Ue 3 /2- 7 POLICY RESEARCH WORKING PAPER 3127 Small and Medium Enterprises across the Globe A New Database Meghana Ayyagari Thorsten Beck Aslh Demirgu,-Kunt The World Bank Development Research Group Finance August 2003 |POLICY RESEARCH WORKING PAPER 3127 Abstract This paper describes a new cross-country database on the definition of SMEs across different countries, allowing importance of small and medium enterprises (SMEs). for consistent cross-country comparisons. Third, while This database is unique in that it presents consistent and we follow the traditional definition of the SME sector as comparable information on the contribution of the SME being part of the formal sector, the new database also sector to total employment and GDP across different includes the size of the SME sector relative to the countries. The dataset improves on existing publicly informal sector. This paper describes the sources and the available datasets on several grounds. First, it extends construction of the different indicators, presents coverage to a broader set of developing and industrial descriptive statistics, and explores correlations with other economies. Second, it provides information on the socioeconomic variables. contribution of the SME sector using a uniform This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to study SME - related issues. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Agnes Yaptenco, room MC3-439, telephone 202-473-1823, fax 202-522-1155, email address ayaptenco@worldbank.org. Policy ResearchWorkingPapersare also posted on the Web athttp://econ.worldbank.org. The authors may be contacted at tbeck@worldbank.org or ademirguckunt@worldbank.org. August 2003. (33 pages) 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 view of the World Bank, its Executive Directors, or the countries they represent. Produced by the Research Support Team Small and Medium Enterprises across the Globe: A New Database Meghana Ayyagari, Thorsten Beck and Asli Demirguf-Kunt Keywords: Small and Medium Enterprises JEL Classification: LI1, L25, 017 Ayyagari: Robert H. Smith School of Business at the University of Maryland; Beck and Demirgt1c-Kunt: World Bank. We would like to thank Nick Stern and Gerard Caprio for encouragement and helpful discussions, Patrick Honohan for useful comments and suggestions, Agnes Yaptenco for assistance with the manuscript, and Leora Klapper and Victor Sulla for help in identifying data sources. I. Introduction The recent World Bank Review on Small Business Activities' establishes the commitment of the World Bank Group to the development of the small and medium enterprise (SME) sector as a core element in its strategy to foster economic growth, employment and poverty alleviation. This year alone, the World Bank Group has approved roughly $2.8 billion in support of micro, small and medium enterprises. There is also a growing recognition of the role that SMEs play in sustained global and regional economic recovery2. However, there is little systematic research in this area backing the various policies in support of SMEs, primarily because of the lack of data. Hallberg (2001) actually suggests that scale-based enterprise promotion is driven by social and political considerations rather than by economic reasoning. This paper introduces a new database that, for the first time, allows researchers to examine the justification for promoting SME development. This database provides comprehensive statistics on the contribution of the SME sector to total employment and GDP across a broad spectrum of countries. The database thus allows for a comparison on how the economic importance of the SME sector varies across countries. It enables researchers to compare the extent of SME activity of a specific country with that of other countries in the same geographical region or countries with similar income levels. It also provides statistics on the contribution of the SME sector to the formal economy as well as the share of the informal economy. XThe Challenge, World Bank Review of Small Business Activities, 2001 2 IFC Country Reports on Indonesia, Thailand, Tajikistan to name a few. 2 This database greatly improves upon existing data on SMEs, which have been very scarce.3 Further, construction of such a broad cross-country database has been plagued by several problems with comparability and consistency. First, different countries adopt different criteria - such as employment, sales or investment - for defmning small and medium enterprises, and different sources of statistics on SME therefore use different criteria.4 Second, even the definition of an SME on the basis of a specific criterion is not uniform across countries. For instance, a specific country may define an SME to be an enterprise with less than 500 employees while another country may define the cut-off to be 250 employees. This new database presents indicators of the relative importance of the SME sector based both on employment and GDP and draws on a wide array of sources. It is a unique database for the following reasons. First, it provides statistics for a uniform definition of SME applied to all countries. Second, it also has an indicator of SME activity adhering to the official country definition of SMEs. And finally, it is the first to provide a measure of the size of the SME sector with respect to the informal sector. The remainder of the paper is organized as follows. Section II gives the definitions of the various variables used in the database. Section III elaborates on the sources used in collecting the SME data. Section IV presents the variation of the relative importance of the SMEs and the informal sector across countries. In Section V we present correlations and descriptive statistics, and Section VI concludes. 3 Previous efforts include Snodgrass and Biggs (1996) and Klapper and Sulla (2002). 