WPS5631 Policy Research Working Paper 5631 Small vs. Young Firms across the World Contribution to Employment, Job Creation, and Growth Meghana Ayyagari Asli Demirguc-Kunt Vojislav Maksimovic The World Bank Development Research Group Finance and Private Sector Development Team April 2011 Policy Research Working Paper 5631 Abstract This paper describes a unique cross-country database than 10 years) have the largest shares of total employment that presents consistent and comparable information on and job creation. Small firms and young firms have the contribution of the small and medium enterprises higher job creation rates than large and mature firms. sector to total employment, job creation, and growth However, large firms and young firms have higher in 99 countries. The authors compare and contrast the productivity growth. This suggests that while small firms importance of small and medium enterprises to that employ a large share of workers and create most jobs in of young firms across different economies. They find developing economies their contribution to productivity that small firms (in particular, firms with less than 100 growth is not as high as that of large firms. employees) and mature firms (in particular, firms older This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ademirguckunt@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 Small vs. Young Firms across the World Contribution to Employment, Job Creation, and Growth Meghana Ayyagari* Asli Demirguc-Kunt* Vojislav Maksimovic* Keywords: Small and Medium Enterprises, employment, job creation, growth JEL Classification: L11, L25, O17 __________ *Ayyagari: School of Business, George Washington University, ayyagari@gwu.edu; Demirgüç-Kunt: World Bank, ademirguckunt@worldbank.org; Maksimovic: Robert H. Smith School of Business at the University of Maryland, vmaksimovic@rhsmith.umd.edu. We thank Miriam Bruhn, Leora Klapper, David McKenzie, Rita Ramalho, and Roberto Rocha for useful comments and suggestions. We would like to thank Yuzhen He for excellent research assistance. This paper's findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. I. Introduction The role played by small and medium enterprises (SMEs) in employment generation and economic recovery is a key question for policy makers. Multi-billion dollar aid portfolios across countries are directed at fostering the growth of SMEs. However, there is little systematic research/data, informing the various policies in support of SMEs, especially in developing countries. Moreover, the empirical evidence that exists on the firm-size growth relationship has been mixed and we do not know whether SMEs or other firms are significant contributors to the creation of jobs, and how this varies across countries. The more recent work of Haltiwanger, Jarmin, and Miranda (2010) in the U.S. suggests that (1) startups and surviving young businesses are critical for job creation and contribute disproportionately to net growth, and (2) there is no systematic relationship between firm size and growth after controlling for firm age. It is not clear whether these findings apply in developing countries where firms, especially small firms, face many institutional constraints. So far there has been little research on the relative importance of age and size in predicting growth in other parts of the world where there are greater barriers to entrepreneurship, and where venture capital markets that finance young firms are not as well developed as in the US. In this paper, we first present comprehensive statistics on the contribution of SMEs and young firms to total employment, job creation, and growth across 99 developing economies. The data compiled are more comprehensive and more comparable across countries than existing cross-country SME databases (e.g. Ayyagari, Beck, and Demirguc-Kunt, 2007). We then examine the relationship between firm size, age, employment, and productivity growth and how this varies with country income. Our analysis shows that small and medium enterprises are the biggest contributors to employment across countries. Our sample consists of 47,745 firms in 99 countries, surveyed in the period 2006-2010. In the median country, firms with 5-250 employees employ 66.76% of the total permanent, full-time employment in the country. 1 The mean across our sample of countries is 66.38%. While SMEs are big contributors to employment in all countries, we do find a negative association between GDP/capita and SME contribution to employment ­ SMEs 1 Note that we do not have micro enterprises, that is, firms with less than 5 employees, in our sample. 2 contribute more to employment in low income countries than high income countries. Other studies, such as Klapper and Love (2010), find a strong positive relation between firm births and income per capita. Taken together, these findings suggest that high income countries are characterized by high rates of entry and turnover of small firms rather than a large SME sector. We find that firms younger than two years represent a very small proportion of total employment in the overall sample, with the mean being 6.75% and median being 4.78%. Across country income groups, firms older than 10 years have the largest shares of total employment ranging from 48.12% in low income countries to 72.76% in high income countries. Across countries, we find that small and old firms (specifically, firms that are over 10 years old and with 5-99 employees) have the largest proportional share of total employment compared to other size- age groupings. Not only do small firms and mature firms employ the largest number of people, they also generate the most new jobs, across country income groups. In the median country in our sample, SMEs with 250 employees or fewer generate 86.01% of the jobs. Their significance is higher (93.05%) in the countries that had a net positive job creation across all firms in the country. Even in countries that had an aggregate net job loss, we find SMEs with 250 employees or fewer to be significant job creators (81.51%). Young firms less than two years old generate only 14% of net jobs in countries that had a net positive job creation and even lesser, only 5.39%, in countries that had a net job loss in our sample. We find that the small firms (1-100 employees) and the young firms (<=2 years) have the highest employment growth rates in regressions controlling for country, industry, and year fixed effects. However, small firms' higher employment growth is not accompanied by higher sales or productivity growth. Large firms and young firms have higher productivity growth. Thus while SMEs employ a large share of workers and create most jobs, their contribution to productivity and growth is uncertain at best. Our results are robust to sub-sample analysis by country income group and by looking at countries with large versus small informal sectors. Our cross-country database improves upon existing databases along several dimensions. First, the data are comparable across countries since they are all sourced from the World Bank Enterprise Surveys (ES) database which samples formally registered firms from over 100 3 countries to study the business climate constraints to private sector growth and performance. The surveys use standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across countries. Second, for the first time ever, we are able to compute statistics on SMEs for a large sample of developing countries. While statistics on size and age distribution are more easily available for the developed countries from sources such as the OECD, there is little to no information available for many developing countries. Third, we are able to construct different size cut-offs for defining SMEs and are able to look at the whole economy, as well as the manufacturing sector separately. Previous databases have been restricted to examining just one size cut-off definition of SME or just the SME share of manufacturing. Fourth, we are also able to look at different age cut-offs from the same database allowing for a direct comparison of SMEs with respect to young firms. Finally, the data set allows us to compute contribution to total employment, labor productivity, and employment generation across the entire size and age distribution in an economy thus allowing for comparisons between SMEs and large firms, and young and old firms. Nevertheless, our findings are subject to a number of caveats. Most importantly, enterprise surveys cover only the formal sector, excluding the informal firms. Hence our results do not speak to informal enterprises. In addition we also do not have data on micro enterprises (less than 5 employees) in our sample. Second, we have data only on surviving firms, which probably overestimates the growth rates for very young firms given they tend to have higher failure rates. While this database is the best available at this point, we recognize these limitations. Overall, our findings contrast with those in Haltiwanger, Jarmin and Miranda (2010a, 2010b). Specifically, they find that in the US large mature firms have the largest share of employment whereas we find that while large firms have a significant employment share, the small mature firms have the largest share of employment in developing economies. On job creation, the US evidence suggests that small mature firms have net job losses whereas in developing countries we find that small mature firms have the largest share of job creation. Moreover, in countries that have had net job losses in the economy as a whole, it is only the small firms, especially small mature firms that have net job gains. 4 Haltiwanger et al also find that there is no systematic relation between firm size and growth once age is controlled for. Specifically, they argue that the "systematic inverse relationship between firm size and net growth rates in prior analyses is entirely attributable to most new firms being classified in small size classes." Since surviving new firms grow much faster than older firms in the US, this classification may make it seem that firm size is a determinant of firm growth. By contrast, in our sample of developing countries, we find that small firms are significant contributors to employment growth even after controlling for age. We find that the higher employment growth of small firms cannot be explained by the sizes of new firms but persists at all ages of firms. The remainder of the paper is organized as follows. In Section II we describe the data and the indicators used in this paper, and present summary statistics. In Section III, we discuss in detail the relationship between the SME sector and young firms and their contribution to employment, productivity, and job creation in our data. In Section IV, we present growth regressions and sensitivity analysis and place our results in the context of existing literature. Section V concludes. II. Data and Variable Construction In this section, we describe the survey dataset and define the various variables used to describe the relative importance of SMEs and young firms in different countries. We use the World Bank Enterprise Surveys (ES) 2 that are an on-going initiative of the World Bank to benchmark the investment climate in different countries across the world and to analyze firm behavior and performance. The Enterprise Surveys survey from the universe of eligible firms obtained from the country's statistical office3 using stratified random sampling with replacement to generate a sample representative of the whole non-agricultural private economy (so fully government 2 The ES surveys and their precursor, the World Business Environment Survey have been used to investigate a series of questions in developmental economics including the relation between property rights and contracting institutions (e.g. Acemoglu and Johnson, 2005; Ayyagari, Demirguc-Kunt, and Maksimovic, 2008a), investment climate and business environment obstacles to growth (e.g. Beck, Demirguc-Kunt, and Maksimovic, 2005; Ayyagari, Demirguc-Kunt, and Maksimovic, 2008b), firm financing patterns (e.g. Beck, Demirguc-Kunt, andMaksimovic, 2008; Cull and Xu, 2005, Ayyagari, Demirguc-Kunt, and Maksimovic, 2010) and dispute resolution via courts (e.g. Djankov, La Porta, Lopez-de-Silanes, and Shleifer, 2003). 3 The master list of firms is sometimes obtained from other government agencies such as tax or business licensing authorities. In some cases, the sampling universe is generated from lists maintained by the Chamber of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration. 5 owned firms are excluded from the sampling universe) in the country. The surveys are stratified according to three criteria: Sector of activity (population of industries include manufacturing sectors, construction, services, transport, storage, communications, and computer and related activities), Firm size (the strata include small firms (5-19 employees),4 medium firms (20-99 employees), and large firms (100 or more employees)), and Geographical location (selected based on centers of economic activity in the country). While the Enterprise Surveys have been produced since 2002, we restrict our sample to surveys administered during 2006-2010 since these provide sampling weights that take care of the varying probabilities of selection across different strata and are thus indispensable to making assertions about the whole population.5 Our final sample consists of surveys across 99 countries. Since the Enterprise Surveys cover mostly the developing economies, we supplement these data with the most recently available data on SME contribution to employment from 44 other countries, most of which are high income countries. The data for these 44 economies are mostly from the year 2008 though this ranges from 1997 to 2009 across the sample. The term SME covers a wide range of definitions and measures, varying across countries and across sources reporting SME statistics. Some of the commonly used criteria include the number of employees, total net assets, sales and investment level, though the most common basis for definition is employment. However, here again, there is variation in defining the upper and lower size limit of an SME across countries. In our data, the SME indicator is based on permanent, full-time employment as reported in the surveys. We construct 6 definitions of SMEs to correspond to varying upper limits in the official country definitions of SMEs adopted around the world ­ SME100, SME150, SME200, SME250, SME300, and SME500. Thus, according to the SME100 definition, an establishment 4 The minimum of 5 employees was imposed when constructing the sample frame for each country so as to limit the surveys to the formal economy. However some of these firms may have shrunk by the time they were surveyed and hence we have some firms (<2.5% of the sample) reporting less than 5 employees. 5 Most surveys contain three sets of weights ­ strict, median, and weak weights depending on the eligibility criteria used to construct the sample universe. Under the strict assumption, eligible establishments are those for which it was possible to directly determine eligibility, under the median assumption, eligible establishments are those for which it was possible to directly determine eligibility and those that rejected the screener questionnaire and under the weak assumption only observed non-eligible units were excluded from universe projections. So under the weak assumption, all establishments for which it was not possible to finalize a contact were assumed eligible. The survey implementation manual recommends the use of median weights for cross-country comparisons. 