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23 01388 3206

This is to certify that the

dissertation entitled

Entry, Exit and Growth Among Small-Scale
Enterprises in Zimbabwe

presented by

Lisa Daniels

has been accepted towards fulﬁllment
of the requirements for

Ph.D. Agricultural Economics

degree in

@wlégm

Major professor

WOW W W4

Thesis Supervisor

 

 

Date 4/10/95

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LlBRARY
Michigan State
University

 

 

 

PLACE N RETURN BOX to remove this Mom from your record.
TO AVOID FlNES return on or baton dot. duo.

DATE DUE DATE DUE DATE DUE

        

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ENTRY, EXIT, AND GROWTH AMONG SMALL-SCALE ENTERPRISES
IN ZIMBABWE

BY

Lisa Daniels

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Economics

1995

ABSTRACT

ENTRY, EXIT, AND GROWTH AMONG SMALL-SCALE ENTERPRISES
IN ZIMBABWE

By

Lisa Daniels

This study examines entry, exit, and growth among micro- and small-scale
enterprises (MSEs) in Zimbabwe. Unlike previous studies that focus on a set of
ﬁrms at one point in time, this study collected information on existing M8135 and
MSEs that folded prior to the survey. Using industrial organization theory and a
random effects model, the factors that inﬂuence entry and exit within industries
were then examined over an eight-year period. An ordinary least squares (OLS)
model was also used to explore the determinants of employment growth within
ﬁrms under periods of increasing and decreasing economic growth.

The results from the entry and exit models show that both the labor-supply
and output-demand hypotheses may play a role in MSE dynamics. The labor-
supply hypothesis suggests that the MSE sector is driven by an excess supply of
labor. This was supported in low-proﬁt industries where turnover was high and
traditional barriers to entry were not signiﬁcant. More importantly, the negative
relationship between economic growth and entry indicated that more people turn to
low-proﬁt industries as the economy declines. Furthermore, the expansion rate
(entry minus exit) increases among low-proﬁt industries as the gross domestic

product (GDP) declines. The output-demand hypothesis suggests that the MSE

sector is driven by market demand. Although this was not conﬁrmed among high-
proﬁt industries, entry was unrelated to economic growth and it was characterized
by high barriers to entry, including experience, capital, and licensing
requirements.

The employment growth model addressed two limitations of previous
studies. First, paid and unpaid employment growth were examined separately.
The results from this analysis showed signiﬁcant differences among the factors that
inﬂuence the two types of growth. Most importantly, paid employment growth
was positively related to proﬁts and credit whereas unpaid employment growth
was not related to either factor. Second, growth was measured in one-year time
periods of increasing and decreasing GDP rather than over the life of the ﬁrm.
The results showed that economic conditions affect employment growth within the

ﬁrm and within industries.

DEDICATION

Nick -- On that warm Friday afternoon of April 7, 1989, did I ask you out
or did you ask me out? Although we may never know the answer, at least we

know there really are happy endings in life.

iv

ACKNOWLEDGEMENTS

There are numerous people who deserve thanks for their support
throughout my graduate program. First and foremost, I would like to thank Dr.
Donald Mead, my dissertation advisor, and Dr. Carl Eicher, my major professor.
Dr. Mead provided both academic and professional guidance at Michigan State
University (MSU) and while I was working abroad. He also facilitated several
consulting assignments that ultimately led to the ﬁeldwork for my dissertation.
Finally, Dr. Mead always treated me as a professional, which I sincerely
appreciated. Thanks to Dr. Mead, my dissertation experience was an enjoyable
and rewarding process.

Dr. Eicher provided invaluable guidance when I ﬁrst arrived at Michigan
State University and throughout my time at MSU. Like Dr. Mead, Dr. Eicher has
the ability to interact with students on a professional level. He also has an
unlimited supply of ideas, contacts, and suggestions that are always useful. I
could not have selected a better major professor.

I have also beneﬁted from the other members of my thesis and guidance

committees. Dr. Carl Liedholm provided invaluable comments on my dissertation

and professional reports over the past three years. I have also beneﬁted from his
insights on the evolution of the small-scale enterprise ﬁeld. Dr. Les Manderscheid
evaluated the econometrics for this dissertation and improved the quality of the
ﬁnal product with his thorough review. Dr. Reardon took the time to provide
extensive comments on the proposal and the dissertation. He also provided a
guide to publishing articles that I use frequently. Dr. James Bonnen introduced
me to U.S. agriculture and the fascinating transaction cost literature. I learned a
great deal from his class and his personal insights. Dr. John Staatz provided
professional guidance and gave useful comments on the early stages of my
dissertation proposal. Dr. Jack Meyer provided a strong base in microeconomic
theory through his excellent teaching.

The research for this dissertation was funded by the United States Agency
for International Development (USAID) and carried out in Zimbabwe. Both
Letwina Dhliwayo and Don Greenberg from the USAID ofﬁce in Zimbabwe
deserve thanks for their continual interest and support of the project. I also owe
thanks to the staff of the Zimbabwean consulting ﬁrm, Human Resources (PVT)
Ltd., that provided the logistical support to carry out the survey. The enumerators
and supervisors also deserve credit for their perseverance over the two-month

period when they visited nearly 12,000 households.

vi

I would also like to acknowledge my parents, Betty and Joe Daniels. The
opportunities that I have had throughout my life would not have been possible
without their love and support.

Finally, I owe my deepest gratitude to my husband, Nick Minot. Within
two weeks of meeting Nick at MSU, we became friends for a lifetime. His
constant support and sense of humor made graduate school a truly enjoyable

experience!

vii

TABLE OF CONTENTS

CHAPTER ONE: INTRODUCTION ........................ 1
1.1 Background ............................... l
1.2 Objectives of the study ........................ 4
1.3 Organization of the study ....................... 5
CHAPTER TWO: BACKGROUND ON ZIMBABWE ............. 7
2 .1 Description of Zimbabwe ....................... 7
2.1.1 Geography ........................... 7
2.1.2 People .............................. 8
2.1.3 History .............................. 8
2.1.4 Structural adjustment program and the 1991/92
drought ............................. 11
CHAPTER THREE: LITERATURE REVIEW .................. 14
3.1 Introduction ............................... 14
3.2 Firm entry ................................ 14
3.2.1 Firm entry rates ........................ 14
3.2.2 Theoretical framework for analysis of entry ....... 16
3.2.3 Measurement of ﬁrm entry ................. 19
3.2.4 Determinants of ﬁrm entry ................. 23
Barriers to entry ........................ 24
Entry-inducing factors .................... 28
Technological environment ................. 29

viii

3.3 Firm exit ................................. 30

3.3.1 Firm exit rates ......................... 30

3.3.2 Theoretical framework for analysis of exit ........ 32

3.3.3 Measurement of ﬁrm exit .................. 32

3.3.4 Determinants of ﬁrm exit .................. 34

Barriers to exit ......................... 34

Exit-inducing factors ..................... 37

3 .4 Theories of entrepreneurial choice ................. 37
3 .5 Comparison of industrial organization and entrepreneurial

choice theories ............................. 39

3.6 Growth Patterns of Micro and Small Enterprises ......... 43

3.6.1 Growth rates .......................... 43

3.6.2 Theoretical framework for analysis of growth ...... 44

3.6.3 Measurement of growth ................... 45

3.6.4 Determinants of growth ................... 46

Proprietor characteristics ................... 46

Firm characteristics ...................... 47

Government regulations ................... 51

CHAPTER FOUR: RESEARCH METHODS ................... 53

4.1 Overview of research approach ................... 53

4.2 Data collection ............................. 53

4.2.1 Sampling method ....................... 54

4.2.2 Survey instruments ...................... 56

4.2.3 Extrapolation of results .................... 56

4.2.4 Survey Limitations ....................... 57

4.3 Econometric techniques for entry and exit ............. 59

4.3.1 Ordinary least squares ..................... 60

4.3.2 Pooled cross-section ...................... 63

4.3.3 Fixed effects or covariance model ............. 63

4.3 .4 Random effects or error components model ........ 66

4.4 Econometric techniques for employment grth model ..... 71

4.5 Data and speciﬁcation ......................... 71

4.5.1 Entry model .......................... 71

4.5.2 Exit model ........................... 79

4.5.3 Employment growth model ................. 83

CHAPTER FIVE: RESULTS OF THE ENTRY AND EXIT MODELS . . . 90

5.1
5.2

5.3

Entry and Exit Rates of MSEs in Zimbabwe ........... 90
Results of the random effects entry model ............. 94
5.2.1 Entry model: all industries combined ........... 94

Barriers to entry ........................ 96

Entry-inducing factors .................... 99
5.2.2 Entry model: high- and low-proﬁt industries ....... 101
Results of the random effects exit model .............. 108

CHAPTER SIX: RESULTS OF THE EMPLOYMENT GROWTH

MODEL ..................................... 114
6.1 Growth rates of MSEs in Zimbabwe ................ 114
6.2 Results of the employment growth model ............. 116
6.2.1 A comparison of paid, unpaid, and total employment
growth .............................. 119
Proprietor characteristics ................... 120
Firm characteristics ...................... 125
Government regulations ................... 131
Summary of paid, unpaid, and total employment
growth differences .................. 132
6.2.2 Paid employment growth under increasing and
decreasing GDP ........................ 133
Proprietor characteristics ................... 135
Firm characteristics ...................... 139
Government regulations ................... 142
Summary of paid employment growth in two time
periods ......................... 142
6.2.3 Unpaid employment growth under increasing and
decreasing GDP ........................ 143
Proprietor characteristics ................... 145
Firm characteristics ...................... 145
Government regulations .................. p . 150
Summary of unpaid employment growth in two time
periods ......................... 151

CHAPTER SEVEN: SUMMARY AND IMPLICATIONS ........... 153

7.1 Objectives ................................ 153
7.2 Summary of results .......................... 154
7.2.1 Entry and exit results ..................... 154

7.2.2 Employment growth results ................. 157

7 .3 Implications for policy ........................ 159
7.3.1 Entry and exit policy implications ............. 159

7 .3 .2 Employment growth policy implications .......... 162

7 .4 Implications for research methods .................. 165
7.4.1 Entry and exit research methods implications ....... 165

7.4.2 Employment growth research methods implications . . . 166

7.5 Need for future research ....................... 167
BIBLIOGRAPHY .................................... 170

Table 3-1:
Table 4-1:
Table 4-2:
Table 4—3:
Table 4-4:
Table 5-1:
Table 5-2:
Table 5-3:
Table 5-4:

Table 5-5:

Table 5-6

Table 5-7 :
Table 5-8:
Table 6-1:
Table 6-2:

Table 6—3:

LIST OF TABLES

A model of industrial organization analysis ............... 18
Sample sizes for the 1991 and 1993 surveys .............. 55
Variables deﬁnitions for the entry model ................ 80
Variable deﬁnitions for the exit model ................. 84
Variable deﬁnitions for the growth model ............... 89
Entry rates by industry, Zimbabwe 1989 to 1993 ........... 91
Exit rates by industry, Zimbabwe 1989 to 1993 ............ 93
Correlations between industry entry and exit rates .......... 94
Random effects entry model results ................... 95
Proﬁts and start-up costs by industry ................. 103

: Random effects entry model results: high- and low-proﬁt industries 104

Results of the random effects exit model ............... 109
Results of the random effects net entry model 111
Average annual employment growth rates by industry ....... 115
OLS employment growth model results ................ 121

Partial derivatives of growth with respect to age and size ..... 127

xii

Table 6—4:
Table 6—5:

Table 6—6:

Table 6-7:

Table 6—8:

Table 6-9:

Differences between paid and unpaid growth models ........ 134
01.8 employment growth model: paid employment ......... 136

Partial derivatives of growth with respect to age and size: paid
employment in two time periods ................... 140

Differences between paid employment growth models in two
time periods ............................... 144

OLS employment growth model: unpaid employment growth . . . 146

Partial derivatives of growth with respect to age and size:
unpaid employment in two time periods .............. 149

Table 6-10: Differences in unpaid employment growth models in

two time periods ............................ 152

xiii

CHAPTER ONE

INTRODUCTION

1.1 Background

Micro- and small-scale enterprises (MSEs) in developing countries are an
important source of employment. Recent studies conducted in the southern
African region show that 17 to 33 percent of the work force in Botswana,
Lesotho, Malawi, Swaziland, and Zimbabwe is engaged in small-scale activities.‘
Although many of these activities are undertaken in addition to farming, half to
three-quarters of the small-scale activities provide 50 percent or more of household
income. These activities include any nonfarm enterprise that employs 50 or fewer
workers and markets at least 50 percent of its output.2

Because of their importance as a source of income and employment, MSEs
have been the focus of many studies since the 1960’s. These studies have covered
a wide range of issues including urban and rural employment, policy effects on
MSEs, training, nonfarm linkages to agriculture, credit, and gender issues.

Despite the large body of literature on the MSE sector, most studies have focused

 

‘ See for example: Daniels, 1994; Daniels and Fisseha, 1992; Fisseha, 1991;
Daniels and Ngwira, 1992; and Fisseha and McPherson, 1991.

2 Nonfarm enterprises may include agricultural production if at least 50
percent of the product is marketed. Other types of agricultural-related activities
are also included such as food processing, trading, and transportation of
agricultural goods.

2

on a set of ﬁrms at a given point in time. Information on entry and exit of MSEs
in developing countries is practically nonexistent.

Entry of MSEs is examined in only two developing countries, Sierra Leone
and Colombia (Chuta and Liedholm, 1985; Cortes er a1. , 1987). While these
studies report entry rates by size of localities, they do not explore the factors that
lead to ﬁrm entry. Furthermore, accurate measures of entry rates require
information on both existing ﬁrms at the time of the survey and ﬁrms that have
opened and closed prior to the survey.

Evidence on ﬁrm disappearance or exit rates is greater, however, it is still
limited. Exit rates by size, age, and sector are examined in ﬁve developing
countries, without providing detailed information on the cause of disappearance
(Cortes et al., 1987; Chuta and Liedholm, 1985; Frishman, 1988; Anderson and
Khambata, 1981; and Nag, 1980). More recently, studies of MSEs in Kenya,
Swaziland, Zimbabwe, Botswana, and Malawi have included limited information
on causes of ﬁrm exit (Parker and Aleke Dondo, 1991; Fisseha and McPherson,
1991; McPherson, 1992; Daniels and Fisseha, 1992; Daniels and Ngwira, 1992).

The literature on growth of MSEs in developing countries has increased
over the past few years, focusing primarily on employment growth rates (Cortes er
al., 1987; Little et a1 1987; Chuta, 1990; Liedholm and Mead, 1993). Growth
rates reported in this literature, however, measure growth over the life of the ﬁrm.

Because this method does not reveal the exact time of growth, the inﬂuence of

3

macroeconomic conditions on grth cannot be determined. Furthermore, an
accurate relationship between growth and other factors cannot be estimated. The
relationship between growth and the age of the ﬁrm, for example, cannot be
measured since the age when growth occurred is unknown.

In addition to concealing the exact time of growth, previous studies have
measured employment growth as the combined change in paid and unpaid
workers. The forces that drive these two types of employment may differ
signiﬁcantly. For example, an increase in paid employees may reﬂect an increase
in proﬁts. Alternatively, an increase in unpaid employees may reﬂect the lack of
employment opportunities for family members.

Based on the current literature described above, there are several gaps in
the information on entry, exit, and growth of MSEs. First, accurate measures of
entry and exit are not available because of the lack of information on MSEs that
opened and closed prior to existing enterprise surveys. Furthermore, the
conditions that drive entry and exit have not been thoroughly explored. By using
information from two surveys on closed enterprises as well as an existing
enterprise survey, this study attempts to measure entry and exit of MSEs in
Zimbabwe. It also explores the conditions that drive entry and exit.

With regard to employment growth, the measurement of growth over the
life of the ﬁrm has limited the ability of previous studies to examine the inﬂuence

of macroeconomic conditions on employment growth. In addition, information on

4

the relationship between growth and other factors, such as proprietor
characteristics, ﬁrm characteristics, and government regulations has been limited
by the methods used to measure growth. By asking retrospective questions about
the two years prior to the survey, this study examines growth in two one-year
periods. In the ﬁrst period, economic growth was declining, while in the second
period economic growth was increasing. Furthermore, paid and unpaid
employment growth are examined separately to determine the inﬂuence of

economic growth and other factors on these two types of employment growth.

1.2 thectimnithumdx
Based on the gap identiﬁed in the literature, this research has three

principal objectives. The ﬁrst objective is to measure MSE entry, exit, and
employment grth in Zimbabwe. This information will shed light on the
employment creation within the MSE sector. Combined with information on
proﬁts, the sustainability of MSE employment can be examined.