4Currently the SME Department of the World Bank works with the following definitions: microenterprise- up to 10 employees, total assets of up to $10,000 and total annual sales of up to $100,000; small enterprise- up to 50 employees, total assets and total sales of up to $3 million; medium enterprise - up to 300 employees, total assets and total sales of up to S 15 million. 3 II. Definitions In this section, we define the various variables used to describe the relative importance of SMEs in different countries. The term SME covers a wide range of definitions and measures, varying from country to country and between the sources reporting SME statistics. Some of the commonly used criteria are the number of employees, total net assets, sales and investment level. However, the most common definitional basis used is employment, and here again, there is variation in defining the upper and lower size limit of an SME. Despite this variance, a large number of sources define an SME to have a cut-off range of 0-250 employees. All our sources focus on SMEs in the manufacturing sector. SMEs are defined as formal enterprises and thus different from informal enterprises. Our main indicator is therefore based on employment. SME250 is the share of the SME sector in the total official labor force when 250 employees is taken as the cutoff for the definition of an SME. For a country to come under the SME250 classification, the SME sector cutoff could range from 200-300 employees. There are few instances of this range occurring, with data for most other countries reported for an exact cut off of 250 employees.5 We have 54 countries in the SME250 sample, 13 of which are low income countries, 24 are middle income and 17 are high income countries. In constructing the employment figures for different countries, we use multiple sources, and any available data from the 1990s. So the SME250 indicator is an average over time and sources. We also construct another set of employment measures where we retain the official country definition of SMEs. SMEOFF is the share of the SME sector in total 5 The source for our data on the African Countries defines an SME to be less than 200 employees and for Japan, the cut-off used is 300 employees. 4 official labor force when the official country definition of SMEs is used, with the official country definition varying between 100 and 500 employees. Countries, which defined SMEs on a category other than employment, were dropped from our sample. For countries, which do not have an official definition of SMEs, and for countries where we do not have data according to the official cut off, the cut-off data from the most reliable source was used as SMEOFF. The choice of source in this case depended largely on the source used for similar countries and was usually one of the five main sources quoted below. Consequently, we have 76 countries in the SMEOFF sample, of which 17 are low income countries, 31 are middle income and 28 are high income countries. Since only some countries have 250 employees as the official cut-off, the number of countries in the SME250 sample is a subset of the number of the countries in the official sample.6 Similar to the SME250 sample, the SMEOFF measures constructed are numbers averaged over the 1990s. To measure the contribution of the SME sector to the economy, we use SME_GDP, which gives the share of the SME sector, as defined by official sources, relative to GDP.7 As in the case of SMEOFF, variance in the official definition of the SME sector may drive part of the variation in this indicator. We have data for 35 countries. To obtain data on the size of the informal sector, we use the estimates reported by Schneider (2000, 2001). He estimates the size of the shadow economy labor force for 76 6 We also explored a sample using employees up to 150 or less as a cut-off. However, we could only collect information for 31 countries and the variation of the actual cut-offs was very high, with some countries reporting figures for cut-offs as low as 10 or 25 employees and others with cut-offs of 100 or 150 employees. 7We also constructed a series of the relative importance of SMEs in GDP using the 250 employee cut-off. However, we could obtain data for only six countries. 5 developing, transition and OECD countries. The paper also gives estimates of the official labor force. Using this data, we obtain the size of the shadow economy as a percent of official labor force, INFORMAL, averaged over the 1 990s for 34 countries in our database.8 To obtain estimates of the informal sector's contribution to GDP, we use data from Friedman, Johnson, Kaufmann and Lobaton(2000). They report two sets of estimates originally from the Schneider and Enste (1998) dataset. We use an average of these two estimates for our database. Values for missing countries in this sample are obtained from Schneider (2000) who uses the currency demand and DYMIMIC approaches to estimate the size of the shadow economy. Both papers report the average size of the shadow economy in percent of official GDP, labeled as INFO_GDP in our sample. Once again, the data averaged over the 1 990s is used for our database. We thus have data on the shadow economy for 55 countries in the sample. III. Sources In this section, we briefly describe the main sources used for compiling the new database. The SME data were drawn from existing cross-country databases, complemented in many cases with information from country-specific sources. The major sources used are listed in the table below and described in the following. The appendix lists the sources used for each country in detail. 8 We also construct the size of the informal economy as a percentage of total labor force(given by informal/(informal+formal)). However, we do not use this statistic because the employment figures for the SME sector, SME250 and SMEEOFF are both reported as a percentage of official labor force. 