EDRI Working Paper 23

October, 2018

Identifying dynamic and constrained entrepreneurs in low income countries: evidence from

Ethiopia Girum Abebe, Berihu Assefa, Mulu

Gebreeyesus and Tigabu Degu

Ethiopian Development Research Institute (EDRI), Ethiopia

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THE ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

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Editors in Chief: Dr. Yohannes Ayalew, Executive Director of EDRI

Bart Minten, Program Leader of IFPRI–ESSP II

Managing Editor: Gebrehiwot Ageba, Director of Programs, EDRI

Editorial Board: Alebel Bayrau, Mulu Gebreeyesus, Biniam Egu, Girum Abebe, Berihu Assefa, Tigabu Degu, Gebrehiwot Ageba (EDRI), Alemayehu Seyoum (IFPRI-ESSP), Tassew Weldehanna (AAU)

Paper citation: Girum Abebe, Berihu Assefa, Mulu Gebreeyesus and Tigabu Degu. 2018.

Identifying dynamic and constrained entrepreneurs in low income countries: evidence from

Ethiopia. EDRI Working Paper 23. Addis Ababa: Ethiopian Development Research Institute.

About the Author(s): Girum Abebe is a Research Fellow at EDRI; Berihu Assefa is a Research Fellow at

EDRI; Mulu Gebreeyesus is a Senior Research Fellow at EDRI; and Tigabu Degu is a Research Fellow at EDRI.

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Identifying dynamic and constrained entrepreneurs in low income countries: evidence from Ethiopia

Girum Abebe, Berihu Assefa, Mulu Gebreeyesus and Tigabu Degu

Ethiopian Development Research Institute (EDRI), Ethiopia

Copyright © 2018 Ethiopian Development Research Institute. All rights reserved. Sections of this material may be reproduced for personal and not- for-profit use without the express written permission of but with acknowledgment to EDRI. To reproduce the material contained herein for profit or commercial use requires express written permission. To obtain permission, please contact Jemal Mohammed at: jemal.mohammed@edri.org.et.

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Table of Contents Abstract ......................................................................................................................................................... 1

1. Introduction ......................................................................................................................................... 2

2. Related Literature ................................................................................................................................ 4

3. Data, methodology and descriptive results ......................................................................................... 5

3.1. Data ............................................................................................................................................... 5

3.2. Sample characteristics .................................................................................................................. 5

3.3. Identifying dynamic enterprises ................................................................................................... 7

3.4. Identifying constrained enterprises .............................................................................................. 9

3.5. Differences between the three groups ....................................................................................... 14

4. Returns to capital ............................................................................................................................... 19

5. Employment Growth and Business Constraints ................................................................................. 23

6. Conclusions and policy implications ................................................................................................... 26

References .................................................................................................................................................. 28

Appendix ..................................................................................................................................................... 32

List of Tables Table 1. Basic entrepreneurial and enterprise characteristics ..................................................................... 6

Table 2. Estimated probit model of being dynamic .................................................................................... 10

Table 3. Entrepreneurial and enterprise characteristics by group of enterprises ...................................... 17

Table 4. Production function estimation and returns to capital................................................................. 21

Table 5. Key differences between the three groups of enterprises (Index approach) ............................... 24

List of Figures Figure 1. Distribution of the predicted probability of being dynamic (GKL) ............................................... 11

Figure 2. Distribution of the predicted probability of being dynamic (index) ............................................ 12

Figure 3. The distibution of capital stock between dynamic, contrained and survivalist enterprises (GKL)

.................................................................................................................................................................... 13

Figure 4. The distibution of capital stock between dynamic, contrained and survivalist enterprises (Index)

.................................................................................................................................................................... 13

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Abstract The small enterprise economy is populated by highly heterogeneous enterprises. The diversity reflects the differences in entrepreneurial and enterprise characteristics with possible implications for the way enterprises are operated. Using a large scale survey of micro and small enterprises in Ethiopia, we employ two independent approaches to differentiate between dynamic, constrained and survivalist enterprises. We find that constrained and dynamic enterprises are operated by entrepreneurs who share similar characteristics, behavioral traits and enterprise structure, but vary substantially in terms of mode of business operations and business constraints that they face. We show that constrained enterprises enjoy high rates of capital returns and that limited capacity utilization rates, poor business practices as well as credit constraint preclude them from growing. On the other hand, survivalists enterprises are not only operated differently, they are also run by entrepreneurs with fundamentally different characteristics, enterprise structure and economic objectives. These results help us draw implications for enterprise support programs. Key words: enterprise, dynamic, constrained, capital, business practice JEL codes: L26, D22

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1. Introduction Entrepreneurial activities in micro and small enterprises (MSEs) are main sources of employment in many developing countries. MSEs’ preponderancy in low income countries and contributions towards employment and poverty reduction has particular ignited a renewed policy and research interest. A nascent but growing literature focuses on the study of heterogeneity in entrepreneurial characteristics and drivers of entrepreneurial successes (Schoar, 2010; Grimm, Knorringa, and Lay 2012; Berner, Gomez and Knorringa et al., 2012; Mano et al., 2012; Baptista, Karaöz, and Mendonça 2014; Gindling and Newhouse, 2014). These studies increasingly identify two distinct groups of entrepreneurs: survivalists and dynamic. Survivalist entrepreneurs operate the vast majority of MSEs in low income countries (Günther and Launov, 2012). Such entrepreneurs initiate their businesses as an occupation of last resort, often lack basic entrepreneurial spirit and skills, have limited business and management knowledge and rarely separate their business from the household (Mead and Liedholm, 1998; Phillips and Bhatia-Panthaki, 2007; Berner, Gomez and Knorringa et al., 2012; Mano et al., 2012; Poschke 2013). Consequently, enterprises operated by survivalist entrepreneurs do not grow rapidly and, instead, are characterized by informality, stagnation and exit (McPherson, 1996; Grimm, Knorringa, and Lay 2012; Poschke 2013; Gindling and Newhouse, 2014). While survivalist entrepreneurs abound throughout the developing world, there are few important exceptions in the small enterprise economy; enterprises operated by opportunity-seeking entrepreneurs who elect self-employment consciously because of higher earnings expectations and growth prospects (e.g., Shane and Venkataraman; 2000; Lee and Venkataraman, 2006; Bennett and Estrin, 2007; Schoar 2010; Falco and Haywood, 2016). These entrepreneurs are dynamic and manage a small number of enterprises with different sets of characteristics than survivalist entrepreneurs. Further, dynamic entrepreneurs are more likely to produce better quality products, expand their market share, enjoy higher productivity and profitability and create more jobs (Gomez, 2008; Henrekson and Johansson, 2010; Mottaleb and Sonobe, 2012; Reeg, 2013). These are sometimes called “transformational entrepreneurs” (Schoar, 2010), “gazelles” (Grimm et al., 2012) or “High Growth Entrepreneurs” (McKenzie, 2017). In the small enterprise economy, there are also a large group of entrepreneurs that lie in a continuum between dynamic and survivalist entrepreneurs. There are, for example, group of enterprises that are operated by entrepreneurs who share the basic characteristics of the dynamic entrepreneurs and the performance profile of survivalists in that despite their potentials, they do not grow because of the constraints that they face. These enterprises are operated by highly motivated entrepreneurs that could benefit from interventions that unlock their potentials and hence are labelled as “constrained gazelles” in earlier work by Grimm et al. (2012). Using a large representative survey of 8,174 enterprises in the 10 largest cities in Ethiopia, this study identifies entrepreneurial and firm-level characteristics that separate high-performing dynamic from high-potential constrained and low-prospect survivalist type enterprises.1 Our contribution to the literature is threefold. First, we adopt the empirical strategy suggested by Grimm, Knorringa and Lay (2012) (GKL, henceforth) to partition enterprises into three groups. Save for GKL’s (2012) study using informal enterprises from Francophone West Africa, we are

1 We use the terms entrepreneurs and enterprise interchangeable in the rest of the paper.

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not aware of any attempt to empirically identify the three distinct enterprise groups. However, unlike GKL (2012), our study sample is drawn from the population of formally registered businesses in the manufacturing sector in 10 major cities in Ethiopia. This enables us to examine the extent to which GKL’s (2012) criteria could be applied to other settings. Further, we compare the three groups of enterprises across six different dimensions: basic entrepreneurial and enterprise characteristics, behavioral traits, enterprise structure, business operations, business constraints and enterprise performance. Second, we identify the three groups of enterprises in two ways: using the GKL (2012) approach that relies on observed performance and using a behavioral approach that constructs a naive index based on Principal Component Analysis of entrepreneurial characteristics and observed behavior (henceforth index approach). Unlike the GKL, the index approach is agonistic to measured enterprise performance and thus presents us with additional test of the validity of our approach. Third, by identifying salient similarities and key differences between the three distinct groups of enterprises, our study contributes towards the effort to tailor effective policy support for MSEs in low income countries. We argue that the effectiveness of policies that target either type of enterprises rests on clearly distinguishing not only between the dynamic and survivalist entrepreneurs but also identifying high potential constrained entrepreneurs. We define ‘dynamic’ enterprises as those that are at the 90th percentile in capital productivity or index score under the GKL and index approaches respectively. We identify ‘constrained’ enterprises as those that have the same predicated probability of being dynamic as the actual dynamic enterprises. The third group are ‘survivalists’ and consists of enterprises that have the lowest observed productivities or index scores. We find crucial similarities and clear distinctions between the three groups of enterprises. While the individual entrepreneur’s characteristics, behavioral traits, firm attributes and enterprise structure show striking similarities between dynamic and constrained enterprises, the two groups seem to be very different in the manner with which they operate their businesses and the business constraints that they face. Despite high returns to capital, we find that low levels of technology, credit constraint, poor business practices, home-based location and low capacity unitization rates prevent the growth and transformation of constrained enterprises. In contrast, survivalist entrepreneurs are found to be “less entrepreneurial” and are very different from both dynamic and constrained entrepreneurs in their human capital, entrepreneurial attitude, enterprise structure, business operations, and the nature of business constraints that they face. We find that the likelihood of being dynamic is positively correlated with entrepreneurial attitude, such as risk taking and being patient, as well as the human capital of the entrepreneur including education and cognitive ability. The remainder of the paper is organized in the following way. The next section presents a brief review of the literature. Section 3 describes the data and partitions enterprises into three mutually exclusive groups. We compute and discuss about returns to capital in Section 4. Section 5 presents results on employment growth and business constraints. The final section concludes the paper and draws policy implications.