6 that employs up to 100 permanent full-time employees in a year is identified as an SME. In addition, we also present data for different size classes ­ 5-9, 10-19, 20-49, 50-99, 100-249, 250- 499, 500-999, and 1000 and above employees. While we report the different cut-offs in our data, we use SME250 in most of our analysis to be consistent with other databases (e.g. OECD and Eurostat) and studies (e.g. Ayyagari, Beck, and Demirguc-Kunt, 2007) that use 250 employees as the cut-off for defining SMEs. Firm age in our data is defined as the number of years since the establishment began operations in the country.6 We define two different cut-offs for young firms ­ YOUNG2 (less than 2 years), and YOUNG5 (less than 5 years). We also construct the following contiguous age intervals - <2 years, 2 and <5 years, 5 and <10 years, and 10 years. We examine and compare the role of SMEs versus young firms in each country along two dimensions. First we construct the SME and young firm share of Total Employment where total employment is the population estimate of the number of permanent, full-time employees in the country derived by aggregating the employment reported each firm in the country multiplied by its sampling weight. Second, we construct the SME and young firm share of Job Creation where job creation is the population estimate of the change in the number of permanent, full-time employees over two years, also derived by aggregating the change in employment reported by each firm in the survey multiplied by its sampling weight.7 Our data are subject to some caveats. First, our results on SMEs are subject to the limitation that the Enterprise Surveys sample only the formal sector in each country and exclude the informal sector. Some of the developing countries in our sample have a large informal sector, which implies that we are underestimating the importance of the SME sector in those countries. In addition, since the sampling frame is restricted to 5 employees or above, our results do not speak to the micro enterprises. 6 The year when the establishment began operations refers to the year in which the establishment actually started producing or providing services. If the establishment was privatized, then the date refers to when the original government-owned establishment began operations. 7 The Enterprise Surveys ask establishments to report the number of permanent, full-time employees at the end of the fiscal year prior to the year of the survey and three fiscal years ago. So we do not have a measure of job creation and destruction in the year the establishment was born, that is, first started operations in the country. 7 Second, our data are only on the continuing/surviving firms and hence we have no data on job destruction by firms which were liquidated over the sampling period. In particular, Haltiwanger et al suggest that very young firms have high failure rates. As a result, we probably overestimate the growth rates of very young firms. Below, we use our estimates of the proportion of surviving firms that report net job losses to get some indirect evidence on job destruction in young firms. On a related note, the surveys are stratified only by industry, firm size, and geographical location and so we may not have a completely representative sample of firm ages, though the firms within the strata are randomly sampled. Finally, our analysis is at the establishment level and not at the firm level since the sampling unit in the Enterprise Surveys is the establishment. 8 While this has the advantage that our job creation measures are well defined and capture actual new jobs at the establishment rather than changes from mergers, acquisitions, and divestitures, we are not able to measure firm size accurately for multi-establishment firms. However we are helped to some extent that the establishments in our sample report whether they are part of a larger firm or whether they are stand-alone. While most of the establishments in our data are stand-alone establishments (86%) and hence can be treated as firms, for robustness, we repeat the analysis on the sub-sample of firms that report that they have a single establishment and find that all our results about relative contributions to employment and growth hold. Henceforth, we will use the term establishment and firm interchangeably. While we recognize fully the above data limitations, we believe that this initial cross- country analysis is useful in understanding the relationship between size, age, job creation and growth in developing countries. However, since our data are based on surveys rather than census and subject to these caveats, the data presented in this paper are best used as cross-country evidence on the role of SMEs versus young firms. 8 In the Enterprise Surveys, the establishment is defined as a physical location where business is carried out and where industrial operations take place or services are provided. In addition, an establishment must make its own financial decisions, have its own financial statements separate from those of the firm, and have its own management and control over its payroll. 8 III. Summary Statistics In this section, we first preview the evidence on the relation between firm size and age by looking at aggregate employment and job creation shares across countries. Where possible we contrast our findings to the U.S. evidence in earlier literature. We then present detailed tables and charts across the entire size and age distribution across country income groups. Like all survey data, our data are subject to the usual sampling errors for surveys and the data caveats discussed in section II.9 Hence, in presenting the summary statistics, we report medians across different sub-populations of firms. A. Aggregate Evidence on SMEs and Young Firms Figure 1 reports the employment shares across the 99 countries in our sample by firm age and firm size classes. Both the employment shares and the size and age classes are defined in the year before the survey. We first compute the employment shares in each country in each size-age bin and then plot the median values. Figure 1 shows that it is the mature SMEs (11+ years and 5-99 employees) that are the largest contributors to total employment (23.7%). Furthermore, in each age bin, the smallest size class (5-99 employees), have the largest employment shares and in each size bin, the oldest firms (11+ years) have the largest employment shares. We get similar patterns if we were to use mean values in each size-age bin rather than median shares across the 99 countries or if we were to repeat the analysis by income groups. After the mature SMEs, it is the large mature firms (500+ employees and 11+ years) that have the next largest share of employment (12.8%). There is very little employment in large young firms. This is consistent with the US evidence in Haltiwanger, Jarmin, and Miranda (2010a) who also find relatively little employment in large young firms. However, in contrast to our findings in developing economies, they find that large (not small) mature firms in the US have the largest share of employment. Figures 2 and 3 present statistics on job creation and loss in our sample of surviving firms. Figure 2 presents the contribution to net job creation over a two year period as a share of total job creation in the economy in that period, by firm age and size classes. The age and size classes are defined in the base year. Of the 99 countries in our original sample, 17 of the 9 The Enterprise Surveys are designed to be representative of large firms as those 100+ employees. While we have sampling weights for the surveys, the surveys may not be representative of the very large firms since the surveys also report higher non-response rates (for the whole survey) for the large firms. 9 countries had a net job loss and for 1 country (Bangladesh) we do not have the employment levels in the base year so we are unable to calculate job creation numbers. Hence, Figure 2 plots the median values in each size-age bin across only 81 countries. Figure 2 shows that net job creation is largest in the small mature firms. There is also substantial job creation in small younger firms (above 10%) but very little job creation in the larger firms irrespective of age. In Figure 3, we focus on the 17 countries that had a net job loss and find that the very large firms and mature firms, that is, firms with over 500 employees and over 11 years old, have the largest job loss. Here again small mature firms have the largest job creation and interestingly, small firms with less than 100 employees across all age groups are the biggest job creators in these economies.10 Our results contrast with the U.S. evidence in Haltiwanger, Jarmin, and Miranda (2010a) who show that the largest job creation in the US is among small young firms (start-ups) though there is also some notable job creation among large mature firms. More importantly, small mature firms which have the largest net job creation in our sample of developing counties have net job losses in the US. There are two caveats to our data. First the job creation shares are computed only on continuing firms and exclude the year of firm birth, and so we are unable to draw conclusions about firm births or the start-ups in our data. However, as shown in Klapper and Love (2010), the rate of firm birth is much lower in developing economies. Second, our job creation is measured over a two year period whereas the US evidence is on an annual basis. In the next sub-sections, we present detailed statistics on size, age, employment, and job creation and in section IV, we turn to a more systematic and rigorous analysis to validate our findings above. B. SME and Young Firm Contributions to Employment In Table 1 in cols. 1-6, we present data on SME share of total employment using six different cut-offs - SME100, SME150, SME200, SME250, SME300, and SME500. We first present data on 99 developing economies covered by the Enterprise Surveys and then supplement with data 10 The 17 countries experiencing job losses are Botswana, Burundi, Cote d'Ivoire, El Salvador, Eritrea, Honduras, Lao PDR, Latvia, Former Yugoslav Republic of Macedonia, Nepal, Panama, Serbia, Sierra Leone, Tonga, Uzbekistan, Western Samoa, and Yemen Republic. Most of these countries have had civil strife and ethnic conflict and it is conceivable that when institutions break down, it is only the small firms that are able to employ people and create jobs. 10 on 44 countries (2 low income, 7 middle income and 35 high income countries) from other data sources including the OECD and European Commission. Appendix Table A1 details out the country-specific sources and also provides the SME shares for just the manufacturing sector for cut-offs other than SME250. For comparison sake, in Col.7, we also present data for the SME250 cut-off for only the manufacturing sector.11 The statistics on SME250 employment from the ES data in Table 1, show that the SME sector's reported share of total employment ranges from less than 20% in countries like Lesotho (16.06%) and Russia (16.62%) to 100% in some of the smaller countries like Angola, Burundi, Eritrea, Micronesia, Tonga, and Vanuatu. The SME250 (Manufacturing) varies from 3.14% in Lesotho to 100% in countries like Kosovo, Niger, Montenegro, Timor-Leste, Sierra Leone, and Gambia. The median SME250 in the sample of 99 countries is 66.76 (Latvia) and SME250 (Manufacturing) is 62.37 (Croatia) suggesting that SMEs play an important role in many economies in contributing to total employment in the economy as well as in the manufacturing sector. When we add in data from sources other than the ES especially on the high income countries, the median value of SME250 is 66.89 (Belgium) and SME250 (Manufacturing) is 60.82 (between Cote d'Ivoire and Norway). The proportions of firms falling under the different SME measures are very highly correlated, with correlation coefficients across the 6 measures ranging from 0.85 to 0.99. The SME250 and SME250 (Manufacturing) are also very highly correlated with a correlation coefficient of 0.86. In Figure 4, we compare the SME250 share of employment with the size of the informal sector and entry density across the 125 countries in Table 1 for which we have data on SME250. Since we do not have data on the informal sector's contribution to total employment, we rely on the measure of size of the informal sector as a percentage of official GDP from Schneider, 11 The SME database in Ayyagari, Beck, and Demirguc-Kunt (2007) only covered the formal labor force in manufacturing. The sample of 54 countries in Ayyagari, Beck, and Demirguc-Kunt (2007) were mostly rich developed nations and thus differ greatly from the developing country sample in the Enterprise Surveys (ES). Of the 54 counties for which SME250 share of manufacturing labor force is reported in Ayyagari et al. (2007), only 30 countries overlapped with the ES database. When we include the 44 countries for which we have additional data from sources other than the ES, we find the SME250 Manufacturing measure in our data to be significantly correlated with that in Ayyagari et al. (2007). 11 Buehn, and Montenegro (2010) and averaged over 2005-2007.12 Entry Density is the number of newly registered limited liability firms per 1000 working-age people (ages 15-64) from the World Bank Entrepreneurship database (Klapper and Love, 2010). Figure 4 shows that relative sizes of the SME sector (as a % of total employment) and the informal economy (as a % of GDP) decreases from Low to High Income countries. However, the differences are not stark, with the SME sector share ranging from 78% in Low Income countries to 67% in High Income countries.13 By contrast, there is considerable variation in entry density from 0.4 in Low income countries to 6.4 in High Income countries. This suggests that entrepreneurship and dynamism, as captured by entry density, show greater covariation with income level than does the absolute size of the SME sector, and thus deserve greater policy attention. Table 2, shows the contribution to employment across the entire size distribution in each country. For better comparability, from here on we use only the data from the ES. The sum of all the employment contributions across the size distribution in an economy should add to 100%. The summary statistics show that the median employment is largest in the smallest size class of 5-19 employees and this holds when we look across income groups. Further, the very large establishments with 1000 employees and over contribute very little to total employment in low income countries, whereas they have the largest share of total employment in upper-middle income countries. Figure 5 shows the contribution to employment by size class for the median country in each income group. Several interesting patterns emerge. Across income groups, establishments that employ less than 100 people have the largest employment shares, ranging from 40% in upper-middle income countries to 57.6% in low income countries. They are followed by firms with 100-249 employees in the low income group countries (15.9% employment share), whereas 12 Schneider et al. (2010) define the informal sector as all market-based legal production of goods and services that are deliberately concealed from public authorities to avoid payment of income, value added or other taxes; to avoid payment of social security contributions; having to meet certain legal labor market standards, such as minimum wages, maximum working hours, safety standards, etc; and complying with certain administrative procedures such as completing statistical questionnaires or administrative forms. 13 In unreported statistics, we find a significant negative correlation between SME250 and Log(GDP/capita) in 2005 (correlation coefficient is -0.17 and significant at the 5% level) as also between SME250 (Manufacturing) and Log(GDP/Capita) in 2005 (correlation coefficient is -0.24 and significant at the 1% level). While it may appear that this result contradicts earlier figures in Ayyagari et al. (2007), it is to be noted that we have a much larger dataset of developing countries in this paper compared to the 54 countries in Ayyagari et al. (2007), most of which were high income countries. The negative association between SME share of employment and GDP/capita is also consistent with anecdotal evidence and empirical figures in Snodgrass and Biggs (1996). 