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Promoting Entrepreneurship in Botswana:Constraints to Micro Business Development

March 2011

Financial and Private Sector Development

Africa Region and The Botswana Institute of Development Policy Analysis (BIDPA)

THE WORLD BANKWashington, DC

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Contents

Acronyms and Abbreviations vii

Acknowledgement ix

Executive Summary xi

Chapter 1 Introduction 11.1 Why should We Care about Microenterprises? 11.2 The Pilot Survey 31.3 Objectives of this Report 41.4 Organization of the Report 6

Chapter 2 Profile of the Microenterprise Sector 72.1 What Do Microenterprises Do? Where? and

How Do They Do It? 72.2 Who are the Micro-Entrepreneurs? 132.3 Capability Groups of Microenterprises 22

Chapter 3 Constraints to Micro-Business Development 413.1 Informality, Productivity and Access to

Services 423.2 Access to Services and Markets 473.3 Constraints to the Growth of Active

Microenterprises 58

iv Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

Conclusion: Lessons for the Design of Market Assessments for Services 65

References 71

Figures1 Registration and licensing status by tax registration status 122 Registration and licensing of the registered or licensed 123 Average net income per person engaged in tax registered

enterprises 134 Difference in probability tax registration by gender

of business owner 155 Difference in probability of tax registration

by education level of make business owners 166 Percent difference in probability of tax registration

by business motive of male entrepreneurs 197 Average net income per person engaged in active

enterprises 218 Fixed investment as % of net income 219 Average annual employment growth rate since

start up, all sectors10 Average annual employment growth rate since start up,

services only 2311 Annual net income of own-account workers by time in

business 2712 Annual net income per worker by time in business and

registration status-Enterprises engaging 2–4 persons 2813 Percent of registered licensed enterprises-own account

work only 2914 Percent of registered licensed enterprises – only

those engaging 2–4 people 2915 Percent distribution of active enterprises by line

of business 3116 Percent distribution of involuntary enterprises by line

of business 3117 Annual net income per worker by age group of owner

(000 pula) – active enterprises 3718 Percent of registered or licensed enterprises by schooling

of business owners 3819 Percent of enterprises of high-school-graduates by line

of business 39

Contents v

20 Percent of enterprises of non-high school grads by line of business 40

21 Respondents rating factors as major barriers registration by registration status 43

22 Respondents rating various factors as major barriers to business registration 43

23 Percent rating factors as major deterrent to business registration by registration status 44

24 Business owners who saw indicated benefits from registration 45

25 Business owners rating factors as major growth obstacles 46

26 Percent rating factors as major constraints to growth by business motivation 49

27 Percent of active enterprises rating various factors as major growth constraints 49

28 Involuntary enterprises rating factors as major growth constraints 50

29 Active enterprises rating factors as major growth obstacles 51

30 Enterprises using external finance for investment or working capital 52

31 Enterprises using supplier and informal finance for investment or working capital 53

32 Enterprises using external finance by source 5433 Percent of enterprises with non-residential business

premises 5534 Percent of enterprises connected to the public

electrical grid 5635 Enterprises with non-residential business premises

by owners’ age group 5736 Enterprises connected to the public electrical grid

by owners’ age groups 5837 Enterprise owners who had graduated from

high school 5938 Vocationally trained business owners 6039 Percent of enterprises keeping books 6140 Percent of enterprises using services of a professional

accountant 62

vi Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

Tables1 Botswana pilot survey sample distribution by location 42 Pilot survey sample distribution by sector and type

of location 83 Distributions of sample enterprises in other

manufacturing and other services 84 Distribution of sample by scale and other business

characteristics 95 Average number of persons engaged per enterprise and net

incomes per person 106 Percent of enterprises by registration status7 Distribution of the sample by registration status 148 Distribution of sample by business owner’s characteristics 159 Distribution business owners by reason for being

in business 1810 Distribution business owners by reason for being

in business 2011 Distribution by business age and size groups and

business motivation 2512 Average Annual net incomes and annual turnover

per enterprise 2613 Annual net incomes per worker and fixed assets

per worker 3014 Percent distribution of business owners by demographic

characteristics 3215 Annual net incomes and turnover per establishment by

owner’s age and schooling 3416 Annual net incomes per worker and fixed assets

per worker by owners’ characteristics 3517 Percent distribution of enterprises by business owners’

characteristics 36

Acronyms and Abbreviations

BDS Business Development ServicesBIDPA Botswana Institute for Development Policy AnalysisCEDA Citizen Enterprise Development AgencyFAP Financial Assistance PolicyIFS Integrated Field ServicesLEA Local Enterprise AuthorityMTI Ministry of Trade and IndustryNDB National Development BankNSO National Strategy OfficeSBPA Small Business Promotion AgencySEEP Small Enterprise Education and PromotionSMME Small, medium, and micro enterpriseUYF Umsobomvu Youth Fund (South Africa)

Acknowledgement

This report was originally prepared as World Bank Report No. 59916-BW by a team drawn from the World Bank Group and the Botswana Institute of Development Policy Analysis (BIDPA). The team was led by Taye Mengistae of the World Bank. Achieng Okatch led BIDPA’s contribution. Andrei Mikhnev, Zeinab Partow, Sandeep Mahajan, Rita Ramalho, and Ravi Ruparel, all of the World Bank Group, kindly served as peer review-ers. Dorothy Judkins provided administrative and editorial assistance.

The team would like to acknowledge the support of the Ministry of Trade and Industry (MTI) of the Government of Botswana (GOB) and Ms. Banny K. Molosiwa, the Permanent Secretary. We are also grateful to the following government officials for critical advice and encouragement given at a very early stage of the design of the study: Mr. Boniface G. Mphetlhe, Deputy Permanent Secretary (MTI), Ms. Peggy Serame, Chief Economist of the Ministry of Finance, and Ms. Neo Mooko, Director of Research and Information Management at the Local Enterprise Authority (LEA).

Comments on an earlier draft were kindly provided by representatives of various departments of the GOB, including from the Ministry of Finance and Development Planning, LEA and the National Strategy Office (NSO). The comments were made at a meeting held on November 30, 2010 at the World Bank Group’s Country Office in Gaborone. Among those in attendance were Keamogetse Molebatsi, Ruth Seipone, and Nancy Mabole of the NSO; Keganele Malikongwa of the Ministry of Finance; and Jerry Mooketsane, Bokete Mokgosi, and Thato Jensen of LEA.

The report was prepared under the overall direction of Ruth Kagia, (World Bank Country Director for Botswana, Lesotho, Namibia, South

x Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

Africa and Swaziland) and Marilou Jane D. Uy (Sector Director, Finance and Private Sector Development, Africa Region, the World Bank). The team received operational guidance from Timothy Gilbo (the then Country Manager for Botswana), Thomas Buckley (Senior Country Officer for Botswana), Michael Fuchs (Acting Sector Manager), and Chunlin Zhang (Sector Leader) of the World Bank.

Executive Summary

Botswana has had active programs of government support to small, medium and micro enterprises (SMMEs) since the 1970s, but none of these have reached microenterprises to a significant degree. Part of the reason for this could be that there are not many financial products and Business Development Services (BDS) that are appropriate or affordable enough for micro businesses.

In June-July 2009 the World Bank and the Botswana Institute of Development Policy Analysis (BIDBPA) carried out a pilot sample survey of 800 microenterprises in the Eastern Corridor of Botswana. The survey was designed to identify major capability groups of micro businesses in the country and institutional constraints under which they operate, as potential input for the design of formal market assessments for financial services and BDS tailored to the needs of this important but little un-derstood part of the economy.

This is a report on the main findings of the survey intended to help inform ongoing efforts to support enterprise development by key agencies such as the Local Enterprise Authority (LEA) and the Citizen Enterprise Development Agency (CEDA) and the Ministry of Trade and Industry. The report identifies capability groups in the sample based on measured productivity and earnings, and sorts the more productive and growth ori-ented businesses that could potentially be a source of effective demand for BDS from those that are not likely to evolve into viable enterprises in the long term. It then assesses the constraints under which various capability groups operate based on business owners’ evaluations of the main obstacles to the operation and expansion of their enterprises.

xii Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

Capability groups are defined in the report at various levels. At the most fundamental level we distinguish between active enterprises and involuntary enterprises based on the business motivation and outlook of their owners, arguing that the relative productivity and dynamism of the two groups indicates that only active enterprises are ultimately viable businesses. The report also further classifies active microenterprises into smaller sub groups of capability, based alternatively on the time they have been in business and the age and level of education of their business owners. Based on time in business, we distinguish between starting up active enterprises and post start-ups. Important distinctions of potential significance to policy are also made between youth owned active enter-prises and enterprises run by older owners, on one hand, and businesses of high school graduates and less educated owners, on the other.

What Makes Microenterprises different?

For the purpose of this report, a microenterprise is a business that en-gages fewer than five workers full time. A small enterprise is defined as employing at least five but no more than thirty workers, and a medium enterprise is defined as employing more than thirty but less than 100 workers. More than 30 percent of enterprises in the survey sample were retail traders. Another third were in services such as catering, transport, personal care, car repairs, printing services, and finance and real estate. One quarter were in food processing, other manufacturing and crafts, mainly in tailoring and knitting, wood work and metal work.

Unlike SMEs, a high proportion of microenterprises operate from no fixed location. Itinerant businesses constituted nearly 30 percent of the sample: most, but not all, of them as street vendors. But an even higher percentage (46 percent) also operated from non-residential business premises or structures. Another 23 percent of businesses were home based.

Microenterprises in Botswana also appear to last longer on average than their counterparts in many other developing economies: just under 23 percent of the sample were businesses that started within the past two years, while nearly one in three had been in business for ten years or longer at the time of the survey.

Productivity

Typically, microenterprises are not as well organized as SMEs. Their work-force also is normally less skilled than that of SMEs and functions with smaller capital per worker. Thus, on average, microenterprises are far less

Executive Summary xiii

productive than SMEs. For the full sample, the average annual net income (or value added) per worker was just under 30,000 pula, and the value of fixed assets per worker was about 57,600 pula. Each of these numbers concealed enormous variation across and within business lines. The median net income per worker was much lower at 10,000 pula, which would have been under one-third of the country’s per capita GDP at the time of the survey. The mean annual net income per worker ranged from just below 19,000 pula for food vendors to 38,000 for transport businesses.

Informality

Most microenterprises in Botswana also are informal in the sense that they are not registered with the tax office and do not hold a business license. The narrowest and probably the most common definition of an informal business is that it is an enterprise that is not registered for tax purposes. On this definition, only 14 percent of enterprises in the pilot survey sample were formal.

Regardless of which definition is used, formal microenterprises are far more productive than informal microenterprises. This is partly because inherently more productive enterprises are more likely to be registered. But registered or licensed businesses also have better access to markets and services, which may help raise their productivity. Indeed, many un-registered and unlicensed micro-businesses recognize that informality bars them from markets and services and from participating in potentially useful business support programs. However, they choose not to register or get a license nonetheless because those benefits have less weight in their view than the costs that come with registration or being licensed. The main cost items include potential tax liabilities, licensing and registration fees, and anticipated compliance costs of labor laws and other aspects of business regulation. Therefore, reducing such regulatory barriers to registration and licensing could be a significant component of formalizing microenterprises to improve their access to services.

Towards Markets in Financial Products and BdS for Microenterprises

At the same time, informality is not necessarily the most important of factors preventing microenterprises from accessing the financial products and business services currently available to SMEs. Most micro businesses in Botswana would not be able to make use of existing products and services regardless of their registration status. Many of them would not

xiv Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

afford those products and services in any case. Even more importantly, existing products and services do not seem to match the circumstances of even the most promising of micro businesses.

Addressing these issues could require public investment in the develop-ment of markets in new financial products and new BDS tailored to the needs and capabilities of the sector. Improved availability of affordable and appropriate services could indeed be the driver rather than consequence of formalization, and might be a greater incentive for registration and licensing than any regulatory reforms.

Active enterprises vs. involuntary enterprises

For public investments in the development of new financial products and BDS for microenterprises to have a positive social payoff, the investments may need to target primarily the more promising and potentially viable businesses within the sector. From this point of view, a more relevant distinction than that between formal and informal micro-businesses is that between micro-businesses of “active entrepreneurs” and those of “involuntary entrepreneurs.” Active entrepreneurs are business owners who could successfully earn a living in the labor market if they chose to, but are self-employed because they make more money with their own business than if they were working for someone else. In contrast, invol-untary entrepreneurs are those who are self-employed by default because they were rationed out of the labor market. They would have taken up paid work at the going rate if there had been enough jobs to go around.

Only one in five of those in the pilot survey sample were active en-terprises. The rest were all involuntary. These proportions are probably close enough to those of the national population of microenterprises in Botswana. A key message of the report is that public investments in the development of markets for new financial products and new business development services for micro-businesses might need to focus, at least initially, on active microenterprises. The reason is that such enterprises have the best chance of evolving into viable and growth-oriented busi-nesses and of providing effective demand for these products and services over the long term.

This is by no means a suggestion that governments, donors or develop-ment agencies should not provide support to involuntary entrepreneurs at all. The message is, rather, that the policy challenge that involuntary entrepreneurs pose is one of integrating the younger among them to the formal labor market through training and skills development schemes, and not necessarily one of providing them with business development services.

