metadata: authors: 0: author: Dinh, Hinh T.
1: author: Mavridis, Dimitris A.
2: author: Nguyen, Hoa B.
count: World
docna: 0: docna: The binding constraint on firms' growth
            in developing countries
docty: Policy Research Working Paper
subtopic: Business Environment,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Financial Economics,Business Cycles and Stabilization Policies,Finance and Development,Access to Finance
teratopic: Environment,Finance and Financial Sector Development
abstracts: cdata!: Firms in developing countries face
            numerous and serious constraints on their growth, ranging
            from corruption to lack of infrastructure to inability to
            access finance. Countries lack the resources to remove all
            the constraints at once and so would be better off removing
            the most binding one first. This paper uses data from World
            Bank Enterprise Surveys in 2006-10 to identify the most
            binding constraints on firm operations in developing
            countries. While each country faces a different set of
            constraints, these constraints also vary by firm
            characteristics, especially firm size. Across all countries,
            access to finance is among the most binding constraints;
            other obstacles appear to matter much less. This result is
            robust for all regions. Smaller firms must rely more on
            their own funds to invest and would grow significantly
            faster if they had greater access to external funds. As a
            result, a low level of financial development skews the firm
            size distribution by increasing the relative share of small
            firms. The results suggest that financing constraints play a
            significant part in explaining the "missing
            middle" -- the failure of small firms in developing
            countries to grow into medium-size or large firms.
repnme: repnme: The binding constraint on firms'
            growth in developing countries
display_title: The binding constraint on
            firms' growth in developing countries
listing_relative_url: /research/2010/11/13157472/binding-constraint-firms-growth-developing-countries
topicv3: Informal Sector m1326258 1168,Manufacturing Sectors m1326521 1281,Finance and Growth m1327404 1081,Access to Finance m1327464 1249,Business Environment m1586411 2530
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Finance and Financial Sector Development
no_of_pages: 42
model_extractions: WPS5485 Policy Research Working Paper 5485 The Binding Constraint on Firms’ Growth in Developing Countries _Hinh T. Dinh Dimitris A. Mavridis Hoa B. Nguyen_ The World Bank Development Economics Operations and Strategy Unit November 2010
Policy Research Working Paper 5485

# **Abstract**

Firms in developing countries face numerous and serious constraints on their growth, ranging from corruption to lack of infrastructure to inability to access finance. Countries lack the resources to remove all the constraints at once and so would be better off removing the most binding one first.

This paper uses data from World Bank Enterprise Surveys in 2006 –10 to identify the most binding constraints on firm operations in developing countries. While each country faces a different set of constraints, these constraints also vary by firm characteristics, especially firm size. Across all countries, access to finance is among the most binding constraints; other obstacles appear to matter much less. This result is robust for all regions.

Smaller firms must rely more on their own funds to invest and would grow significantly faster if they had greater access to external funds. As a result, a low level of financial development skews the firm size distribution by increasing the relative share of small firms. The results suggest that financing constraints play a significant part in explaining the “missing middle”—the failure of small firms in developing countries to grow into medium-size or large firms.

This paper—a product of the Operations and Strategy Unit, Development Economics—is part of a larger effort in the department to develop new practical insights as to how African countries can significantly improve their production of light manufacturing products. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at HDinh@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
# **The Binding Constraint on Firms’ Growth in Developing Countries**

**Hinh T. Dinh, Dimitris A. Mavridis, Hoa B. Nguyen**<sup>**1**</sup>

> 1 The authors would like to thank Anders Isaksson, Justin Lin and Vincent Palmade for their helpful comments on a previous draft of this paper.
# **1. Introduction**

Private sector growth remains one of the main challenges facing developing countries in their quest for development and poverty reduction. Extensive evidence shows that a favorable business environment helps promote the growth of firms. As shown in recent research, however, firms in developing countries face a tougher business environment than their counterparts in the developed world.

Our aim in this paper is twofold. First, we seek to go beyond the traditional menu of constraints on firm growth to find out which of these constraints is the most binding. As the growth diagnostics approach points out, developing countries have scarce resources and therefore need to focus on removing the most binding constraint. Second, we examine the effects of the most binding constraint on firm growth not only across countries but also by firm characteristics.

We first explore the relationship between the business environment and firm growth as measured by employment growth. Among 15 components of the business environment, we identify the most binding constraint using both subjective and objective measures. Our focus is on the most binding constraints for existing firms and, more specifically, the binding constraint that matters the most for firm growth. The methodology follows two steps. The first is to find out which constraints are statistically significant among all regressions after controlling for firm characteristics and country fixed effects. The second is to identify the most binding constraint. We find that besides informal sector competition, access to finance is the obstacle that matters the most for growth. This result is robust for all regions and all sectors.

Our analysis contributes to the existing literature in several ways. First, using a large sample, containing more than 39,000 firms across 98 countries, we identify the most binding constraint on firms using subjective measures, then evaluate the importance of this constraint to firm growth using objective measures and controlling for firm characteristics. The sample comes from World Bank Enterprise Surveys conducted in 2006–2010 in mostly emerging and developing countries. The surveys provide both subjective data on perceived obstacles and objective measures of many constraints.

Second, we investigate the effect of financial access variables on firm growth by using firm-level regressions across countries controlling for the effects of different firm sizes, firm ages, sectors, and regions. Our results show that having access to finance in the form of a loan, sales credit, or external finance helps micro firms the most. This finding holds not only for the full sample but also for different regions. Sales credit is important only to micro and small firms, probably because it substitutes for bank loans. Having a loan or overdraft facility and receiving external finance for investment help growth for firms of all sizes across regions.

Third, we find clear evidence that a low level of financial sector development affects the firm size distribution and therefore contributes to the phenomenon of the “missing middle” in developing countries. Firm size distribution is skewed toward small and medium-size firms—and more so in 2
Africa, among firms that are credit constrained, and among firms that perceive access to finance as an obstacle. Our analysis shows that firm size and age are significantly correlated with firm growth. Distinguishing between different types of ownership, we find that firms tend to have higher growth if they are an exporter, are part of entities with multiple establishments, are foreign owned, or are privately owned.

The paper is organized as follows. The next section reviews the literature and shows how this paper relates to it. Section 3 presents an overview of the data and describes the sample used. Section 4 examines the most binding constraint of the business environment. Section 5 examines the effect of access to finance on employment growth in the full sample and by region, while section 6 looks at determinants of access to finance. Section 7 investigates differences in the effect of financial access variables on employment growth, and section 8 looks at the relationship between firm size and financial constraints. The last section concludes.

# **2. Literature Review**

Understanding the firm growth process is important for designing appropriate policies for job creation and pro-poor growth. Many studies are devoted to understanding the determinants of firm growth, especially employment growth. Most of these studies focus on the manufacturing sector and large firms (Evans 1987a, 1987b; Hall 1987; Dunne, Roberts, and Samuelson 1989). They find that firm age and firm size are important in the analysis of firm growth.

## **2.1 Effect of the Business Environment on Firm Growth**

A number of recent studies use World Business Environment Survey data (a firm-level data set covering 4,000 firms across 54 countries) to study the effect of the business environment on firm growth. Using subjective, firm-level data on the business environment, some of these studies show the importance of finance, corruption, and property rights (Batra, Kaufmann, and Stone 2003; Ayyagari, Demirgüç-Kunt, and Maksimovic 2006). Others examine the relationship between the business environment and firm growth in individual countries or a small group (Dollar, HallwardDriemeier, and Mengistae 2005 in Bangladesh, China, India, and Pakistan; Fisman and Svensson 2007 and Reinikka and Svensson 2002 in Uganda; Bigsten and Söderbom 2006 in Africa).

Other studies also assess the effect of different dimensions of the business environment on firm growth. Some focus on the importance of access to finance for firm development and growth using subjective data (Rajan and Zingales 1998; Galindo and Micco 2007). Others investigate the impact of employment regulations on firm creation and growth (Djankov and others 2002; Klapper, Laeven, and Rajan 2004).

Several papers have emphasized the importance of financing obstacles. Using firm-level data, Demirgüç-Kunt and Maksimovic (1998) provide evidence on the importance of the financial system and legal enforcement on firm growth. Rajan and Zingales (1998) present supporting evidence on 3
the role of external finance for faster growth in countries with better developed financial systems. These papers focus on only a small set of obstacles that firms confront without discussing the motivation for choosing that set.

It is essential to explore the relationship between the business environment and firm growth not only across countries but across regions and by firm characteristics within countries—by firm size, age, sector, and ownership type. In examining this relationship, the literature has focused largely on the effect of difficulties in access to finance by firm type, particularly firm size. Generally the finding is that smaller firms are more constrained (Love and Mylenko 2003; IDB 2007). Beck, DemirgüçKunt, and Maksimovic (2005), using the World Business Environment Survey data set, include measures of corruption and property rights. Based on firms’ perceptions of potential constraints, they also find patterns across countries, with small firms benefiting the most from greater financial and institutional development.

Aterido, Hallward-Driemeier, and Pagés (2007, 2009, 2010) analyze the effect of several aspects of the business environment—access to finance, corruption, and regulations—on the growth of firms. Their findings show that the business environment affects small, medium-size, and large firms differently. The reason is that small firms are exposed to a different set of constraints than large firms are. Access to electricity, for example, has heterogeneous effects: small and medium-size firms are often affected by power cuts, while large and micro firms tend not to be. The main reason is that micro firms use less energy-intensive tools and large firms are more likely to secure their own energy supply (Gelb and others 2007). Thus infrastructure such as the electricity grid affects the growth rate of small and medium-size firms directly, but has only an indirect effect on the growth rate of micro and large firms. Micro firms are much more credit constrained and must rely less on external funds to finance investment. Improving access to finance might boost the entry rate and the growth of small firms, perhaps at the expense of larger incumbents.