12 in the middle and high income group countries, the largest establishments with more than 500 employees have the second highest employment shares (ranging from 23.3% in lower-middle to 28.2% in upper-middle income countries). Both Table 2 and Figure 5 show that while small firms are the largest contributors to employment, the contribution by large firms and medium sized firms is not insignificant. We further explore our data and their implications for the "missing-middle" phenomenon in Ayyagari, Demirguc-Kunt, and Maksimovic (2011). In Table 3, we present data on the contribution of young firms, both less than 2 years and less than 5 years as well as across the entire age distribution of firms in the economy. Note that the sum of all the employment contributions across the age distribution in each country does not sum to 100% in all cases because of missing data on age for some firms. Further, all our statistics on age are subject to the caveat that we only have the surviving firms. Focusing on the contribution of young firms, we find that firms less than 2 years old generate little or no employment in countries like Eritrea (0%) to a high of 43.14% in Timor- Leste. The sample mean is 6.75% and the sample median is 4.78%. Overall, we find that across countries, firms less than two years contribute a very small fraction of overall employment. In Figure 6, we show the contribution to employment by different age bins for the median country in each income group. The contribution to employment of firms less than two years old and between two-five years old is clearly decreasing across income groups and is below 15% in all cases. When we look at establishments that are between 6-10 years old, the employment contribution is more substantial, ranging from 17% in the median upper-middle income group country to 23.2% in the median country in the low income group. Across income groups, firms older than 10 years have the largest share of total employment. Overall, we find that small firms and mature firms have the largest shares of employment across countries. 13 C. Job Creation Shares of SMEs versus Young Firms Next we analyze how job creation is affected by characteristics of firms: age and size. We first examine the job creation in each size/age class as a share of total job creation in the economy, where job creation is defined as the employment change over a two year period. The size and age classifications are in the base year. Of the 99 countries in our sample, 17 countries had a net job loss and we do not have job creation data for Bangladesh. To allow for easier interpretation, we report the data in the tables for the two samples, that is the 81 countries which had a net positive job creation and 17 countries which had a net job loss, separately. In Panel A of Table 4, we present the job creation shares by size class in the 81 countries that had a net positive job creation. The first column of Table 4 shows that job creation share in the SME250 sector ranges from 20.3% (Chile) to 766.29% (Kyrgyz Republic). Overall, the sector generates a significant share of overall jobs in the economy as indicated by the high sample mean of 105.38%14 and median of 93.04%. Figure 7 shows the split across income groups in the 81 countries that had a net positive job creation and we find that the job creation share for firms with less than 100 employees ranges from 67.5% in upper-middle income countries (median) to 95.4% in low income countries. In unreported statistics where we examine a more detailed breakdown of size classes, we find that in the median countries across income groups, the 20-49 employees size class has the largest share of job creation. In Panel B of Table 4 we focus on the 17 countries that had a net job loss and report the job creation/destruction in each bin as a share of overall job loss in the country. Interestingly, we find that only in 4 of the 17 countries (Eritrea, Lao, Tonga, and Uzbekistan), the SME250 sector has a net job loss. When we look at the summary statistics across the countries we find that the median value in all the bins with less than 1000 employees is positive suggesting that it is the very large firms that are losing jobs in these economies. This is also seen in Figure 8 where we find that across income groups the firms with 500+ employees are losing jobs where as even in these economies the smallest firms with less than 100 employees are creating jobs. 14 The mean over 100% implies that larger firms on average lost jobs and hence the SME sector creates more jobs than the overall jobs in the economy. 14 In Table 5, we look at the two year employment generation across establishment age. Here again we split the sample into countries that had a net job gain and those that had a net job loss. In Panel A, we examine the job creation in each size-age bin for the 81 countries with a net job gain. In this sample, the mean and median job creation for firms less than 2 years old is 21.7% and 14% respectively. For firms less than five years old, the mean is 36.5% and median is 19.6%. Figure 9 graphs the median values in Table 5 across age classes and across income groups in countries that had a net positive job creation. Figure 9 shows that except in the low income countries, there is a monotonic increase in job creation share from young to mature firms in all other income groups. The share of job creation in firms that are older than 10 years is 24.4% in lower-middle income countries, 45.5% in upper-middle income countries, and 47% in high income countries. In low income countries, the largest share of job creation is in firms that are 6-10 years old (31.4%). Panel B of Table 5 presents the data for the sample of 17 countries that had a net job loss. The summary statistics show that the mean value for firms older than 10 years is negative. Figure 10 shows that across income groups, the mature firms over 10 years old had the largest portion of job losses. Overall, we find that small firms and mature firms have the largest shares of job creation but large and mature firms have the largest share of job losses. Even in countries which had a net job loss we find the small firms to be creating jobs. IV. Regression Analysis In this section, we turn to a more systematic analysis of the summary statistics using regression analysis. Our primary objective is to understand the relationship between growth, size, and age. Hence we run regressions of the form: Growth = a + b1 Size + b2 Age + b3 Industry Dummies + b4 Year Dummies + b5 Country Dummies + e (1) Our main measure of growth is Employment Growth defined as the log difference between employment three years back and employment last year divided by two. We also use Sales Growth and Labor Productivity (Sales/Worker) Growth, constructed similarly, to see if 15 there is an association between size, age and increase in sales and productivity. We use three dummies for size ­ 1-100 employees, 101-250 employees and 250+ employees (reference category). We use three dummies for age ­ 2 years, 3-5 years and 6+ years (reference category). Both size and age dummies are constructed in the base year. In addition to country and year fixed effects, we control for industry fixed effects since firm size and firm age distributions vary by industry as do net growth rate patterns. While there are several approaches to the use of survey data in regression analysis, we follow the "model approach" (see Cameron and Trivedi, 2005) used in the literature which utilizes data collected in the sample directly, without weighing. Hence we use simple OLS regressions to estimate (1), with standard errors clustered at the country level. As robustness, we also report weighted estimates below. Cols.1-5 of Panel A in Table 6 present employment growth regressions, cols. 6-8 present sales growth regressions and cols. 9-11 present productivity growth regressions. In cols. 1-3 we first enter only size dummies, only age dummies and then both size and age dummies to replicate the specifications in Haltiwanger, Jarmin, and Miranda (2010b) who look at the impact of size and age on employment growth in the US.15 Col.1 shows that when size dummies are entered into the regression without age controls we find all firms with 250 employees or less to have higher employment growth rates than firms with more than 250 employees, with the smallest size bin of 5-100 employees growing the fastest. Col. 2 shows that firms that are five years old or less have higher employment growth than more mature firms with the youngest firms that are two years or less growing the fastest. In col. 3 we enter both size and age dummies, and find that small firms have higher employment growth than large firms controlling for firm age and that young firms have higher employment growth than old firms, controlling for firm size. These relations also hold when we look at manufacturing firms in col. 4 and non-manufacturing firms in col. 5 though the Size Dummy for 101-250 employees is not significant in the sample with just non-manufacturing firms. 15 If Eit is the employment in year t for establishment i, our employment growth is [log(E it)-log(Eit-2)]/2. We treat establishments as firms in our sample since 86% of our sample is single establishment firms and our results are robust to restricting it to single establishments. By contrast, the establishment growth rate in Haltiwanger et al. is (E it - Eit-1)/(0.5 * (Eit + Eit-1)) . The firm growth rate in their regressions is a weighted sum of establishment growth rates, taking into account only organic growth at the establishment level and correcting for mergers and acquisitions. 16 Our results on size are in contrast to Haltiwanger, Jarmin, and Miranda (2010b) who find that once they control for firm age there is no systematic relationship between firm size and growth. Clearly in developing economies small firms have higher employment growth, even after controlling for age. The sales growth regressions in cols. 6-8 show that that there is no evidence that small firms (less than 250 employees) have higher sales growth than larger firms controlling for firm age though young firms (both 2 years and 3-5 years) have significantly higher sales growth than more mature firms (6+ years) controlling for firm size and these results hold for both the sub-samples of manufacturing and non-manufacturing firms. When we examine productivity growth regressions we find that small firms have significantly lower productivity growth than large firms controlling for firm age and the youngest firms ( 2 years) have higher productivity growth than the most mature firms (6+ years) controlling for firm size. These results hold for the manufacturing sector and the non-manufacturing sector separately, though in the latter the significance levels are much weaker. In unreported results, we obtain similar findings when we examine just the Food industry across all the countries in our sample. Manufacture of food products and beverages (ISIC 15) is one of the manufacturing industries found in all of the 99 countries in our sample. Here again the smallest firms with 250 employees or less have higher employment growth, as good or lower sales growth, and lower productivity growth than firms with more than 250 employees. The youngest firms in the Food industry have higher employment and sales growth but there is no evidence of higher productivity growth than in more mature firms that are older than five years. Panel A of Table 6 enables us to separate out the effects of size and age on firms' employment, sales and productivity growth. However, it is often more convenient to examine the growth rates of certain categories of firms directly. Accordingly, in Panel B we look at distinct categories of size-age classifications by entering 9 dummies for the intersection of the three size (5-100, 101-250, and 251+ employees) and three age classifications ( 2 years, 3-5 years, 6+ years) with the largest and oldest (that is 251+ employees and 6+ years) being the reference category. Col. 1 shows that compared to the largest and most mature firms, the smallest firms across different ages are growing faster ­ the coefficients for 5-100 employees and 2 years, 5- 17 100 employees and 3-5 years, and 5-100 employees and 6+ years are all positive and significant at the 1% levels. The mid-size firms that are "middle-aged" and older are also growing faster than the largest and most mature firms ­ the coefficients for 101-250 employees and 3-5 years and 101-250 employees and 6+ years are both positive and significant at the 1% levels while the coefficient for the 101-250 employees and 2 years is positive but not significant. Col. 1 also shows that the largest firms with 251+ employees, irrespective of age, are not growing fast. These results hold when we look at the sub-sample of manufacturing firms in col. 2. In col. 3 when we look at non-manufacturing firms, our results on SMEs are stronger because we find only the size-coefficients of 5-100 employees and 2 years, 5-100 employees and 3-5 years, and 5-100 employees and 6+ years to be positive and significant at the 1% levels. None of the med- sized or large firm coefficients are significant irrespective of age. Cols. 4 to 6 present sales growth regressions. Here we find a significant effect of age on size because we find that in the full sample and in the sub-sample of manufacturing firms, only the SMEs (5-100 employees) that are 5 years or below are growing faster than larger more mature firms. This is even more apparent in the non-manufacturing sector where we find that only the smallest and youngest (firms with 5-100 employees and 2 years) have higher sales growth than the largest, most mature firms. Across all three samples we find the constant which is the reference category of the largest most mature firms to be positive and significant at the 1% level. The productivity growth regressions in cols. 7-9 show that in developing economies it is the largest and oldest firms (reference category) that have the highest productivity growth. SMEs with 5-100 employees irrespective of age have significantly lower productivity growth than the larger firms. While the mid-sized firms (101-250 employees) that are 2 years old or 3-5 years have as good or slightly higher productivity growth than the largest mature firms, the mature mid-sized firms (101-250 employees and 6+ years) have significantly lower productivity growth than the largest mature firms. Overall panel A and B show that small firms have higher employment growth but lower productivity growth than large firms and these results hold controlling for firm age. In the sub- sections below we put our results through a battery of robustness tests. 