metadata: authors: 0: author: Adoho,Franck M.
1: author: Doumbia,Djeneba
count: Congo, Democratic Republic of
docna: 0: docna: Informal sector heterogeneity and income
            inequality : evidence from the Democratic Republic of Congo
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Poverty Reduction Strategies,Inequality
teratopic: Industry,Science and Technology Development
abstracts: cdata!: This paper uses 1-2-3 survey data on the
            Democratic Republic of Congo to analyze heterogeneity in the
            informal sector. It empirically identifies three types of
            entrepreneurs in the sector. The first group of
            entrepreneurs—top performers -- is growth oriented and
            enjoys greater access to capital. The second
            group—constrained gazelles -- includes entrepreneurs who
            share many characteristics, especially management skills,
            with the top performers, but operate with less capital. The
            third group—survivalists—comprises firms struggling to grow.
            Based on logit and fixed effect ordinary least squares
            models, the results presented in this paper show that
            poverty and income inequality are more common among
            constrained gazelles and survivalists. The paper also shows
            that income inequality is explained mainly by educational
            disparities and lack of credit access among entrepreneurs.
            Additionally, the outcomes of a Blinder-Oaxaca decomposition
            show that the performance of firms is a key factor in
            explaining differences in income. Examining the drivers of
            performance, the paper finds that human capital and
            managerial skills are important engines of performance.
geo_reg_key: 517196,517191
repnme: repnme: Informal sector heterogeneity and income
            inequality : evidence from the Democratic Republic of Congo
display_title: Informal sector heterogeneity and
            income inequality : evidence from the Democratic Republic of Congo
listing_relative_url: /research/2018/02/29555601/informal-sector-heterogeneity-income-inequality-evidence-democratic-republic-congo
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Informal Sector m1326258 1168,Poverty m1326687 695,Education m1327048 470,Income Inequality m1503891 2668
publishtoextweb_dt: 2018-02-01T00:00:00Z
historic_topic: Industry,Science and Technology Development
no_of_pages: 45
author_keys: 0: author_key: 944874
1: author_key: 1269551
model_extractions: WPS8328 Policy Research Working Paper 8328 _Franck M. Adoho Djeneba Doumbia_ Poverty and Equity Global Practice Group February 2018
Policy Research Working Paper 8328

# **Abstract**

This paper uses 1–2–3 survey data on the Democratic Republic of Congo to analyze heterogeneity in the informal sector. It empirically identifies three types of entrepreneurs in the sector. The first group of entrepreneurs—top performers—is growth oriented and enjoys greater access to capital. The second group—constrained gazelles—includes entrepreneurs who share many characteristics, especially management skills, with the top performers, but operate with less capital. The third group—survivalists—comprises firms struggling to grow. Based on logit and fixed effect ordinary least squares models, the results presented in this paper show that poverty and income inequality are more common among constrained gazelles and survivalists. The paper also shows that income inequality is explained mainly by educational disparities and lack of credit access among entrepreneurs. Additionally, the outcomes of a Blinder-Oaxaca decomposition show that the performance of firms is a key factor in explaining differences in income. Examining the drivers of performance, the paper finds that human capital and managerial skills are important engines of performance.

This paper is a product of the Poverty and Equity Global Practice 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 authors may be contacted at fadoho@worldbank.org; ddoumbia@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
# **Informal Sector Heterogeneity and Income Inequality: Evidence from the Democratic Republic of Congo**<sup>**1**</sup>

## **Franck M. Adoho World Bank**

## **Djeneba Doumbia Paris School of Economics World Bank**

_Keywords:_ Informal sector, income inequality, firm performance. _JEL classification:_ 017; 012; D21.

> 1 The authors gratefully acknowledge the comments of Jean-Bernard Chatelain, Kathleen Beegle, Jean-Louis Combes, Chadi Bou Habib, Tidiane Kinda, Aly Sanoh, Clarence Tsimpo Nkengne, Pierella Paci, Philippe de Vreyer, Romeo Gansey as well as participants to the 12th Doctorissimes of University Paris 1 Pantheon-Sorbonne, the International Monetary Fund African Department Seminar, and the 57<sup>th</sup> Conference of the Canadian Association for Economic Science.
# **1. Introduction**

In 2012, the urban informal sector represented 81.5 percent of employment in the Democratic Republic of Congo (World Bank 2016). This large proportion highlights the sector’s critical importance for job creation and income growth in the country. The sector is also vital for the achievement of the twin goals of poverty reduction and shared prosperity, as it tends to offer opportunities to the most vulnerable, namely, the poorest, women, and youth. While the informal sector is defined in various ways in the literature, it is generally said to encompass economic activities that are not subject to taxes and other regulations (De Soto 1989; Loayza 1996). This paper defines enterprises in the informal sector as enterprises without formal accounting books and national identification numbers as firms. This definition seems more appropriate because it allows for comparability with studies in other developing countries.

Many aspects of the informal economy have been studied. Earlier studies have focused on explaining the reasons underlying the emergence of the informal economy, the productivity of the informal sector, and why firms remain informal. For instance, Johnson, Kaufmann, and Shleifer (1997) and Johnson, Kaufmann, and Zoido-Lobatón (1998) argue that the most striking factors explaining the emergence of informal firms are rigid labor regulations, high registration costs, bribery, and constraints in the delivery of public goods and services. Using data on Latin America, Loayza (1996) finds that the size of the informal sector is negatively correlated with the quality of government institutions and positively correlated with proxies for tax burden and labor market restrictions. Still, little attention has been paid to heterogeneity in the informal sector and even less to inequality within and between the various categories of informal firms. Understanding this heterogeneity and income inequality is crucial to the design of appropriate policies.

This paper contributes to the empirical literature by analyzing heterogeneity in the informal sector in the Democratic Republic of Congo and the implications for the performance of firms and income inequality. Relying on a representative sample of informal firms derived from the 1- 2-3 survey in the country, the paper identifies three distinct groups of informal entrepreneurs: top performers, constrained gazelles, and survivalists. The first group of entrepreneurs is growth oriented and enjoys a greater access to capital. The second group includes entrepreneurs who share many characteristics, especially management skills, with the top performers, but operate with less capital. The survivalists, entrepreneurs who are struggling to grow, represent the third group. Furthermore, the paper makes three other major contributions. First, it analyzes poverty and income inequality among these three distinct categories of firms. Second, using a BlinderOaxaca decomposition, it explains income differences across the three groups. Third, it examines the drivers of performance and profitability among informal firms.

The results highlight a striking heterogeneity among informal firms. Poverty rates are higher among survivalists and constrained gazelles than among top performers. Income inequality, which is more common among constrained gazelles and survivalists, is mainly explained by 2
performance gaps, educational disparities, and a lack of credit access. The characteristics of entrepreneurs, such as age, educational attainment, and managerial skills, are important drivers of the quality of the performance of firms.

The rest of the paper is structured as follows. Section 2 reviews the literature on the informal sector. Section 3 describes the data and descriptive statistics. Section 4 explains the identification strategy. Section 5 compares various characteristics associated with these groups. Section 6 investigates urban poverty and inequality among informal firms. Section 7 analyzes the drivers of the performance of firms. Section 8 provides concluding remarks and policy recommendations.

# **2. Informal sector heterogeneity and inequality: Literature review**

The distinction between the formal sector and the informal sector has been studied by various authors over the last four decades. Hart (1973) relies on the distinction between wage and nonwage (self-employment) activities to distinguish the two sectors (see also ILO 1972). Since then, the debate about the nature of the diversity of formal and informal firms has shifted appreciably. Now, specific attributes and issues in the informal sector tend to vary depending on the region.

In Latin America and in economically dynamic parts of Southeast Asia, discussions about the informal sector focus on whether most informal entrepreneurs would prefer to remain in the informal sector or possess more secure jobs in the formal sector (Maloney 2004). In Africa, interest has centered on the division between a rather small group of successful firms and a much larger group of firms that struggle to survive. The available evidence tends to confirm that SubSaharan Africa is characterized by a dual industrial structure characterized by large numbers of small firms, small numbers of large firms, and few small and medium enterprises (Fafchamps 1994).

Rogerson (1996) recognizes a conceptual distinction between two groups of informal enterprises. The first group consists of survivalist firms. These are involved in activities initiated by people unable to secure regular wage employment or access to an economic sector of their choice. These firms are characterized by poverty and must struggle to survive. The second group includes microenterprises or growth enterprises that are small. Using empirical data on Costa Rica and Malaysia, Fields (1990) defines the growth-oriented entrepreneurs who can afford financial and human capital as the “upper-tier informal sector.” The International Labour Organization (ILO 2002) stresses the importance of recognizing the diversity of the workers in the informal economy because these workers have varied needs. In this vein, Nichter and Goldmark (2009) defend their argument that practitioners should be clear about their goals and avoid focusing interventions on the broad-based expansion of micro- and small enterprises. These enterprises are heterogeneous, and the class possesses numerous 3
segments, such as latent gazelles and survivalist middle-size enterprises, making the establishment of common, effective programs across groups difficult. Whereas all such enterprises require assistance through interventions, the latent gazelles are more likely than survivalists to need significantly varied types of assistance.

The empirical literature highlights many features of the heterogeneity in the informal sector, including involuntary wage labor, queuing for formal employment, and voluntary selfemployment. These are similar in many ways to the characteristics of the entrepreneurial small firm sector in developed countries (Grimm, Lay, Roubaud and Vaillant 2011). Fields (2004) differentiates between rationed upper-tier activities, which are based on methods of production and types of jobs that are similar to those of formal firms, and a lower tier, which is concentrated on easy entry activities and corresponding to the residual subsistence sector in the dualistic view (Bosch and Maloney 2010). However, Cunningham and Maloney (2001), using data on Mexico, argue that there is strong heterogeneity among small firms that is of the same nature as the heterogeneity in developed countries, whereby small firms that have reached their optimal longrun size coexist with profitable starting firms and start-up firms that will fail.

The key policy implication of the existence of distinct groups of entrepreneurs is that these groups call for distinct policy approaches: a directed business development method for top performers and a more widespread program to help improve employability and reduce poverty among the survivalists (Berner, Gomez, and Knorringa 2012).

Using a sample of informal entrepreneurs in seven West African countries, Grimm, Knorringa, and Lay (2012) in addition to the groups of top performers and survivalists, empirically identify a third segment of informal entrepreneurs, constrained gazelles. These entrepreneurs show characteristics—managerial abilities and motivation—similar to those of growth-oriented entrepreneurs. The authors mostly find a larger share of constrained gazelles in more dynamic economies. They also show that the size of the group of constrained gazelles relative to the group of survivalists is correlated with structural characteristics, such as the urbanization rate, the share of agriculture in the economy, and the size of the public and formal private sectors of the respective countries.

Gindling and Newhouse (2014) investigate heterogeneity among the self-employed, using the international Income Distribution Database (I2D2) on almost 100 developing countries. Unlike Grimm, Knorringa and Lay (2012), this paper uses two different measures of success (a) whether the self-employed worker is an employer and (b) whether the self-employed worker belongs to a family with per capita consumption above the $2 per day poverty line. The paper relies on the methodology developed in Grimm, Knorringa and Lay (2012) to identify self-employed with a high potential to become successful. Results show that one-third of the unsuccessful entrepreneurs have the potential to become successful.

4
Using qualitative information, performance-based measures and employment growth, Diao et al. (2016) identified a group of firms in the Micro, Small and Middle-Sized Enterprises (MSME) sector in Tanzania– the so-called “in-between”. These firms operate mainly in the sectors of manufacturing and trade services, employ approximately the same number of employees as compared to those in the formal sector and have significant potential for future growth.

Few studies have specifically investigated inequality in the informal sector. Most that do have focused only on the causality between rising inequality and the size of the informal economy at the macroeconomic level (Chong and Gradstein 2004; Winkelried 2005).