6 Source Coverage IADB The Inter-American Development Bank: SME Observatory 1990-99 UNECE United Nations European Economic Commission 1994-97 OECD OECD: Globalization and SME, Synthesis Report 1990-99 APEC The APEC Survey on SMEs 1991-95 WB RPED Regional Program on Enterprise Development Paper 1990-99 L4DB: SME Observatory For Latin American Countries, we used as the primary source the SME data published by the Inter-American Development Bank (The Latin American SME Observatory). This database has time series observations on SME size and activity in about 18 Latin American countries. In most cases, it also includes the definition of the SME sector used in presenting the statistics. The data presented is either census data or collected from surveys. Observations, which did not represent contribution of the SME sector to formal employment or to total GDP, were not included in our sample. The same is true for observations where the size of the SME sector was not defined. This gave us data on the SME share of employment for 9 and SME share of GDP for 4 Latin American countries. UNECE The UN-ECE produces annual statistics and trends in national SME development for the countries in transition (CIT). The statistics are calculated from survey questionnaires and the data available are for the years 1994-95 and 1996-97. Each annual report also gives the latest official definition of the SME sector in the various CIT. Data for 20 transition economies were obtained from this source. Once again, observations that did not report the size of the SME sector were dropped. For two countries, Albania and 7 Ukraine, the latest data were not taken because of discrepancy from the previous years' statistics and from data published by other country specific sources. OECD For the OECD countries, the primary data source used were the SME data published by the OECD (Globalization and SMEs, 1997 ed.vol I and II). The OECD adopts the following convention for categorizing SMEs --micro: 1-4 employees; very small: 5-19 employees; small: 20-99 employees; medium: 100-500 employees. The broad definition for OECD countries used for our database is that an SME has less than 500 employees. For two countries, Japan and Sweden, the country specific definition of the SME was used. The statistics compiled were from survey data. APEC The Asia Pacific Economic Council publishes statistics compiled from a field survey conducted in selected APEC countries. The definition of the SME sector varies largely in the APEC countries, not only in the cut off used for employment but also in the criteria used for categorization. Countries like India have SMEs defined only according to the investment level and hence do not figure in our sample of countries. After adopting the usual criteria for inclusion, we have eight APEC countries included in our database. WB_RPED The Regional Program on Enterprise Development (World Bank) has several country- specific studies on the structure of labor markets in Africa. The studies contain statistics on SME contribution to employmnent. The numbers are calculated on the basis of surveys collected through interviews from manufacturing firns in seven African countries. The general classification of the SME sector used in this source (and in our database) is- 8 micro: less than 10 employees; small: 10-49 employees; medium: 50-200 employees. We obtain data on SME share of employment for eight Africa countries from this source. IV. SME across countries Table 1 presents the share of SMEs and the informal sector in total employment and GDP, as well as GDP per capita. The importance of the SME sector varies greatly across countries. While in Azerbaijan, Belarus and Ukraine less than 5% of the formal work force is employed in SMEs, this share is more than 80% in Chile, Greece, and Thailand (SME250). Similarly, the ratio of the informal economy relative to GDP varies from 9% in Switzerland to 71% in Thailand. Table 2 presents the correlation matrix for all these variables. The SME sector's contribution to both employment and GDP shows a strong positive correlation with GDP per capita, while INFORMAL and INFO_GDP are significantly negatively correlated with GDP per capita.9 We see strong positive correlations between the SME variables themselves, i.e. between SME250 and SME_GDP and between SMEOFF and SME_GDP, while we see only a weak (10% significance level) correlation between the two measures of the relative importance of the informal sector. Some, but not all of the SME measures are negatively correlated with the measures of the informal economy. 9 This result contradicts anecdotal evidence and earlier empirical figures in Snodgrass and Biggs (1996) who report that the SME share in employment reduces with GNP per capita. Their finding is based on census data from 34 countries in the 1960s and 1970s and they define SMEs to have less than 100 employees. The reason for the discrepancy between our results could be the small sample or the lower employment cut-off for the SME definition. We cannot check the results only using their sample because they do not report the countries for which census data were available. However, when we use our limited data for SME 150, we find that its correlation with GDP per capita is no longer significant although the positive sign remains. 