18 A. Across Income Groups In this section we examine how our results vary across country income groups. In Table 7, in cols. 1-4 we look at employment growth, in cols. 5-8 we look at sales growth and in cols. 9-12 we look at productivity growth. We find that across all income groups, controlling for size and age in all regressions, small firms (especially those with 5-100 employees) and young firms (especially those that are two years old or lesser) have higher employment growth than large firms (more than 250 employees) and mature firms (older than five years) respectively. While we do not find small firms to have significantly different sales growth compared to large firms, we do find that firms 5 years old or below have significantly higher sales growth than those over 5 years old. We also find that firms that have 5-100 employees have significantly lower productivity growth than those with more than 250 employees except in high income countries where the coefficient is negative but not significant. The age coefficients are not significant in the productivity regressions except in the high income countries where we find the youngest firms that are two years old or less have significantly higher (at the 10% level) productivity growth than firms over five years old. However we are inclined to rely less heavily in our findings on the youngest age bin because these firms are most likely to be subject to survivorship bias given the data limitations. B. Size of Informal Sector In this section we examine whether the contribution of size and age to growth varies depending on the size of the informal sector in the economy. Of the 98 countries for which we have data on firm growth rates, we have data on the informal sector's contribution to GDP in 89 countries from Schneider et al. (2010). In Table 8, in cols. 1-3 we report results for countries with a large informal sector (above the median value) and in cols. 4-6 we report results for countries with a small informal sector (below the median value). Table 8 shows that when we look at countries with large informal sector we find that the smallest firms that have 100 employees or fewer have higher employment growth but lower productivity growth than firms with more than 250 employees. Firms with 101-250 employees have significantly higher employment growth than those with over 250 employees and while 19 they also seem to have lower productivity growth, it is not significant. Firms younger than 5 years have higher employment and sales growth than those over 5 years and only the youngest firms that are 2 years old or less have significantly higher productivity growth than the more mature firms. We find similar results across size and age in the sample with small informal sectors in cols. 4-6. This suggests that the size of the informal sector does not make a material difference to our results. C. Stand-Alone Establishments vs. Establishments That Are Part of a Larger Firm Since all our data are at the establishment level, in this section, we split our sample into establishments that state that they belong to a bigger firm and those that are stand alone. Cols. 1-3 of Table 9 report results for a sample of single establishment firms. We find that the small firms (5-100 employees and 101-250 employees) have higher employment growth and lower productivity growth than firms with over 250 employees. While all firms that are five years or below have higher employment and sales growth, only the youngest firms that are two years old or less have higher productivity growth than firms that are over 5 years old. In the sample of establishments that are part of a larger firm in cols. 4-6, we again find that small establishments have higher employment growth than large establishments. The size coefficients are not significant in the sales growth or productivity growth regressions. While young establishments that are 5 years old or less have higher employment growth and higher sales growth than more mature firms, they do not have significantly higher productivity growth than the more mature firms. Overall, across both sub-samples, we find consistent results that small establishments and young establishments have higher employment growth than large establishments and mature establishments correspondingly. While we also find that stand-alone small establishments (<=250 employees) have lower productivity growth than stand-alone large establishments (over 250 employees) and stand alone young establishments (<=2 years) have higher productivity growth than more mature stand alone establishments (over 5 years), these results on productivity growth are much weaker and not significant in the sample of establishments that are part of a large firm. 20 D. Additional Robustness In this section we perform additional robustness tests of our main results. In cols. 1- 3 of Table 10 we include country x sector interaction effects and find all our results to hold. We do not include the interaction effects in all tables so as to not to overwhelm the sample with so many interaction effects. Cols. 1-3 show that small firms with less than 250 employees have higher employment growth and lower productivity growth than larger firms. Firms younger than five years old have higher employment growth and sales growth than more mature firms and the youngest firms that are two years old or less have higher productivity growth than the more mature firms. In 64 surveys in our sample, for each firm, we have a unique stratification identifier. 16 Hence in cols. 4-6 we restrict the sample to these 64 countries and run OLS regressions clustering standard errors by survey strata. None of our results are changed. Small firms have higher employment but lower productivity growth. Young firms have higher employment growth, sales growth, and productivity growth. In cols. 7-9 we use survey regression techniques that adopt a "census approach" 17 where in, the firms are weighed by their sampling weights. This approach gives more weight to firms in the larger countries, and thus provides a better description of the outcomes to typical firms across the world. The standard errors take weights, clustering and stratification into account. The weighted survey regressions show that the smallest firms with 5-100 employees have significantly higher employment growth than firms with more than 500 employees. Firms that are five years old or less have significantly higher employment growth than more mature firms. When we look at sales growth, we find that small firms have as good or lower sales growth than larger firms ­ the size dummy for 101-250 employees is negative and significant at the 10% level, which is stronger than the result with the OLS specification in col. 3 of Table 6. Firms that are five years old or less have significantly higher sales growth than more mature firms. When we look at productivity growth, we find that small firms with 101-250 employees have significantly lower productivity growth than firms with more than 250 employees. Overall, we 16 For the remaining surveys we do not have a stratification identifier because block enumeration was used to overcome the lack of a reliable sample frame 17 For a discussion of the census and model based approaches see Cameron and Trivedi (2005). For a practical illustration of the differences in the two approaches see Frohlich, Carriere, Potvin, and Black (2001). 21 find that using weighted survey regressions on a smaller sample does not make a material difference to our results. E. Discussion In this section, we discuss our findings in the context of the existing literature on firm size, age, and growth. The empirical literature on firm size and growth has largely focused on understanding the role of firm size and age for growth dynamics, and why Gibrats Law, the proposition that firm growth is independent of size, does not hold. 18 In the most recent evidence on this subject, Haltiwanger, Jarmin, and Miranda (2010a, 2010b) study U.S. census data and find that over the period 1992-2005, the large and mature firms (over 500 employees and 10+ years) account for about 45% of employment and most job creation (and destruction). They find that while small firms seem to have large shares of employment and job creation and grow faster, this results needs to be qualified since it is the small-young firms, especially startups that disproportionately create or destroy jobs. Startups in their sample contribute to less than 5% of employment but more than 15% to job creation. Furthermore, while size is inversely related to growth without controlling for age in the US, there is no systematic relation between size and growth once age is controlled for. Our results, on the other hand, suggest that in developing economies small firms, especially small mature firms, are significant contributors to employment and job creation. We do not have data on job destruction. In employment growth regressions, we find that size remains a significant predictor for employment growth even after controlling for age. The importance of small firms in developing economies is of significance since we know that in these countries, small firms face many institutional constraints such as limited access to finance (e.g. Demirguc- Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Beck, Demirguc-Kunt, and Maksimovic, 2005), poorly functioning judicial systems and legal enforcement (e.g. La Porta, Lopez-de-Silanies, Shleifer, and Vishny, 1997), and weak property rights protection (e.g. 18 See Sutton, 1997 for a review. Early studies such as Birch (1979, 1981, and 1987) found an inverse relation between growth and size and found small firms to be particularly important in job creation. Evans (1987), Dunne, Roberts, and Samuelson (1989), and Dunnes and Hughes (1994) focus on unraveling the roles played by firm age and size as determinants and find that larger firms have lower growth rates but are more likely to survive. 22 Claessens and Laeven, 2003).19 Our findings on SMEs are broadly consistent with the OECD evidence in Haltiwanger, Scarpetta, and Schweiger's (2010) where they study net employment and find that small firms account for a higher pace of job creation and destruction. Our findings are also consistent with the results in Beck, Demirguc-Kunt, and Levine (2005) who find a large SME contribution to employment across 54 (mostly developed) countries and a strong association between the SME sector and GDP/capita growth but no evidence of causality. On age, we also find that the youngest firms (two years old or less) have higher employment growth, sales growth, and productivity growth. Our results pertain to continuing firms, so it is important to bear in mind that the youngest firm class is most subject to survivorship bias in our data. In addition, we do not have growth rates in the year of the birth. However, our findings on the importance of SMEs for employment growth persist at all ages of firms and are not driven by the sizes of new firms alone. We also find that while small firms are important for employment and job creation, the large firms have the highest productivity growth in our sample. This is consistent with the evidence in other work such as Banejee and Duflo (2005), Maksimovic and Phillips (2002), and Bartelsman, Haltiwanger, and Scarpetta (2009) who find that larger firms are more productive. Other studies such as Beck, Demirguc-Kunt, and Maksimovic (2006) also suggest that there is a positive relationship between firm size and the development of financial and legal institutions in a country. V. Conclusion We present a unique cross-country database on the contribution of SMEs and young firms to total employment, job creation, and growth across 99 developing economies. We find that small firms are important contributors to total employment and job creation. Unlike in the US, the relationship between size and job creation exists even when we control for firm age. However small firms also have lower productivity growth than large firms, which explains why job creation does not translate into faster growth. While the youngest firms have the highest 19 Note however that Rauch (2010) shows that in less developed countries institutional reforms that disproportionately benefit small businesses may have adverse consequences such as interfering with the impact of trade reform since SMEs tend not to be export oriented and produce low quality output. 23 employment growth rates and highest productivity growth, these results are subject to greater qualification since young firms are also subject to greater survivorship bias. With countries all around the world struggling to recover from the crisis, job creation policies are at the top of the agenda for policymakers. Our results caution that the challenge for policymakers is not only to create more jobs, but also to create better quality jobs to promote growth. 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Journal of Economic Literature 38(1), 11-44. 27 Figure 1: Employment Shares across countries by Size and Age 25 20 15 10 5 0 5-99 100-249 250-499 500+ 11+ years Size 6-10 years 3-5 years <=2 years Age Figure 2: Job Creation Shares across countries by Size and Age In Countries with net job creation (81 countries) 30 25 20 15 10 5 0 5-99 100-249 250-499 Size 11+ years 500+ 6-10 years 3-5 years <=2 years Age 28 Figure 3: Job Creation Shares across countries by Size and Age In Countries with net job losses (17 countries) 40 20 0 5-99 100-249 250-499 500+ -20 11+ years 6-10 years 3-5 years <=2 years -40 -60 -80 -100 Size Age Figure 4: SME, Informal Sector, and Entry Density 80 7 70 6 Informal Sector's Share of GDP SME250 Share of Employment 60 5 Entry Density 50 4 40 3 30 2 20 10 1 0 0 Low Income Lower Middle Upper Middle High Income Income Income Informal Sector (% of GDP) SME250 Share of Employment Entry Density 29 Figure 5: Employment Shares across countries by Size Employment Shares by Size Class (Medians) Low Inc Lower-Middle Inc 57.6 60 50.9 40 23.3 15.9 20 13.3 15.5 12.0 9.5 0 Upper-Middle Inc High Inc 60 46.9 40.0 40 28.2 24.4 17.5 17.0 20 11.1 12.7 0 5-99 employees 100-249 employees 250-499 employees 500+ employees Graphs by income Figure 6: Employment Shares across countries by Age Employment Shares by Age Distribution (Medians) Low Inc Lower-Middle Inc 80 59.1 60 48.1 40 23.2 20.4 20 13.8 9.3 9.3 4.8 0 Upper-Middle Inc High Inc 80 72.8 61.0 60 40 17.0 17.2 20 9.2 7.0 3.8 0.9 0 <=2 years 3-5 years 6-10 years 11+ years Graphs by income 30 Figure 7: Job Creation Shares across countries by Size In Countries with net job creation (81 countries) Job Creation Shares by Size Countries with net job creation Low Inc Lower-Middle Inc 95.4 100 20 40 60 80 71.1 8.6 10.6 3.4 0.0 0.4 0.0 0 Upper-Middle Inc High Inc 100 88.7 20 40 60 80 67.5 11.5 8.9 6.8 7.9 4.3 0 -4.5 5-99 employees 100-249 employees 250-499 employees 500+ employees Medians across income groups Figure 8: Job Creation Shares across countries by Size In Countries with net job loss (17 countries) Job Creation Shares by Size Countries with net job loss Low Inc Lower-Middle Inc 200400 80.2 1.3 0.0 4.9 0.1 0.0 0 -200 -100.3 -181.5 -400 Upper-Middle Inc High Inc 200400 319.5 44.8 15.7 9.6 0 -200 -40.4 -57.2 -170.1 -400 -381.1 5-99 employees 100-249 employees 250-499 employees 500+ employees Medians across income groups 31 Figure 9: Job Creation Shares across countries by Age In Countries with net job creation (81 countries) Job Creation Shares by Age Countries with net job creation Low Inc Lower-Middle Inc 10 20 30 40 50 31.4 24.3 23.0 24.7 24.4 20.2 19.7 14.1 0 Upper-Middle Inc High Inc 47.0 10 20 30 40 50 45.5 30.3 23.9 17.2 12.3 9.5 5.0 0 <=2 years 3-5 years 6-10 years 11+ years Medians across income groups Figure 10: Job Creation Shares across countries by Age In Countries with net job loss (17 countries) Job Creation Shares by Age Countries with net job loss Low Inc Lower-Middle Inc 50 24.7 5.7 4.4 0.5 0.2 0 -50 -2.0 -200 -100 -51.5 -81.7 -150 Upper-Middle Inc High Inc 55.7 35.6 27.2 26.1 33.0 50 2.3 0 -50 -200 -100 -150 -161.7 -205.8 <=2 years 3-5 years 6-10 years 11+ years Medians across income groups 32 Table 1: SME Contribution to Employment Shares This table presents the contribution of small and medium enterprises (SMEs) to total employment in each country. For 99 countries for which we have data from the World Bank Enterprise Surveys, we construct total employment to be the population estimate of the number of permanent, full-time employees in a particular year in each country. We construct 6 definitions of SMEs also based on permanent, full-time employment ­ SME100, SME150, SME200, SME250, SME300, and SME500. In col. 7 we report the share of Manufacturing SMEs with 250 employees or less as a share of total manufacturing employment, also derived from the survey. For 44 countries for which we don't have data from the Enterprise Surveys we use several other sources as described in the Appendix. We report summary statistics and median values across income groups and regions at the foot of the table. 