA TGO IRQ TGO NIC CHN THA 80 CHN PRY COL HNDMARGTM 80COL HND PRYSLV GTM DOMPER MAR VEN PER DOM SLV PHL VEN PHL ECU MEX LKA JOR IRN MEX ECU DZA LKA LBN JORIRN DZA BIH KGZ 60 ALB TUR KAZ 60 ALB KAZ TUR ARG BRA JAM MYS BRA JAM ARG YUG GEO AZE PAN MUS CRI MYS EGY TUN PAN AZE CRI EGY TUN 40 ROM MKD URY MDA ROM MNG CHL 40 URY MNG MDA CHL BGR ARM BGR HRV SVK ARM LBY SVK UKR KOR LVA SGP HRV POL UKR KOR SGP LTU HUN POL LVA 20 GRCCZE EST HUN LTU SVN 20 GRC EST ITA BEL PRT ESP AUT FRA DEUGBR SWE DNK FIN CZE SVNBEL USA NLD IRLAUS NOR ITA ESP FRAPRT AUT FIN JPN NZL SWE DNK DEU USA GBR AUS NLD 0 CHE IRL NOR JPN NZL 0 CHE 1 2 3 4 5 6 3 4 5 6 7 8 Law and Order (index: higher, better) Business Regulatory Freedom (index: higher, less regulated) (% of labor force) (% of labor force) 100 MOZ BGD NPL TZA SLE correlation: -0.84*** 100 GIN NGA MOZ TZA NPL SEN BEN UGA MRT SDN ZMB IDN GHA PAK N = 78 BGD MRTPAK BEN BFA BDI SEN MDG SLEUGA RWA IND IDN ZMB CIV SDN IND GHA VNM RWA KENBOL CMR ZWE GAB BOL NIC YEM TGO NIC ZWE KEN CMR IRQTHA PRY CHN YEM TGO 80 GTM DOM COL SLVHND MARVEN PER COLTHA HND IRQ WBGMAR CHN PHL 80 DOM PRY SLV GTM VEN PER PHL ECU JOR DZA MEX LKA IRN 60 TUR ECU IRN MEX DZA LKA LBN JOR BIH KGZ BRA ARG JAM 60 KAZ TUR ALB MYS CRI MUSPAN TUN EGY BRA JAM ARG YUG URY MYS 40 CHL PANGEO AZEEGY CRI ROM TUN URY MKDMUS MDAMNG SGP KOR 40 CHL POL HUN BGR ARM 20 SVK HRV GRC SGP KOR UKR BEL PRT ESPITA AUT LVA FRA HUN POL IRL FIN GBR NLD DNK AUS NOR SWE USA DEU 20 LTU NZL JPN CHE GRC CZEEST 0 AUT SVN BEL ITA PRT ESP correlation: 0.87*** DNKFRAFIN IRL SWE DEU GBR USA AUS NLD NOR JPNNZL N = 109 0 CHE 0 1 2 3 4 5 -2 -1 0 1 2 Average Years of Secondary Schooling Sociodemographic Factors, standardized Note: *** denotes significance at the 1 percent level. 24 Figure 3. Predicted and Actual Levels of Informality A. Schneider Shadow Economy index B. Heritage Foundation Informal Market index 70 Actual values BOL 5 Actual values NER TGO (% of GDP) PAN (Higher, more informality) COGBGD IRN MLI SYR PRY HTI SLE IDN 60 PERTZA ZWE KEN CMR HTI BOL PAK NICVEN MOZ IND THA 4 GUY HND ECU UGA ZWE ZMB PHL TZA 50 URY ZMB COG GTM HND COL GTM MWI ZAR ARG BRA DOM PER CHN SLV NIC SEN LKAUGA PHL MLI SLVPAN EGY SEN POLMEX TUR SLE 40 TUN BRA NER COLGHA MOZ MWI TGO JAM GHA JORZAFTHALKA DZA MAR JAM KEN MAR EGY PAK BGD PNG 3 URYCRIPNG GRC VEN CMR ECU BWA TUR DOMDZA KOR MYS TTO TUN BWA MYSMEX PRY HUN 30 KOR ARE ARGZAF POL GRC ITA HUN CRI IND ITA KWT KWT PRT JOR BELESP CHL SYR IDN 2 ESP PRT 20 NOR SWE HKG FIN DNK IRN FRA JPN BEL DEU CAN IRLFRA CHL ARE AUS SGP NLD NZL JPN CHN HKG USA AUT IRL DEU GBR AUT DNKGBR 10 CHE USA 1 FIN NZL SWECAN NLD SGP CHE AUS ISL NOR Predicted values Predicted values 0 (% of GDP) (Higher, more informality) 0 10 20 30 40 50 60 70 1 2 3 4 5 C. Self Employment D. Non-contributor to Pension Scheme 60 Actual values CMR 100 Actual values MOZ TZA (% of total employment) (% of labor force) PAK SLEBGD SEN UGA IDN IND GHA ZMB BOL ZWE KEN TGO CMR 50 THA NIC CHN 80 COL PER MAR PRY SLV DOM HND GTM DOMIDN VEN PHL BOL COL PAK ECU MEXIRN DZA 40 HND PER ZMB BGD JOR LKA IRN PHL GTM JAM 60 TUR ECU THA SYR VEN ARG BRA JAM GRC PAN SLV LKA MYS 30 BRA PAN CRI EGY CHL MEX DZA KOR URYCRI EGY MAR TUN ARG PRT TUN 40 CHLURY ITA 20 TTO MYS ZAF NZL SGP KOR IRL ESP HUN POL ISL CAN CHE BEL SGP 20 AUS GRC HKG GBRJPN AUT FRA NLD AUT BEL ESP 10 DEU SWE DEU AUS FRA FIN DNK NLD IRL ITA PRT DNK USA NOR NOR GBR USA Predicted values NZLJPN 0 CHE Predicted values (% of total employment) (% of labor force) 0 0 10 20 30 40 50 60 0 20 40 60 80 100 Note: In each graph, a 45-degree line is drawn to show a distance between predicted and actual levels. 