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2. Related Literature A common finding across studies is that enterprises are highly heterogeneous and that interventions, such as credit supply and businesses skills training, have different effects for various group of enterprises ( see McKenzie and Woodruff 2014 for comprehensive review of training effect on enterprise practice and performance). A critically important source of enterprise heterogeneity is the nature of entrepreneurship: survivalist vs. dynamic. The bifurcation of enterprises into dynamic and survivalist starts with the clear difference in the motivation to start up a business. Survivalist entrepreneurs often start the business as a last resort failing to find wage job. Since business earnings are often insufficient to cover basic household expenses, survivalist enterprises also partition their time between the business and paid work to supplement their income (Portes and Hoffman 2003). The enterprise is also barely separated from the household and entrepreneurs are forced to surrender to competing household interests due to social pressure. Enterprise owners, for example, often have to finance household resource requirements from the business (Nichter and Goldmark, 2009). Further, survivalist entrepreneurs exhibit limited desire and ability to grow, are less innovative, create no or few jobs and might be more inclined to join wage employment when such opportunities arise (Phillips and Bhatia-Panthaki, 2007; Berner, Gomez and Knorringa, 2012). Even when they have the desire to grow and expand their businesses, however, survivalist entrepreneurs are often met with severe internal constraint such as lack of business skills, and external constrains, such as access to finance, that are difficult to overcome (Mead and Lidelholm, 1998; GKL, 2012). At the other end of the distribution lie dynamic entrepreneurs who are more likely to start their businesses seeking better opportunities for growth. They also exhibit better entrepreneurial spirit in that they are more willing to take risk and save and reinvest their business proceed back to the business with the ambition of expansion (Berner, Gomez and Knorringa, 2012). They are also more likely to spend more time working on their business and less likely to engage in time-competing supplementary income generating activities (GKL 2012). Dynamic entrepreneurs’ commitment towards running and expanding their business is also manifested in the employment of paid workers even at the early stage of operation (Mead and Liedholm, 1998). They also have better business skills and operate their businesses more efficiently. Using a sample of representative informal enterprises from seven Francophone African countries, GKL (2012), for example, find that ‘gazelle’ entrepreneurs have better business and management skills and thus have adopted superior business and management practices, such as book keeping and marketing, than necessity (survivalist) entrepreneurs. These difference notwithstanding, we point out that the distinction between survival and dynamic entrepreneurs is not clean-cut and the dichotomous categorization does not imply that there are no within group differences. GKL (2012), for example, categorize enterprises into three groups: survivalist, constrained gazelles and top performers. The authors’ categorization is based on observed and predicted performance measured by capital accumulation and value added. With high marginal returns to capital, they show that ‘constrained gazelles’ closely match the entrepreneurial and business profile of ‘top performers’ than ‘survivalist’ despite low levels of capital stock. For example, ‘top performers’ and ‘constrained gazelles’ are more likely to be operated by male entrepreneurs with better education and industry experience than ‘survivalist’ entrepreneurs. Moreover, both ‘top performers’ and ‘constrained gazelles’ are likely to use hired staff and work more hours in the business and operate their business from fixed

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location than mobile shops. Yet ‘constrained gazelles’ differ significantly from ‘top performers’ in the external business constraints they faced; they are more likely to be constrained by the external environment, such as access to finance and infrastructure. This crucial distinction between the three groups of enterprises is rarely made in the literature. This is mainly because of the limited enterprise transition (graduation) observed from the survivalist to dynamic entrepreneurs group (Schoar, 2010; Berner et al., 2012). This paper thus provides a further test on whether treating the survivalist as a homogenous bunch is the right approach.

3. Data, methodology and descriptive results

3.1. Data

Our data is a combination of a representative sample of microenterprises and a census of small businesses in the 10 largest cities in Ethiopia.2 The 10 cities are categorized into two broad groups: Addis Ababa, the capital city, and regional cities. For sampling purposes, the 10 sub cities within Addis Ababa are considered as 10 separate strata.3 The population frame for enterprises is created by combining three separate administrative lists compiled by the Bureau of Labor and Social Affairs, the Trade and Industry Bureau and the Urban Job Creation and Food security Agency (formerly the Federal Micro and Small Enterprise Development Agency). We decided to conduct a census of all small firms in all the 10 cities and generate a random sample of microenterprises.4 The sample selection process for microenterprises involved stratification by cities using a sampling weight drawn from the population distribution of the cities. Reflecting the relative concentration of enterprises, 55% of our samples of enterprises were thus drawn from the 10 sub cities in Addis Ababa. Using face-to-face interviews, we implemented a survey instrument that contains a rich set of questions on demographic characteristic of the entrepreneur, business profile, credit history, saving culture, number of workers, capital stock, cognitive ability, risk and time preference. The interviews were mostly close-ended and were conducted on the working premise of the enterprises. In total, we collected data from 8174 enterprises.

3.2. Sample characteristics

Table 1 introduces key variables from the sample. 19% of the sample entrepreneurs are female and the average age in the sample is 38, and 76% are married. About 82% of entrepreneurs identify themselves as Christians and 17% as Muslims. Entrepreneurs have attended 11 years of schooling on average. Table 1 also shows measures of cognition, time and risk preferences. Cognitive score is measured using five selected questions drawn from the Raven test reflecting analytical skills (McKenzie and Woodruff, 2015). Time preference is measured using a hypothetical question that asks respondents whether they would delay current gratification when confronted with a choice between 1000 birr tomorrow and 1100 birr a month from tomorrow. Patient

2 The cities are Adama, Addis Ababa, Bahir Dar, Dessie, Dire Dawa, Gonder, Hawassa, Jigjiga, Jimma and Mekelle.

3 Addis Ababa is structured into 10 sub cities and 116 woredas. Sub cities and woredas are equivalent to boroughs

and counties in the US context respectively. 4 Micro enterprises employ at most five workers and small enterprises employ six to 50 workers.

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entrepreneurs are those whose decide to wait one month to receive 1100 birr. Risk is measured using a lottery game. Table 1 indicates that on average respondents score 2.2 out of 5 on the Raven’s test, about 6 % are patient, 23% are willing to take risk and 76 % are risk averse. Table 1 also shows that one in four entrepreneurs speak English. Table 1 also shows that 56% of entrepreneurs had a wage employment experience, the majority of them in the private sector. On average, entrepreneurs have 8 years of self-employment experience and, remarkably, nearly two third of the fathers of entrepreneurs had the experience of running own business. Entrepreneurs also had on average 0.4 siblings that started their own business before they had entered into the self-employment sector. Table 1. Basic entrepreneurial and enterprise characteristics

All

Basic entrepreneur characteristics All

Female (%) 18.9

Age 38.0

Married (%) 75.8

Christian (%) 82.0

Muslim (%) 16.9

Years of schooling 11.0

Cognition, time and risk preferencesa

Cognitive test score (max=5) 2.26

Speaks English (%) 25.1

Patient (%) 6.0

Risk taker (%) 23.2

Risk averse (%) 76.2

Experience

Public sector employment experience 11.4

Private sector employment experience 44.2

Wage employment experience 55.6

Self-employment experience (number of years) 7.68

Family business background

Father was self-employed (Yes==1) 74.3

Number of siblings before owning the current business 0.43

Basic firm characteristics

Firm age 6.11

Enterprise is self-initiated (%) 95.7

Cooperative (%) 14.5

Percent of total start-up from

Own saving 67.1

Bank loan 0.47

MFI loan 4.84

Credit and saving association loan 1.51

Loan applied for the business 39.4

Use power-driven machinery 93.0

Located commercially 57.1

Number of observations 8174

Note: aCognitive score is measured using five selected questions drawn from Raven test reflecting analytical skills

(McKenzie and Woodruff, 2015). Time preference is measured using a hypothetical questions that ask respondents

whether they would delay current gratification when confronted with a choice between 1000 birr tomorrow and

1100 birr a month from tomorrow. Patient entrepreneurs are those whose decide to wait one month to receive 1100

birr. Risk is measured using a lottery game that asked respondents to choose between a sure outcome and a bet with

equal expected values. Those who chose to play the lottery games are considered as “risk takers” and those who

chose the sure outcome as “risk averse”.