1 2 3 4 5 6 7 SME250_ Nation Income Region year SME100 SME150 SME200 SME250 SME300 SME500 Manufacturing Afghanistan Low income SAR 2007 59.75 66.08 74.21 76.00 77.94 86.92 77.33 Albania Upper middle income ECA 2006 64.77 77.44 89.71 96.90 96.90 98.17 94.78 Angola Lower middle income AFR 2005 88.44 100.00 100.00 100.00 100.00 100.00 100.00 Argentina Upper middle income LAC 2005 18.62 23.63 26.10 27.59 31.38 42.65 29.18 Armenia Lower middle income ECA 2008 37.42 51.86 56.44 61.17 66.86 74.89 73.50 Azerbaijan Upper middle income ECA 2008 30.25 37.41 40.13 43.00 48.48 53.75 54.69 Bangladesh Low income SAR 2006 10.08 12.58 15.54 20.54 26.74 41.39 18.12 Belarus Upper middle income ECA 2007 20.62 27.23 32.41 35.58 41.31 51.07 18.93 Benin Low income AFR 2008 59.10 64.42 66.53 69.13 77.23 79.41 40.42 Bhutan Lower middle income SAR 2008 53.61 64.50 73.05 78.66 82.57 90.18 70.97 Bolivia Lower middle income LAC 2005 60.43 65.90 69.67 79.34 80.64 88.87 78.61 Bosnia and Herzegovina Upper middle income ECA 2008 44.83 53.08 61.02 66.19 68.24 82.43 65.04 Botswana Upper middle income AFR 2005 49.00 61.16 66.18 68.19 70.80 87.06 64.05 Brazil Upper middle income LAC 2008 21.35 28.76 34.25 37.10 38.05 49.96 36.69 Bulgaria Upper middle income ECA 2006 44.58 53.07 58.59 60.24 67.01 75.18 59.68 Burkina Faso Low income AFR 2008 45.79 66.18 69.76 79.00 80.40 83.06 79.91 Burundi Low income AFR 2005 90.95 96.66 100.00 100.00 100.00 100.00 100.00 Cameroon Lower middle income AFR 2008 29.46 41.67 45.45 46.50 48.24 63.69 35.62 Cape Verde Lower middle income AFR 2008 65.66 74.38 80.56 82.34 93.35 93.35 84.89 Chad Low income AFR 2008 70.80 84.72 84.72 84.72 92.09 100.00 64.47 Chile Upper middle income LAC 2005 15.24 19.31 22.16 23.10 25.83 59.49 41.34 Colombia Upper middle income LAC 2005 64.06 69.52 71.28 74.09 76.12 79.72 78.92 Congo, Dem. Rep. Low income AFR 2005 78.17 86.71 94.43 96.60 100.00 100.00 93.49 Congo, Rep. Lower middle income AFR 2008 54.21 59.43 61.79 76.20 80.72 80.72 53.10 Cote d'Ivoire Lower middle income AFR 2008 49.55 57.91 61.80 64.95 67.69 68.31 59.95 Croatia High income: nonOECD ECA 2006 51.51 58.53 61.46 73.05 75.34 86.03 62.37 Czech Republic High income: OECD ECA 2008 46.14 54.39 59.38 64.41 66.52 75.58 55.66 Ecuador Lower middle income LAC 2005 40.35 51.29 59.35 62.12 65.77 77.34 65.91 El Salvador Lower middle income LAC 2005 38.45 48.18 52.63 63.87 66.33 72.35 42.67 Eritrea Low income AFR 2008 87.51 100.00 100.00 100.00 100.00 100.00 100.00 33 1 2 3 4 5 6 7 SME250_ Nation Income Region year SME100 SME150 SME200 SME250 SME300 SME500 Manufacturing Estonia High income: nonOECD ECA 2008 60.66 67.77 73.42 77.82 82.66 89.60 82.38 Fiji Upper middle income EAP 2008 43.58 48.28 53.81 57.07 59.62 67.36 52.28 Gabon Upper middle income AFR 2008 36.83 43.52 52.42 57.24 57.24 61.76 54.51 Gambia Low income AFR 2005 69.68 69.68 79.49 85.74 92.00 100.00 100.00 Georgia Lower middle income ECA 2007 22.05 23.56 26.01 27.66 28.03 35.59 27.79 Ghana Low income AFR 2006 35.95 45.77 57.99 59.04 74.32 83.68 55.48 Guatemala Lower middle income LAC 2005 55.16 67.77 72.77 74.15 75.65 82.37 62.35 Guinea Low income AFR 2005 62.47 65.38 75.14 85.52 85.52 85.52 81.46 Guinea-Bissau Low income AFR 2005 75.39 75.39 75.39 85.91 100.00 100.00 72.10 Honduras Lower middle income LAC 2005 21.10 31.54 34.00 34.58 52.68 74.98 72.89 Hungary High income: OECD ECA 2008 33.20 39.51 42.58 45.61 48.80 56.48 40.87 Indonesia Lower middle income EAP 2008 41.13 44.22 46.43 47.46 48.40 52.56 45.10 Kazakhstan Upper middle income ECA 2008 36.44 45.55 53.33 58.15 60.67 72.20 51.15 Kenya Low income AFR 2006 33.12 42.55 47.06 53.69 58.47 63.57 41.75 Kosovo Lower middle income ECA 2008 67.78 86.57 89.32 91.24 93.24 93.24 100.00 Kyrgyz Republic Low income ECA 2008 42.92 52.85 55.85 58.60 82.72 88.33 47.91 Lao PDR Low income EAP 2008 56.79 64.00 67.60 72.50 76.44 80.86 50.68 Latvia High income: nonOECD ECA 2008 48.21 55.79 63.77 66.76 67.94 78.16 75.86 Lesotho Lower middle income AFR 2008 12.97 13.42 15.82 16.06 16.65 18.96 3.14 Liberia Low income AFR 2008 87.45 91.17 93.58 96.52 96.52 100.00 89.35 Lithuania Upper middle income ECA 2008 58.07 72.16 75.37 77.83 84.75 91.77 69.16 Macedonia, FYR Upper middle income ECA 2008 53.27 61.10 64.87 66.44 70.45 75.35 59.03 Madagascar Low income AFR 2008 35.35 42.78 46.43 48.47 52.34 65.49 30.71 Malawi Low income AFR 2008 24.71 31.20 34.03 36.38 38.01 53.16 23.11 Mali Low income AFR 2006 77.44 82.16 84.25 93.28 93.28 100.00 91.13 Mauritania Low income AFR 2005 81.18 84.56 93.84 93.84 100.00 100.00 91.54 Mauritius Upper middle income AFR 2008 35.22 46.05 53.66 62.06 66.71 75.65 52.59 Mexico Upper middle income LAC 2005 54.50 66.47 69.37 71.66 76.80 81.79 59.58 Micronesia, Fed. Sts. Lower middle income EAP 2008 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Moldova Lower middle income ECA 2008 52.58 58.04 65.42 69.28 73.96 83.70 53.48 Mongolia Lower middle income ECA 2008 57.48 65.40 71.10 73.60 76.01 85.81 74.37 Montenegro Upper middle income ECA 2008 71.72 78.00 88.35 91.43 94.94 94.94 100.00 Mozambique Low income AFR 2006 46.46 56.74 61.07 65.44 73.37 84.69 95.82 Namibia Upper middle income AFR 2005 77.15 86.96 89.70 90.47 90.47 93.70 74.51 Nepal Low income SAR 2008 74.41 77.11 80.52 85.09 85.74 94.80 81.72 34 1 2 3 4 5 6 7 SME250_ Nation Income Region year SME100 SME150 SME200 SME250 SME300 SME500 Manufacturing Nicaragua Lower middle income LAC 2005 54.96 71.99 73.72 75.19 79.42 88.11 64.51 Niger Low income AFR 2008 82.71 90.34 94.42 94.42 94.42 94.42 100.00 Nigeria Lower middle income AFR 2006 79.26 85.57 91.13 91.85 93.34 96.60 87.81 Panama Upper middle income LAC 2005 37.75 46.71 51.60 57.35 60.95 70.19 72.30 Paraguay Lower middle income LAC 2005 53.72 66.62 74.37 79.43 83.67 100.00 77.91 Peru Upper middle income LAC 2005 27.78 36.77 37.70 42.33 54.53 58.92 26.31 Philippines Lower middle income EAP 2008 32.13 38.74 42.59 45.87 50.91 55.92 47.49 Poland High income: OECD ECA 2008 38.98 48.32 59.53 71.09 77.78 89.22 63.90 Romania Upper middle income ECA 2008 48.87 58.80 65.63 70.06 75.07 82.66 61.80 Russian Federation Upper middle income ECA 2008 9.49 12.23 14.45 16.62 19.28 27.19 26.26 Rwanda Low income AFR 2005 43.62 50.14 58.46 66.27 72.86 72.86 53.31 Senegal Lower middle income AFR 2006 46.35 52.09 56.26 56.26 60.52 68.26 43.27 Serbia Upper middle income ECA 2008 34.94 46.69 51.17 56.57 59.55 72.53 54.11 Sierra Leone Low income AFR 2008 67.33 72.45 74.25 83.85 83.85 86.49 100.00 Slovak Republic High income: OECD ECA 2008 53.32 60.06 63.39 64.54 65.53 71.34 53.84 Slovenia High income: OECD ECA 2008 33.82 38.61 44.86 48.28 56.29 74.74 39.96 South Africa Upper middle income AFR 2006 40.10 50.02 53.98 57.92 61.34 70.44 56.80 Swaziland Lower middle income AFR 2005 35.29 40.97 46.65 50.28 61.32 67.36 34.64 Tajikistan Low income ECA 2007 30.97 36.79 40.50 47.48 49.76 59.16 39.53 Tanzania Low income AFR 2005 55.32 63.16 75.62 77.50 87.61 94.25 74.51 Timor-Leste Lower middle income EAP 2008 67.42 67.42 67.42 67.42 67.42 67.42 100.00 Togo Low income AFR 2008 64.53 68.84 78.06 79.90 86.71 92.93 67.25 Tonga Lower middle income EAP 2008 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Turkey Upper middle income ECA 2007 23.20 26.72 30.44 33.25 36.62 42.55 44.02 Uganda Low income AFR 2005 50.72 60.61 64.85 66.28 68.89 82.07 45.85 Ukraine Lower middle income ECA 2007 32.40 38.02 40.82 44.02 47.93 56.17 31.31 Uruguay Upper middle income LAC 2005 59.30 66.58 71.99 75.03 75.70 79.18 86.44 Uzbekistan Lower middle income ECA 2007 58.07 68.30 70.88 73.88 76.24 82.52 65.95 Vanuatu Lower middle income EAP 2008 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Venezuela, RB Upper middle income LAC 2005 55.39 62.12 70.07 72.40 78.77 80.00 73.75 Vietnam Lower middle income EAP 2008 22.58 29.50 36.06 37.04 45.26 52.99 28.29 Western Samoa Lower middle income EAP 2008 51.22 59.16 63.97 63.97 71.34 71.34 30.24 Yemen, Rep. Lower middle income MNA 2009 45.38 48.10 53.75 56.82 58.56 62.30 68.51 Zambia Low income AFR 2006 40.83 49.57 54.91 61.74 68.11 81.35 58.71 Data from Sources other than World Bank Enterprise Surveys 35 1 2 3 4 5 6 7 SME250_ Nation Income Region year SME100 SME150 SME200 SME250 SME300 SME500 Manufacturing Austria High ECA 2008 67.26 54.66 Belgium High ECA 2008 66.89 54.98 Canada High NAmer 2009 48.05 59.27 63.51 Costa Rica Upper-Middle LAC 2000 54.3 Cyprus High ECA 2008 83.52 87.00 Denmark High ECA 2008 65.97 54.46 Finland High ECA 2008 59.69 48.74 France High ECA 2008 61.72 53.73 Germany High ECA 2008 60.50 46.85 Greece High ECA 2008 87.05 78.24 Iceland High ECA 2008 41.12 6.70 Ireland High ECA 2008 68.51 53.40 Israel High MENA 2008 57.80 51.19 Italy High ECA 2008 80.95 77.91 Japan High EAP 2007 67.80 Korea, Rep. High EAP 2004 86.5 Liechtenstein High ECA 2007 65.23 31.61 Luxembourg High ECA 2008 66.76 39.49 Malta High MENA 2008 76.80 59.26 Netherlands High ECA 2008 67.18 67.20 New Zealand High EAP 2004 70.9 Norway High ECA 2008 69.64 61.69 Portugal High ECA 2008 81.45 81.55 Singapore* High EAP 2008 55.83 65.72 Spain High ECA 2008 78.04 74.04 Sweden High ECA 2008 63.73 50.60 Switzerland High ECA 2005 72.66 63.26 Taiwan, China High EAP 2006 77.76 85.84 Thailand Lower-Middle EAP 2006 60.77 68.31 78.26 United Kingdom High ECA 2008 54.83 56.47 United States High NAmer 2007 35.40 39.34 41.99 49.64 American Samoa Upper middle EAP 2007 Australia High EAP 2007 68.8 Bahrain High MENA 2006 72.7 Brunei Darussalam High EAP 1997 70.0 36 1 2 3 4 5 6 7 SME250_ Nation Income Region year SME100 SME150 SME200 SME250 SME300 SME500 Manufacturing Egypt, Arab Rep. Lower middle MENA 2006 31.2 Cambodia Low EAP 2001 7.7 Lebanon Upper middle MENA 2004 12.00 Morocco Lower middle MENA 2002 21.6 Oman High MENA 2007 5.90 Pakistan Low SAR 2005 78 Puerto Rico Upper middle LAC 2007 43 Saudi Arabia High MENA 2008 19.5 Trinidad and Tobago High LAC 2007 75 Summary Statistics Minimum 5.90 12.23 7.70 16.06 16.65 18.96 3.14 Mean 50.06 57.90 61.94 66.30 70.52 76.99 62.00 Median 49.55 58.67 63.87 66.89 73.37 79.86 60.82 Maximum 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Median across Income Groups Low 59.43 65.73 74.21 78.00 83.29 86.71 73.31 Lower-Middle 52.58 59.16 64.70 66.19 71.34 77.80 65.91 Upper-Middle 41.84 49.15 53.90 58.15 64.03 73.86 57.92 High 48.13 54.39 61.46 66.89 67.23 75.16 55.66 Median across Regions AFR 54.77 63.79 68.15 76.85 80.56 85.11 65.86 EAP 56.79 61.58 67.42 65.70 71.34 71.34 52.28 ECA 44.71 53.08 59.46 66.32 67.48 75.47 55.32 LAC 53.72 56.71 64.36 67.77 70.99 78.26 65.21 MNA 31.20 48.10 36.63 57.31 58.56 62.30 59.26 NAmer 41.73 39.34 41.99 59.27 56.58 SAR 56.68 65.29 73.63 78.00 80.26 88.55 74.15 37 Table 2: Contribution to Employment Shares by Size Class This table presents the contribution of different size classes to total employment in each country. Total employment is the population estimate of the number of permanent, full-time employees in a particular year in each country, derived from the World Bank Enterprise Surveys. In col. 1 we report the SME250 contribution to total employment where SME250 consists of all firms with 250 permanent full time employees. In cols. 2-8, we report employment shares across 7 size classes based on permanent full time employment ­ 5-19 employees, 20-49 employees, 50-99 employees, 100-249 employees, 250-499 employees, 500-999 employees and 1000+ employees. We report summary statistics and median values across income groups and regions at the foot of the table. 1 2 3 4 5 6 7 8 SME Size Class Nation SME250 5-19 20-49 50-99 100-249 250-499 500-999 1000+ Afghanistan 76.00 22.40 16.85 16.20 18.76 12.70 13.08 0.00 Albania 96.90 24.62 23.85 14.91 33.52 1.26 0.00 1.83 Angola 100.00 59.66 23.30 5.48 11.56 0.00 0.00 0.00 Argentina 27.59 4.25 6.71 7.14 8.97 12.29 11.46 49.17 Armenia 61.17 10.86 12.81 12.54 23.00 15.68 8.39 16.71 Azerbaijan 43.00 9.54 9.57 10.72 13.17 10.75 3.34 42.91 Bangladesh 20.54 3.23 4.45 1.79 7.04 21.04 26.65 35.80 Belarus 35.58 4.83 7.23 6.82 16.48 11.80 33.07 19.77 Benin 69.13 28.79 29.48 0.83 10.04 8.10 22.77 0.00 Bhutan 78.66 24.97 17.78 9.87 21.87 15.70 9.82 0.00 Bolivia 79.34 18.66 24.68 15.94 16.26 12.31 11.08 1.07 Bosnia and Herzegovina 66.19 10.67 15.53 16.46 22.79 16.97 3.72 13.85 Botswana 68.19 17.98 14.54 12.06 23.62 18.86 9.89 3.06 Brazil 37.10 2.66 6.79 10.74 14.86 13.92 17.27 33.75 Bulgaria 60.24 12.92 18.24 11.80 16.84 13.81 4.90 21.48 Burkina Faso 79.00 20.39 17.47 6.25 34.89 4.06 0.00 16.94 Burundi 100.00 49.70 21.73 15.31 13.27 0.00 0.00 0.00 Cameroon 46.50 10.39 11.39 6.78 17.93 16.05 3.20 34.25 Cape Verde 82.34 28.01 24.18 13.46 16.69 11.01 6.65 0.00 Chad 84.72 20.14 29.61 11.32 23.64 15.28 0.00 0.00 Chile 23.10 4.09 5.08 4.80 8.77 36.65 7.08 33.53 Colombia 74.09 20.31 30.06 10.31 13.12 5.59 11.42 9.19 Congo, Dem. Rep. 96.60 40.14 22.83 14.07 19.57 3.40 0.00 0.00 Congo, Rep. 76.20 13.73 23.57 15.91 15.46 12.06 19.28 0.00 Cote d'Ivoire 64.95 28.94 14.91 4.06 16.55 3.86 5.88 25.80 Croatia 73.05 11.24 23.21 16.84 21.10 13.64 8.99 4.98 Czech Republic 64.41 13.83 15.58 16.25 18.12 11.80 10.45 13.97 Ecuador 62.12 13.05 13.13 12.75 23.19 14.57 13.26 10.06 El Salvador 63.87 15.66 12.51 8.36 21.16 13.88 13.11 15.32 Eritrea 100.00 40.13 28.55 18.83 12.49 0.00 0.00 0.00 Estonia 77.82 21.50 13.90 24.06 16.28 13.87 3.91 6.48 Fiji 57.07 13.29 14.68 12.64 13.19 13.55 16.27 16.37 Gabon 57.24 13.87 12.95 9.15 21.27 4.52 11.11 27.13 Gambia 85.74 23.70 22.62 23.36 9.81 20.51 0.00 0.00 Georgia 27.66 6.46 6.42 9.03 5.65 7.08 2.90 62.46 Ghana 59.04 16.42 13.06 6.47 23.09 24.64 6.43 9.89 Guatemala 74.15 19.55 16.86 17.45 19.51 8.48 4.40 13.74 Guinea 85.52 41.50 11.16 9.81 23.04 0.00 14.48 0.00 Guinea-Bissau 85.91 45.32 11.84 18.23 10.52 14.09 0.00 0.00 Honduras 34.58 5.56 4.05 10.31 14.42 40.64 20.11 4.92 Hungary 45.61 8.35 10.25 13.62 12.58 10.64 17.02 27.55 Indonesia 47.46 26.03 7.60 7.11 6.61 4.22 10.26 38.18 Kazakhstan 58.15 8.18 17.34 8.58 23.30 11.48 9.41 21.72 Kenya 53.69 11.11 11.33 10.19 18.91 12.05 13.25 23.17 Kosovo 91.24 36.49 20.04 11.24 23.46 2.00 0.00 6.76 Kyrgyz Republic 58.60 12.29 13.81 15.02 15.67 31.53 11.67 0.00 Lao PDR 72.50 26.69 19.60 9.86 15.37 9.33 3.31 15.83 Latvia 66.76 16.53 14.88 16.64 17.66 10.00 13.08 11.20 Lesotho 16.06 4.49 4.07 4.31 2.95 2.62 5.19 76.37 Liberia 96.52 62.85 7.20 17.41 9.07 3.48 0.00 0.00 Lithuania 77.83 17.20 23.50 15.32 19.79 14.50 5.05 4.64 Macedonia, FYR 66.44 16.10 14.51 20.81 15.03 8.62 3.79 21.15 Madagascar 48.47 10.27 11.40 12.42 13.90 17.51 17.18 17.33 Malawi 36.38 7.39 6.36 10.03 12.61 15.60 5.69 42.33 Mali 93.28 37.13 24.66 15.66 11.04 11.52 0.00 0.00 Mauritania 93.84 42.67 25.78 9.86 15.53 6.16 0.00 0.00 Mauritius 62.06 10.00 10.04 14.16 24.51 12.33 9.44 19.53 38 1 2 3 4 5 6 7 8 SME Size Class Nation SME250 5-19 20-49 50-99 100-249 250-499 500-999 1000+ Mexico 71.66 19.27 14.99 18.21 18.48 10.37 10.12 8.57 Micronesia, Fed. Sts. 100.00 31.94 43.03 25.03 0.00 0.00 0.00 0.00 Moldova 69.28 17.11 18.93 13.94 18.31 15.42 9.88 6.42 Mongolia 73.60 13.71 21.07 22.52 15.75 11.81 10.96 4.19 Montenegro 91.43 37.30 23.24 10.07 17.74 6.59 5.06 0.00 Mozambique 65.44 14.16 17.53 13.28 17.91 12.50 24.60 0.00 Namibia 90.47 37.59 29.09 10.48 13.32 1.36 4.06 4.10 Nepal 85.09 49.16 17.53 6.96 9.12 12.03 5.20 0.00 Nicaragua 75.19 19.30 21.22 12.18 22.48 9.28 5.21 10.32 Niger 94.42 42.64 29.20 10.88 11.71 0.00 5.58 0.00 Nigeria 91.85 36.37 24.19 16.78 14.51 3.44 4.71 0.00 Panama 57.35 12.06 10.51 13.31 19.67 10.70 12.11 21.64 Paraguay 79.43 14.43 20.91 15.59 27.16 16.79 5.11 0.00 Peru 42.33 5.04 10.21 12.19 14.60 16.19 30.93 10.84 Philippines 45.87 8.58 9.80 11.75 14.98 9.47 22.52 22.90 Poland 71.09 15.08 11.13 10.69 29.45 20.79 11.96 0.89 Romania 70.06 19.68 14.77 13.08 22.20 11.85 4.85 13.58 Russian Federation 16.62 1.74 3.32 4.25 7.16 8.41 8.72 66.40 Rwanda 66.27 16.97 16.74 9.91 22.65 6.59 27.14 0.00 Senegal 56.26 24.97 9.83 8.51 12.96 12.00 3.82 27.93 Serbia 56.57 9.83 12.43 11.71 22.11 16.46 7.61 19.86 Sierra Leone 83.85 41.35 12.88 12.44 17.18 2.63 13.51 0.00 Slovak Republic 64.54 19.84 14.52 18.85 11.32 6.80 10.09 18.57 Slovenia 48.28 10.97 11.97 10.54 14.54 23.77 18.41 9.81 South Africa 57.92 7.97 14.21 17.18 18.14 10.56 9.88 22.07 Swaziland 50.28 15.58 9.68 8.76 16.26 17.09 18.50 14.14 Tajikistan 47.48 7.29 9.10 13.00 18.09 11.69 23.65 17.18 Tanzania 77.50 22.63 18.67 13.65 21.62 17.67 3.50 2.25 Timor-Leste 67.42 22.14 32.63 12.65 0.00 0.00 0.00 32.58 Togo 79.90 44.90 13.58 5.06 16.36 13.03 7.07 0.00 Tonga 100.00 83.51 16.49 0.00 0.00 0.00 0.00 0.00 Turkey 33.25 7.50 7.57 6.96 10.51 9.22 17.58 40.67 Uganda 66.28 19.51 18.84 11.81 16.12 13.11 4.71 15.90 Ukraine 44.02 9.28 11.22 10.10 11.90 11.65 9.14 36.71 Uruguay 75.03 22.63 22.05 12.12 17.52 4.85 3.91 16.90 Uzbekistan 73.88 27.58 17.10 12.60 16.60 8.63 12.07 5.41 Vanuatu 100.00 28.99 42.39 28.62 0.00 0.00 0.00 0.00 Venezuela, RB 72.40 29.64 13.17 12.12 17.47 7.31 12.08 8.21 Vietnam 37.04 5.13 8.29 8.33 14.69 14.59 11.41 37.57 Western Samoa 63.97 19.26 22.12 9.84 12.76 7.37 0.00 28.66 Yemen, Rep. 56.82 30.39 7.69 7.16 10.72 4.66 4.92 34.46 Zambia 61.74 9.28 12.39 15.43 24.37 19.89 0.00 18.65 Summary Statistics Minimum 16.06 1.74 3.32 0.00 0.00 0.00 0.00 0.00 Mean 66.38 21.00 16.28 12.12 16.04 11.06 8.88 14.62 Median 66.76 17.20 14.77 12.06 16.26 11.65 7.61 10.32 Maximum 100.00 83.51 43.03 28.62 34.89 40.64 33.07 76.37 Median across Income Groups Low 78.25 23.17 17.16 12.12 15.89 12.04 5.39 0.00 Lower-Middle 67.42 19.26 16.86 11.24 15.46 9.47 5.88 10.32 Upper-Middle 59.2 12.49 14.36 11.93 17.50 11.12 9.43 19.65 High 65.65 14.46 14.21 16.45 16.97 12.72 11.20 10.50 Median across Regions AFR 76.85 23.17 15.83 11.57 16.31 11.27 5.39 0.00 EAP 65.7 24.09 18.05 10.81 9.69 5.79 1.66 19.64 ECA 64.47 12.61 14.51 12.80 17.25 11.74 9.07 13.91 LAC 67.76 15.04 13.15 12.15 17.50 12.30 11.44 10.58 MNA 56.82 30.39 7.69 7.16 10.72 4.66 4.92 34.46 SAR 77.33 23.69 17.19 8.41 13.94 14.20 11.45 0.00 39 Table 3: Contribution to Employment Shares by Age This table presents the contribution of young firms as well as firms of different age bins, to total employment in each country. Total employment is the population estimate of the number of permanent, full-time employees in a particular year in each country, derived from the World Bank Enterprise Surveys. Age is defined as Survey Year-Year the company started operations. We use two definitions of young firms - 2 years and 5 years. We also report employment shares in the following age bins ­ 3-5 years, 6-10 years, 11-20 years, 21-50 years, and 51+ years. We report summary statistics and median values across income groups and regions at the foot of the table. 