Classifying firms in the informal sector and recognizing the sector’s role as a setting environment for profitable activity may contribute to economic growth (AfDB 2013). Accounting for more than half the labor force in Sub-Saharan Africa, the informal sector represents prospects for better living standards among many people. Nonetheless, there is evidence that registering with tax authorities (formalization) increases the profits of firms with two to five workers and the middle third of capital stock (the midsize group), while leading to losses among smaller and larger informal firms (McKenzie and Sakho 2010). Discovering how to reduce inequality and poverty between and within the three distinct categories of informal firms—top performers, constrained gazelles, and survivalists—is thus an important policy matter. It could enhance the productivity of firms and help governments coordinate strategies and policies to support the formalization of the sector.

# **3. Data and descriptive statistics**

## **3.1. 1-2-3 survey**

The empirical investigation reported in this paper relies on survey data from the 1-2-3 survey collected in the Democratic Republic of Congo in 2012. The 1-2-3 survey was conducted by the National Institute of Statistics, in partnership with other actors, including Afristat and the World Bank.<sup>2</sup> The 1-2-3 survey is a representative, multilayer survey that covers three nested surveys, three phases involving separate statistical populations: individuals, informal production units, and households.

Phase 1 provides detailed information on employment, unemployment, and household and individual sociodemographic characteristics. It includes a household module, an individual questionnaire for individuals ages 5 or above, and a community questionnaire. Phase 2 is a specific survey covering the heads of informal production units. This informal sector survey collects information on the characteristics of firm owners, production units, economic performance, business requirements (costs, investment), and employees. This phase is important as it allows measuring the Congolese informal economy. Phase 3 is a survey on household

> 2 See INS. http://www.ins-rdc.org/sites/default/files/Rapport%20enquete%20123.pdf.

5
expenditures that involves interviews among a subsample of the informal production units identified in phase 1. The paper focuses on a representative subsample of 4,504 informal firms. Table A1 in the appendix defines the variables used in the paper.

# **3.2. Characteristics of the informal sector in DRC**

The informal sector in the Democratic Republic of Congo is heterogeneous in many ways. Differences are noticeable across the individual characteristics of the owners of informal firms (reason for the creation of the firm, educational attainment), but also across the distribution of consumption<sup>3</sup> and capital.

Most people who have created informal enterprises have done so because they lack alternative employment opportunities. Many entrepreneurs started their businesses because they could not find jobs in the formal wage sector. Of entrepreneurs who have created their own businesses because of the lack of jobs in large firms, 63 percent have received secondary education, 65 percent are men while only 35 percent are women (appendix A, figure A.1). The informal sector is dominated by women entrepreneurs, who own approximately 64 percent of all informal firms. This reinforces the finding that, in many developing countries, women manage the majority of small and medium enterprises because they lack access to alternative opportunities (Rubio 1991).

The vast majority of entrepreneurs in the informal sector are poor. Poverty incidence is high among workers in the sector as well. An average 61 percent of informal entrepreneurs are poor, compared with the nationwide poverty incidence, which is 64 percent. However, the incidence of poverty among workers in the informal sector varies dramatically by province. For example, 31 percent of entrepreneurs in Kinshasa are poor, compared with approximately 90 percent in SudKivu (figure 1). There also are striking differences across the provinces in the sociodemographic characteristics of entrepreneurs in the informal sector.

**Figure 1.  Poverty Incidence among informal Entrepreneurs by province, 2012 (%)**

<!-- Start of picture text --> 100 89.43<br>81.23 80.65 79.42<br>80 75.56 73.73 68.15<br>60 54.33<br>44.04 42.87<br>40 30.78<br>20<br>0<br>Sud-Kivu Nord-Kivu Bandundu Kasaï Equateur Province Kasaï Katanga Maniema Bas-Congo Kinshasa<br>Oriental Orientale Occidental<br><!-- End of picture text --> _Source:_ Calculations based on 1-2-3 Survey data.

> 3 The rest of the paper uses consumption as a proxy for income and will use the two interchangeable terms.

6
Most of the entrepreneurs in the informal sector have received education. Respectively, 59 percent and 14 percent have attained secondary and postsecondary education. Only 7 percent have no formal education, while 16 percent have completed primary education (table 1).

There is a strong correlation between educational attainment and poverty status because informal entrepreneurs with higher consumption levels tend to be more educated. Approximately 40 percent of informal entrepreneurs who possess postsecondary education rank among the informal entrepreneurs with the highest consumption (top quintile). Only 12 percent of the informal entrepreneurs who have no formal education have reached such consumption. Meanwhile, approximately 6 percent of entrepreneurs with postsecondary education exhibit consumption that ranks in the bottom consumption quintile, where approximately 24 percent of the entrepreneurs with no formal education are found. Educational attainment correlates positively with consumption because the relatively more educated entrepreneurs are more well off (see table 1).

**Table 1. Consumption Distribution across Informal Firms**

||All|Bot. decile|1st qt.|2nd qt.|3rd qt.|4th qt.|5th qt.|Top decile|
|---|---|---|---|---|---|---|---|---|
|**Owner characteristics**|||||||||
|Male|0.364|0.427|0.394|0.387|0.325|0.327|0.391|0.376|
|Age owner|0.378|0.385|0.383|0.375|0.373|0.380|0.382|0.387|
|No diploma|0.077|0.083|0.240|0.273|0.204|0.166|0.118|0.080|
|Primary completed|0.165|0.107|0.224|0.293|0.208|0.140|0.135|0.060|
|Some secondary|0.588|0.055|0.137|0.227|0.212|0.214|0.210|0.095|
|Post secondary|0.143|0.024|0.060|0.130|0.165|0.261|0.385|0.221|
|Other post primary|0.026|0.023|0.129|0.218|0.190|0.259|0.205|0.137|
|Can read|0.888|0.818|0.809|0.856|0.865|0.927|0.961|0.964|
|Can write|0.878|0.806|0.798|0.858|0.857|0.908|0.947|0.940|
|Poor|0.611|0.099|0.243|0.376|0.333|0.048|0|0|
|**Firm characteristics**|||||||||
|Annual value added|4,304,138|1,916,232|1,623,570|1,457,256|13,900,000|2,124,842|2,408,441|2,956,779|
|Physical capital|145,255.6|54,340.23|75,616.27|95,276.93|131,650|138,510.2|279,270.4|327,061.7|
|Gross operating profit|125,703.4|115,867.2|106,092.7|99,184.71|129,174.5|130,322.6|163,759|181,338.4|
|Workers, total|1.3|1.41|1.31|1.24|1.27|1.3|1.37|1.44|
|Paid workers|0.14|0.2|0.11|0.08|0.13|0.17|0.18|0.25|
|Wage worker, male|0.23|0.186|0.189|0.166|0.366|0.182|0.271|0.328|
|Wage worker, female|0.13|0.082|0.11|0.119|0.157|0.146|0.198|0.286|
|Water|0.0845|0.0202|0.0393|0.1867|0.1857|0.2326|0.3557|0.2343|
|Electricity|0.1201|0.0296|0.0396|0.1291|0.1844|0.2192|0.4277|0.2358|
|Land line, phone|0.1534|0.0395|0.0987|0.1474|0.2252|0.2114|0.3174|0.1832|
|Fixed location|0.5797|0.0508|0.1317|0.2349|0.2039|0.2167|0.2128|0.1041|
|Mobile location|0.4051|0.0729|0.1722|0.2244|0.2012|0.1902|0.212|0.1087|
|Other location|0.0152|0.129|0.1605|0.2019|0.2474|0.1599|0.2303|0.062|
|Observations|4,424|440|1,002|1,007|864|840|711|346|

_Source:_ Calculations based on 1-2-3 survey data _Note:_ This table considers 4,424 informal firms as there are 80 missing points in our variable per equivalent adult consumption. Means of characteristics of interest are presented in the table. Means are weighted using sampling weights.

7
The staff size of urban informal firms varies widely, but most of the firms are staffed by only the owner. Appendix A, figure A.2 illustrates the distribution of informal firms by staff size. It shows that 82 percent of entrepreneurs in the informal sector are staffed by one person. Whereas 11 percent of informal firms are staffed by two people, only around 7 percent have from two to five employees.<sup>4</sup> The distribution of the staff size of informal firms by gender shows that 87 percent of firms owned by women are staffed by one person while this is the case for 72 percent of firms owned by men (appendix A, figure A.3).

Approximately 65 percent of informal firms are active in the agroindustry and food trade, retail trade, and repair services. The breakdown among these branches indicates that 38 percent of entrepreneurs operate in the agroindustry and food trade, while 26 percent have retail shops and repair services. The manufacturing branch represents a minority, with 10 percent of all informal firms. The wholesale, construction, and mining branches each represents less than 2 percent of informal firms (appendix A, table A.2).

About 85 percent of entrepreneurs in the informal sector work from a fixed place of business, whereas 15 percent do not. However, the locations of the fixed places of business often do not meet basic requirements in a way that sustains business growth. Informal entrepreneurs without a fixed workplace tend to be mobile. Home is the most common workplace among entrepreneurs who work from a fixed place. However, 21.7 percent of informal entrepreneurs who work from their homes do not have the appropriate equipment (appendix A, figure A.4).

The majority of informal firms lack access to basic infrastructure such as water, electricity, and telecommunication. Only 8 percent, 12 percent, and 15 percent of the firms, respectively, have access to water, electricity, and telecommunication. Entrepreneurs in the bottom quintile of the consumption distribution have less access to basic infrastructures mainly because they are less likely to have a fixed workplace compared with entrepreneurs in the top quintile of the consumption distribution (see table 1).

Access to basic infrastructure and a fixed workplace is more constrained among women entrepreneurs than among men entrepreneurs. Whereas 24 percent of men entrepreneurs have access to telecommunication, only 10 percent of women entrepreneurs have such access. Similarly, relative to men entrepreneurs (12 percent), more women entrepreneurs (27 percent) operate from their homes without the appropriate infrastructure (appendix A, figure A.5).

The performance of informal firms is related to their access to capital and the source of their labor. Firms with large amounts of capital generate the most profits. Such firms are disproportionately concentrated among entrepreneurs who exhibit high consumption. Highly

> 4 About 93 percent of informal firms were created by one person. This may indicate a high potential for cooperatives. The mean of paid employees is 0.14 (Table 1), which is below the share (0.205) found in Grimm, Knorringa and Lay (2012) using 1-2-3 survey data for seven West African countries.

8
productive firm owners tend to have at least one wage worker (see table 1). Firm characteristics and performance vary according to the socioeconomic characteristics of the owner.

The average performance of informal firms, measured by the hourly value added, is CGF 20,697.02, and the average amount of capital is CGF 145,255.60. Both firm performance and access to capital vary by gender. Overall, informal firms owned by men tend to perform better. These firms are more likely to have access to more capital and to generate more value added per hour worked relative to informal firms headed by women. Man-headed informal firms typically have more workers who are paid. The informal firms with the most capital are active more often in transport, mining and other primary activities in production and the retrieval of raw materials, construction, and hotels and restaurants. Around 42 percent of informal firms in the top capital decile are engaged in transport. Informal firms operating in the food trade, agribusiness, and wholesale tend to have the least access to capital (appendix A, table A.2).

In general, the richer the entrepreneur, the better the economic performance. Larger hourly value added is generated by entrepreneurs with higher levels of consumption compared with their counterparts in the bottom consumption quintile. Informal entrepreneurs in the bottom income decile exhibit lower value added, about two-thirds as much as the entrepreneurs in the top decile.

Entrepreneurs with greater capital are typically more well educated. Over a third of the entrepreneurs with greater access to capital (the top quartile) have received vocational education, that is, other post-primary education. Only 11 percent of the entrepreneurs in the bottom quartile of the capital distribution have received so much training. Around 36 percent of the entrepreneurs in the top quartile of the capital distribution have some postsecondary education compared with 19 percent of the entrepreneurs in the bottom quartile (appendix A, table A.3).

Informal firms with less capital are more constrained by a lack of basic infrastructure (water, electricity, and phone lines) than their peers. Firms in the bottom quartile of the capital distribution have less access to basic infrastructure. While close to a third of these firms operate from mobile locations, this is true of only a fifth of the firms in the top capital quartile (appendix A, figure A.6). Informal firms in provinces such as Katanga and Kinshasa tend to have access to more capital and better access to water and electricity (appendix A, table A.2).