9 In Figure 1, we graph the SME sector's contribution to total employment'0 and GDP across different income groups. The graph shows a marked increase in the SME sector's contribution to total employment from the low-income countries (17.56%) to the high income (57.24%). The SME share of GDP follows a similar trend increasing from 15.56% of GDP in the low-income countries to 51.45% in the high-income countries. Therefore, an increase in SME sector's contribution to employment is accompanied by an increase in its share of GDP as well. Figure 2 shows a steady decline in the contribution of the informal sector to GDP, from the low-income countries (47.2%) to the high-income countries (13%). The sector's contribution to total employment" also shows a general decline from the low-income group (29.41 %) to the high-income group (15.16%), though it increases slightly in the middle-income group. Figure 3 presents the contribution of each sector across different income groups in a single graph. As the figure shows, the SME sector generates a much smaller portion of median employment in the low-income countries than in the high-income countries. In the developing countries of the low and middle-income group, the INFORMAL sector generates a significantly higher portion of median employment than the SME sector. For instance, in the low-income countries, while the informal sector generates 29.14% of total employment, the SME sector generates only 17.56%. In stark contrast, at the high-income '0 Up till now, we have been using the SME share of formal sector employment. For Figure 1, we calculate SME share of total labor force in the country. Therefore SMEOFF_Total = SMEOFF * FORMAL_Total, where FORMAL_Total gives the proportion of the formal labor force as a percentage of total labor force. Data on FORMAL_Total is obtained from our calculations of the shadow economy. The informal sector's share of total labor force is given by INFORMAL_Total = INFORMAL/(I+INFORMAL). Therefore, FORMAL_Total = I-INFORMAL_Total. Since the data sample on INFORMAL is limited to 34 countries, this also limits our sample on SMEOFF_Total to 34 countries. "Here again, we graph INFORMAL-Total (contribution to total employment) instead of INFORMAL(contributi6n to formnal employment) 10 level, while the INFORMAL sector generates only 15.16%, the SME sector generates 57.24% of the total employment of the country (as shown in Figure 2). Figure 4 portrays the contribution to GDP of the two sectors in a single graph. The SME sector generates only 15.56% of total GDP in the low-income group compared to 39% in the middle-income group and 51.45% in the high-income group countries. The informal sector follows a reverse trend and is the largest contributor to GDP at 47.2% in the low-income group and contributes only 13% in the high-income group. Interestingly, the joint contribution of the informal and SME sectors to GDP remains approximately constant across income groups at around 65-70 percent. As income increases however, there is a marked shift from the informal to the SME sector. V. The Importance of SMEs: correlations with policies, the business environment, growth obstacles, and historic factors This section relates the variation in the importance of the SME sector across countries to differences in economic policies, the business environment in which firms operate, growth obstacles reported by SME and historic determinants. While these correlations do not imply any causality in either direction, they provide helpful information to better understand the variation in SME across countries and form the basis for more rigorous analysis. A. SMEs and Macroeconomic Policy Variables In Panel A of Table 3, we examine correlations between the SME sector's share of total labor force, the INFORMAL sector's share of GDP12 and some possible determinants, which empirical economic literature has shown to be associated with economic growth (Barro 1991; Easterly, Loayza and Montiel, 1997). The determinants investigated are also the ones used as a conditioning information set in Levine, Loayza and Beck (2000) and include the following: Government Consumption (government expenditures as a share of GDP) and Inflation (the inflation rate) as measures of macroeconomic stability and Education (secondary school enrollment) as a measure of the level of human capital. We also use Trade (the sum of exports and imports to GDP) to capture the degree of openness of an economy and Black Market Premium to capture the extent of policy distortions. As a measure of financial development, we use Private Credit (claims of financial institutions on the private sector as a share of GDP)'3. Panel A of Table 3 shows that the SMEs are more important in economies with higher levels of education, lower inflation rates and higher levels of financial intermediary development. They tend to be less important in more open economies and in countries with greater policy distortions. The informal sector, on the other hand, has a larger importance in economies with lower levels of human capital accumulation, lower levels of government expenditures and lower levels of financial intermediary development. B. SMEs and the Business Environment 12 Results for the SME sector's share of GDP and INFORMAL sector's share of total labor force are not 1,resented due to the small number of data points. Levine, Loayza and Beck(2000) find EXPEN, EDUCATION, TRADE, PRIVO to have a large and significant impact on economic growth 12 Panel B of Table 3 investigates the correlations of indicators of the business environment with the SME sector's contribution to formal sector employment and the INFORMAL sector's share of GDP. The business environment indicators are obtained from the Word Bank's Doing Business database that provides indicators of the cost of doing business by identifying specific regulations that enhance or constrain business investment, productivity, and growth. The Cost of Entry, is the cost of registration relative to Gross National Income (GNI) that a start-up must bear before it becomes legally operational. Data are from Djankov et al (2002). The correlations indicate that countries where it is more costly to register a new enterprise have smaller share of SMEs and larger informal sectors. This