Executive Summary xv

On average, active entrepreneurs are younger and more educated. Their businesses also are younger on average, and are far more likely to be registered for taxes. Active enterprises tend to concentrate in relatively high value-added business lines such as manufacturing and crafts, trans-port, and non-retail services. Involuntary enterprises tend to concentrate more in food vending and micro-retail more generally. Most significantly, active enterprises are far more productive and have far higher growth rates than otherwise comparable involuntary enterprises.

Active enterprises also are far more growth-oriented than involun-tary enterprises. The typical active enterprise invests a greater share of its net income and grows faster than the typical involuntary enterprise. For example, in the survey sample, an active enterprise that started out as own-account work 3 to 5 years prior to the survey is likely to have approximately tripled in scale by the time of the survey. In contrast, an otherwise comparable involuntary enterprise starting from the same base is likely to have expanded by approximately 83 percent over the same time interval.

More promising active enterprises as potential markets for BDS

Youth owned enterprises

As potential participants in new markets for financial products and BDS, active microenterprises constitute a very diverse group in terms capabili-ties and constraints. It may be advisable to focus initial public investment in the new markets on the more promising businesses among the group. These can be defined in terms of characteristics of businesses or of their owners. Thus, based on analysis of relative productivity and growth record in the survey sample, youth owned active enterprises in general and those of young high school graduates in particular constitute the most promising group of enterprises. The second most promising group of active enterprises defined by owner characteristics is that of business of older high school graduates.

Startups

Youth-owned active enterprises largely overlap start-up active enterprises, by which is meant active enterprises that came into existence within the last five years. Start-up active enterprises are as promising and produc-tive as the active enterprises of young high school graduates. This is not surprising since this particular group of entrepreneurs account for a high

xvi Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

proportion of startups. Although some start-up active enterprises are own-account businesses, the vast majority are micro-employers, mean-ing they engage up to three people other than the owner. The second most promising group of active enterprises is that of post-start-up active micro-employers.

Therefore, investments in the development of markets in financial services and BDS for microenterprises initially should target youth-owned active enterprises. These constituted approximately 13 percent of the pilot survey sample and overlap more or less exactly the subpopulation of start-up active enterprises. The second most promising target of the programs seems to be active enterprises of older high school graduates. These constituted approximately 9 percent of the sample and more or less coincide with the subsample of post-start-up active micro-employers.

What next?

The information that the pilot survey has generated on the various ca-pability and constraints groups of microenterprises could be useful input to future efforts to make available new financial products and new BDS to active microenterprises in Botswana. An essential component of such efforts should be a formal assessment of the market for existing and potential products and services among the two most promising groups of active enterprises identified in the report: active startups and active youth-owned enterprises, with a particular focus on micro-employers and on businesses of young high school graduates.

The scope of a formal market assessment for a given financial product or BDS includes four analytic tasks: (a) an evaluation of a target group’s awareness and willingness and ability to pay for existing products; (b) an evaluation of the group’s willingness to pay for potential products; (c) an assessment of the extent of segmentation of the wider markets for existing or potential products in which the group may be supported to participate; and (d) an assessment of the potential for crowding out private demand or supply by public interventions in markets.

The findings of this report would be useful input to the design of tasks (a) and (b). The tasks will require much more data on each constraints group than has been generated by the pilot survey, which would need to be collected through focus group discussions with micro enterprise owners within each group, more open in-depth interviews with enterprise owners and BDS providers, market observation, and focused market surveys.

C h a P T e r 1

Introduction

1.1 Why Should We Care about Microenterprises?

This report analyzes data from the pilot sample survey of 800 microenter-prises that was carried out in selected localities of the Eastern Corridor of Botswana in the summer of 2009. The aim of the survey was to identify major capability and constraints groups of micro-businesses in the coun-try as groundwork for formal market assessments for financial services and Business Development Services (BDS) tailored to the needs of this important, but relatively neglected, part of the economy. The intended audience of the report therefore includes policy makers concerned with the development of the SMME sector in Botswana and experts involved in the design and monitoring and evaluation of support programs target-ing micro businesses and SMEs.

Botswana is an upper middle income, high growth economy with a long record of sound macroeconomic management and good governance. However, it has long been dominated by diamond mining, which cur-rently accounts for a third of the country’s GDP and 80 percent of its export earnings. And yet mining employs less than 5 percent of the work force. As a result, unemployment and the incidence of poverty have both been persistently high, and SMME support programs have figured prominently in the government’s strategies for economic diversification, poverty reduction and employment generation. However, there are also

2 Promoting Entrepreneurship in Botswana: Constraints to Micro Business Development

indications that the success of government efforts to support SMMEs is being hampered by limited capacity to design and implement appropri-ate intervention.

For the purpose of this report, a microenterprise is a business that engages fewer than five workers full time. A small enterprise is defined as employing at least five but no more than thirty workers, and a medium enterprise is defined as employing more than thirty but less than 100 workers. According to the 2006 Labor Force Survey (Government of Botswana 2008), approximately 300,000 people worked in the nonfarm private sector in Botswana, of which 80 percent were engaged in small, micro-, and medium enterprises (SMMEs). Approximately 1 in 3 of the workforce engaged in the SMME sector ran microenterprises. About 46,000 of these were own-account workers. The rest worked in some 20,000 micro-and small enterprises that provided paid employment to workers other than their owners. These statistics indicate fairly large sectors of microenterprises and SMEs for a country of Botswana’s size. However, many experts also believe that Botswana’s SME sector should be much larger than it is, not least of all because more than one-third of the country’s labor force is unemployed. The development of a more vibrant SME sector also is a major component of the government’s strategy for economic diversification and poverty reduction.

As a result, Botswana has had active programs of government support to SMME since the 1970s. The earliest framework of these programs was the Financial Assistance Policy (FAP), which was introduced in the early 1980s to provide direct government financial assistance to small-scale manufacturers. In the late 1980s, the Integrated Field Services (IFS) was launched as a program of training for SMMEs in recordkeeping, costing, business planning, marketing, buying, and stock control. In 1999 the government set up the Small Business Promotion Agency (SBPA) to co-ordinate all SMME support programs. This action was followed in 2001 by the establishment of the Citizen Enterprise Development Agency (CEDA) as the implementing agency of FAP, now refocused on providing subsidized loans to indigenous enterprises. Currently, CEDA offers loans ranging from P500 to P150 000 to micro-and small-scale projects at an interest rate of 5.0%, repayable in 5 years; and loans of up to P2 million to medium-scale projects with a 7.5% interest rate repayable in 7 years. CEDA’s micro-lending activities are intended to complement those of the National Development Bank (NDB), which has provided a range of regular-term loans to SMMEs since 1999.

In 2004, the Ministry of Trade and Industry (MTI) established the Local Enterprise Authority (LEA) to incorporate the functions of the IFS and

Introduction 3

the SBPA. LEA offers highly specialized development and support services including facilitating business planning; providing training, mentoring, and advisory services; identifying business opportunities for existing and future SMMEs; promoting domestic and international linkages; facilitating access to markets; facilitating exploitation of government and large firms’ procure-ment opportunities by SMMEs; facilitating access to finance; facilitating technology adoption and diffusion; and promoting general entrepreneur-ship and SMME awareness.

Unfortunately, none of these or the earlier programs has significantly reached microenterprises. Microenterprises are very much part of the intended beneficiaries of existing programs. Nevertheless, not many financial products or BDS have been tailored to the needs of microen-terprises. To the contrary, a common feature of existing and past SMME support programs is that they all have been based on a top-down, one-size-fits-all approach.

Moving away from this approach to one whereby products and services on offer are differentiated enough to match the diversity of needs and demands of their intended beneficiaries is a necessary next step. However, doing so requires reasonably detailed knowledge of the diversity of businesses in the SMME sector in capability and constraints. The pilot survey was intended to help bridge this knowledge gap with respect to micro-businesses.

1.2 The Pilot Survey

The survey was designed and implemented by the Botswana Institute for Development Policy Analysis (BIDPA) during June and July 2009. It involved administering a common written questionnaire to the 800 sampled business owners through face-to-face interviews by trained enumerators. The subjects of the questions were:

• Demographic characteristics of business owners;• Location, activities, staffing, organization, start-up history, and cur-

rent registration and legal status of their businesses;• Owners’ assessment of what they thought were obstacles to the

day-to-day operations and growth of their businesses;• Information on turnover, employment, revenue, staff remuneration,

cost of purchases, and sources of finance; and• Indicators of the business environment in such areas as interaction

with government agencies, property crime, and quality of public utilities services.


The sample was drawn from localities in Gaborone and surround-ing areas (including Tlokweng, Mogoditshane, Ramotswa, Gabane, and Mochudi), Francistown and surrounding areas (including Tonota, Sebina, Tsamaya, and Matsiloje), and Selebi Phikwe and surrounding areas (in-cluding Bobonong, Mmadinare, and Tsetsebjwe), Palapye, and Serowe. The distribution of the sample among these locations is shown in table 1.

1.3 Objectives of this report

The report analyzes the returns to the survey with the broad objective of identifying capability groups of micro-businesses and the constraints under which they operate as potential input to formal assessments of markets in financial products and BDS. Prior market assessments are a critical link in the methodology of the market development paradigm for public support to small and microenterprise development that the Donor Committee (2001) has recommended for the design and delivery of BDS. The purpose of a market assessment is to determine, (a) whether or not there is significant actual or potential (effective) demand for par-ticular types or categories of BDS by some populations of enterprises; (b) whether BDS providers in the region have sufficient capacity to meet that demand; and (c) whether there is lack of (effective) demand or sup-ply, the reasons for it, the kind of interventions that would be needed to remedy the observed deficiency in demand or supply, and whether these interventions should come from local development agencies or other sources. The interventions could come from the supply side and involve the design of new products or the introduction or marketing of existing products from other parts of the country. They also could come from the

Table 1 Botswana pilot survey sample distribution by location

District Count Percent Town Count Percent

Southern 254 31.75 Gaborone 256 32

South East 59 7.38 Mogoditshane 46 5.75

Kweneng 61 7.63 Mochudi 55 6.88

Kgatleng 55 6.88 Ramotswa 32 4

Central 242 30.25 Serowe 61 7.63

North East 129 16.13 Palapye 38 4.75

Total 800 100 Selibe Phikwe 89 11.13

Francistown 118 14.75

Other 105 13.13

Total 800 100

Introduction 5

demand side and involve financing or subsidy schemes such as vouchers and matching grants designed to make existing products accessible to the group in question (box 1).

Because the scope of the survey instrument did not extend to service providers, the report cannot say anything of relevance to objective (b) of a market assessment. However, the survey provides a useful starting point for some of the analysis needed for objectives (a) and (c) of such an assess-ment in two ways. First, it identifies capability groups based on measured productivity and earnings, and, on this basis, sorts the more productive

Box 1

Market Paradigm for Public Intervention in the Provision of Business development ServicesA key element of the market development paradigm of the Donor Committee (2001) for public intervention in the provision of BDS is that the intervention would be justified only insofar as there is evidence that a significant segment of targeted enterprises would not have access to these services in the absence of the inter-ventions, despite the fact that their access would be socially profitable. In other words, interventions would be justified only on grounds of evident market failure in provision.

A second element of the same paradigm is that public intervention should not crowd out existing or potential commercial providers of services. Doing so would undermine the sustainability of the market over the long term and its reach to the wider population of enterprises.

A third element is that, in principle, interventions should eschew free or highly subsidized provision of BDS by the public in favor of facilitating the evolution of a genuine market in the service over the long term.

We are not aware of specific schemes for the provision of BDS that reflect these principles in Botswana, although several examples exist in neighboring South Africa. Perhaps the best known is the Umsobomvu Youth Fund (UYF) voucher scheme, which enables young participating entrepreneurs to obtain business planning services or training from a registered private sector service provider in the areas of services that they otherwise could not afford. At the same time, the beneficiaries of the scheme have a say in which provider to use from a number of competing suppliers.

In a second South African example of BDS provision, government agencies play the role of facilitators rather than direct providers. This program is the BDS provision cluster that Investec established under the name, Business Place. This program is one-stop provision of a wide range of services––from access to finance, business planning, legal services, IT services, computer training, marketing, to export-import advice––from a variety of private sector providers. These providers share a common service infrastructure but otherwise compete with rival suppliers for clients. The cluster operates in partnership with a number of public partners including the City of Johannesburg and the City of Cape Town.


and growth-oriented businesses that are likely to have effective demand for BDS from those that are not likely to evolve into viable enterprises in the long term. Second, it highlights the differences between capability groups in terms of the constraints they face based on business owners evaluation of growth factors.

A key consideration in the identification of capability groups of mi-croenterprises in the context of Southern Africa is the role that business informality plays in determining access to services and markets. We char-acterize a business as formal if it is registered for tax purposes or operates on an official business license. Gelb and others (2009) show that, unlike in other parts of Sub-Saharan Africa, informality in Southern Africa is strongly associated with lower productivity and with poorer access to services. The other dimensions on which enterprise capability varies are the scale of the business, its age, and the human capital of business own-ers. All three of these variables are significant correlates of productivity, registration and licensing status, and access to markets and services.