The second objective is to examine factors that inﬂuence MSE entry and
exit. As discussed above, limited information on patterns of ﬁrm entry and exit is
available while factors that drive ﬁrm entry and exit have not been rigorously
examined in developing countries. An understanding of these factors may help
policy makers to create a supportive environment for the formation of MSEs and

for a longer life span. For example, governments policy often includes the

5

development of an ”enabling environment“ for micro and small enterprises. This
environment cannot be created, however, without a thorough understanding of the
forces that drive entry and exit of MSEs. Information on these forces could lead
to a more supportive policy toward MSEs.

The third objective is to determine factors that inﬂuence MSE employment
growth, particularly related to economic growth. As stated earlier, the exact time
of growth has not been measured in previous studies. Therefore, economic
conditions that affect growth in a given set of ﬁrms cannot be assumed to be the
same for all ﬁrms. By measuring growth within the two-year period prior to the
survey, all ﬁrms faced the same economic conditions. Growth related to these
conditions can therefore be examined. Also, by measuring the inﬂuence of
macroeconomic conditions and other factors on paid and unpaid employment
separately, a more accurate assessment of the forces that drive each type of growth
can be determined. This information will help policy makers to determine the
type of growth that should be supported and a better understanding of how to

create a supportive environment.

1.3 Qrganizan’onhnhesmdx

This study is organized into seven chapters. Chapter Two describes
Zimbabwe, including both current and historical events which inﬂuence the MSE

environment. This is followed in Chapter Three by a detailed review of the

6

literature on entry, exit, and growth of MSEs. The methods used to meet the
research objectives are presented in Chapter Four followed by the results in
Chapters Five and Six. Finally, Chapter Seven offers a brief summary of the
results, implications for policy and research methods, and suggestions for further

research.

CHAPTER TWO

BACKGROUND ON ZIMBABWE

This chapter provides a brief description of Zimbabwe, including the
geography, people, and the history leading up the current economic reforms. This
information is necessary to understand the operating environment of MSEs in

Zimbabwe today.

2.1 W

2.1.1 92mm
Zimbabwe is a landlocked country surrounded by Zambia,

Botswana, South Africa, and Mozambique. It covers a land area of 390,700
square kilometers, which is divided into ﬁve natural regions based on soil types
and climatic factors. Within these regions, there are four primary types of
agricultural systems: large-scale commercial farms, small-scale commercial farms,
resettlement areas, and communal land areas. Natural Region 1 has the highest
rainfall, receiving more than 1,000 millimeters per year in areas below 1,700
meters. Seventy-four percent of this area is occupied by large-scale commercial
farms. Natural Region 5 has the lowest rainfall, which is too low and erratic for
most crops including drought-resistant grain crops (Tabex, 1987). Forty-ﬁve

percent of Natural Region 5 comprises communal lands with the remaining area

8

divided between large-scale commercial farms and national parks. This
organization of farming areas is an outgrowth of historical events which are

described in Section 2.1.3.

2.1.2 Bernie
The 1992 census in Zimbabwe revealed a population of 10,407,767

with an intercensal growth rate of 3.13 percent. The population density was
estimated at 26.62 persons per square kilometer. Twenty percent of the population
is located in the two largest cities of Harare (including Chitungwiza) and
Bulawayo.

The population can be divided into several ethnic groups including: Shona,
Ndebele, Europeans, Asians, and Mixed Race groups. The Shona represent the
largestgroupandoccupythenorth, centerandeasternpartofthecormtry. The
Ndebele occupy the western part of the country. The non-Africans in the
remaining groups have never represented more than 6 percent of the population

(Tabex, 1987). There are also some smaller Aﬁican groups scattered throughout

the country.

2.1.3 History
Four distinct historical periods in Zimbabwe have led to the cru'rent

operating environment of MSEs in Zimbabwe: (1) the colonization of Zimbabwe

9
from 1894 to 1965; (2) the Unilateral Declaration of Independence (UDI) in 1965

up to independence in 1980; (3) independence in 1980 up to 1990; and (4) the
introduction of the economic structural adjustment program in 1991 to the current
day including the 1991/92 drought. These are described brieﬂy below.

With the death of a local chief in 1894, Cecil Rhodes claimed all arable
and grazing land in Zimbabwe for the British South Africa Company and the
Crown. The indigenous population was settled in "Native Reserves" and "Native
Purchase Areas,” now know as communal and small-scale commercial lands
described above. Within these areas, no land ownership rights were granted. This
reallocation of land as well as treatment of the indigenous population led to
periodic uprisings against colonial domination from 1894 to 1965.

In 1965, Ian Smith, leader of the colonial government at the time, declared
an illegal ”Unilateral Declaration of Independence. " The British government
responded by imposing economic sanctions. These included termination of
assistance and trade. Other countries joined Britain by discontinuing trade with
Zimbabwe, then know as Rhodesia. The colonial government responded by
implementing a highly regulated economic system. For example, controls were
imposed on foreign exchange, prices, wages, interest rates, and trade. Emphasis
was also placed on import substitution industries and foreign exchange earning
potential. These regulations led to a protected operating environment for

businesses, or the lack of competition from outside forces.

10
During this same period from 1965 to 1979, the indigenous population

began the war for liberation. Fighting between the colonialists and the indigenous
population ﬁnally led to independence in 1980. At this time, general elections
were held and the ZAN U (PF) party, led by Robert Mugabe, won the victory at
the polls. Independence was granted on 18 April 1980 with Canaan Banana as the
ﬁrst president and Robert Mugabe as the Prime Minister.

From 1980 to 1990, the new government adopted socialist policies and a
stricter set of industrial policies. In particular, four key policies were used to
control the industrial sector. First, extensive price controls were implemented
including set prices for essential products and cost-plus pricing for other products.
Second, the government set wages and implemented restrictive labor laws that
inhibited ﬁring of employees. Third, foreign exchange controls were tightened.
Finally, the government obtained shares in large companies to reduce monopoly
and oligopoly power (USAID, 1992). Because of these policies, monopoly
powers were limited, however, barriers to entry were created. The foreign
exchange allocation, price controls, and licensing system acted as disincentives to
enter the industrial sector. For those ﬁrms already operating, the policies created
incentives to focus on the domestic market without improving quality of their
products.

By the end of the 1980’s, the government realized the need to change

economic policies to foster investment and growth. It was also necessary to

11
reduce the ﬁscal deﬁcit which had grown substantially due to increased spending

on health and education. At this time, the government decided to introduce the

Economic Structural Adjustment Program (ESAP) which ofﬁcially began in 1991.

 

The structural adjustment program was introduced in Zimbabwe to
promote higher medium- and long-term growth and reduce poverty (Government
of Zimbabwe, 1991). It is a ﬁve-year program with four primary components: (1)
deregulation, (2) trade liberalization, (3) ﬁscal policy reforms, and (4) monetary
policy reforms. The ﬁrst component, deregulation, deals with the removal of
many regulations currently controlling business activities. For example, zoning,
licensing, registration, labor market regulations, and price and marketing controls
have often been identiﬁed as impediments to MSE activities (Harbin, 1993: Hess,
1993; Robinson, 1991; ENDA, 1990; IMANI 1990; Saito and Van Dijk, 1990;
ILO/SATEP, 1989; Konrad Adenauer Foundation, 1988; UNIDO, 1988; Jasset
and Jirira, 1987; and Price Waterhouse, 1986). Under ESAP, plans to change
these regulations should ease the operating environment for MSEs in Zimbabwe.

Trade liberalization, the second component of ESAP, also has implications
for MSEs. Foreign exchange allocation changes and removal of import
restrictions under ESAP may have both a positive and negative impact on MSEs.

For example, foreign exchange liberalization and removal of import restrictions

12

should alleviate input or raw material shortages frequently identiﬁed as a major
constraint to MSE activity. (Mead and Kunjeku, 1993; Zimconsult, 1992;
McPherson 1991; IMANI, 1990; Price Waterhouse 1986). Stronger competition
with imported goods, however, may increase MSE failure rates.

The third component of ESAP, ﬁscal policy reforms, includes tax and
expenditure reductions. Currently, corporate taxes are 45 percent while individual
taxes are as high as 60 percent. This tax structure has repeatedly been cited as an
impediment to MSE activity (Robinson, 1991; Human Resources, 1990; IMANI,
1990; USAID, 1990; Price Waterhouse, 1986).

Under the fourth component, monetary policy reforms, interest rates have
been liberalized and now play a greater role in allocating credit. Previously banks
had a strong incentive to limit credit to larger, well-established ﬁrms. With higher
interest rates, investment by larger ﬁrms may decline, however, credit is now
available to a wider range of ﬁrms. The beneﬁciaries of this policy are more
likely to be medium-sized ﬁrms rather than micro or small enterprises.

In addition to the key components of structural adjustment reviewed above,
sectoral initiatives have also been introduced both prior to ESAP and since ESAP
began. In particular, micro and small enterprises have been targeted though
several promotional measures including: the Small Enterprise Development
Corporation (SEDCO), the small enterprise scheme of Zimbank, the Zimbabwe

Development Bank (ZDB) fund for capital equipment, the African Development

13
Bank (ADB) soft loan window, and ZDB surplus funds facility (Government of

Zimbabwe, 1991). These programs may provide more capital to the MSE sector.
While many changes since 1991 have been attributed to the structural
adjustment program, the MSE operating environment has also been affected by the
drought of the 1991/92 agricultural season. During this period, the southern
Aﬁican region experienced the worst drought of the century. Rainfall in
Zimbabwe was only 43 percent of average annual rainfall from 1970 to 1991.
Although it is diﬁicult to separate the effects on the economy from the
drought and ESAP reforms, the World Bank estimated that the external debt
increased to over 2350 billion in 1992 because of the drought (The Herald,
12/3/92).’ This was partially caused by an 80 percent reduction in the maize
harvest and a 35 % reduction in agricultural output compared to the previous year.
Based on these shortages, high interest rates, and reduced consumer demand, real
Gross Domestic Product (GDP) fell by 7.7 percent in 1992. While the economy
has improved since 1992, ripple effects created by the drought are expected for

several years.

 

3 The exchange rate in December 1992 was US$1.00 = 255.40.

CHAPTER THREE

LITERATURE REVIEW

3.1 Tuneduction

As mentioned in the introduction, a large body of literature exists on the
MSE sector that covers a wide range of topics. The literature review in this
section focuses on the two key areas identiﬁed in the objectives of this study:
entry and exit of MSEs, and growth of MSEs. Sections 3.2 and 3.3 begin with an
examination of the rates of entry and exit, respectively. The rates are followed by
a theoretical framework for analysis of entry and exit based on microeconomic
theory and industrial organization. Using this framework, 3 review of the
measurement and determinants of entry and exit follows. Section 3.4 introduces
theories of entrepreneurial choice as an alternative framework to analyze enuy.
This is followed by a comparison of industrial organization and entrepreneurial
choice theories in Section 3.5. Finally, Section 3.6 presents growth rates of MSEs

followed by a literature review of the factors that inﬂuence growth.

3.2 Eitmentrx
32.1 W

Firm entry has rarely been studied in developing countries. This is

partly due to the lack of statistics on micro and small enterprises in developing

l4

15

countries and the sm'vey methods used to study MSEs. Frequently, survey
methods involve repeated visits to a set of ﬁrms, yielding information on ﬁrm
growth and exit for that particular set of ﬁrms. This method, however, does not
provide information on ﬁrm entry for the MSE sector. Other studies have used a
sin e-visit survey to estimate annual entry rates based on the existing set of ﬁrms
at the time of the sm'vey. This method underestimates entry rates since it excludes
ﬁrms that opened and closed prior to the survey. Nonetheless, it does give some
indication of entry rates in developing countries. Entry rates estimated from
existing ﬁrms in developing countries are reviewed below as well as a comparison
toentryratesintheUnitedStatesandCanada.

Liedholm (1990) reviewed the frequency of ﬁrm births in Sierra Leone.
He found a 12.8 percent annual average birth rate. He also showed that birth
rates were higher in localities with more than 20,000 residents. In Colombia,
from 1970 to 1975, Cortes er a1. (1987), reported an average annual birth rate of
8.1 percent. These ﬁgures closely correspond with estimates of birth rates in the
U.S. where Phillips and Kirchhoff (1988) showed that small ﬁrm birth rates
ranged form 8.9 to 12.1 percent while large ﬁrm birth rates ranged from 2.4
percent to 4.9 percent. Combining all ﬁrm sizes, Baldwin and Gorecki (1991)
estimated an entry rate of 5 percent for Canadian manufacturing ﬁrms in the

19705.

l6
3 .2.2 WW

While the estimates reviewed above indicate the magnitude of entry,
they do not reveal factors that inﬂuence entry. According to microeconomic
theory, ﬁrms will enter an industry characterized by free entry whenever proﬁts
are positive. An industry is deﬁned as "the set of ﬁrms that produce products that
are viewed as close substitutes by consumers (Varian, 1987). " This entry process
into an industry continues until proﬁts are driven to zero. Similarly, if proﬁts are
negative, ﬁrms will exit an industry until proﬁts are zero. In a long-run
equilibrium, factors of production are paid their market price. Under this
scenario, there are no incentives to attract new factors of production to the
industry since each factor of production is earning the same amount that it could
earn elsewhere.

The long-run equilibrium described above is based on the assumptions of a
perfectly competitive industry. These assumptions include: a large number of
ﬁrms producing homogeneous products; proﬁt maximization within ﬁrms; each
ﬁrm is a price taker; free mobility of all resources, including free entry and exit
of ﬁrms; and perfect knowledge. Although perfect competition is often used as a
framework to study industry organization and behavior, economists recognize that
the assumptions of perfect competition are not realistic. Based on the weaknesses

of this model, a new framework of analysis called industrial organization (10)

17
emerged in the 19303 and was later developed by other scholars (Mason, 1939;

Bain, 1959; Shaffer, 1980; Scherer, 1980; Caves, 1982; and Marion, 1986).

Industrial organization has been deﬁned by Scherer as "how productive
activities are brought into harmony with society’s demands for goods and services
through some organizing mechanism such as a free market, and how variations
and imperfections in the organizing mechanism affect the degree of success
achieved by producers in satisfying society’s wants (Scherer, 1980, p.1)."
Industrial organization uses the basic theory of the market from microeconomics
and adds an institutional dimension and an emphasis on behavior. More
speciﬁcally, the ultimate performance of the market depends on the conduct of
buyers and sellers, which in turn depends on market structure. Both conduct and
market structure also depend on basic conditions that are a function of the
particular industry being reviewed. Combined, these elements, illustrated in Table
3-1, make up the structure-conduct—performance (SCP) paradigm.

Rather than using pure microeconomic theory, IO theorists use the SCP
paradigm to study ﬁrm entry. Entry no longer depends strictly on proﬁt levels in
a perfectly competitive industry. Instead, entry is studied in a more realistic
setting where basic conditions, market structure, and conduct can affect entry

patterns. These studies of entry are reviewed below.

‘18
Table 3-1: A model of industrial organization analysis

 

 

 

 

 

BASIC OUIDITIOIS

Smell Ian

Raw laterials Price elasticity

Technology Substitutes

Unionization Rate of growth

Product durability Cyclical and seasonal character
Value/weight Purchasing rethod

Public policies Marketing type

III!!! STRUCTURE

 

Hulber of sellers and buyers
Product differentiation
Barriers to entry

Cost structures

Vertical integration
Conglorerateness

 

 

 

CDIDOCT

 

Pricing behavior

Product strategy and advertising
Research and innovation

Plant investlent

Legal tactics

 

 

 

 

Production and allocative efficiency
progress

Full erployrent

Equity

 

 

 

Source: Scherer (1980)

19
3.2.3 WW

Many authors have used 10 theory to examine the factors that
inﬂuence ﬁrm entry in the United States and Canada. Under this theory, entry is
examined at the industry level since basic conditions, structure, and conduct
speciﬁc to an industry will affect ﬁrm entry patterns. More generally, Dunne,
Roberts, and Samuelson (1988) suggest that impact of entry is related to markets.
Therefore, entry measures should be speciﬁc to markets rather than aggregated
across markets. Assuming that industries, deﬁned by microeconomics above,
represent separate markets, industries are typically speciﬁed as the Standard
Industrial Classiﬁcation (SIC) four—digit industries in the United States and three-
digit industries in Canada.

There are several ways to measure entry as illustrated by different studies.
MacDonald (1986) and Khemani and Shapiro (1986), for example, use gross entry
as the dependent variable to examine the factors that inﬂuence ﬁrm births. Gross
entry is deﬁned as the absolute number of new ﬁrm entrants in a given industry
over a speciﬁed time period. Alternatively, Orr (1974) uses net entry as the
dependent variable to examine factors that inﬂuence ﬁrm births. In this case, net
entry is deﬁned as the absolute number of ﬁrm births minus ﬁrm closures in a
given industry over a speciﬁed time period. Hamilton (1985) and Khemani and
Shapiro (1986) argue that gross entry is more appropriate than net entry because

net entry conceals the magnitude of gross entry over time. Furthermore, Khemani

20
and Shapiro (1986) maintain that net entry is simply calculated from published

data as the change in the number ﬁrms from period t to t + n. This calculation
leads to measurement errors since it aggregates entry, exit, mergers, and
diversiﬁcation of ﬁrms. Orr (1974) also acknowledges that gross entry is more
appropriate than net entry, however gross entry data were not available for his
study.