suggests that costly registration requirements constitute an impediment for informal firms to convert themselves into formal enterprises. As important as low entry barriers are for a thriving corporate sector, so is an efficient exit mechanism. We therefore look at the correlation of the SME and INFORMAL sector with corporate bankruptcy procedures in different countries. We use Bankruptcy, an index documenting the success of a jurisdiction in attaining the three goals of insolvency as defined in Hart (1999): the cost of insolvency (rescaled from 0 to 1, where higher scores indicate less cost), time of insolvency (rescaled from 0 to 1, where higher scores indicate less time), the observance of absolute priority of claims, and the efficient outcome achieved. A 1 on the Bankruptcy Index means perfect efficiency while a 0 means that the insolvency system does not function. The results show that the SME sector is weakly positively correlated with Bankruptcy while the INFORMAL sector is strongly negatively correlated. Therefore, countries with efficient insolvency procedures 13 have larger SME sectors and smaller INFORMAL sectors as compared to countries with weaker and less efficient procedures. We also look at the Cost of Contract Enforcement, which is the cost - in attorney fees and court costs - of dispute resolution relative to Gross National Income (GNI). The data is from Djankov et al (2003). Contract enforcement is not only imnportant for firms in their commercial transactions, but also for access to finance. The correlations indicate that countries with higher costs of dispute resolution have larger informal sectors. This implies that an inefficient judicial system is an impediment to the conversion of informal enterprises into formal ones. The data also includes the Credit Registry, which is an index of the extent to which the rules of credit information registries facilitate lending. It is constructed on the basis of the scope of information collected, scope of information distributed, ease of access to information and the quality of information. The correlation matrix shows that there is no correlation of the Credit Registry with either the SME or the INFORMAL sectors of the economy. The correlation matrix also examines whether the importance of the SME sector is related to the Labor market regulation, an index for the regulation of labor markets. The index is constructed by examining detailed provisions in labor laws. While the SME sector does not appear to be correlated with the Labor market regulation, the correlation matrix shows that countries with more severe labor marker regulations have larger INFORMAL sectors. Rigid labor markets thus seem to impede conversion of informal enterprises into formal ones. 14 We also consider the general institutional environment, in which firms operate. The institutional variables include Property Rights, an index of the degree to which the legal system protects private property and Regulatory Environment, a measure of extent of regulation of the various institutions (both measures from the Heritage Foundation). Institutional Development is the average of six institutional variables - voice and accountability, government effectiveness, regulatory quality, rule of law, political stability and control of corruption -, as constructed by Kaufman, Kraay, and Zoido- Lobaton.(1999a, 1999b). We find strong positive correlations between the SME variables and the institutional variables, suggesting that the SMEs thrive more in countries with better- developed institutions. The correlation matrix also shows a negative relation between entry regulation and the importance of the SME sector, indicating that high entry regulation in terms of greater number of procedures and higher cost and time act as a deterrent to SME sector's development. The findings for the INFORMAL sector are exactly reverse of those for the SME sector. We find positive correlations between the informal sector and the entry regulation and contract enforcement variables and negative correlations between the institutional variables and the importance of the informal sector. C SMEs and Growth Obstacles In Panel C of Table 3, we try to examine the correlations between the importance of SME and informal sectors and various growth obstacles as reported by the SMEs themselves. We use data from the World Business Environment Survey (WBES), a major cross-country survey of small, medium and large enterprises that included questions on the severity of certain obstacles for the firm's growth and operation. They 15 include the following: Financing Obstacle, Infrastructure Obstacle, Political Instability Obstacle, Inflation Obstacle, Exchange Rate Obstacle, Street Crime Obstacle, Organized Crime Obstacle, Taxes and Regulation Obstacle, Corruption Obstacle, Judiciary Obstacle and Anticompetitive Practices Obstacle. Only the financing and inflation obstacles are negatively and robustly correlated with both SME measures, while the infrastructure obstacle is negatively correlated at the 5% significance level with SME250 and the corruption obstacle with SME250 at the 10% significance level. The importance of the informal sector, on the other hand is positively correlated with most of the growth obstacles. This shows that in countries where there are many obstacles to firm growth and particularly on SMEs, firms tend to migrate to the informal sector to overcome these obstacles. These correlation also underline the importance of access to financial services for a thriving SME sector. D. SMEs and Historic Determinants In this section we examine the impact of historical determinants on the SME sector. Panel D of Table 3 investigates whether ethnic composition, natural endowments, legal origin and religious composition are related with the SME share of the economy. We explore the