In the process of identifying capability groups, the report draws a profile of Botswana’s microenterprise sector insofar as the survey sample can be thought of as representative of the national population of micro-businesses. It also describes the roles that business owners’ human capital plays in the choice of lines of business, in productivity, and in access to markets and services.

1.4 Organization of the report

The rest of the report is organized as follows. Chapter 2 defines the main capability groups of microenterprises in terms of scale of activities, length of time in business, and the experience, education, and business motives of their owners. The capability of each group is measured by the average productivity and growth record of its members. Chapter 3 then discusses how the capability groups identified in the second chapter dif-fer in access to markets and services. Chapter 4 concludes the report by laying out the implications of the survey results for the design of public interventions to promote the development of markets in new financial products and BDS.

C h a P T e r 2

Profile of the Microenterprise Sector

2.1 What do Microenterprises do? Where? and how do They do It?

Approximately one-third of enterprises in the survey sample were retail traders (tables 2 and 3). Another one-third worked in services such as catering, transport, personal care, car repairs, printing services, and finance and real estate. One-quarter were employed in food processing and other manufacturing and crafts, mainly in tailoring and knitting, wood work, and metal work.

Unlike SMEs and larger businesses, a very large proportion of microen-terprises operate from no fixed location. Itinerant businesses constituted nearly 30 percent of the sample. Most, but not all, of them were street vendors. However, a surprisingly high 46 percent of the sample also op-erated from nonresidential premises or structures. Another 23 percent of businesses in the sample were residence based. The latter is quite low in comparison with other developing economies. It probably reflects the fact that zoning regulations are enforced more stringently in Botswana than in many other developing economies.

Scale and longevity

Very much in line with the results of the 2006 Labor Force Survey of the Government of Botswana, and the 2007 Informal Sector Survey, half


of the microenterprises covered by the pilot survey were run by own-account workers. The other half provided employment to 1–3 people other than the business owner as paid employees or as unpaid full-time family workers (table 4).

Table 2 Pilot survey sample distribution by sector and type of location

Industry Count Percent Type of location Count Percent

Food and beverages

65 8.13 Non-residential location

375 46.88

Textiles 37 4.63 Located in residence

187 23.38

Garments 51 6.38 No fixed location 238 29.75

Transport 17 2.13

IT 14 1.75 Total 800 100Retail trade 273 34.13

other services 286 35.75

Other 57 7.13

Total 800 100

Table 3 distributions of sample enterprises in other manufacturing and other services

Manufacturing Count Services (excluding retail trade) Count

Food and beverages 68 Barber Shop and hair saloon 49

Textiles 37 Car repairs 36

Garments 52 ITC 20

Basic metals 6 Transport 20

Fabricate metal products 7 Shoe repair service 20

Machinery and equipment

1 Printing / secretarial services/ Photography

17

Electronics 14 Health/education 12

Other manufacturing 18 Financial and real estate services 9

Total 203 Hotels and restaurants 5

Advertising 5

Wholesale trade 4

Laundry and dry cleaning 4

Car Wash and tire repair 3

Entertainment 3

Driving schools 2

Total 209

Profile of the Microenterprise Sector 9

The median microenterprise engages two people full time. This number is consistent with any of the following plausible pairings: a husband and a wife, the household head or the spouse with a child helper, or either of the two with a hired hand from outside of the household. Many of the larger enterprises rely on 2–3 paid employees. Such enterprises are common in tailoring and similar crafts, and typify such services providers as barber and car repair shops. On the other hand, the modal enterprise in transport would be an own-account cab driver, and the modal retail trader would be an own-account street vendor (table 5).

Microenterprises in Botswana appear to last longer than their coun-terparts in many other developing economies (table 4). At the time of the survey, just under 23 percent of the sample were businesses that had started within the past two years, whereas close to 30 percent had been in business for 10 years or longer.

Productivity

Less than 50 percent of enterprises in the sample kept books, and only approximately 7 percent employed professional accountants for this

Table 4 distribution of sample by scale and other business characteristics

Scale Count Percent

Own-account work 387 48.38

Engaging 2 to 4 people 339 42.38

Engaging 5 or more people 74 9.25

Total 800 100

Business age group Count Percent

2 years or less 183 22.88

2–5 years old 238 29.75

5–10 years old 130 16.25

Older than 10 years 249 31.13

Total 800 100

Registration status:

Registered with some authority 397 49.3

Registered with tax authorities 111 13.98

Holds a trading license 361 45.41

Book keeping practice:

Kept books 358 44.75

Have an accountant 53 6.63


task (table 4). These low rates no doubt have to do with the fact that the microenterprise workforce is smaller and typically less educated and skilled than that of larger businesses. Combined with low fixed assets per worker, these factors also make microenterprises less productive than larger businesses.

The average annual net income (or value added) per worker for the full sample was just under 30,000 pula, and the value of fixed assets per worker was approximately 57,600 pula. However, each of these num-bers concealed enormous variation across and within business lines. The median net income per worker was much lower at 10,000 pula, which would be under one-third of the country’s per capita GDP at the time of the survey. The median value of fixed assets per worker was 12,000 pula. Comparing subsamples across sectors, the mean annual net income per worker ranged from just below 19,000 pula for food vendors to 38,000 for transport businesses.

Informality

Most microenterprises also are informal in the sense that they are not registered with the tax office and do not hold a business license. The nar-rowest and probably the most common definition of an informal business is that it is an enterprise that is not registered for tax purposes. By this definition, only 14 percent of enterprises in the pilot survey sample were formal (table 4). However, approximately 50 percent of the enterprises in the sample held a business license or had registered with some govern-ment authority other than the tax office.

Table 5 average number of persons engaged per enterprise and net incomes per person

Number of Persons engaged

Net income per worker (‘000 pula)

Fixed assets per worker (‘000 pula)

Mean Median Mean Median Mean Median

Food and beverages

2.1 2 18.7 5.3 57.5 9.5

Manufacturing/crafts

2.6 2 29 11.2 50.5 20.2

Transport 2.4 1 38 26.4 55.5 40

Retail trade 1.5 1 26.8 8.7 48.6 7.2

Other services 2.9 2 30.9 11.6 74.2 16.3

All sectors 2.2 2 27.9 9.8 57.6 12


The tax registration rate varies enormously across the sample by line of business, ranging from a low of 6 percent for food vendors to a high of 40 percent for transport services (table 6). In contrast, there is not much variation in the rate of licensing, which ranges from 43 percent for food vendors to 50 percent for other sectors. When we use the weaker defini-tion of formality to characterize enterprises that either are registered for tax or hold a business license, but not necessarily both, just a little over 50 percent of businesses in the sample were informal.

If we choose the stricter definition of formality (tax registration only), 77 percent of the micro-businesses we would classify as formal would be holding a business license, and 93 percent of them would be registered with the Office of Company Registration (figure 1).

On the other hand, by using the stricter definition, we would leave out of the category some 40 percent of microenterprises that hold busi-ness licenses and some 43 percent that are registered with the company registration office. The advantage of the second, weaker definition is that it allows us to categorize as formal all license-holders and two-thirds of those registered with the office of company registration (figure 2).

A disadvantage of the weaker definition is that it somewhat blurs the performance gap between what we would classify as formal businesses and their informal counterparts compared to what would emerge from the stricter definition of business formality. For example, the average net income per worker for tax-registered enterprises in the sample was 57 percent larger (figure 3). Approximately 10 percent of this gap arose from tax-registered enterprises having more fixed assets per person engaged. Another 5 percent reflects the fact that tax-registered enterprises normally

Table 6 Percent of enterprises by registration status

Registration or licensing status

Industry

Registered with

non-tax authority

Registered for tax

Holds a business license

Registered for tax or licensed

Registered with some authority or licensed

Food and beverages

29.4 6.1 52.9 54.4 58.8


73.0 23.9 48.9 58.0 80.7

Transport 55.0 40.0 50.0 55.6 72.2

Retail trade 40.8 8.4 43.3 45.4 62.4

Other services 59.1 17.8 44.4 48.1 66.1

All sectors 49.4 13.6 45.2 48.7 65.5


operate from better locations or better business premises than do the unregistered. Table 7 shows that the tax registration rate of enterprises operating from nonresidential business premises is twice the average rate.

Some 20 percent of the productivity gap reflects the skills advantage of tax-registered enterprises. For example, more of them keep books. In addition, well over half of the productivity gap in favor of tax-registered businesses reflects other sources, possibly including unobserved advantages in skills and know-how, and better or cheaper access to resources and publicly provided goods and services. One observed source is economies of scale: tax-registered enterprises are significantly larger (table 7).

Figure 1 Registration and licensing status by tax registration status

76.6

92.8 97.3

40.1 42.5

59.6

0

20

40

60

80

100

120

% holding atrade license

% registered withthe registrar

% registered orholding a license

of the tax registered of those not registered for tax

Figure 2 Registration and licensing of the registered or licensed

0

20

40

60

80

100

120

30

67.7

93.3100

32.5

% taxregistered

% registered wihthe registrar

% hodlinga license

% registered withregistrar or for tax or

holding a license

of those which are tax registered or hold a licenseof those which are neither tax registered nor hold a license

0 0 0


2.2 Who are the Micro-entrepreneurs?

Gender, experience, and schooling

Important as they are as covariates of its productivity, the scale of an enterprise and its registration and licensing status ultimately result from, among other things, predetermined attributes of the business owner. These attributes include schooling, duration and type of prior economic experience, and other significant social characteristics such as gender and ethnicity.

Nearly 70 percent of microenterprises in Botswana are owned and run by women (table 8)––quite a high rate compared to other developing countries. Women-owned enterprises are less likely to be registered for tax partly because the women in the sample tend to be less educated than the men (figure 4), and the more educated among business owners are more likely to have their businesses registered with the tax authorities. Average schooling levels of micro-business owners in Botswana are quite high compared to those in the region and other developing economies. Less than 10 percent of owners in the pilot survey sample had no formal schooling at all, while at the other end of the educational attainment spectrum, a comparable proportion had some tertiary education. Between these extremes were the business owners who had graduated from high school, those who had started but had not completed primary schools, and those who had some secondary schooling but had not graduated from high school. Business owners who had at least completed high school constituted approximately one-third of the full sample. Those with some schooling but who had not yet completed high school made up another 60 percent of the sample.

Figure 3 Average net income per person engaged in tax registered enterprises (Net income per person for unregistered = 100)

136

148

151

157

125 130 135 140 145 150 155 160

Controlling for fixed assets,type of premises, bookkeeping practice

Controlling for fixed assets per worker,type of business location

Controlling for fixed assets per worker

No controls


A business owner’s schooling is a reasonably strong predictor of the scale of her or his business and its productivity, and the likelihood of the business being registered for tax or holding a trade license. More educated business owners are significantly more likely to run registered or licensed enterprises. For example, the business of a male owner randomly picked from the sample would be 14 percent more likely to have been registered

Table 7 distribution of the sample by registration status

Business registered for tax?

Yes No Total

(1) Number of persons engaged

1 person (own-account work) 13 371 384

% 3.39 96.61 100

2–4 persons 64 271 335

% 19.1 80.9 100

5 or more persons 34 41 75

% 45.33 54.67 100

Total 111 683 794

% 13.98 86.02 100

(2) Number of years in business

2 years or less 18 165 183

% 9.84 90.16 100

2–5 years old 36 199 235

% 15.32 84.68 100

5–10 years old 23 107 130

% 17.69 82.31 100

Older than 10 years 34 212 246

% 13.82 86.18 100

(3) Type of location of transactions

Non-residential premises 90 279 369

% 24.39 75.61 100

Located in residence 12 175 187

% 6.42 93.58 100

No fixed location of operation 9 229 238

% 3.78 96.22 100


Table 8 distribution of sample by business owner’s characteristics

Characteristics Count Percent

Gender:

Female 550 68.75

Education:

No schooling 57 7.13

Primary incomplete 64 8.01

Primary Completed 139 17.4

Junior Secondary Completed 278 34.79

Senior Secondary Completed 119 14.89

Vocationally trained 82 10.26

Some University Training 60 7.51

Total 799 100Reason for setting up this business:

Absence of alternative employment 375 46.93

Pay in alternative too low 149 18.65

Enjoy running it 127 15.89

This is what I am best at 84 10.51

Other 64 8.01

Total 799 100

Figure 4 Difference in probability tax registration by gender of business owner (%)

0

–1

–2

–3

–4

–5

–6

–7

–8

–9

–10–8.9

–5.8–5.2 –5

With no controls Controlling forschooling

Controlling forschooling and age

Controlling forschooling, age andbusiness motive


for tax if the owner had at least completed high school than if the owner had completed only primary schooling (figure 5).

Active entrepreneurs vs. involuntary entrepreneurs

Part of the reason that more educated business owners are more likely to run tax-registered or licensed enterprises is that a larger proportion of them are in business as a positive career choice, not because they have no alternative ways of earning their livelihoods. Ultimately, people are found in occupations as the outcome of rational choices that they make subject to the constraints imposed on them by market parameters and their skills and wealth. At the same time, not everyone makes choices from the same set of alternatives. Even when their underlying preferences are similar to those of everyone else, some people’s choices are more restricted than those of others. In other words, not everyone found in self-employment or running a micro-business––or, indeed, any business––is there because s/he would not want to be anywhere else given a wider choice set.