# **4. Identification strategy: Informal firms**

## **4.1. Defining a top performer**

Following Grimm, Knorringa, and Lay (2012), the strategy to identify the three categories of informal entrepreneurs involves three steps. The first step consists in defining growth-oriented entrepreneurs or top performers in terms of physical capital and value added. Physical capital is defined as the replacement value of all business-related assets that have been used in the operation of the enterprise in the previous year, including the business establishment, machinery, furniture, vehicles, and utilities. In the second step, owner and firm characteristics are identified 9
that are correlated with physical capital and value added, and the empirical probability that a firm will be highly productive is assessed based on these correlations. In the third step, entrepreneurs are classified into the three groups—top performers, constrained gazelles, and survivalists— conditional on the actual status as a top performer and the predicted probability of this status.

The paper defines top performers as those entrepreneurs in the sample who are the most productive (the top 50 percent in terms of capital productivity, that is, value added per physical capital) among the top 40 percent of the capital distribution. Because capital accumulation is part of the economic growth process, it is considered an accurate factor in identifying a growthoriented firm.

# **4.2. Sample selection bias**

Because of missing values in the variable of capital and because the definition of the top performers is based mainly on this variable, the Heckman (1979) two-step estimation is viewed as a reliable way to test for selection bias. The first step consists in analyzing the determinants of capital possession. To do so, a probit model is estimated, as the follows: `′` Pr(Capital = 1) = θ(∝0+ Xi ∝1+ ωi) (1) where _capital_ is a dummy variable taking 1 when a firm owns capital and 0 otherwise;  is the cumulative standard distribution function;  i is a vector of the following factors: business association, financial knowledge that is measured by a variable equal to 1 if the firm’s owner has knowledge about microfinance institutions and 0 otherwise, family tradition taking 1 if the owner started a business because of family tradition, the age of the owner, the age squared; a gender dummy equal to 1 if the owner is a man and 0 otherwise, and firm age. Dummies are included for sectors. The equation also includes region fixed effects.

The results show that membership in a business association, the possession of financial knowledge, and being a man increase the probability of owning capital (table 2). They also support the evidence that industry is a highly capital-intensive sector. Entrepreneurs in the industry sector are more likely (around 6.7 percent) than entrepreneurs in other sectors to have capital. Entrepreneurs operating in hotels and restaurants are also likely to own capital. Entrepreneurs in retail trade and in repair services show less likelihood of capital ownership.

10
**Table 2. Step 1: Determinants of Capital Ownership among Firms**

|(1)<br>Coefficients|(2)<br>Marginal effects|
|---|---|
|Business association<br>0.209**<br>(0.0972)|0.0548|
|Financial knowledge<br>0.151***<br>(0.0585)|0.0397|
|Family tradition<br>0.104<br>(0.0896)|0.0273|
|Male(=1)<br>0.0887*<br>(0.0522)|0.0233|
|Age firm<br>0.00571<br>(0.0129)|0.00150|
|Age owner<br>0.00370<br>(0.00805)|0.000972|
|Age squared owner<br>-0.000<br>(0.000)|-0.000|
|Constant<br>0.802***<br>(0.184)||
|Sector fixed effects<br>yes||
|Region fixed effects<br>yes||
|Pseudo-R2<br>0.1138||
|Observations<br>4,410|4,410|
|Standard errors in parentheses *** p<0.01, ** p<0.05,|* p<0.1|

In the next step, the paper derives the predicted probabilities from the estimation of (1) to calculate the inverse Mills ratio, that is, the ratio of the probability density function to the cumulative distribution function, as follows<sup>5</sup> : The paper then includes this ratio in estimating the following equation: Top where Yi is the binary variable indicating a firm that is a top performer;  is the cumulative distribution function of the logistic distribution; β1 is the vector of coefficients that defines the link between owner and firm characteristics and the probability that the firm is a top performer; and  i is the vector of owner and firm characteristics. The vector  i encompasses the predetermined factors that are correlated with identity as a top performer. To address the issue of endogeneity, predetermined variables are included, as follows: age, age squared, gender, educational attainment, and the motivation of the entrepreneur to set up the business, which is measured by a dummy variable taking the value of 1 if the entrepreneur created the firm because s/he could not find a paid job in a large company. Besides these predetermined variables, which allow to control for the firm’s age and avoid selection against younger firms with a high potential, a sector dummy is included.

> 5 Box A1 (in appendix A) provides a detailed explanation about the inverse Mills ratio, the probability density and the cumulative distribution functions.

11
Table 3 displays the estimation results. It highlights that men have a greater chance (5.2 percent) than women of being top performers. In addition, the probability of inclusion as a top performer rises with age, albeit at a decreasing pace. Vocational training is a key determinant of being a top performer. Individuals who have started businesses because they could not find jobs in large firms exhibit a higher probability (approximately 4.2 percent) of inclusion among the top performers. Moreover, the inverse Mills ratio is not significant, that is, there is no selection bias in the model estimation.

**Table 3. Step 2: Probability of being a Top Performer**

||(1)<br>Coefficients|(2)<br>Marginal effects|
|---|---|---|
|Age owner|0.117***<br>(0.0287)|0.00878|
|Age squared owner|-0.00142***<br>(0.000352)|-0.000107|
|Male(=1)|0.701***|0.0528|
||(0.156)||
|Age firm|0.0239<br>(0.0426)|0.00180|
|No diploma|Ref.||
|Primary completed|0.128<br>(0.399)|0.00740|
|Some secondary|0.439<br>(0.311)|0.0288|
|Post-secondary|0.599<br>(0.374)|0.0419|
|Other post primary|1.057**<br>(0.432)|0.0886|
|No opportunity in large firms|0.564***<br>(0.183)|0.0424|
|Family tradition|0.171<br>(0.266)|0.0129|
|Inverse Mills ratio|-2.129<br>(1.341)|-0.160|
|Constant|-4.769***<br>(0.924)||
|Sector fixed effects|yes||
|Region fixed effects|yes||
|Pseudo-R2|0.08||
|Observations|3,401|3,401|
|Standard errors in parentheses ***|p<0.01, ** p<0.05, *|p<0.1|

# **4.3. Identification of the constrained gazelles and survivalists**

Based on regression (3), the paper predicts, for each entrepreneur in the country, the statistical probability of being a top performer. This probability is conditional on the estimated parameters  _0_ and  _1_ and on the vector of observed variables  . The following model is performed: where β̂ is the vector of estimated coefficients. To separate the sample of informal entrepreneurs into three categories: top performers, constrained gazelles and survivalists, the paper uses these predicted probabilities.

Top To determine the shares of the other segments, the cut-off point is selected for Pr̂(Yi = 1) between constrained gazelles – a group of entrepreneurs who have business skills that resemble 12
in many respects those of the top performers but are clearly different from those skills characterizing survivalists – and survivalists such that the mean of this predicted probability is similar in the group of top performers and the group of constrained gazelles. This suggests that the distribution of the observable factors is the same in both categories and that on average constrained gazelles should be as likely as the actual top performers to be top performers. The survivalists are defined as all entrepreneurs for whom Pr̂(YiTop = 1) is below the threshold.6 Estimates based on equation (3) illustrate that constrained gazelles and top performers share some characteristics. Appendix A, table A.5 illustrates the determinants of inclusion among the three distinct groups of informal firms that have been identified. The results show that firms managed by men, people who created businesses because they could not find jobs as wage earners in large firms, more well educated, and older people have a higher probability of being constrained gazelles. However, these determinants also decrease the probability of being survivalists.

Table 4 and map 1 show the shares of the three groups of informal entrepreneurs by province. One may note that, whereas almost 26 percent of the constrained gazelles are located in Kinshasa, only 1 percent are based in Sud-Kivu. Katanga and Kinshasa, where firms are the least constrained by the lack of basic infrastructure and the most highly endowed in capital, are the provinces with the most top performers and constrained gazelles.

Table 5 presents the shares of top performers, constrained gazelles and survivalists within province. Within province, most of entrepreneurs are either survivalists or constrained gazelles. Among entrepreneurs in Sud-Kivu, 78 percent are survivalists. In Kinshasa, more than a half of entrepreneurs are constrained gazelles. In Equateur, 16 percent of entrepreneurs are top performers whereas in only 4 percent of entrepreneurs in Nord-Kivu are top performers.

**Table 4. Percentage Share of Firms, by Province**

||Top performers|Constrained gazelles|Survivalists|
|---|---|---|---|
|Equateur|13.68|9.47|6.90|
|Bandundu|8.88|10.14|13.31|
|Bas-Congo|3.74|4.09|5.14|
|Katanga|15.83|16.98|10.40|
|Kasaï Oriental|10.46|7.54|10.53|
|Kasaï Occidental|8.37|8.39|18.21|
|Kinshasa|25.66|25.96|13.37|
|Nord-Kivu|1.36|1.75|4.39|
|Sud-Kivu|3.78|1.03|5.30|
|Maniema|1.67|3.62|2.46|
|Province Orientale|6.57|11.03|9.97|

_Source:_ Calculations using 1 **-** 2-3 surveys. Percentages are weighted using sampling weights

> 6 As a robustness check, the paper relies on the full information maximum likelihood (FIML) model to estimate equation (3).  The FIML model handles missing values problems well by using all the information available. The results remain unchanged (appendix A, table A.4). The two groups (constrained gazelles and survivalists) are then reclassified using the predicted probabilities based on the model regressions. The groups of constrained gazelles and survivalists using Heckman (1979) two step and FIML estimations match at about 96 percent.

13
<!-- Start of picture text --> Top performers Constrained gazelles<br>8-14 Bett<br>45 4.8<br>= 14-28Share198 of top performers : 11-26Share1-4 of cons. gazolles<br>Survivalists<br>i re<br>Shary of sutvivalists<br>1318 :<br>5-19 ote<br>: 2-50-13 acd E TM<br><!-- End of picture text -->
<!-- Start of picture text --> gN<br>oy<br>2<br>go<br>8<br>rr) y;Cs po Ne tes\<br>yf = S.<br>/ Se...<br>o<br>0 oe A 6<br>Predicted probability<br>—--—-- Top performers ——— Constrained gazelles<br>——— Sunivalists<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>&<br>2<br>2<br>a<br>8 =<br>y N NC<br>t mS 7<br>===<br>°o -<br>0 50 100 150 200<br>Capital<br>—---- Top performers ©———— Constrained gazelles<br>——— Sunvalists<br><!-- End of picture text -->
Educational differences are pronounced, with top performers and constrained gazelles more highly educated than survivalists. The difference in educational attainment between top performers and constrained gazelles is not statistically significant. However, there is a noticeable difference between the educational attainment of top performers and survivalists. While 14 percent of top performers have a postsecondary education, only 8 percent of survivalists have attained this level. Survivalists have the lowest overall educational attainment.

Vocational training correlates positively with the performance of informal firms. This correlation may provide a useful link between education and firm performance. Through better education and training, entrepreneurs may develop greater capability, acquire new management and marketing skills, and learn efficient production processes (Tan and Batra 1995; Burki and Terrell 1998).