According to Dollar, Hallward-Driemeier, and Mengistae (2005), improving the business environment is an important complement to trade policies aimed at increasing international trade integration. Factors such as fast customs clearance times, good infrastructure, and availability of financial services have a significant impact on the probability of a firm’s exporting and receiving foreign investment. Freund and Rocha (2010) provide more evidence of the link between the business environment and international trade. Using data from Africa, they find that even though poor trade infrastructure is one of the main obstacles to trade, most of the burden is due to heavy “red tape,” bureaucratic customs practices that increase the time and cost of trade.

Gelb and others (2007) use subjective data on the business environment from 26 African countries to show that perceived constraints are not always independent of scale. Complaints about access to finance and land are more common among small firms, while complaints about infrastructure and corruption are more evenly distributed. They also find that a country’s level of development strongly determines which constraints are present (country fixed effects are more important than within4
country variations). This finding is shared by the World Bank’s _Africa Competitiveness Report 2009_ , which shows that as a country’s income rises, its set of constraints changes.

All these studies share a common result: business environment variables affect firms’ growth, in the expected direction. The results are heterogeneous by firm size, and they are robust.

# **2.2 Financial Development and Firm Size Distribution**

A common finding in the literature is that the firm size distribution in developing countries is skewed toward small and medium-size firms. Small firms are often credit constrained and cannot borrow to engage in productive investments, which limits their growth and can prolong the skewness. If lack of access to finance prevents small firms from growing, the allocation of resources will be distorted. Capital and labor will not be able to flow to where they are most productive, and growth will suffer.

Cooley and Quadrini (2001) and Cabral and Mata (2003) present different models of firms’ growth, showing that capital constraints can cause a skewness in the firm size distribution. Their prediction is verified empirically. Cabral and Mata (2003) find that the size distribution of firms is skewed toward small firms and that the skewness decreases with firm age. Many subsequent papers confirm the skewness of the firm size distribution, such as Angelini and Generale (2008), Beck, DemirgüçKunt and Maksimovic (2005), and Desai, Gompers, and Lerner (2003).

Desai, Gompers, and Lerner (2003) find that in countries with less developed capital markets, the firm size distribution is significantly more skewed. They also find that a better legal environment favors entry (more small firms will enter) while the growth of small firms reduces the skewness. Angelini and Generale (2008) and Beck, Demirgüç-Kunt and Maksimovic (2005) find that capitalconstrained firms grow more slowly than their counterparts.

# **2.3 Growth Diagnostics Approach**

The growth diagnostics approach proposed by Hausmann, Rodrik, and Velasco (2005) (hereafter, the HRV approach) provides a theoretical framework to identify the most binding constraints on economic growth in general. This methodology recognizes that constraints on the growth of a developing economy are numerous and that previous approaches to reforms and growth are either unrealistic (as with wholesale reform that attempts to eliminate all obstacles at the same time) or wrong (by hoping to do as many reforms as possible, the current prevailing approach goes against the principle of second best).

The HRV approach is based on the theory of second best (Lipsey and Lancaster, 1956). According to this theory, if there are many distortions in the economy, fixing any one distortion would not necessarily lead to a better Pareto outcome. The HRV approach shows that if there are many distortions, whether removing one growth constraint will have a positive effect on growth depends 5
on the interaction effects and coefficients of the other constraints. In the face of uncertainty about these effects, Hausmann, Rodrik, and Velasco recommend a practical approach based on removing the most binding constraint, with the “most binding constraint” defined as the one with the largest effect where issues of second-best effects are likely to be minimal.

## **2.4 This Study’s Contribution to the Literature**

In this paper, based on the HRV approach, we investigate the most binding constraint on the growth of firms, with the “most binding constraint” defined as the one with the largest estimated coefficient across all models and across regions and sectors. Compared with studies using the World Business Environment Survey data set, our paper uses a much larger sample. And while other studies use subjective firm responses as measures of the business environment at the firm level, we also include objective measures, in part to deal with endogeneity and in part to avoid measurement errors of perceptions at the country level.

In exploring the relationship between the business environment and firm growth, we go beyond distinguishing effects by firm size. We look closely at the effect of financial access variables—loan, credit constraint, sales credit, and external investment finance—on firm growth by firm size and age in different sectors and regions. We combine multiple financial access variables in a single regression in addition to evaluating the effect of each variable on employment growth controlled for firm size, age, and other characteristics. This allows an understanding of the impact of each dimension of finance on firm growth as firm characteristics change.

Moreover, our paper emphasizes which element of the business environment matters most for firms, especially for small firms. And it analyzes how different financial access variables affect the firm size distribution across regions and sectors.

# **3. Data**

In this paper we use a newly available firm-level data set from the World Bank Enterprise Surveys. The surveys cover more than 100,000 firms across more than 120 economies and six regions during 2006–10. We use a sample of 39,538 firms in 98 countries for which data are complete. The unit in the sample is the establishment; one firm may have more than one establishment. For simplicity, we use the term firms throughout the paper, though the analysis is based on establishment data.

Our outcome variable of interest is employment growth, measured by the number of permanent employees. Our policy interest is in understanding the determinants that are important to the longterm business operation and employment growth of firms.<sup>2</sup> Because there are no data on temporary

> 2 Like other researchers, we use employment growth rather than sales growth, for several reasons. Sales growth is more volatile and is also more prone to reporting and measurement biases, especially when survey respondents are reporting sales realized three years before. Moreover, for tax reasons, firms may not choose to report actual sales.

6
employees collected three fiscal years before the survey fiscal year, we focus on permanent full-time employees rather than general full-time employees.

The firm growth rate is calculated as the log difference between the current number of employees and the number of employees three fiscal years before the survey fiscal year. The formula for employment growth is as follows: where ��� is firm size, and ���� employment growth, for firm _i_ at time _t_ .<sup>3</sup> The description and summary statistics for the employment growth variable are reported in table 1.

World Bank Enterprise Surveys are conducted to provide information on different aspects of the business environment and the performance of firms. The core questionnaire, which contains survey questions answered by business owners and top managers around the world, provides both subjective and objective information on the business environment that firms confront. The questionnaire includes a section asking firms to rank 15 components of the business environment, indicating which represent the biggest obstacles, and to evaluate these 15 components on a scale of 0–4 (0 being no obstacle, 1 a minor obstacle, 2 a moderate obstacle, 3 a major obstacle, and 4 a very severe obstacle). Summary statistics for the related variables are provided in table 1.

These subjective evaluations show the severity of obstacles across regions and countries. This makes it possible to identify the top obstacles and examine which obstacles firms consider to be the most important. But because the data are subjective—reflecting entrepreneurs’ perceptions of the impact of the business environment on firm operation, with successful entrepreneurs perhaps likely to consider the business environment to be less restrictive—we need to control for firm characteristics in explaining firm growth. In addition, we need to include objective measures of business environment constraints.

The World Bank Enterprise Surveys provide a large set of objective measures of business environment constraints. In addition to subjective information on access to finance as an obstacle, the questionnaire also collects objective information on aspects of financial access, allowing us to create several variables: _Loan_ is a dummy variable indicating whether a firm has a loan or line of credit from a financial institution or an overdraft facility _. Credit constraint_ is a dummy variable indicating whether an establishment did not apply for loans or lines of credit for one or more of the following reasons: application procedures for loans or lines of credit are complex, interest rates are not favorable, collateral requirements are too high, the size and maturity of loans are insufficient, getting bank loans requires making informal payments, or the establishment did not think its application would be approved.<sup>4</sup> _Sales credit_ is a dummy variable indicating whether the firm has positive purchase of its material inputs or services paid for after delivery (about 70 percent of firms

> 3 ln(1+ _X_ ) is considered approximately equal to ln( _X_ ). We therefore use ln(1+ _X_ ) to compute the log of the number of employees, since some firms have zero employees in a specific year but not in both years.

> 4 No dummy variable is included for firms applying for new loans or lines of credit whose applications were rejected because this information is available for only 14 percent of firms in the sample.

7
in the sample have sales credit). We also include a dummy variable indicating whether a firm has a positive share of investment financed with external funds (this applies to 24 percent of firms in the sample).

The World Bank Enterprise Surveys also provide important information on firm characteristics, including size, age, sector,<sup>5</sup> export activity, and ownership as well as whether a firm is an independent, single establishment. The sample used in this paper is stratified by size, age, sector, region, and other firm characteristics. (Variable descriptions and distributions are reported in tables 1, 2 and 3.) Firms are divided into four categories by size: _micro_ (1–10 permanent employees), _small_ (11–50), _medium_ (51–200), and _large_ (more than 200). The sample includes mostly micro firms (39 percent of the total) and small firms (37 percent); only 16 percent are medium-size and 7 percent large. Firms are divided into three categories by age: _young_ (1–5 years),<sup>6</sup> _mature_ (6–15), and _older_ (more than 15). Most are mature (47 percent) or older (41 percent); only 11 percent are young firms. Ownership is defined as being _foreign_ or _government_ if “10 percent or more” of the firm is foreign or government owned; 12 percent of the firms in the sample are foreign owned and only 2 percent are government owned. _Exporter_ is a dummy variable indicating that direct exports account for 10 percent or more of a firm’s sales; 13 percent of the sample firms are exporters.

Whether a firm has a single establishment or multiple ones matters for firm growth, especially in the manufacturing sector (see Dunne, Roberts, and Samuelson 1989). We therefore include a dummy variable indicating whether a firm is an independent, single establishment. Most of the firms in the sample are single establishments (85 percent), while 14 percent are part of multi-establishment entities. Finally, we divide the firms into three sectors: _manufacturing_ (55 percent), _sales_ (23 percent are in the retail and wholesale sector), and other _services_ (20 percent). The sample includes firms from six regions: 31 percent from Sub-Saharan Africa, 28 percent from Latin America and the Caribbean, 27 percent from Europe and Central Asia, 11 percent from East Asia and Pacific, and only 3 percent from the Middle East and North Africa and South Asia.<sup>7</sup> Table 1 provides an overview of firm growth by firm characteristics and by region. Young, small firms experience rapid growth in their labor force. The mean growth rate for micro firms is twice that for small firms and three times that for medium-size firms. There appears to be little growth in employment for large firms on average. The mean growth rate for young firms is nearly twice that for mature firms and more than three times that for older firms. On average, there is little difference in growth rate between single, independent establishments and those that are part of multiestablishment entities or between the manufacturing, sales, and services sectors. Firms in Africa and Latin America grow faster than those in Europe and Central Asia and East Asia and Pacific.