correlations of the relative importance of the SME and informal sectors with Latitude, absolute value of the latitude of the country, Good Crops, proxying for agricultural endowments conducive to the emergence of a middle class and institutional development, and Settler Mortality, log of deaths per thousand soldiers per year. According to the endowments theories (Engerman and Sokoloff 1998, Acemoglu, Johnson and Robinson, 2001, 2002) natural endowments and disease environment may have influenced institutional development in countries, resulting in different income 16 distributions and economic systems and consequently different firm size distributions. While Latitude and Good Crops are positive indicators of endowments, Settler Mortality is a negative indicator. We do not find a significant association of the relative importance of SMEs with Latitude and Good Crops, but we find a negative and significant correlation with Settler Mortality, indicating that countries with endowments less conducive to institutional development have relatively few SMEs. Similarly, we find that countries with endowments less conducive to institutional development have larger informal sectors. The correlation matrix also includes Ethnic Fractionalization, the ethnic composition of a country. This variable measures the probability that two randomly selected individuals from a country are from different ethno-linguistic groups. Panel D shows a significant negative correlation between Ethnic Fractionalization and the relative importance of the SME sector and a positive correlation with the relative importance of the informal sector, suggesting SME (informal enterprises) have a larger (smaller) role in ethnically more homogenous countries. Panel D also examines the effect of religious composition. Catholic, Muslim, Protestant, Other Religion equal the fraction of population that is Catholic, Muslim, Protestant or of another religion, with data coming from LLSV(1999). The correlation results show that countries with a larger share of Catholic population and a smaller share of Muslims and adherents of other religions have larger SME sectors. On the other hand, countries with a larger share of Muslim and a smaller share of Protestant population have a larger share of informal enterprises. 17 To analyze the effect of legal tradition, we also use data from LLSV(1998, 1999) who identify the legal origin of each country's Company/Commercial Law. Thus the Common-Law equals one if the country adopted its commercial! company law from the British Common Law System and zero otherwise. The French-Civil Law, German Civil-Law and Socialist Law dummy variables are defined similarly. In our sample of 76 countries, we have 17 common-law countries, 6 German civil law countries, 27 French civil law, 5 Scandinavian legal origin countries, and 21 transition countries. The correlation analysis shows that transition economies have smaller SME sectors and French Civil Law countries have larger SME sectors, in terms of their contribution to total formal employment. French civil law countries also have larger informal sectors. There is no robust correlation between the German and British legal origin and the relative importance of the SME and informal sectors. VI. Conclusion This paper introduces a new and unique set of cross-country indicators of the contribution of small and medium enterprises (SMEs) to employment and wealth creation. The dataset reveals a significant variation in the size and economic activity of the SME sector across income groups. Countries with a higher level of GDP per capita have larger SME sectors in terms of their contribution to total employment and GDP. However, it is also interesting to note that the overall contribution of small firms - formal and informal - remain about the same across income groups. As income increases, the share of the informal sector decreases and that of the formal SME sector increases. 18 The paper also suggests that a variety of macro-economic variables and historical determinants show significant correlations with the relative importance of the SME and informal sectors. This database is part of a broader research project that aims to investigate the impact of the SME sector on growth and poverty alleviation. Specifically, the compiled data allows researchers to run cross-country regressions to evaluate the relation between the size of the SME sector and economic development. The indicators can also be used to investigate the empirical link between the SME sector and other possible determinants of size such as natural endowments, ethnic composition, legal origin, and other regulatory and policy variables. We turn to these issues in Beck, Demirguc-Kunt and Levine (2002). 19 REFERENCES Acemoglu, D., Johnson, S., Robinson, J.A.(2001): The colonial origins of comparative development: an empirical investigation. American Economic Review 91, 1369-140 1. Acemoglu, D., Johnson, S., Robinson, J.A.(2002):Reversal of fortunes: geography and institutions in the making of the modem world income distribution. Quarterly Journal of Economics 117, forthcoming. Beck, T., Demirguc-Kunt, A., Levine, R. (2002): Law, Endowments and Finance. Working Paper. Beck, T., Demirguc-Kunt, A., Levine, R. (2002): Small and Medium Enterprises, Economic Growth and Development. World Bank Mimeo. Beck, T., Levine, R., Loayza, N. (2000): Finance and the Sources of Growth. Journal of Financial Economics 58, 261-300. Beck, T., Levine, R., Loayza, N. (2000): Financial Intermediation and Growth: Causality and Causes. Journal of Monetary Economics 46, 31-77. Boyd, J., Levine, R., Smith, B. 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Hallberg, Kristin(2001): A Market-Oriented Strategy For Small and Medium-Scale Enterprises. IFC Discussion Paper # 48. 