1 2 3 4 5 6 7 Young Firms Establishment Age Nation 2 years 5 years 3-5 years 6-10 years 11-20 years 21-50 years 51+ years Afghanistan 20.56 56.50 35.95 21.79 13.56 7.65 0.37 Albania 9.80 26.46 16.66 29.39 39.46 4.69 0.00 Angola 25.67 52.93 27.26 20.40 16.86 9.67 0.14 Argentina 3.46 11.30 7.84 18.14 11.07 27.05 32.43 Armenia 5.65 18.62 12.98 41.19 33.34 6.17 0.68 Azerbaijan 1.42 10.66 9.23 13.64 45.70 16.57 4.16 Bangladesh 12.05 26.16 14.11 24.38 29.78 17.12 2.57 Belarus 1.67 7.25 5.58 15.45 30.46 8.32 37.20 Benin 9.57 21.54 11.97 32.82 29.98 15.31 0.29 Bhutan 14.43 29.50 15.08 12.05 28.86 29.59 0.00 Bolivia 2.69 9.81 7.12 31.75 22.29 28.05 8.07 Bosnia and Herzegovina 1.50 7.17 5.68 13.71 25.84 23.47 28.80 Botswana 13.99 28.15 14.16 15.17 33.16 21.76 1.76 Brazil 0.21 1.67 1.47 27.62 15.04 29.96 25.59 Bulgaria 3.43 22.46 19.03 21.70 50.48 4.55 0.81 Burkina Faso 6.04 14.62 8.58 42.02 25.76 14.00 0.93 Burundi 19.68 39.54 19.85 21.91 24.58 13.07 0.90 Cameroon 0.69 4.98 4.29 14.07 13.86 54.73 11.70 Cape Verde 8.63 22.11 13.48 28.58 19.21 16.77 12.30 Chad 11.63 17.42 5.79 15.62 39.07 24.43 1.22 Chile 0.57 4.89 4.32 8.98 9.87 30.59 45.50 Colombia 5.97 15.20 9.22 15.89 34.12 23.43 9.73 Congo, Dem. Rep. 15.19 31.46 16.27 24.46 27.26 14.03 2.78 Congo, Rep. 2.69 13.83 11.14 28.87 18.70 18.76 8.55 Cote d'Ivoire 12.99 27.52 14.53 23.45 20.95 13.03 15.02 Croatia 0.43 6.47 6.04 10.01 56.81 8.17 17.51 Czech Republic 0.93 9.86 8.93 21.12 57.15 0.32 10.63 Ecuador 3.86 9.67 5.82 7.08 18.50 55.48 9.12 El Salvador 2.61 15.97 13.36 11.55 27.04 29.66 15.42 Eritrea 0.00 6.03 6.03 22.95 39.73 12.82 6.62 Estonia 0.65 8.67 8.02 17.71 63.40 6.09 4.14 Fiji 3.96 10.28 6.32 21.14 13.35 35.51 7.53 Gabon 5.33 28.95 23.62 19.27 11.74 39.56 0.47 Gambia 12.66 33.28 20.62 16.20 25.72 23.87 0.63 Georgia 17.25 25.16 7.92 35.06 25.52 13.96 0.29 Ghana 2.89 6.00 3.11 14.55 39.69 23.21 16.52 Guatemala 4.78 12.87 8.10 16.17 36.87 24.94 8.64 Guinea 8.89 35.09 26.20 35.20 17.58 10.76 0.58 Guinea-Bissau 12.49 39.19 26.70 19.20 26.32 15.28 0.00 Honduras 0.80 4.28 3.48 25.11 18.10 47.76 1.05 Hungary 1.91 6.23 4.32 20.83 59.69 10.03 2.19 Indonesia 1.97 33.31 31.35 13.55 25.94 23.47 2.04 Kazakhstan 4.38 18.99 14.62 45.68 27.82 4.54 0.47 Kenya 6.96 15.61 8.65 14.23 17.32 38.40 12.46 Kosovo 1.11 7.87 6.76 31.80 52.14 6.76 0.99 Kyrgyz Republic 11.54 15.13 3.59 18.91 34.31 25.16 5.75 Lao PDR 7.28 28.52 21.24 23.43 42.22 5.74 0.09 Latvia 0.89 9.93 9.03 23.24 63.72 0.36 2.33 Lesotho 0.98 4.34 3.36 15.94 3.93 75.47 0.00 Liberia 11.86 34.86 23.00 34.06 12.95 17.45 0.68 Lithuania 4.57 11.53 6.96 20.45 56.88 5.98 2.94 Macedonia, FYR 3.72 17.00 13.28 13.28 29.83 9.32 28.68 Madagascar 8.32 29.44 21.12 17.50 30.05 14.67 7.83 Malawi 6.07 13.74 7.67 13.22 19.64 20.75 32.48 Mali 10.67 24.89 14.23 27.07 25.41 21.11 1.52 Mauritania 8.99 33.32 24.34 24.28 25.25 17.15 0.00 Mauritius 13.87 24.42 10.55 12.50 15.98 25.12 17.94 Mexico 5.88 16.05 10.17 13.53 29.93 20.94 7.72 40 1 2 3 4 5 6 7 Young Firms Establishment Age Nation 2 years 5 years 3-5 years 6-10 years 11-20 years 21-50 years 51+ years Micronesia, Fed. Sts. 8.20 15.46 7.26 10.13 37.33 36.00 0.00 Moldova 5.16 14.18 9.02 26.70 53.79 1.31 4.02 Mongolia 2.16 15.08 12.92 30.01 38.27 11.19 5.18 Montenegro 2.67 17.45 14.78 28.41 47.87 3.77 2.08 Mozambique 8.56 13.79 5.24 23.65 39.18 18.18 4.35 Namibia 16.46 33.16 16.71 22.01 23.65 18.17 3.01 Nepal 12.47 26.16 13.70 19.31 29.27 25.06 0.14 Nicaragua 1.11 9.80 8.68 39.40 21.26 19.97 9.57 Niger 10.84 21.29 10.45 22.20 13.13 21.64 7.21 Nigeria 9.74 28.56 18.81 31.79 19.74 18.97 0.58 Panama 0.42 1.45 1.03 7.86 37.11 28.59 24.16 Paraguay 2.06 8.77 6.72 14.23 22.04 42.05 12.56 Peru 0.45 9.89 9.44 32.95 19.67 22.54 14.90 Philippines 1.65 7.30 5.65 18.96 30.13 34.78 7.27 Poland 1.19 3.66 2.46 16.23 55.13 8.06 15.78 Romania 4.45 12.19 7.74 27.91 50.92 2.41 4.99 Russian Federation 0.38 7.19 6.80 45.07 26.25 7.58 13.25 Rwanda 10.24 26.00 15.76 16.14 19.23 38.17 0.46 Senegal 6.13 12.29 6.16 14.96 19.02 53.17 0.56 Serbia 3.26 8.77 5.51 9.62 35.14 12.58 32.27 Sierra Leone 0.53 25.76 25.23 26.06 28.46 16.11 3.61 Slovak Republic 4.39 16.19 11.80 16.70 40.06 3.71 18.12 Slovenia 0.56 5.96 5.39 8.57 40.12 12.02 33.34 South Africa 4.37 10.77 6.41 15.49 19.11 27.30 27.32 Swaziland 14.04 54.05 40.00 11.67 16.53 16.31 0.00 Tajikistan 4.69 13.90 9.21 41.27 13.02 14.71 16.20 Tanzania 5.37 19.28 13.92 38.26 22.03 16.49 3.15 Timor-Leste 43.14 60.94 17.80 33.06 3.79 0.43 0.00 Togo 14.48 23.84 9.36 27.34 21.26 25.26 0.52 Tonga 14.86 33.18 18.33 25.95 18.89 14.43 7.45 Turkey 1.40 12.66 11.26 22.95 30.88 31.03 1.14 Uganda 2.82 10.01 7.19 31.11 41.82 10.68 4.62 Ukraine 6.15 15.45 9.30 20.42 20.62 7.06 35.58 Uruguay 6.11 10.06 3.95 13.39 15.28 34.89 26.37 Uzbekistan 1.98 20.03 18.05 18.82 29.74 19.18 11.80 Vanuatu 10.48 27.20 16.72 9.98 33.68 25.85 1.66 Venezuela, RB 14.19 31.07 16.88 11.23 10.56 35.93 9.06 Vietnam 5.00 24.90 19.90 22.18 20.59 29.98 1.39 Western Samoa 2.47 6.74 4.28 6.73 54.96 25.23 3.38 Yemen, Rep. 0.72 5.75 5.02 17.34 47.06 28.02 1.30 Zambia 4.17 17.21 13.04 15.89 28.41 32.03 6.39 Summary Statistics Minimum 0.00 1.45 1.03 6.73 3.79 0.32 0.00 Mean 6.75 18.75 12.00 21.52 29.34 20.34 8.60 Median 4.78 15.46 9.36 20.42 27.04 18.18 4.16 Maximum 43.14 60.94 40.00 45.68 63.72 75.47 45.50 Median across Income Groups Low Inc 9.28 24.37 13.81 23.19 26.04 17.13 2.04 Lower-Middle Inc 4.78 15.45 9.30 20.40 22.04 23.47 3.38 Upper-Middle Inc 3.84 11.86 9.23 17.02 28.83 22.15 9.39 High 0.91 7.57 7.03 17.20 56.98 7.08 13.21 Median across Regions AFR 8.94 24.13 13.70 21.96 21.65 18.47 2.27 EAP 6.14 26.05 17.26 20.05 28.04 25.54 1.85 ECA 2.41 12.42 8.98 20.98 39.76 7.82 5.09 LAC 2.65 9.85 7.48 15.06 20.46 29.13 11.14 MNA 0.72 5.75 5.02 17.34 47.06 28.02 1.30 SAR 13.45 27.83 14.59 20.55 29.06 21.09 0.26 41 Table 4: Job Creation as a share of total job creation by Size Class This table presents the contribution to job creation by different size classes. Job Creation is the population estimate of the change in the number of permanent, full-time employees over a two year period, derived from the World Bank Enterprise Surveys. In col. 1 we report the SME250 contribution to job creation where SME250 consists of all firms with 250 permanent full time employees in the base year. In cols. 2-8, we report 7 size classes based on permanent full time employment in the base year ­ 5-19 employees, 20-49 employees, 50-99 employees, 100-249 employees, 250-499 employees, 500-999 employees and 1000+ employees. In Panel A we report data for 81countries that had a net positive job creation (across all sizes) over the two period. In Panel B we report data for 17 countries that had a net job loss (across all sizes) over the two period. We report summary statistics and median values across income groups and regions at the foot of each panel. Panel A: Countries with net job creation 1 2 3 4 5 6 7 8 SME Size Class Nation SME250 5-19 20-49 50-99 100-249 250-499 500-999 1000+ Afghanistan 207.00 147.19 62.14 19.89 -22.22 96.30 0.00 -203.29 Albania 98.55 51.43 32.74 0.31 14.08 1.45 0.00 0.00 Angola 100.00 77.43 9.66 20.01 -7.09 0.00 0.00 0.00 Argentina 46.01 13.48 10.70 8.29 11.45 9.62 20.89 25.57 Armenia 64.54 22.11 13.61 3.49 25.33 4.11 9.57 21.78 Azerbaijan 52.95 15.61 18.75 1.68 15.27 -1.99 -15.67 66.36 Belarus 48.02 9.10 20.29 23.91 -5.28 44.68 -6.60 13.90 Benin 145.69 101.96 25.19 0.00 18.53 -30.88 -14.81 0.00 Bhutan 99.22 61.00 13.15 -1.63 26.71 0.78 0.00 0.00 Bolivia 108.69 63.18 18.20 19.56 6.40 7.39 -9.16 -5.57 Bosnia and Herzegovina 77.77 38.29 23.93 20.93 -4.33 36.85 -3.96 -11.72 Brazil 63.96 4.65 7.73 6.93 44.07 5.71 11.38 19.52 Bulgaria 88.46 31.32 22.37 15.86 18.76 3.67 18.43 -10.42 Burkina Faso 302.59 118.42 61.93 114.09 8.16 8.66 0.00 -211.25 Cameroon 54.29 13.66 18.42 5.55 12.57 3.58 2.92 43.29 Cape Verde 102.70 40.70 27.14 17.92 16.95 -14.91 12.21 0.00 Chad 100.00 56.38 27.41 20.00 -3.79 0.00 0.00 0.00 Chile 20.30 5.72 3.72 4.28 4.81 39.35 6.01 36.11 Colombia 80.47 56.46 2.18 12.97 7.91 18.60 -0.85 2.74 Congo, Dem. Rep. 100.00 78.16 15.90 5.26 0.68 0.00 0.00 0.00 Congo, Rep. 89.16 22.43 25.59 12.22 28.92 0.00 10.84 0.00 Croatia 101.96 11.78 73.52 13.35 3.31 2.55 -0.93 -3.58 Czech Republic 92.98 45.34 19.24 10.03 19.22 14.37 1.18 -9.38 Ecuador 68.11 22.34 15.42 19.58 10.58 6.71 23.89 1.47 Estonia 67.30 22.64 26.43 12.96 3.21 6.75 28.01 0.00 Fiji 44.60 24.37 8.67 -1.93 11.48 7.80 49.62 0.00 Gabon 142.51 103.26 34.73 69.92 -65.41 0.00 19.80 -62.31 Gambia 98.72 60.30 30.52 24.97 -17.07 1.28 0.00 0.00 Georgia 25.49 8.97 5.32 4.54 6.16 3.10 66.25 5.67 Ghana 82.69 40.82 3.56 0.50 37.80 17.31 0.00 0.00 Guatemala 70.04 20.93 7.44 32.50 9.15 3.11 12.67 14.20 Guinea 74.17 43.52 9.64 12.86 8.14 25.83 0.00 0.00 Guinea-Bissau 100.00 104.32 19.82 -16.36 -7.78 0.00 0.00 0.00 Hungary 149.50 50.30 12.15 53.56 33.48 23.33 5.17 -77.99 Indonesia 41.83 72.45 -3.46 -12.80 -13.46 -0.31 -11.66 69.24 Kazakhstan 54.13 20.38 5.54 16.79 7.43 11.85 37.98 0.03 Kenya 82.37 24.37 21.76 13.40 23.13 5.89 -14.01 25.45 Kosovo 98.53 40.26 20.25 -10.94 15.58 34.86 0.00 0.00 Kyrgyz Republic 766.29 464.40 336.88 34.92 -208.53 -348.20 -179.48 0.00 Lesotho 27.76 28.26 -2.13 -2.37 4.00 16.37 56.54 -0.68 Liberia 139.80 220.94 -83.79 3.84 -1.19 0.00 -39.80 0.00 Lithuania 81.01 30.30 39.63 8.31 2.38 7.53 4.29 7.56 Madagascar 58.07 45.43 8.13 20.19 -11.69 27.79 21.32 -11.16 Malawi 92.77 13.54 18.15 14.24 46.84 32.15 -11.94 -12.99 42 1 2 3 4 5 6 7 8 SME Size Class Nation SME250 5-19 20-49 50-99 100-249 250-499 500-999 1000+ Mali 100.00 73.99 18.15 -0.01 7.87 0.00 0.00 0.00 Mauritania 203.77 128.13 22.69 19.72 33.23 -103.77 0.00 0.00 Mauritius 84.94 64.02 2.35 4.58 13.99 8.13 7.75 -0.82 Mexico 78.12 31.80 22.18 11.48 12.00 3.79 11.61 7.13 Micronesia, Fed. Sts. 100.00 77.15 22.85 0.00 0.00 0.00 0.00 0.00 Moldova 98.40 36.48 10.12 10.82 39.93 -10.46 -2.13 15.24 Mongolia 96.72 43.56 24.60 1.49 22.76 8.60 -1.01 0.00 Montenegro 89.77 56.49 10.92 24.00 -1.63 10.23 0.00 0.00 Mozambique 67.60 15.65 10.27 14.62 7.62 51.84 0.00 0.00 Namibia 85.88 43.09 17.05 -0.46 26.20 9.80 4.32 0.00 Nicaragua 75.44 36.20 21.79 -7.25 24.70 25.83 3.92 -5.18 Niger 117.72 102.15 7.29 14.21 11.39 17.66 -52.70 0.00 Nigeria 103.17 58.57 22.75 15.13 6.71 1.27 -4.43 0.00 Paraguay 110.84 70.34 27.86 -5.93 18.56 -2.73 -8.11 0.00 Peru 66.55 6.24 17.89 27.26 14.02 27.85 2.68 4.07 Philippines 97.11 8.28 11.59 75.27 1.34 19.05 -2.42 -13.11 Poland 171.06 49.13 34.55 46.80 40.14 -23.13 -47.50 0.00 Romania 146.73 95.77 24.12 25.58 0.59 -16.92 -16.14 -13.01 Russian Federation 304.29 65.90 35.83 31.74 164.39 -10.79 459.31 -646.37 Rwanda 93.04 47.08 17.72 2.22 26.02 0.00 6.96 0.00 Senegal 140.04 77.03 23.45 26.45 13.11 -3.64 -34.81 -1.59 Slovak Republic 73.72 34.35 28.47 23.90 -13.36 2.38 30.85 -6.59 Slovenia 99.37 23.53 17.21 48.00 7.89 4.34 -5.85 4.88 South Africa 100.94 30.37 24.15 23.98 22.70 4.94 1.47 -7.61 Swaziland 43.89 16.46 8.61 8.17 10.64 -3.55 15.90 43.77 Tajikistan 86.13 21.38 15.44 22.87 26.44 13.29 -19.93 20.51 Tanzania 89.36 44.32 24.15 11.77 9.12 5.57 5.07 0.00 Timor-Leste 100.00 89.17 20.39 -9.56 0.00 0.00 0.00 0.00 Togo 117.82 80.00 19.33 -0.60 13.26 -24.38 12.39 0.00 Turkey 62.40 47.57 2.97 5.71 2.72 14.55 14.86 11.61 Uganda 101.48 40.14 24.26 21.72 14.21 9.71 -10.03 0.00 Ukraine 120.45 99.13 -4.62 31.90 -0.39 -15.51 8.47 -18.98 Uruguay 84.90 54.47 23.64 6.51 -1.05 13.24 3.81 -0.61 Vanuatu 100.00 71.41 24.28 21.08 -16.77 0.00 0.00 0.00 Venezuela, RB 77.73 45.79 14.63 3.88 13.43 22.27 0.00 0.00 Vietnam 126.31 34.99 42.96 20.54 26.26 -11.42 -102.45 89.10 Zambia 108.83 39.56 22.98 28.45 17.84 -11.11 -21.90 24.18 Summary Statistics Minimum 20.30 4.65 -83.79 -16.36 -208.53 -348.20 -179.48 -646.37 Mean 105.38 55.65 22.26 15.55 9.27 2.10 4.57 -9.39 Median 93.04 43.56 19.24 12.97 10.58 4.11 0.00 0.00 Maximum 766.29 464.40 336.88 114.09 164.39 96.30 459.31 89.10 Median across Income Groups Low Inc 100.00 58.34 19.58 14.23 8.64 3.43 0.00 0.00 Lower-Middle Inc 98.47 40.48 18.31 9.50 10.61 0.39 0.00 0.00 Upper-Middle Inc 79.30 35.05 18.32 9.89 11.46 8.87 4.31 0.00 High Inc 99.37 34.35 26.43 23.90 7.89 4.34 1.18 -3.58 Median across Regions AFR 100.00 47.08 19.33 13.40 11.39 1.27 0.00 0.00 EAP 100.00 71.41 20.39 0.00 0.00 0.00 0.00 0.00 ECA 91.38 37.38 20.27 16.32 7.66 4.23 0.00 0.00 LAC 75.44 31.80 15.42 8.29 11.45 9.62 3.92 2.74 SAR 153.11 104.10 37.64 9.13 2.24 48.54 0.00 -101.65 43 Panel B: Countries with net job loss 1 2 3 4 5 6 7 8 SME Size Class Nation SME250 5-19 20-49 50-99 100-249 250-499 500-999 1000+ Botswana 1307.79 1560.04 401.44 512.35 -1166.05 -585.54 -484.76 -337.49 Burundi 81.51 66.90 10.89 2.40 1.31 0.00 0.00 -181.51 Cote d'Ivoire 0.61 0.37 0.12 0.01 0.11 0.00 -0.19 -100.42 El Salvador 1119.33 465.15 163.81 271.31 203.31 -1130.76 56.60 -129.41 Eritrea -100.00 -25.39 -20.61 -7.63 -46.36 0.00 0.00 0.00 Honduras 151.61 18.76 1.38 7.48 102.11 124.85 -24.64 -329.93 Lao PDR -0.06 2.45 0.11 0.70 -3.31 -0.11 -0.44 -99.39 Latvia 57.91 22.30 8.85 13.62 15.69 9.60 -3.78 -166.28 Macedonia, FYR 331.16 278.58 63.76 67.98 -79.15 -40.38 0.43 -391.21 Nepal 879.31 826.97 -11.01 -34.40 97.75 42.05 95.34 -1116.70 Panama 336.15 90.43 91.05 47.21 99.29 -56.51 -218.73 -152.74 Serbia 61.40 36.16 19.95 7.45 -1.69 -57.82 -34.39 -69.67 Sierra Leone 159.89 70.19 12.40 11.95 65.35 5.50 20.99 -286.37 Tonga -0.03 0.31 -0.34 0.00 0.00 0.00 0.00 -99.97 Uzbekistan -76.81 -0.41 -22.21 -22.74 -31.45 -13.45 -9.33 -0.41 Western Samoa 0.30 12.96 2.14 -10.22 -4.58 0.00 0.00 -100.30 Yemen, Rep. 167.48 68.54 32.76 49.15 17.03 7.35 3.32 -278.15 Summary Statistics Minimum -100.00 -25.39 -22.21 -34.40 -1166.05 -1130.76 -484.76 -1116.70 Mean 263.39 205.55 44.38 53.92 -42.98 -99.72 -35.27 -225.88 Median 81.51 36.16 8.85 7.45 0.11 0.00 0.00 -152.74 Maximum 1307.79 1560.04 401.44 512.35 203.31 124.85 95.34 0.00 Median across Income Groups Low Inc 81.51 66.90 0.11 0.70 1.31 0.00 0.00 -181.51 Lower-Middle Inc 0.61 12.96 1.38 0.01 0.11 0.00 0.00 -100.42 Upper-Middle Inc 333.66 184.50 77.40 57.60 -40.42 -57.16 -126.56 -245.11 High Inc 57.91 22.30 8.85 13.62 15.69 9.60 -3.78 -166.28 Median across Regions AFR 81.51 66.90 10.89 2.40 0.11 0.00 0.00 -181.51 EAP -0.03 2.45 0.11 0.00 -3.31 0.00 0.00 -99.97 ECA 59.65 29.23 14.40 10.54 -16.57 -26.92 -6.55 -117.97 LAC 336.15 90.43 91.05 47.21 102.11 -56.51 -24.64 -152.74 MNA 167.48 68.54 32.76 49.15 17.03 7.35 3.32 -278.15 SAR 879.31 826.97 -11.01 -34.40 97.75 42.05 95.34 -1116.70 44 Table 5: Job Creation as a share of total job creation by Age This table presents the contribution to job creation by young firms as well as firms in different age bins. Job Creation is the population estimate of the change in the number of permanent, full-time employees over a two year period, derived from the World Bank Enterprise Surveys. Age is defined as Survey Year-Year the company started operations. We use two definitions of young firms - 2 years and 5 years. We also report job creation shares in the following age bins ­ 3-5 years, 6-10 years, 11-20 years, 21-50 years, and 51+ years. In Panel A we report data for 81countries that had a net positive job creation (across all sizes) over the two period. In Panel B we report data for 17 countries that had a net job loss (across all sizes) over the two period. We report summary statistics and median values across income groups and