# **4. The Most Binding Constraint of the Business Environment**

> 5 The questionnaire provides information on industry, and we use this information to establish the sector variable.

> 6 Firms operating for less than one year are classified as young firms.

> 7 Because of space limitations, abbreviations are sometimes used for the regions in tables: AFR for Sub-Saharan Africa, LAC for Latin America and the Caribbean, ECA for Europe and Central Asia, and EAP for East Asia and Pacific.

8
Having the managers of firms rate constraints on the firms’ operation and growth is a useful start for identifying important obstacles in the business environment. We analyze these obstacles not only by using econometric tools but also by examining the importance of these obstacles across regions and sectors.

# **4.1 Understanding Obstacles to Firms’ Operation**

In the World Bank Enterprise Survey, as noted, firms rate 15 obstacles in their business environment. These are access to finance, practices of competitors in the informal sector, electricity, corruption, crime, inadequately educated workforce, labor regulations, business licensing and permits, political instability, tax administration, tax rates, transport, customs and trade regulations, courts, and access to land.

A review of firm responses shows that the biggest reported obstacles differ across regions and countries (see appendix). Using model 2 (as explained in greater detail in the next section), we find that different sectors also confront different obstacles. For example, in the manufacturing sector access to finance, informal sector competition, tax rates, and labor regulations matter the most, while in the sales and services sectors only access to finance and informal sector competition are negatively and significantly correlated with firm growth (see table 6). Estimation results for the same model show that each country faces its own set of significant obstacles.<sup>8</sup> So does each region (see table 5).

Many of these obstacles are linked directly or indirectly to poor firm performance. In an ideal world a country would address all these problems in order to improve firm performance. But governments in developing countries have limited financial and human resources and, as argued by the growth diagnostics approach, should therefore prioritize reform efforts to remove the most important constraints.

The top three obstacles to firms’ operation emerging from the survey data for our sample are electricity, access to finance, and tax rates (figure 1). But we do not know whether these are the top obstacles to employment growth. We therefore need to analyze which obstacles have a significant effect on employment growth.

> 8 The estimation results are available upon request.

9
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of permanent employees at the beginning of period _t_ − 3), labor size squared, age, age squared, and indicators of whether a firm is part of a multi-establishment entity (multi), is in manufacturing (manuf), is an exporter, is foreign owned (foreign), and is government owned (govt).

The results suggest that access to finance and competition from the informal sector are the most binding constraints, with statistically significant effects in all models. Columns 1–15 in table 4, presenting the estimation results for model 1 for each obstacle, show that only access to finance and competition from the informal sector have a significant negative effect on employment growth. Column 16 shows the estimation results for model 2, run for all 15 obstacles together, and column 17 presents the results for model 3, which includes all significant obstacles. Once again we find that access to finance and competition from the informal sector are the most binding constraints. We also examine the significance of the effect of these obstacles on firm growth across regions and sectors to check the robustness of the findings. Tables 5 and 6 confirm that access to finance and competition from the informal sector matter the most after controlling for firm characteristics.

Our results demonstrate that both econometrically and economically access to finance and competition from the informal sector matter the most for firms’ employment growth—findings that are in line with the starting point of the rankings of reported obstacles shown in the appendix. While statistically both constraints are equally binding, the meaning of the second constraint is ambiguous. The survey asks firms whether they see competition from the informal sector as an obstacle. To any individual firm, competition poses a threat to survival. Yet at the level of the economy it is competition that drives firms to improve productivity and therefore drives growth. So it is not clear to us that competition from the informal sector should be considered an obstacle to firms’ operation. The finding that competition from the informal sector is the second most important binding constraint may indicate that the formal firms covered by the survey are not the appropriate firm organization form in developing countries. Moreover, this survey question is not followed by other questions on related aspects of competition, allowing too little information to assess the importance of informal sector competition. Therefore we do not further address this issue in the paper.

While perception-based indicators like those applied in the analysis discussed here are useful, quantitative indicators may give a more accurate picture of the business environment. Firm managers within a country may have different perceptions of the same obstacle, and firm managers in different countries and regions have different frames of reference. A problem perceived as a moderate obstacle by one firm may be perceived as a severe obstacle by another, even though the problem imposes a smaller cost on the second firm.

In the next three sections we use objective measures to examine the importance of access to finance. As discussed, we cannot analyze informal sector competition because of its ambiguity and because the data do not provide sufficient information. We leave further analysis of this constraint with objective measures for future work, when the data are available.

11
# **5. Impact of Financial Access Variables on Employment Growth**

In this section we examine the effect of financial access variables on firm employment growth, controlling for individual firm characteristics. The model is set up with the following specification: _EG_ = _b_ 0 + _b_ 1Laborsize + _b_ 2Age + _b_ 3Multi + _b_ 4Manuf + _b_ 5Exporter + _b_ 6Foreign + _b_ 7Govt + _b_ 8 _FC(s)_ + Country Fixed Effects _+ e_ (4) where _EG_ refers to the employment growth of firm _i_ at time _t_ (the growth in the number of permanent employees between _t_ − 3 and _t_ ) and _FC_ denotes each of the financial access variables— loan, credit constraint, sales credit, and external finance.

Our specification accounts for heteroskedasticity and country fixed effects. All outliers have been removed. We also emphasize the importance of ownership structure by varying the type of establishment: single or multiple, foreign or government owned, exporter or nonexporter. The negative relationship between firm growth and firm size shown in table 7—along with the supportive evidence in table 1 showing that smaller firms grow faster than larger firms—suggests that Gibrat’s law does not hold in this sample of firms. This finding is true across regions and sectors. The negative and statistically significant coefficient on firm age tells us that there is an inverse relationship between firm growth and firm age, which is consistent with Jovanovic’s model (1982) of disproportionate growth.

With other firm characteristics held constant, the rate of growth is significantly lower for independent, single-establishment firms and government-owned firms. Exporters and foreignowned firms tend to have greater employment growth. In Africa and East Asia firms in the manufacturing sector have higher employment growth than firms in the sales and services sectors.

On average, a 1 percent increase in beginning-of-period firm size is associated with a 0.93 percent increase in end-of-period size (after three years) when the beginning-of-period age is held constant (based on the results in table 7, column 1). Based on the analysis across regions, we get estimated elasticities of end-of-period size with respect to beginning-of-period size of approximately 0.9 for Africa, East Asia and Pacific, Europe and Central Asia, and Latin America and the Caribbean (table 7, columns 6–9). With beginning-of-period size held constant, a 1 percent increase in beginning-ofperiod firm age is associated with a 0.07 percent decrease in end-of-period size (table 7, column 1).

The results in table 7 show that financial access variables have a significant effect on firm growth. Columns 1–4 indicate that with other factors held constant, having a loan or overdraft facility increases the growth in a firm’s number of permanent employees by 3.1 percent; being credit constrained reduces a firm’s employment growth by 1.9 percent; having sales credit increases firm’s growth by 2.6 percent; and having external investment funds increases growth by 4.2 percent. If we include all these significant financial access variables in one model after controlling for firm characteristics, they still have significant effects on employment growth, though the effects are of smaller magnitude. And if we use the same model and run the regressions in different regions, the 12
significance and signs of the effects remain the same across regions. These strong results show that access to finance does indeed matter for firm growth.

# **6. Determinants of Financial Access**

In this section we estimate the probability of a firm having access to finance based on its characteristics. We use the following model: _FC_ = _b_ 0 + _b_ 1Small + _b_ 2Medium + _b_ 3Large + _b_ 4Mature + _b_ 5Older + _b_ 6Multi + _b_ 7Manuf + _b_ 8Exporter + _b_ 9Foreign + _b_ 10Govt + _e_ (5) where _FC_ denotes each of the financial access variables—loan, credit constraint, sales credit, and external finance.

We estimate this model by probit. We focus on firms of different sizes (micro, small, medium-size, and large, with micro as the base category that is omitted from the regression) and different ages (young, mature, and older, with young as the base category that is omitted from the regression). The results show that a firm’s size, age, and status as an exporter are strong determinants of its access to finance (table 8).

There is a bigger difference in access to finance between micro and large firms than between small and large firms. With the analysis controlling for firm characteristics and using country fixed effects, micro firms are more likely to be credit constrained. With other factors held constant, large firms are 85 percent less likely to be credit constrained than micro firms. In addition, large firms are 97 percent more likely to have a loan or overdraft facility and 75 percent more likely to have a share of investment financed externally than micro firms. Medium-size and large firms are about 32 and 43 percent more likely to have sales credit than micro firms, while small firms are about 19 percent more likely to offer sales credit than micro firms.

Older firms are 29 percent more likely to have a loan, 8 percent more likely to have sales credit, and 20 percent less likely to be credit constrained than young firms. Mature firms are only 6 percent less likely to be credit constrained and about 10 percent more likely to have a loan than young firms, with other factors held constant.

Other interesting results also emerge. Firms in the manufacturing sector are 20 percent more likely to be credit constrained. With other factors held constant, firms that are exporters are 41 percent more likely to have a loan, 26 percent less likely to be credit constrained, and 20 percent more likely to have external finance for investment than non-exporters.

13
# **7. Effect of Financial Access on Employment Growth by Firm Size and Age**

In this section we investigate the effect of financial access on employment growth by firm size and firm age first for each of the financial access variables individually and then for all the variables combined.