In addition to the net and gross entry of the absolute number of ﬁrms
described above, several authors use net and gross entry rates to examine the
factors that inﬂuence entry. For example, Mansﬁeld (1962), Gorecki (1975), and
Duetsch (1975) deﬁne the net entry rate as the change in the number of existing
ﬁrms from period t-l to t divided by the number of existing ﬁrms at the end of
the period H (or the beginning of period t). Hamilton (1975) and MacDonald
(1986) use the gross entry rate deﬁned as the number of new ﬁrms in period t
divided by the existing stock of ﬁrms at the beginning of period t. In
distinguishing between net entry rates and gross entry rates, Duetsch (1984) points
out a number of ﬂaws with the net entry rate. These include the following: (1)
the net entry may be much lower than gross entry if a large number of ﬁrms have
exited the industry; (2) the net entry rate does not reveal any change when a ﬁrm
is taken over by a new owner; and (3) when one ﬁrm enters and two ﬁrms merge,
thae is no change in the entry rate. Overall, these ﬂaws reduce the explanatory

power of the model according to Duetsch. Similarly, Mansﬁeld and Gorecki

21
acknowledge that gross entry rates would be preferred for their studies, however,

the available data dictated the use of net entry rates.

Comparing the absolute number of new ﬁrms to entry rates, Mansﬁeld
(1962) argues that entry rates are more appropriate since the absolute measurement
does not control for industry size. He points out, for example, that the entry of
two new ﬁrms into an industry that only has two ﬁrms represents a different set of
dynamics than the entry of two new ﬁrms in an industry that has 100 ﬁrms. Orr
(1974) also argues that entry must be correlated with industry size. Since larger
industries will tend to have a higher number of entries, this factor must be
controlled for in the model. In contrast, Khemani and Shapiro (1986) argue that
thesizeoftheindustryispartly determinedbythebarrierstoentry. An
examination of the factors that inﬂuence entry should not, therefore, use industry
size in the dependent variable. They claim that the coefﬁcients on barriers to
entry will be biased toward zero since the positive correlation between absolute
entry and size of an industry will lead to relatively constant entry rates despite
inter-industry differences in barriers to entry.

While Khemani and Shapiro argue against entry rates, all of the remaining
authors reviewed in this section have used rates or controlled for size of the
industry by including a size measurement in the dependent variable or among the
independent variables. For example, Mansﬁeld (1962), Gorecki (1975), Duetsch

(1975), Duetsch (1984), Hamilton (1985), and MacDonald (1986) used entry rates

22
as their dependent variables. Acs and Audretsch (1989) and Hause and Du Rietz

(1984) use the ratio of new ﬁrm employment to initial industry employment to
control for the size of the industry. Finally, Orr (1974) adds industry sales as an
independent variable to account for the correlation between industry entry and
size. Considering the signiﬁcant results estimated by these studies that control for
size, Khemani and Shapiro’s argument that coefﬁcients are biased toward zero is
not supported.

As mentioned above, two studies measure entry through employment rather
than the number of ﬁrms (Acs and Audretsch, 1989; Hause and Du Rietz, 1984).
This alternative measure of ﬁrm entry is employed to account for entry by ﬁrm
size. Acs and Audretsch argue, for example, that treating each new ﬁrm as one
entry tends to understate entry when new ﬁrms are large and overstate entry when
new ﬁrms are small. Duetsch (1975) supports this argument by stating that entry
rates based on ﬁrms give too much weight to the turnover of a large number of
small ﬁrms. Hause and Du Rietz employ a different argument to use employment
rather than ﬁrms. They claim that entry measured through employment is a proxy
for product market shares of new ﬁrms which is a more direct measure of the
impact of entry than the traditional use of the number of ﬁrms.

The entry measurements reviewed above included gross and ad absolute
ﬁrm entry, gross and net ﬁrm entry rates, and entry measured through

employment. In addition to the debate on how to numerically measure entry,

23
there is also a debate on the deﬁnition of entry. For example, Gorecki (1975) and

MacDonald (1986) distinguish between diversiﬁed ﬁrms and new or specialized
ﬁrms. In both cases, the determinants of entry vary by the type of entry under
examination. Similarly, Hamilton (1985) makes the distinction between
independent ﬁrms that are not part of another organization and dependent ﬁrms
that are branches or subsidiaries within larger corporations. Again, he ﬁnds by
estimating separate regressions that independent and dependent ﬁrms respond
differently to the same entry determinants. Khemani and Shapiro (1986) and
Duetsch (1975) also acknowledge differences in entry patterns by limiting their
studies to new plants created by new ﬁrms and independent ﬁrms, respectively.
Finally, Acs and Audretsch (1989) distinguish between size categories within
industries by estimating separate regressions for ﬁrms with fewer than 100
workers, fewer than 500 workers, and 500 or more workers. They ﬁnd
contrasting entry behavior among size categories within the same industries.
Combined, these results show that reﬁned deﬁnitions of entry are an improvement

over the aggregation of all types of ﬁrms.

3.2.4 Deterrninantmﬂﬁnnem
The factors that inﬂuence ﬁrm entry are typically categorized as

barriers to entry and entry-inducing factors. Barriers to entry reﬂect market

structrn'e characteristics and are expected to be inversely related to entry. High

24

levels of market concentration and economies of scale, for example, are expected
to inhibit new ﬁrm entrants. Alternatively, entry-inducing factors are expected to
be positively correlated with entry. These factors are represented by industry
proﬁtability and growth. In addition to barriers to entry and entry-inducing
factors, Acs and Audretsch (1989) suggest a third category called the technological
environment. While many authors have included technological innovations in their
models of ﬁrm entry, there is a debate over the relationship between ﬁrm entry
and technology. Because of the mixed results, it is difﬁcult to classify
technological innovations as a barrier or an entry-inducing factor. Results from all

three categories are presented below.

W

As mentioned above, barriers to entry are typically market
structure characteristics that inhibit new ﬁrrn entrants. One of the most frequently
cited barriers to entry is the level of market concentration. High levels of market
concentration are expected to deter new ﬁrm entrants. Khemani and Shapiro
(1986), Orr (1974), and Hause and Du Rietz (1984) provide evidence that support
this hypothesis using three measurements of concentration: (1) the Herﬁndahl
Hirschman Index; (2) a ranking based on concentration ratios; and (3) a dummy
variable to represent the existence of a signiﬁcant registered cartel agreement.

Market concentration was not, however, a signiﬁcant factor in all studies. For

25
example, based on the percent of industry employment in plants with more than

500 people, and a measurement of market power, Hamilton (1985), and Duetsch
(1984) found that market concentration was not signiﬁcant. In a third case,
Duetsch (197 5) found that market concentration measured by the concentration
ratio (CR4) was positive. Acs and Audretsch (1989) suggest that these mixed
results reﬂect the aggregation of all ﬁrm sizes. By running separate regression for
ﬁrms with one to 99 workers, one to 499 workers, and 500 or more workers, Acs
and Audretsch show that market concentration is a barrier to entry for ﬁrms with
499 or fewer workers. For larger ﬁrms, however, market concentration is
positively correlated with entry. Acs and Audretsch suggest that this positive
relationship reﬂects a large—ﬁrm advantage in concentrated markets.

Higher levels of capital expenditure required to start a business are also
expected to pose barriers to entry. This hypothesis is supported by numerous
enrpirical results that used various measurements of capital expenditure. For
example, Orr (1974) and Mansﬁeld (1962) used the level of investment required to
establish a ﬁrm of minimum efﬁcient size to show that capital is a barrier to entry.
Hamilton (1985) employed the capital expenditure associated with entry at the
modal employment size in each industry as a measure of capital expenditures.
Macdonald (1986) measured capital barriers as the asset value of an efﬁciently
sized plant. This was calculated as the ratio of ﬁxed assets to sales in industry

multiplied by the minimum efﬁcient scale of plant. Khemani and Shapiro (1986)

26

used the cost of fuel and electricity required for a minimum efﬁcient size plant.
Finally, Duetsch (1984) calculated capital requirements for each industry by the
average shipments of the largest plants accounting for 50 percent of industry
shipments multiplied by the industry assets/ shipments ratio. In all cases, capital
was a signiﬁcant factor and negatively correlated with entry.

Advertising or product differentiation by established ﬁrms is also expected
to discourage new or potential entrants. In this situation, new ﬁrms must spend a
large sum of money to win market shares from existing ﬁrms. Acs and Audretsch
(1989), Duetsch (1975), Gorecki (1975), Khemani and Shapiro (1986), and Orr
(1974) have shown that advertising expenditure by established ﬁrms is a barrier to
entry.

Econonries of scale have also been identiﬁed as barriers to entry. When
economies of scale exist, new entrants must capture a substantial portion of market
share to generate proﬁts. Because economies of scale are difﬁcult to measure,
authors have used proxies such as the minimum efﬁcient plant size and the small—
ﬁrm cost disadvantage. For example, the former was measured by Duetsch (1984)
as the ratio of the value added per worker in the largest plants that generate 50
percent of industry output to value added per worker in the remaining plants.
Gorecki (1975) measured economies of scale as the ratio of the minimum efﬁcient
size plant size to the size of the industry as a whole. The minimum efficient plant

size was estimated by the average size of the plant in terms of net output.

27

Similarly, Hause and Du Rietz (1984) measured the minimum efﬁcient size as the
average size of ﬁrms in the industry. In their study, however, employment was
used instead of net output. The small-ﬁrm cost disadvantage was measured by
Acs and Audretsch (1989) as the small—ﬁrms sales-per-employee divided by the
industry sales-per-employee. In all cases, econonries of scale was a statistically
signiﬁcant barrier to entry.

In addition to the more common barriers to entry reviewed above, some
authors have tested other barriers to entry such as human capital requirements,
tariffs, risk, and multiplant operations. Acs and Audretsch (1989), for example,
show that high levels of human capital present a barrier to entry for ﬁrms with
one to 499 employees, however it is not a barrier for larger ﬁrms. Tariffs were
tested by Khemani and Shapiro (1986) to account for foreign competition. Using
the nominal tariff rate, they found that tariffs do pose a barrier to entry. Orr
(1974) included risk as deviations from the average proﬁts within an industry and
found that it was negatively correlated with entry. Finally, Duetsch (1984) found
that the extent of multiplant operations within an industry was a barrier to entry.
He hypothesized that multiplant ﬁrms "have potential economies . . . over
single-plant ﬁrms in spreading risks, raising capital, procming materials,
supporting research and development, and engaging in sales promotion activities

(p. 477)."

28

E _. l . E

As discussed above, nricroeconomic theory predicts that an
industry characterized by positive proﬁts will attract new ﬁrms, assuming there are
no barriers to entry. Entry-inducing factors are, therefore, typically represented
by industry proﬁtability measures. These include the price-cost margin, industry
sales growth, industry employment growth, or some other measure of proﬁtability.
Measures of these variables and the signiﬁcance of their impact on entry are
reviewed below.

Numerous studies have incorporated published data on industry price—cost
margins as a determinant of ﬁrm entry. For example, Duetsch (1975), Duetsch
(1984), Hamilton (1985), and Khemani and Shapiro (1986) found that price-cost
margins were signiﬁcant and positively correlated with entry. In the studies
conducted by Acs and Audretsch (1989) and MacDonald (1986), however, price-
cost margins were not signiﬁcant. In the latter case, MacDonald (1986) used the
residual from a regression of price-cost margin as a function of capital and
advertising.

Industry sales growth, measured as the percentage growth in sales, was a
positive determinant of new entrants in the studies conducted by Acs and
Audretsch (1989) and Gorecki (1975). Similarly, industry employment growth
was also a positive determinant of new entrants in studies conducted by Hanrilton

(1985) and Hause and Du Rietz (1984).

29

Other measures of industry proﬁts or growth were also incorporated into
studies of new ﬁrm entrants. For example, Duetsch (1975) estimated the entry
rate as a function of the price-cost margin which in turn was a function of demand
growth. In this case, demand growth was a positive deternrinant of the price—cost
margin. Duetsch (1984) measured industry growth as the percentage growth in the
value of shipments and found that it was a signiﬁcant determinant of entry.
Khemani and Shapiro (1986) divided industry growth by the minimum efﬁcient
plant size as a percentage of the market and found that it was signiﬁcantly
positive. They argue that their measurement is more accurate than industry
growth alone because entry will only rise if industry growth is high enough to
allow efﬁcient operation of plants of minimum efﬁcient size. Finally, Orr
measured industry growth as the percentage change in value added over a certain

time period, which was a positive determinant of ﬁrm entry.

I l l . l .
As mentioned above, barriers to entry are negatively correlated
with entry while entry-inducing factors are positively related to entry.
Technological innovations, however, do not ﬁt neatly into one of these categories.
Some authors argue that established ﬁrms use technological innovation to deter
new entrants. Alternatively, a technological environment may increase entry since

new ﬁrms can use innovations as a means to attract market shares.

30

Using research and development (R&D) expenditures as a proportion of
industry sales, Orr (1974) found a negative relationship between R&D and entry.
In contrast, Highﬁeld and Smiley (1987) found a positive relationship and
Khemani and Shapiro (1986) found no signiﬁcant relationship using the same
measurement.

Acs and Audretsch (1989) calculated the number of small ﬁrm innovations
divided by the total number of innovations as a measurement of R&D within an
industry. Their results indicate a positive relationship between R&D and entry,
however, a high technological environment is more conducive to large ﬁrm

entrants.

3.3 W

3 .3.1 Einnexitrates

Similar to ﬁrm entry rates, ﬁrm exit rates have rarely been studied

in developing countries. Again, this reﬂects a lack of statistics on micro and small
enterprises in developing countries and the survey methods used to study MSEs.
While repeated visits to a set of ﬁrms yields information on exit for that set of
ﬁrms, it does not reveal ﬁrm exit rates or behavior for the MSE sector as a whole.
Surveys that cover MSEs that folded would be a more appropriate method to

record exit rates and behavior over time. Because these types of studies have not

31

previously been available, exit rates based on visits to the same set of ﬁrms in
developing countries are reviewed below.

Exit rates were studied by Frishman (1988) in Nigeria and Chuta and
Liedholm (1985) in Sierra Leone. Frishman found only 39.7 percent of a subset
of ﬁrms one year after the original contact. After a seven—year period, Frishman
tried to ﬁnd a subset of ﬁrms from permanent sites. Only 27.5 percent of these
ﬁrms were located. Similarly, Chuta and Liedholm tried to ﬁnd 60 ﬁrms after a
six-year period. They found 43 percent of the ﬁrms in Freetown and only 34
percent of ﬁrms in smaller localities.

Compared to exit rates studied elsewhere, the exit rates described above are
high. Using data from ﬁve developing countries and the United States, Liedholm
(1990) showed that exit rates ranged from 1.3 percent to 12.5 percent in the
manufacturing sector. He also showed that annual exit rates for micro enterprises
are higher than exit rates for small and medium ﬁrms with ten to 199 workers.
For example, in the U.S., micro enterprises had an annual exit rate of 12.5
percent compared to 8.3 percent for medium ﬁrms in the 1976 to 1982 period.
Baldwin and Gorecki (1991) also estimated a low exit rate of 6.5 percent for

manufacturing ﬁrms in the U.S. during the 19705.

32
3.3.2 Iheomhcalﬁanmrkicunalxmofsxu

While the estimates reviewed above give some indication of the
magnitude of ﬁrm exit, they do not reveal factors that inﬂuence ﬁrm exit. The
study of exit behavior is based on the same theoretical framework as the analysis
of ﬁrm entry. In the case of ﬁrm exit, microeconomic theory predicts that ﬁrms
will exit an industry when proﬁts are negative. This process will continue until
economic proﬁts are zero. Again, this theory is based on the assumptions of a
perfectly competitive industry. Because these assumptions are unrealistic, the 10
framework reviewed above is a more appropriate method to study the factors that
inﬂuence exit behavior. Similar to ﬁrm entry, the 10 framework acknowledges

the inﬂuence of basic conditions, market structure, and conduct on ﬁrm exit.

3 .3.3 Meamemenmffmnrxit
Unlike ﬁrm entry, very few authors have studied ﬁrm exit in the

United States and Canada. For those studies that are available, 10 theory is used
to examine the factors that inﬂuence ﬁrm exit. Again, similar to entry, exit is
examined at the industry level since basic conditions, structure, and conduct
speciﬁc to the industry will affect exit behavior.

Both MacDonald (1986) and Dunne, Roberts, and Samuelson (1988) use
gross exit rates to examine the determinants and patterns of ﬁrm exit. The gross

exit rate is deﬁned as the number of ﬁrms that exit from a given industry in a

33
speciﬁed time period divided by the number of ﬁrms alive at the beginning of the

time period for that industry.