**Table 6. Comparison across Top Performers, Survivalists, and Constrained Gazelles**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Top|Constrained||t-Test|t-Test|t-Test|
||performers|Gazelles|Survivalists|(1) vs (2)|(1) vs (3)|(2) vs (3)|
|Age owner|37.753|38.132|36.582|0.5606|0.1994|0.001***|
|Male (=1)|0.58|0. 712|0.132|0.000***|0.000***|0.000***|
|No diploma|0.046|0.034|0.119|0.2983|0.000***|0.000***|
|Primary completed|0.11|0. 098|0.231|0.5264|0.000***|0.000***|
|Some secondary|0.658|0.666|0.561|0.7969|0.001***|0.000***|
|Post-secondary|0.140|0.158|0.082|0.4481|0.001***|0.000***|
|Other post-primary|0.043|0.042|0.005|0.9606|0.000***|0.000***|
|No opportunity in large firms|0.201|0.195|0.026|0.8320|0.000***|0.000***|
|No opportunity in small firms|0.110|0.148|0.133|0.091*|0.2846|0.222|
|By family tradition|0.087|0.09|0.077|0.7995|0.5736|0.1553|
|Per equivalent adult consumption|708203.7|588719.4|487817|0.002***|0.000***|0.000***|
|Poor|0.568|0.644|0.733|0.014**|0.000***|0.000***|
|Food/Agribusiness|0.23|0.184|0.532|0.073*|0.000***|0.000***|
|Clothing and apparel|0.083|0.104|0.054|0.2726|0.047**|0.000***|
|Industry|0.156|0.162|0.147|0.811|0.6747|0.2503|
|Construction|0.03|0.033|0.001|0.787|0.000***|0.000**|
|Transport|0.026|0.033|0.025|0.528|0.910|0.1721|
|Hotels and restaurants|0.08|0.049|0|0.037**|0.000***|0.000***|
|Wholesale|0.02|0. 018|0. 005|0.889|0.006***|0.000***|
|Primary and mining|0.046|0.048|0.014|0.874|0.000***|0.000***|
|Retail and repair services|0.266|0.290|0.172|0.4046|0.000***|0.000***|
|Other activities|0.06|0.073|0.046|0.407|0.3232|0.001***|
|Firm set up with other person|0.256|0.216|0.115|0.1306|0.000***|0.000***|
|<br>Wage worker, male|0 .216|0.229|0.244|0.8311|0.6277|0.5629|
|Wage worker, female|0.15|0.111|0.137|0.3813|0.7948|0.2096|
|Size of firm|1.756|1.496|1.191|0.001***|0.000***|0.000***|
|Age of firm|2.263|2.295|2.03|0.7657|0.021**|0.000***|
|Mobile location|0.146|0.152|0.165|0.7986|0.4046|0.3126|
|Fixed location on the street|0.176|0.170|0.126|0.7998|0.018**|0.000***|
|Firm located at home|0.313|0.349|0.423|0.2352|0.000***|0.000***|
|Firm located at clients' home|0.033|0.048|0.017|0.2471|0.065*|0.000***|
|Fixed location on market|0.033|0.033|0.015|0.9808|0.027**|0.000**|
|Improvised location on market|0.183|0.160|0.210|0.3420|0.2734|0.000***|
|Shop/restaurant|0.03|0.027|0.013|0.7781|0.027**|0.004***|
|Other location|0.083|0.057|0.027|0.099*|0.000***|0.000***|
|Water connection|0.083|0.068|0.049|0.3663|0.016**|0.023**|
|Electricity connection|0.133|0.142|0.053|0.6896|0.000***|0.000***|
|Telecommunication|0.2|0.202|0.077|0.9253|0.000***|0.000***|
|Trade register|0.016|0.024|0.006|0.4009|0.0672*|0.000***|
|Knowledge of micro-finance inst.|0.216|0.228|0.143|0.6520|0.001***|0.000***|
|Self-employment|0.656|0.752|0.878|0.000***|0.000***|0.000***|
|Member of a business association|0.113|0.101|0.051|0.5468|0.000***|0.000***|
|Observations|300|1331|1839||||
|Notes: *** p<0.01, ** p<0.05, * p<0.|1||||||

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# **5.2. Firm typology and the choice of sector**

More than 50 percent of survivalists are engaged in food trade and agribusiness compared with only 23 percent of top performers and 18.4 percent of constrained gazelles. This sector comprises for instance retail trades of palm oil, tomatoes and other agricultural products. The primary sector of top performers and constrained gazelles (26.6 percent and 29.0 percent, respectively) is repair services and the retail trade, which includes retail trade in coal, fuel, and kitchen appliances and the repair of bicycles, motorcycles, and household items. Our results are similar to those of Gindling and Newhouse (2014). Their results show that the successful selfemployed are more likely to operate in retail and services compared to unsuccessful selfemployed.

Top-performing firms are generally larger than constrained gazelles and survivalists. Top performing firms are approximately 20 percent larger than constrained gazelles and almost 50 percent larger than survivalist firms. Firm size is measured as the total number of paid and nonpaid workers.

Setting up an informal firm with more than one owner may reduce credit market constraints. Enterprises with multiple owners are more likely to be top performers or constrained gazelles. About 20 percent of the top performers have founded their businesses as joint ventures. Only 11 percent of survivalists are joint ventures.

Constrained gazelles are generally older than both top performers and survivalists, and survivalists are, on average, the youngest of the three groups. This age dimension may reflect accumulated business experience. Firms may also experience both positive and negative shocks during their existence. The learning-by-doing channel enables firms to learn and to perform better as they age, making them more likely to survive (Baker and Kennedy 2002). However, older firms in developing countries may suffer from productivity losses, for example, if their owners become too old to adopt new technologies (Burki and Terrell 1998; Nichter and Goldmark 2009).

Top performers and constrained gazelles tend to operate from fixed locations (streets, markets, shops), while survivalists tend to operate from home and mobile selling points. The transitory locations of survivalists may explain the greater constraints the survivalists face in taking advantage of basic infrastructure.

Survivalists possess less financial knowledge, are less likely to register their businesses, and are less likely to be involved in a business association than either of the other two groups. Top performers and constrained gazelles are not significantly different in these characteristics. This highlights that top performers and constrained gazelles share similar management skills and professional networks.

17
# **6. Urban poverty and income inequality in the informal sector**

Poverty and income inequality are particularly significant in the informal sector. This section examines these two issues among the three distinct groups of informal firms.

Table 7 shows the distribution of quintile of income by informal firms. The top performers are better off than the other categories of informal firms. While more than 17 percent of survivalists are in the bottom quintile of revenue only respectively 13 percent of constrained gazelles and 9 percent of top performers are found in this quintile. Moreover, the logit estimates of the probability of being poor confirm that survivalists and constrained gazelles are more likely to be poor than top performers (table 8).<sup>7</sup>

**Table 7. Quintiles of Per Adult Equivalent Consumption, Informal Firms, %**

|Quintiles of consumption|Top performers|Constraine<br>Gazelles|d<br>Survivalists|
|---|---|---|---|
|1|8.94|12.85|17.55|
|2|18.43|21.17|26.23|
|3|21.37|17.17|21.94|
|4|23.04|21.47|19.66|
|5|28.23|27.35|14.63|
|Total<br>_Source:_Calculations using 1-2-3<br>Notes: Percentages are weighted|100<br>Survey data<br>using sampling weigh|100<br>ts.|100|

**Table 8. Poverty between Informal Firms, Logit Estimates**

||(1)<br>Coefficients<br>Poor=1|(2)<br>Marginal effects|(3)<br>Coefficients<br>Poor=1|(4)<br>Marginal effects|
|---|---|---|---|---|
|Top-performers|Ref.||Ref.||
|Constrained gazelles|0.320**|0.0681**|0.409***|0.0715***|
||(0.146)|(0.0309)|(0.154)|(0.0270)|
|Survivalists|0.736***|0.157***|0.582***|0.102***|
||(0.132)|(0.0277)|(0.154)|(0.0269)|
|Constant|0.276**<br>(0.124)||1.339***<br>(0.193)||
|Sector fixed effects|||yes||
|Region fixed effects|||yes||
|Pseudo-R2|0.0111||0.1517||
|Observations|3,398|3,398|3,398|3,398|
|Standard errors in parent|heses *** p<0.01,|** p<0.05, *p<0.1|||

A Lorenz curve illustrates inequality across the three distinct groups of informal firms (figure 3). It shows that the profit – which constitutes entrepreneurs’ revenue – is more equally distributed across top performers than across the other two groups of informal firms. For instance the poorest 25 percent of top performers share about 8 percent of the profits, whereas the poorest 25 percent of the constrained gazelles possess only 1.5 percent of the profits. The Gini coefficient is about 0.40 among top performers whereas this index is higher among constrained gazelles (0.57) 7These results are robust to two other classification methods (using the mean in capital and in value added per hour worked) for the three groups of informal firms.

18
<!-- Start of picture text --> Lorenz Curves for informal firms<br>=<br>So<br>ao<br>2<br>&<br>ge<br>g<br>[o)<br>ot<br>=<br>g<br>Sa<br>Se<br>|<br>(S)<br>o<br>oO ee 4 6 8 1<br>Cumulative % of entrepreneurs<br>————_ line of 45° (perfect equality) —————— Top-performers<br>——— Constrained gazelles ———— Survivalists<br><!-- End of picture text -->
Table A.6 (in appendix A) displays the estimates of regression (5). The results show that for an increase of CGF 1,000 in the gross operating surplus, the odds of being in the top quintile versus the (combined) other categories are 1.001 times (or 10 percent) greater (all sample). This means that there is inequality among the informal entrepreneurs. An increase in profits is more likely to positively impact the income of the richest entrepreneurs than the others’ incomes. When considering each category of firms, the constrained gazelles and survivalists show the same pattern. However, among the top performers there is an equal chance of being in the top quintile of income, even if the estimated coefficient is not significant.

Human capital, including educational attainment and the ability to read in any language is a key factor, that is, it increases the probability of being in the top quintile for all types of informal firms. Still, the impact of postsecondary educational attainment among top performers is the greatest. Attainment of other post-primary education, including vocational training, increases the odds of being in the top income quintile among constrained gazelles, though this is not the case for the two other groups. Moreover, firms with less access to credit are less likely to be in the top income quintile. Because of a lack of credit access, top performers, constrained gazelles, and survivalists are less likely to be among the richest 20 percent. The impact of the lack of credit access on income inequality is greater among constrained gazelles and survivalists (see table A.6).

Firm size and the income of the owners appear to be independent among top performers and survivalists. However, among constrained gazelles, there is a significant negative relationship between firm size and the probability of being in the highest income quintile. Regarding the factors underlying firm organization, firms constrained by credit access are less likely to be in the top income quintile.

These results highlight that pro-poor policies aimed at improving education and providing access to finance ~~a~~ re the most favorable to increasing income among the poorest among informal firms.

The results of inequality regressions are robust to two other classification methods of the three groups of informal firms and to two other definitions of top performers. First, the groups of top performers and constrained gazelles are reclassified based on the median value of capital (appendix A, table A.7). This gives 690 top performers and 941 constrained gazelles. Second, the top performers are redefined as those entrepreneurs in the top 10 percent of the distribution of value added per hour worked (appendix A, table A.8). The paper also tests whether the classification changes according to the definition of a top performer. First, top performers are defined as those entrepreneurs in the sample who are in the top 40 percent in terms of capital productivity among the top 40 percent of the capital distribution. Second, the paper defines top performers as those entrepreneurs in the sample who are in the top 40 percent in terms of capital productivity among the top 50 percent of the capital distribution. Then, the paper classifies the 20
two other groups, the constrained gazelles and survivalists. Appendix A, tables A.9 and A.10 present the results.<sup>8</sup>

# **7. Drivers of the performance of informal firms**

In the previous section, poverty and income inequality are analyzed among informal firms. The findings show that policies aimed at reducing the performance gap between top performers and the two other groups, namely, constrained gazelles and survivalists, may also help lift entrepreneurs in the latter two groups out of poverty and reduce income inequality.

This section first corroborates these results based on a Blinder-Oaxaca decomposition and then examines the factors that make one informal firm more successful than another.

The Blinder-Oaxaca decomposition (Blinder, 1973; Oaxaca, 1973) explains the gap in the means of an outcome variable—the logarithm of per adult equivalent revenue in this case—between two groups. This paper compares top performers with constrained gazelles and top performers with survivalists. It uses the Blinder-Oaxaca to decompose income differences into a part that is due to differences between the two groups in endowments or characteristics (explained component), on the one hand, and group differences in returns to those endowments (unexplained component), on the other.