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Table 1 also presents firm characteristics and shows that the average firm is 6 years old and 15% are cooperatives.5 96% of the enterprises are initiated by the current owner and 67% of start-up capital were mobilized from owner’s own savings, while about 0.5% and 5% of it was borrowed from banks and MFI respectively. Yet this is despite the fact that 40% of the enterprises have applied for a formal business loan. While bank loan is a rarity, grant or loan from friends and relatives, contribution by partners, and support from NGO and government were also some of the ways enterprises raised seed money to finance their start-ups. More than 93% of enterprise use power driven machinery for production and 57 % are located in commercially areas. Table A1 in the appendix also shows that although enterprise in the sample engage in a wide range of sub sectors, nearly two thirds of them are active in three sectors: food and beverage, metalworking, furniture and wood work. The lower panel of Table A1 also shows that the majority of enterprises are drawn from Addis Ababa.

3.3. Identifying dynamic enterprises

An important question that undergirds this study is how to make distinction between dynamic, constrained and survivalist entrepreneurs. In the literature, dynamic enterprises have been defined in different ways. For example, Sims and O’Regan (2006) use ‘the DNA’ approach to identify the ‘gazelles’. They use combination of measures of growth such as increased employees, increased sales, increased profits and increased profit margins over the three years to calculate a firm’s ‘growth footprint’. The weights assigned to each of these measures, however, are often arbitrarily decided. Delmar et al. (2003) set a criterion that a ‘high growth’ firm had to be among the top 10% of all firm in terms of “annual average” either in employment or sales growth or both. Eurostat-OECD (2007) considers ‘high-growth firms’ as those that attain “an average annualized growth greater than 20 per cent per annum, over a three-year period” (Eurostat-OECD 2007: 61). In this approach, 10 employees in the beginning of the growth period is suggested as provisional size threshold. Goedhuys and Sleuwaegen (2009) use slightly lower growth rate and size threshold to define dynamic firms; high-growth firms are those that experienced 10 percent or more rate of annual growth over a three years period and had more than five workers at the initial year of business start-up. The classifications indicated in the Eurostat-OECD manual and by Goedhuys and Sleuwaegen (2009) are not, however, tenable given the majority of firms in low income countries like Ethiopia have smaller number of workers. Further, absent longitudinal data, it is not clear how such classifications could be used practically. We employ two approaches to define dynamic enterprises. First, reflecting the cross-sectional nature of our data, we closely follow the classification introduced by GKL (2012). GKL (2012) define dynamic enterprises as ‘top performers’ based on levels and profitability of capital; the ‘top performers’ are those that are at the top 40% of the capital profitability distribution among the 75th percentile in capital stock distribution. To identify dynamic enterprises, we thus first select enterprises at the top quartile of capital distribution and take 40% of them with the highest

5 Cooperatives are enterprises that are formed by a group of young unemployed individuals through a wide-range

of support by the state as part of its strategy to reduce urban youth unemployment (Abebe, 2015). Their membership typically range from 5 to 15 individuals.

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profit capital ratio as dynamic.6, 7

(1)

Equation 1 shows how the sample is portioned into the two groups more formally.

represents profit capital ratio and shows the 60th percentile value of capital profit ratio. is

total number of enterprises and is number of enterprises meeting the condition that capital stock of the enterprise is at the top quartile of the distribution represented by the condition that

. The condition implies that the capital stock of the enterprise should be

greater than the capital stock obtained at the 3rd quartile of the distribution. is equal to 60 in

our context and indicates the value of the 60th percentile for profit capital ratio. Equation 1 creates a dummy that takes the value of 1 for enterprises meeting this condition (dynamic) and zero otherwise. The second approach to identify dynamic enterprises relies on a Principal Component Analysis (PCA) method to determine main variables that are likely correlated with entrepreneurial success. We start with a large set of variables (17) and employ PCA to identify variables that load onto one similar factor (defined mostly by one factor). This way, we identify eight factors that seem to jointly load onto one factor.8 We aggregate these variables to create a naive index with a score ranging from 0 to 8. We then define dynamic enterprises as those whose index score lie at the top 10 % of the index distribution (are at 90th percentile of the index score).

Equation 2 shows how the index is created. and indicates the variables that are

selected through PCA. Appendix Table A1 presents the factor analysis tables for all the variables used to determine factor loading. Equation 2 states that dynamic enterprises have an index score that is greater than or equal to the 90th percentile score.

6 We measure capital stock using the resale market value of the enterprise’s own machinery, equipment, building,

vehicles and other capital goods. 7 The GKL measure is based on a combination of size (in terms of capital) and profitability of capital. By

construction, smaller enterprises would thus be less likely to be defined in the dynamic group. This is, of course, a valid approach as capital constraint is a key barrier to firm start-up and enterprise growth (Schoar, 201), and, hence enterprise size proxy earlier performance history. This may not be, however, always the case given that some enterprises may start larger from the outset and stagnate during the course of operation. In such cases, the link between enterprise size and performance would break. By contrast, the behavioral approach based on the index is ignorant of enterprise size.

8 For independent comparison with the GKL methodology, we do not include enterprise performance indicators in

the set of variables used for factor analysis as the GKL methodology purely relies on grouping of enterprises based on performance. The factor analysis suggest that variables related with opportunity seeking (for starting the business), entrepreneur’s determination to stay with the business, scoring cognitive test above median, prior experience in wage sector, ability to speak English, having college degree, initially employing paid workers and readiness to sacrifice for business can be combined to create a naïve index.

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(2)

3.4. Identifying constrained enterprises

To identify constrained enterprises, we first examine factors that influence the likelihood of being a dynamic enterprise. Our objective is not to draw casual inference on the relationship between entrepreneurs’ characteristics and entrepreneurial success; it is rather to identify correlates of success. To attenuate the problem of endogeneity, we restrict these factors to firm and entrepreneurial characteristics that are less likely to be affected by enterprise performance. To further avoid correlation by design, the index based estimation omits the variables that are used to construct the index. Table 2 presents coefficients and marginal effects from a probit model estimation of enterprise and entrepreneurial characteristics on the dynamic enterprise dummy defined by the GKL (columns 1 and 2) and index approaches (columns 3 and 4). Table 3 shows that female entrepreneurs are less likely to be dynamic entrepreneurs; the probability of being top performer is 2.6% to 3.4 % lower for female entrepreneurs than their male counterparts. Similarly, GKL (2012) find that female entrepreneurs have a 9 % lower probability of being a ‘top performer’ in their sample. Six variables appear to significantly affect the probability of being dynamic: schooling, cognitive test score, patient, risk taking, English speaking and cooperative. Columns 2 and 4, for example, shows that the probability of being dynamic increases by nearly 0.8 % to 2.8 % for every additional year of schooling. Similarly, an increase in cognitive test score by 1 point increases the probability of being dynamic by 1.1 %. English speaking also positively affects the likelihood of being dynamic. The magnitude of the English speaking effect of 2.2 % indicated in column 2 is strikingly similar to the French speaking effect that GKL find in seven West African countries. These results are consistent with earlier studies that find that dynamic entrepreneurs possess higher education levels and cognitive ability (De Mel, McKenzie and Woodruff 2008; Poschke 2013) and suggests that the human capital of the entrepreneur is a key factor influencing entrepreneurial success. The probability of being dynamic is also higher by as much as 5.3 % for patient entrepreneurs than impatient ones as indicated in column 4. On the other hand, cooperatives are less likely to be dynamic compared to non-cooperatives enterprises, such as sole-proprietorship, partnerships and private limited companies (PLCs). This might possibly be due to negative selection where entrepreneurs who form cooperatives are less capable than entrepreneurs who decide to set up their businesses on their own. Alternatively, low performance might be explained by the presence of multiple owners/workers that create operational and management difficulties in cooperative businesses (Abebe, 2015).9

9 Since “Members of the cooperatives are all working-owners of the enterprises who also take part in the joint

management of the enterprise” (Abebe, p7, 2015), these variables are constructed for the lead person or manager in the cooperative.