20 Hart (1999): Different Approaches to Bankruptcy. Harvard Institute of Economic Research Working Paper No. 1903. Kaufiman, D., Kraay, A., Lobaton, P.Z.(1999): Governance Matters. World Bank Policy Research Department Working Paper No. 2196. Klapper, L. and V. Sulla (2002): SMEs Around the World: Where Do they Matter Most? World Bank Mimeo. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R. (1999): The quality of Government. Journal of Law, Economics and Organization 15, 222-279. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R. (1998): Law and Finance. Journal of Political Economy 106, 1113-1155. Schneider, F. (2000): The Size and Development of the Shadow Economies and Shadow Economy Labor Force of 18 Asian and 21 OECD Countries: First Results for the 90s. Forthcoming. Schneider, F., Enste, D.(1998). Increasing shadow economies all over the world-fiction or reality: a survey of the global evidence of its size and of its impact from 1970 to 1995, IMF and University of Linz, August 21. Snodgrass, D. and Biggs, T. (1996) Industrialization and the Small firm. International Center for Economic Growth. 21 Table 1. Firm Size and Employment/GDP Share The variables are defined as follows: GDP/CAP is the real GDP per capita in US$. SME250 is the SME sector's share of formal employment when 250 employees is used as the cut-off for the definition of SME. SMEOFF is the SME sector's share of formal employment when the official country definition of SME is used. NFORMAL is the share of the shadow economy participants as a percentage of the formal sector labor force. INFO GDP is the share of the shadow economy participants as a percentage of GDP. SME_GDP is the SME sector's contribution to GDP(The official country definition of SME is used).Values are 1990-99 averages for all the variables Nation GDP/CAP SME250 SMEOFF INFORMAL SME GDP INFO GDP Albania 744.07 9.49 Argentina 7483.77 70.18 70.18 . 53.65 21.80 Australia 20930.40 . 50.60 . 23.00 15.30 Austria 29619.35 66.10 66.10 16.00 . 10.45 Azerbaijan 558.29 5.34 5.34 . . 47.20 Burundi 170.59 . 20.51 Belgium 27572.35 69.25 69.25 . . 18.65 Bulgaria 1486.74 50.01 50.01 63.00 39.29 31.25 Belarus 2522.94 4.59 4.59 . 9.00 16.65 Brazil 4326.55 59.80 59.80 49.21 . 33.40 Brunei 17983.77 . 69.40 Canada 19946.50 . 58.58 . 57.20 11.75 Switzerland 44716.54 . 75.25 . . 8.55 Chile 4476.31 86.00 86.50 40.00 . 27.60 Cote d'lvoire 746.01 18.70 18.70 59.65 Cameroon 652.67 20.27 20.27 61.40 Colombia 2289.73 67.20 67.20 53.89 38.66 30.05 Costa Rica 3405.37 . 54.30 . . 28.65 Czech Republic 5015.42 64.25 64.25 . . 12.35 Germany 30239.82 59.50 70.36 22.00 42.50 12.80 Denmark 34576.38 68.70 78.40 15.40 56.70 13.60 Ecuador 1521.39 55.00 55.00 58.80 20.03 31.20 Spain 15361.80 80.00 74.95 21.90 64.70 20.00 Estonia 3751.59 65.33 65.33 . . 17.85 Finland 26813.53 59.15 59.15 . . 13.30 France 27235.65 67.30 62.67 9.00 61.80 12.10 United Kingdom 19360.55 56.42 56.42 . 51.45 10.40 Georgia 736.79 7.32 7.32 36.67 . 53.10 Ghana 377.18 51.61 51.61 71.76 Greece 11593.57 86.50 74.00 . 27.40 24.20 Guatemala 1460.47 32.30 32.30 50.25 . 55.70 Hong Kong, China 21841.82 . 61.50 . . 13.00 Honduras 706.01 . 27.60 . . 46.70 Croatia 4453.72 62.00 62.00 70.00 . 23.50 Hungary 4608.26 45.90 45.90 . 56.80 29.85 Indonesia 963.33 . 79.20 37.45 Ireland 19528.13 67.20 72.10 . . 14.25 22 Nation GDP/CAP SME250 SMEOFF INFORMAL SME GDP INFO GDP Iceland 27496.90 . 49.60 Italy 19218.46 79.70 73.00 39.00 58.50 22.20 Japan 42520.01 71.70 74.13 . 56.42 11.10 Kazakhstan 1496.16 . 12.92 40.00 . 28.25 Kenya 340.85 33.31 33.31 41.10 Kyrgyz Republic 972.25 63.22 63.22 40.00 Korea, Rep. 10507.69 76.25 78.88 19.62 45.90 38.00 Luxembourg 45185.23 70.90 70.90 . 76.30 Latvia 2418.82 . 20.63 . . 29.80 Mexico 3390.17 48.48 48.48 . . 38.05 Nigeria 256.55 16.72 16.72 48.85 . 76.00 Nicaragua 432.34 . 33.90 Netherlands 27395.01 61.22 58.50 . 50.00 12.65 Norway 33657.02 . 61.50 . . 11.30 New Zealand 16083.78 . 59.28 9.20 35.00 10.15 Panama 2998.63 72.00 72.00 . 60.12 51.05 Peru 2162.12 67.90 67.90 54.56 55.50 50.95 Philippines 1099.31 66.00 66.00 30.63 31.50 50.00 Poland 3391.08 63.00 61.81 . 48.73 16.45 Portugal 11120.81 79.90 81.55 . 67.25 16.20 Romania 1501.08 37.17 37.17 42.73 33.60 17.55 Russian Federation 2614.38 13.03 13.03 42.18 10.50 34.30 Singapore 22873.66 . 44.00 . . 13.00 El Salvador 1608.91 . 52.00 46.67 44.05 Slovak Republic 3651.45 56.88 32.07 . 37.10 10.00 Slovenia 9758.43 . 20.26 31.00 16.65 Sweden 27736.18 61.30 56.50 19.80 39.00 13.80 Thailand 2589.83 86.70 86.70 . . 71.00 Tajikistan 566.44 . 35.91 Turkey 2864.80 61.05 61.05 . 27.30 Taiwan, China 12474.00 68.60 68.60 14.50 . 16.50 Tanzania 182.85 32.10 32.10 42.24 . 31.50 Ukraine 1189.84 5.38 5.38 . 7.13 38.65 United States 28232.07 . 52.54 . 48.00 12.20 Vietnam 278.36 74.20 74.20 . 24.00 Yugoslavia, Fed. Rep. 1271.12 44.40 44.40 South Africa 3922.60 . 81.53 Zambia 418.93 36.63 36.63 Zimbabwe 643.84 15.20 15.20 33.96 23 Table 2. Correlations Correlations between the SME sector and INFORMAL sector are presented in the table. The variables are defined as follows: GDP/Cap is the real GDP per capita in US$. SME250 is the SME sector's share of formal employment when 250 employees is used as the cut-off for the definition of SME. SMEOFF is the SME sector's share of formal employment when the official country definition of SME is used. INFORMAL is the share of the shadow economy participants as a percentage of the formal sector labor force. INFORMAL_GDP is the share of the shadow economy participants as a percentage of GDP. SME_GDP is the SME sector's contribution to GDP(The official country definition of SME is used). GDP/CAP SME250 SMEOFF INFORMAL INFORMAL_GDP SME250 0.43a SMEOFF 0.44' 0.98a INFORMAL -0.72a -0.35c -0.31c INFORMAL-GDP -0.65a -0.32b 0.3lb 0.51' SME_GDP 0.51a 0.68a 0.708 -0.32 -0.17 a, b and ' stand for significance levels at 1, 5 and 10 percent, respectively. 