25 Figure 4. Differences in Informality, LAC Countries and Chile A. Schneider Shadow Economy index B. Heritage Foundation Informal Market index (% of GDP) (Higher, 60 6 more informality) Max Max HTI 50 HTI 5 GTM HND HND COL PRY GTM VEN PRY BRA BOL DOM GUY JAM 4 BOL ECU GUY NIC 40 ECU MEXNIC SLV VEN ARG BRA DOM MEX JAM COL PAN PANPER PER ARG CRI SLV CRI URY 3 URY 30 CHL 2 CHL 20 1 Min 10 Min C. Self Employment D. Non-contributor to Pension Scheme (% of total employment) 120 Max Max (% of labor force) HTI 50 HTI 100 GTM HND GUY GTM HND NIC PRY 40 GUY 80 BOL PRY BOL NIC DOM ECU SLV COL BRA DOM ECU JAM VEN COL BRA JAM SLV VEN MEX 60 CRI MEX PANPER CRI PANPER ARG ARG 30 URY URY 40 20 CHL CHL 20 10 0 Min Min Note: Presented are all predicted levels, which may be above/below the actual max/min values. 26 Figure 5-A. Explanation of Differences in Informality, LAC Countries and Chile Schneider Shadow Economy index (% of GDP) ARGENTINA BOLIVIA BRAZIL (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 COLOMBIA COSTA RICA DOMINICAN REPUBLIC (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 ECUADOR EL SALVADOR GUATEMALA (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 GUYANA HAITI HONDURAS (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 JAMAICA MEXICO NICARAGUA (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 PANAMA PARAGUAY PERU (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 URUGUAY VENEZUELA, RB Legend (%) (%) 80 80 Law and Order 60 60 40 40 Regulatory Freedom 20 20 Education 0 0 -20 -20 Sociodemographic Factors 27 Figure 5-B. Explanation of Differences in Informality, LAC Countries and Chile Heritage Foundation Informal Market index (range 1-5: higher, more informality) ARGENTINA BOLIVIA BRAZIL (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 COLOMBIA COSTA RICA DOMINICAN REPUBLIC (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 ECUADOR EL SALVADOR GUATEMALA (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 GUYANA HAITI HONDURAS (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 JAMAICA MEXICO NICARAGUA (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 PANAMA PARAGUAY PERU (%) (%) (%) 80 80 80 60 60 60 40 40 40 20 20 20 0 0 0 URUGUAY VENEZUELA, RB Legend (%) (%) 80 80 Law and Order 60 60 Regulatory Freedom 40 40 20 20 Education 0 0 Sociodemographic Factors 28 Figure 5-C. Explanation of Differences in Informality, LAC Countries and Chile Self Employment (% of total employment) ARGENTINA BOLIVIA BRAZIL 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 COLOMBIA COSTA RICA DOMINICAN REPUBLIC 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 ECUADOR EL SALVADOR GUATEMALA 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 GUYANA HAITI HONDURAS 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 JAMAICA MEXICO NICARAGUA 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 PANAMA PARAGUAY PERU 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 URUGUAY VENEZUELA, RB Legend 80 (%) 80 (%) 60 60 Law and Order 40 40 Regulatory Freedom 20 20 0 Education 0 -20 Sociodemographic Factors -20 29 Figure 5-D. Explanation of Differences in Informality, LAC Countries and Chile Non-contributor to Pension Scheme (% of labor force) ARGENTINA BOLIVIA BRAZIL 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 COLOMBIA COSTA RICA DOMINICAN REPUBLIC 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 ECUADOR EL SALVADOR GUATEMALA 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 GUYANA HAITI HONDURAS 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 JAMAICA MEXICO NICARAGUA 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 PANAMA PARAGUAY PERU 80 (%) 80 (%) 80 (%) 60 60 60 40 40 40 20 20 20 0 0 0 -20 -20 -20 URUGUAY VENEZUELA, RB Legend (%) 80 (%) 90 60 Law and Order 60 40 Regulatory Freedom 30 20 0 Education -25 0 -50 -20 Sociodemographic Factors 30 Appendix 1. Sample of Countries in the Informality Regressions Schneider Shadow Heritage Foundation