## **7.1  Effect of Individual Financial Access Variables**

To examine the effect of the financial access variables individually, we use the following model: _EG = b_ 0 + _b_ 1 _FC + b_ 2Small* _FC + b_ 3Medium* _FC + b_ 4Large* _FC + b_ 5Mature* _FC + b_ 6Older* _FC_ + _b_ 7Multi + _b_ 8Manuf + _b_ 9Exporter + _b_ 10Foreign + _b_ 11Govt + Country Fixed Effects + _e_ (6) where _EG_ refers to the employment growth of firm _i_ at time _t_ (the growth in the number of permanent employees between _t_ − 3 and _t_ ) and _FC_ denotes each of the financial access variables— loan, credit constraint, sales credit, and external finance.

Table 9 shows the effect of each of the financial access variables—loan, credit constraint, sales credit, and external finance—in turn on employment growth. Micro firms are again the base category that is omitted from the regression.

Among size categories, micro firms appear to benefit the most from having access to finance. Column 1 shows that having a loan increases employment growth by 9 percent in micro firms, but by only 4 percent in medium-size firms and 2 percent in large firms, with other factors held constant. The results in columns 3 and 4 tell the same story. Micro and small firms gain the most from finance in forms ranging from simple to more sophisticated—from having a loan or overdraft facility to sales credit to external finance for investment. Column 2 supports the argument that micro and small firms benefit the most from having access to finance. Being credit constrained will make larger firms suffer more than smaller firms.

Young firms expand more than older firms with access to the same forms of finance. Having a loan or overdraft facility increases employment growth by 9 percent for young firms, but by 6 percent for mature firms and 3 percent for older firms, with other factors held constant. Similarly, having sales credit or external finance increases growth more for young firms than for mature and older firms. Being credit constrained reduces firm growth as firms age. This finding emphasizes the importance of firm age to firm growth. The effect of being credit constrained also varies by sector, appearing to be stronger in manufacturing than in the sales or services sector.

We also look at the effect of financial access on employment growth across regions. The estimation results by region are presented in table 10 for each financial access variable at a time. The finding that having a loan, sales credit, or a share of investment financed externally helps micro firms the most still holds. Indeed, this finding holds for all regions. The finding that young firms expand more than older firms with access to the same forms of finance also holds across regions.

14
## **7.2  Effect of Combined Financial Access Variables**

In this section we look at the effect of all four financial access variables combined on employment growth, by firm size, age, sector, and region. We use the following model: _EG_ = _b_ 0 + _b_ 1Small + _b_ 2Medium + _b_ 3Large + _b_ 4Mature + _b_ 5Older + _b_ 6Multi + _b_ 7Manuf + _b_ 8Exporter + _b_ 9Foreign + _b_ 10Govt + _b_ 11Loan + _b_ 12Credit Constraint + _b_ 13Sales Credit + _b_ 14 External Finance + Country Fixed Effects + _e_ (7) where _EG_ refers to the employment growth of firm _i_ at time _t_ (the growth in the number of permanent employees between _t_ − 3 and _t_ ).

Table 11 shows the estimation results for equation 7. The effects of all the financial access variables are statistically significant and have the right signs. The results in column 1 indicate that firm growth slows both as a firm expands its labor force and as it ages, controlling for other firm characteristics. Columns 2–5 suggest that having a loan and having external finance are important for firms of all sizes, though the effects are largest for small firms. The effects for medium-size and large firms are similar in size. The effect of being credit constrained increases with firm size.

Columns 6–8 show that having a loan is very important for firms of all ages, with the largest effect on young firms. Being credit constrained has the largest effect on medium-size firms followed by large firms. The effects of having external finance are statistically significant for firms of all ages. In other words, trust and external finance matter to firms, regardless of their age.

Columns 9–11 show that most forms of financial access are important to firms, no matter their sector. Having a loan has the largest effect on employment growth for firms in the manufacturing sector. Being credit constrained has a negative effect on firms in all sectors, with the largest effect in the manufacturing and sales sectors. Having external finance matters in all sectors, and the effects are of similar magnitude.

Columns 12–15 present the estimation results across regions. Having a loan and external finance matters to firms in different regions, with the largest effects in Latin America. Being credit constrained has a significant effect on firm employment growth only in Europe and Central Asia and Latin America. From these results, together with the results in tables 7–10, we find that the interaction between financial access variables and firms’ size or age is significant in explaining firms’ employment growth.

# **8. Firm Size Distribution**

Because a firm’s size plays a significant part in determining its employment growth, we further assess the relationship between firm size and financial constraints. The survey data allow the creation of a variable showing which firms are credit constrained, which we identify as those that applied for a 15
loan and were rejected or that were discouraged from applying for a loan.<sup>9</sup> The data also include extensive information on the sources of firms’ investments in fixed assets. These sources can be external (formal or informal) or internal.<sup>10</sup> Confirming the findings of Cabral and Mata (2003), figure 2 shows that the firm size distribution is skewed to the right and that the skewness tends to diminish with age. The size distribution of older firms is more symmetric than that of young firms.

As shown in figure 3, the firm size distribution is skewed more to the right in the manufacturing and services sectors, where micro and small firms make up about two-thirds of the sample. Figure 4 suggests that Africa has the largest share of micro and small firms while other regions have more medium-size and large firms. This again provides evidence of the “missing middle” in Africa.

Using the indicator of credit constrained created for this analysis, we split the sample into two groups, credit-constrained and non-credit-constrained firms. As figure 5 shows, the firm size distribution is skewed to the right for credit-constrained firms. This result is in line with the finding that being credit constrained has a negative effect on firm growth—and, especially, that this effect is largest for small firms. Taking this analysis further, we investigate the firm size distribution using the survey data on firms’ perceptions of access to finance, splitting the sample between those perceiving it as a major or very severe obstacle and those viewing it as a minor obstacle or no obstacle. Figure 6 shows that the size distribution of firms perceiving access to finance as a major or very severe obstacle is skewed to the right. This result is confirmed by the data for our sample showing that most of the firms regarding access to finance as a major or very severe obstacle are micro or small. The size distribution for firms perceiving access to finance as a minor obstacle or no obstacle is more symmetric.

The findings in this and previous sections suggest that a low level of financial development results in a skewed firm size distribution, with a larger relative share of small firms. Policies favoring the development of the financial sector should therefore have an effect on the firm size distribution and, ultimately, favor the adoption of different technologies and an improved allocation of resources if the industry in question is in line with the country’s comparative advantages (Lin, 2010).

# **9. Conclusion**

Using a newly available data set from the World Bank Enterprise Surveys (2006–10) for 39,538 firms across 98 countries, we investigate the binding constraints on firms’ employment growth. With an econometric model and subjective measures, we find that access to finance and informal sector competition are the most binding constraints—both globally and in each region. Using objective

> 9 Our measure of credit-constrained firms comprises those that applied for a loan and were rejected and those that did not apply for one or more of the following reasons: fear of rejection, collateral requirements too high, interest rates not favorable, or a belief that the application would not be approved.

> 10 Formal sources are private or public banks, nonbank financial institutions, issues of new debt, and suppliers’ credit. Informal sources are friends and moneylenders. Internal sources consist of issuances of new shares and own funds.

16
measures and controlling for firm characteristics, we evaluate the importance of access to finance for firms’ employment growth. We find that access to different forms of finance matters. These results from our cross-country firm-level analysis suggest that governments seeking to improve the business environment and promote firm growth should make financial sector reforms a priority.

Objective business conditions vary systematically across firms of different sizes and ages, and good business conditions favor smaller firms, especially micro firms. Micro and small firms gain the most from access to finance in forms ranging from simple to more sophisticated—from a loan or overdraft facility to sales credit to external finance for investment. This finding holds not only globally but also for different regions. While sales credit is important only for micro and small firms, having a loan or overdraft facility and receiving external finance for investment promote employment growth for firms of all sizes across regions. And sales credit and external finance matter for firms of all ages.

The firm size distribution is skewed toward smaller firms. The skewness declines with firm age—and is more present in Africa, among firms that are credit constrained, and among those that perceive access to finance as a serious obstacle. These findings call for policies favoring the development of the financial sector, which can help small firms grow into medium-size and large firms.

The findings have several implications for developing countries. First, because the constraints faced by firms differ across countries and, within countries, across sectors, policies to promote firm growth need to be tailored to each country and sector. Second, finance appears to be the most binding constraint across sectors and countries, suggesting that reforms in this sector could yield broad benefits—including by helping to address the problem of the “missing middle” in developing countries. Third, while access to finance is a binding constraint on firms’ growth in all developing countries, the fact that industrial development in some countries took off faster and sooner than others indicate that there are other constraints that are not captured by the Enterprise Survey. Finally, reforms in finance take time, and a quicker development strategy could be to identify the binding constraints in a specific subsector and try to address them through direct policy measures.