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Table 2. Estimated probit model of being dynamic

Dynamic (GKL) Dynamic (Index)

VARIABLES Coefficients Marginal effects Coefficients Marginal effects

(1) (2) (3) (4)

Age of entrepreneur 0.006** 0.001** 0.002 0.000 (0.003) (0.000) (0.003) (0.000) Female -0.292*** -0.036*** -0.214*** -0.026*** (0.063) (0.007) (0.059) (0.006) Married 0.134** 0.018** -0.035 -0.005 (0.056) (0.007) (0.050) (0.007) Christian -0.024 -0.003 0.050 0.006 (0.063) (0.009) (0.066) (0.008) Years of schooling 0.057*** 0.008*** 0.216*** 0.028*** (0.007) (0.001) (0.007) (0.001) Cognitive test score 0.083*** 0.012*** (0.017) (0.002) Is patient 0.128 0.019 0.325*** 0.053*** (0.084) (0.014) (0.080) (0.015) Is a risk taker 0.130*** 0.019** 0.204*** 0.029*** (0.050) (0.008) (0.048) (0.007) Speaks English 0.149*** 0.022** (0.055) (0.009) Had a wage employment experience -0.076* -0.011* (0.045) (0.006) Father had a self-employment experience 0.014 0.002 -0.041 -0.005 (0.050) (0.007) (0.045) (0.006) Number of siblings in businesses 0.003 0.000 0.017 0.002 (0.017) (0.002) (0.016) (0.002) Age of the firm 0.020*** 0.003*** -0.017*** -0.002*** (0.004) (0.001) (0.004) (0.001) The enterprise is self-initiated -0.169* -0.026 0.081 0.010 (0.100) (0.017) (0.105) (0.012) Enterprise is cooperative -0.117* -0.015* -0.271*** -0.031*** (0.069) (0.009) (0.069) (0.007) Controls for sector Yes Yes Yes Yes Controls for city Yes Yes Yes Yes Observations 7,421 7,421 8,133 8,133

Note. The dependent variable takes the value 1 if the enterprise is classified as dynamic and

otherwise zero. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

We follow the approach used by GKL (2012) to partition the sample of non-dynamic enterprises into two groups; constrained and survivalists. We compute the predicated probability of being dynamic for all enterprises in the sample using both the GKL and index approaches. Equation 3 shows how the predicated probabilities are computed from a probit model.

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(3)

where shows the cumulative density function of standard normal distribution. is estimated

probabilities of being dynamic, as defined earlier, is a matrix of entrepreneurial and enterprise characteristics that are less likely to be affected by enterprise performance. We define constrained enterprises as those that share similar observed characteristics with the dynamic enterprises and hence are identified using their predicted probabilities of being dynamic,

. We start from enterprises with the highest predicted probability of being

dynamic and go down the probabilities until we end up with a group of enterprises that have mean predicted probability of being dynamic equal to those of dynamic. This way, we will have three distinct groups under the GKL and index approaches independently: 1) dynamic are those that are actually dynamic as defined earlier, 2) constrained are those who have equal mean predicted probability of being dynamic as the actual dynamic enterprises, 3) survivalists are those with the least predicated probability of being dynamic. Following GKL (2012), we employ two criteria to show that our enterprise partitioning produces the three distinct groups. First, the distribution of predicted probabilities of dynamic and constrained enterprises should substantially overlap and should be situated well to the right of the survivalist enterprises. Figures 1 and 2 show the distribution of predicated probabilities for the three groups of enterprises under the GKL and index partitioning. As expected, the predicted probabilities of dynamic and constrained enterprises highly overlap and are distinctly distributed more to the right of the survivalists.

Figure 1. Distribution of the predicted probability of being dynamic (GKL)

05

10

15

Den

sity

0 .2 .4 .6 .8Predicted probability

Growth-oriented

Constrained

Survivalist

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Figure 2. Distribution of the predicted probability of being dynamic (index)

05

10

15

Den

sity

0 .2 .4 .6 .8 1Predicted probability

Growth-oriented

Constrained

Survivalist

Second, dynamic enterprises should have capital stock distribution well to the right of constrained and survivalist enterprises. Figures 3 and 4 present the kernel density distribution of capital stock for the three groups of enterprises using GKL and index approaches respectively. As indicated in the figures, capital stock of survivalist enterprises lies well to the left of the other type of enterprises.

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Figure 3. The distibution of capital stock between dynamic, contrained and survivalist enterprises (GKL)

0.1

.2.3

.4.5

Den

sity

5 10 15 20Log of capital stock (2016)

Dynamic

Constrained

Survivalist

Figure 4. The distibution of capital stock between dynamic, contrained and survivalist enterprises (Index)

0.1

.2.3

Den

sity

5 10 15 20Log of capital stock (2016)

Dynamic

Constrained

Survivalist

14

3.5. Differences between the three groups

We now turn to the important question of how much constrained enterprises are similar to dynamic enterprises and different from survivalists and what constraints prevent them from attaining their full potentials. Table 3 presents six set of panels to better elucidates the distinctions between the three groups of enterprises. In the table, we mainly examine to what extent constrained enterprises are similar to or different from dynamic and survivalist enterprises. Columns 1 to 5 present descriptive information when enterprises are partitioned based on GKL and Columns 6 to 10 when partitioning is based on index. Columns 4 and 10 present the p values of mean equality tests between dynamic and constrained enterprises. Similarly, Columns 5 and 9 show the p values of mean equality tests between constrained and survivalist enterprises. The first panel of Table 3 shows basic entrepreneurial and firm characteristics by the three group of enterprises. As indicated in columns 1 through 4, dynamic and constrained enterprises are highly similar in basic entrepreneurial and firm characteristics. The only statistically significant difference between the two groups is in the years of schooling where constrained enterprises are operated by entrepreneurs with 0.3 more years of schooling. Partitioning based on the index approach also indicates that the two groups of enterprises are similar aside from the difference in years schooling, which again is 0.3 years.10 By contrast, Table 3 shows that entrepreneurial and enterprises characteristics between constrained and survivalist enterprises are very different as indicated by very low p values in columns 5 and 10. For example, compared to constrained entrepreneurs, survivalist entrepreneurs are more likely to be female, younger, single and non-Christian. Further, their human capital measures are much lower as indicated by lower levels of education, cognitive skills, knowledge of English and prior work experience. The second panel of Table 3 shows behavioral traits of the three group of entrepreneurs. Mirroring the similarities in basic characteristics, there is no statistically significant differences in time and risk preferences between dynamic and constrained enterprises irrespective of how the enterprises are partitioned. The attitude towards the business measured by opportunity seeking, active wage job search and the propensity to decline a wage job offer, is also very similar between the two groups of enterprises.11 The behavioral traits are, however, statistically significantly different between constrained and survivalist entrepreneurs. For example, constrained entrepreneurs are more likely to be patient and risk takers than survivalists, while they are less likely to search for and take up a wage job. The third panel of Table 3 presents structural factors related with enterprise ownership and access to government support. Across all the six factors considered, dynamic and constrained enterprises are very similar and no statistically significant differences under both the GKL and index approaches. On the other hand, there is substantial structural difference between constrained and survivalist enterprises. For example, survivalist enterprises are more likely to be cooperatives, have more competitors locally and are more likely to get access to government

10

Note that the differences observed in cognitive tests scores, English speaking and wage employment experiences in column 6 to 10 are true by the very construction of the index itself (see Table A1 in the Appendix). 11

Note that opportunity-seeking is one of the building components of the index variable (see Table A1 in the Appendix) and hence the difference between the dynamic and constrained enterprises is true by construction.

15

support. 12 The latter suggests a negative selection effect where government support particularly targets less-performing and low-prospect enterprises. The fourth panel of Table 3 presents key indicators of business operations for the three groups. While constrained entrepreneurs tend to operate their businesses more professionally than survivalists, they strikingly fare much less than dynamic entrepreneurs in several measures of business operations. For example, constrained entrepreneurs spend on average one and half less hours on their business per week and operate their businesses from non-commercial locations, such as home. Nearly one in five of constrained enterprises also assess their level of production technology to be far below their main competitors, while only 12 % of dynamic enterprises have the same assessment of their production technologies. Constrained enterprises also have lower business practice scores reflecting sub-optimal business operations and larger scopes for improvements.13 We also find that constrained entrepreneurs are less likely to use paid workers than dynamic entrepreneurs. This represents more reliance on the use of employment opportunities at the enterprise as a support and protection means for family workers with possible implications for enterprise performance (Mead and Liedholm, 1998; Poschke 2013).14 Further, the measure of credit constraint appear to be significantly different between the two groups in both GKL and index approaches suggesting that constrained enterprises are more credit constrained than dynamic enterprises.15 Compared to dynamic enterprises, smaller share of constrained enterprises also have a separate saving account for their businesses. There is also a significant difference between constrained and dynamic enterprises in quarterly average saving; while savings by constrained enterprises is 3.6 fold larger than the survivalists’, it is only 43% of the saving amount by dynamic.16 Table 3 also shows nearly all dynamic enterprises use power driven machinery, while 3.5 % of constrained enterprises do not use such machines. Constrained enterprises are also much less likely to use ICT (information communication technologies, such as email, website and fax) for their businesses. Capacity utilization rate is also different between the three groups. Constrained enterprises are, for example, producing well below their full capacity; their capacity utilization rate is lower than dynamic enterprises by 5 to 7 percentage points. The fifth panel of Table 3 indicates four important business constraints that impeded the operation of the enterprise as identified by entrepreneurs. Not surprisingly, constrained entrepreneurs are more likely to identify limited credit access as the most important business constraint followed by infrastructure difficulties. Credit access problem reflects a combination of

12

Government support includes eight key areas of interventions designed to promote the micro and small enterprise sector in Ethiopia. These are production skills training, business skills training, access to credit, access to working premises, market linkage services, government procurement, tax exemption and material supplies. 13

To measure business practices, we use closed-ended binary questions that are coded 1 for carrying out a task or zero for not carrying out a task (see Appendix Table A3 for the list of questions asked). These business practices questions are drawn from 17 of the 26 questions used by McKenzie and Woodruff (2015). 14

Using cross-country microdata from the Global Entrepreneurship Monitor (GEM) survey, Poschke (2013) find that the probability of employing paid workers is lower by 8% for necessity entrepreneurs.