Non-contributor to Self Employment Country Code Country Economy index Informal Market index Pension Scheme (84 countries) (86 countries) (57 countries) (70 countries) DZA Algeria ARG Argentina AUS Australia AUT Austria BGD Bangladesh BEL Belgium BOL Bolivia BWA Botswana BRA Brazil CMR Cameroon CAN Canada CHL Chile CHN China COL Colombia ZAR Congo, Dem. Rep. COG Congo, Rep. CRI Costa Rica DNK Denmark DOM Dominican Rep. ECU Ecuador EGY Egypt, Arab Rep. SLV El Salvador FIN Finland FRA France DEU Germany GHA Ghana GRC Greece GTM Guatemala GUY Guyana HTI Haiti HND Honduras HKG Hong Kong, China HUN Hungary ISL Iceland IND India IDN Indonesia IRN Iran, Islamic Rep. IRL Ireland ITA Italy JAM Jamaica JPN Japan JOR Jordan KEN Kenya KOR Korea, Rep. KWT Kuwait MWI Malawi MYS Malaysia MLI Mali MEX Mexico MAR Morocco MOZ Mozambique NLD Netherlands NZL New Zealand NIC Nicaragua NER Niger NOR Norway PAK Pakistan PAN Panama PNG Papua New Guinea PRY Paraguay PER Peru PHL Philippines POL Poland PRT Portugal SEN Senegal SLE Sierra Leone SGP Singapore ZAF South Africa ESP Spain LKA Sri Lanka SWE Sweden CHE Switzerland SYR Syrian Arab Rep. TZA Tanzania THA Thailand TGO Togo TTO Trinidad and Tobago TUN Tunisia TUR Turkey UGA Uganda ARE United Arab Emirates GBR United Kingdom USA United States URY Uruguay VEN Venezuela, RB ZMB Zambia ZWE Zimbabwe 31 Appendix 2. Definitions and Sources of Variables Used in Regression Analysis Variable Definition and Construction Source Schneider Shadow Estimated shadow economy as the percentage of official GDP. Average of 2001-2002 by Schneider (2004). Economy index country. Heritage Foundation An index ranging 1 to 5 with higher values indicating more informal market activity. The Miles, Feulner, and O'Grady (2005). Informal Market index scores and criteria are: (i) Very Low: Country has a free-market economy with informal market in such things as drugs and weapons (score is 1); (ii) Low: Country may have some informal market involvement in labor or pirating of intellectual property (score is 2); (iii) Moderate: Country may have some informal market activities in labor, agriculture, and transportation, and moderate levels of intellectual property piracy (score is 3); (iv) High: Country may have substantial levels of informal market activity in such areas as labor, pirated intellectual property, and smuggled consumer goods, and in such services as transportation, electricity, and telecommunications (score is 4); and (v) Very High: Country's informal market is larger than its formal economy (score is 5). Average of 2000-2005 by country. Self Employment Self employed workers as the percentage of total employment. Country averages but ILO. Data retrieved from periods to compute the averages vary by country. Average of 1999-2006 by country, but laborsta.ilo.org. countries in Europe and Central Asia (ECA) are excluded (Loayza and Rigolini 2006). Non-contributor to Labor force not contributing to a pension scheme as the percentage of total labor force. World Development Indicators, Pension Scheme Average of 1993-2005 by country. various years. Per Capita GDP Growth Log difference of real GDP per capita (2000 US$). World Development Indicators, various years. Initial GDP per capita Real GDP per capita (2000 US$) in 1985, in logs. World Development Indicators, various years. Initial Government Ratio of general government final consumption expenditure to GDP in 1985. World Development Indicators, Expenditure various years. Poverty Headcount The fraction of the population with income below a given poverty line. The poverty line Loayza and Raddatz (2006). index is $1 per person a day, converted into local currency using a PPP-adjusted exchange rate. The latest/final year of each country's poverty spell is used. Initial Gini index A measure of