17
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**Table 1. Variable Descriptions and Summary Statistics**

|Variable|Description|Mean|SD|
|---|---|---|---|
|Employment growth|Employment growth [(ln_S it_- ln_S i,t-3_)/3]|0.052|0.127|
|Laborsize|Number of permanent employees [ln_S i,t-3_]|3.112|1.350|
|Age|Years of firm's operation|2.602|0.741|
|Multi|Equal to 1 if firm is independent, single establishment; 0 otherwise|0.138|0.345|
|Manuf|Equal to 1 if firm is in manufacturing or construction sector; 0 otherwise|0.555|0.497|
|Exporter|Equal to 1 if direct exports account for more than 10 percent of firm's sales; 0 otherwise?|0.130|0.336|
|Foreign|Equal to 1 if firm has 10 percent or more of foreign ownership; 0 otherwise|0.117|0.321|
|Govt|Equal to 1 if firm has 10 percent or more of government ownership; 0 otherwise|0.017|0.129|
|Loan|Equal to 1 if firm has loan, line of credit, or overdraft facility; 0 otherwise|0.573|0.495|
|Credit constraint|Equal to 1 if firm did not apply for loan for some reason; 0 otherwise|0.334|0.472|
|Sales credit|Equal to 1 if firm has positive sales paid for after delivery; 0 otherwise|0.702|0.458|
|External finance|Equal to 1 if firm has a positive amount of external funds; 0 otherwise|0.237|0.425|
|Access to finance|How much of an obstacle to firm's operation is access to finance?|1.725|1.564|
|Informal competition|How much of an obstacle to firm's operation are informal sector competitors?|1.627|1.453|
|Labor regulations|How much of an obstacle to firm's operation are labor regulations?|0.958|1.181|
|Inadequate education|How much of an obstacle to firm's operation is an inadequately educated workforce?|1.408|1.353|
|Electricity|How much of an obstacle to firm's operation is electricity?|1.843|1.526|
|Transport|How much of an obstacle to firm's operation is transport of goods, supplies, and inputs?|1.224|1.310|
|Customs and trade|How much of an obstacle to firm's operation are customs and trade regulations?|0.954|1.242|
|Access to land|How much of an obstacle to firm's operation is access to land?|1.031|1.334|
|Courts|How much of an obstacle to firm's operation are courts?|1.025|1.280|
|Crime|How much of an obstacle to firm's operation are crime, theft, and disorder?|1.423|1.382|
|Tax rates|How much of an obstacle to firm's operation are tax rates?|1.828|1.374|
|Tax administration|How much of an obstacle to firm's operation is tax administration?|1.439|1.319|
|Licensing and permits|How much of an obstacle to firm's operation are business licensing and permits?|1.095|1.238|
|Political instability|How much of an obstacle to firm's operation is political instability?|1.615|1.504|
|Corruption|How much of an obstacle to firm's operation is corruption?|1.780|1.530|

_Source:_ World Bank Enterprise Surveys (2006-10).

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|Characteristic<br>Frequency<br>**Table 2. Firm Characteristics by Differe**|Percent<br>**nt Groups o**|Cumulativepercent<br>**f Controls**|
|---|---|---|
|_By size_|||
|Micro<br>15,357|38.84|38.84|
|Small<br>14,791|37.41|76.25|
|Medium<br>6,499|16.44|92.69|
|Large<br>2,845|7.2|99.88|
|Unknown<br>46|0.12|100|
|_By age_|||
|Young<br>4,440|11.23|11.23|
|Mature<br>18,551|46.92|58.15|
|Older<br>16,146|40.84|98.99|
|Unknown<br>401|1.01|100|
|_By establishment number_|||
|Multi-establishment<br>5,397|13.65|13.65|
|Single establishment<br>33,729|85.31|98.96|
|Unknown<br>412|1.04|100|
|_By sector_|||
|Manufacturing<br>21,783|55.09|55.09|
|Sales<br>8,901|22.51|77.6|
|Services<br>7,845|19.84|97.44|
|Unknown<br>1,009|2.55|100|
|_By trade orientation_|||
|Nonexporter<br>34,405|87.02|87.02|
|Exporter<br>5,133|12.98|100|
|_By foreign ownership_|||
|Domestically owned<br>34,587|87.48|87.48|
|Foreign owned<br>4,579|11.58|99.06|
|Unknown<br>372|0.94|100|
|_By government ownership_|||
|Government owned<br>37,858|95.75|95.75|
|Privately owned<br>649|1.64|97.39|
|Unknown<br>1,031|2.61|100|
|Total establishments<br>39,538<br>_Source:_World Bank Enterprise Surveys (2006-10).|||

22
|**Characteristic or region**<br>**Table 3. Employment Growth**<br>**Re**|**Mean**<br>**by Firm C**<br>**gion**|**Min**<br>**haracterist**|**Max**<br>**ic and by**|
|---|---|---|---|
|_By size_(number of employees)||||
|Micro (1-10)|0.086|-0.536|0.866|
|Small (11-50)|0.035|-0.732|0.844|
|Medium (51-200)|0.025|-0.638|0.594|
|Large (201+)|0.007|-0.562|0.753|
|Unknown|0.606|0.231|0.880|
|_By age_(years of operation)||||
|Young (1-5)|0.100|-0.732|0.880|
|Mature (6-15)|0.061|-0.623|0.807|
|Older (16+)|0.029|-0.584|0.866|
|Unknown|0.022|-0.458|0.448|
|_By establishment number_||||
|Multi-establishment|0.051|-0.732|0.880|
|Single establishment|0.055|-0.452|0.866|
|_By sector_||||
|Manufacturing|0.049|-0.638|0.880|
|Sales|0.057|-0.525|0.855|
|Services|0.054|-0.732|0.799|
|_By region_||||
|Sub-Saharan Africa|0.066|-0.732|0.813|
|East Asia & Pacific|0.025|-0.638|0.880|
|Europe & Central Asia|0.043|-0.547|0.799|
|Latin America & Caribbean|0.056|-0.510|0.697|
|Middle East & North Africa|0.043|-0.384|0.462|
|South Asia<br>_Source:_World Bank Enterprise|0.076<br>Surveys (20|-0.623<br>06-10).|0.866|

23
|||||**Tabl**|**e 4. Effect**|**of Busines**|Dependent varia<br>**s Environment Obsta**|ble: emplo<br>**cles on E**|yment grow<br>**mploymen**|th<br>**t Growth**|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|1|2|3|4|5|6|7<br>8|9|10|11|12|13|14|15|16|17|
|Access to finance|-0.002***||||||||||||||-0.004***|-0.002***|
||(0.000)||||||||||||||(0.001)|(0.000)|
|Informal sector competition||-0.003***|||||||||||||-0.003***|-0.003***|
|||(0.000)|||||||||||||(0.001)|(0.001)|
|Inadequate education|||0.005***||||||||||||0.007***|0.007***|
||||(0.001)||||||||||||(0.001)|(0.001)|
|Electricity||||0.002***|||||||||||0.001*||
|||||(0.000)|||||||||||(0.001)||
|Customs and trade|||||0.005***||||||||||0.005***||
||||||(0.001)||||||||||(0.001)||
|Access to land||||||0.003***|||||||||0.003***||
|||||||(0.001)|||||||||(0.001)||
|Political instability|||||||-0.001||||||||-0.002**||
||||||||(0.001)||||||||(0.001)||
|Courts|||||||-0.000||||||||-0.001||
||||||||(0.001)||||||||(0.001)||
|Crime||||||||0.001**|||||||0.000||
|||||||||(0.001)|||||||(0.001)||
|Tax rates|||||||||-0.001*||||||-0.001*||
||||||||||(0.001)||||||(0.001)||
|Tax administration||||||||||-0.000|||||-0.000||
|||||||||||(0.001)|||||(0.001)||
|Licensing and permits|||||||||||0.001||||0.001||
||||||||||||(0.001)||||(0.001)||
|Corruption||||||||||||-0.000|||-0.001*||
|||||||||||||(0.000)|||(0.001)||
|Transport|||||||||||||0.002***||0.000||
||||||||||||||(0.001)||(0.001)||
|Labor regulations||||||||||||||0.002***<br>|-0.001<br>||
|||||||||||||||(0.001)|(0.001)||
|Adjusted_R_-squared|0.123|0.122|0.124|0.121|0.125|0.122|0.121<br>0.117|0.121|0.122|0.122|0.122|0.122|0.121|0.121|0.130|0.127|
|Number of observations|35,837|35,466|36,216|36,554|32,967|35,399|35,814<br>32,794|36,278|36,287|36,154|35,350|35,435|36,222|36,297|26,574|34,359|
|Number of countries|96|96|96|96|96|96|96<br>95|96|96|96|96|96|96|96|95|96|
|<br>_Note:_Standard errors (in pa|rentheses)|are robust|to heterosk|edasticity|and cluster|ed on count|ries. Model 1 (col. 1-1|5):_EG_=_b_|0 +_b_1Indi|vidual Obst|acle +_b_2F|irm Charact|eristics +|Country Fix|ed Effects|+_e_1.|
|* Significant at the 10 perce<br>_Source:_Authors' estimation<br>Model 2 (col. 16):_EG_=_b_0<br>is tested and rejected.|nt level. **<br>s based on<br>+_b_1All 15|Significant<br>data from<br>Obstacles|at the 5 per<br>World Ban<br>+_b_2Firm|cent level.<br>k Enterpris<br>Characteris|*** Signific<br>e Surveys (<br>tics + Cou|ant at the 1<br>2006-10).<br>ntry Fixed E|percent level.<br>ffects +_e_2. The hypot|hesis that|the coeffici|ents for ac|cess to fina|nce and inf|ormal sect|or competiti|on differ||