regions at the foot of each panel Panel A: Countries with net job creation 1 2 3 4 5 6 7 Young Firms Establishment Age 6-10 11-20 21-50 51+ Nation 2 years 5 years 3-5 years years years years years Afghanistan 146.9 259.9 113.0 7.6 16.4 -185.2 1.0 Albania 17.6 40.2 22.6 38.8 17.7 3.3 0.0 Angola 41.1 71.7 30.5 21.8 -3.6 10.2 0.0 Argentina 5.3 16.0 10.7 17.2 12.6 31.3 22.8 Armenia 9.6 53.9 44.3 23.2 22.5 0.8 -0.4 Azerbaijan 6.7 19.6 12.8 69.3 5.8 14.0 5.8 Belarus 12.1 19.7 7.6 24.7 9.5 20.5 27.4 Benin 33.0 90.8 57.8 -57.3 51.3 15.4 -0.3 Bhutan 27.1 35.2 8.1 11.9 45.5 7.4 0.0 Bolivia -3.0 52.0 54.9 28.6 13.4 10.5 -4.6 Bosnia and Herzegovina 3.7 19.5 15.9 35.9 51.8 -11.8 4.5 Brazil 0.3 12.1 11.8 44.1 3.1 16.4 24.2 Bulgaria 16.0 39.7 23.7 13.8 44.6 3.7 -1.9 Burkina Faso 44.0 289.8 245.8 71.8 -283.0 25.0 -6.4 Cameroon 4.0 8.1 4.1 23.2 -1.4 53.1 16.3 Cape Verde 21.2 35.2 14.0 26.4 31.6 0.6 6.0 Chad 23.4 26.6 3.2 27.7 31.4 14.1 -0.5 Chile 4.4 4.3 -0.1 11.4 9.0 24.1 51.1 Colombia 1.1 5.4 4.3 32.5 51.0 -1.8 6.3 Congo, Dem. Rep. 30.7 57.2 26.6 20.6 9.8 9.0 3.4 Congo, Rep. 8.3 30.9 22.6 26.8 13.6 18.9 9.0 Croatia 1.0 8.1 7.1 23.9 81.0 -3.4 -9.0 Czech Republic 19.8 45.5 25.7 15.3 48.7 -0.7 -7.1 Ecuador 8.1 17.6 9.5 13.5 33.9 39.4 -4.4 Estonia 5.0 26.5 21.5 26.5 42.0 2.8 2.2 Fiji 14.3 16.0 1.7 31.9 16.0 19.9 -3.6 Gabon 46.5 49.9 3.4 9.6 -17.1 59.5 -1.9 Gambia 38.4 59.3 20.9 14.1 5.5 19.1 2.5 Georgia 10.8 13.7 2.8 82.7 3.9 0.0 -0.3 Ghana 5.6 18.3 12.7 75.2 27.1 18.7 -39.3 Guatemala 6.3 9.6 3.3 21.2 50.1 11.1 8.0 Guinea 23.8 39.0 15.2 41.3 2.3 16.3 0.0 Guinea-Bissau 29.7 77.8 48.1 9.1 12.1 1.0 0.0 Hungary 6.4 23.7 17.2 133.5 -35.9 0.2 -9.5 Indonesia 116.0 126.5 10.5 16.0 2.1 -32.8 1.5 Kazakhstan 9.0 28.8 19.8 61.2 7.7 1.0 0.1 Kenya 14.0 30.6 16.6 20.5 23.4 42.0 -20.5 Kosovo 6.1 25.3 19.2 2.6 66.3 6.9 -1.6 Kyrgyz Republic 105.1 131.6 26.5 340.9 -309.6 793.3 -687.1 Lesotho 2.3 15.8 13.5 58.3 0.6 25.3 0.0 Liberia 24.7 26.1 1.4 87.3 17.5 -31.4 0.5 Lithuania 6.1 15.1 9.0 55.3 30.4 -5.8 1.7 45 1 2 3 4 5 6 7 Young Firms Establishment Age 6-10 11-20 21-50 51+ Nation 2 years 5 years 3-5 years years years years years Madagascar 45.1 54.7 9.7 -0.7 49.6 -2.6 -1.1 Malawi -10.2 -1.5 8.7 59.8 2.0 36.7 3.1 Mali 17.5 29.9 12.3 33.1 20.2 9.2 7.6 Mauritania 31.4 126.2 94.8 25.4 -68.3 16.8 0.0 Mauritius 52.1 54.6 2.6 11.2 11.1 15.1 6.4 Mexico 1.7 8.9 7.2 7.0 20.6 37.2 11.7 Micronesia, Fed. Sts. 36.4 45.3 9.0 10.4 0.9 46.3 0.0 Moldova 22.7 62.7 40.0 38.9 3.6 -1.7 -3.5 Mongolia 12.5 28.6 16.1 53.1 15.2 1.6 1.5 Montenegro 10.1 12.6 2.5 39.9 51.3 -4.5 0.5 Mozambique 4.1 7.8 3.7 34.4 44.5 8.2 3.6 Namibia 11.0 31.9 20.9 30.4 14.6 23.3 -0.2 Nicaragua 15.2 64.8 49.6 30.9 2.4 8.9 -7.0 Niger 37.6 57.2 19.6 -24.6 2.5 15.1 36.9 Nigeria 16.3 46.3 30.0 26.6 18.6 7.1 1.2 Paraguay 18.2 27.5 9.3 52.4 4.3 12.4 3.7 Peru 5.1 18.3 13.2 23.9 18.7 36.0 3.2 Philippines -5.1 0.1 5.2 19.6 6.3 69.3 4.7 Poland 16.1 15.1 -1.1 4.9 82.9 19.3 -22.9 Romania 31.7 61.1 29.4 5.6 54.3 -0.7 -21.6 Russian Federation 14.0 -734.3 -748.3 162.4 317.0 -24.3 379.2 Rwanda 19.8 35.3 15.4 29.8 15.1 19.0 0.8 Senegal 17.9 38.1 20.2 26.2 35.7 0.9 -0.9 Slovak Republic 0.0 50.3 50.3 36.7 13.4 -6.9 22.8 Slovenia 4.9 15.3 10.4 13.3 37.5 2.0 32.0 South Africa 6.6 23.0 16.3 30.2 28.9 16.2 1.8 Swaziland 53.0 73.6 20.5 15.6 7.0 3.3 0.0 Tajikistan 12.5 42.0 29.4 52.9 -8.6 19.9 -6.8 Tanzania 7.1 60.7 53.7 21.5 14.0 0.0 2.7 Timor-Leste 13.1 81.1 68.1 16.8 3.3 -1.3 0.0 Togo 39.7 25.6 -14.1 43.9 38.7 -9.6 0.1 Turkey 4.9 50.8 45.9 31.7 18.0 -0.9 0.6 Uganda 6.6 31.4 24.8 41.7 28.4 0.1 -4.7 Ukraine 64.3 102.7 38.4 60.8 12.7 -8.0 -63.5 Uruguay 16.5 32.2 15.7 16.2 23.0 36.2 -7.6 Vanuatu 2.6 24.5 21.9 19.3 32.2 23.5 0.5 Venezuela, RB 20.4 39.4 19.0 9.3 9.0 40.5 1.8 Vietnam 106.8 167.6 60.8 53.6 -7.2 -110.7 -3.3 Zambia 22.6 52.5 29.9 36.1 32.2 2.5 -23.6 Summary Statistics Minimum -10.2 -734.3 -748.3 -57.3 -309.6 -185.2 -687.1 Mean 21.7 36.5 14.8 35.3 15.6 17.5 -3.0 Median 14.0 31.9 16.1 26.5 15.2 9.0 0.1 Maximum 146.9 289.8 245.8 340.9 317.0 793.3 379.2 Median across Income Groups Low Inc 24.3 47.2 20.2 31.4 15.7 14.6 0.0 Lower-Middle Inc 14.1 36.7 19.7 24.7 9.8 7.3 0.0 Upper-Middle Inc 9.5 19.6 12.3 30.3 17.9 15.6 1.8 High Inc 5.0 23.7 17.2 23.9 42.0 0.2 -7.1 Median across Regions AFR 22.6 38.1 16.6 26.6 14.6 15.1 0.0 EAP 14.3 45.3 10.5 19.3 3.3 19.9 0.0 46 1 2 3 4 5 6 7 Young Firms Establishment Age 6-10 11-20 21-50 51+ Nation 2 years 5 years 3-5 years years years years years ECA 10.4 27.5 19.5 37.8 20.3 0.5 -0.1 LAC 5.3 17.6 10.7 21.2 13.4 24.1 3.7 SAR 87.0 147.5 60.6 9.8 30.9 -88.9 0.5 Panel B: Countries with net job loss Young Firms All Firms by Age Nation 2yrs 5yrs 3-5years 6-10years 11-20years 21-50years 51+years Botswana 288.54 1275.59 987.05 218.74 -1012.30 -634.46 52.43 Burundi 26.40 -132.44 -158.84 4.37 22.37 5.70 0.00 Cote d'Ivoire -75.06 -74.84 0.23 0.23 0.02 -25.42 0.01 El Salvador -400.69 -497.49 -96.81 327.90 523.50 -498.93 45.02 Eritrea 3.52 9.19 5.66 -18.59 -35.01 -17.33 -29.40 Honduras 6.73 114.54 107.81 -3.86 79.19 -288.65 -1.21 Lao PDR -0.31 1.00 1.30 -97.47 -3.11 -0.41 0.00 Latvia 2.28 28.40 26.12 33.04 -161.17 -0.66 0.10 Macedonia, FYR 54.61 177.45 122.85 24.32 138.23 -10.50 -427.40 Nepal 344.09 542.88 198.80 147.17 -895.61 104.98 0.00 Panama 16.50 -264.01 -280.51 30.10 62.71 7.54 51.97 Serbia 5.39 -6.04 -11.44 18.76 70.59 -33.57 -149.02 Sierra Leone 24.69 39.00 14.31 31.05 98.57 -274.12 5.50 Tonga -99.94 -99.44 0.49 -0.04 0.05 -0.52 -0.05 Uzbekistan -1.95 -17.31 -15.35 -31.01 -15.49 -25.28 -10.71 Western Samoa -0.67 5.17 5.84 3.33 -101.46 -3.76 -0.97 Yemen, Rep. 10.54 29.28 18.73 36.27 -268.58 98.31 3.13 Summary Statistics Minimum -400.69 -497.49 -280.51 -97.47 -1012.30 -634.46 -427.40 Mean 12.04 66.53 54.49 42.61 -88.09 -93.95 -27.09 Median 5.39 5.17 5.66 18.76 0.02 -10.50 0.00 Maximum 344.09 1275.59 987.05 327.90 523.50 104.98 52.43 Median across Income Groups Low Inc 24.69 9.19 5.66 4.37 -3.11 -0.41 0.00 Lower-Middle Inc -1.95 -17.31 0.49 0.23 0.02 -25.28 -0.05 Upper-Middle Inc 35.55 85.71 55.70 27.21 66.65 -22.03 -48.52 High Inc 2.28 28.40 26.12 33.04 -161.17 -0.66 0.10 Median across Regions AFR 24.69 9.19 5.66 4.37 0.02 -25.42 0.01 EAP -0.67 1.00 1.30 -0.04 -3.11 -0.52 -0.05 ECA 3.84 11.18 7.34 21.54 27.55 -17.89 -79.86 LAC 6.73 -264.01 -96.81 30.10 79.19 -288.65 45.02 MNA 10.54 29.28 18.73 36.27 -268.58 98.31 3.13 SAR 344.09 542.88 198.80 147.17 -895.61 104.98 0.00 47 Table 6: Establishment Size, Age, and Growth The regressions estimated in this table are: Employment Growth/Sales Growth/Productivity Growth = a + b 0 Size Dummy for 5-100 employees + b1Size Dummy for 101-250 employees + b2 Size Dummy for 251+ employees (reference category) + b3 Age Dummy for 2 years + b4 Age Dummy for 3-5 years + b5 Age Dummy for 6+ years (reference category) +Country Dummies + Sector Dummies + Year Dummies + e. Employment Growth is defined as the log difference in permanent, full-time employment over a two year period. Sales Growth is defined as the log difference in sales over a two year period and Labor Productivity Growth is defined as the log difference in labor productivity (Sales/Employment) over a two year period. In cols. 1-3, 6, and 9 we report results for the full sample. In cols. 4,7, and 8 we report results for just the manufacturing sector and in cols. 5, 8, and 11 we report results for non-manufacturing firms. All data is at the firm level from the World Bank Enterprise Surveys. All regressions are OLS regressions with standard errors clustered at the country level. Panel A: 1 2 3 4 5 6 7 8 9 10 11 Employment Growth Sales Growth Productivity Growth Manu- Non-Manu Full Manu- Non-Manu Full Manu- Non-Manu Full Sample facturing facturing Sample facturing facturing Sample facturing facturing Size Dummy (5-100 employees) 0.113*** 0.100*** 0.105*** 0.087*** 0.005 0.007 -0.002 -0.091*** -0.094*** -0.085** (0.009) (0.009) (0.010) (0.015) (0.021) (0.019) (0.041) (0.017) (0.018) (0.039) Size Dummy (101-250 employees) 0.029*** 0.026*** 0.031*** 0.014 -0.015 -0.025 0.003 -0.037** -0.051*** -0.008 (0.008) (0.008) (0.010) (0.016) (0.018) (0.016) (0.046) (0.016) (0.016) (0.043) Age Dummy (<=2 years) 0.118*** 0.110*** 0.115*** 0.105*** 0.133*** 0.136*** 0.129*** 0.028** 0.032* 0.022 (0.009) (0.008) (0.012) (0.008) (0.015) (0.020) (0.018) (0.011) (0.017) (0.014) Age Dummy (3-5 years) 0.063*** 0.057*** 0.058*** 0.056*** 0.047*** 0.045*** 0.049*** -0.007 -0.009 -0.006 (0.005) (0.005) (0.006) (0.006) (0.007) (0.009) (0.012) (0.007) (0.009) (0.012) Constant -0.011 0.065*** -0.021* -0.012 0.018 0.104*** 0.121*** 0.192*** 0.120*** 0.112*** 0.135*** (0.012) (0.009) (0.011) (0.012) (0.018) (0.024) (0.022) (0.048) (0.020) (0.018) (0.044) # of Observations 40750 40129 40129 22974 17155 33220 19112 14108 33205 19109 14096 # of Countries 98.000 98.000 98 98 98 98 98 98 98 98 98 R-sq 0.058 0.065 0.078 0.085 0.079 0.052 0.057 0.052 0.042 0.046 0.044 Adjusted R-sq 0.056 0.062 0.076 0.08 0.074 0.049 0.052 0.045 0.039 0.04 0.037 *, **, and *** represent significance at 10, 5, and 1% respectively. 48 Panel B: 1 2 3 4 5 6 7 8 9 Employment Growth Sales Growth Productivity Growth Non- Non- Non- Full Sample Manufacturing Manufacturing Full Sample Manufacturing Manufacturing Full Sample Manufacturing Manufacturing Size (5-100 employees) and Age (<=2 years) 0.208*** 0.222*** 0.184*** 0.137*** 0.142*** 0.129*** -0.062*** -0.064*** -0.057 (0.011) (0.015) (0.018) (0.023) (0.025) (0.043) (0.019) (0.020) (0.039) Size (5-100 employees) and Age (3-5 years) 0.150*** 0.157*** 0.134*** 0.049** 0.049** 0.047 -0.095*** -0.101*** -0.085** (0.009) (0.011) (0.016) (0.021) (0.021) (0.040) (0.017) (0.018) (0.038) Size (5-100 employees) and Age (6+ years) 0.089*** 0.094*** 0.074*** 0.003 0.004 -0.001 -0.082*** -0.085*** -0.074* (0.008) (0.008) (0.016) (0.021) (0.020) (0.040) (0.017) (0.017) (0.038) Size (101-250 employees) and Age (<=2 years) 0.018 0.009 0.019 0.120* 0.087 0.160 0.106* 0.084 0.145 (0.023) (0.034) (0.028) (0.065) (0.086) (0.104) (0.059) (0.074) (0.094) Size (101-250 employees) and Age (3-5 years) 0.052*** 0.062*** 0.034 0.046 0.045 0.053 0.012 0.003 0.032 (0.014) (0.016) (0.032) (0.043) (0.041) (0.102) (0.038) (0.039) (0.086) Size (101-250 employees) and Age (6+ years) 0.024*** 0.030*** 0.011 -0.019 -0.030* 0.002 -0.040** -0.055*** -0.010 (0.007) (0.008) (0.016) (0.019) (0.018) (0.044) (0.017) (0.016) (0.043) Size (251+ employees) and Age (<=2 years) -0.059 -0.058 -0.064 0.064 0.102 -0.039 0.130 0.175 0.013 (0.096) (0.124) (0.086) (0.073) (0.064) (0.190) (0.115) (0.143) (0.171) Size (251+ employees) and Age (3-5 years) 0.015 0.020 -0.007 0.048 0.023 0.122 0.040 0.020 0.111 (0.024) (0.025) (0.054) (0.058) (0.033) (0.170) (0.056) (0.041) (0.165) Constant (reference category of Size(251+ employees) and Age (6+ years)) -0.012 -0.004 0.028 0.106*** 0.124*** 0.191*** 0.113*** 0.108*** 0.127*** (0.011) (0.011) (0.018) (0.025) (0.023) (0.047) (0.020) (0.018) (0.043) # of Firms 40129 22974 17155 33220 19112 14108 33205 19109 14096 # of Countries 98.000 98.000 98.000 98.000 98.000 98.000 98.000 98.000 98.000 R-Squared 0.080 0.087 0.080 0.052 0.057 0.053 0.042 0.046 0.045 Adjusted R-Sq 0.078 0.083 0.075 0.049 0.052 0.045 0.039 0.040 0.037 *, **, and *** represent significance at 10, 5, and 1% respectively. 49 Table 7: Establishment Size, Age, and Growth ­ Across Income Groups The regressions estimated in this table are: Employment Growth/Sales Growth/Productivity Growth = a + b 0 Size Dummy for 5-100 employees + b1Size Dummy for 101-250 employees + b2 Size Dummy for 251-500 employees + b3 Age Dummy for 2 years + b4 Age Dummy for 3-5 years + b5 Age Dummy for 6-10 years +Country Dummies + Sector Dummies + Year Dummies + e. Employment Growth is defined as the log difference in permanent, full-time employment over a two year period. Sales Growth is defined as the log difference in sales over a two year period and Labor Productivity Growth is defined as the log difference in labor productivity (Sales/Employment) over a two year period. In cols. 1, 5, and 9, we report results for a subpopulation of firms in low income countries. In cols. 2, 6, and 10, we report results for a subpopulation of firms in lower-middle income countries. In cols. 3, 7, and 11, we report results for a subpopulation of firms in upper middle income countries. In cols. 4, 8, and 12, we report results for a subpopulation of firms in high income countries. All data is at the firm level from the World Bank Enterprise Surveys. All regressions are OLS regressions with standard errors clustered at the country level. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Employment Growth Sales Growth Productivity Growth Lower- Upper- Lower- Upper- Lower- Upper- Low Middle Middle High Low Middle Middle High Low Middle Middle High Size Dummy (5-100 employees) 0.143*** 0.097*** 0.097*** 0.071*** 0.017 -0.008 0.009 0.024 -0.117** -0.095*** -0.089*** -0.039 (0.024) (0.016) (0.014) (0.013) (0.036) (0.038) (0.033) (0.040) (0.043) (0.034) (0.025) (0.041) Size Dummy (101-250 employees) 0.047* 0.035* 0.016 0.017 0.016 -0.031 -0.025 0.056 -0.031 -0.057** -0.039 0.047 (0.025) (0.018) (0.011) (0.017) (0.035) (0.031) (0.029) (0.047) (0.042) (0.023) (0.026) (0.051) Age Dummy (<=2 years) 0.090*** 0.099*** 0.135*** 0.124** 0.091*** 0.131*** 0.170*** 0.228*** 0.006 0.036 0.037 0.109* (0.012) (0.015) (0.014) (0.040) (0.022) (0.027) (0.026) (0.055) (0.016) (0.021) (0.023) (0.051) Age Dummy (3-5 years) 0.043*** 0.053*** 0.064*** 0.096** 0.038*** 0.052*** 0.041*** 0.101** -0.003 0.001 -0.023 0.023 (0.008) (0.006) (0.011) (0.030) (0.013) (0.013) (0.014) (0.036) (0.013) (0.014) (0.014) (0.032) Constant -0.113*** -0.005 -0.049*** -0.043 0.184** 0.035 0.038 -0.096* 0.251*** 0.127*** 0.092*** -0.036 (0.032) (0.021) (0.015) (0.026) (0.071) (0.032) (0.040) (0.049) (0.055) (0.024) (0.030) (0.067) # of Observations 7760 14502 15480 2387 7037 11982 12254 1947 7034 11978 12246 1947 # of Countries 29.000 33.000 28.000 8.000 29.000 33.000 28.000 8.000 29.000 33.000 28.000 8.000 R-sq 0.087 0.088 0.068 0.067 0.085 0.052 0.041 0.038 0.074 0.046 0.028 0.029 Adjusted R-sq 0.082 0.085 0.066 0.059 0.079 0.048 0.037 0.027 0.067 0.042 0.024 0.018 *, **, and *** represent significance at the 10, 5, and 1% respectively. 50 Table 8: Establishment Size, Age, and Growth ­ Large vs. Small Informal Sector The regressions estimated in this table are: Employment Growth/Sales Growth/Productivity Growth = a + b 0 Size Dummy for 5-100 employees + b1Size Dummy for 101-250 employees + b2 Size Dummy for 251-500 employees + b3 Age Dummy for 2 years + b4 Age Dummy for 3-5 years + b5 Age Dummy for 6-10 years +Country Dummies + Sector Dummies + Year Dummies + e. Employment Growth is defined as the log difference in permanent, full-time employment over a two year period. Sales Growth is defined as the log difference in sales over a two year period and Labor Productivity Growth is defined as the log difference in labor productivity (Sales/Employment) over a two year period. Cols 1-3 present results for countries that have a large informal sector (above the median value) and cols. 4-6 present results for countries with a small informal sector (below the median value) where informal sector is defined by the informal sector's contribution to GDP in Schneider et al. (2010). All data is at the firm level from the World Bank Enterprise Surveys. All regressions are OLS regressions with standard errors clustered at the country level. (1) (2) (3) (4) (5) (6) Employment Sales Productivity Employment Sales Productivity Growth Growth Growth Growth Growth Growth Large Informal Sector Small Informal Sector Size Dummy (5-100 employees) 0.105*** -0.004 -0.100*** 0.089*** 0.016 -0.072*** (0.016) (0.035) (0.029) (0.009) (0.023) (0.019) Size Dummy (101-250 employees) 0.037** -0.028 -0.054 0.018** -0.002 -0.021 (0.017) (0.035) (0.033) (0.008) (0.018) (0.017) Age Dummy (<=2 years) 0.112*** 0.133*** 0.029* 0.115*** 0.129*** 0.018 (0.014) (0.023) (0.017) (0.011) (0.019) (0.016) Age Dummy (3-5 years) 0.060*** 0.051*** -0.004 0.057*** 0.039*** -0.017 (0.006) (0.010) (0.011) (0.008) (0.011) (0.011) Constant -0.019 0.192*** 0.196*** -0.023* -0.023 0.120*** (0.024) (0.041) (0.037) (0.012) (0.028) (0.022) # of Observations 17313 14488 14485 20907 17220 17211 # of Countries 45.000 45.000 45.000 43.000 43.000 43.000 R-sq 0.083 0.059 0.050 0.070 0.048 0.030 Adjusted R-sq 0.079 0.055 0.046 0.067 0.044 0.027 *, **, and *** represent significance at the 10, 5, and 1% respectively. 