A widely held view is that a large proportion of those who are self-employed or running micro-businesses in developing economies are people who have been rationed out of formal labor markets and who would be found working for someone else if there were enough jobs to go around at the going wage rate or even less. A term occasionally used to describe people who run micro-businesses by default, or because they cannot earn their livelihood as employees in the formal sector, is “necessity

Figure 5 Difference in probability (%) of tax registration by education level of make business owners (High school grads – primary school complete)

Controlling for owners ageand business motive

With no controls Controlling forowner’ age

13.9 13.8

8.9

0

2

4

6

8

10

12

14

16


entrepreneurs”. At the other end of the spectrum are the “opportunity entrepreneurs”, who comprise business owners whose opportunity set is large enough to enable them to earn a living as formal sector employees at the going wage rate, yet have chosen an entrepreneurial career as they would make more money or find more self-fulfillment this way. Alternative designations for “necessity entrepreneurs” are “involuntary entrepreneurs” and “survivalist entrepreneurs.” “Opportunity entrepreneurs” often are referred to as “active entrepreneurs.”

One way of telling “active entrepreneurs” from “involuntary entrepre-neurs” in business surveys is asking enterprise owners why they decided to be in business rather than work for someone else. In the Botswana survey, owners were asked to pick what best fit their actual business mo-tive from the list of five alternatives shown in table 8. The idea behind the question was that people who had been pushed into self-employment due to lack of opportunities for paid employment at “living wages” would not claim to enjoy their current occupation or accept that their current earnings are what they would inherently be worth in the labor market. On the other hand, those who were self employed by choice would. If this reasoning is correct, some 26 percent of the sample, or 211 business owners, would quality as active entrepreneurs. The remainder would be “involuntary entrepreneurs, who reportedly were in business only because they could not find any alternative employment at all, or they could not live on the wages offered to them by potential employers.

Table 9 breaks down various demographic strata of business owners by what their responses would be to the hypothetical question of whether they would rather work for someone else at the going wage rates based on the reasons they gave to the actual survey question described in table 8. In effect, the responses classify each stratum into active entrepreneurs and involuntary entrepreneurs. Table 9 suggests that the proportion of ac-tive entrepreneurs is slightly smaller among women and younger business owners. However, this particular comparison does not take into account the role of differences in educational attainment by gender and between business owner age groups. Indeed, by far the sharpest pattern in the table is that the likelihood of a business owner being an active entrepreneur consistently rises with his or her educational attainment. Thus, 45 percent of the subsample of those who had at least completed high school would quality as active entrepreneurs, compared to 18 percent of those who had at most completed primary school. This correlation between schooling and active entrepreneurship accounts for approximately one-third of the correlation between schooling and business formality as indicated by business registration for tax (figure 5).


The direct correlation between business formality and active entre-preneurship is depicted in figure 6. According to it, a randomly chosen male active entrepreneur is approximately 17 percent more likely to have his business registered for tax than a randomly selected male involuntary entrepreneur. Conversely, more than half of the businesses in the sample that are registered for tax are run by active entrepreneurs, whereas less than one-quarter of businesses that are not registered for tax are owned by active entrepreneurs. Approximately half of this correlation between tax registration and motives for entrepreneurship (figure 6) is explained by active entrepreneurs being more likely to run larger businesses, which are more likely to be registered for tax. For example, the proportion of

Table 9 distribution business owners by reason for being in business

Prefer to work for someone else?

Yes No Total

(1) Level of Education

At most primary complete 199 43 242

% 82.23 17.77 100

Junior secondary complete 198 65 263

% 75.29 24.71 100

At least secondary complete 128 103 231

% 55.41 44.59 100

(2) Age Group

24 yrs. or younger 311 103 414

% 75.12 24.88 100

25–29 yrs 92 49 141

% 65.25 34.75 100

30 yrs. or older 122 59 181

% 67.4 32.6 100

(3) Gender

Female 374 136 510

% 73.33 26.67 100

Male 151 75 226

% 66.81 33.19 100

Total 525 211 736

% 71.33 28.67 100


active entrepreneurs among micro-businesses engaging 2–4 people in the sample averages 38 percent, compared to 16 percent for own-account workers (table 10).

Active entrepreneurs are also more likely to operate from nonresiden-tial premises, and they are more likely to operate in sectors subject to greater regulation, such as transport services, manufacturing and crafts, and nonretail services (table 10). Both factors would make a business more visible to authorities, and thus probably more likely to register with them. These two factors are simultaneously associated with higher rates of active entrepreneurship. For example, the proportion of active entrepre-neurs in manufacturing and crafts or transport was 45 percent, compared to 21 percent of retail traders and 8 percent of food vendors. Likewise, 39 percent of businesses operated from nonresidential premises are run by active entrepreneurs, compared to less than 20 percent each of those operated from home and those operated from no fixed location at all.

Relative productivity and relative dynamism of active enterprises

When we control for scale, line of business, and type of location of activi-ties, an active enterprise is only approximately 6 percent more likely to be registered for tax than an involuntary enterprise (figure 6). Allowing

Figure 6 Percent difference in probability of tax registration by business motive of male entrepreneurs (active enterprises – involuntary enterprises)

0

2

4

6

8

10

12

14

18

16

Controlling for scale, years

in business, type of

location, line of business,

labor productivity

Controlling for scale, years

in business, type of

location and line of business

Controlling for scale, mode of

location and years in business

Controlling for scale and

mode of location

Controlling for scale

No controls

16.8

8.9

7.1 75.9

4.1


Table 10 distribution business owners by reason for being in business

Prefer to work for others?

Yes No Total

(1) Scale

1 person (own-account work) 300 59 359

% 83.57 16.43 100

Engaging 2 to 4 people 196 119 315

% 62.22 37.78 100

Engaging 5 or more people 29 33 62

% 46.77 53.23 100

(2) Age group

start up=<5 yrs 291 101 392

% 74.23 25.77 100

Young=5 to 10 yrs 81 35 116

% 69.83 30.17 100

Established>10 yrs 153 75 228

% 67.11 32.89 100

(3) Location of business activities

Non-residential business premise 207 134 341

% 60.7 39.3 100

Located in residence 134 33 167

% 80.24 19.76 100

No fixed location 184 44 228

% 80.7 19.3 100

(4) Line of business

Food and beverages 60 5 65

% 92.31 7.69 100

Manufacturing/crafts 46 38 84

% 54.76 45.24 100

Transport 11 9 20

% 55 45 100

Retail trade 268 71 339

% 79.06 20.94 100

Other services 135 86 221

% 61.09 38.91 100

(5) Registered for tax?

Yes 45 52 97

% 46.39 53.61 100

No 477 156 633

% 75.36 24.64 100


for the facts that active enterprises are more productive on average and that the more productive businesses are more likely to register takes away two more percentage points from the gap in the probability of tax registration between active and involuntary enterprises.

Regardless of tax registration status, active enterprises are on aver-age far more productive than involuntary enterprises (figure 7). For example, the average net income per worker for tax-registered active enterprises is 29 percent greater than the average net income per worker of tax-registered involuntary enterprises. The productivity advantage of unregistered active enterprises over unregistered involuntary enterprises is even higher at 46 percent.

Approximately half of the productivity advantage of the tax-registered active enterprises over tax-registered involuntary enterprises reflects the

Figure 7 Average net income per person engaged in active enterprises (Involuntary enterprises = 100)

160

No controls

138

142

143

146

114

124

126

129

0 20 40 60 80 100 120 140

Controlling for scale, fixed assets per worker,type of location of activities, bookkeeping practices

Controlling for scale, fixed assets per worker,type of location of activities

Controlling for scale and forfixed assets per worker

Unregistered, unlicensed enterprises onlyTax registered or licensed enterprises only

Figure 8 Fixed investment as % of net income

160 180 2000 20 40 60 80 100 120 140

All sectors


Other services

Retail trade

Transport

Active enterprises Involuntary enterprises


advantages that active enterprises have in scale economies, better tech-nology, fixed assets per worker, and skills. The other half could reflect unobserved advantage in skills or better access to services (figure 7).

Active enterprises also are far more growth oriented than involuntary enterprises. For the year leading up to the survey, fixed investment rates averaged well over 90 percent of the net income of active enterprises, com-pared to approximately 30 percent for involuntary enterprises (figure 8). Although investment rates varied enormously across sectors, the pattern in every line of business was that the rates were several times higher for active enterprises than for involuntary enterprises. As a result, the typical active enterprise grows faster than the typical involuntary enterprise. For example, an active enterprise that started out as own-account work 3–5 years prior to the survey would have grown by approximately 300 percent by the time of the survey (figure 9). In contrast, an otherwise comparable involuntary enterprise starting from the same base would have expanded by approximately 83 percent over the same interval. Similarly, an active enterprise that started out with 2 workers 3–5 years prior to the survey would have increased in scale by approximately 84 percent by the time of the survey while an otherwise comparable default enterprise would have expanded by only 16 percent over the same period. This pattern applies across all four sectors and is particularly well illustrated for the nonretail-trade service sector by figure 10.

Figure 9 Average annual employment growth rate since start up (%), all sectors

306

84

4318

83

15 16 120

50

100

150

200

250

300

350


Started out as own-account work

3–5 years ago (n=15 for active, n=24 for

involuntary)

Started out with 2 workers 3–5 years

ago (n=24 for active, n=28 for

involuntary)

Started out as own-account work 2 years ago or later

(n=21 for active, n=97 for involuntary)

Started out with 2 workers 2 years ago

or later (n=28 for active, n=112 for

involuntary)


2.3 Capability groups of Microenterprises

The distinctions between formal and informal enterprises, on one hand, and active and involuntary enterprises on the other, relate to only two of the dimensions in which capability groups of microenterprises can be defined as segments of the sector that may respond differently to a given set of policy reforms, new financial products, or new markets in BDS. Of these two dimensions, whether a business is formal or informal is an outcome variable. Ultimately, owners choose to have their busi-nesses registered for tax or otherwise depending on their determination of which of the two courses of action would make them better off. In contrast, whether these same owners are active or involuntary entre-preneurs is not subject to their choice any more than as their gender, age, and ethnicity are. Rather, whether they are active or involuntary entrepreneurs is a characterization of their prior labor market options as one of the exogenous determinants of their choice to pursue a business career. We classify business owners as active entrepreneurs to indicate that, given their skills, they would have successfully earned a living in the labor market had they chosen to. Business owners that do not meet this criterion are classified as involuntary entrepreneurs as the alternative to self-employment in their case would be unemployment or employment at below subsistence wages.

Figure 10 Average annual employment growth rate since start up, services only

050

100150200250300

500

350400450


Non-retail services, started out with 2

workers 2 years ago or later (n=9 for active, n=13 for

involuntary).

Non-retail services, started out with 2 workers 3–5 years

ago (n=12 for active, n=9 for involuntary).

Retail trade, started out with one person 2 years ago or later

(n=12 for active, n=71 for

involuntary).

Retail trade, started out with one person 3–5 years ago (n=18 for active, n=69 for

involuntary)

433

7346

14

92

458 5


In this section, we identify narrower and more homogenous capabil-ity and constraints groups among active microenterprises, based, first, on the scale of each enterprise and how long it has been in business and, secondly, the age and schooling of the business owner. Like registration and licensing status, the scale and age of an enterprise are outcomes of the decisions that the entrepreneur has made since setting up the business and are strong correlates of the business owner’s decision on whether or not to operate formally. In contrast, the age and schooling of the business owner are largely predetermined in relation to these decisions and in relation to the decision to set up the business in the first place. A business owner’s age and schooling also are key determinants of the labor market earnings potential of the entrepreneur. They consequently are likely determinants of whether the business owner is an active entrepreneur or an involuntary one.

Startups vs. post-startups

The most natural division of the survey sample by scale of activities is probably that between own-account work (or single-person enterprises) and micro-employers, that is, microenterprises that provide full-time em-ployment to people other than the owner as paid workers or as unpaid but full-time family workers. We can further classify each scale group of enterprises by how long they have been in business into “startups” and “post startups.” The first term refers to enterprises that have been in operation for no more than five years. “Post-startups” are those who have been in business for longer than five years. The first half of the upper panel of table 11 shows the distribution of the 211 active enterprises of the sample across the 4 categories of this 2-way classification. The second half of the upper panel is the corresponding distribution of 525 of the involuntary enterprises in the sample.