Although both studies use the same numerical measurement of exit, their
deﬁnitions of exit differ. MacDonald (1986), for example, distinguishes between
the exit of "specialist" ﬁrms and "diversiﬁed" ﬁrms. Specialist ﬁrms represent
those ﬁrms for which the largest share of employment is engaged in production of
the product for their industrial classiﬁcation. Alternatively, diversiﬁed ﬁrms
represent those ﬁrms for which the industry is secondary. In this case, the largest
share of employment within the ﬁrm is engaged in the production of products
outside of its industrial classiﬁcation. Dunne, Roberts, and Samuelson (1988)
deﬁne exit in ways that are consistent with deﬁnitions used for entry. For
example, exit of ﬁrms that started as new or diversiﬁed ﬁrms is examined as well
as the method of entry. The method of entry includes ﬁrms that started by
constructing a new plant or ﬁrms that started in an industry by changing the
product mix within existing plants.

Caves and Porter (1976) use a completely different approach for both the
numerical measure of exit and the deﬁnition of exit. Instead of aggregating ﬁrms

within an industry, they assume that one business represents an industry. The

34

dependent variable is then a binary variable which equals one if a major

competitor has exited from the industry and zero if otherwise.‘

3.3 .4 Determinantmfﬁmexit

Similar to ﬁrm entry, determinants of ﬁrm exit can be categorized
as barriers to exit and exit-inducing factors. Some authors argue that there is a
high degree of symmetry between exit and entry determinants. Khemani and
Shapiro (1986), for example, report high degrees of correlation between entry and
exit barriers. Caves and Porter (1976) also note the ”structural symmetries"
between barriers to entry and exit. For example, high expenditures on capital
equipment that act as barriers to entry will also act as barriers to exit due to the
high level of sunk costs. While there is very little statistical evidence to support
the relationships between entry and exit determinants, the effects of exit

determinants, reported hypotheses, and evidence are reviewed below.

E . .
Barriers to exit are expected to be negatively correlated with exit

rates. One of the most frequently cited barriers to exit is the level of capital

 

‘ In a second set of analyses, Caves and Porter change the dependent variable
to equal one if the before-tax return on investment was less than a cutoff value and
zero if otherwise. In this case, the association between exit barriers and the presence
of ﬁrms with persistently low proﬁts was measured. Most of the hypotheses discussed
below were supported by this alternative measurement of exit behavior.

35
expenditure. More speciﬁcally, Caves and Porter (1976) deﬁne this barrier as the

ownership of "durable and speciﬁc assets." Because durable and speciﬁc assets
cannot be easily transferred to another type of activity, ﬁrms may continue to
operate despite low proﬁts. According to Caves and Porter, ”assets may be
speciﬁc to the particular business or productive activity, to the company
employing them, to the physical location, or to any combination of these (p. 40). "

Although durable and speciﬁc assets are typically associated with large
machinery or indivisible equipment, Caves and Porter (1976) describe both
tangible and intangible ﬁxed assets. Tangible assets may include skilled labor,
indivisible equipment, and inventories of inputs or ﬁnal products with limited
salvage. Intangible assets include trademarks and good will developed by the
business. Investrrrent in either type of asset may impede exit.

To test the signiﬁcance of durable and ﬁxed assets, Caves and Porter
(1976) employed three variables related to plant and equipment and one variable
related to trademarks. In the ﬁrst case, the variables related to plant and
equipment were not signiﬁcant. These included the following: (1) the average
investment of ﬁxed and working capital divided by full capacity sales; (2) the
annual depreciation charge on major ﬁxed assets divided by the net book value of
plant and equipment; and (3) the net book value of plant and equipment divided by
gross book value of plant and equipment. MacDonald (1986), however, found a

signiﬁcant negative relationship between capital commitments and the exit rate by

36

using the ratio of ﬁxed assets to sales in an industry multiplied by the minimum
efﬁcient scale of a plant. As a proxy for intangible ﬁxed capital, Caves and
Porter (1976) used advertising and sales promotion expenses plus product research
and development expense divided by net sales. They found a signiﬁcant negative
relationship between this proxy and the exit rate.

In addition to durable and speciﬁc assets, Caves and Porter (1976) also
included a set of variables that represented ”joinmess. " For example, a diversiﬁed
ﬁrm may share marketing channels, facilities, and equipment for several product
lines. In this case, a higher level of joinmess may lead to lower exit rates as ﬁrms
subsidize low-proﬁt products with revenue from high-proﬁt products. The results,
however, indicated a positive relationship between several of the joinmess
variables and the exit rate. Caves and Porter suggest that this may be due to the
inadequacies of using a single ﬁrm to represent the industry as a whole.

Caves and Porter (197 6) also suggest that managerial behavior can affect
exit rates. For example, managers may be reluctant to close a ﬁrm since it may
reﬂect on their managerial abilities. Also, managers may be loyal to a community
or hesitant to lay off workers. Although these are difﬁcult to measure, Caves and
Porter included measures of diversity and the growth rate of the company as
proxies for the managerial factor. They suggest that higher levels of diversity and
growth rates should reduce the pressure to eliminate unproﬁtable businesses.

Neither of these variables, however, was signiﬁcant. Again, Caves and Porter

37

suggest that the insigniﬁcance may reﬂect the inadequacy of the dependent
variable.

A generational or turnover variable was also included in the analysis
conducted by Caves and Porter (1976). This is measured as a binary variable that
indicates if a major competitor has entered the business. Caves and Porter (1976)
suggest that this entry variable represents the ”unknown underlying forces that

speed the generational turnover of sellers (p. 56). "

E . _. l . E

Exit-inducing factors are expected to be negatively correlated with
exit rates in contrast to entry-inducing factors that are positively correlated with
entry. For example, as industry growth decreases, exit rates should rise. Both
MacDonald (1986) and Caves and Porter (1976) use measures of industry growth
to test this relationship. MacDonald included the percentage change in industry
employment as a measure of growth, which was not signiﬁcant. Caves and Porter
measured growth through the change in industry shipments, which was inversely
related to exit as expected.

3.4 jljhegn'es of entrepreneurial choice
The models reviewed above used 10 theory to study ﬁrm entry and exit.

Firms were aggregated at the industry level under the assumption that each

38
industry represented a separate market. Industrial organization theory was then

used as a framework to examine the impact of the basic conditions, market
structure, and the conduct of market participants on entry and exit.

In addition to the use of IO theory, theories of entrepreneurial choice have
been developed to study entry and exit. Under these theories, the individual
choice to become an entrepreneur or to work for someone else is examined.
Kihlstrorn and Laffont (1979), for example, develop a model that focuses on risk.
The individual has a choice between operating a risky ﬁrm or working for a
riskless wage. The choice is based on a comparison of the expected utility derived
from proﬁts or wages from the two options. To make this choice, the individual
considers his or her abilities, labor skills, attitudes toward risk, and initial access
to capital. The model shows that less risk averse individuals become entrepreneurs
while more risk averse individuals work as laborers.

Kanbur (1979) also developed a model of entrepreneurial choice based on
risk. In his model, individuals choose between becoming an entrepreneur or a
laborer. Using a general equilibrium framework, he shows that societies that are
less risk averse do not necessarily have higher national income or greater income
inequalities.

In a model that focuses on managerial ability, Lucas (1978) examines
entry, exit, and the size distribution of businesses. In his model, people with

greater managerial ability become entrepreneurs while those with less managerial

39
ability supply labor. Entry and exit occur as those with marginal ability move

between roles as entrepreneurs and workers. Based on this model and the
assumption of decreasing returns to scale, Lucas predicts the size-distribution of
ﬁrms assuming a given distribution of managerial talent. He also uses empirical
results to show that ﬁrm sizes increase as the economy becomes wealthier.

Brock and Evans (1986) also developed a model to study entrepreneurial
choice. In their model, an individual maximizes expected utility which is a
function of wage or earnings. Wages or earnings are in turn a function of
individual characteristics, including demographic and human capital indicators.
Using only men who worked full time in the preceding year, Brock and Evans
examine the characteristics of self-employed versus wage workers. Their results
indicate that education, income earned by a spouse, imnrigrant status, and marital
and family status are not signiﬁcant determinants in the choice between self-
employment and labor supply. Signiﬁcant determinants include age, race, and
location. More speciﬁcally, as men become older, self-employment increases at a
decreasing rate and African Americans and urban dwellers are less likely to

become entrepreneurs.

3.5 our. r' .H t H ‘m. 0 ‘a! nu .u ‘H am m‘ .r.. H' r‘or'
Both the entrepreneurial choice and industrial organization theories attempt

to explain ﬁrm entry. Although there are differences, entrepreneurial choice and

4o

industrial organization do not represent competing theories of ﬁrm entry. Instead,
the two theories complement each other and have a large degree of overlap. A
comparison of the two theories is presented below beginning with the question
addressed by the two theories, the unit of analysis, the assumptions, and the

independent variables of the two theories.

3.5.1 Questionaddressedandunimtanalxsis

The entrepreneurial choice theory addresses the question "What
factors affect an individual decision to start a business?" The question under
industrial organization theory is “What factors affect entry within speciﬁc
industries?" Under entrepreneurial choice theory, the unit of analysis is the
individual. Industrial organization theory takes a different perspective by
reﬂecting the sum of individual decisions to start a business and using the industry

entry rate as the unit of analysis.

3.5.2 Assumptions

Under the entrepreneurial choice theory, the individual assumes that
industry structure, proﬁts, and prices are given. The individual then compares
expected income from various industries as well as other alternatives based on his

or her skills, attitudes toward risk, and access to capital.

41
Industrial organization theory incorporates the effect of individual decisions

in determining market structure for speciﬁc industries. It then assumes that the
market structure will affect market conduct and ultimately market performance.
For example, an industry represented by a large number of sellers (or individuals
making the decision to enter a certain industry) should be characterized by more

competitive pricing and thus more efﬁcient allocation of productive resources.

3.5.3 Independenuatiahles

Because industrial organization theory implicitly reﬂects the sum of
individual decisions from entrepreneurial choice theory, there is a large degree of
overlap between the independent variables used in the two theories. For example,
entrepreneurial choice theory incorporates human capital as one component of an
individual decision to start a business. Industrial organization theory combines
individual human capital to determine the average human capital level within
industry as a barrier to entry. Similarly, capital requirements are considered in
both theories. Under entrepreneurial choice theory, an individual examines his or
her initial access to capital and the amount of capital required to start a business.
Industrial organization theory combines individual capital expenditures within an
industry to determine the average capital expenditure within an industry as a

potential barrier to entry.

42

In addition to incorporating individual characteristics from entrepreneurial
choice theory into industrial organization theory, individual alternatives are
incorporated into both models. Under entrepreneurial choice theory, the
individual considers expected proﬁts from various alternatives. The expected
proﬁts from becoming an entrepreneur are derived from the average proﬁts
generated within speciﬁc industries being considered. Industrial organization
theory also uses proﬁts as a determinant of entry. High proﬁts are expected to
attract new entrants whereas declining proﬁts should lead ﬁrms to exit from the
industry to pursue other alternatives.

In summary, entrepreneurial choice theory considers the decisions of
individuals to start a business. It begins with the set of all individuals, whether or
not they will become entrepreneurs. Industrial organization theory reﬂects the
combined decisions of those individuals who choose to become entreprenerus to
determine industry characteristics. Thus, entrepreneurial choice theory is
incorporated into industrial organization theory by combining individual decisions
and characteristics to determine industry-level characteristics.

This study examines industry-level characteristics to examine ﬁrm entry.
Although industrial organization theory is the basis for this study, entrepreneurial
choice theory is incorporated into the analysis as described above. In addition,
however, this study extends the industrial organization model by including other

”non—entrepreneurial" alternatives in the analysis. For example, as mentioned

43

above, the industrial organization model assumes that proﬁts within the industry
are a key determinant of ﬁrm entry and exit. In a more dynamic setting,
however, entrepreneurs will continue to examine not only the proﬁts of their own
business, but proﬁts in other industries and expected earnings from other activities
such as working for someone else or farming. For this reason, wages outside of
the MSE sector and farming are included as other alternatives that may affect

entry and exit rates. These are described in Chapter Four.

3.6 W

3.6.1 W

Employment growth has been more widely studied than entry and

exit of ﬁrms in developing countries. In a cross-country comparison, for example,
Liedholm and Mead (forthcoming) show that the average annual growth rate of
MSEs, deﬁned as the percentage change in the number of employees since the
ﬁrm started divided by the number of years the ﬁrm has operated, is 11.3 percent
for ﬁve countries in southern and eastern Africa. Including Niger and the
Dominican Republic, the average annual growth rate is 11.4 percent. Within these
countries, the growth rate ranges from 6.6 percent in Swaziland to 29 percent in
Kenya.

Using only urban growth rates, Liedholm and Mead (forthcoming) use 13

countries in Asia, Africa, and Latin America to show that the growth rate is 14.9

44

percent on average. Again, there is a wide range from 8.5 percent in Niger to 25

percent in Kenya.

3.6.2 Theoreticaljameraorkmanalxsimfgmmh

While the estimates reviewed above give some indication of the
magnitude of growth, they do not reveal factors that affect employment growth.
Neoclassical economic theory suggests that ﬁrms increase employment when the
value of marginal product of labor increases beyond the market wage. This may
occur with an increase in the product price, a technological improvement, or a
decrease in market wages.

Stochastic theories of growth contend that ﬁrm growth is a random
process. These theories are based on Gibrat’s "Law" which hypothesizes that ﬁrm
growth is independent of ﬁrm size (Gibrat, 1931). Furthermore, growth is not
dependent on the proprietor in stochastic theories. Numerous empirical studies,
however, have shown that Gibrat’s Law does not hold (Evans, 1987; McPherson,
1992; Liedholm and Mead, forthcoming; Hall 1987).

More recently, Jovanovic (1982) developed a growth model that focuses on
the manger. He suggests that each manager is aware of the distribution of
abilities of other managers but uncertain about his or her own ability. The
manager, therefore, assumes that his or her own abilities are average. Over time,

managers learn about their abilities and adjust their perceptions which become

45
increasingly more accurate. Based on this theory, growth is dependent on both

ﬁrm age and size. Pakes and Ericson (1987) broaden this theory by allowing
managerial efﬁciency to increase through human capital investment. As this level

of efﬁciency increases, ﬁrrns grow more rapidly.

3.6.3 Meammentntamrath
Firm growth can be measured through a variety of ways including

increases in employment, sales, proﬁts, or ﬁxed assets. Many studies typically
use employment growth due to the ease of measurement. For example, most
ﬁrms in the MSE sector do not keep written records of sales and proﬁts.
Employment change, however, is easier to measure and recall in a single-visit
survey. Although sales or proﬁts may be more desirable than employment
changes, Parker and Aleke Dondo (1991) showed that there is a signiﬁcant
positive correlation between sales and employment growth. In most of the studies
reviewed below, employment change is used as the measure of growth. It is
typically calculated as the annual average growth rate or the percentage change in
the number of employees since birth divided by the number of years that the ﬁrm

has been in operation.

46
3.6.4 Detenninantsnfammh

Determinants of growth can be divided into three categories
including: proprietor characteristics, ﬁrm characteristics, and government

regulations. Empirical evidence on each of these categories is presented below.

2.! ..

As discussed in Jovanovic’s theory above, proprietor
characteristics are expected to have an impact on ﬁrm growth. Furthermore,
additional investment in human capital, as suggested by Pakes and Ericson (1987)
should lead to more efﬁcient management and subsequent growth. Pack (1993)
also suggests that improvements in industrial productivity can only be achieved by
using more experienced managers. The impact of education on growth in Aﬁica,
however, is not uniform. For example, McPherson (1992) found that education
did not affect growth rates in Swaziland and Botswana, however, proprietors that
had completed secondary school in Zimbabwe experienced higher employment
growth rates.

Experience is also be expected to be positively correlated with growth.
This is supported by both Jovanovic (1982) and Pakes and Ericson (1987)
theoretical work. Empirical evidence in two southern African countries, however,

is mixed. McPherson (1992) showed that the years of proprietor experience was

47

positively related to growth in Swaziland and not a signiﬁcant determinant of
growth in Botswana.

The gender of the proprietor is expected to inﬂuence employment growth
within the ﬁrm. Downing (1990) suggests, for example, that female proprietors
avoid risk and are therefore less likely to expand their businesses. Rather than
investing in one business, Downing suggests that women diversify their activities
as a source of income security. McPherson (1992) found, however, mixed results.
In Lesotho, Botswana, and Zimbabwe, employment growth rates did not differ by
male- and female-owned ﬁrms. In contrast, female-owned ﬁrms exhibited
signiﬁcantly lower growth rates in South Africa and Swaziland. Liedholm and
Mead (forthcoming) also found that female-owned ﬁrms experienced signiﬁcantly
lower growth rates in a regression that combined growth rates from six countries
in southern and eastern Africa.