The formalization of income differences is as follows: where lnIncomeTt and lnIncomeit represent the mean of the logarithm of per adult equivalent income for respectively top performers and constrained gazelles or survivalists in time t. X is the vector of control characteristics for individuals. The first term is the explained component, and the second term represents the unexplained component as described above.

Tables 9 and 10 display the results. We observe that differences in firms’ performance explain more than half a percent of the revenue gap between top performers and constrained gazelles. Differences in other factors including the individual characteristics of entrepreneurs such as age and human capital, and other managerial characteristics such as management and employee discipline issues, explain more than 20 percent of the revenue gap even though the coefficient is not significant. Nonetheless more than 25 percent of the gap remains unexplained.

> 8 We performed an endogeneity test to assess the potential endogeneity of our profit variable (gross operating surplus). Using the sector-region average of value added per hour worked as an instrument for gross operating profit, the test did not find evidence of endogeneity.

21
# **Table 9. Blinder-Oaxaca Decomposition of Per Adult Equivalent Income, Top Performers versus Constrained Gazelles**

|Per adult equivalent consumption, Log|Coefficients|Std. Err.|P>z|
|---|---|---|---|
|Differential||||
|Mean prediction (top-performers)|13.18|0.044|0.000|
|Mean prediction (gazelles)|13.05|0.019|0.000|
|Difference|0.12|0.048|0.009|
|Explained||||
|Value added per hour worked, Log|0.065|0 .017|0.000|
|Other factors|0.026|0 .018|0.154|
|Total|0.091|0.024|0.000|
|Unexplained|0.033|0 .046|0.468|
|Observations= 1,427||||

_Source:_ Calculations using 1-2-3 Survey data, 2012.

# **Table 10. Blinder-Oaxaca Decomposition of Per Adult Equivalent Income, Top Performers versus Survivalists**

|Per adult equivalent consumption, Log|Coefficients|Std. Err.|P>z|
|---|---|---|---|
|Differential||||
|Mean prediction (top-performers)|13.18|0.044|0.000|
|Mean prediction (survivalists)|12.89|0.014|0.000|
|Difference|0.29|0.046|0.000|
|Explained||||
|Value added per hour worked, Log|0.039|0.016|0.018|
|Other factors|0.117|0.026|0.000|
|Total|0.157|0.029|0.000|
|Unexplained|0.133|0.049|0.007|
|Observations= 1,889||||

_Source:_ Calculations using 1-2-3 Survey data, 2012.

Overall, the performance of firms is an important factor in explaining income differences between top performers and constrained gazelles while it explains less than 15 percent of income differences between top performers and survivalists. The results of the Blinder-Oaxaca decomposition are in line with the previous findings that well performing informal firms (the top performers) are less poor and exhibit less income inequality relative to constrained gazelles and survivalists.

In the next step, the drivers of the performance of firms are analyzed. The following regression explains the value added per hour worked by a number of characteristics of the entrepreneurs such as human capital including educational attainment, financial knowledge, a proxy for social networks and proxies for management skills. The equation<sup>9</sup> is as follows: `′ ′ ′ ′ ′` lnvahi = α0 + α1lnKi + α2nocapitali + Xi α3 + Hi α4 + Mi α5 + Si α6 + Ri α7 + εi (7)

> 9 The regressions on the drivers of firm performance rely on the Cobb-Douglas production function.

22
where the explained variable lnvahi is the log of the ratio of value added with the total hours worked in firm _i_ . lnKi stands for the log of physical capital. Given that in our sample some firms do not possess any capital, a dummy variable nocapitali that equals 1 when the firm has no capital is also introduced. This allows us to avoid obtaining unbiased estimates of the returns to capital when some firms have zero observations of capital (Battese 1997; Grimm, Knorringa, and Lay 2012). The vector Xi includes standard characteristics of the firm’s owner, (age squared, gender and the age of the firm). The vector Hi includes educational attainment, financial knowledge, reading knowledge and a dummy taking the value 1 if the owner is member of a business association. The vector Mi includes proxies for firm’s management skills such as lack of customers, lack of credit access, management issues, employees’ discipline problems, and problems of location and lack of equipment. The equation also includes sector dummies Si and region fixed effects Ri.

Equation (7) explains the performance drivers of the three groups of firms. Appendix A, figure A.7 shows that the higher a firm’s value added per hour worked, the better the firm performs. The results are displayed in table 11. The first column shows the results when we estimate equation (7) with only some basic characteristics of the firm’s owner, including education. The second column includes the variables estimated by vectors Xi and Hi . And the third column displays the results if one estimates the equation with all the variables.

The estimated capital elasticity is about 0.115 (column 3). This elasticity is lower than the capital elasticity (0.150) found using the 1-2-3 survey for seven Sub-Saharan African countries in the paper of Grimm, Knorringa and Lay (2012). The results show that the older the firm’s owner, the better is the firm’s, but at a narrowing rate. Human capital, including secondary educational attainment, vocational training, and reading knowledge, is significantly and positively correlated with the performance of a firm. For instance, some other post-primary educational attainment, such as vocational training, raises the performance of firms by around 41 percent (column 1).

Membership in a business association is another important factor in the success of firms. The availability of a professional network seems to be a key advantage that can help entrepreneurs gain access to resources such as information and credit.<sup>10</sup> However, there is no clear gender effect. The estimated coefficient is positive, but not significant. All the coefficients associated with the factors used as proxies for management skills have the expected signs except the variable for the lack of equipment, which may not be a great problem for those entrepreneurs providing services such as hairdressers or luggage carriers. In any case, building performance-enhancing skills is always central to enhancing productivity and reducing inequality and poverty among informal firms. Entrepreneurs in the informal sector,

> 10 The lack of credit access is not significant (table 11, column 3). Regressions include another financial variable – which is not subject to endogeneity. While we are concerned about the potential reverse causality between access to credit and performance of firms, this paper does not discuss the literature analyzing the link between financial development and economic growth and bi-directional causality.

23
especially survivalists, need formal (secondary) education. Including constrained gazelles and top performers, they also require specific vocational training programs to upgrade their business skills.

Employees’ discipline issues and the lack of customers are the most relevant challenges to the good performance of informal firms in the Democratic Republic of Congo. Several sectors exhibit high growth potential. Besides construction and other household service activities (including hairdressing), apparel, and mining and other primary activities in production and the retrieval of raw materials are the sectors in which entrepreneurs are most likely to increase their productivity. Moreover, the analysis shows that, relative to survivalists, more top performers and constrained gazelles operate in these sectors.

**Table 11. Drivers of Performance**

|Dependent variable: Log of value<br>added per hour worked|(1)|(2)|(3)|
|---|---|---|---|
|Capital, log|0.112***|0.105***|0.115***|
||(0.0187)<br>|(0.0169)<br>|(0.0162)<br>|
|No capital|0.943*<br>|0.888*<br>|1.268***<br>|
||(0.486)|(0.511)|(0.471)|
|Age owner|0.0585***|0.0571***|0.0579***|
||(0.0105)|(0.0103)|(0.00947)|
|Age squared owner|-0.000695***<br>|-0.000679***<br>|-0.000685***<br>|
||(0.000114)|(0.000117)|(0.000109)|
|Male (=1)|0.0497|0.0459|0.0553|
||(0.0693)|(0.0626)|(0.0661)|
|No diploma|Ref.|Ref.|Ref.|
|Primary completed|0.0373|0.0336|0.00483|
||(0.124)|(0.117)|(0.123)|
|Some secondary|0.227**|0.223**|0.209*|
||(0.116)|(0.110)|(0.121)|
|Post-secondary|0.222|0.211|0.180|
||(0.138)|(0.131)|(0.144)|
|Other post primary|0.344*|0.325|0.261|
||(0.191)|(0.224)|(0.262)|
|Can read|0.331***<br>|0.320***<br>|0.300***<br>|
||(0.0872)|(0.0915)|(0.0968)|
|Firm age|0.0361**|0.0354**|0.0428**|
||(0.0183)|(0.0179)|(0.0186)|
|Financial Knowledge||0.147*|0.171**|
|||(0.0783)|(0.0750)|
|Business association||0.140*|0.169*|
|||(0.0809)|(0.0897)|
|Lack of customers|||-0.134**|
||||(0.0603)|
|Lack of credit access|||-0.0365<br>00669|
||||(.)|
|Management problems|||-0.109|
||||(0.0690)|
|Location problem|||-0.106<br>|
||||(0.0745)|
|Lack of equipment|||0.169**<br>(0.0739)|
|Discipline problem|||-0.430***<br>(0.125)|
|Constant|||5.544***|
||||(0.232)|
|Sector fixed effects|||yes|
|Region fixed effects|||yes|
|R-squared|||0.106|
|Observations|||3,119|
|Standard errors in parentheses *** p<0|.01, ** p<0.05, * p|<0.1||

24
These results are robust to three other measures of firm performance, namely, value added per worker, labor productivity and real labor productivity.<sup>11</sup> The logarithm of total hours of labor is included in the regressions as an additional explanatory variable. We observe that the more are the hours worked in a firm, the better this firm performs (appendix A, table A.11).

Additionally, simple simulations indicate that a number of characteristics, including human capital and managerial skills, could improve the performance of informal firms. The simulation relies on ordinary least squares (OLS) estimates to show how the performance of constrained gazelles and survivalists would improve if these firms shared the same environment with top performers. The findings reveal that the performance of constrained gazelles (value added per hour worked) would be enhanced by approximately 5 percent if the entrepreneurs in these firms had the same or similar educational attainment, financial knowledge, business networks, and managerial abilities as top performers. Similarly, given backgrounds and preparation resembling those of the top performers, the performance of survivalists would improve by 26 percent.

Simulations also indicate that human capital—especially vocational training (post-primary education)—is an important factor in the performance of firms (appendix A, figure A.8). Policies aimed at enrolling all top performers and constrained gazelles in vocational training could enhance their performance by 33 percent. Among survivalists, these policies could improve performance by 34 percent. The impacts of secondary education and reading knowledge on the performance of firms are not as strong. For instance, if all survivalists had access to secondary education, their performance would increase by 10 percent.

Additional factors have important effects on the success of firms. Discipline problems have a relatively greater impact on the performance of survivalists, which thereby decreases by 41 percent, than of top performers and constrained gazelles (approximately 39 percent). Likewise, wider access to financial knowledge and business associations could improve the performance of firms, but the impact is slightly stronger among survivalists.

# **8. Conclusion and Policy Recommendations**

The informal sector accounts for an important share of the economic activities in Sub-Saharan Africa. The literature on the informal sector has focused mainly on explaining the causes of the emergence and productivity of informal firms and on ways to formalize the informal sector. Using 1-2-3 survey data on the Democratic Republic of Congo, this paper contributes to the empirical literature in four main areas. First, following Grimm, Knorringa, and Lay (2012), the

> 11 Labor productivity is the ratio of production to the number of workers. Real labor productivity is the ratio of turnover to the number of workers.

25
paper identifies a third category of informal firms in the country, constrained gazelles, who are neither top performers nor survivalists, and assesses the heterogeneity across these three classifications of firms. Constrained gazelles show substantial potential to become top performers, but are constrained by their external environment (lack of water, electricity, telecommunication infrastructure, and finance). Second, the paper examines poverty and income inequality within these three distinct categories of firms. Third, using a Blinder-Oaxaca decomposition, it explains income differences across the three groups. Fourth, the paper analyzes the drivers of performance and profitability among informal firms.

The paper underlines that poverty and income inequality are more prevalent among constrained gazelles and survivalists than among top performers. Income inequality among informal firms is explained primarily by disparities in educational attainment and infrastructure, including the lack of access to credit. Vocational training raises the odds of being in the top quintile among constrained gazelles. Access to finance, including financial knowledge and access to credit, is a more important factor among constrained gazelles and survivalists than among top performers.

The results also show that performance is a key factor in explaining income differences between informal firms. For example, differences in the performance of firms explain more than 50 percent of the income gap between top performers and constrained gazelles. Thus, policies aimed at reducing the performance gap between top performers and the two other categories of informal firms—constrained gazelles and survivalists—could narrow income inequality and reduce poverty.