A little over one-quarter of all active enterprises in the sample were businesses of own-account workers. These are divided more or less equally between startups and post startups. This section compares the two age groups of own-account active enterprises with each other and with active micro-employers in productivity and other characteristics of the business and of the owner. The section also will compare both age groups of active micro-employers with one another, with own-account active enterprises, and with their respective matching groups of involuntary enterprises. Approximately one-third of the 211 active microenterprises in table 11 are “startup” micro-employers. Another one-third of active enterprises are “post-startup” micro-employers. There are 123 involuntary enterprises that match the first group in workforce size and time in business. The


Table 11 distribution by business age and size groups and business motivation

Number of enterprises by size groups

Own-account work Engaging 2–4 people Total

Active enterprises

Start ups 29 72 101

Post-start up 30 80 110

Total 59 152 211

Involuntary enterprises

Start ups 168 123 291

Post-start up 132 102 234

Total 300 225 525

Number of enterprises by business owners’ level of schooling

Had not completed high school

Had at least completed high school Total

Active enterprises

Youth owned 72 79 151

Non-youth owned 35 22 57

Total 107 101 208


Youth owned 298 105 403

Non-youth owned 99 23 122

Total 397 128 525

matching age-size group of involuntary enterprises for post-startup active micro-employers comprises 102 businesses.

Own-account enterprises

Of the 211 active enterprises in the sample, 29 were start-up own-account enterprises. Another 30 were post-startup own-account enterprises. These two groups contrast sharply with each other and with their respective matching groups of involuntary enterprises in productivity, line of busi-ness, informality, and the owners’ human capital. In the second half of the upper panel of table 11 are 168 businesses in the matching group of involuntary enterprises for the 29 start-up own-account active enter-prises. The matching group for the 30 post-startup own-account active enterprises is the 132 post-startup own-account involuntary enterprises (same section of table 11).

26 Promoting Entrepreneurship in Botswana: Constraints to Micro Business DevelopmentTa

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148

100.

62.

977

60.9


Start-up own-account active enterprises are far more productive than start-up own-account involuntary enterprises. The former also are more productive than post-startup own-account active enterprises. The average net income of start-up own-account active enterprises is approximately 59,700 pula a year and corresponds to an average annual turnover of 67,900 pula (table 12 and figure 11). These sums are several times higher than the average annual net incomes of start-up own-account involun-tary enterprises. The sums also are higher than the average annual net incomes and average annual turnover of 44,200 pula and 57,400 pula, respectively, of post-startup own-account active enterprises. Finally, start-up own-account active enterprises are more likely to register for tax than post-startup own-account active enterprises or start-up own-account involuntary enterprises (figure 13).

Part of the productivity advantage of own-account active enterprises over own-account involuntary enterprises and the higher propensity of the active enterprises to register for tax is explained by the fact that ac-tive enterprises tend to concentrate more in higher productivity indus-tries and in sectors in which tax registration is more difficult to avoid. These include manufacturing and crafts, transport services, and nonretail services. For example, more than 40 percent of start-up own-account active enterprises operated in such sectors, compared to approximately 12 percent of start-up own-involuntary enterprises also were in the same sectors. Some 45 percent of post-startup own-account active enterprises also were in these sectors, compared to approximately 27 percent of post-start up own-account involuntary enterprises that operate in the same sectors (figures 15 and 16).

Figure 11 Annual net income of own-account workers by time in business (000 pula)

0

22.3

30.6

28.4

64.2

14.7

21.8

42

69.1

10 20 30 40 50 60 70 80

Unregistered, unlicensed enterprises only

Tax registered or licensed enterprise only



Invo

lun

tary

ente

rpri

ses

Act

ive

ente

rpri

ses

Startups Post-startups


Some of the productivity advantage of starting-up own-account active enterprises over post-startup own-account active enterprises is that their owners are, on average, younger and more educated. The owner of the typical start-up active own-account enterprise is significantly younger than the owner of the typical post startup active own-account enterprise. This correlation holds despite the fact that the owner-age composition of each group of active own-account enterprises is very similar to that of the respective involuntary match groups. The average schooling of owners of start-up active own-account enterprises also is significantly higher than that of the owners of post-startup own-account enterprises. The amount of schooling also is higher than the average schooling level of the owners of its match group of involuntary enterprises.

Micro-employers

There were 72 active micro-employers in the start-up age group and 80 active micro-employers in the post-startup age group of active startups (table 11). Their involuntary-enterprise match groups included 123 startup businesses and 102 post startups, respectively.

Both age groups of active micro-employers were far more productive than their involuntary enterprise match groups. Average net incomes per person engaged were 35,900 pula for start-up active micro-employers, and 43,100 pula for post-start up micro-employers, which, while signifi-cantly lower than the average annual net incomes per person for start-up own-account active enterprises, are comparable to the average annual net income per person for post-start up own-account active enterprises,

Figure 12 Annual net income per worker by time in business and registration status-Enterprises engaging 2–4 persons

0 10 20 30 40 50 60 70





Invo

lun

tary

ente

rpri

ses

Act

ive

ente

rpri

ses

Post-startups Startups

11.3

20.7

34.8

37.8

23.5

21.8

19.1

62.5


but higher than those of their respective involuntary enterprise match groups by more than 50 percent (figures 11 and 12, table 12). Moreover, the average active micro-employer of either age group generated twice the aggregate net income of the average own-account active enterprise and nearly twice the aggregate net income of the average involuntary micro-employer (table 13).

Some of the productivity advantage of active micro-employers over involuntary micro-employers probably has to do with factors that make active micro-employers more likely to register than involuntary

Figure 13 Percent of registered licensed enterprises-own account work only

0 10 20 30 40 50 60

% registered for tax

% holding a license

% registered with non-tax authority


% holding a license


Star

tup

sPo

st-s

trat

up

s


Figure 14 Percent of registered licensed enterprises – only those engaging 2–4 people

0 10 20 30 40 50 9060 70 80


% holding a license



% holding a license


Star

tup

sPo

st-s

trat

up

s



micro-employers. The average start-up active micro-employer is more than likely to register for tax than an involuntary micro-employer of either age group (figure 14). One such factor is that active enterprises are far more concentrated in more productive sectors including manu-facturing and crafts, and transport and other nonretail services. In these sectors, the benefits that enterprises derive from registering for tax or holding a license might outweigh the costs. Some 70 percent of active micro-employers were found in these sectors, compared to 35 percent of involuntary micro-employers (figures 15 and 16).

A second factor behind the productivity advantage of active micro-employers over involuntary micro-employers is that owners of active micro-employers are more educated on average. Although the age distri-bution of the two groups of business owners is quite similar, 49 percent of active micro-employers are high school graduates, compared to 22 percent of involuntary micro-employers.

Table 13 annual net incomes per worker and fixed assets per worker

Annual net income per

worker (‘000) pula

Fixed assets per worker (‘000) pula

I. Active enterprises only

Startups

Tax registered or licensed enterprise only 37.8 28.4

Unregistered, unlicensed enterprises only 34.8 138.8

All 35.9 138.9

Post-startups


Unregistered, unlicensed enterprises only 19.1 20

All 43.1 39.5

II. Involuntary enterprises only

Startups



All 14.8 32.2

Post-startups



All 22.8 39


Figure 15 Percent distribution of active enterprises by line of business

Own-account work Engaging 2–4 people

0

10

20

30

40

50

60

70

Foo

d a

nd

bev

erag

es

Man

ufa

ctu

rin

g/c

raft

s

Tran

spo

rt

Reta

il tr

ade

Oth

er s

ervi

ces

Foo

d a

nd

bev

erag

es

Man

ufa

ctu

rin

g/c

raft

s

Tran

spo

rt

Reta

il tr

ade

Oth

er s

ervi

ces


Figure 16 Percent distribution of involuntary enterprises by line of business

Own-account work Engaging 2–4 people

0

10

20

30

40

50

60

80

70

Foo

d a

nd

bev

erag

es

Man

ufa

ctu

rin

g/c

raft

s

Tran

spo

rt

Reta

il tr

ade

Oth

er s

ervi

ces

Foo

d a

nd

bev

erag

es

Man

ufa

ctu

rin

g/c

raft

s

Tran

spo

rt

Reta

il tr

ade

Oth

er s

ervi

ces



Youth-owned businesses and enterprises of older owners

More than three-quarters of enterprises in the sample were youth owned, meaning that they were run by people who were no more than 29 years old (table 14). We can further classify the schooling of youth-owned and “non-youth-owned” businesses owners’ into those run by people who had at least completed high school and those run by people who had not. The distribution of the subsample of active entrepreneurs between the four categories of this two-way classification is shown in the first half of the lower panel of table 11.

A particularly interesting entry in this panel is the 79 active youth entrepreneurs who had graduated from high school. The entry corre-sponds to a group of enterprises that stands out in productivity in rela-tion to the 72 enterprises of less-educated active young entrepreneurs (shown in same panel) as well as to the involuntary enterprises of the 105 high school graduates shown in the table. This section compares and contrasts the three groups of enterprises in productivity and other business characteristics.

Table 14 Percent distribution of business owners by demographic characteristics

24 years or

younger

29 years or

younger Female

High school

graduate


Startups

Own account work 82.1 92.9 67.9 25

Engaging 2–4 people 67.8 84.7 61 71.2

Post-startups

Own account work 28.6 50 75 14.3

Engaging 2–4 people 23.7 59.3 68 49.2


Startups

Own account work 86.1 93.4 82.5 19.9

Engaging 2–4 people 85.5 93.6 57.3 41.8

Post-startups

Own account work 25.2 52.7 74 13

Engaging 2–4 people 26.7 59.3 70.9 22.1


Businesses of high school graduates

Approximately half of the active enterprises in the sample were run by people who have had at least high school education. The vast majority of these-79, to be precise—were young people. The respective involuntary match groups of youth-owned and non-youth-owned active enterprises of high school graduates are 105 youth-owned involuntary enterprises and 23 non-youth-owned involuntary enterprises respectively (table 11).

When we cross-classify the subsample of active enterprises by scale and by time in business, start-up own-account active enterprises turn out to be the most productive of the resulting four categories of micro-businesses, but the productivity gap between them and active micro-employers of either age group is not that large. When we cross classify the same sub-sample of active enterprises by the schooling and age groups of business owners, youth-owned businesses of high school graduates are by far the most productive group, followed at some distance, by businesses of older high-school graduates. On average, a business run by a young high school graduate generated a net income per person of 57,700 pula a year, with an average workforce of 2.9 and an annual turnover of 184,000 pula (tables 15–17). In comparison, businesses run by older high school graduates generate an average net income per worker of 42,000 pula a year on an annual turnover of 171,900 pula and with a workforce of 3.2. Within each group, average scale and average productivity both were higher for businesses that were registered for tax or held business licenses than for those that were neither nor licensed (figure 17). Each of the two groups of businesses run by high school graduates also was, on average, more productive and had far larger turnover than active enterprises of owners who had not graduated from high school and involuntary enterprises of high school graduates of the same age group (figure 17 and tables 17 and 18).

Part of the productivity advantage of active enterprises of high school graduates of either age group reflects that these enterprises have more fixed assets per person engaged than the match group of involuntary enterprises, and also compared to active enterprises of owners who had not completed high school. The higher turnovers and larger fixed assets of active enterprises of high school graduates, compared to involuntary enterprises of the same education level of group owners, also translate to greater propensity of active enterprises to formalize. For example, 37 percent of active enterprises of young high school graduates were registered for tax compared to 14 percent of involuntary enterprises of young high school graduates that had done the same (figure 18). On the

34 Promoting Entrepreneurship in Botswana: Constraints to Micro Business DevelopmentTa

ble

15

ann

ual n

et in

com

es a

nd tu

rnov

er p

er e

stab

lishm

ent b

y ow

ner’s

age

and

sch

oolin

g

Act

ive

ente

rpris

es o

nly

Invo

lunt

ary

ente

rpris

es o

nly

Aver

age

num

ber

of p

erso

ns

enga

ged

Aver

age

annu

al

turn

over

(‘0

00 p

ula)

Aver

age

annu

al

net i

ncom

e (‘0

00 p

ula)

Aver

age

num

ber

of p

erso

ns

enga

ged

Aver

age

annu

al

turn

over

(‘0

00 p

ula)

Aver

age

annu

al

net i

ncom

e (‘0

00 p

ula)

I. You

th o

wne

d bu

sines

ses

Ow

ner h

as c

ompl

eted

hig

h sc

hool

Tax

regi

ster

ed o

r lic

ense

d en

terp

rises

onl

y3.

225

1.1

175.

72.

818

786

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

2.7

86.1

72.7

2.5

75.4

59.2

All

2.9

184

133.

82.

713

3.8

73.2

Ow

ner h

as le

ss th

an h

igh

scho

ol e

duca

tion

Tax

regi

ster

ed o

r lic

ense

d en

terp

rises

onl

y2.

710

2.5

80.1

1.5

59.9

45.9

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

2.3

66.8

54.3

1.6

25.3

29

All

2.5

84.9

67.4

1.5

38.6

29.3

2. B

usin

esse

s of

old

er o

wne

rs

Ow

ner h

as c

ompl

eted

hig

h sc

hool

Tax

regi

ster

ed o

r lic

ense

d en

terp

rises

onl

y3.

325

0.7

184

2.4

157.

213

2.4

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

3.1

47.9

26.9

1.6

26.5

19.8

All

3.2

171.

912

2.9

296

.279

.9

Ow

ner h

as le

ss th

an h

igh

scho

ol e

duca

tion

Tax

regi

ster

ed o

r lic

ense

d en

terp

rises

onl

y2

5232

.31.