24
**Table 5. Effect of Business Environment Obstacles on Employment Growth by Region**

||Dep|endent varia|ble: emplo|yment gro|wth|
|---|---|---|---|---|---|
||**World**|**AFR**|**EAP**|**ECA**|**LAC**|
|**Variable**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Laborsize|-0.022***<br>|-0.026***<br>|-0.029***<br>|-0.018***<br>|-0.019***<br>|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Age|-0.023***|-0.021***|-0.019***|-0.031***|-0.024***|
||(0.001)|(0.002)|(0.004)|(0.003)|(0.002)|
|Multi|0.018***|0.012***|0.010|0.020***|0.022***|
||(0.002)|(0.003)|(0.007)|(0.006)|(0.004)|
|Manuf|0.003|0.012***|0.012**|-0.009**|0.005|
||(0.002)|(0.003)|(0.006)|(0.004)|(0.003)|
|Exporter|0.020***|0.020***|0.018**|0.020***|0.025***|
||(0.002)|(0.004)|(0.007)|(0.004)|(0.004)|
|Foreign|0.011***|0.008**|0.009|0.021***|0.011**|
||(0.002)|(0.003)|(0.006)|(0.006)|(0.005)|
|Govt|-0.017***|-0.013|0.017|-0.022***|-0.020|
||(0.005)|(0.011)|(0.012)|(0.008)|(0.017)|
|Access to finance|-0.004***|-0.002*|-0.008***|-0.004***|-0.005***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Informal sector competition|-0.003***|-0.003***|-0.008***|-0.002*|-0.003***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Inadequate education|0.007***|0.005***|0.008***|0.006***|0.010***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Electricity|0.001*|-0.001|0.004**|0.001|0.002**|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Customs and trade|0.005***|0.004***|0.006**|0.006***|0.003*|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|
|Access to land|0.003***|-0.000|0.004*|0.004***|0.005***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Political instability|-0.002**|-0.001|-0.002|-0.001|-0.003**|
||(0.001)|(0.001)|(0.003)|(0.001)|(0.002)|
|Courts|-0.001|-0.001|0.001|-0.004**|0.000|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|
|Crime|0.000|0.001|0.002|-0.000|-0.001|
||(0.001)|(0.001)|(0.003)|(0.001)|(0.001)|
|Tax rates|-0.001*|-0.002|-0.000|-0.000|-0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Tax administration|-0.000|0.002*|-0.002|-0.002|-0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Licensing and permits|0.001|-0.001|0.003|0.002|0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Corruption|-0.001*|-0.001|-0.002|0.000|-0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|
|Transport|0.000|-0.002|0.002|0.001|0.001|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Labor regulations|-0.001|-0.001|-0.001|-0.002|-0.001|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Constant|0.176***|0.182***|0.156***|0.184***|0.174***|
||(0.004)|(0.006)|(0.011)|(0.008)|(0.008)|
|Adjusted_R_-squared|0.130|0.129|0.148|0.130|0.112|
|Number of observations|26,574|8,600|3,079|6,596|7,592|
|Number of countries|95|37|10|30|15|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Regressions for the Middle East and North Africa and South Asia are excluded because of insufficient data. Model: _EG_ = _b_ 0 + _b_ 1All 15 Obstacles + _b_ 2Firm Characteristics + Region + Country Fixed Effects + _e._ * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on World Bank Enterprise Surveys (2006-10).

25
|**Table 6. Effect of Business En**|**vironment Obstacles on**<br>Depend|**Employment Growth by**<br>ent variable: employment|**Sector**<br> growth|
|---|---|---|---|
||**Manufacturing**|**Sales**|**Services**|
|**Variable**|**(1)**|**(2)**|**(3)**|
|Laborsize|-0.026***<br>|-0.014***<br>|-0.021***<br>|
||(0.001)|(0.002)|(0.002)|
|Age|-0.024***|-0.025***|-0.018***|
||(0.002)|(0.002)|(0.003)|
|Multi|0.022***|0.014***|0.014***|
||(0.003)|(0.004)|(0.005)|
|Exporter|0.026***|0.017**|0.010|
||(0.003)|(0.007)|(0.007)|
|Foreign|0.011***|0.009*|0.012**|
||(0.003)|(0.005)|(0.005)|
|Govt|-0.009|-0.036**|-0.020*|
||(0.007)|(0.015)|(0.011)|
|Access to finance|-0.004***|-0.002*|-0.004***|
||(0.001)|(0.001)|(0.002)|
|Informal sector competition|-0.004***|-0.002*|-0.004***|
||(0.001)|(0.001)|(0.001)|
|Inadequate education|0.008***|0.007***|0.003**|
||(0.001)|(0.001)|(0.002)|
|Electricity|0.002***|0.001|-0.002|
||(0.001)|(0.001)|(0.001)|
|Customs and trade|0.004***|0.006***|0.007***|
||(0.001)|(0.001)|(0.002)|
|Access to land|0.003***|0.003***|0.001|
||(0.001)|(0.001)|(0.002)|
|Political instability|-0.002|-0.001|-0.002|
||(0.001)|(0.001)|(0.002)|
|Courts|-0.001|-0.001|-0.002|
||(0.001)|(0.002)|(0.002)|
|Crime|0.001|-0.001|0.001|
||(0.001)|(0.001)|(0.002)|
|Tax rates|-0.002**|0.001|-0.001|
||(0.001)|(0.002)|(0.002)|
|Tax administration|0.001|-0.002|-0.001|
||(0.001)|(0.002)|(0.002)|
|Licensing and permits|0.000<br>|0.000<br>|0.002<br>|
||(0.001)|(0.002)|(0.002)|
|Corruption|-0.001|-0.001|-0.001|
||(0.001)|(0.001)|(0.002)|
|Transport|0.000|0.001|-0.001|
||(0.001)|(0.001)|(0.002)|
|Labor regulations|-0.002*|-0.003|0.004**|
||(0.001)|(0.002)|(0.002)|
|Constant|0.191***|0.151***|0.167***|
||(0.005)|(0.007)|(0.009)|
|Adjusted_R_-squared|0.146|0.118|0.114|
|Number of observations|15,322|6,014|5,237|
|Number of countries|95|95|95|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Model: _EG_ = _b_ 0 + _b_ 1All 15 Obstacles + _b_ 2Firm Characteristics + Region + Country Fixed Effects + _e_ . * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

26
**Table 7. Effect of Objective Financial Access Variables on Employment Growth**

||||De|pendent var|iable: empl|oyment gro|wth|||
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**Variable**|**World**|**World**|**World**|**World**|**World**|**AFR**|**EAP**|**ECA**|**LAC**|
|Laborsize|-0.024***|-0.023***|-0.022***|-0.024***|-0.026***|-0.029***|-0.032***|-0.022***|-0.023***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Age|-0.024***|-0.023***|-0.023***|-0.022***|-0.023***|-0.020***|-0.018***|-0.030***|-0.023***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.003)|(0.002)|(0.002)|
|Multi|0.019***|0.018***|0.019***|0.019***|0.019***|0.013***|0.017***|0.020***|0.022***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.006)|(0.005)|(0.004)|
|Manuf|0.0003|0.001|-0.000|-0.001|-0.000|0.010***|0.010**|-0.010***|-0.002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.005)|(0.003)|(0.003)|
|Exporter|0.022***|0.024***|0.024***|0.023***|0.021***|0.022***|0.023***|0.015***|0.028***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.004)|(0.007)|(0.004)|(0.004)|
|Foreign|0.013***|0.012***|0.013***|0.014***|0.014***|0.008**|0.019***|0.020***|0.016***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.006)|(0.005)|(0.005)|
|Govt|-0.009**|-0.011**|-0.011**|-0.010**|-0.008*|-0.008|0.024**|-0.015**|-0.016|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.010)|(0.011)|(0.007)|(0.020)|
|Loan|0.031***||||0.020***|0.014***|0.018***|0.023***|0.026***|
||(0.002)||||(0.002)|(0.003)|(0.004)|(0.003)|(0.003)|
|Credit constraint||-0.019***|||-0.010***|-0.004*|0.001|-0.024***|-0.012***|
|||(0.002)|||(0.002)|(0.002)|(0.004)|(0.003)|(0.003)|
|Sales credit|||0.026***||0.009***|0.010***|0.006|0.012***|0.005|
||||(0.003)||(0.002)|(0.002)|(0.005)|(0.003)|(0.003)|
|External finance||||0.042***|0.036***|0.021***|0.041***|0.036***|0.041***|
|||||(0.002)|(0.002)|(0.003)|(0.005)|(0.003)|(0.003)|
|Constant|0.163***|0.182***||0.166***|0.162***|0.174***|0.144***|0.165***|0.153***|
||(0.003)|(0.003)||(0.003)|(0.003)|(0.005)|(0.009)|(0.006)|(0.006)|
|Number of observations|34,894|35,641|36,722|36,722|34,524|10,270|3,971|9,423|9,911|
|Adjusted_R_-squared|0.131|0.125|0.123|0.138|0.146|0.144|0.155|0.154|0.128|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Regressions for the Middle East and North Africa and South Asia are excluded because of insufficient data.

Model: _EG_ = _b_ 0 + _b_ 1Laborsize + _b_ 2Age + _b3_ Multi + _b_ 4Manuf + _b_ 5Exporter + _b_ 6Foreign + _b_ 7Govt + _b_ 8 _FC(s)_ + Country Fixed Effects + _e. ; * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

27
|**Table 8**|**. Objective Finan**|**cial Access Variables**|**by Firm Characteris**|**tic**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|**Variable**|**Loan**|**Credit constraint**|**Sales credit**|**External finance**|
|Small|0.467***|-0.412***|0.187***|0.338***|
||(0.041)|(0.043)|(0.035)|(0.030)|
|Medium|0.779***|-0.721***|0.320***|0.572***|
||(0.063)|(0.068)|(0.049)|(0.038)|
|Large|0.973***|-0.852***|0.427***|0.747***|
||(0.072)|(0.073)|(0.078)|(0.045)|
|Mature|0.097**|-0.063*|0.055|0.023|
||(0.040)|(0.035)|(0.055)|(0.037)|
|Older|0.287***|-0.200***|0.079|0.034|
||(0.066)|(0.057)|(0.063)|(0.048)|
|Multi|0.032|-0.128***|0.023|0.007|
||(0.051)|(0.036)|(0.047)|(0.048)|
|Manuf|-0.089|0.196***|-0.031|-0.015|
||(0.062)|(0.048)|(0.058)|(0.048)|
|Exporter|0.412***|-0.261***|0.084*|0.204***|
||(0.064)|(0.053)|(0.049)|(0.044)|
|Foreign|-0.101|-0.024|-0.026|-0.157***|
||(0.070)|(0.049)|(0.050)|(0.043)|
|Govt|-0.361***|0.199**|0.042|-0.118|
||(0.119)|(0.082)|(0.099)|(0.094)|
|Constant|-0.325***|-0.071|0.070|-1.039***|
||(0.104)|(0.091)|(0.063)|(0.070)|
|Number of observations<br>* Significant at the 10 percen<br>_Source:_Authors' estimations<br>_Note:_Standard errors (in par<br>Model:_FC_=_b_0 +_b_1Small<br>_b_9Foreign +_b_10Govt +_e_.|34,916<br>t level. ** Significa<br>based on data fro<br>entheses) are rob<br>+_b_2Medium +_b_3|35,663<br>nt at the 5 percent level.<br>m World Bank Enterpris<br>ust to heteroskedasticity<br>Large +_b_4Mature +_b_5|36,746<br>*** Significant at the 1<br>e Surveys (2006-10).<br>and clustered on cou<br>Older +_b_6Multi +_b_7|36,746<br>percent level.<br>ntries.<br>Manuf +_b_8Exporter +|