The amount of capital, secondary educational attainment, vocational training, and professional networks are important determinants of the performance of firms. Thus, membership in a business association boosts the performance of firms by around 17 percent. Lack of demand and employee discipline issues are two important impediments to performance. The Congolese informal sector is tremendously heterogeneous in human and physical capital. This heterogeneity means that the establishment of effective broad-based programs is difficult.

The informal sector is the critical lifeline of the Democratic Republic of Congo. It accounts for 81.5 percent of all employment in the country. Because of this importance, policies to reduce poverty in the country should be tailored to each of the three groups of firms in the informal sector. For the survivalists, the priorities should involve providing vocational training to improve technical and managerial skills and raise the sense of entrepreneurship. Improving financial literacy and easing the access to finance are also essential for this group. Unlocking the potential of the constrained gazelles would require facilitating the access to credit and supplying training programs to enhance managerial skills. The assistance for top performers should seek to strengthen managerial skills and improve the access to infrastructure, enabling these firms to expand markets.

26
# **Appendix A. Tables and Figures**

## **Table A.1. Definition of the Variables**

|**_Variable_**|_Description_|
|---|---|
|_Owner characteristics_<br>Age|Age of firm's owner, years|
|<br>Male|<br>Takes value of 1 if entrepreneur is a man|
|Can read in any language|"Yes" responded to the question, "Do you know how to read in any language?"|
|Can write in any language<br>|"Yes" responded to the question, "Do you know how to write in any language?"<br>|
|Education|Measured by highest diploma achieved in school system|
|No other opportunity in large firm|<br>Entrepreneur created business because s/he could not find paid job in large firm|
|No other opportunity in small firm|Entrepreneur created business because s/he could not find paid job in small firm|
|By family tradition|Entrepreneur created business by family tradition|
|<br>Trade register|<br>Entrepreneur keeps a trade register on her/his activity|
|<br>Business association|<br>Entrepreneur is a member of business association relevant to her/his activity|
|Financial knowledge|Takes the value of 1 if entrepreneur knows some microfinance institutions|
|Income per adult equivalent<br>|Per adult equivalent expenditure, adjusted for provincial price differences<br>|
|Poor|Takes value of 1 if entrepreneur is poor and 0 otherwise|
|_Household characteristics_||
|Size|Number of all household members|
|Wage earner is a woman|The wage earner in the household is a woman|
|<br>Wage earner is a man|<br>The wage earner in the household is a man|
|_Firm characteristics_||
|Age|Age since firm was established|
|Set up with one person|Firm was established by one person|
|<br>No capital|<br>Firms operates without any physical capital|
||<br>Replacement value of all business-related assets that have been used to operate the|
|Capital|enterprise in the last year, including the business establishment, machines, furniture,<br>vehicle, and utilities<br>|
||Gross profit measured as sales turnover, excluding taxes, minus amount of tax-free costs|
|Gross operating surplus|<br>of inputs (raw materials, merchandise), minus purchase amount, excluding services taxes,|
||minus taxes, minus cost of payroll|
|Annual value added|<br>Value added measured as turnover, minus the costs for intermediate inputs|
|Total annual hours|Self-reported total number of hours worked in firm in last 12 months|
|Hired paid workers|Number of hired staff who are paid|
|Nonpaid workers|Number of nonpaid staff in firm|
|<br>Size|<br>Number of total workers in firm|
|Self-employment|Firm is staffed by one person|
|Lack of credit access|Takes value of 1 if firm has poor access to credit|
|Lack of customers|Takes value of 1 if firm encounters problems to sell (lack of customers)|
|Management problem|<br>Takes value of 1 if firm encounters management problems|
|<br>Discipline problem|<br>Takes value of 1 if employee discipline issues in firm|
|Lack of qualified workforce|Takes value of 1 difficulties in recruitment of qualified workforce|
|Lack of equipment|Takes value of 1 if firm encounters lack of equipment issues|
|<br>Location problem|<br>Takes value of 1 if lack of space in firm and/or unsuitable location|
|Water connection|Water connection available in firm|
|Electricity connection|Electricity connection available in firm|
|<br>Telecommunication|<br>Telephone connection available in firm|
|Street serves as location|<br>No fixed location for activity|
|Mobile (improvised) street location|<br>No fixed location for activity|
|Fixed street location|Entrepreneur operates her/his activity on street|
|<br>Vehicle serves as location|<br>Firm located in a vehicle|
|Customer’s home serves as location<br>Firm located at home|Activity operated in customer’s home<br>Firm located at home (with/without amenities)|
|Fixed location in market|Fixed location in market|
|<br>Improvised location in market|<br>Improvised location in market|
|<br>Shop, restaurant|<br>Firm is shop or restaurant|
|Other location|Any other type of location not listed above|
|Sector dummies|Take value of 1 if firm is in respective sector (exclusive); zero otherwise|
|Regional/Provincial dummies|<br>Take value 1 if firm is in respective region/province (exclusive); zero otherwise|

27
**Figure A.1. Informal Businesses Started Because of Job Shortages in Large Firms, by Owner Educational Attainment and Gender, 2012** **_(%)_**

<!-- Start of picture text --> a. By Owner educational attainment<br>b. By gender<br>70 70 64.76<br>62.63<br>60 60<br>50 50<br>40 40 35.24<br>30 30<br>20 15.32 20<br>12.66<br>10 5.02 4.37 10<br>0 0<br> Primary Some Post other post Men Women<br>No diploma completed secondary secondary primary<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data

# **Figure A.2. Size of Informal Firms** **_(%)_**

<!-- Start of picture text --> Self-employed 2 3 - 5 6 and up<br>0.99<br>6.6<br>10.59<br>81.82<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

28
# **Figure A.3 Size of Informal Firms** **_(%)_ , by Gender**

<!-- Start of picture text --> a. Women b. Men<br>1.84<br>0.5<br>4.35 10.53<br>7.73<br>15.57<br>72.06<br>87.42<br>Self-employed 2 3 - 5 6 and up Self-employed 2 3 - 5 6 and up<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

**Table A.2. Descriptive Statistics, Capital Distribution, by Sector and Province**

||All<br>B|ot. decile|1st q.|2nd q.|3rd q.|4th q.|Top decile|
|---|---|---|---|---|---|---|---|
|**Sectors**||||||||
|Food/Agribusiness|0.3840|0.1431|0.3646|0.2826|0.2259|0.1268|0.0556|
|Clothing and apparel|0.0839|0.0475|0.0955|0.1663|0.2775|0.4607|0.1158|
|Industry|0.1082|0.0478|0.1556|0.2627|0.3462|0.2355|0.0928|
|Construction|0.0154|0.0000|0.0570|0.3706|0.2665|0.3059|0.1793|
|Transport|0.0305|0.0641|0.0837|0.1155|0.1192|0.6816|0.4243|
|Hotels and restaurants|0.0269|0.0329|0.1083|0.0458|0.3068|0.5391|0.1372|
|Wholesale|0.0153|0.1730|0.2136|0.2352|0.1053|0.4458|0.1059|
|Primary and mining|0.0195|0.0558|0.1352|0.1209|0.1958|0.5481|0.2489|
|Retail shops and repair services|0.2601|0.1092|0.2733|0.2406|0.2854|0.2007|0.0649|
|Other activities|0.0562|0.1440|0.2153|0.2303|0.1248|0.4296|0.2602|
|**Regions/Provinces**||||||||
|Equateur|0.0753|0.0677|0.2009|0.2853|0.3553|0.1585|0.0332|
|Bandundu|0.1026|0.1751|0.3569|0.3245|0.1995|0.1192|0.0408|
|Bas-Congo|0.0488|0.1036|0.2136|0.2845|0.2001|0.3018|0.1550|
|Katanga|0.1129|0.0625|0.1579|0.2027|0.2296|0.4098|0.1999|
|Kasaï Oriental|0.1013|0.1331|0.3403|0.2358|0.1782|0.2456|0.0512|
|Kasaï Occidental|0.1080|0.1203|0.3514|0.2657|0.2284|0.1545|0.0325|
|Kinshasa|0.2347|0.0639|0.1936|0.2107|0.3089|0.2868|0.1325|
|Nord-Kivu|0.0427|0.2353|0.4230|0.2303|0.2282|0.1185|0.0094|
|Sud-Kivu|0.0489|0.2161|0.3539|0.3271|0.2024|0.1166|0.0649|
|Maniema|0.0221|0.0423|0.2734|0.1812|0.2453|0.3000|0.1535|
|Province Orientale<br>Note: Means are weighted using sam|0.1027<br>pling weights.|0.0914|0.1750|0.1705|0.2827|0.3718|0.1644|

29
# **Table A.3. Descriptive Statistics, Capital Distribution, by Owner and Firm Characteristics**

||All|Bot. decile|1st q.|2nd q.|3rd q.|4th q.|Top decile|
|---|---|---|---|---|---|---|---|
|**Owner characteristics**||||||||
|Male|36.42|21.93|22.49|31.35|39.62|58.51|64.65|
|Age owner|37.8|38.44|37.71|38.26|37.33|37.23|38.18|
|No diploma|7.73|16.67|33.71|27.18|26.63|12.48|2.58|
|Primary completed|16.53|12.52|30.31|26.51|25.93|17.25|5.13|
|Some secondary|58.82|10.71|26.33|25.28|23.89|24.5|9.45|
|Post secondary|14.31|7.81|18.65|17.18|27.71|36.46|18.11|
|Other post primary|2.62|1.84|10.69|27.14|27.29|34.88|14.37|
|Can read|88.76|82.72|85.77|86.12|91.88|95.36|96.25|
|Can write|87.82|80.50|83.73|84.76|91.50|95.65|97.06|
|Poor|61.13|13.37|30.34|26.16|22.99|20.5|7.06|
|**Firm characteristics**||||||||
|Annual value added|4304138|981232.9|1275135|1438029|1901095|3226659|3782644|
|Physical capital|145255.6|1469.54|3215.51|11281.62|35296.27|532807.8|1137005|
|No capital|0.005|0.05|0.02|0.00|0.00|0.00|0.00|
|Value added per hour worked|20697.02|5340.53|6780.46|9360.12|9523.56|12800.06|14938.48|
|Gross operating profit|125703.4|59326.88|85340.87|108583.8|139324.8|184766.5|198145.5|
|Workers, total|1.30|1.11|1.13|1.2|1.4|1.74|2.13|
|Paid workers|0.14|0.041|0.035|0.06|0.16|0.41|0.77|
|Wage worker, male|0.23|0.202|0.204|0.24|0.19|0.23|0.32|
|Wage worker, female|0.13|0.16|0.12|0.13|0.1|0.14|0.22|
|Water|8.45|3.95|10.19|14.70|25.1|50.01|31.38|
|Electricity|12.01|2.33|6.41|8.74|20.62|64.22|37.92|
|Land line, phone|15.34|3.44|9.00|15.74|27.25|48.02|24.58|
|Fixed location|57.97|10.11|24.12|22.54|25.45|27.89|10.9|
|Mobile location|40.51|11.37|28.42|27.36|23.73|20.49|8.47|
|Other location<br>_Note:_Means and percentages are we<br>upper panel. For the bottom panel, t<br>regarding endowments in infrastruct<br>_Source_: Authors' calculations using|1.52<br>ighted using sa<br>he first segment<br>ure and location<br>1-2-3 survey dat|8.69<br>mpling weights<br>presents mean<br>s of firms.<br>a.|21.44<br>. Percentage<br>s of firms ch|10.58<br>s of characte<br>aracteristics|42.16<br>ristics of inte<br>while the sec|25.82<br>rest are pres<br>ond shows p|13.35<br>ented in the<br>ercentages|

# **Figure A.4. Location of Activities, Informal Firms**

<!-- Start of picture text --> 25<br>21.65<br>20 18.81 18.14<br>15 12.78<br>10.62<br>10 8.49<br>5 3.25 2.38 2.26 1.53<br>0<br>Home Fixed Improvised Home with Mobile Improvised Customers' Fixed Shop, Other<br>without location on sellpoint in equipment location location on house location in a restaurant,<br>equipment streets a market streets public hotel<br>market<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data 30
**Figure A.5. Infrastructure and Business Activity, Location by Gender** **_(%)_**