946

.228

.7

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

1.9

53.8

44.2

2.5

4628

.6

All

1.9

53.1

39.4

2.2

46.1

28.6

Profile of the Microenterprise Sector 35Ta

ble

16

ann

ual n

et in

com

es p

er w

orke

r and

fixe

d as

sets

per

wor

ker b

y ow

ners

’ cha

ract

eris

tics

Act

ive

ente

rpris

es o

nly

Invo

lunt

ary

ente

rpris

es o

nly

Annu

al n

et in

com

e pe

r w

orke

r (‘0

00) p

ula

Fixe

d as

sets

per

wor

ker

(‘000

) pul

aAn

nual

net

inco

me

per

wor

ker (

‘000

) pul

aFi

xed

asse

ts p

er w

orke

r (‘0

00) p

ula

I. You

th o

wne

d bu

sines

ses

Ow

ner h

as c

ompl

eted

hig

h sc

hool

Tax

regi

ster

ed o

r lic

ense

d en

terp

rise

only

75.3

75.5

2558

.7

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

31.9

26.1

31.2

22.2

All

57.7

55.4

2841

.3

Ow

ner h

as le

ss th

an h

igh

scho

ol e

duca

tion

Tax

regi

ster

ed o

r lic

ense

d en

terp

rise

only

44.2

28.3

2626

.4

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

2628

12.6

33.3

All

34.9

28.1

17.7

30.6

2. B

usin

esse

s of

old

er o

wne

rs

Ow

ner h

as c

ompl

eted

hig

h sc

hool

Tax

regi

ster

ed o

r lic

ense

d en

terp

rise

only

6430

7.3

43.1

57.9

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

17.6

26.9

14.5

31.3

All

4219

8.3

29.8

45.5

Ow

ner h

as le

ss th

an h

igh

scho

ol e

duca

tion

Tax

regi

ster

ed o

r lic

ense

d en

terp

rise

only

15.3

22.1

23.3

132.

4

Unr

egist

ered

, unl

icen

sed

ente

rpris

es o

nly

13.5

41.6

19.1

18.5

All

14.3

33.3

20.7

63.8


other hand, tax registration rates among active enterprises of high school graduates do not vary by much by age groups of owners.

Again, some of the differences in productivity and registration status between active enterprises of high school graduates and their involun-tary enterprises match group, and the same differences between the two owner-age groups of the active enterprises of high school graduates are related to differences in choice of lines of business. Active enterprises of young high school graduates tend to concentrate in relatively high productivity sectors, for which tax registration rates tend to be higher in any case. In contrast, involuntary enterprises of either age group are more concentrated in retail trade, in which productivity generally is lower and tax registration rates also are comparatively low (figure 19).

A related factor in the higher productivity of active enterprises of young high school graduates compared to active enterprises of older high school graduates is that businesses of younger people also are younger and larger on average. Some 62 percent of businesses run by young high school graduates in the sample were in the start-up business age group, compared to only 29 percent of active enterprises of older high school

Table 17 Percent distribution of enterprises by business owners’ characteristics

startups (%)

Engaging 2–4 people

(%)

Female owned

(%)


Youth owned businesses

Owner has completed high school 61.5 85.9 48.7

Owner has less than high school education

56.3 49.2 73.2

Business run by older owner

Owner has completed high school 29.2 88.9 75


14.7 54.8 73.5


Youth owned businesses



64.7 34.5 74.6

Business run by older owner



14.1 35.1 78.8


graduates in the same category (table 17). Moreover, approximately 86 percent of the active enterprises of young high school graduates engaged 2–4 people––twice the proportion of involuntary enterprises of young high school graduates who engaged that many people.

Businesses of the less educated

A little over half of the active enterprises in the sample were run by people who had less than a high school education. Nevertheless, again, the vast majority of these (72) were youth owned. The respective in-voluntary match groups of youth-owned, and non-youth-owned active enterprise owners who had less than high school education consisted of 298 youth-owned involuntary enterprises and 99 non-youth-owned involuntary enterprises, respectively (table 11).

Active enterprises of youth who had not completed high school op-erated on a smaller scale: a 65 percent smaller turnover and 49 percent smaller fixed assets per worker. Furthermore, they were on average 40 percent less productive than active enterprises of young high school graduates (tables 15 and 16 and figure 17). However, the non-graduate youth-owned active enterprises were nearly 60 percent more productive than active enterprises of older owners who had not completed high school (table 16 and figure 17).

The productivity advantage that active enterprises of non-graduate youth had over businesses owned by older people with comparable school-ing is reflected in the former’s higher rates of registration and licensing

Figure 17 Annual net income per worker by age group of owner (000 pula) – active enterprises

0 10 20 30 40 50 70 8060

Youth owned Run by older owners

13.5

15.3

17.6

64

26

44.2

31.9

75.5

Unregistered, unlicensed

tax registered or licensed

Unregistered, unlicensed

tax registered or licensed

Ow

ner

gra

du

ated

fr

om

hig

h s

cho

ol

Ow

ner

did

no

t g

rad

uat

e fr

om

h

igh

sch

oo

l


relative to comparator groups (figure 18). Their productivity advantage also was reflected in the fact that these youth were more concentrated in high productivity sectors than their involuntary enterprise match group (figure 20) and that the active enterprises owned by these youth tended to be younger than active enterprises of older owners who had not completed high school (table 17).

Figure 18 Percent of registered or licensed enterprises by schooling of business owners

0 10 20 30 40 50 60 70 80 90 100


% holding a license

% registered withnon-tax authority


% holding a license


invo

lun

tary

en

terp

rise

s


% holding a license

% registered withnon-tax authority


% holding a license


Act

ive

ente

rpri

ses

11.8

15.7

14.4

34.8

37.2

No

n-y

ou

tho

wn

eden

terp

rise

s

Yo

uth

ow

ned

ente

rpri

ses

No

n-y

ou

tho

wn

eden

terp

rise

s

Yo

uth

ow

ned

ente

rpri

ses

Run by high school graduates Run by owners who did not graduate from high school


Figure 19 Percent of enterprises of high-school-graduates by line of business

0 10 20 30 40 50 60 70

Run

by

old

er o

wn

ers

Yo

uth

ow

ned

invo

lun

tary

ente

rpri

ses

Act

ive

ente

rpri

ses

invo

lun

tary

ente

rpri

ses

Act

ive

ente

rpri

ses

Food and beverages

Transport


Retail trade

Other services

Food and beverages

Transport


Retail trade

Other services

Food and beverages

Transport


Retail trade

Other services

Food and beverages

Transport


Retail trade

Other services


Figure 20 Percent of enterprises of non-high school grads by line of business

0 10 20 30 40 50 60 70

Run

by

old

er o

wn

ers

Yo

uth

ow

ned

invo

lun

tary

ente

rpri

ses

Act

ive

ente

rpri

ses

invo

lun

tary

ente

rpri

ses

Act

ive

ente

rpri

ses

Food and beverages

Transport


Retail trade

Other services

Food and beverages

Transport


Retail trade

Other services

Food and beverages

Transport


Retail trade

Other services

Food and beverages

Transport


Retail trade

Other services

C h a P T e r 3

Constraints to Micro-Business Development

Some of the productivity gap observed among various categories of mi-croenterprises was related to differences in the skills and effort of their owners. However, another part of the gap was associated with differences in access to key markets and services. This access may not be related to business owners’ talent or effort. Some businesses have easier or cheaper access to external finance than others for reasons that may have nothing to do with how well run they are or how profitable their projects might be. Some may have better access to public utilities or cheaper access to markets and suppliers for reasons unrelated to their productivity or their prior or potential market performance.

In Botswana, as in many other developing economies, SMMEs do not have as good access to credit, markets, business services more generally, or public utilities as do larger companies. Within the SMME sector, mi-croenterprises are at a particular disadvantage not least because they are less capable than SMEs of taking advantage available business support schemes for improving access to services.

There also is some consensus that business informality often is a significant barrier to microenterprise access to markets and services. Part of the effort to support the integration of the microenterprises in financial and BDS markets might therefore need to go into formalizing micro-businesses.


The next section assesses how much micro business owners think of finance and other services as constraints to the productivity and growth of their enterprises and the role that business informality may have played in impeding access to markets and services by the various capability groups identified in section 2.

3.1 Informality, Productivity and access to Services

Drivers of informality

Why do so many microenterprise owners choose to operate informally, that is, opt not to register with the tax authority and choose even not to get a business license? A short answer would be that those who operate informally are doing so only because they would be worse off had they registered for tax or taken out a license. This answer is correct but prob-ably trivial if we do not know exactly how formality would affect net profits (or net income). Enterprises would choose to operate formally only if the act of registering for tax or getting a license would make their net profits higher than otherwise. Getting registered or obtaining a license would raise an enterprise’s net profits only if either of them increased the enterprise’s revenue by more than it would add to the cost of the enterprise’s activities. Then the enterprise owners who operate informally would be only those who had decided that registration for tax or being licensed would increase their costs of operation by more than these ac-tions would increase their revenues.

This line of reasoning is, by and large, correct. However, it also as-sumes that all micro-business owners know approximately what they need to do to register or license their enterprises, or what net benefits they would get directly or indirectly from doing either. In fact, one of the main reasons that survey respondents gave for not registering their businesses was ignorance. In the survey, ignorance of how and where to get registered––and, in many cases, even whether they were expected to register their businesses––was cited by approximately one-third of non-registered respondents as a major reason for not registering. Moreover, this response rate applied to active as well as involuntary entrepreneurs.

However, many survey respondents also pointed to several items that would discourage registration by adding to the cost of running their businesses. These items are listed in figures 21–23. The items seem to be given the same weight by formal and informal enterprises as a deterrent of registration. In figure 21, the comparison is limited to enterprises that are unregistered for tax or those not holding business licenses, and shows that there probably is no one regulatory factor in Botswana that we could

Constraints to Micro-Business Development 43

single out as the main driver of informality among microenterprises. At the same time, the figure suggests that a number of regulatory require-ments may have combined to make the cost of formalization too high and beyond any possible benefit that entrepreneurs might expect to gain from operating formally.

Taxes represent one of the more prominent of these cost items. Approximately 30 percent of unlicensed enterprises and almost as high a percentage of those unregistered for tax cited the desire to avoid taxes as a major reason for not having registered their businesses. Only a slightly smaller fraction of each of these groups of respondents also reported that registration fees were prohibitive. Some 26 percent–27 percent of business owners cited the desire to avoid complying with labor laws as a

Figure 21 Respondents rating factors as major barriers registration by registration status (%)

0

23.5

24.6

26.2

27.1

29.4

24.2

25.4

26.7

29.8

30.4

5 10 15 20 25 30 35

Compliance costs of regulation

Time cost of registration

Labor laws

Registration fees

Taxes on the registered

Unlicensed Unregistered for tax

Figure 22 Respondents rating various factors as major barriers to business registration (%)

0 5 10 15 20 25 30 4035

26.7

30.3

24.5

30.6

28.2

17.9

23.8

28

29.8

35.4


Labor laws



Registration fees



major reason that they had avoided registering. Approximately 25 percent of each group thought that businesses were discouraged from registering by the length of the time needed to complete the registration. A slightly smaller percentage of each group cited the cost of compliance combined with other aspects of business regulation as a major factor.

In figure 22, we compare ratings of the factors listed in figure 21 as potential deterrents of registration. However, this time we limit ourselves to businesses that are registered for tax or hold a business license. This time we also distinguish between active and involuntary enterprises. On the whole, the relative weight attached by both groups to individual factors is not very different from what is shown in figure 21. There also registration fees and taxes are marginally the most prominent of all the items. However, the concern with labor laws seems to be slightly lower for this group. Moreover, the cost of complying with aspects of regula-tion other than those relating to labor or taxes is of significantly less of a concern for active than for involuntary enterprises.

Figure 23 describes ratings of deterrents of registration by registered and unregistered enterprises separately. Here, too, the basic pattern is that the composition of the factors that seem to matter and the relative weights respondents attach to each does not vary much from figures 21 and 22.

Private costs of informality: Does informality reduce access to services?

Survey respondents identified potential tax liabilities, licensing and registration fees, and the compliance costs of labor laws as key items

Figure 23 Percent rating factors as major deterrent to business registration by registration status

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of the cost to formally operate a business. However, respondents also readily recognized that enterprises that failed to register for tax or get a license would forgo the advantages of potentially cheaper access to markets and services, which would have enhanced their revenue. For example, more than 70 percent of respondents whose businesses were neither registered for tax nor licensed thought that registration would enable them to participate in government business support or incentive programs (figure 24). The proportion of those who thought of registra-tion as a requirement for participation in government programs was even higher among owners of tax-registered or licensed enterprises. The vast majority of respondents also thought that registered businesses would have cheaper access to finance and improved access to business space and utilities. Many even thought that registration would provide some protection from demands for bribes.

The effects that respondents expected that formalization would have on their businesses’ access to markets and services (figure 24) are consistent with the differences between the ratings that tax-registered enterprises gave to various potential growth constraints and the ratings given by those unregistered for tax (figure 25). For example, the proportion of businesses that considered lack of access to finance to be a major growth constraint was significantly smaller for those that were tax-registered than for those that were not (60 percent vs. 80 percent, respectively). Tax-registered businesses also were significantly less likely to rate access to business space or access to public utilities as major constraints than enterprises that were unregistered for tax.