15

We define credit constrained enterprises as those that applied for the loan and whose loan application was denied plus those who wanted to apply but did not apply because of the high cost of financing (interest rate, collateral, and mistrust of lenders). 16

The difference is statistically significant only under the GKL (2012) approach.

16

limited credit supply, large collateral requirements and high interest rates. Infrastructure combines electricity, telecom, roads and transport. 6 % to 7 % of constrained and dynamic enterprises also report that administrative problem related with government bureaucracy is a key business constraint that negatively affects their operations.17 The last panel of Table 3 presents performance measures for the three group of enterprises. Across all enterprise measures, dynamic enterprises tend to perform better than constrained enterprises under the GKL partitioning, while performance profile does not differ under the index approach except for number of workers and employment growth. Similarly, constrained enterprises have much larger sales and number of workers than survivalists under the GKL, while are comparable in the other performance measures. When enterprises portioning is based on index, however, constrained enterprises tend to perform much better in all the six performance measures compared to survivalist enterprises. In short, Table 3 shows that constrained and dynamic enterprises are highly similar in basic entrepreneurial and firm characteristics as well as behavioral traits of entrepreneurs and structural factors that affect their performance. Such similarities are in stark contrast with survivalists group of enterprises, which seem to be operated by less “entrepreneurial” entrepreneurs. The similarity between dynamic and constrained enterprises, however, no longer holds when one examines the manner with which the enterprises are operated. Table 3 shows that dynamic entrepreneurs operate their businesses more professionally than constrained entrepreneurs. Further, constrained enterprises appear to struggle to get access to credit, government support and infrastructure services.

17

These includes problems related with business licensing and registration, labor regulations, tax administration,

penalty fee payments (related with informal or illegal business activities), inability to use the judicial system to enforce contracts, corruption and lengthy customs clearance processes.

iv

Table 3. Entrepreneurial and enterprise characteristics by group of enterprises

GKL Index

Dynamic Constr

ained

Survivali

st

P value of

t-test

between

(1) and (2)

P value of t-

test between

(2) and (3)

Dynamic Constrained Survivalist

P value of t-

test between

(6) and (7)

P value of t-

test between

(7) and (8)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Basic characteristics

Female 13.3 11.0 23.6 0.091 0.000 15.2 13.6 20.7 0.271 0.000

Age 40.4 40.5 36.2 0.855 0.000 38.3 38.9 37.7 0.117 0.000

Married 81.7 81.8 71.4 0.937 0.000 75.2 74.3 76.3 0.627 0.140

Christian 84.8 86.0 80.3 0.405 0.000 89.1 89.0 78.9 0.520 0.000

Years of schooling 13.3 13.6 9.0 0.006 0.000 14.8 15.1 9.5 0.018 0.000

Cognitive test score (max=5) 2.7 2.8 1.93 0.233 0.000 3.4 2.5 2.0 0.000 0.000

Speaks English 46.5 46.8 10.0 0.875 0.000 77.1 48.6 9.8 0.000 0.000

Wage employment experience 57.2 59.6 54.5 0.242 0.000 83.2 52.9 50.7 0.000 0.150

Self-employment experiencea 9.1 8.9 6.8 0.538 0.000 6.62 7.5 7.9 0.000 0.065

Firm age 7.77 7.4 5.2 0.227 0.000 5.8 6.1 6.2 0.229 0.373

Behavioral traits

Patient 8.4 8.7 4.2 0.799 0.000 9.8 9.1 4.6 0.540 0.000

Risk taker 29.8 30.5 19.3 0.667 0.000 30.8 29.6 20.4 0.539 0.020

Risk averse 69.6 68.9 80.3 0.719 0.000 68.6 69.4 79.1 0.676 0.013

Opportunity seekingb 36.7 36.9 32.0 0.911 0.000 39.5 32.0 34.2 0.000 0.134

Actively searching for a job 4.4 5.1 6.8 0.479 0.006 4.5 5.5 6.5 0.306 0.158

Will not take a wage job if offered 86.7 84.6 77.9 0.157 0.000 94.5 94.5 78.3 0.267 0.093

Structural factors

Enterprise is self-initiated 93.3 93.9 96.7 0.520 0.000 96.2 95.4 95.6 0.281 0.673

Father was self-employed 71.2 68.9 76.9 0.207 0.000 64.6 62.8 78.6 0.365 0.000

Number of siblings before owning

the current business 0.53 0.56 0.35 0.694 0.000 0.51 0.49 0.40 0.642 0.012

Cooperative 9.8 9.0 18.4 0.485 0.000 9.3 8.7 16.7 0.647 0.000

Number of local competitors 13.4 15.2 19.5 0.101 0.000 12.3 13.4 20.0 0.276 0.000

Access to government supportc

9.2 8.7 14.9 0.639 0.000 8.0 7.3 14.0 0.522 0.000

Business operations

Labor hours of owner per week 53.7 52.4 52.3 0.085 0.946 53.5 52.1 52.3 0.070 0.692

Located commercially 61.1 55.8 56.8 0.020 0.429 59.0 55.0 57.2 0.048 0.046

Technology level is below main

competitors

11.6 19.4 22.8 0.000 0.001 11.7 21.6 23.0 0.000 0.305

Business practice score 12.2 10.6 7.8 0.000 0.000 11.5 10.8 8.2 0.000 0.000

18

Percentage share of paid workers in

total work force 79.7 68.7 55.8 0.000 0.000 75.6 67.8 58.1 0.000 0.000

Is credit constrained 40.3 45.1 51.3 0.020 0.000 41.3 46.7 50.5 0.008 0.014

Saving account 93.3 89.5 88.9 0.002 0.366 91.5 92.0 87.5 0.721 0.012

Separate business account 65.5 56.7 35.5 0.000 0.000 59.0 54.5 39.4 0.029 0.000

Three months savings (in 000 birr) 51.3 21.4 5.9 0.000 0.000 28.6 29.1 9.3 0.955 0.000

Use power-driven machinery 99.4 96.6 90.7 0.000 0.000 98.2 96.5 91.3 0.012 0.000

Use ICT for the business 45.9 33.1 17.1 0.000 0.000 44.8 36.9 19.4 0.000 0.000

Capacity utilization rate 65 56.2 52.2 0.000 0.000 60.2 55.6 53.2 0.000 0.001

Key business constraintsd

Credit accesse 17.2 17.2 19.1 0.964 0.027 16.7 21.8 17.7 0.002 0.001

Infrastructuref 19.4 14.8 11.9 0.003 0.027 14.2 15.9 13.0 0.261 0.008

Government bureaucracyg 6.0 7.1 4.7 0.302 0.000 6.6 6.9 5.2 0.774 0.023

Finding market 7.3 10.4 10.6 0.011 0.819 9.2 9.0 10.5 0.862 0.096

Performance (2016)

Sales (in 000 birr) 7378 1204 409.2 0.000 0.000 3078 2691 726.1 0.672 0.000

Value added (in 000 birr) 5246 535.6 192.5 0.000 0.957 1710 1920 253.3 0.813 0.000

Gross profit (in 000 birr) 4879 357.2 137.2 0.000 0.553 1449 1744 162.8 0.740 0.000

Number of workers 17.3 10.8 6.6 0.000 0.000 10.9 12.6 7.8 0.000 0.000

Value added per worker(in 000 birr) 296.7 36.6 30.8 0.000 0.217 119.9 104.4 37.9 0.777 0.000

Employment growth (annual) 14.6 9.7 9.7 0.000 0.938 13.1 11.2 9.1 0.049 0.000

Number of observations 776 2344 4620 1144 1226 5804

Note a

Number of years of self-employment experience. b

opportunity seeking entrepreneurs are those who stated that the main reason for staring their businesses is

that they “saw market opportunities” that they can exploit. Other responses included lack of wage job alternatives, being laid off, source of post-retirement

income, wanting to continue a family business, to enjoy better life-work balance, wanted to work for self to be “own boss”.

c

Government support includes eight key areas of interventions designed to promote the micro and small enterprise sector in Ethiopia. These are production

skills training, business skills training, access to credit, access to working premises, market linkage services, government procurement, tax exemption and

material supplies. If the enterprise gets any one of the support services the variables takes the value one and zero otherwise. d

These variables are drawn from the

questions “what is the 1st most important factor which impede the operation and growth of your business”. e

Credit constrained enterprises are those who stated

that access to credit or large collateral requirements or high interest rates as the first most import business constraints that they faced. f infrastructure consists of

electricity, telecom, roads and transport services. g

Government bureaucracy reflects administration problems related with business licensing and registration,

labor regulations, tax administration, penalty fee payments (related with informal or illegal business activities), inability to use the judicial system to enforce

contracts, corruption and lengthy customs clearance processes

iv

4. Returns to capital

Table 3 showed similarities and key differences between the three groups of enterprises. But what do these differences mean for performance of enterprises? In this section, we compare the differences in returns to capital between the three groups of enterprises more explicitly. Following GKL (2012), we estimate a production function to calculate the marginal returns to capital for the three groups using both the GKL and index approaches. We estimate production function for each of the enterprise groups separately using equation 4. Because of the way we partition enterprises using the GKL methodology, estimating the production function on the selected sample of constrained enterprises might lead to biased results.18 Following GKL (2012), we compute the median capital stock of dynamic enterprises and redefine dynamic enterprises as those with above the median capital stock and constrained enterprises as those with capital stock lower than the median irrespective of their productivity. This procedure reclassifies 534 constrained enterprises to the group of dynamic enterprises and 383 dynamic to the group of constrained enterprises. No such adjustment is, however, required for the index-based enterprise partitioning.