28
**Table 9. Differences in Effects of Objective Financial Access Variables on Employment Growth by Firm Size and Age**

||<br>**by F**<br>|<br>**irm Size and Ag**<br>Dependent va<br>|<br>**e**<br>riable: employment<br>|<br> growth<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|||**Credit**|||
||**Loan**|**constraint**|**Sales credit**|**External finance**|
|Financial access variable<br>(_FC_)|0.091***|0.053***|0.106***|0.105***|
||(0.004)|(0.004)|(0.012)|(0.006)|
|Small*_FC_|<br>-0.040***|<br>-0.057***|<br>-0.043***|<br>-0.039***|
||(0.002)|(0.002)|(0.007)|(0.004)|
|Medium*_FC_|<br>-0.051***|<br>-0.065***|<br>-0.057***|<br>-0.056***|
||(0.003)|(0.004)|(0.008)|(0.004)|
|Large*_FC_|-0.069***|-0.078***|-0.066***|-0.073***|
||(0.003)|(0.007)|(0.009)|(0.005)|
|Mature*_FC_|<br>-0.035***|<br>-0.026***|<br>-0.042***|<br>-0.030***|
||(0.004)|(0.004)|(0.012)|(0.006)|
|Older*_FC_|-0.061***|-0.044***|-0.062***|-0.051***|
||(0.004)|(0.004)|(0.012)|(0.006)|
|Multi|<br>0.008***|<br>0.006***|<br>0.003|<br>0.004**|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Manuf|-0.005***|-0.006***|-0.009***|-0.009***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Exporter|<br>0.009***|<br>0.003|<br>0.001|<br>0.004*|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Foreign|0.003|0.003|0.000|0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Govt|<br>-0.035***|<br>-0.041***|<br>-0.046***|<br>-0.042***|
||(0.005)|(0.005)|(0.005)|(0.005)|
|Constant|<br>0.045***|<br>0.055***|<br>0.055***|<br>0.048***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Adjusted_R_-squared|<br>0.090|<br>0.065|<br>0.095|<br>0.078|
|<br>Number of observations|34,894|35,641|36,722|36,722|
|Number of countries<br>_Note:_Standard errors (in par|95<br>entheses) are ro|96<br>bust to heteroske|96<br>dasticity and clust|96<br>ered on countries.|

_<mark>Note:</mark>_ <mark>Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries.</mark> Model: _EG_ = _b_ 0 + _b_ 1 _FC_ + _b_ 2Small* _FC_ + _b_ 3Medium* _FC_ + _b_ 4Large* _FC_ + _b_ 5Mature* _FC_ + _b_ 6Older* _FC_ + _b_ 7Multi + _b_ 8Manuf + _b_ 9Exporter + _b_ 10Foreign + _b_ 11Govt + Country Fixed Effects + _e_ . * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level.

_<mark>Source:</mark>_ <mark>Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).</mark> 29
||**Table 10.**|**Differenc**<br>|**es in Effect**<br>|**s of Objec**|**tive Finan**|**cial Acces**<br>|**s Variable**<br> <br>Depen|**s on Empl**<br>dent variabl|**oyment Gro**<br>e: employme|**wth by Firm**<br> <br>nt growth|**Size and Ag **<br>|**e across Re**|**gions**||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Loa**|**n**|||**Credit co**|**nstraint**|||**Sales c**|**redit**|||**Externa**|**finance**||
||**AFR**|**EAP**|**ECA**|**LAC**|**AFR**|**EAP**|**ECA**|**LAC**|**AFR**|**EAP**|**ECA**|**LAC**|**AFR**|**EAP**|**ECA**|**LAC**|
|Financial access (_FC_)|0.063***|0.061***|0.108***|0.123***|0.044***|0.090***|0.044***|0.071***|0.056***|0.077***|0.109***|0.140***|0.070***|0.089***|0.108***|0.136***|
||(0.007)|(0.011)|(0.008)|(0.008)|(0.004)|(0.012)|(0.010)|(0.014)|(0.018)|(0.034)|(0.020)|(0.021)|(0.010)|(0.017)|(0.012)|(0.011)|
|Small*_FC_|-0.026***|-0.033***|-0.035***|-0.054***|-0.049***|-0.076***|-0.043***|-0.066***|-0.019***|-0.051***|-0.031***|-0.069***|-0.027***|-0.018|-0.033***|-0.057***|
||(0.004)|(0.007)|(0.004)|(0.004)|(0.003)|(0.008)|(0.006)|(0.006)|(0.012)|(0.031)|(0.013)|(0.012)|(0.007)|(0.014)|(0.006)|(0.006)|
|Medium*_FC_|-0.036***|-0.045***|-0.057***|-0.057***|-0.049***|-0.082***|-0.067***|-0.064***|-0.026***|-0.057***|-0.051***|-0.086***|-0.035***|-0.039***|-0.058***|-0.071***|
||(0.005)|(0.008)|(0.005)|(0.005)|(0.006)|(0.011)|(0.008)|(0.010)|(0.014)|(0.037)|(0.014)|(0.015)|(0.007)|(0.014)|(0.007)|(0.007)|
|Large*_FC_|-0.047***|-0.069***|-0.084***|-0.069***|-0.051**|-0.111***|-0.070***|-0.065***|-0.079***|-0.024***|-0.067***|-0.087***|-0.057***|-0.061***|-0.080***|-0.083***|
||(0.008)|(0.009)|(0.006)|(0.006)|(0.021)|(0.014)|(0.011)|(0.016)|(0.023)|(0.046)|(0.015)|(0.016)|(0.011)|(0.017)|(0.008)|(0.008)|
|Mature*_FC_|-0.039***|-0.018|-0.035***|-0.043***|-0.018***|-0.040***|-0.037***|-0.035**|-0.033***|0.012**|-0.043***|-0.049***|-0.037***|-0.025|-0.024**|-0.038***|
||(0.007)|(0.011)|(0.008)|(0.008)|(0.004)|(0.013)|(0.010)|(0.014)|(0.020)|(0.033)|(0.019)|(0.022)|(0.010)|(0.018)|(0.012)|(0.011)|
|Older*_FC_|-0.062***|-0.037***|-0.059***|-0.076***|-0.037***|-0.052***|-0.058***|-0.063***|-0.036***|-0.036***|-0.069***|-0.073***|-0.048***|-0.055***|-0.049***|-0.063***|
||(0.007)|(0.011)|(0.008)|(0.008)|(0.005)|(0.012)|(0.011)|(0.014)|(0.021)|(0.031)|(0.020)|(0.022)|(0.011)|(0.018)|(0.012)|(0.011)|
|Multi|0.001|0.001|0.014***|0.012***|0.002|-0.001|0.008*|0.006|-0.003|-0.006|0.008***|0.006***|-0.001|-0.005|0.010**|0.007*|
||(0.003)|(0.006)|(0.005)|(0.004)|(0.003)|(0.006)|(0.005)|(0.004)|(0.003)|(0.006)|(0.004)|(0.004)|(0.003)|(0.006)|(0.004)|(0.004)|
|Manuf|0.00002|0.001|-0.014***|-0.002|0.001|-0.001|-0.015***|-0.006**|-0.003|-0.004|-0.017***|-0.007|-0.003|-0.004|-0.017***|-0.006**|
||(0.002)|(0.005)|(0.003)|(0.003)|(0.002)|(0.005)|(0.003)|(0.003)|(0.002)|(0.004)|(0.003)|(0.003)|(0.002)|(0.004)|(0.003)|(0.003)|
|Exporter|0.004|-0.007|0.011***|0.020***|-0.002|-0.008|0.003|0.012***|-0.005*|-0.014|0.004***|0.012***|-0.003|-0.011*|0.006|0.014***|
||(0.004)|(0.006)|(0.004)|(0.004)|(0.004)|(0.006)|(0.004)|(0.004)|(0.004)|(0.006)|(0.004)|(0.004)|(0.004)|(0.006)|(0.004)|(0.004)|
|Foreign|-0.003|0.003|0.017***|0.004|-0.003|0.006|0.012**|0.004|-0.008|0.002|0.011***|0.004|-0.007**|0.002|0.013***|0.006|
||(0.003)|(0.006)|(0.005)|(0.005)|(0.003)|(0.006)|(0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.005)|
|Govt|-0.036***|-0.008|-0.041***|-0.031|-0.041***|-0.017|-0.048***|-0.036*|-0.048***|-0.026|-0.051***|-0.036*|-0.044***|-0.019*|-0.047***|-0.032|
||(0.010)|(0.011)|(0.006)|(0.020)|(0.009)|(0.011)|(0.006)|(0.021)|(0.009)|(0.011)|(0.006)|(0.020)|(0.009)|(0.011)|(0.006)|(0.020)|
|Constant|0.068***|0.024***|0.028***|0.033***|0.063***|0.025***|0.056***|0.058***|0.070***|0.030***|0.049***|0.053***|0.068***|0.026***|0.038***|0.042***|
||(0.002)|(0.004)|(0.003)|(0.004)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|(0.003)|(0.002)|(0.002)|(0.002)|(0.003)|(0.002)|(0.003)|
|Adjusted_R_-squared|0.069|0.060|0.099|0.097|0.062|0.062|0.070|0.045|0.083|0.086|0.090|0.092|0.055|0.056|0.082|0.079|
|<br>Number of observations|10,358|4,100|9,527|9,949|10,636|4,076|9,907|10,067|10,878|4,431|10,237|10,203|10,878|4,431|10,237|10,203|
|Number of countries<br>_Note:_Standard errors (in par<br>Model:_EG_=_b_0 +_b_1_FC_+<br>* Significant at the 10 percen<br>_Source:_Authors' estimation|37<br>entheses) are ro<br>_b_2Small*_FC_+_b_ <br>t level. ** Signific<br>s based on data f|10<br>bust to hete<br>3Medium*_F_<br>ant at the 5<br>rom World|30<br>roskedastic<br>_C_+_b_4Lar<br>percent le<br>Bank Enter|15<br>ity and clu<br>ge*_FC_+_b_ <br>vel. *** Sign<br>prise Surve|38<br>stered on c<br>5Mature*_F_<br>ificant at th<br>ys (2006-10|10<br>ountries. R<br>_C_+_b_6Old<br>e 1 percen<br>).|30<br>egressions<br>er*_FC_+_b_ <br>t level.|15<br>for the Mid<br>7Multi +_b_8|38<br>dle East and<br>Manuf +_b_9E|10<br>North Africa<br>xporter +_b_1|30<br>and South Asi<br>0Foreign +_b_1|15<br>a are exclud<br>1Govt + Re|38<br>ed becaus<br>gion + Cou|10<br>e of insuffi<br>ntry Fixed|30<br>cient data.<br>Effects +_e_|15<br>.|