Marital status may also affect employment growth rates. As suggested
above, married women with children may diversify rather than expand one
business in order to maintain income security. Alternatively, married men may be

able to afford the risk of expansion if their wives provide an income security base.

E. l . .
There are several ﬁrm characteristics that may affect employment

growth. These include the following: age, size, industry, location, linkages of the

48

ﬁrm, proﬁts, investment, credit, and support from agricultural income. In the
ﬁrst case, Jovanovic’s theory of ﬁrm growth hypothesizes an inverse relationship
between ﬁrm growth and ﬁrm age. Numerous studies have found that this
relationship holds. For example, Cortes et al. (1987), found a distinct drop in the
growth rate after ﬁrms reach the age of ten years in Colombia. Chuta (1990) also
found a strong inverse relationship between growth and age in northern Nigeria for
existing ﬁrms in all subsectors. Comparing ﬁve countries, McPherson found a
negative relationship between age and growth in South Africa, Swaziland, and
Zimbabwe. Similarly, combining six countries in southern and eastern Africa,
Liedholm and Mead (forthcoming) found a negative relationship. Finally, in the
U.S., Evans (1987) studied the relationship between growth and age for 100
industries and found a signiﬁcant negative relationship in three-fourths of the

The relationship between the size of the ﬁrm and growth is also expected to
be negative according to Jovanovic (1982). This was true in several African
countries where McPherson (1992) found a negative relationship between growth
and ﬁrm size in Swaziland, Lesotho, South Africa, and Botswana. He did not
ﬁnd a signiﬁcant relationship, however, in Zimbabwe. Liedholm and Mead
(forthcoming) also found a negative relationship between growth and size when
combining six African countries. In the U.S., Evans (1987) shows a strong

negative relationship between ﬁrm grth and size even when controlling for

49

sample censoring arising from ﬁrm exit. Using the U.S. manufacturing sector,
Hall (1987) weakly rejects Gibrat’s Law for smaller ﬁrms, but accepts it for larger
ﬁrms.

Grth may also vary by industry as illustrated by Phillips and Kirchoff
(1988) who showed signiﬁcant differences in small ﬁrm growth rates by industry
in the U.S. McPherson (1992) also showed varying growth rates among industries
within the same country in southern Africa, however, he did not identify a pattern
across countries. Using one-digit industries and combining six countries,
Liedholm and Mead (forthcoming) also show a signiﬁcant difference in the growth
rates of trading, manufacturing, and services.

Growth rates are also expected to differ by regional location. Urban-based
MSEs, for example, should have higher grow rates because of a larger market for
MSE products. McPherson (1992), however, found mixed results. Urban-based
MSEs did not have higher growth rates in Zimbabwe, Lesotho and Botswana,
however, they did exhibit higher growth rates in Swaziland. Liedholm and Mead
(forthcoming) also showed that MSEs in rural towns and villages had signiﬁcantly
lower growth rates in a model that combined six countries in southern and eastern
Aﬁica. Chuta and Liedholm (1985) also found that ﬁrms in localities with less
than 2,000 inhabitants in Sierra Leone did not grow, while two-thirds of the ﬁrms

in Freetown with more than 100,000 inhabitants expanded.

50

MSEs located in commercial or industrial shops are also expected to have
higher growth rates than those located at home, in markets, or on the roadside.
McPherson (1992) showed, for example, that commercial-based shops had higher
growth rates in Botswana, Swaziland, and Zimbabwe. Liedholm and Mead
(forthcoming) also showed similar results by combining six African countries.

Forward and backward linkages are expected to have a positive effect on
employment growth. For example, businesses that sell products to retailers,
wholesalers, or the export market may display higher employment growth rates.

Proﬁts, investment, credit, and support from agricultural income are
expected to have an impact on employment growth. Because these are difﬁcult to
measure in developing countries, however, there is very little empirical evidence
to document these relationships. Parker and Aleke Dondo (1991) showed a
positive correlation between sales and employment growth for two manufacturing
industries near Nairobi, Kenya. Proﬁts, however, were not measured by Parker
and Aleke Dondo. The relationship between investment and growth was positive
in a study of the U.S. manufacturing sector as demonstrated by Hall (1987). He
found that a four million-dollar increase in physical investment is associated with a
1 percent increase in industry employment growth. Although there is no empirical
evidence on the relationship between credit and growth, higher levels of credit and
corresponding investment may lead to higher growth rates. Finally, some studies

suggest that the transfer of income from farm activities to nonfarm activities may

51
affect employment growth. According to Haggblade, Hazel], and Brown (1987)

for example, diversiﬁed activities allow households to reduce risk by shifting
capital between activities. This diversiﬁcation may lead to lower proﬁts for each

activity as suggested by Kilby (1971).

Goremmemrsaulaticns

As discussed in Chapter Two, there are numerous government
regulations in Zimbabwe that may inhibit MSE growth. Licensing, registration,
and title deeds are three areas that are repeatedly cited as impediments to the MSE
sector. In the case of licensing, there are numerous types of licenses required by
speciﬁc types of businesses in Zimbabwe. These include shop, trading, factory,
vendors, hawkers, and liquor licenses. Each license serves a different purpose and
is administered by a different branch of the government. Although the process to
obtain a license may not be costly or time consuming, information on which types
of businesses need which licenses is difﬁcult to obtain.

Registration is a separate process from licensing that is both costly and time
consuming. For example, a recent study found that registration could take from
two to eight months (Hess, 1993) and cost from Z8200 to 231,700.s Although
registration offers the advantages of limited liability, and access to credit and

subcontracting, it also has several disadvantages. These include preparation of

 

’ US$1.00 = 256.70

52

ﬁnancial statements and payment of taxes based on income. Because most MSEs
do not keep records, these requirements are difﬁcult to meet.

Some studies have suggested that ﬁrms remain small to avoid the licensing
and registration process and government taxation. If this is true, ﬁrms that do not
have licenses or are not registered may exhibit lower growth rates. Alternatively,
title deeds, or property rights, are expected to be positively related to employment
growth. For example, proprietors that own title deeds can use title deeds as
collateral for loans. Also, title deeds may provide the incentives to proprietors to

invest more in their property.

3.6.5 Iiminantgmnuh

While numerous studies have explored determinants of growth as
discussed above, very few studies have addressed the timing of growth. This
partially reﬂects the nature of single-visit surveys that cannot accurately portray
ﬁrm histories. Liedholm (1990) suggests that ﬁrm growth does not occur
gradually. Instead, it takes place very rapidly in a limited period of time. This is
supported by Chuta’s (1990) work in northern Nigeria that revealed limited
periods of rapid growth for over two-thirds of the ﬁrms studied. Chuta also
showed that 51 percent of growth took place between the ages of ﬁve and ten
years old. Similarly, Phillips and Kirchhoff (1988) showed that few furns grew

during the ﬁrst four years in their study of Canadian ﬁrms.

CHAPTER FOUR

RESEARCH METHODS

4.1 Wham

This chapter describes the research methods used in this study. It begins
with a description of the data collection, including the sampling method, survey
instruments, extrapolation of the results, and survey limitations. The econometric
techniques are then described including: a review of econometric techniques for
panel data, justiﬁcation for using a random effects model for the entry and exit
models, and an explanation of the random effects model. Finally, the data and
speciﬁcations used in the three models, including the expected sign of each

variable, are presented.

4.2 Datamllectinn

The data for this study were collected by two nationwide surveys of micro
and small enterprises in Zimbabwe funded by the United States Agency for
International Development (USAID). An MSE was deﬁned as any business
activity that employed 50 or fewer employees and marketed at least 50 percent of
its product. The ﬁrst study, conducted in 1991, provided information on the
number and types of MSEs in Zimbabwe as well as MSE constraints and credit

sources. The second study, conducted in 1993, examined changes in the MSE

53

54

sector from 1991 to 1993. Particular emphasis was placed on the impact of the
Economic Structural Adjustment Program (ESAP) on the MSE sector (see Section

2.2 for information on ESAP).

4.2.1 Samulinarnethod

The sample for the 1991 survey was selected by using a stratiﬁed,
one-stage cluster sampling technique. This involved three steps. First, the
country was divided into eight strata based on population density and commercial
activities. Urban areas were deﬁned as cities with more than 20,000 inhabitants as
estimated by the 1982 census. Within this group, four strata were used: high-
density areas, low-density areas, commercial districts, and industrial areas.‘5 The
remaining four strata in rural areas included small towns, growth points, district
councils, and rural councils.’ Second, a random sample of enumeration areas
(EAs) within each stratum was selected. Finally, all households, businesses, and
mobile vendors in each selected EA were interviewed.

In the 1993 survey, a subset of EAs from the 1991 study was revisited.
The reduction in the number of EAs was based on an ex post analysis of the

sample size in 1991 that indicated over-sampling in low density areas, commercial

 

‘ High-density areas are typically inhabited by low-income households while
low-density areas are inhabited by hi gh-income households.

7 Growth points are towns designated by the government to promote rural
development. Incentives are provided in these towns to promote establishment and
growth of businesses.

55

Table 4-1: Sample sizes for the 1991 and 1993 surveys

 

 

 

 

 

 

Survey 1991 Survey 1993 Survey
EAs Strata Number Strata Number
of EAs of EAs
High-density Areas 9 High-density Areas 8
Low-density Areas 12 Low-density Areas 6
Commercial Districts 9 Commercial Districts 4
Industrial Areas 8 Industrial Areas 4
District Councils 7 District Councils 7
Rural Councils 4 Rural Councils 4
Smaller Towns 4 Smaller Towns 4
Growth Points 5 Growth Points 3
Total EAs 58 Total EAs 40
Sample Households Visited 14,035 Households Visited 11,762
Size MSEs Interviewed 6,769 MSEs Interviewed 5,356
Folded MSEs 1,101 Folded MSEs 706

 

 

districts, industrial areas, and growth points and under-sampling in the remaining
strata (McPherson and Parker, 1993). Ideally, all EAs should have been revisited
in addition to new EAs in the under-sampled areas. Due to budget constraints and
a longer questionnaire, however, the number of EAs in the over-sampled areas
was reduced and all EAs in the under—sampled strata were revisited. Information

on the sample size for the two surveys is illustrated in Table 4.1.

56
4.2.2 W15

At each household or site, enumerators asked if anyone was
currently operating an MSE on the premises or if anyone had operated an MSE
that had folded.‘ If a business was currently in operation, an existing enterprise
questionnaire (EEQ) was administered. Similarly, a dead enterprise questionnaire
(DEQ) was administered to anyone who had previously operated a business. The
1991 survey also administered a supplementary questionnaire that provided more
detailed information on a subset of MSEs. Because the 1993 EEQ was

signiﬁcantly longer than the 1991 BBQ, a supplementary questionnaire was not

necessary.

4.2.3 Empolationntresults

The sample data collected by the survey were extrapolated to
produce national estimates of the MSE sector in 1991 and 1993. The weights for
each stratum were based on the probability of a household being selected, and the
probability of a respondent being home to answer the questions. This is

represented in equation 4-1.

 

‘ Only existing enterprises on the premises are enumerated to avoid double
counting. For example, two enumerators may visit members of the same households
in different locations. If the enumerators inquired about businesses operated from the
household, businesses would be counted twice. Micro and small enterprises that had
operated in the past, however, are based on businesses operated anywhere in the
country. This does not lead to double counting since individuals are asked about
businesses that they personally operated.

57

 

_ HHS, MSEOP, + MSECL,
‘ H115,

MSEOP'.

where:
VI = weight
1’ = stratumi, i = 1,2,..,8
HHS = total number of households in the stratum
HHE = total number of households enumerated in the stratum
MSEOP = number of MSEs at open households where someone was home
MSECL = number of MSEs at closed households where no one was home

(4- 1)
The ﬁrst term in the equation is the reciprocal of the probability of being
selected. The second term is used to account for closed households. Closed
households are deﬁned as households visited but where no one was at home at the
time of the survey. The weighting factors take these households into account by
assuming that a proportion of closed households in each stratum have MSEs. This
assumption was based on a return visit to 17 percent of all closed households

during the survey period.

4.2.4 Suermitaﬁnns

There are several limitations of the survey which should be
recognized when interpreting the results. First, only 55.6 percent of the
businesses enumerated in 1991 were found during the 1993 survey. The 1993
survey also identiﬁed 2,457 MSEs born prior to or during 1991 that were not

included in the 1991 survey. Because of these differences in the two surveys,

58

information on existing MSEs from the 1991 survey was not included in the entry
model. Instead, information on existing MSEs from the 1993 survey and closed
MSEs from the 1991 and 1993 surveys was used in the entry model.

Closed households, or households where no one was home at the time of
the survey, also represent a survey limitation. In 1993, for example, there were
2,884 households where no one was at home. To determine what proportion of
these households had MSEs, enumerators returned to 758 closed households. The
proportion of closed households with businesses in each stratum was then used to
make assumptions about the number of businesses missed at closed households.
Unfortunately, however, these households were not randomly selected.’

In addition to the sampling errors described above, non-sampling errors
should also be considered. In particular, the respondents in 1991 were asked to
recall all businesses that they operated during their lifetime. Since entrepreneurs
may open and close numerous businesses in their lifetime, they may not be able to
or want to accurately recall all past businesses and the year in which in each
business was started. This inability to recall past businesses may reduce the

number of ﬁrm entries reported in earlier years.

 

’ Closed households were not randomly selected partly due to the lack of
addresses in rural areas. Without speciﬁc addresses, enumerators could not return to
closed households in these areas.

CU

em

res;
in l
by ;
the
mm

0W.

59
4.3 W

By using retrospective data on ﬁrm entry from the 1993 existing enterprise
survey and the two closed enterprise surveys, the information on ﬁrm entry and
exit are represented by panel data sets. The N industries represent the cross-
sectional units, which are observed over T time periods. Both data sets, therefore,
consist of NT observations.

Panel data sets offer several advantages. First, they allow for variation
across ﬁrms and over time. Additionally, panel data collection techniques may
also increase the reliability of the data. For example, repeated sampling may
reduce the imprecision due to poor respondent recall if the interviews are spaced
close together. Furthermore, repeated contact with respondents may raise the
quality of the data by increasing respondent trust and the credibility of the
enumerator.

Panel data may also have some disadvantages. While repeated interviews
may raise the quality of the data, multiple interviews could also affect the
respondents behavior between interviews and their responses. Because the surveys
in this study were two years apart, however, the responses should not be affected
by repeat visits. Another disadvantage is that many respondents may drop out of
the sample over time. As noted above in Section 4.2.4, many respondents were
not available for the second round of data collection. This may affect the results

of the survey.

C3?

at

my

60
Methods for analyzing panel data can be grouped into three basic

categories: (1) pooled cross-section models; (2) ﬁxed effects or covariance models;
and (3) random effects or error components models. These models are described
below following a brief review of ordinary least squares (OLS) that is presented as

a basis for a discussion of the three models.

4.3.1 Wares

The classical normal linear regression model comprises the

(4-2)
regression equation 4.2 and the ﬁve basic assumptions below.
1. Normality: e, is normally distributed
2. Zero mean: Ele,] = O for all i
3. Homoskedasticity: Var[e,] = a’, a constant for all i
4. Independent Errors”: Cov[e,,e,-] = 0 if i¢j.
5. Nonstochastic X: X is a nonstochastic variable with values ﬁxed

in repeated samples

 

‘° Nonautocorrelation is typically used to describe this assumption under OLS.
The term independent errors is used throughout the remainder of the discussion,
however, because the assumption with pooled data must be that there is no correlation
of errors in observations of different ﬁrms or different time periods.

61

By deﬁnition, the variance of e is E[e - E(e)]2 and the covariance of as, is

E[(e, - E(e,)(ej - E(e,-))]. According to assumption two, however, E(e) = 0.

Therefore, the variance of 6 can be written as E(e’) and the covariance of 6,6, can

be written as E[e,e,-]. Using matrix notation, this is written as follows.

E(e e’) =

 

P E(ei') E(€1€2) Kelsi
age-pas?) ...E(e,e_)

bﬂenel) E(enez) E(e,’;) .

(4-3)

 

Using assumptions three and four, however, E[e,2] is a2 and all of the

covariances are equal to zero. Therefore, 4-3 can be rewritten as follows:

E(e e’) =

 

 

. = 02, (44)

Under these assumptions, the best, linear, unbiased estimator of B is

62

1‘30” = (X’X)'1X'y (4—5)
which is derived by minimizing the sum of squared residuals with respect to B.