(4)

where refers to log of value added, which is measured by the difference between value of

sales and the combined costs of raw materials, electricity and other utilities. and refer to firm

and the three partition segments respectively. , and indicates the logs of capital stock, hours of work and material cost respectively. Additional controls of entrepreneurial and

enterprise characteristics are given by .

We estimate equation 4 for all enterprises in the sample and for the three groups defined by the GKL and index approaches separately. The estimated results are reported in Table 4 (Table A4 in the Appendix present the estimation results with no controls). Column 1 presents the results for all observations in the sample. Columns 1 through 3 indicates inputs elasticities when enterprise partitioning is done using the GKL approach and columns 5 through 7 using index approach. The results for dynamic enterprises is indicated in columns 2 and 5, for constrained enterprises in columns 3 and 6, and for survivalists’ in columns 4 and 7. Table 4 shows that capital elasticity is about 0.12 in the whole sample. There is, however, a wide variation when estimation is done separately for the three groups. For example, under the GKL approach, capital elasticity ranges from 0.05 for the survivalist enterprises (column 4) to 0.23 for dynamic enterprises (column 2). In the index approach, the elasticity gap, while is

18

As indicated in Figure A1 in the appendix, the value added capital stock distribution of constrained enterprises is largely missing in the upper left corner of the figure as the enterprises with the lowest capital stock but highest value added are classified as dynamic enterprises under the GKL approach. Regression adjustment to account for the group selection is thus difficult and a production function estimation to obtain capital elasticities could lead to biased results (see GKL 2012, pp. 1362). Indeed, a model that does not adjust for the group selection based on capital stock and productivity leads to a much higher capital elasticities for dynamic enterprises (not reported but available on request).

20

smaller than the first, is still sizable. Particularly, constrained enterprises appear to have much higher capital elasticity than survivalists in both partitioning approaches. The chow test of equality of capital elasticity between constrained enterprises and survivalists suggest that the differences are statistically significant. We now turn to formally tests whether returns to capital differ between the three groups. If constrained enterprises are really constrained but have a potential to grow and expand, they exhibit high returns to capital even at low levels of capital. To explore whether there are differences in returns to capital between the three groups, we compute the monthly marginal productivity of capital. Following GKL (2012), to calculate marginal returns to capital, we multiply the production elasticities indicated in Tables 4 with the annual value added to capital ratio. We then divide the value by 12 to obtain monthly returns to capital. Table 4 shows that marginal returns to capital is close to 9 % among Ethiopian enterprises operating in urban cities. Yet there are substantial variabilities across groups. Dynamic enterprises, for example, enjoy a monthly return of 5.4 % (Column 1) to 6.7 % (Column 5). More notably, constrained enterprises have much higher returns to capital ranging from 10 % to 12 %. This implies that a 100 birr investment in the constrained enterprises could potentially yield a monthly return of 10 to 12 birr. This suggests that there is unexploited potential in the group of constrained enterprises and hence have a larger scope for growth and improvement. Monthly returns to capital in survivalist enterprises ranges from 4.6 % to 8.8 %.19

19

The returns to capital among Ethiopian micro and small enterprises (9%) is slightly higher than what is reported by GKL (2012) for West Africa (5 %).

iv

Table 4. Production function estimation and returns to capital

GKL Index

VARIABLES All Dynamic Constrained Survivalists Dynamic Constrained Survivalists

(1) (2) (3) (4) (5) (6) (7)

Capital Stock (log) 0.118*** 0.225*** 0.146*** 0.052*** 0.124*** 0.154*** 0.105***

(0.016) (0.023) (0.009) (0.014) (0.011) (0.020) (0.016)

Hours of work (log) 0.336*** 0.463*** 0.317*** 0.307*** 0.339*** 0.349*** 0.326***

(0.021) (0.031) (0.032) (0.029) (0.066) (0.040) (0.017)

Material cost (log) 0.465*** 0.368*** 0.479*** 0.472*** 0.496*** 0.469*** 0.459***

(0.024) (0.014) (0.025) (0.052) (0.059) (0.024) (0.034)

Age of entrepreneur

-0.000 -0.000 0.001 -0.002 0.001 -0.001 -0.001

(0.001) (0.003) (0.002) (0.002) (0.002) (0.002) (0.001)

Female

-0.041* 0.184*** 0.144*** -0.085*** 0.008 0.147*** -0.093***

(0.019) (0.038) (0.036) (0.025) (0.066) (0.029) (0.018)

Married

0.048** -0.090** 0.009 0.057* 0.081 -0.012 0.047**

(0.016) (0.032) (0.020) (0.029) (0.049) (0.038) (0.018)

Christian

-0.028 -0.020 -0.054* -0.009 -0.094*** 0.227*** -0.139

(0.022) (0.048) (0.028) (0.031) (0.014) (0.011) (0.214)

Years of schooling

0.009*** 0.012 -0.016*** 0.009* 0.003 0.018* 0.014**

(0.003) (0.013) (0.003) (0.005) (0.004) (0.009) (0.005)

Cognitive test score

0.033*** 0.004 0.015 0.040

(0.008) (0.009) (0.009) (0.025)

Is patient

0.008 0.187 -0.006 -0.009 0.011 0.112 -0.020

(0.055) (0.120) (0.053) (0.087) (0.081) (0.064) (0.059)

Is a risk taker

0.028 -0.066 -0.027 0.064 -0.066 -0.066** 0.087

(0.069) (0.069) (0.030) (0.079) (0.046) (0.023) (0.083)

Speaks English

-0.006 0.030 -0.045 -0.034

(0.017) (0.051) (0.033) (0.024)

Had a wage employment

experience

0.040** -0.063*** 0.099*** 0.045

(0.015) (0.019) (0.012) (0.028)

Father had a self-employment

experience

0.045 -0.113*** 0.033 0.063 0.017 0.001 0.052

(0.041) (0.033) (0.055) (0.036) (0.060) (0.032) (0.037)

Number of siblings in businesses

-0.037*** -0.016 -0.062*** -0.029** -0.030*** -0.091*** -0.016

(0.008) (0.016) (0.007) (0.012) (0.008) (0.004) (0.010)

Age of the firm

0.012*** 0.005 0.005 0.012** -0.002 0.020*** 0.012**

(0.002) (0.003) (0.004) (0.004) (0.006) (0.003) (0.004)

The enterprise is self-initiated

-0.149** -0.169** -0.130** -0.096 -0.122** -0.391*** -0.146

(0.053) (0.068) (0.053) (0.164) (0.040) (0.116) (0.081)

Enterprise is cooperative -0.097 -0.207* 0.007 -0.066 -0.093 0.028 -0.102

22

(0.066) (0.112) (0.051) (0.088) (0.065) (0.063) (0.070)

Controls for sector Yes Yes Yes Yes Yes Yes Yes

Controls for city Yes Yes Yes Yes Yes Yes Yes

Test of equality of capital

elasticity between dynamic and

constrained

0.001 0.188

Test of equality of capital

elasticity between constrained and

survivalists

0.000 0.001

Monthly marginal returns to

capital (%)

9.1 5.4 11.7 4.6 6.7 9.8 8.8

R-squared 0.664 0.480 0.582 0.567 0.716 0.656 0.618

Observations 7,160 906 2,008 4,246 1,028 1,059 5,073

Note. The dependent variable in all the specification is log of value added. Marginal return to capital is computed by multiplying annual capital productivity with

elasticity of capital derived from the production function estimation with additional controls for entrepreneurial and firm characteristics. We divide the ratio by

12 to obtain the monthly returns.

iv

5. Employment Growth and Business Constraints

Table 3 has shown that a fundamental difference between dynamic and constrained enterprises lies in the nature of business constraints that they have faced. Despite having high returns to capital, these business constraints could potentially impede constrained enterprises from growing and earning higher profit. In this section, we examine more formally whether constrained enterprises have different growth performances and are affected differently by these constraints after controlling for entrepreneurial and firm characteristics. We consider employment growth, technology level, credit, business practice score, commercial location and capacity utilization rates to assess to what extent such factors affect constrained enterprises differently. Due to the very construction of the main variables of interest, there is a risk that the left hand-side variables could be correlated with the performance indicators presented on the right-hand side. To attenuate the problem of endogeneity associated with the link between performance-defined enterprise partitioning under GKL approach and the business constraints, we thus restrict our estimation to the index model.20 We calculate annual employment growth following the common approach in the literature (see, for example, Evans 1987).21 Column 1 of Table 5 shows that dynamic enterprises grow on average 2.4 % per year, while the growth profile of constrained enterprises is not only low (0.6 % per year), it is not also statistically different from the growth rates of survivalist enterprises. This suggests that despite similarities in characteristics and behavioral traits with dynamic enterprises, constrained enterprises fail to attain high growth trajectories.