24 _ i _ E < g r 3= -R w 00 0 e c M M M g t; 3 =3 e e o e' e O O 4 5~~~~~~ E§555S55e g 0 C, o i5 E¢ngoot ° !.;2p ,E°oS Z )9 of ~ o X u cj i] 9 o]ni- w ; ° VCCm S i; ci; -ca 2~~~~~~~~~~0 3 '.,SE3 g - r. r4 as" " .- 6 U 0 r 0.0 ' o N =~~~~c .~~~~ 9 * 0 ~~~~0 2 -0 C 00~~~~~~~ 0 g C2 ci 20 6 W CA ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 E~~~~~~~~~~ . : DE 6~~~~~~~~~~~~~~~~ o __fi Cor L ~~~~~~~~Cl CD CD (%)dG9 (%);uawxuojdwg 0~~~~~~~~ -~~~~~~~~~~~~~7 C -- 0~~~~~~~~~~~~~~ 0 ~~~~~~~~~~~~~~~~~~~~~~~~~~~00 0 c -_ .0. C4~~~~~~~~~4 __ r-~(% dU _ _ )juaw(ojdtug oo som0. 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E 2~~~~Cl t5~~~~~~~~~~~~~~~~~~~~~~~4 B a o C O o ffi oF ch > ^ o~~~~~~c 00 Oa OO 0O t^s O Os g os '> > O o fi Os b o o o o 0 o-o C 08 Oh0o° * sz O O : o O O O O O O O o O = O O O O O O i e cO _z o 0 o 0 o ; 0 0 0 0 v~~~~~~~~~~~~~~ ce Wbe se s- < sf sN f S N v Sv O ,,,8 m ,, . eE S San e E E a E o E, o~~~~~~~G 40 0~~~~~~~~~~~~~~l if .5.1.5 "0. 4 4 a~~ ~ ~~~~~~~~ A -a I 2 0 ~~~~~ I. ~~~~~~~~~~~~~~.E o~~~~~~~~~~~~~~~~~~~~~ E 2 o O O o o 00' 0 °°O° ° x}o °o ° ' t 2U 3, a X x ff b 3 3 U 3 9 S £ O E a Z U 0 ~- U 0 ~~LU ~~ 00 0 ~~~~ ~ ~ LU .2 ~~~ .~0 U I- 0~ ~ ~ ~ ~ ~ ~ ~~~~~~~~CD0C C .C C~~~~~~~~~~~~~~~~~~~C a~~~ 0~~~~~~~~~~~~~P U 53 -' O 5 or o >o > > 0 ~ ~ ~ ~ 0' E E 0~~~~~~~~~~~~~~~~ . . - ; ;0 ; o, (a a a ,E n 0 m c - .0 z 0 c o * c : c * oQ Q i c D o 0 00 >DD 000 Ds >ut; 0 c a a ao a oa o0Mgg g g h. cQ~~~~~~~~ o _~ ~ ~~~~~~0 Co o ooo S.. I- - E~~~~~~91I. en -n WI0 US)-00r. v)1 v o o o or o D ,o o) o4 o C a q e } s _ v S vN m a, &s &S ciNc cy, ZY, c, N S c oo 2 S 0 C0 S 00 C o c> 2 o> C | <= C = *> * 0~~~~~~~0 ~~~~~ E~~~~~~~ 0 0 0 u~~~~~0 a 00 >~~ ~ ~0 >.2 o 0. a,: a.. -a :9, ~ ~ ~ ul g2 M M ro cn cn n M F-- - F. 9 F- D: N R - Policy Research Working Paper Series Contact Title Author Date for paper WPS31 01 Portfolio Preferences of Foreign Reena Aggarwal July 2003 A. Yaptenco Institutional Investors Leora Klapper 31823 Peter D. Wysocki WPS3102 Investing in Infrastructure: What is Marianne Fay July 2003 M. Fay Needed from 2000 to 2010? Tito Yepes 87200 WPS3103 Ownership Structure and Initial Reena Aggarwal July 2003 A. Yaptenco Public Offerings Leora Klapper 31823 WPS3104 Does Strict Employment Protection Jan Rutkowski August 2003 J. Rutkowski Discourage Job Creation? Evidence 84569 from Croatia WPS3105 Further Evidence on the Link between Allen N. Berger August 2003 A, Yaptenco Finance and Growth: An International Iftekhar Hasan 31823 Analysis of Community Banking and Leora F. Klapper Economic Performance WPS3106 Governance Matters III: Governance Daniel Kaufmann August 2003 A. Bonfield Indicators for 1996-2002 Aart Kraay 31248 Massimo Mastruzzi WPS3107 More Favorable and Differential Bernard Hoekman August 2003 P. Flewitt Treatment of Developing Countries: Constantine Michalopoulos 32724 Toward a New Approach in the L. Alan Winters World Trade Organization WPS3108 Stabilization and Association Process Bartiomiej Kaminski August 2003 F. Sy in the Balkans: Integration Options Manuel de la Rocha 89750 and their Assessment WPS3109 The Impact of China's WTO Elena lanchovichina August 2003 S. Lipscomb Accession on East Asia Terri Walmsley 87266 WPS31 10 Governance of Public Pension Funds: David Hess August 2003 P. Braxton Lessons from Corporate Governance Gregorio Impavido 32720 and International Evidence WPS31 11 International Trade and Wage Gunseli Berik August 2003 M. Ponglumjeak Discrimination: Evidence from Yana van der Meulen Rodgers 31060 East Asia Joseph E. Zveglich, Jr. WPS3112 On the Timing of Marriage, Cattle, Johannes Hoogeveen August 2003 P. Sader and Weather Shocks in Rural Bas van der Klaauw 33902 Zimbabwe Gijsbert van Lomwel WPS3113 What Drives Bank Competition? Stijn Claessens August 2003 R. Vo Some International Evidence Luc leaven 33722 WPS3114 The Dynamics of Foreign Bank Giovanni Majnoni August 2003 H. Issa Ownership: Evidence from Hungary Rashmi Shankar 30154 Eva Varhegyi WPS3115 Integrating Housing Wealth into the Robert Buckley August 2003 0. Himid Social Safety Net: The Elderly in Kim Cartwright 80225 Moscow Raymond Struyk Edward Szymanoski Policy Research Working Paper Series Contact Title Author Date for paper WPS3116 Dollarization of the Banking System: Gianni De Nicol6 August 2003 A. Yaptenco Good or Bad? Patrick Honohan 38526 Alain Ize WPS3117 Policy Research on Migration and David Ellerman August 2003 B. Mekuria Development 82756 WPS3118 To Share or Not to Share: Does Local Beata Smarzynska August 2003 P. Flewitt Participation Matter for Spillovers from Javorcik 32724 Foreign Direct Investment? Mariana Spatareanu WPS3119 Evaluating the Impact of Conditional Laura B. Rawlings August 2003 M. Colchao Cash Transfer Programs: Lessons Gloria M. Rubio 38048 from Latin America WPS3120 Land Rights and Economic Quy-Toan Do August 2003 P. Sader Development: Evidence from Vietnam Lakshmi lyer 33902 WPS3121 Do Bilateral Investment Treaties Mary Hallward-Driemeier August 2003 A. Bonfield Attract Foreign Direct Investment? 31248 Only a Bit ... and They Could Bite WPS3122 Individual Attitudes Toward Roberta Gatti August 2003 N. Obias Corruption: Do Social Effects Matter? Stefano Paternostro 31986 Jamele Rigolini WPS3123 Production and Cost Functions and Beatriz Tovar August 2003 G. Chenet-Smith Their Application to the Port Sector: Sergio Jara-Dfaz 36370 A Literature Survey Lourdes Trujillo WPS3124 The Impact of Structural Reforms on Neil McCulloch August 2003 M. Faltas Poverty: A Simple Methodology with 82323 Extensions WPS3125 Economic Analysis of Health Care Vicente B. Paqueo August 2003 R. Guzman Utilization and Perceived Illness: Christian Y. Gonzalez 32993 Ethnicity and Other Factors WPS3126 Public Disclosure of Environmental Jong Ho Hong August 2003 Y. D'Souza Violations in the Republic of Korea Benoit Laplante 31449 Craig Meisner