If all of the assumptions described above hold, OLS is an appropriate
technique to model the relationship between the dependent and independent
variables. The assumptions about the regression disturbance, however, are
restrictive. For example, the ﬁrm size used in this study ranges from one-person
ﬁrms up to ﬁrms with 50 employees. Variances of ﬁrm entry and growth rates
over this wide range of ﬁrm sizes may vary and therefore violate the
homoskedasticity assumption. Similarly, correlation among errors may be present
when using time—series data. For example, a random shock such as the drought in
Zimbabwe could have ripple effects for several time periods. If either
heteroskedasticity or non-independent errors are present, OLS procedures no
longer produce minimum-variance estimates because the classical assumptions are
violated. More important, the estimated variance of the OLS coefﬁcient estimates
will be biased. This bias invalidates standard statistical tests by producing wider
or narrower conﬁdence intervals than they should be depending on the direction of
the bias.

In summary, the consequences of using OLS when the assumptions are
violated leads to unbiased but inefﬁcient estimators and biased estimates of 02 that
invalidate statistical tests. With these consequences in mind, three techniques to

analyze panel data are reviewed below.

63
4.3.2 Won

The pooled cross-section model simply pools all cross-section units
over T time periods and treats each observation as independent. Ordinary least
squares (OLS) is then used to estimate the model. While this method is simple
and yields unbiased estimates of the model parameters, the assumptions of
homoskedasticity and independent errors under OLS are likely to be violated. As
mentioned above, heteroskedasticity is likely to occur in a cross—section data set
that measures employment grth as a function of ﬁrm size. Similarly, non-
independent errors may occur in a time-series data set.

In addition to violating the classical assumptions, using OLS procedures on
pooled cross-section data assumes a constant intercept and slope. This implies that
regression parameters are the same for all units, at all times.

Given the limitations of OLS applied to pooled cross-section data, the ﬁxed
effect and random effects models should be considered. These models are

described below.

4.3 .3 Eixedsffectsnurmriancemndel
The general form of both the ﬁxed effects and random effects model

is illustrated in equation 4-6.

64

Y“ = Xnﬂ + “a + 6,, i=1,...,N, t=l,...,T (4-6)

Y. is the dependent variable for unit i and time period t; Xit is a lxK vector of
explanatory variables; 8 is a le vector of parameters; a, is the unobserved
individual effect; and 5,, is the random error. The errors 6,, are assumed to be
independently and identically distributed (i.i.d.).

The ﬁxed effects and random effects models differ primarily in their
assumptions about (1,. In the ﬁxed effects model, the a, are treated as ﬁxed or
constant over time, whereas the random effects model treats the a. as random.
These assumptions lead to different estimation techniques that are described below.

The ﬁxed effect model is generally treated within the framework of the
classical regression model. Under the assumptions described in Section 4.3.1, the
least squares dummy variable estimator is the best linear unbiased estimator, and is
consistent as either N, T, or both go toward inﬁnity (Hsiao, 1986). Nonetheless,
the assumptions of homoskedasticity and independent errors used in the classical
regression model may be violated as in the pooled cross-section techniques
described above.

An advantage of the ﬁxed effect model is that it introduces dummy
variables that allow the intercept to vary over cross-section units and/or time
periods. This is a more reasonable assumption than the pooled cross-section

estimation that assumed that the regression parameters were the same for all units

65

over all time periods. The addition of dummy variables, however, also reduces
degrees of freedom and may substantially decrease the statistical power of the
model. Fruthermore, the dummy variables do not identify the variables that cause
the regression line to shift over time or ﬁrms.

Addition of dummy variables in the ﬁxed effects model also creates
complications if there are time invariant regressors. For example, if gender is
included in a model that speciﬁes ﬁrm growth as the dependent variable, gender
will not vary over time for that ﬁrm. The ﬁxed effect for that ﬁrm is then
perfectly collinear with gender and the least squares dummy variable estimator
cannot be estimated (Greene, 1993).

Another limitation of the ﬁxed effects model is that it only considers
variation within ﬁrms or individuals. It does not consider the variation between
ﬁrms. Therefore, the estimator is not efﬁcient for a small number of time periods
(Judge er al., 1985).

In addition to the advantages and disadvantages listed above, the decision
to use the ﬁxed effects model may be based on the choice between conditional and
unconditional inference. When the a, are ﬁxed, the inference is conditional on the
cross-sectional units or the ﬁrms in the sample. This is appropriate if the ﬁrms in
the data set cannot be regarded as a random sample from some larger population
or if the research is particularly focused on those ﬁrms in the sample. If

inferences will be drawn about a larger population from which the ﬁrms are

TC

66

drawn, then the unconditional inference used in the random effects model is more
appropriate (Judge, et al., 1982).

In summary, there are numerous disadvantages of the ﬁxed effect model.
These include the following: (1) the restrictive assumptions concerning the
behavior of the regression disturbance; (2) the loss of degrees of freedom due to
the addition of dummy variables; (3) the inability to use time-invariant regressors;
and (4) the inability to consider variation between ﬁrms. Also, while the ﬁxed
effect model may be appropriate for conditional inference, the data in this study
are drawn from a larger population and therefore require unconditional inference.
Based on these limitations, the random effects model is more appropriate for this

study. This model is described in detail below.

4.3.4 W
The random effects model is speciﬁed by equation 4-6 above and

assumes that the unobserved individual effects, al., are random. Unlike the pooled
cross-section technique and the ﬁxed effect model that use the classical regression
model, the random effect model uses generalized least squares (GLS) to estimate
the model. In this case the assumptions about the regression disturbance are not as

restrictive. For example, repeating 4—3 below

67

P Heb E(€1€2) E(ele")q
505261) 1205;) axe-,5")

E(e 6’) = (4’7)

 

 

_E(En€1) E(eue'2) E(e,’;) ‘
Using the assumptions of homoskedasticity and independent errors, 4—3 was
rewritten as 4-4 or E[ee’] = 021.. When the assumptions do not hold, however,

the more general case can be written as

2
al 012 01”
2
021 02 02,.
E(e 6’) = n = (4‘3)
. 2
_°n1 0:12 . on

 

 

which allows for the variances to differ across the conditional distributions of e
and allows for correlation among the disturbance terms.

Derivation of a best linear unbiased estimator for B when E(ee’) = (l =
a"! is presented in Judge et al. (1982). First, the model y = XB + e must be
transformed by multiplying it by a (T X T) matrix P which has the property

PYP’=I,. This yields the following

68

Py = PXB + Pe (4-9)

01‘

yt = Xep + C. (4'10)

The transformed error vector e‘ has the mean

E[e '1 = E[Pe] = PE[e] = 0 (+11)

and the covariance matrix

E[e’e"] = E[Pee’P’] = PE[ee’]P’ = azpnr’ = 021, (+12)

Based on 4-11 and 4-12, the transformed error vector maintains the assumptions of
zero mean, homoskedasticity, and independent errors of the error term in the
classical normal linear regression model. The best linear unbiased estimator is

therefore

ﬁ ___ (Xt’Xt)-1Xt’y¢ (4-13)

69
Filling in the values for X' and y'

a = (x’P’Pn-lx’P’Py (4-14)

Since P‘I'P’ = 1,, then ‘1': PP"1 and ‘1’“ = P’P. Therefore, the least squares

estimator of the transformed equation is

361.3 = (xlw-lm-lxlw-ly (4‘15)

which is the GLS estimator. This estimator is consistent and asymptotically
efﬁcient. Furthermore, it is asymptotically more efﬁcient that the estimator used
in the ﬁxed effects model. This efﬁciency gain is due to the consideration of both
within and between (across ﬁrms) variation. The efﬁciency gain disappears,
however, as T goes to inﬁnity. The disadvantage of the GLS estimator is that the
estimates may be biased if there are any omitted variables that are correlated with
the explanatory variables, i.e. , if the a, are correlated with X.

Using GLS, the regression equation for the random effects model can be

written as

[be

are]

70

Y. =X. +v. i=1,...N, t=l,...T
. ..B .. (4—16)

where vi, = a, + 6,,

The a, are assumed to be i.i.d, with the E(a,)=0, and the variance of or, equal to

of. It is also assumed that a is independent of X and e.

Combining all NT observations, the regression equation can be written as

Y z XB + v (447)
where v = Ga + e
and
e, 0 0 1
0 e,. 0
G = Iﬁ®er = . . . (4'18)
Ldb .,

 

 

Because the variance components are not known, a two-stage approach is
used to derive the GLS estimator. First, variance components are estimated using
the residuals from a simple OLS regression. Feasible GLS coefﬁcient estimates

are then computed based on this estimated variance.

 

Unlike the entry and exit models, the employment growth model is based
on a cross-section of N ﬁrms. Although different time periods of growth are
examined, the analyses are no longer based on a panel data set. Instead, separate
ordinary least squares regressions are estimated for growth in each time period.
The data and speciﬁcation for this model are described below following a

description of the entry and exit models.

4.5 W

Based on the literature review in Chapter Three, the situation in
Zimbabwe, and the available data, variables were selected for the ﬁrm entry, exit,

and growth models. These are presented below.

4.5.1 Entthodel

A random effects model using a linear functional form will be used
to estimate the entry and exit models. As described in Chapter Three, the
empirical analysis uses industrial organization theory as a framework to analyze
the determinants of ﬁrm entry and exit. Following previous work in this area,
entry is examined through industry-level statistics. More speciﬁcally, the
dependent variable, ENTRY, is measured as the gross entry rate. This is deﬁned

as the number of ﬁrms that enter industry i in period t divided by the number of

72
ﬁrms existing in industry i at the beginning of period t. In this case, 21 industries

at the International Standard Industrial Classiﬁcation (ISIC) four-digit level will be
examined.

The gross entry rate was chosen over other measures of entry such as net
entry, gross entry, net entry rate, and employment based on arguments presented
in the literature review. For example, net entry and net entry rates conceal
information on the actual level entry that occurs within an industry over time.
Also, gross entry rates are preferred to the absolute number of entrants in order to
control for the size of the industry. Finally, in studies that include ﬁrms with one
to more than 500 employees, employment is an appropriate measure to account for
the impact of the size of new ﬁrms. In this study, however, more than 90 percent
of all new ﬁrms have four or fewer employees. Determinants of entry for
various ﬁrm sizes will, therefore, not be examined.

Explanatory factors to be included in the regression are categorized as
barriers to entry and entry-inducing factors as suggested by previous literature.
The technological environment is not included as a category of entry determinants
since the majority of micro and small enterprises in Zimbabwe do not engage in
research and development. The barriers to entry included in the model are deﬁned
ﬁrst followed by entry-inducing factors. All deﬁnitions and expected signs are

summarized in Table 4—2.

C0]

C0

do

0i ﬁn
ﬁrms
mm
from I.
Eleni,-
m‘LCﬁ‘jg
“L of
Wt 0
Wm

M

73

The ﬁrst barrier to entry described in the literature review, market
concentration, is expected to be negatively correlated with entry. Market
concentration (CONC) for this study is deﬁned as the percentage of ﬁrms in
industry i with 20 or more employees for all existing ﬁrms in period t.‘1 While
market concentration ratios would be preferred to this measure, data on sales and
market shares are not available for the majority of micro and small enterprises in
Zimbabwe. Capital expenditure (CAPITAL) is also hypothesized to be
negatively correlated with entry. In this case capital is deﬁned as the average
amount of expenditures in hundreds of Zimbabwe dollars on equipment and
buildings to start a business in industry i. The square of capital (SQCAPITAL) is
also included. Working capital (WORKCAP) may also be a barrier to entry in
Zimbabwe where access to capital throughout the life of the business is limited.
This is measured as the average value of working capital in hundreds of Zimbabwe

dollars per ﬁrm on a weekly basis in industry i.

 

“ The market concentration variable (CONC) is based on the weighted sample
of ﬁrms with up to 100 employees. Although a concentration variable based on all
ﬁrms in an industry would be more appropriate, complete information on ﬁrms with
greater than 100 employees was not available. This was due to a lack of cooperation
from larger ﬁrms during the ﬁeldwork. For example, enumerators were instructed to
interview all ﬁrms, regardless of size. Many larger ﬁrms, however, refused to be
interviewed. In these cases, the type of ﬁrm is recorded, however, information on the
size of the ﬁrm is missing. Nonetheless, this does not represent a serious limitation
since only 0.5 percent of all ﬁrms were recorded with missing information on the
number of employees and only 0.1 percent of larger ﬁrms that did cooperate had more
than 100 workers.

en

El

74
Economies of scale (SIZE) is expected to be negatively correlated with

entry as illustrated by several studies (Duetsch, 1984; Gorecki, 1975; Hause and
Du Rietz, 1984; and Acs and Audretsch, 1989). The average size of a ﬁrm in
industry i during period t is used as a proxy for economies of scale.12

Human capital requirements are also included as a barrier to entry by
including proprietor characteristics such as education (EDUCATN) and experience
(EXPERNC). As illustrated by Acs and Audretsch (1989), high levels of human
capital are expected to be negatively correlated with entry. Education is measured
as the average number of years of schooling completed by proprietors in industry i
and experience is measured as the average number of years that proprietors have
worked in industry i.

In addition to the traditional barriers to entry included in previous studies,
two other barriers are considered in this analysis. First, government regulations
are frequently cited as an impediment to micro and small enterprises in Zimbabwe.
These include zoning, licensing, labor market regulations, and price and marketing
controls (Harbin, 1993: Hess, 1993; Robinson, 1991; ENDA, 1990; IMANI 1990;
Saito and Van Dijk, 1990; ILO/SATEP, 1989; Konrad Adenauer Foundation,
1988: UNIDO, 1988; Jasset and Jirira, 1987; and Price Waterhouse, 1986).

While proxies are not available for each of these regulations, the proportion of

existing ﬁrms in industry i during period t that have licenses (LIC) is included as

 

‘2 Similar to the market concentration variable, the size variable is limited by
the lack of cooperation from larger ﬁrms. See the previous footnote for more details.

cm

of

sit

in:

Fir

75
an explanatory variable. Licensing is expected to be negatively correlated with

entry. A higher proportion of licenses is expected to signify stronger enforcement
of licensing within certain industries. This enforcement should discourage new
ﬁrm entrants.

The second barrier to entry not included in previous studies is the type of
site where the ﬁrm operates. In most previous studies, the site of the ﬁrm, an
industrial plant, is homogeneous across industries. In Zimbabwe, however, micro
and small enterprises are located in homes, commercial shops, traditional markets,
on the roadside, or they are mobile. Although most MSEs are located at the
home, the proportion of each type of location varies by industry. The type of site
(SHOP) is therefore included to control for ﬁrm site. It is deﬁned as the
proportion of all existing ﬁrms in industry i located in commercial shops during
period t. The proportion of commercial shops was chosen because it is the most
difﬁcult site to obtain in Zimbabwe where zoning regulations are enforced and
rents are high in commercial areas. Location is, therefore, expected to be
negatively correlated with entry.

Entry-inducing factors are typically represented by industry proﬁtability
and growth. As reviewed in the literature, the price-cost margin, industry sales
growth, and industry employment growth are three typical measures used as
proxies for entry-inducing factors. Similar measures will be used in this study.

First, proﬁts (PROFITS) will be measured as the average level of proﬁts per ﬁrm

76
in industry i, or the returns to both labor and capital. Although proﬁt per unit of

capital or investment could also be used, very few ﬁrms in Zimbabwe reported
capital expenditures. For example 46 percent of all ﬁrms used in the entry model
reported that they spent nothing on capital to start the business and two-thirds
spent less than US$10.00 on capital. Only 13 percent spent more than US$100 on
capital to start the business. Capital expenditures following start-up were even
lower. For example, 82 percent of proprietors reported that they spent no
additional money on capital after starting the business and 90 percent spent less
than US$10.00. Only three percent of all ﬁrms had spent more than US$100 on
capital equipment following their start-up. Because of these low capital
expenditures, proﬁts per ﬁrm is a fairly uniform measure to estimates proﬁts.
Industry growth will be measured by employment growth. Unlike the
studies reviewed earlier, however, MSEs in Zimbabwe employ both paid and
unpaid employees. Unpaid employees are typically immediate family members or
distant relatives that may not have other employment opportunities. This type of
employment does not necessarily represent industry growth or positive proﬁts.
Paid employment growth, however, should represent industry growth. These two
types of employment will be measured separately as the number of paid
(GROWTHPD) or unpaid (GROWTl-IUP) employees added or subtracted since the

start of the business.

77

As mentioned earlier, positive proﬁts and industry employment growth are
expected to attract new entrants. This may not be true in Zimbabwe, however,
where businesses are driven by different factors than businesses in the U.S. or
Canada. For example, the businesses included in the U.S. studies were typically
large, employing from 100 to 500 employees. In Zimbabwe, however, 96 percent
of the existing businesses in 1993 had only one to four workers. Furthermore,
Zimbabwean proprietors typically have low levels of education, skills, and access
to capital. For example, only 16.7 percent of proprietors had completed
secondary school and only 0.7 percent of all ﬁrms had received credit from a
formal institution. With these constraints, proprietors in Zimbabwe may not have
the option to choose an industry based on proﬁts. Instead, proprietors compare
the option of starting a business in an industry that requires low skills, capital, and
can be Opened at their location to the alternatives of working for someone else at a
low wage, being unemployed, or possibly turning to agriculture on a full-time
basis as a source of income.