20

To prevent multicollinearity, we further remove three variables that are the building blocks of the index dummy from the regression equation. These variables are cognitive test score, English speaking ability and prior wage employment experience. 21

Growth is measure by , where and subscripts enterprise and time. is natural log of

number of workers workers at the initial year of business operation and is current number of workers and

is age of the firm.

iv

Table 5. Key differences between the three groups of enterprises (Index approach)

VARIABLES Employment

growtha

(OLS)

Technology

level is lower

than main

competitors b

(Probit)

Credit is a

major

business

problem c

(Probit)

Is credit

constrainedd

(Probit)

Business

practice

score

(OLS)

Located

commercially

(Probit)

Capacity

utilization

rate

(OLS)

(1) (2) (3) (4) (5) (6) (7)

Dynamic 0.024** -0.319*** -0.010 -0.054 0.086*** -0.004 3.080***

(0.008) (0.029) (0.102) (0.108) (0.017) (0.097) (0.899)

Constrained 0.006 0.108*** 0.187*** 0.107*** 0.041*** -0.118** -2.180***

(0.005) (0.041) (0.047) (0.037) (0.010) (0.049) (0.423)

Age of entrepreneur

-0.002*** 0.003 -0.004 -0.011*** -0.000 -0.004*** -0.025**

(0.000) (0.002) (0.004) (0.001) (0.000) (0.001) (0.011)

Female

-0.038*** 0.083 -0.077* -0.112*** -0.019** -0.063* -1.023

(0.008) (0.058) (0.041) (0.035) (0.008) (0.034) (0.884)

Married

0.001 -0.058 -0.048 0.044 0.020* 0.070*** 1.372***

(0.005) (0.042) (0.029) (0.034) (0.009) (0.013) (0.337)

Christian

-0.004 0.006 0.059 0.201*** 0.004 -0.012 0.051

(0.006) (0.043) (0.040) (0.068) (0.006) (0.061) (0.625)

Years of schooling

0.002** -0.035*** -0.002 -0.021*** 0.011*** 0.001 0.353**

(0.001) (0.009) (0.009) (0.008) (0.001) (0.010) (0.145)

Is patient

-0.003 -0.108 -0.189 -0.035 0.010 0.266*** 2.403

(0.007) (0.093) (0.120) (0.077) (0.012) (0.050) (2.176)

Is a risk taker

0.002 -0.095** 0.063 0.059* 0.017 0.041 -0.088

(0.010) (0.040) (0.045) (0.034) (0.014) (0.030) (1.821)

Father had a self-employment

experience

0.002 -0.113*** 0.028 0.053 -0.028 -0.069*** 2.136

(0.003) (0.028) (0.034) (0.040) (0.018) (0.021) (1.769)

Number of siblings in businesses

0.001 -0.023* 0.058*** 0.019 0.027*** -0.017*** 0.283

(0.001) (0.012) (0.021) (0.013) (0.005) (0.006) (0.290)

Age of the firm

-0.005*** -0.016*** -0.008*** -0.019*** 0.004*** 0.011*** 0.295***

(0.000) (0.004) (0.002) (0.005) (0.000) (0.002) (0.041)

The enterprise is self-initiated

0.040*** 0.055 -0.178*** -0.406*** -0.053** 0.761*** -0.069

(0.006) (0.113) (0.048) (0.125) (0.018) (0.137) (0.826)

Enterprise is cooperative -0.080*** -0.017 0.064** -0.050 0.033* 0.181*** -0.303

(0.012) (0.070) (0.030) (0.069) (0.015) (0.066) (0.493)

Controls for sector Yes Yes Yes Yes Yes Yes Yes

Controls for city Yes Yes Yes Yes Yes Yes Yes

P value of F-test for equality of

coefficients (Top vs Constrained)

0.078 0.000 0.000 0.013 0.001 0.000 0.000

R-squared 0.081 0.195 0.065

Observations 8,127 8,133 8,133 8,133 8,133 8,133 8,133

Note: a Annual employment growth from start-up. Controlling for initial size using employment does not alter the

results. bTakes the value 1 if the enterprises assessed its production technology levels to be lower than that of its

competitors, otherwise takes zero. c takes the value 1 if enterprises stated that access to credit or large collateral

requirements or high interest rates are the first most import business constraints.d take the value 1 for enterprises that

applied for a loan and whose loan application was denied or those who wanted to apply but did not apply because of

the high cost of financing (interest rate, collateral, and mistrust of lenders). Robust standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

iv

Columns 2 to 7, present some of the key constraints that can potentially affect the growth performance of constrained enterprises. Column 2 indicates that compared to both constrained and survivalist enterprises, constrained enterprises are likely to report that their production technology levels are inferior to their main competitors.22 Lower levels of technology is often associated with wasteful production process that may not allow constrained enterprises to introduce improved line of products and penetrate new markets, thereby reducing enterprise profitability and growth. In columns 3 and 4, we employ two different measures of credit constraints.23 Both credit measures indicate that constrained enterprises are more credit constrained than dynamic and survivalist enterprises. Presumably, access to credit and inability to save sufficient amounts are key factors that inhibited the growth of constrained enterprises. There is emerging experimental evidence that alleviating credit constraint can potentially trigger growth. McKenzie (2017), for example, finds that a large grant to business competition winners in Nigeria help unleashed the growth of constrained enterprises by increasing access to capital and labor. Similarly, Blattman et al. (2014) show that financial grants enhances business income in self-employment in Uganda, and, in Sri Lanka, De Mel et al. (2008) find a small grant amounting to US$100 to US$200 generates returns to capital amounting to 4.6%–5.3% per year. Column 5 of Table 5 also shows that constrained enterprises have much lower business practice scores compared to dynamic enterprises. Lower business practice score is reflective of poor operations with implication for enterprise growth, productivity and profitability. Using a sample of micro and small firms pooled from seven countries, McKenzie and Woodruff (2015), for example, find a 35% increase in productivity following a one standard deviation improvement in businesses practices. Column 6 indicates that constrained enterprises are less likely to be located in commercial areas (are more likely to be operated from home). This reduces both the size of their manufacturing operations and their output market, confining sales to their immediate neighborhood. Further, constrained enterprises have much lower capacity utilization rates than both dynamic and survivalist type of enterprises (column 7). Low levels of capacity utilization implies high levels of unexploited potentials, which could be put to productive use through improved technologies, better access to credit and improved business practices.

22

We also test equality of coefficients between dynamic and constrained dummies. The P values of the tests are presented at the bottom end of the table. 23

Column 2 indicates a measure that is solicited from an open-ended question on the most important business constraints. Enterprises are considered credit constrained if the stated that their most important business constraint is access to credit, high collateral requirement for credit or high credit financing costs (interest rate). As discussed earlier, column 3 indicate credit constraint variable that takes the value 1 if the enterprise applied for a loan and its application was denied or if the enterprises did not apply for loan because of higher costs of financing (interest rate, collateral, and mistrust of lenders).

26

6. Conclusions and policy implications

MSEs are not only increasingly recognized as important instruments for poverty alleviation, they are also often considered as breeding grounds for the next generation of industrialists in low income countries. In view of that, government policies and current aid strategies have focused on promoting their growth through a wide array of assistances. In the design of such polices and strategies, the distinct characteristics, behavioral and business operations differences between entrepreneurs is rarely factored in. Enterprise policy support programs in low income countries have been all encompassing making little distinction between high-potential dynamic and low-prospect survivalist enterprises. The micro and small enterprise development strategy paper that underpins support for the sector in Ethiopia, for example, identify growth-oriented enterprises as those that operate in priority sectors, such as leather goods and articles, textile and garment, food processing and construction (FDRE, 2011). No further enterprise or entrepreneur level criteria is employed to identify truly dynamic businesses and thus state support, such as credit, business skills training and working premises are granted to all businesses that operate in these sectors. This is not unique to Ethiopia. Shane (2009), for example, states that policy makers “provide people with transfer payments, loans, subsidies, regulatory exemptions, and tax benefits if they start businesses, any businesses.” To tailor effective policy support towards subset of enterprises with high growth potentials and provide protection for survival type of enterprises, the first crucial step is to clearly identify enterprise and entrepreneurial characteristics that are good predicators of entrepreneurial success. For example, entrepreneurs who have the will, capacity and ambition to grow respond to policies and supports systems very differently to those who take up self-employment out of necessity or being rationed out of the labor market. In light of this, policies geared towards job creation on the one hand and enterprise transformation and industrial growth, on the other hand, will have to make distinctions between dynamic and survivalist enterprises for better targeting. Using entrepreneurial and firm-level characteristics, this study thus differentiates between three groups of enterprises: high-performing dynamic, high-potential constrained and low-prospect survivalist type enterprises. Using observables, we construct two measures to partition enterprises into the three categories based on the relative position of the enterprise in the empirical distribution of the predicted probability of being dynamic. We find that there are crucial similarities and fundamental differences between the three groups of enterprises. Dynamic and constrained enterprises appear to be very similar in terms of basic entrepreneurial and firm characteristics including behavioral traits and economic objectives of the entrepreneurs and structural features of the firms. Reflecting high potentials, constrained enterprises also enjoy a high monthly returns to capital reaching 10 % to 12 %. Yet constrained enterprises seem to face severe constraints that made them lag behind dynamic enterprises. For example, their similarities in characteristics with dynamic enterprises does not extend to business operations. We find that constrained entrepreneurs employ less advanced technologies than their competitors, are more credit constrained, their shop floor operations are less efficient, they are less likely to operate their businesses from commercial locations, and have much lower capacity utilization rates than dynamic enterprises. By contrast, survivalist entrepreneurs are “less entrepreneurial” and are very different from both dynamic and constrained enterprises across a host of dimensions. The difference between the three groups of enterprises in entrepreneurial trait, motivation, and