30
**Table 11. Effect of Combined Objective Financial Access Variables on Employment Growth by Firm Characteristic and by Region**

||||**By **|**size**|||**By age**<br>Depen|dent variable|: employmen|**By sector**<br>tgrowth|||**By re**|**gion**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**World**|**Micro**|**Small**|**Medium**|**Large**|**Young**|**Mature**|**Older**|**Manufactu**<br>**ring**|**Sales**|**Services**|**AFR**|**EAP**|**ECA**|**LAC**|
|**Variable**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|**(13)**|**(14)**|**(15)**|
|Small|-0.052***|||||-0.082***|-0.051***|-0.044***|-0.060***|-0.037***|-0.056***|-0.046***|-0.058***|-0.042***|-0.061***|
||(0.002)|||||(0.006)|(0.002)|(0.002)|(0.002)|(0.003)|(0.004)|(0.002)|(0.005)|(0.003)|(0.003)|
|Medium|-0.067***|||||-0.113***|-0.071***|-0.054***|-0.080***|-0.041***|-0.065***|-0.056***|-0.080***|-0.065***|-0.067***|
||(0.002)|||||(0.010)|(0.003)|(0.003)|(0.003)|(0.005)|(0.005)|(0.004)|(0.006)|(0.004)|(0.004)|
|Large|-0.091***|||||-0.137***|-0.089***|-0.080***|-0.110***|-0.032***|-0.082***|-0.078***|-0.107***|-0.093***|-0.085***|
||(0.003)|||||(0.019)|(0.005)|(0.004)|(0.004)|(0.008)|(0.008)|(0.007)|(0.008)|(0.005)|(0.006)|
|Mature|-0.030***|-0.036***|-0.016***|-0.014|-0.017||||-0.038***|-0.025***|-0.019***|-0.024***|-0.021***|-0.041***|-0.037***|
||(0.003)|(0.003)|(0.005)|(0.009)|(0.018)||||(0.004)|(0.004)|(0.005)|(0.003)|(0.007)|(0.006)|(0.006)|
|Older|-0.053***|-0.064***|-0.038***|-0.032***|-0.043**||||-0.060***|-0.047***|-0.040***|-0.046***|-0.036***|-0.065***|-0.065***|
||(0.003)|(0.003)|(0.005)|(0.009)|(0.018)||||(0.004)|(0.005)|(0.006)|(0.004)|(0.007)|(0.006)|(0.006)|
|Multi|0.014***|0.013***|0.017***|0.012***|0.010*|0.009|0.015***|0.014***|0.015***|0.012***|0.014***|0.008***|0.009|0.018***|0.015***|
||(0.002)|(0.004)|(0.003)|(0.004)|(0.006)|(0.008)|(0.003)|(0.003)|(0.003)|(0.004)|(0.005)|(0.003)|(0.006)|(0.005)|(0.004)|
|Manuf|-0.001|0.011***|-0.002|-0.018***|-0.032***|0.022***|-0.001|-0.007***||||0.007***|0.009*|-0.011***|-0.001|
||(0.001)|(0.002)|(0.002)|(0.004)|(0.006)|(0.005)|(0.002)|(0.002)||||(0.002)|(0.005)|(0.003)|(0.003)|
|Exporter|0.014***|0.028***|0.024***|0.009**|-0.001|0.035***|0.011***|0.015***|0.020***|0.017**|0.007|0.011**|0.009|0.013***|0.022***|
||(0.002)|(0.006)|(0.004)|(0.004)|(0.005)|(0.011)|(0.003)|(0.003)|(0.002)|(0.007)|(0.006)|(0.004)|(0.006)|(0.004)|(0.004)|
|Foreign|0.009***|0.006|0.013***|0.008*|0.009|0.019**|0.014***|0.001|0.010***|0.008*|0.011**|0.002|0.012**|0.020***|0.010**|
||(0.002)|(0.005)|(0.004)|(0.004)|(0.006)|(0.008)|(0.003)|(0.003)|(0.003)|(0.005)|(0.005)|(0.003)|(0.006)|(0.005)|(0.005)|
|Govt|-0.023***|-0.031*|-0.007|-0.020***|-0.028***|-0.030*|-0.019**|-0.022***|-0.015**|-0.032***|-0.032***|-0.024**|0.002|-0.028***|-0.024|
||(0.005)|(0.018)|(0.010)|(0.007)|(0.009)|(0.017)|(0.008)|(0.006)|(0.006)|(0.012)|(0.008)|(0.010)|(0.010)|(0.006)|(0.022)|
|Loan|0.019***|0.014***|0.023***|0.017***|0.015**|0.027***|0.018***|0.017***|0.022***|0.015***|0.017***|0.011***|0.016***|0.023***|0.026***|
||(0.002)|(0.003)|(0.003)|(0.004)|(0.007)|(0.006)|(0.002)|(0.002)|(0.002)|(0.003)|(0.004)|(0.003)|(0.004)|(0.003)|(0.003)|
|Credit constraint|-0.009***|-0.003|-0.014***|-0.017***|-0.018**|-0.005|-0.010***|-0.008***|-0.009***|-0.010***|-0.007*|-0.002|0.003|-0.023***|-0.010***|
||(0.002)|(0.002)|(0.003)|(0.005)|(0.008)|(0.006)|(0.002)|(0.002)|(0.002)|(0.003)|(0.004)|(0.002)|(0.004)|(0.003)|(0.003)|
|Sales credit|-0.004|0.001|-0.008|-0.013**|0.004|0.011|-0.007|-0.006|-0.004|-0.007|-0.003|-0.002|0.006|-0.006|-0.003|
||(0.003)|(0.007)|(0.005)|(0.006)|(0.008)|(0.014)|(0.005)|(0.004)|(0.004)|(0.006)|(0.007)|(0.006)|(0.012)|(0.005)|(0.005)|
|External finance|0.036***|0.034***|0.042***|0.030***|0.025***|0.032***|0.035***|0.037***|0.036***|0.036***|0.036***|0.019***|0.036***|0.038***|0.041***|
||(0.002)|(0.004)|(0.003)|(0.004)|(0.006)|(0.008)|(0.003)|(0.003)|(0.002)|(0.004)|(0.004)|(0.004)|(0.006)|(0.003)|(0.003)|
|Constant|0.105***|0.106***|0.035***|0.036***|0.041**|0.102***|0.077***|0.048***|0.112***|0.087***|0.091***|0.107***|0.073***|0.109***|0.112***|
||(0.003)|(0.003)|(0.005)|(0.010)|(0.019)|(0.005)|(0.002)|(0.003)|(0.004)|(0.005)|(0.006)|(0.004)|(0.007)|(0.006)|(0.007)|
|Adjusted_R_-squared|0.135|0.107|0.100|0.097|0.105|0.130|0.115|0.100|0.151|0.116|0.130|0.119|0.122|0.143|0.135|
|<br>Number of observations|34,524|13,169|13,060|5,739|2,527|3,734|16,387|14,403|21,675|7,978|4,870|10,270|3,971|9,423|9,911|
|Number of countries|95|95|95|94|89|95|95|95|95|95|95|37|10|30|15|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Regressions for the Middle East and North Africa and South Asia are excluded because of insufficient data. Model: _EG_ = _b_ 0 + _b_ 1Small + _b_ 2Medium + _b_ 3Large + _b_ 4Mature + _b_ 5Older + _b_ 6Multi + _b_ 7Manuf + _b_ 8Exporter + _b_ 9Foreign + _b_ 10Govt + _b_ 11Loan + _b_ 12Credit Constraint + _b_ 13Sales Credit + _b_ 14 External Finance+ Country Fixed Effects + _e_ * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

31
<!-- Start of picture text --> Figure 2. Firm Size Distribution by Age<br>fee}<br>oO : R<br>“ : a > ~<br>yy » SQQe--8<br>0) 2 4 6 8<br>Size (Log of Employees)<br>soreness YWOung = ——— Mature<br>——-—-—- Older<br><!-- End of picture text --> Figure 3. Firm Size Distribution by Sector <!-- Start of picture text --> foe)<br>°f<br>;<br>3<br>;<br>= p<br>ea } ‘<br>® Jf<br>fe) 2 4 6 8<br>Size (Log of Employees)<br>sven Manufacturing ————- Sales<br>————_ Services<br><!-- End of picture text -->
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