Because proﬁts may not be a driving force of ﬁrm entry and individuals
continually examine the other alternatives mentioned above, two additional
variables are included in the analysis to represent these alternatives. First, wage
(WAGE) and the square of wage (SQWAGE) will be included to represent the
opportrmity cost of lost wages outside of the MSE sector. It will be deﬁned as the

real minimum wage in time period t as measured by the monthly salary of

78

domestic workers in 1990 Zimbabwe dollars. This wage was chosen because it is
the minimum wage of all industries monitored by the government. Although some
entrepreneurs may ﬁnd higher paying jobs outside of the MSE sector, many
entrepreneurs do not have the skills or experience necessary to acquire these jobs.
It is therefore hypothesized that as the minimum wage decreases, entry may rise as
proprietors compare lower wages of working for someone else with potential
earnings from starting a business.

Second, agriculture will be included as an alternative to the MSE sector. It
will be measured by the average kilograms of maize harvested by MSE owners in
industry i from 1991 to 1993 in thousands of kilograms. It is hypothesized that
industries characterized by proprietors that are also engaged in agriculture will
experience lower entry rates. This is because agriculture may provide a source of
income and subsequently reduce the search for alternative income sources in the
MSE sector.

The gross domestic product (GDP) will be included as a measure of the
performance of the economy. More speciﬁcally, GDP reﬂects primarily changes
in the formal sector, or activities which are included national statistics. Small
agricultural units and small businesses in rural areas are not typically included in
national statistics because of collection difﬁculties. Therefore, changes in entry
caused by changes in the formal sector are measured by including GDP. In this

case, as GDP decreases, entry may increase as individuals have fewer alternatives

79
in the formal sector. GDP will be measured as the percent change in GDP in

period t expressed in constant 1980 dollars using 1990 weights.

Finally, the regional location of the proprietor, should be controlled for in
the analysis of entry. As mentioned above, proprietors are constrained by their
location. They may not have the option of moving to new location where market
demand is greater. The regional location (URBAN) will therefore be included as
me proportion of all exiting MSES in industry i during period t that are located in
urban areas. This is expected to be positively correlated with entry for two
reasons. First, entry rates in urban areas may be higher due to a larger market for
MSE products. Second, agriculture may not be a viable alternative source of
income in urban areas. Therefore, more people may turn to MSES as a source of

income in urban areas.

4.5.2 Eximebl

Similar to ﬁrm entry, exit is examined using industrial organization
as a framework for analysis. Exit (EXIT) is deﬁned as the number of ﬁrms that
exit an industry in period t divided by the number of existing ﬁrms in industry i at
the beginning of period t. This is the gross exit rate, which again is more
appropriate than the net exit rate or absolute measures of exit based on the
arguments presented in Sections 3.2.3 and 3.3.3 above. The 21 sectors at the

ISIC four-digit level used in the entry analysis will also be used for the exit

8()

Table +2: Variables deﬁnitions for the entry model

Variable Definition Expected Sign
nale

BABEIER§_IQ_EEIRX

CONC The proportion of firls in industry 1, period t, with 20 or lore elployees

CAPITAL Average expenditure in hundreds of Zilbabve dollars on equiprent and -
buildings to start a business in industry i

socxprru Square of CAPITAL

WORKCAP Average value of expenditures on variable costs during the week prior to
the survey in industry i in hundreds of Zilbabve dollars

SIZE Average size of firls in industry i, period t leasured by the nurber of -
workers

EDUCATI Average nulber of years of schooling corpleted by proprietors in industry

1
EXPERNC Average nunber of years that proprietors have operated in industry 1
LIC Percentage of firls that have licenses in industry i during period t
SHOP Percentage of firrs in industry i during period t located in a connercial
shop

E!!RX:I!DQ§IE§.EA§IQB§

PROFITS Average annual profits in industry i in Zilbabwe dollars ns

GROHTHPD Average nulber of paid elployees added or subtracted since the start of ns
the business in industry i

GROWTBUP Average nurber of unpaid employees added or subtracted since the start of ns
the business in industry 1

WAGE Dorestic worker’s salary in 1990 Zilbabve dollars per Ionth

SQWAGE Square of WAGE

GDP Percentage change in GDP in period t expressed in constant 1980 dollars
using 1990 weights

HAIZE Average kilogrars of Iaize harvested by proprietors in industry i fro: -
1991 to 1993 in thousands of kilograns

URBAN Percentage of fir-s in industry i during period t located in urban areas +

ns =ITEESe variables are expected to be not significant despite their significance in

0.3. and Canadian studies.

++r

 

81

analysis. Again, a random effects model using a linear functional form is
estimated.

Explanatory factors to be included in the regression include barriers to exit
and exit-inducing factors. As pointed out by Caves and Porter (1976) and
Khemani and Shapiro (1986), symmetries between barriers to entry and barrier to
exit may exist. Some of the same variables from the entry model will, therefore,
be included in the exit model and retain the same deﬁnitions. The variable
deﬁnitions and expected signs for the exit model are illustrated in Table 4—3.

One of the most common barriers to exit, capital expenditures (CAPITAL),
is expected to be negatively correlated with exit. As discussed in Section 3.3.4,
high levels of capital expenditure represent high levels of sunk costs. If the assets
in the industry are not easily transferrable, ﬁrms may continue to operate despite
low proﬁts.

Economies of scale (SIZE) is also included as an explanatory factor.
Although economies of scale may not typically exist in micro and small
enterprises, exit may be lower in industries characterized by larger ﬁrms. Within
these industries, for example, ﬁrms may have easier access to credit and other
resources and therefore be less likely to exit.

Working capital (W ORKCAP) may not be symmetrical in the entry and
exit models. For example, working capital is expected to be a barrier in the entry

model, however, it may act as an exit-inducing factor in Zimbabwe. Industries

82
characterized by high levels of working capital may exhibit higher levels of exit as

ﬁrms struggle to survive. Working capital is therefore hypothesized to be
negatively correlated with exit.

Exit-inducing factors, similar to entry-inducing factors, will be included as
determinants of exit. These include proﬁtability and growth. Proﬁts (PROFITS)
are expected to be negatively correlated with exit. Again, however, Zimbabwean
proprietors may not necessarily make decisions based on proﬁts. For example, 25
percent of MSES closed from 1991 to 1993 due to personal or health reasons
compared to 22 percent that closed for ﬁnancial reasons.

Industry growth will again be measured by growth in paid (GROWTHPD)
and unpaid (GROWTHUP) employees. Growth in paid employment should
indicate positive growth and therefore lower exit rates. Industries characterized by
growth in unpaid employment may be a sign of unproductive growth and lead to
higher exit rates.

Similar to the entry model, proprietor alternatives such as working for
someone else, being unemployed, or turning to agriculture need to be considered
as determinants of exit. The real minimum wage (WAGE) and a quadratic term
will again be used to represent the possibility of working for someone else and is
expected to be positively correlated with exit. Increases in GDP (GDP) may also
lead to higher exit rates as more jobs become available in the formal sector.

Alternatively, GDP increases could lead to higher demand for MSE products and

83

thereby fewer exits. Industries characterized by high levels of agriculture
(MAIZE) within the proprietors household could exhibit higher exit rates since
proprietors have the option of turning to agriculture in good agricultural years or
when business is slow.

The regional location (URBAN) will be used in the exit model to control
for location. In this case, the sign is ambiguous. Exit rates may be higher in
industries with a high proportion of urban enterprises due to a larger turnover.
Controlling for turnover as discussed below, however, exit may be negatively
correlated with a high proportion of urban enterprises if people do not have many
other sources of income.

Entry (ENTRY) will also be included as an explanatory factor. As
suggested by Caves and Porter (1976), entry represents the generational turnover

that is typical of an industry.

4.5.3 Wants]
As described above, growth patterns will be explored using
OLS estimation with a linear functional form that includes a set of interactive and
squared terms for age and size. Unlike the entry and exit models, where rates
were more appropriate than absolute numbers for the dependent variable, the
employment growth model will use the absolute number of employees added or

subtracted in the speciﬁed time period as the dependent variable. This is because

5&4

 

Table 4—3: Variable deﬁnitions for the exit model
_

Variable Definition Expec-

nale ted Sign
BARRIERS TO EXII

CAPITAL Average expenditure in hundreds of Zilbabwe dollars on -
equiplent and buildings to start a business in industry i

SIZE Average size of a firl in industry i, period t as measured by -
the total nulber of workers

- UC

WORKCAP Average value of expenditures on variable costs during the week +
prior to the survey in industry i in hundreds of Zilbabwe
dollars

PROFITS Average annual profits in industry 1 in Zilbabwe dollars -

GROWTHPD Average nunber of paid erployees added or subtracted since the -
start of the business in industry 1

GROWTHUP Average nulber of unpaid elployees added or subtracted since +
the start of the business in industry i

WAGE Doaestic worker’s salary in 1990 Zilbabwe dollars per Ionth +

GDP Percentage change in GDP in period t expressed in constant 1980 ?
dollars using 1990 weights

HAIZE Average kilograls of Iaize harvested by proprietors in industry +
i froa 1991 to 1993 in thousands of kilograls

URBAN Percentage of firls in industry 1 during period t located in -
urban areas

ENTRY Gross entry rate of industry i during period t +

85

size has been identiﬁed in numerous studies as a signiﬁcant determinant of ﬁrm
growth. Therefore, rather than controlling for size by using the growth rate, the
relationship between growth and size should be estimated.

Explanatory factors to be included in the regression are categorized as
proprietor characteristics, ﬁrm characteristics, and government regulations. The
variable names, deﬁnitions, and expected signs for the growth model are
summarized in Table M. Within the ﬁrst category, proprietor characteristics,
education (EDUC) is expected to be positively correlated with employment
growth. Higher education levels may lead to more efﬁcient management
techniques and thereby increase employment growth. Education is measured as
the number of years of schooling completed by the proprietor in ﬁrm i.

Experience (EXPERNC) is also expected to be positively correlated with
growth. As proprietors gain more experience over time, their ﬁrms should grow
as they become more efﬁcient. Experience is deﬁned as the number of years the
proprietor in ﬁrm i has been engaged in the current type of business in time t.
Similarly, experience in other types of businesses (OTHEXP) should also be
positively correlated with growth. This is measured as the number of years the
proprietor in ﬁrm i has been engaged in some other type of business.

The gender of the proprietor is expected to inﬂuence employment growth
within the ﬁrm. Downing (1990) suggests, for example, that female proprietors

avoid risk and are therefore less likely to expand their businesses. Based on this

86
hypothesis, female-owned ﬁrms are expected to have lower growth than male-

owned ﬁrms. This will be measured by including a dummy variable for the
gender of the proprietor.

The marital status of the proprietor may also inﬂuence growth. Married
proprietors may, for example, have access to capital through their spouses. Also,
married proprietors may be more willing to take risks if their spouse is also
providing an income to the household. This will be measured by including a
dummy variable for the marital status of the proprietor.

Firm characteristics that may affect growth include age, size, industry,
location, linkages, proﬁts, investment, credit, and support from agricultural
income. Beginning with age (AGEPER) and size (SIZESTPR), several studies
have shown an inverse relationship between growth and age and size as mentioned
earlier. Age will be measured as the number of years that the ﬁrm has been in
operation in period t. Size will be measured as the number of workers at the
beginning of the period. A set of interactive terms between size and age will also
be included.

Industries are expected to exhibit different growth patterns depending on
market demand and supply conditions. A dummy variable for industry will,
therefore, be included in the model.

Growth patterns are also expected to differ by regional location. Urban-

based MSES, for example, may have higher growth rates because of a larger

87
market for MSE products. This will be measured by including a dummy variable

for the regional location. Similarly, the type of location should have an impact on
growth. For example, MSES based in commercial shops are expected to have
higher growth rates than MSES based at home. Again, a dummy variable will be
included to measure these differences.

Forward and backward linkages are expected to have a positive impact on
growth. As suggested earlier, businesses that sell products to retailers,
wholesalers, or the export market may display higher growth rates. This will be
measured by including a set of dummy variables for the types of linkages
associated with each ﬁrm.

Proﬁts (PROFITS) are also expected to be positively related to employment
growth. As proﬁts increase, the business should expand and therefore add more
employees. This will be measured by the amount of annual proﬁts in Zimbabwe
dollars earned by the business.

Higher initial capital investments (CAPITAL) are expected to be positively
related to employment growth. Capital will be deﬁned as the amount of
expenditures in hundreds of Zimbabwe dollars on equipment and buildings to start
a business.

Many studies suggest that credit is a primary consuaints faced by MSEs.

If this constraint were relieved, MSEs would expand. Credit (CREDIT) will

88

therefore be included as the amount of credit received over the life of the business
in Zimbabwe dollars.

In addition to proﬁts, investment, and credit, several studies suggest that
rural households supplement business activities with income generated from
agricultural activities. Kilby (1971) argues that this may lead to lower proﬁts for
each activity as people diversify. This type of ﬁnancial support will be measured
by including a dummy variable for those households that use agricultural income
to support their business activities.

The last set of determinants is related to government regulations. As
suggested in Chapter Two, numerous regulations have been cited as impediments
to MSE growth. Licensing and registration, for example, may force ﬁrms to
remain small in order to avoid government regulations and taxation. If this is
true, ﬁrms that do not have licenses or are not registered may exhibit lower
growth rates. This will be measured by including dummy variables for both
licensing and registration.

Finally, ﬁrms with title deeds may experience higher growth rates since
title deeds may be used as collateral for loans. Also, people may be more willing
. to invest in their businesses if they own the property. Again, a dummy variable

will be included to indicate if a proprietor owns a title deed.

89

Table 44: Variable deﬁnitions for the growth model

 

Variable Definition Expected
sale 519“
l . ! :l ! . !'
EDUC Wulber of years of schooling coapleted by the proprietor in +
firl i.

EXPERIC Wulber of years the proprietor has been engaged in the current +
type of business in tire t

OTEEXP llulber of years the proprietor has been engaged in other types +
of businesses

HALE Duny for sale-owned fins (feaale oaitted) +

ltIXED Duny for fins owned by Ien and woren +

MARRIED Duny for Iarried proprietors +

E' :1 ! . !°

AGEPER Age of fin i, period t -

SIZESTPR Size of fin i at beginning of the period t as leasured by the -
total nulber of workers

INDUSTRY Industry of fit: as leasured by drmy variables *

URBAN Duny for urban-based fins (rural oaitted) +

DOCATIOII Set of duny variables for bone vs. shop—based HSEs *

IMAGES Set of duny variables for linkages *

E E'! l I t ; '! 1
PROFITS Profits earned in fin i in Zilbabwe dollars +

CAPITAL Expenditure on equiplent and buildings to start a business in +
hundreds of Zilbabwe dollars

CREDIT Alount of credit received over the life of the fit! in zilbabwe +
dollars

AGIllmlE Dtmy for fins supported by agricultural incoae -

WW

LIC Duny for fir-s that own licenses +

REG Duny for fins that are registered +

TITLE Duny for fins that own title deeds +

 

CHAPTER FIVE

RESULTS OF THE ENTRY AND EXIT MODELS

This chapter begins with entry and exit rates of MSES in Zimbabwe. This
is followed by the results of the random effects entry and exit models in sections
5.2 and 5.3, respectively. Descriptive statistics are also used throughout Chapter

Five to support the interpretation of the random effects model.

5.1 MW

As described in the literature review, entry rates of MSES are available in
only two developing countries. In Sierra Leone, Liedholm (1990) found a 12.8
percent annual average entry rate with higher entry rates in localities with more
than 20,000 residents. Cortes et al. (1987) reported an annual average entry rate
of 8.1 percent in Colombia. These results are similar to a U.S. study where entry
rates ranged from 8.9 to 12.1 percent for small ﬁrms (Phillips and Kirchhoff,
1988).

The evidence on exit rates indicates a wider variation than entry rates. For
example, Liedholm (1990) estimated exit rates from 1.3 percent to 12.5 percent
for the manufacturing sector in ﬁve developing countries. In the United States,
Baldwin and Gorecki (1991) estimated a low exit rate of 6.5 percent for

manufacturing ﬁrms in the U.S. during the 1970s.

91

Table 5-1: Entry rates by industry, Zimbabwe 1989 to 1993

 

 

Industry Entry Rates by Industry, Zilbabwe Five
Year

1989 1990 1991 1992 1993 Avg.