27

enterprise performance has important policy implications. The lack of growth ambitions and prospects among survivalist enterprises imply that enterprise support programs such as business skills training and microcredit will have limited impacts on their performance. This is not, however, a recommendation to recklessly abandon and completely withdraw policy support from survival type of enterprises. We are aware that protecting and promoting survival entrepreneurs have important welfare implications. Ensuring the survival of businesses operated by such entrepreneurs is often considered as a key social policy geared towards job creation and poverty alleviation. In the absence of social insurance schemes and few alternative employment opportunities in the formal wage sector, such enterprises provide income security (albeit precarious) for a vast number of unskilled and semi-skilled individuals in developing countries. Such enterprises help “poor people become a little less poor” (Mead and Liedholm, 1998, P.70). However, business development services and business skills training may not expand the growth opportunity of survival enterprises and hence will not transform the lives of their owners and workers. Further credit and grant policies that encourage businesses start-ups might end up selecting ill-prepared individuals who do not have the entrepreneurial attitude to form and operate businesses. In such cases, the support systems might create poorly operated enterprises that do not survive for long and hence will be provisional bridges towards unemployment. Appropriate support system to survival entrepreneurs might involve more flexible labor market policies that promote wage employment opportunities and social protection to those who are unable to enter the labor market. Pushing for the expansion of survival type of enterprises even with well-intentioned and-designed support systems, however, will lead to the expansion of micro and small businesses with limited prospect of growth and transformation. By contrast, higher returns to capital among constrained enterprises suggest the presence of untapped potentials and possibly larger returns when enterprise support programs target those whose ambition is to grow their businesses. In their attempt to expand their businesses, dynamic and constrained enterprises will face a wide range of obstacles stemming from the complexity of larger and frequent transactions and transactions involving high quality inputs and products. Financial constraint related with working capital and non-financial constraint related with technology, business practices, the reliable supply of electricity, parts, materials and inputs as well as access to market locations are crucial to constrained entrepreneurs who aspire to expand their businesses. It is thus important for government policy to reflect the clear distinctions between dynamic, constrained and survival enterprises in designing and implementing enterprise support programs. In addition to simple and rule-of-thump types of businesses skills training, business development services targeting industrial growth and expansion in particular should increasingly offer more advanced services reflecting the changing needs of dynamic and constrained entrepreneurs. It is evident that the expansion of businesses, reduction of unemployment and sustainable poverty reduction rests on the potential of dynamic and constrained entrepreneurs to successfully upgrade their production processes and expand employment.

28

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Appendix

Table A1. Factor analysis of reasons on basic entrepreneurial characteristic and choosing self-employment

Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7 Factor8

Eigen value 2.497 1.084 0.739 0.554 0.248 0.192 0.125 0.058

Factor loading Opportunity seeking 0.011 0.06 0.043 0.168 0.013 -0.024 0.082 -0.092

Determined to stay in the business 0.017 0.311 0.053 0.149 0.013 0.097 0.06 0.041

Training before starting the business 0.004 -0.011 0.012 0.071 0.373 -0.019 0.056 0.017

Is not searching for a wage job 0.012 0.141 -0.009 -0.015 0.033 0.168 0.021 0.04

Has an expansion plan 0.001 0.111 0.031 0.086 0.068 -0.094 0.056 0.007

Is ready to make significant sacrifices to stay in business 1.025 0.054 -0.004 0.145 0.053 0.244 -0.034 0.036

Will not take a wage job if offered -0.015 0.341 0.023 0.074 -0.029 0.056 0.035 0.033

Has a cognitive test score above the median score 0.008 0.074 0.135 0.163 0.017 -0.029 0.14 0.054

Is a risk taker -0.005 0.021 0.029 -0.001 0.011 -0.028 0.011 0.268

Is patient 0 0.009 0.014 -0.008 0.013 0.032 -0.023 0.248

Had a wage employment experience -0.002 0.036 0.04 0.149 0.053 -0.025 0.039 0.047

Prefer to run own business than wage employment -0.039 0.088 0.016 0.013 0.045 0.324 0.063 -0.033

Have attended training after starting the business 0.006 0.014 -0.026 0.036 0.38 0.046 0.039 0.032

Can speak English 0.013 0.063 0.398 0.205 -0.001 -0.02 0.226 0.057

Has a degree -0.003 0.024 0.369 0.146 -0.033 -0.024 -0.114 0.081

Has a diploma 0 0.018 0.023 0.087 0.033 0.006 0.319 -0.008

Has employed paid workers when starting the business -0.004 0.098 0.076 0.101 -0.1 0.016 0.091 0.041

Source: Author’s compliation based on survey data.

Note: maximum likelihood method; loadings >0.1 in bold italic. The table shows a high levels of heterogeneity in both characteristics

and drivers of entrepreneurial choice to join self-employment. We also observe that relatively a large number of variables load

similarly onto factor 4 with factor loading greater than 0.1, we thus choose to combine these variables to construct our index.

33

Table A2. Enterprise distribution by sector and location

Sector All

Food and beverage 20.2

Textile and garment 13.4

Chemical, plastic and rubber 2.45

Non-metallic minerals 6.78

Metal working 20.5

Printing 3.94

Machinery, equipment and electronics 1.06

Furniture and woodwork 23.0

Leather and leather products 3.62

Construction inputs 4.49

Others 0.51

Cities

Adama 3.19

Addis Ababa 55.0

Bahir Dar 6.92

Dessie 2.89

Dire Dawa 2.99

Gonder 4.12

Hawassa 5.62

Jigjiga 1.27

Jimma 3.60

Mekelle 14.4

Number of observations 8174

34

Table A3. business practise questions

Business practice % of

enterprises

that said yes

1 Visited one of your competitor’s businesses to see what prices they are charging? 67.8

2 Visited one of your competitor’s businesses to see what products he/she offers? 70.5

3 Asked your customers if there are any other products or services they would like you to offer? 74.2

4 Talked to at least one former customer to find out why former customers have stopped buying from this

business? 58.2

5 Compared the prices or quality offered by alternate suppliers to your current suppliers? 80.6

6 Do you record every purchase and sale made by the business? 55.9

7 Are you able to use your records to see how much cash the business has on hand at any point in time? 48.8

8 Do you use the records regularly to know whether sales of a particular product are increasing or decreasing

from one month to another? 47.7

9 Knows which goods you make the most profit per item selling? 75.4

10 Has a written budget, which states how much is owed each month for rent, electricity, equipment

maintenance, transport, advertising, and other indirect costs to business? 29.3

11 Has records documenting that there exists enough money each month after paying business expenses to

repay a loan in the hypothetical situation that this business wants a bank loan? 32.0

12 Review the financial performance of their business and analyze where there are areas for improvement at

least monthly? 41.3

13 Has a target been set for sales over the next year? 43.9

14 Compares their sales achieved to their target at least monthly? 39.2

15 Has an annual profit and loss statement? 52.1

16 Do you currently have business/strategic plan for your company? 45.6

17 Does this business have a specific name which is posted? 42.1

35

Table A4. Production function estimation and returns to capital (with no controls)

GKL Index

All Dynamic Constrained Survivalists Dynamic Constrained Survivalists

(1) (2) (3) (4) (5) (6) (7)

Capital Stock (log) 0.152*** 0.258*** 0.146*** 0.067*** 0.155*** 0.187*** 0.132***

(0.014) (0.027) (0.007) (0.013) (0.008) (0.014) (0.015)

Hours of work (log) 0.315*** 0.415*** 0.276*** 0.282*** 0.311*** 0.367*** 0.303***

(0.027) (0.020) (0.032) (0.031) (0.065) (0.056) (0.021)

Material cost (log) 0.480*** 0.388*** 0.485*** 0.474*** 0.503*** 0.484*** 0.466***

(0.030) (0.013) (0.024) (0.056) (0.048) (0.026) (0.037)

Test of equality of capital

elasticity between dynamic and

constrained

0.001 0.084

Test of equality of capital

elasticity between constrained and

survivalists

0.000 0.000

Monthly marginal returns to

capital (%)

11.6 6.2 11.7 5.8 8.4 11.9 11.1

R-squared 0.648 0.460 0.557 0.542 0.692 0.627 0.601

Observations 7,192 920 2,026 4,246 1,028 1,091 5,073

36

Figure A1. Distribution of capital stock and value added by group of enterpises.

0

2.0

0e

+0

84

.00e

+0

86

.00e

+0

88

.00e

+0

8va

20

08

0 5.00e+07 1.00e+08 1.50e+08 2.00e+08Capital stock

Constrained Dyanmic

Survivalist