51 Table 9: Establishment Size, Age, and Growth ­ Stand-Alone Establishments vs. Establishments that are part of a larger firm The regressions estimated in this table are: Employment Growth/Sales Growth/Productivity Growth = a + b 0 Size Dummy for 5-100 employees + b1Size Dummy for 101-250 employees + b2 Age Dummy for 2 years + b3 Age Dummy for 3-5 years + Country Dummies + Sector Dummies + Year Dummies + e. Employment Growth is defined as the log difference in permanent, full-time employment over a two year period. Sales Growth is defined as the log difference in sales over a two year period and Labor Productivity Growth is defined as the log difference in labor productivity (Sales/Employment) over a two year period. Cols. 1 to 3 present results for only single establishment firms. Cols. 4 to 6 present results for establishments that report being part of a larger firm. All data is at the firm level from the World Bank Enterprise Surveys. All regressions are OLS regressions with standard errors clustered at the country level. (1) (2) (3) (4) (5) (6) Employment Sales Productivity Employment Sales Productivity Growth Growth Growth Growth Growth Growth Single Establishment Part of a Larger Firm Size Dummy (5-100 employees) 0.108*** -0.001 -0.103*** 0.099*** 0.048 -0.048 (0.011) (0.021) (0.018) (0.011) (0.036) (0.034) Size Dummy (101-250 employees) 0.029*** -0.029 -0.051** 0.033*** 0.018 -0.015 (0.010) (0.024) (0.022) (0.011) (0.032) (0.033) Age Dummy (<=2 years) 0.112*** 0.132*** 0.024** 0.097*** 0.145*** 0.054 (0.009) (0.015) (0.011) (0.014) (0.036) (0.035) Age Dummy (3-5 years) 0.060*** 0.043*** -0.013 0.039*** 0.057*** 0.015 (0.005) (0.008) (0.009) (0.012) (0.020) (0.023) Constant -0.039*** 0.110*** 0.175*** -0.135*** 0.117 0.267*** (0.014) (0.023) (0.019) (0.025) (0.082) (0.069) # of Observations 34318 28574 28564 5429 4408 4405 # of Countries 97.000 97.000 97.000 96.000 96.000 96.000 R-sq 0.081 0.056 0.045 0.098 0.064 0.057 Adjusted R-sq 0.078 0.052 0.041 0.079 0.039 0.032 *, **, and *** represent significance at the 10, 5, and 1% respectively. 52 Table 10: Establishment Size, Age, and Growth ­ Additional Robustness The regressions estimated in this table are: Employment Growth/Sales Growth/Productivity Growth = a + b 0 Size Dummy for 5-100 employees + b1Size Dummy for 101-250 employees + b2 Size Dummy for 251-500 employees + b3 Age Dummy for 2 years + b4 Age Dummy for 3-5 years + b5 Age Dummy for 6-10 years +Country Dummies + Sector Dummies + Year Dummies + Country x Sector Dummies + e. Employment Growth is defined as the log difference in permanent, full-time employment over a two year period. Sales Growth is defined as the log difference in sales over a two year period and Labor Productivity Growth is defined as the log difference in labor productivity (Sales/Employment) over a two year period. In Cols. 1-3 we include country x sector interaction effects and use OLS regressions with standard errors clustered by country. In cols. 4 to 6 we use OLS regressions but cluster standard errors by survey strata. In cols. 7 to 9 we use weighted survey regressions. (1) (2) (3) (4) (5) (6) (7) (8) (9) Employment Sales Productivity Employment Sales Productivity Employment Sales Productivity Growth Growth Growth Growth Growth Growth Growth Growth Growth Country x Sector Effects Clustering by strata Weighted Survey Regression Size Dummy (5-100 employees) 0.100*** 0.003 -0.093*** 0.092*** 0.004 -0.084*** 0.069*** -0.056 -0.112 (0.009) (0.022) (0.019) (0.006) (0.018) (0.018) (0.020) (0.132) (0.130) Size Dummy (101-250 employees) 0.028*** -0.019 -0.043** 0.025*** -0.021 -0.043** 0.062 -0.439* -0.506* (0.009) (0.019) (0.017) (0.007) (0.020) (0.021) (0.049) (0.265) (0.302) Age Dummy (<=2 years) 0.110*** 0.130*** 0.026** 0.127*** 0.169*** 0.043** 0.092*** 0.106** 0.010 (0.008) (0.015) (0.012) (0.008) (0.016) (0.017) (0.020) (0.048) (0.054) Age Dummy (3-5 years) 0.057*** 0.046*** -0.009 0.061*** 0.056*** -0.000 0.057*** 0.106* 0.046 (0.005) (0.008) (0.008) (0.005) (0.012) (0.011) (0.017) (0.060) (0.072) Constant 0.045*** 0.310*** 0.262*** 0.094*** 0.391*** 0.294*** 0.115*** 0.516*** 0.388** (0.008) (0.018) (0.015) (0.013) (0.028) (0.028) (0.035) (0.161) (0.161) # of Observations 40129 33220 33205 27748 21757 21746 27748 21757 21746 # of Countries 98.000 98.000 98.000 64 64 64 64 64 64 R-sq 0.108 0.087 0.074 0.077 0.050 0.036 0.050 0.082 0.082 Adjusted R-sq 0.083 0.056 0.043 0.074 0.047 0.032 *, **, and *** represent significance at 10, 5, and 1% respectively. 53 Appendix: Full Sample Manufacturing Source (Common for all tables) Nation year SME50 SME50 SME100 SME150 SME200 SME250 SME300 SME500 Afghanistan 2007 41.76 36.51 62.19 72.91 77.33 77.33 77.33 77.33 Enterprise Surveys Albania 2006 50.47 37.58 54.35 65.43 82.65 94.78 94.78 96.91 Enterprise Surveys Angola 2005 83.85 78.16 84.36 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Argentina 2005 11.60 12.33 19.49 26.16 28.86 29.18 34.16 43.21 Enterprise Surveys Armenia 2008 26.97 19.29 36.82 59.58 63.44 73.50 85.89 88.56 Enterprise Surveys Azerbaijan 2008 20.54 17.16 30.51 41.46 47.37 54.69 72.79 85.85 Enterprise Surveys Bangladesh 2006 7.96 6.29 8.09 9.77 12.78 18.12 23.32 37.33 Enterprise Surveys Belarus 2007 12.96 5.50 10.39 13.88 17.37 18.93 25.05 38.65 Enterprise Surveys Benin 2008 58.27 17.47 17.47 32.89 32.89 40.42 40.42 40.42 Enterprise Surveys Bhutan 2008 43.58 26.11 40.38 54.94 65.55 70.97 83.57 100.00 Enterprise Surveys Bolivia 2005 44.67 45.53 58.16 64.57 67.75 78.61 79.34 91.39 Enterprise Surveys Bosnia and Herzegovina 2008 27.41 22.80 47.52 54.66 59.48 65.04 68.33 81.12 Enterprise Surveys Botswana 2005 34.42 19.31 36.77 54.89 59.31 64.05 64.05 69.56 Enterprise Surveys Brazil 2008 11.26 10.80 21.39 28.68 33.41 36.69 37.69 48.49 Enterprise Surveys Bulgaria 2006 32.48 31.55 43.90 52.49 58.19 59.68 66.74 74.66 Enterprise Surveys Burkina Faso 2008 38.53 33.47 44.36 61.47 62.91 79.91 84.46 90.79 Enterprise Surveys Burundi 2005 74.79 60.90 81.71 93.24 100.00 100.00 100.00 100.00 Enterprise Surveys Cameroon 2008 22.26 11.59 23.55 30.60 35.62 35.62 38.15 51.62 Enterprise Surveys Cape Verde 2008 52.91 69.00 75.46 84.89 84.89 84.89 100.00 100.00 Enterprise Surveys Chad 2008 52.02 25.08 44.16 64.47 64.47 64.47 85.22 100.00 Enterprise Surveys Chile 2005 9.48 17.14 25.13 32.89 38.17 41.34 48.18 53.16 Enterprise Surveys Colombia 2005 51.90 58.38 72.42 74.54 76.53 78.92 79.42 83.09 Enterprise Surveys Congo, Dem. Rep. 2005 62.97 55.15 65.84 74.53 89.32 93.49 100.00 100.00 Enterprise Surveys Congo, Rep. 2008 37.30 30.09 53.10 53.10 53.10 53.10 53.10 53.10 Enterprise Surveys Cote d'Ivoire 2008 44.31 36.76 42.29 51.21 54.57 59.95 62.89 64.51 Enterprise Surveys Croatia 2006 34.86 28.30 38.41 50.31 56.01 62.37 66.12 83.94 Enterprise Surveys Czech Republic 2008 31.15 18.53 34.47 45.61 52.77 55.66 57.76 69.72 Enterprise Surveys Ecuador 2005 27.26 25.42 34.32 52.96 64.64 65.91 73.42 87.22 Enterprise Surveys El Salvador 2005 28.49 16.77 28.36 34.42 40.22 42.67 48.34 56.18 Enterprise Surveys Eritrea 2008 74.98 65.03 84.22 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Estonia 2008 37.82 29.92 55.92 63.54 73.61 82.38 87.85 100.00 Enterprise Surveys Fiji 2008 29.71 17.12 31.90 45.51 52.28 52.28 52.28 59.74 Enterprise Surveys Gabon 2008 27.25 23.85 34.01 34.01 38.25 54.51 54.51 54.51 Enterprise Surveys Gambia 2005 50.84 62.22 71.49 71.49 84.26 100.00 100.00 100.00 Enterprise Surveys Georgia 2007 13.35 14.27 23.08 25.51 26.52 27.79 29.03 31.81 Enterprise Surveys 54 Full Sample Manufacturing Source (Common for all tables) Nation year SME50 SME50 SME100 SME150 SME200 SME250 SME300 SME500 Ghana 2006 29.83 25.38 30.73 41.40 54.34 55.48 72.09 82.27 Enterprise Surveys Guatemala 2005 36.87 33.70 43.25 47.75 59.79 62.35 63.12 78.13 Enterprise Surveys Guinea 2005 52.66 46.08 58.64 62.36 68.17 81.46 81.46 81.46 Enterprise Surveys Guinea-Bissau 2005 60.93 58.30 72.10 72.10 72.10 72.10 100.00 100.00 Enterprise Surveys Honduras 2005 9.88 26.92 60.96 67.15 71.16 72.89 75.22 81.67 Enterprise Surveys Hungary 2008 19.79 14.81 28.23 34.89 38.36 40.87 46.13 56.54 Enterprise Surveys Indonesia 2008 34.74 32.32 38.43 41.63 44.04 45.10 46.18 51.00 Enterprise Surveys Kazakhstan 2008 27.87 16.97 23.92 33.65 43.53 51.15 55.50 72.41 Enterprise Surveys Kenya 2006 24.08 8.19 20.09 30.74 38.01 41.75 47.59 53.82 Enterprise Surveys Kosovo 2008 62.25 82.95 96.10 96.10 96.10 100.00 100.00 100.00 Enterprise Surveys Kyrgyz Republic 2008 28.49 21.60 35.32 41.56 47.91 47.91 75.53 87.39 Enterprise Surveys Lao PDR 2008 48.34 28.78 36.33 40.53 45.90 50.68 56.79 64.89 Enterprise Surveys Latvia 2008 33.19 27.44 49.02 57.61 70.56 75.86 78.48 96.44 Enterprise Surveys Lesotho 2008 8.82 1.64 2.70 2.94 3.14 3.14 3.84 6.57 Enterprise Surveys Liberia 2008 72.64 81.01 82.82 82.82 85.85 89.35 89.35 100.00 Enterprise Surveys Lithuania 2008 43.84 28.89 49.86 58.42 63.45 69.16 76.76 89.38 Enterprise Surveys Macedonia, FYR 2008 30.89 19.87 43.73 52.54 57.44 59.03 64.60 71.31 Enterprise Surveys Madagascar 2008 22.07 9.48 19.35 25.78 29.62 30.71 34.03 48.00 Enterprise Surveys Malawi 2008 14.42 4.93 14.20 19.24 21.92 23.11 24.80 30.17 Enterprise Surveys Mali 2006 63.30 59.31 70.24 76.47 79.23 91.13 91.13 100.00 Enterprise Surveys Mauritania 2005 70.72 59.78 74.14 78.78 91.54 91.54 100.00 100.00 Enterprise Surveys Mauritius 2008 21.84 19.81 30.93 33.15 45.83 52.59 53.19 68.07 Enterprise Surveys Mexico 2005 34.80 24.52 43.83 56.30 57.79 59.58 62.92 69.89 Enterprise Surveys Micronesia, Fed. Sts. 2008 74.97 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Moldova 2008 37.33 22.45 32.42 38.11 48.24 53.48 58.48 70.81 Enterprise Surveys Mongolia 2008 36.96 36.06 54.13 67.73 69.82 74.37 77.94 84.29 Enterprise Surveys Montenegro 2008 60.54 49.03 69.73 84.41 100.00 100.00 100.00 100.00 Enterprise Surveys Mozambique 2006 33.47 55.25 75.74 91.06 92.32 95.82 95.82 100.00 Enterprise Surveys Namibia 2005 69.37 52.81 63.53 68.15 72.43 74.51 74.51 83.14 Enterprise Surveys Nepal 2008 68.25 61.85 63.34 68.62 73.35 81.72 83.50 94.67 Enterprise Surveys Nicaragua 2005 42.00 46.32 58.20 59.27 60.73 64.51 72.41 87.84 Enterprise Surveys Niger 2008 72.91 62.98 75.55 93.66 100.00 100.00 100.00 100.00 Enterprise Surveys Nigeria 2006 63.05 47.20 70.64 78.84 86.57 87.81 88.66 94.21 Enterprise Surveys Panama 2005 24.23 27.93 50.00 60.59 67.75 72.30 77.19 82.37 Enterprise Surveys Paraguay 2005 36.93 34.87 51.72 66.94 74.62 77.91 81.15 100.00 Enterprise Surveys Peru 2005 15.68 9.90 20.90 24.54 26.09 26.31 44.30 52.70 Enterprise Surveys Philippines 2008 20.35 15.90 30.63 37.33 41.37 47.49 50.94 60.89 Enterprise Surveys Poland 2008 27.45 15.16 26.54 37.27 47.14 63.90 73.56 82.48 Enterprise Surveys 55 Full Sample Manufacturing Source (Common for all tables) Nation year SME50 SME50 SME100 SME150 SME200 SME250 SME300 SME500 Romania 2008 35.94 20.38 37.56 48.78 57.78 61.80 65.59 76.56 Enterprise Surveys Russian Federation 2008 5.94 6.67 14.76 18.45 21.59 26.26 33.84 49.88 Enterprise Surveys Rwanda 2005 34.45 21.69 29.72 38.75 46.62 53.31 62.43 62.43 Enterprise Surveys Senegal 2006 36.08 20.61 31.68 39.54 43.27 43.27 49.09 56.54 Enterprise Surveys Serbia 2008 22.60 17.38 31.35 42.18 48.91 54.11 58.61 71.57 Enterprise Surveys Sierra Leone 2008 54.24 84.42 96.00 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Slovak Republic 2008 34.41 25.96 39.92 47.33 52.67 53.84 55.33 64.89 Enterprise Surveys Slovenia 2008 23.15 14.53 26.09 30.16 36.18 39.96 39.96 65.59 Enterprise Surveys South Africa 2006 23.86 19.90 36.97 47.81 52.18 56.80 60.80 70.77 Enterprise Surveys Swaziland 2005 25.57 10.00 17.63 22.41 29.87 34.64 49.16 57.10 Enterprise Surveys Tajikistan 2007 17.36 10.38 24.90 30.68 32.87 39.53 42.51 49.90 Enterprise Surveys Tanzania 2005 41.70 30.20 45.31 54.98 70.65 74.51 74.51 88.18 Enterprise Surveys Timor-Leste 2008 54.77 91.66 100.00 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Togo 2008 59.87 22.11 29.56 35.83 67.25 67.25 67.25 100.00 Enterprise Surveys Tonga 2008 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Turkey 2007 15.64 17.29 28.50 33.58 39.30 44.02 48.21 57.22 Enterprise Surveys Uganda 2005 39.65 25.34 34.21 39.36 43.55 45.85 50.05 71.21 Enterprise Surveys Ukraine 2007 21.35 11.10 21.21 25.88 28.02 31.31 35.97 45.87 Enterprise Surveys Uruguay 2005 47.78 56.65 71.35 79.86 83.11 86.44 87.18 92.03 Enterprise Surveys Uzbekistan 2007 44.86 29.30 51.36 63.06 65.51 65.95 68.32 73.98 Enterprise Surveys Vanuatu 2008 73.46 33.46 100.00 100.00 100.00 100.00 100.00 100.00 Enterprise Surveys Venezuela, RB 2005 44.23 41.71 53.68 60.61 69.53 73.75 80.05 81.85 Enterprise Surveys Vietnam 2008 14.64 6.56 14.57 20.79 27.16 28.29 32.54 41.30 Enterprise Surveys Western Samoa 2008 44.96 25.01 30.24 30.24 30.24 30.24 30.24 30.24 Enterprise Surveys Yemen, Rep. 2009 40.21 40.06 46.17 49.44 60.15 68.51 69.58 80.41 Enterprise Surveys Zambia 2006 21.92 16.06 35.19 44.80 51.07 58.71 69.21 91.02 Enterprise Surveys Data from Sources other than World Bank Enterprise Surveys Australia 2002 52.30 IFC-OECD Austria 2008 54.66 European Commission on Enterprise& Industry Belgium 2008 54.98 European Commission on Enterprise& Industry Statistics Canada, Survey of Employment, Payrolls Canada 2009 36.36 53.63 61.71 and Hours (SEPH) Cyprus 2008 87 European Commission on Enterprise& Industry Denmark 2008 54.46 European Commission on Enterprise& Industry Finland 2008 48.74 European Commission on Enterprise& Industry France 2008 53.72 European Commission on Enterprise& Industry Germany 2008 46.85 European Commission on Enterprise& Industry Greece 2008 78.24 European Commission on Enterprise& Industry Hong Kong, China 2008 53.71 Hong Kong Trade & Industry Dept 56 Full Sample Manufacturing Source (Common for all tables) Nation year SME50 SME50 SME100 SME150 SME200 SME250 SME300 SME500 Iceland 2008 6.70 European Commission on Enterprise& Industry Ireland 2008 53.40 European Commission on Enterprise& Industry Israel 2008 51.19 European Commission on Enterprise& Industry Italy 2008 77.91 European Commission on Enterprise& Industry Japan 2006/2007 55.27 67.75 67.8 74.20 JPN Census/OECD Liechtenstein 2007 31.61 European Commission on Enterprise& Industry Luxembourg 2008 39.49 European Commission on Enterprise& Industry Malaysia 2008 33.2 Malaysian Dept of Statistics Malta 2008 59.26 European Commission on Enterprise& Industry Netherlands 2008 67.20 European Commission on Enterprise& Industry Norway 2008 61.69 European Commission on Enterprise& Industry Portugal 2008 81.55 European Commission on Enterprise& Industry Spain 2008 74.04 European Commission on Enterprise& Industry Sweden 2008 50.60 European Commission on Enterprise& Industry Switzerland 2005 63.26 European Commission on Enterprise& Industry Taiwan, China 2006 64.49 77.03 National statistics of Taiwan Thailand 2006 38.54 48.04 62.82 National Statistics Office United Kingdom 2008 56.47 European Commission on Enterprise& Industry United States 2007 26.07 44.43 US Small Business Administration IFC- (Central Bureau of Statistics Netherlands Netherlands Antilles 2010 50.59 Antilles) Bermuda 2008 49.32 IFC-(Bermuda Dept of Statistics) Guam 2007 59.94 IFC-US Census Bureau Virgin Islands (U.S.) 2007 64.77 IFC-US Census Bureau West Bank and Gaza 2007 82.00 IFC-Palestinian Central Bureau of Statistics Summary Statistics Minimum 5.94 1.64 2.70 2.94 3.14 3.14 3.84 6.57 Mean 39.72 32.87 45.79 53.45 59.13 62.00 67.42 74.95 Median 36.90 25.96 40.15 51.85 58.19 60.82 68.33 78.13 Maximum 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Median across Income Groups Low Inc 49.59 31.84 44.84 61.92 67.71 73.31 79.40 90.91 Lower-Middle Inc 37.32 30.09 42.77 53.10 60.44 65.91 69.58 79.27 Upper-Middle Inc 27.64 19.89 36.87 47.81 54.86 57.92 63.49 71.44 High 34.64 22.25 37.38 46.47 52.77 55.66 61.94 69.72 Median across Regions AFR 43.01 30.15 44.26 54.94 63.69 65.86 73.30 85.66 EAP 48.34 30.55 38.54 41.63 52.28 52.28 56.79 63.86 ECA 29.69 20.13 36.07 46.47 52.67 55.32 65.86 74.32 LAC 35.84 27.43 46.92 57.79 62.69 65.21 72.92 81.76 MNA 61.11 40.06 46.17 49.44 60.15 59.26 69.58 80.41 NAmer 49.30 31.21 53.63 53.07 SAR 42.67 31.31 51.29 61.78 69.45 74.15 80.42 86.00 57