Figure 24 Business owners who saw indicated benefits from registration (%)

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Protects from demand forbribes from officials

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Registered for tax or holding a license Unlicensed and unregistered


Does informality cost society? Informality and productivity

That a large majority of microenterprises operate informally despite the very same owners’ belief that being formal would have advantages in access to services can mean only that the private costs of informality (figures 21 and 22) outweigh the advantages. The question then arises whether society at large would lose as a result. In other words, would raising the formalization rate of microenterprises in Botswana increase the aggregate productivity of the sector?

This question is difficult to answer based on cross section survey data. The evidence that the Botswana survey data provided on the issue was rather inconclusive. The data showed that microenterprises that were registered for tax or held business licenses were significantly more pro-ductive than those that were neither tax registered nor held a license. For example, controlling for business location, business line, and relevant characteristics of the business owner (experience, education, gender, and business motivation), the net income per person engaged in the enterprise was 57 percent higher for a tax-registered micro-business.

However, there are likely to be unobserved attributes of enterprises or of business owners that influence productivity, but at the same time make businesses more likely to register for tax. Once we control for such unobserved attributes, there is no statistical significant difference in pro-ductivity between microenterprises that are registered for tax and those that are not. Basically the same pattern of results is observed when we compare productivity between formal and informal microenterprises on alternate definitions of informality. This pattern suggests that we cannot rule out, on the basis of survey data, the possibility that the correlation

Figure 25 Business owners rating factors as major growth obstacles (%)

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Lack of skills

Lack of power connection

Lack of access to land

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Unregistered, unlicensed Registered for tax or holding license


that we see in the data between formalization and productivity stems solely from the fact that inherently more productive microenterprises also are more likely to register for tax for some reason.

On the other hand, the survey data provide some evidence that improvement in access to external finance increases the productivity of microenterprises. The same data show that registration for tax in-creases access to finance. This is indirect evidence that formalization does lead to productivity gains. When controlling for relevant observ-able business characteristics (other than scale and input mix) and relevant attributes of the business owner (including age, gender, and schooling), microenterprises that reported being constrained by lack of access to finance had net incomes per worker that were approxi-mately 36 percent less than the average. If we limit the comparison to active enterprises only, the productivity shortfall of enterprises that are reportedly constrained by lack of access to finance is much higher at approximately 55 percent.

Again, these productivity gaps could merely reflect that inherently more productive firms tend to complain less about lack of finance, or rely less on external finance, or be more successful in obtaining credit. However, the negative correlation between being constrained by lack of access to finance and productivity persists even when we control for unobservables that may influence productivity as well as access to finance. Indeed, the use of these controls suggests that the true effect on microenterprises of improved access to finance could be much larger than suggested by the 36 percent labor productivity gap that we observe in the data between financially constrained enterprises and others.

3.2 access to Services and Markets

Getting registered for tax and operating with a license would improve a business’ access to markets and services. However, informality is not necessarily the most important factor preventing microenterprises from accessing the financial products and business services that may be avail-able to SMEs. Most micro-businesses in Botswana probably would not be able to make use of existing products and services regardless of their registration status. The reason is that a combination of factors, including that many enterprises probably could not afford them, that the prod-ucts and services seem to be ill suited to the needs and circumstances of most, and that many business owners seem to be ignorant of what is available on the market. Addressing these factors to improve the access of microenterprises to services probably requires public investment in


the development of markets in new financial products and new BDS tailored to the needs and capabilities of the sector.

The results of the pilot survey suggest that the social payoff in higher productivity from such investment could be substantial. The data show, for instance, that improved access to finance would raise the average produc-tivity of microenterprises. The data also suggest that improved allocation of business space, better provision of infrastructure, and the development of markets for training services and other business development schemes probably would have a similar effect and help make the microenterprise sector a more likely foundation for a larger and more dynamic SME sector.

However, the microenterprise sector is not homogeneous as a potential market for new financial products and new BDS. The survey data show that the sector is extremely diverse in productivity and development potential. In fact, efforts at introducing new financial products and new BDS tailored to the sector would probably benefit only the most active enterprises. These enterprises comprise the most productive segment of the sector and therefore are the most likely to be able afford the new services over the long term. Productivity levels, growth prospects, and reported constraints to business development also seem to vary considerably across the differ-ent sections of the population of active enterprises that we described in the previous section. It could be important to take these differences into account in the design and introduction of new products and services. In the previous section, we discussed potentially relevant capability differ-ences among groups of active enterprises as measured by productivity. In the rest of this section, we will highlight the differences and similarities in reported constraints to business development among those groups.

For active and involuntary enterprises alike, the most important reported constraints are access to finance, access to business space and utilities, crime, and lack of skills (figure 26). However, the key difference between the two groups of enterprises is that active enterprises that are tax-registered or licensed are significantly less likely to report any of these constraints as “major” than are unregistered and unlicensed active enterprises (figure 27). In contrast, there is no significant difference among registered or li-censed involuntary enterprises in ratings of individual constraints. Overall, “complaint” rates against individual constraints also are consistently lower for active enterprises, which particularly are less likely to report being held by lack of access to finance or by lack of access to business space.

Yet another striking contrast (figures 27, 28 and 29) between active enterprises and involuntary enterprises is that complaint rates do not vary much by business age groups (startup vs. post- startups), by scale groups (own-account enterprises vs. micro-employers), or even by age and


Figure 26 Percent rating factors as major constraints to growth by business motivation

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Figure 27 Percent of active enterprises rating various factors as major growth constraints

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education groups among involuntary enterprise business owners, whereas issues these very much do matter among active enterprises.

Across both scale groups of active microenterprises, and for startups as well as for post-startups, tax-registered or licensed businesses are less constrained by lack of access to finance or by lack of access to land. Tax-registered businesses also are more likely to have better access to electricity and suffer from less crime. However, there also is indication that the association between business formality and access to services in Botswana may have weakened in recent years. The association was that the advantage that tax-registered or licensed businesses had over the unregistered and unlicensed was larger among startups (own-account enterprises as well as micro-employers) than it was among post startups.

Access to finance

A sizable proportion of active enterprises use external sources to finance fixed investments and working capital. In the survey sample, the proportion

Figure 28 Involuntary enterprises rating factors as major growth constraints (%)

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ranged between 40 percent and 50 percent for startups, and from 50 per-cent to 60 percent among post-startups (figure 30). However, the external finance was almost invariably obtained from informal sources and suppliers (figure 31). Access also was significantly lower among active enterprises that were not tax registered or licensed; only 20 percent to 30 percent of these had used external sources. Among youth-owned enterprises, only 30 percent to 40 percent had used external sources (figure 32)

Therefore, it was surprising that lack of access to finance was the top constraint for almost all categories of active enterprises that had not registered for tax or did not have a business license. Active enterprises comprised both scale groups of active startups, post-startup active micro-employers, businesses of young active entrepreneurs who had not gradu-ated from high school, and businesses of older active entrepreneurs who had graduated from high school (figures 27 and 29). The only group of active enterprises for which access to finance came as the second most important constraint was businesses of young high school graduates, for which access to finance was a close second to business space and utili-ties (figure 29).

Figure 29 Active enterprises rating factors as major growth obstacles (%)

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Overall, access to finance was less of a problem for tax-registered or licensed businesses for two reasons. First, a smaller percentage of them rate it as a major problem than do unregistered and unlicensed active enterprises. Second, historical rates of access to external sources happen to be higher for active enterprises that are registered for tax or are licensed. The gap between the ratings of registered or licensed vs. unregistered and unlicensed was particularly large among active startups. The gap was evident both in actual rate of access to external finance and in ratings of lack of such access as a business constraint (figures 29, 30). Interestingly, it appears that the gap has decreased substantially in recent years. An indication of the decrease was that the rate was much lower for active startups in the sample than it was for active post-startups (figure 30). The decrease suggests that business informality may have become less of a barrier to access to informal external finance than it used to be.

Figure 30 Enterprises using external finance for investment or working capital (%)

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Tax registered or licensed Unregistered for tax and unlicensed


Business space and access to utilities

On average, active enterprises are significantly more likely to operate from nonresidential business premises connected to the public electricity grid. Among active enterprises in the survey sample, those registered for tax or holding a license also were more likely to operate from a nonresidential business premise and to be connected to the public power grid than those that were not registered for tax or hold a license (figures 33 and 34). Nevertheless, again, the difference between the two groups of active enterprises has narrowed in recent years, suggest-ing that business informality also may have become less of a barrier to access to business space and utilities than it used to be. Moreover, the proportion of active enterprises operating from nonresidential prem-ises appears to have increased markedly. This growth was indicated by the fact that the proportion in the sample was significantly higher for startups than it was for post-startups––own-account enterprises and micro-employers included. For example, 80 percent of start-up active micro-employers in the sample––including those registered for tax or

Figure 31 Enterprises using supplier and informal finance for investment or working capital (%)

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holding a license as well as the unregistered and unlicensed––operated from nonresidential business premises. In contrast, half that many (40 percent) unregistered and unlicensed post-startup micro-employers and 60 percent of registered or licensed post-startup micro-employers did so (figure 34).

The proportion of those operating from nonresidential premises con-nected to public utilities also was relatively high—70 percent to 80 percent for active enterprises of high school graduates in the sample (figure 35 and 36). In contrast, the same proportion was quite low—less than 40 percent––among own-account active enterprises (figures 33 and 34), and among active enterprises of youth who had not completed high school (figures 35 and 36). Thus, lack of business space and access to utilities figured prominently in business owners’ ratings of constraints to growth. These two issues were the second most important constraints reported by startup active enterprises and a major problem for more than half of post-startup active enterprises in the sample (figure 27). In fact, these

Figure 32 Enterprises using external finance by source

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issues were the top problems for active enterprises of young high school graduates. They were rated as major constraints by more than 70 percent of active entrepreneurs who had graduated from high school––both young and older (figure 29). More than 60 percent of all active enterprises in the sample including startups, post-startups, own-account enterprises, and micro-employers considered not being connected to the public power grid as a major problem.

Skills constraint

Lack of skills was a major constraint for approximately 40 percent of active enterprises in the sample (figures 27 and 29). The percentage of active enterprises for which it was a major constraint was significantly higher among own-account startups (more than 60 percent) than it was for start-up micro-employers. This differential is not surprising because the average level of schooling of own-account startups was far lower than that of start-up micro-employers. Less than 30 percent of owners running own-account active startups had not completed high school, whereas more

Figure 33 Percent of enterprises with non-residential business premises

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than 70 percent of start-up micro-employers had (figure 37). Similarly, approximately 25 percent of owners of start-up micro-employers were vocationally trained whereas only 10 percent of start-up own-account entrepreneurs are similarly trained (figure 38).

The differences in levels of training between the two groups of active entrepreneurs were reflected in the way that they ran their businesses. For example, a start-up micro-employer was approximately 20 percent more likely to have kept proper books than the typical own-account enterprise, and more likely also to have used the services of a professional accountant for the purpose (figures 39 and 40).

On the other hand, the proportion of post-startup micro-employers who rated lack of skills as a constraint to growth was significantly lower than that of start-up active micro-employers (figure 29). This response was notable since the average schooling level of owners of post-startup micro-employers was lower (figure 37), the proportion of the vocationally trained also was significantly smaller for the same group (figure 38), and post-startup micro-employers were less likely to keep books or use the services of professional accountants for the purpose (figures 39 and 40).

Figure 34 Percent of enterprises connected to the public electrical grid

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Business environment issues: Crime

Crime was a major business environment problem for more than 60 percent of active enterprises across all age and size groups of microenterprises in the sample (figures 27 and 29). It was particularly a problem for post-startup active enterprises and for active enterprises run by older or less educated business owners. Crime was rated as a major business obstacle by an even higher percentage of involuntary enterprises. It was the second most important constraint after access to finance (more important than land) for all size-age categories of involuntary enterprises (figure 28).

Crime is a business environment issue, and the provision of protection against criminals by the state should not be rationed. Part of the solution to crime as a business environment problem includes private provision of security and insurance. Provision of secure business premises also could offer a significant part of that solution.

Figure 35 Enterprises with non-residential business premises by owners’ age group (%)

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3.3 Constraints to the growth of active Microenterprises

Constraints facing start-up active enterprises

Own-account startups

Let us say hypothetically that we are targeting active own-account startups as the beneficiaries of a business support program limited to the group’s most pressing needs. Such a program would focus on access to business space and utilities for the tax registered or licensed of the group, and on access to finance for the unregistered and unlicensed. These emphases would be based on the assumption that the most pressing need of a group would be what affects the largest percentage of its membership. Skills and crime are not major issues for the tax registered or licensed, but both affect almost as many of the unregistered and unlicensed as does the lack of access to business space and utilities.

Figure 36 Enterprises connected to the public electrical grid by owners’ age groups (%)

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The percentage of the unregistered and unlicensed of active own-account startups who complained of lack of access to finance in the survey sample was second only to the percentage of post-startup own-account enterprises that complained about the same issue. Active own account startups should therefore be a priority beneficiary, for example, of a program that introduced a microfinance product, just as they should be for, say, a lease program for business space or market stalls. Active own-account startups should also be among the priority beneficiaries of a business skills development program since it is the group for which the highest percentage of members rate skills as a major constraint.