<!-- Start of picture text --> a. Types of Location b. Water, electricity, phone<br>Men Women Men Women<br>30<br>27.08 25 23.69<br>25<br>20<br>21.3<br>19.64<br>20<br>18.36<br>15.49<br>17.42<br>15<br>15 12.9<br>10.6<br>12.15 12.64 12.73 10.03<br>8.59<br>10<br>10 8.9 8.22<br>8.27<br>6.78<br>6.33<br>5<br>5 3.43 3.61<br>2.98<br>1.51 1.78<br>1.49 0.7<br>0 0<br>Water Electricity Land line,<br>phone<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

**Figure A.6. Firm Location, by Quartile of Capital**

<!-- Start of picture text --> 45 42.16<br>40<br>35<br>30 28.42 27.36 25.45 27.89 25.82<br>24.12 23.73<br>25 21.44 22.54 20.49<br>20<br>15<br>10.58<br>10<br>5<br>0<br>Q1 Q2 Q3 Q4<br>Fixed location Mobile location Other<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

31
**Box A1.** Probit model with selection bias – Heckman two-step procedure

The paper used the Heckman procedure to correct for sample bias. The first step consists in estimating the probit model (equation (1)), i.e., calculating the probability that a firm has capital and then obtaining the linear predictors from the model. The second step consists in calculating the inverse Mills ratio and including it in equation (1). The inverse Mills ratio is the ratio of the probability density function and the cumulative distribution function divided by the standard error of the probit estimation. This ratio is calculated as follows: ∅(X<sup>′</sup> i∝1⁄σω) MR̃ = . The probability density and cumulative distribution functions are Φ(X<sup>′</sup> i∝1⁄σω)̂ such that: Pr(Capital = 1| X) = ∫−∞X∝1 ∅(t)dt = Φ(X∝1).

# **Table A.4. Step 2: Probability of being a Top Performer, Full Information Maximum Likelihood Model Estimation**

|Dependent variable: Probability<br>of being a top performer|Coefficients|
|---|---|
|Age owner|0.00595***<br>(0.0016)|
|Age squared owner|-0.000071***<br>(0.000)|
|Male(=1)|0.0606***|
||(0.0106)|
|Age firm|0.00226|
||(0.00315)|
|No diploma|Ref.|
|Primary completed|0.006|
||(0.02)|
|Some secondary|0.0281<br>(0.0180)|
|Post-secondary|0.0404*<br>(0.0220)|
|Other post primary|0.0956***<br>(0.0354)|
|No opportunity in large firms|0.0591***<br>(0.0156)|
|Family tradition|0.0161<br>(0.0171)|
|Constant|-0.0821*|
||(0.0373)|
|Log likelihood|-54212.128|
|LR test of model vs. Satured|Prob > chi2 = 1|
|Observations|4,504|
|Model|FIML|
|OIM Standard errors in parentheses ***<br>The test of model vs. Satured chi2 indic|p<0.01, ** p<0.05, * p<0.1<br>ates the model is a good fit.|

32
# **Table A.5. Probability of being Top Performers, Constrained Gazelles, or Survivalists, Logit Model**

||(1)<br>Coefficients<br>Top-p|(2)<br>Marginal effects <br>erformers|(3)<br> Coefficients<br>Constrai|(4)<br>Marginal effects <br>ned Gazelles|(5)<br> Coefficients<br>Surv|(6)<br>Marginal effects<br>ivalists|
|---|---|---|---|---|---|---|
|Age owner|0.123***<br>(0.0275)|0.00924|0.451***<br>(0.0368)|0.0494|-1.001***<br>(0.0806)|-0.0591|
|Age squared owner|-0.00151*** <br>(0.000346)|-0.000113|-0.00547*** <br>(0.000448)|-0.000598|0.0122***<br>(0.000968)|0.000722|
|Male(=1)|0.806***<br>(0.144)|0.0604|3.313***<br>(0.163)|0.362|-6.884***<br>(0.441)|-0.407|
|Age firm|0.0345<br>(0.0495)|0.00259|0.0961**<br>(0.0383)|0.0105|-0.278***<br>(0.0491)|-0.0164|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|0.168<br>(0.350)|0.00966|0.797***<br>(0.226)|0.0777|-1.427***<br>(0.417)|-0.0716|
|Some secondary|0.455<br>(0.342)|0.0295|1.676***<br>(0.237)|0.174|-3.668***<br>(0.437)|-0.201|
|Post-secondary|0.607*<br>(0.366)|0.0419|2.424***<br>(0.290)|0.263|-4.977***<br>(0.505)|-0.288|
|Other post primary|1.090**<br>(0.472)|0.0912|3.802***<br>(0.558)|0.433|-9.732***<br>(0.834)|-0.576|
|No opportunity in large firms|0.489***<br>(0.180)|0.0367|1.288***<br>(0.262)|0.141|-4.376***<br>(0.402)|-0.258|
|No opportunity in small firms|-0.276<br>(0.200)|-0.0207|-0.0842<br>(0.120)|-0.00922|0.707***<br>(0.156)|0.0418<br>(0.00969)|
|Sector fixed effects|yes||yes||yes||
|Province fixed effects|yes||yes||yes||
|Pseudo-R2|0.0792||0.4721||0.7199||
|Observations|3,416|3,416|3,416|3,416|3,326|3,326|

33
# **Table A.6.  Income Inequality: Ordered Logit**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Coefficients <br>All s|Odds ratio<br>ample|Coefficients<br>Top-pe|Odds ratio<br>rformers|Coefficients<br>Constraine|Odds ratio<br>d gazelles|Coefficients <br>Survi|Odds ratio<br>valists|
|Gross operating profit|0.00108*** <br>(0.000188)|1.001|0.000489<br>(0.000374)|1.000|0.000980*** <br>(0.000338)|1.001|0.00204***<br>(0.000450)|1.002|
|Age owner|-0.0276***|0.973|0.0298|1.030|-0.0260|0.974|-0.0252*|0.975|
||(0.0104)||(0.0656)||(0.0353)||(0.0151)||
|Age squared owner|0.000344**<br>(0.000121)|* 1.000|-0.000445<br>(0.000788)|1.000|0.000215<br>(0.000438)|1.000|0.000338*<br>(0.000172)|1.000|
|Male(=1)|-0.149**<br>(0.0688)|0.862|-0.677**<br>(0.326)|0.508|-0.0722<br>(0.151)|0.930|-0.355**<br>(0.159)|0.701|
|No diploma|Ref.||Ref.||Ref.||Ref.||
|Primary completed|0.0351|1.036|1.123|3.075|-0.0648|0.937|-0.0165|0.984|
||(0.125)||(0.687)||(0.356)||(0.167)||
|Some secondary|0.532***|1.702|1.142*|3.133|0.427|1.532|0.429***|1.536|
||(0.115)||(0.591)||(0.325)||(0.159)||
|Post secondary|1.135***|3.111|2.098***|8.150|1.134***|3.110|0.902***|2.464|
||(0.141)||(0.667)||(0.358)||(0.219)||
|Other post primary|0.619***<br>(0.219)|1.856|1.313<br>(0.871)|3.718|0.687*<br>(0.418)|1.988|0.876<br>(0.582)|2.400|
|Can read|0.447***<br>(0.0974)|1.563|0.0436<br>(0.665)|1.045|0.909***<br>(0.276)|2.482|0.404***<br>(0.129)|1.497|
|No opportunity in small firms|-0.000629<br>(0.0866)|0.999|0.795*<br>(0.444)|2.215|-0.0311<br>(0.156)|0.969|-0.0724<br>(0.136)|0.930|
|Firm size|0.00340|1.003|-0.0217|0.979|-0.205**|0.814|0.0680|1.070|
||(0.0561)||(0.174)||(0.0871)||(0.105)||
|Trade register|0.830***<br>(0.258)|2.294|0.397<br>(0.959)|1.487|1.469***<br>(0.374)|4.346|0.604<br>(0.600)|1.829|
|Financial knowledge|0.421***|1.523|-0.326|0.722|0.404***|1.498|0.396***|1.486|
||(0.0822)||(0.347)||(0.141)||(0.141)||
|Business association|-0.0507|0.951|0.359|1.431|-0.341*|0.711|0.167|1.182|
||(0.113)||(0.367)||(0.182)||(0.208)||
|Lack of customers|0.0401|1.041|0.174|1.191|0.160|1.174|-0.0109|0.989|
||(0.0602)||(0.275)||(0.111)||(0.0942)||
|Lack of credit access|-0.193***<br>(0.0645)|0.825|-0.479*<br>(0.270)|0.619|-0.237**<br>(0.119)|0.789|-0.246**<br>(0.101)|0.782|
|Management problems|0.0895|1.094|-0.208|0.812|0.269*|1.309|0.0827|1.086|
||(0.0781)||(0.363)||(0.145)||(0.122)||
|Sector fixed effects|yes||yes||yes||yes||
|Region fixed effects|yes||yes||yes||yes||
|<br>Pseudo-R2|0.0999||0.1789||0.1176||0.0902||
|Observations|4,012|4,012|265|265|1,194|1,194|1,681|1,681|
|Standard errors in parentheses **|* p<0.01, ** p<|0.05, * p<0.1|||||||

Note: Discipline problems, no opportunity in large firms, firms age and lack of qualified workforce are non significant additional control variables which are included in the regressions but not presented in this table 34
<!-- Start of picture text --> = a ~~ i<br>, ‘<br>A\ ‘<br><2 Pe 1\\<br>\<br>== !! ‘.<br>on ' \<br>rea 7 \<br>11<br>' \<br>_=Zs7/i \A‘ “<br>Sf 7 ‘ “<br>— - oe N = —_—— —_ ~s<br>o Ss 10 15<br>Value added per hours worked, Log<br>——-—-—- Top performers ———- Constrained gazelles<br>———— Survivalists<br><!-- End of picture text -->
# **Table A.8. Income Inequality, Reclassification: Top Performers Are Firms in the Top 10 Percent of the Distribution by Value Added per Hour Worked**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Top-pe|rformers|Constrain|ed gazelles|Survi|valists|
||Coefficients|Odds ratio|Coefficients|Odds ratio|Coefficients|Odds ratio|
|Gross operating profit|0.000105|1.000|0.00122***|1.001|0.00200***|1.002|
||(0.000306)||(0.000355)||(0.000433)||
|Age owner|0.0147<br>|1.015|0.00162<br>|1.002|-0.0574***<br>|0.944|
||(0.0357)||(0.0195)||(0.0150)||
|Age squared owner|-0.000218<br>(0.000411)|1.000|-0.000<br>(0.000242)|1.000|0.000652*** <br>(0.000168)|1.001|
|Male(=1)|-0.0195<br>(0.207)|0.981|-0.0981<br>(0.0990)|0.907|-0.287**<br>(0.115)|0.750|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|-0.286<br>(0.456)|0.751|0.110<br>(0.237)|1.117|0.0420<br>(0.161)|1.043|
|Some secondary|0.305<br>(0.423)|1.356|0.753***<br>(0.220)|2.123|0.407***<br>(0.155)|1.502|
|Post secondary|1.324***<br>(0.491)|3.757|1.376***<br>(0.248)|3.959|0.805***<br>(0.215)|2.237|
|Other post primary|0.724|2.062|0.677**|1.967|1.048*|2.852|
||(0.621)||(0.315)||(0.578)||
|Can read|0.356|1.428|0.603***|1.828|0.283**|1.327|
||(0.357)||(0.163)||(0.133)||
|No opportunity in large firms|0.115<br>|1.122|0.194<br>|1.214|0.0155<br>|1.016|
||(0.263)||(0.123)||(0.235)||
|No opportunity in small firms|-0.362<br>|0.696|0.189<br>|1.208|-0.114<br>|0.892|
||(0.311)||(0.131)||(0.128)||
|Firm size|0.127<br>|1.135|-0.0429<br>|0.958|0.00233<br>|1.002|
||(0.228)||(0.0788)||(0.0918)||
|Firm age|-0.0290<br>|0.971|0.0181<br>|1.018|0.0152<br>|1.015|
||(0.0561)||(0.0280)||(0.0356)||
|Trade register|0.405|1.499|1.056***|2.874|0.422|1.524|
||(0.829)||(0.324)||(0.538)||
|Financial knowledge|0.336|1.400|0.528***|1.696|0.359***|1.432|
||(0.224)||(0.121)||(0.136)||
|Business association|0.311|1.365|-0.169|0.844|0.116|1.123|
||(0.352)||(0.163)||(0.186)||
|Lack of customers|0.230|1.258|0.134|1.143|-0.117|0.890|
||(0.186)||(0.0898)||(0.0932)||
|Lack of credit access|-0.335*|0.716|-0.111|0.895|-0.226**|0.798|
||(0.202)||(0.0948)||(0.100)||
|Management problems|0.230|1.259|0.108|1.114|0.0204|1.021|
||(0.241)||(0.116)||(0.122)||
|Lack of qualified workforce|0.375<br>|1.455|0.812**<br>|2.251|-0.488<br>|0.614|
||(0.899)||(0.373)||(0.439)||
|Discipline problem|-0.262|0.769|-0.103|0.902|0.319|1.376|
||(0.957)||(0.290)||(0.376)||
|Sector fixed effects|yes||yes||yes||
|Region fixed effects|yes||yes||yes||
|<br>Pseudo-R2|0.1255||0.1155||0.0918||
|Observations|470|470|1,862|1,862|1,680|1,680|
|Standard errors in parentheses **|* p<0.01, ** p<|0.05, * p<0.1|||||