income inequality ranging 0 to 100 with higher values indicating more Loayza and Raddatz (2006). inequal income distribution. The initial year of each country's poverty spell is used. Law and Order An index ranging 0 to 6 with higher values indicating better governance. Law and Order ICRG. Data retrieved from are assessed separately, with each sub-component comprising 0 to 3 points. Assessment www.icrgonline.com. of Law focuses on the legal system, while Order is rated by popular observance of the law. Average of 2000-2005 by country. Business Regulatory An index ranging 0 to 10 with higher values indicating less regulated. It is composed of Gwartney, Lawson, Sobel, and Leeson Freedom following indicators: (i) Price controls: extent to which businesses are free to set their (2007), The Fraser Institute. Data own prices; (ii) Burden of regulation / Administrative Conditions/Entry of New Business; retrieved from www.freetheworld.com. (iii) Time with government bureaucracy: senior management spends a substantial amount of time dealing with government bureaucracy; (iv) Starting a new business: starting a new business is generally easy; and (v) Irregular payments: irregular, additional payments connected with import and export permits, business licenses, exchange controls, tax assessments, police protection, or loan applications are very rare. Average of 2000-2005 by country. Average Years of Average years of secondary schooling in the population aged 15 and over. The most Barro and Lee (1993, 2001); and Secondary Schooling recent score in each country is used, while figures are computed for countries data are not authors' calculations. available. Sociodemographic Simple average of following three variables: (i) Youth (aged 10-24) population as the Authors' calculations with data from Factors percentage of total population; (ii) Rural population as the percentage of total population; World Development Indicators, ILO and (iii) Agriculture as the percentage of GDP. All three variables are standardized before and UN. the average is taken. Average of 2000-2005 by country. 32 Appendix 3. Descriptive Statistics Data in country averages; periods vary by informality measure (a) Univariate (regression sample) Variable Obs. Mean Std. Dev. Minimum Maximum Schneider Shadow Economy index 84 32.960 14.735 8.550 68.200 (% of GDP) Heritage Foundation Informal Market index 86 3.055 1.251 1.000 5.000 (range 1-5: higher, more informality) Self Employment (% of total employment) 57 26.204 12.028 7.132 59.335 Non-contributor to Pension Scheme 70 53.198 33.482 1.450 98.000 (% of labor force) (b) Univariate (full sample) Variable Obs. Mean Std. Dev. Minimum Maximum Schneider Shadow Economy index 145 34.838 13.214 8.550 68.200 (% of GDP) Heritage Foundation Informal Market index 159 3.409 1.201 1.000 5.000 (range 1-5: higher, more informality) Self Employment (% of total employment) 86 25.158 12.118 1.119 59.335 Non-contributor to Pension Scheme 110 55.999 31.905 1.450 98.500 (% of labor force) (c) Bivariate Correlations between Informality Measures (upper triangle for regression sample (in italics) and lower triangle for full sample) Schneider Heritage Fndn. Self Non-contributor Variable Shadow Economy Informal Market Employment to Pension Schneider Shadow Economy index 1.00 0.68*** 0.71*** 0.72*** (% of GDP) 145 | 84 83 55 70 Heritage Foundation Informal Market index 0.65*** 1.00 0.88*** 0.90*** (range 1-5: higher, more informality) 132 159 | 86 57 70 0.65*** 0.79*** 1.00 0.89*** Self Employment (% of total employment) 69 76 86 | 57 51 Non-contributor to Pension Scheme 0.59*** 0.77*** 0.88*** 1.00 (% of labor force) 104 107 57 110 | 70 Notes: 1. Sample sizes are presented below the corresponding coefficients. 2. *** denotes significance at the 1 percent level. 33