Start-up micro-employers

For start-up micro-employers also, lack of business space and access to utilities are of greater concern than lack of access to finance for the tax registered or licensed; and the reverse for the unregistered and unlicensed. Skills constitute a major constraint only for the tax registered or licensed of the group.

Figure 37 Enterprise owners who had graduated from high school (%)

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Again, let us hypothesize that we chose start-up micro-employers as a priority target for a business support scheme limited to the group’s most pressing need. The focus of the scheme would be access to business space, not access to finance, for the tax registered or licensed; and the reverse for the unregistered and unlicensed. Crime would be the third most pressing problem for this group as a whole.

The complaint rate against lack of business space was larger for the tax registered or licensed of this group than for any other group of active enterprises in the survey sample. The complaint rate against shortage of skills also was the highest for the tax registered and licensed of this group than for any other. Consequently, the tax registered or licensed of this group should be the priority target for an innovative lease program for business space or a voucher program for training in business skills. To put it another way, let’s say, for example, that we had a business sup-port program focused entirely on easing the business space constraint for microenterprises, and, for some reason, we had to limit participation to sections of the sector that are most in need. In this scenario, the tax registered or licensed among start-up active micro-employers would be

Figure 38 Vocationally trained business owners (%)

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the group most in need, not the tax registered or licensed among start-up active own-account enterprises.

The unregistered and unlicensed of the start-up active micro-employers also would be a priority group of programs targeted for improving access to finance since the percentage of them that complain of lack of access was comparable to that of any group of active enterprises.

Constraints to youth-owned active enterprises

Businesses of young high school graduates

This group’s top concern is access to business space and utilities, followed by access to finance. Because it had the highest complaint rate against ac-cess to business space, this group would be a priority target for programs focused on improving access to space. This group also would be a priority target of interventions to improve access to finance. The rationale would be not because it had the highest complaint against access to finance in the survey sample (in fact it had the third highest rate), but because

Figure 39 Percent of enterprises keeping books

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it was the most productive of all groups and hence would be a likelier source of effective demand for new financial products.

Businesses of youth who had not completed high school

This group’s reported top concern is lack of access to finance. The group would be a priority target for programs to improve access to finance for a second reason: its complaint rate against lack of access to finance was almost as high as any other group in the survey sample including non-youth-owned enterprises of older high school graduates. The non-graduate business owners also are one of the groups most affected by crime.

Other groups of active enterprises as constraints groups

Access to finance is the most complained about issue for active post-startup micro-employers and was rated by the group as far more important than access to business space and utilities. Lack of skills also was reported as a

Figure 40 Percent of enterprises using services of a professional accountant

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major constraint. This group would be a priority beneficiary of a program for improved access to finance.

The top problem of active enterprises of older high school graduates also is access to finance. Thus, this group also would be a priority ben-eficiary for access to finance programs as well as programs to strengthen access to business space and utilities.

C h a P T e r 4

Conclusion: Lessons for the Design of Market Assessments for Services

There are two kinds of micro-businesses in Botswana today. One group sometimes is known as “opportunity entrepreneurs” or “active entre-preneurs.” This group comprises people who would successfully earn a living in the labor market if they chose to, but are self employed because they are better off in business than they would be working for someone else. In contrast, those who are alternatively referred to as “necessity entrepreneurs,” “involuntary entrepreneurs,” or “survivalists” are self employed by default. They were rationed out of the labor market even though they would have taken paid work at the going rate if there had been enough jobs.

active enterprises as Potential Markets for BdS

This report on the results of the Botswana Pilot Survey of Microenterprises has highlighted the sharp contrast that exists between the two segments of the microenterprise sector in productivity and development potential. The degree of contrast is not surprising given the underlying differ-ence between the two groups in business motivation. We think that the contrast has far-reaching implications for the scope of programs of interventions for the development of markets in financial products and BDS tailored to the needs of the sector. The contrast suggests that, in principle, such programs should target only businesses of active


entrepreneurs, not those of involuntary entrepreneurs. The appropri-ate interventions for the latter should be those relating to markets in training programs in labor market skills. Productivity and growth patterns in the survey data suggest that only active enterprises have a realistic chance of evolving into viable and growth-oriented businesses that will have sustainable effective demand for these financial products and BDS over the long term.

Nevertheless, the subpopulation of active microenterprises is ex-tremely heterogeneous in enterprise capabilities and constraints. This diversity may need to be taken into account in the design of public investment programs to develop markets for financial products and BDS tailored to the needs of the sector. Indeed, it may be advisable to concentrate initial investments in the participation of the more promising of segments of the subpopulation in the evolving markets. The report has identified such segments via characteristics of both businesses and their owners.

More Promising active enterprises

An analysis of the relative productivity of many of these segments suggests that youth-owned active enterprises in general, and those of young high school graduates in particular, constitute the most promising group on which to focus initial efforts at developing markets for financial products and BDS. The second most promising group of active enterprises identi-fied by owner characteristics is that of businesses of older high school graduates. Youth-owned active enterprises and active enterprises of older high school graduates constitute 13 percent and 9 percent, respectively, of the pilot survey sample.

Youth-owned active enterprises largely overlap what we have termed “start-up active enterprises,” defined as active enterprises that came into existence within the last five years. Start-up active enterprises are as promising and productive as active enterprises of young high school graduates. This finding is not surprising since high school graduates ac-count for a high proportion of start-ups. Initial public investments in the development of markets for financial products and BDS probably should also concentrate on inducing the participation of start-up active enter-prises. A minority of these are own-account enterprises, but 75 percent of them are micro-employers (engage 2–4 people). The second most promising group of active enterprises identified by business characteristics is that of post-startup active micro-employers. Start-up active enterprises and post-startup active micro-employers constitute 13 percent and 10


percent, respectively of the sample. Together, they overlap the combined subsamples of youth-owned active enterprises.

Thus, the basic message of this report is that intervention programs for the development of markets in financial services and BDS for micro-enterprises initially should target youth-owned active enterprises. This group constitutes approximately 13 percent of the pilot survey sample and overlaps almost exactly the subpopulation of start-up active enter-prises. The second most promising target of the programs should be active enterprises of older high school graduates. This second group constitutes approximately 9 percent of the sample and more or less coincides with the subsample of post-startup active micro-employers. Only to the de-gree that programs have reached these first and second most promising groups are they likely to succeed in extending markets and services to a third group of active microenterprises, namely, post-startup own-account active enterprises. Essentially, they are active enterprises of older owners who did not graduate from high school and constitute approximately 4 percent of the pilot survey sample.

Thus, the maximum initial target that public investment in the devel-opment of markets in financial products and BDS should aim to reach in Botswana is probably no more than 26 percent of the population of microenterprises. This estimate assumes that the proportion of active enterprises of the pilot survey sample is a reliable proxy for the true proportion of active enterprises in the national population.


If a given mix of financial products and BDS does not eventually find a ready market in active microenterprises, it is extremely unlikely to find a market in the involuntary microenterprises, which constitute as high as 75 percent of micro-businesses in the country. The report shows that, controlling for observable human capital variables of owners, location, and line of business, involuntary enterprises consistently underperform active entrepreneurs in both productivity and growth by very large margins.

More than providing BDS, the policy challenge that the population of involuntary entrepreneurs poses is probably integrating the younger among them in the formal labor market through training and skills development schemes. In this context, it is very significant that this sub-group of business owners is overwhelmingly dominated by young people. Approximately 68 percent of the group are aged 29 years or less. The youth of this group means that, in principle, the majority are trainable in new labor market skills.


This conclusion is reinforced by the fact that approximately 49 per-cent–51 percent of the group have been in business for fewer than 5 years, and are own-account workers. These factors may mean that they are not too locked into their current occupation to join or rejoin the formal labor market. This leaves approximately one-third or less of the group who have been in their current occupations for too long and prob-ably are too old to be retrained for a labor market career. These workers probably would benefit more from business support schemes than from schemes for labor market training.

The question is really whether the businesses of this last group are dynamic and productive enough to ultimately provide effective demand for micro-financial products and micro-BDS. Unfortunately we do not have the data needed to answer this question. What we can say at this point is that active enterprises in general, and certain segments of them in particular, are far more likely to eventually provide the market for these products and services than any involuntary enterprises. If public investments in developing these markets have to be prioritized to en-terprises in which the return to the efforts is most likely to be positive, then priority should be given to investing in the participations of active enterprises in the markets over investing in the participation of invol-untary enterprises.

Youth-owned active enterprises as a Constraints group

The survey shows that as potential markets for new financial products and BDS, youth-owned active enterprises rate lack of access to financing, business space and utilities, and skills as major constraints to business de-velopment. However, the weight they attach to each of these constraints relative to the others depends on the business owners’ education. For example, business space and access to public utilities is the top concern for active enterprises of high school graduates and is followed by access to finance as the second most important issue of concern for that group. Although it rates skills and security of property as important issues as well, this group is far less concerned about either of these two issues than other active enterprises.

In contrast, businesses of active young entrepreneurs who had not completed high school form the group that is most affected by the skills constraints and by property crime. Nevertheless, both of these come second and third behind access to finance as the top of the list of concerns of this group.


active Startups as a Constraints group

Looking at differences in needs from another perspective, namely, across the age groups of businesses rather than the age groups of their owners, we see a high degree of interaction between the tax registration and licens-ing status of active enterprises and the relative weights that their owners attach to different constraints to business development. In particular, as the more promising age group of active enterprises, active startups that are registered for tax or hold a license are more likely to be constrained by business space and access to public utilities than they are by financing, although both constraints top their list of concerns.

In contrast, access to finance is more of a constraint than lack or shortage of business space and utilities for active startups that are not registered for tax and do not carry a business licenses. Active startups also are the group that is reportedly most constrained by lack of skills. This is true both of own-account startups and start-up micro-employers, but with significant difference in the relative weight given to this particular con-straint by tax registration and licensing status within each group. Among start-up micro-employers, those registered for tax or holding licenses are far more likely to be affected by the skills constraint than the unregistered and the unlicensed. Conversely, among own-account start ups, the skills constraint is felt more strongly by the unregistered and the unlicensed.

Formalization and access to Services

Priority needs vary by tax registration and licensing status because whether a business is formal is an important determinant of its access to markets and services. In particular, formal businesses are more likely to have bet-ter access to finance, more likely to operate from nonresidential business premises connected to public utilities, and less exposed to property crime (probably because of the better location of their activities and assets). Moreover, formal microenterprises are far more productive than infor-mal ones, in part because of their better access to services. Thus, there is some evidence that improving microenterprises’ access to BDS may need to encourage formalization by removing some of the impediments to registration that survey respondents cited.

The most common reasons that respondents gave for not registering their businesses included the desire to avoid four things: paying taxes, compliance with labor laws, prohibitive registration fees, and the cost of compliance with other aspects of business regulation. Respondents who cited any of these reasons recognized that the lack of official status and


recognition that not having registered or not holding a license entailed also would bar them from access to key services and markets. The reason that they did not register regardless was that they did not think that there were attractive enough services and products whose value would outweigh the anticipated costs of becoming and staying registered or licensed.

Toward Markets in Financial Products and BdS for active Microenterprises

The information that the pilot survey has generated on the various ca-pability and constraints groups of microenterprises will be useful input to future efforts to make available new financial products and new BDS to active microenterprises in Botswana. An essential component of the efforts will be a formal assessment of the market for existing and potential products and services among the two most promising groups of active enterprises identified in this report. They are active startups and active youth-owned enterprises, with a particular focus on micro-employers and businesses owned by young high school graduates.

The Donor Committee (2001) guidelines define the scope for formal market assessments to include four analytic tasks: (a) evaluate a target group’s awareness and willingness and ability to pay for existing prod-ucts, (b) evaluate the group’s willingness to pay for potential products, (c) assess the extent of segmentation of the wider markets for existing or potential products that the group may be supported to participate in, and (d) assess the potential for crowding out private demand or supply by public interventions in markets. The main findings of this report will be useful input to the design of tasks (a) and (b). The tasks will require much more data on each constraints group than has been generated by the pilot survey. The additional data will have to be collected through focus group discussions with microenterprise owners within each group, more open in-depth interviews with enterprise owners and BDS provid-ers, market observation, and focused market surveys (SEEP Network 2005). These methods also would be useful in generating much of the information needed for tasks (c) and (d) of a formal market assessment.

References

Committee of Donor Agencies for Small Enterprise Development. 2001. “Business Development Services for Small Enterprises: Guiding Principles for Donor Intervention.” World Bank.

Gelb, A., T.A. Mengistae, R. Ramachandran, and M. Shah. 2009. “To Formalize or Not to Formalize? Comparisons of Microenterprise Data from Southern and East Africa.” Center for Global Development Working Paper 175. July.

Government of Botswana. 2008. “Labor Force Survey Report 2005/2006.” Central Statistical Office. Gaborone.

_____. 2009. “Informal Sector Survey Report.” Central Statistical Office. Gaborone.

SEEP Network. 2005. International Labour Organization. www.seepnetwork.org

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