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# **Table A.9. Income Inequality, Reclassification: Top Performers Are Firms in the Top 40 Percent in Capital Productivity among the Top 40 Percent of the Capital Distribution**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Top-pe|rformers|Constrain|ed gazelles|Sur|vivalists|
||Coefficients|Odds ratio|Coefficients|Odds ratio|Coefficients|Odds ratio|
|Gross operating profit|0.00112**<br>(0.000504)|1.001|0.000899***<br>(0.000339)|1.001|0.00183*** <br>(0.000384)|1.002|
|Age owner|0.0775|1.081|-0.0465|0.955|-0.0291**|0.971|
||(0.0914)||(0.0369)||(0.0143)||
|Age squared owner|-0.000784<br>|0.999|0.000512<br>|1.001|0.000364** <br>|1.000|
||(0.00110)||(0.000470)||(0.000162)||
|Male(=1)|-0.974**<br>(0.484)|0.377|-0.167<br>(0.145)|0.846|-0.394***<br>(0.128)|0.674|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|1.011|2.749|0.277|1.319|-0.0498|0.951|
||(1.012)||(0.435)||(0.164)||
|Some secondary|0.835|2.306|0.947**|2.579|0.300**|1.350|
||(0.930)||(0.419)||(0.151)||
|Post secondary|2.143**|8.529|1.663***|5.273|0.876***|2.400|
||(1.051)||(0.451)||(0.194)||
|Other post primary|2.089*|8.073|0.946*|2.575|0.861**|2.366|
||(1.241)||(0.505)||(0.400)||
|Can read|-0.943<br>(0.887)|0.389|0.656**<br>(0.286)|1.928|0.509***<br>(0.126)|1.664|
|No opportunity in small firms|1.104*|3.017|-0.156|0.856|0.0233|1.024|
||(0.604)||(0.156)||(0.133)||
|Firm size|-0.00573|0.994|-0.0889|0.915|-0.102|0.903|
||(0.228)||(0.0900)||(0.0969)||
|Trade register|-|-|1.196***|3.306|1.207***|3.343|
||||(0.385)||(0.464)||
|Financial knowledge|-0.369|0.691|0.341**|1.406|0.397***|1.487|
||(0.505)||(0.150)||(0.129)||
|Business association|0.228|1.256|-0.248|0.780|0.00599|1.006|
||(0.470)||(0.207)||(0.176)||
|Lack of customers|-0.00695|0.993|0.141|1.151|-0.00625|0.994|
||(0.385)||(0.113)||(0.0906)||
|Lack of credit access|-0.342|0.710|-0.295**|0.744|-0.217**|0.805|
||(0.384)||(0.122)||(0.0955)||
|Management problems|0.106|1.111|0.292**|1.338|0.0610|1.063|
||(0.583)||(0.148)||(0.116)||
|Sector fixed effects|yes||yes||yes||
|Region fixed effects|yes||yes||yes||
|Pseudo-R2|0.1813||0.1205||0.0970||
|Observations|159|159|1,132|1,132|1,849|1,849|
|Standard errors in parentheses ***|p<0.01, ** p<0.|05, * p<0.1|||||
|<br>Note: Discipline problems, no opp|<br>ortunity in large|<br>firms, firms ag|e and lack of qualif|ied workforce a|re non significa|nt additional|
|control variables which are include<br>because of collinearity and limited|d in the regressi<br>observations.|ons but not pres|ented in this table|. Trade register i|s dropped from|the regression|

37
# **Table A.10. Income Inequality, Reclassification: Top Performers Are Firms in the Top 50 Percent in Capital Productivity among the Top 50 Percent of the Capital Distribution**

||(1)<br><br>Top-pe|(2)<br> <br>rformers|(3)<br><br>Constrain|(4)<br> <br>ed gazelles|(5)<br><br>Surv|(6)<br> <br>ivalists|
|---|---|---|---|---|---|---|
||Coefficients|Odds ratio|Coefficients|Odds ratio|Coefficients|Odds ratio|
|Gross operating profit|0.000691**<br>(0.000326)|1.001|0.00122***<br>(0.000348)|1.001|0.00179***<br>(0.000557)|1.002|
|Age owner|0.0132<br>(0.0469)|1.013|-0.0419<br>(0.0328)|0.959|-0.0228<br>(0.0169)|0.977|
|Age squared owner|-0.000244<br>(0.000562)|1.000|0.000468<br>(0.000406)|1.000|0.000307<br>(0.000192)|1.000|
|Male(=1)|-0.244<br>(0.226)|0.784|-0.0225<br>(0.122)|0.978|-0.509***<br>(0.185)|0.601|
|No diploma|Ref.||Ref.||Ref.||
|Primary completed|1.087**|2.966|-0.0922|0.912|-0.0784|0.925|
||(0.483)||(0.318)||(0.178)||
|Some secondary|1.212***|3.362|0.348|1.416|0.419**|1.520|
||(0.415)||(0.275)||(0.176)||
|Post secondary|1.751***<br>(0.478)|5.760|1.123***<br>(0.306)|3.075|0.819***<br>(0.250)|2.268|
|Other post primary|1.024|2.784|0.732**|2.079|0.818|2.266|
||(0.626)||(0.373)||(0.938)||
|Can read|0.0655<br>|1.068|1.016***<br>|2.763|0.341**<br>|1.407|
||(0.465)||(0.225)||(0.140)||
|No opportunity in small firms|0.115<br>(0.301)|1.122|-0.0837<br>(0.142)|0.920|-0.0515<br>(0.159)|0.950|
|Firm size|0.205|1.228|-0.191**|0.826|-0.0611|0.941|
||(0.141)||(0.0864)||(0.118)||
|Trade register|0.623|1.864|1.307***|3.694|0.705|2.024|
||(0.806)||(0.347)||(0.907)||
|Financial knowledge|0.172|1.187|0.372***|1.450|0.370**|1.448|
||(0.236)||(0.133)||(0.170)||
|Business association|0.0330|1.034|-0.249|0.779|0.113|1.120|
||(0.295)||(0.181)||(0.231)||
|Lack of customers|0.190|1.210|0.155|1.168|-0.0528|0.949|
||(0.202)||(0.102)||(0.107)||
|Lack of credit access|-0.395**|0.674|-0.199*|0.820|-0.263**|0.769|
||(0.200)||(0.109)||(0.116)||
|Management problems|0.361|1.435|0.163|1.177|0.0854|1.089|
||(0.279)||(0.133)||(0.136)||
|Sector fixed effects|yes||yes||yes||
|Region fixed effects|yes||yes||yes||
|Pseudo-R2|0.1277||0.1161||0.0903||
|Observations|436|436|1,380|1,380|1,324|1,324|
|control variables which are includ<br>Standard errors in parentheses ***<br>Note: Discipline problems, no opp|ed in the regress<br>p<0.01, ** p<0<br>ortunity in large|ions but not pr<br>.05, * p<0.1<br>firms, firms a|esented in this tab<br>ge and lack of qu|le.<br>alified workfor|ce are non signifi|cant additional|

38
qd) @) GB) Coefficients Coefficients Coefficients Value added Labour Real labour per worker, log productivity, log productivity, log Hours oflabour, log 0.124*** 0.118*** 0.168*** (0.0303) (0.0246) (0.0289) Capital, log 0.124*** 0.168*** 0.137*** (0.0165) (0.0152) (0.0150) No capital 1.309*** 1.632*** 1.492*** (0.454) (0.456) (0.349) Age owner 0.0616*** O0.0547*** 0.0598*** (0.00837) (0.008 16) (0.00771) Age squared owner -0.0007 11 *** -0.000646* ** -0.000704*** 0.000093 0.000097 0.000088 Male(=1) 0.136** -0.0498 0.000514 (0.0632) (0.0506) (0.0480) No diploma Ref. Ref. Ref. Primary completed -0.0275 -0.0630 -0.0846 (0.112) (0.110) (0.104) Some secondary 0.208** 0.199** 0.157* (0.0966) (0.0990) (0.0950) Post-secondary 0.147 0.104 0.0837 (0.125) (0.119) (0.118) Other post primary 0.260 0.192 0.223 (0.191) (0.168) (0.171) Can read 0.356*** 0.378*** 0.370*** (0.0863) (0.0794) (0.0735) Firm age 0.0378** 0.0342** 0.0133 (0.0193) (0.0158) (0.0152) Financial Knowledge O0.251*** 0.199*** 0.238*** (0.0677) (0.0648) (0.0657) Business association 0.312*** 0.348*** 0.429*** (0.0935) (0.0775) (0.0724) Lack of customers -0.140*** -0.129*** -0.114** (0.0489) (0.0437) (0.0487) Lack of credit access -0.0522 -0.0452 -0.0694 (0.0562) (0.0456) (O.0SOO) Management problems -0.115 -0.0644 -0.00239 (0.0758) (0.0652) (0.0598) Location problem -0.0109 -0.0167 0.0314 (0.0590) (0.0558) (0.0597) Lack of equipment -0.00921 0.00400 -0.132** (0.0745) (0.0670) (0.0558) Discipline problem -0.478*** -0.539*** -0.696*** (0.143) (0.124) (0.121) Constant 9.693 *** 9.832*** 10.75*** (0.262) (0.233) (0.236) Sector fixed effects yes yes yes Region fixed effects yes yes yes R-squared 0.172 0.193 0.225 Observations 3,119 3,123 3,124 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0O.1
# **Figure A.8. Changes in firms’ performance (%)**

# **a. Educational attainment b. Expertise, networking, employee discipline**

<!-- Start of picture text --> 40 20 13.51 13.19 14.68 14.88 15.08 15.99<br>32.9 34.21<br>35 32.9 10<br>30 0<br>25<br>-10<br>20<br>-20<br>15 9.95<br>10 7.74 7.56 6.42 -30<br>1.67<br>5 1.22 -40<br>-39.72 -39.39<br>0 -50 -41.00<br>Post-primary Secondary Can read Financial Business Discipline<br>education education Knowledge association problem<br>Top-performers Gazelles Survivalists Top-performers Gazelles Survivalists<br><!-- End of picture text --> _Source:_ a: Calculations based on OLS estimates from performance drivers regression (1) (column 1, table 11). b: Calculations based OLS estimates from performance drivers regression (3) (column 3, table 11).

40