Ianufacturing Total 15.4 21.8 15.4 21.1 11.6 17.1
DreSSIaking 13.6 13.4 11.4 14.0 34.2 17.3
Tailoring 11.5 31.3 6.7 19.6 5.1 14.8
Knitting 10.2 18.7 15.0 22.7 11.6 15.6
Other Textile 22.4 18.7 7.5 6.7 7.8 12.6
crocheting 20.8 23.9 17.0 28.7 11.0 20.3
Shoe work 13.3 33.3 8.2 24.7 42.5 24.4
Grass/Cane 10.9 34.9 25.9 22.2 10.7 20.9
Wood 18.7 6.0 5.5 17.7 .2 9.6
Carpentry 19.3 3.4 7.9 1.7 2.3 6.9
Welding 78.1 47.5 9.0 9.9 9.7 30.8
Auto works 1.2 7.0 1.7 2.1 4.9 3.4
Electrical Repair 19.7 5.6 2.7 6.5 28.5 12.6
construction 11.3 0.6 1.3 7.7 17.3 7.6
Trade Total 15.8 24.1 15.6 40.2 44.1 28 0
Vending Foods 5.8 11.6 51.7 20.8 34.6 24.9
Vending Par] Prod. 23.2 35.4 20.9 66.3 55.9 .40.4
Vending Garlents 18.5 21.5 13.0 21.7 44.1 23.8
Grocery 24.0 19.9 .6 20.2 20.9 17.1
Retail Carlents 14.4 32.4 14.3 15.0 20.3 19.3
General Trader .6 10.7 1.2 25.5 24.8 12.6
Services Total 8.7 25.0 7.5 8.3 5.4 11.0
Traditional Healer 2.5 1.2 2.4 0.0 0.0 1.2
Hairdresser/Barber 31.2 96.2 15.8 19.8 11.7 35.0
_MAL, ALL W 15.0 20.9 14.4 23.5 18.2 18.4

 

Note: Only industries with rare than 30 observations are included in this table.

The entry rates at the ISIC one-digit level and in the last row are not siaple averages
of the ooluan figures. They are based on the total nuaber of enterprises born in the
industry, e.g., Ianufacturing, divided by the nuaber alive in the industry.

92

Enu'y and exit rates of MSES in Zimbabwe are illustrated in Tables 5-1 and
5-2, respectively. Overall, the average annual entry rate for the ﬁve-year period
from 1989 to 1993 was 18.4 percent. This is signiﬁcantly higher than entry rates
estimated in previous studies. Table 5-1 also shows that the entry rates vary
widely by industry. Among four-digit industries, the lowest entry rates are in the
traditional healing industry (2.4%) compared to the highest entry rates in vending
of farm products (40.4%). Overall, among one-digit industries, the highest entry
rates occurred in trade, while the lowest entry rates were in construction.

The average annual exit rate for the same ﬁve-year period was 6.2
percent." Again, however, there is a range of exit rates among industries. The
highest exit rates occurred in the garment vending industry (19.7%), compared to
the lowest exit rates in general traders (0.2 %) and traditional healers (0.2 96).
Overall, the highest exit rates occurred in trade, while the lowest exit rates were in
services.

The high levels of entry and exit in trade suggest a positive relationship
between entry and exit. Dunne, Roberts, and Samuelson (1988), for example,

found a positive correlation between entry and exit rates. Using four-year periods,

 

‘3 Although proprietors are asked about all MSEs that they owned over their
lifetime, entry and exit rates are under-reported due to recall difﬁculties. For
example, an effort was made to locate all MSES that had opened and closed in a
one-year period in the Dominican Republic (Cabal, 1994). Cabal found that
including these MSEs would increase the number of new ﬁrms by 12 percent and
the entry rate would increase by 6.5 percent. Similarly, the exit rate would also
increase by including these ﬁrms.

€93

Table 5-2: Exit rates by industry, Zimbabwe 1989 to 1993

 

 

 

Industry Exit Rates by Industry, Zilbabwe Five
Year
1989 1990 1991 1992 1993 Avg,
lanufacturing Total 1.8 6.8 1.4 8 6 5.2 4 8
Dressraking .2 7.5 .6 12.6 10.3 6.2
Tailoring 4.2 17.2 4.0 7.0 1.2 6.7
Knitting .8 8.5 2.1 6.0 7.7 5.0
other Textile 1.8 4.5 1.8 22.3 1.7 6.4
Crocheting 2.0 3.1 .7 6.6 6.2 3.7
Shoe work 0.0 .7 .7 3.6 0.0 1.0
Grass/Cane 3.6 10.2 2.2 11.1 3.2 6.1
Wood .3 7.8 0.0 9.3 5.0 4.5
Carpentry .2 .8 0.0 .2 .3 .3
Welding 1.9 7.4 0.0 22.3 1.9 6.7
Auto works 0.0 0.0 0.0 0.0 2.5 .5
Electrical Repair 6.7 0.0 0.0 0.0 0.0 1.3
Construction .4 2.8 .8 1.1 10.2 3.7
Trade Total 10.2 13.2 19.1 11.7 12.9 13.4
Vending Foods 13.1 1.2 32.8 15.8 17.5 16.1
Vending Farr Prod. 13.7 18.8 26.5 12.5 13.8 17.1
Vending Garlents 16.3 17.8 20.2 20.0 24.3 19.7
Grocery .6 11.5 12.5 0.0 .6 5.0
Retail Garlents 15.0 30.1 26.4 15.1 6.1 18.5
General Trader 0.0 1.0 0.0 0.0 .3 .2
Services Total 2.5 1.9 0.9 1.0 1.2 1.5
Traditional Healer 1.1 0.0 0.0 0.0 0.0 0.2
Hairdresser/Barber 7.8 7.8 2.2 2.5 2.5 4.6
.TOIAL, ALL EITERPRISES 3.4 7.7 4.6 8.6 6.9 6.2
Note:

Only sectors with lore than 30 observations are included in this table.

The exit rates for the ISIC one-digit level and in the last row (Total, all
enterprises) are not silple averages of the colunn figures. They represent the nurber
of firls that exit in a sector, e.g. Ianufacturing, divided by the nulber of firls
alive in the sector.

94

Table 5-3: Correlations between industry entry and exit rates
—

 

1989 1990 1991 1992 1993
Exit rate

Entry rate

1989 .035 .143 -.077 .457 -.088
1990 .296 .350 .086 .210 -.061
1991 .557* .132 .640** .450 .546*
1992 .421 .419 .487 .183 .388
1993 .556* .243 .587* .232 .585*

 

* = 1-tail significance at a = 0.01
** = l-tail significance at a = 0.001

they showed that correlation is highest between entry in one four-year period and
exit in the following four-year period. The correlation between entry and exit
rates in Zimbabwe is illustrated in Table 5-3. Similar to the results of Dunne,
Roberts, and Samuelson, entry is positively correlated with exit in the same year

and all succeeding years, except for three correlations that are close to zero.

5.2 Resumdfmuandnmsffecmuxmndel

5.2.1 WWW
As described in Section 4.3.4, a random effects model was used to

examine factors that inﬂuence entry rates of MSES in Zimbabwe. The results of
the model are presented in Table 5—4. The correlation coefﬁcient (R-square) is .29

and the F-statistic rejects the null hypothesis that all coefﬁcients are equal to zero.

95

Table 54: Random effects entry model results

Dependent variable: gross entry rate

 

Variable Coefficient t-ratio
Capital -0.40** —3.29
chapital 0.78E-05** 3.25
Workcap 1.03** 2.08
Expernc -1.56** -5.25
Lic -0.13** -2.15
Growthup 103.60** 4.27
Hage -3.04** -2.87
Sqwage 0.14E-01** 2.85
GDP -0.60** -2.26
Maize -0.59* -1.75
Urban O.21** 2.65
Constant 182.87** 3.32

 

Regression Statistics
No. of observations
(21 industries x 8

 

time periods) 168

R-square .29

=_.F-statistic 5.88
* 2 significant at a = 0.10
** = significant at a = 0.05

The dependent variable is the gross entry rate of ﬁrms within a four-digit industry
during time period t. All variables that were not signiﬁcant at the 10 percent level
were dropped from the estimation, however, an explanation for their insigniﬁcance
if offered in the discussion below.

The coefﬁcients can be interpreted as follows. There are three types of
variables in the model: (1) those that vary over industry (cross-section), (2) those

that vary over time (time-series), and (3) those that vary over industry and time

96

(cross-section and time-series combined). In the ﬁrst case, the coefﬁcient of
working capital, for example, can be interpreted as a comparison of working
capital use across industries. For every Z$100 increase in working capital used
across industries, the entry rate increases by 1.03 percent, holding all other
variables constant. Variables that vary over time, such as GDP, can be interpreted
as a 1 percentage point increase in the real GDP growth rate leads to a 0.6 percent
decrease in the entry rate, holding other variables constant. Finally, variables that
vary over industry and time, such as license, can be interpreted as a one
percentage point increase in the proportion of ﬁrms that have licenses across
industries and over time leads to a 0.13 percentage point decrease in the entry

rate, holding all other variables constant.

W
The results in Table 5-4 can be used to examine the predicted signs
of the variables described in Section 4.4. Beginning with barriers to entry, market
concentration is not a signiﬁcant determinant of entry and therefore was removed
from the model. This is not surprising given the low levels of concentration in
micro and small enterprise industries. For example, 71.4 percent of all industries
over the six-year time period had no market concentration as measured by the
proportion of ﬁrms in industry i with 20 or more employees. Furthermore, 98.9

percent of all industries had less than 1.7 percent of all ﬁrms with 20 or more

97

employees. The highest level of concentration was in the other textile industry
with 10.7 percent of all ﬁrms with 20 or more employees in 1991.

The second barrier to entry, capital expenditure, was a signiﬁcant
determinant of entry. The results show that high levels of expenditure on building
and equipment to start a business lead to lower entry rates. This is not surprising
given the limited amount of credit available to the MSE sector. Among all
existing ﬁrms in 1993, only 0.7 percent had received credit from a formal
institution. Furthermore, 89 percent of all existing ﬁrms had not received any
type of credit, including loans from family and friends.

Working capital was also predicted to be a barrier to entry. Surprisingly,
however, working capital was positively correlated with entry. This may reﬂect
the positive correlation between working capital and proﬁts. “ Rather than posing
a barrier to entry, higher working capital may, therefore, reﬂect more proﬁtable

Economies of scale were not a signiﬁcant determinant of why. Again,
this is not surprising given the large proportion of micro and small enterprises in
Zimbabwe. Ninety-six percent of all ﬁrms had four or fewer workers in 1993.

The results for human capital barriers to entry were mixed. Education was
not a signiﬁcant determinant of entry. This may reﬂect the small range of average

education levels within the industries. For example, average education levels at

_ “ The correlation coefﬁcient between working capital and proﬁts was .482
Wild! was signiﬁcant at the .001 level.

98
the industry level ranged from some primary education to completion of secondary

school. This low range of education level may not lead to barriers to entry within
an industry. Experience, however, was a signiﬁcant determinant of entry rates.
Assuming that experience is a proxy for skills required to enter an industry, the
results indicate that higher levels of experience act as barriers to entry.

Using licensing as a proxy for government regulation, entry rates were
lower in industries characterized by higher levels of licensing. More speciﬁcally,
a one percentage point increase in the number of ﬁrms with licenses in industry i,
time t, leads to a 0.13 percent decrease in the entry rate for that industry and time
period, holding all other variables constant. As discussed in section 4.4.1, a
higher proportion of licenses is expected to signify stronger enforcement of
licensing within certain industries. The results indicate that this enforcement may
discourage new ﬁrm entrants.

The type of site where a business operates was not a signiﬁcant barrier to
entry. As discussed in Section 4.4.1, micro and small enterprises in Zimbabwe
are located in homes, commercial shops, markets, on the roadside, or they are
mobile. Shops are the most difﬁcult location to maintain due to high rents and
zoning regulations in Zimbabwe. It was expected, therefore, that industries
characterized by a high proportion of businesses in commercial shops would
exhibit low entry rates. The insigniﬁcance of the type of site may reﬂect the fact

that only two of the 21 industries used in the study had two-thirds or more

99

businesses located in shops. The remaining industries had zero to 18.9 percent of

businesses in shops.

As suggested by the literature review, proﬁts and industry growth
are the most common variables included as entry-inducing factors. High proﬁts in
an industry are expected to attract new entrants until proﬁts are zero. In
Zimbabwe, however, proﬁt was not a signiﬁcant determinant of entry. As
mentioned earlier, Zimbabwean proprietors typically have low levels of education,
skills, and access to capital. They may also be conﬁned to their physical location.
The choice to open a business may, therefore, be based on other factors. In
particular, proprietors may compare the option of starting a business in an industry
that requires low skills and capital to the alternatives of working for someone else
at a low wage, being unemployed, or possibly turning to agriculture on a full-time
basis as a source of income. It is not smprising, therefore, that proﬁts were not a
driving force of entry in Zimbabwe. Similarly, growth in paid employment was
not a signiﬁcant entry—inducing factor.

Growth in unpaid employment was positively related to entry. As
discussed earlier, unpaid employment growth may reﬂect expansion in low-proﬁt
industries. For example, micro enterprises may use family members or distant

relatives who do not have other employment opportunities. Addition of these

100
members, therefore, may not represent proﬁtable growth of the business. The

hypothesis that unpaid employment growth is not positively correlated with proﬁts
is supported by the employment growth model in Chapter Six. Using two periods
of decreasing GDP and increasing GDP, proﬁts were negatively correlated with
unpaid employment growth under decreasing GDP and not signiﬁcant during the
period of increasing GDP.

The wage rate and GDP were also included to incorporate alternatives
faced by Zimbabwean proprietors. Unlike traditional entry-inducing factors,
however, these should be negatively correlated with entry. For example, as wage
rates decline in the formal sector, entry is expected to increase as people search
for better sources of income. Also, low wage rates may indicate an excess supply
of labor and thus very few alternatives. As predicted, wages were inversely
related to entry as illustrated by Table 5-4. There was also, however, a turning
point. At higher wage levels, entry rates begin to rise. This could imply that the
demand for labor in the formal sector is inelastic. As minimum wage levels rise,
the demand for labor will decrease signiﬁcantly, forcing people to turn to the MSE
industry for employment. Higher wage rates could also create greater demand for
MSE products, thereby leading to higher MSE entry rates.

GDP, which was included as a proxy for the performance of the economy,
was negatively related to entry. This may indicate that decreases in GDP lead to

higher entry rates as people search for alternative income sources. It also supports

101
the hypothesis that entry is driven by an excess supply of labor rather than demand

for MSE products.

Considering agriculture as an alternative source of income, the level of
maize production was included as a proxy for the average agricultrual production
by proprietor households within each industry. It was hypothesized that industries
characterized by proprietors that are also engaged in agriculture will experience
lower enu'y rates. As predicted, the level of maize production was negatively
correlated with entry. Again, this suggests that may does not occur as frequently
in industries with high levels of agricultural production by proprietors due to the
availability of alternative sources of income.

A proxy was also included to control for the dominant regional location of
an industry. It was represented by the proportion ofﬁrms in an industry that are
located in urban areas. As hypothesized, the proportion of ﬁrms located in urban
areas was positively related to entry. Entry may be higher in urban—based
industries due to a larger market for MSE products. Also, with high
unemployment rates in urban areas, alternative income sources may not be

available.

5 .2.2 EnmLdeehhghMJoimuoﬁtindustnes
As suggested throughout the discussion above, micro and small

enterprise industries in Zimbabwe comprise a range of skills, proﬁts, access to

102

capital, and growth patterns. This range may help explain two conﬂicting
hypotheses about the forces that drive ﬁrm entry: the output-demand hypothesis
and the labor-supply hypothesis. The output-demand hypothesis assumes that ﬁrm
entry is primarily driven by consumer demand for particular products. This
implies that most ﬁrms would be proﬁtable and may require high capital and skill
levels. The output-demand hypothesis has been supported by several studies that
have shown that the demand for small-scale enterprise products increases as rural
household income increases (Deb and Hossain, 1984; Hazell and Roell, 1983; and
King and Byerlee, 197 8). Alternatively, the labor-supply hypothesis assumes that
ﬁrm births are driven by an excess supply of labor. In this case, people enter the
MSE industry in search of alternative income sources regardless of demand for
MSE products. Firms in this category would be characterized by low proﬁts and
low costs of entry. This hypothesis has not been empirically supported, however,
proﬁt measures taken in Zimbabwe showed that the majority of ﬁrms are in low-
proﬁt industries (Daniels, 1994).

Rather than assuming that only one hypothesis is correct, both the output-
demand and labor-supply hypotheses may play a role in driving ﬁrm entry. For
example, high-proﬁt industries with corresponding high levels of skills and capital
are more likely to be driven by market demand forces. Alternatively, low-proﬁt
industries may be driven by an excess supply of labor. If these statements are

true, then the barriers to entry and entry-inducing factors reviewed above should

103
Table 5-5: Proﬁts and start-up costs by industry

 

Industry Avg. Annual Avg. Cost of
Profits Entry (Zilbabwe

(Zirbabwe Dollars)

Dollars)

Retail Garnents 97,630 2,060
Auto Works 71,388 3,034
General Trader 28,970 5,911
Grocery 13,817 40,408
Construction 9,126 787
Welding 8,619 4,040
Hairdresser/Barber 7,078 3,783
Vending Garlents 6,027 534
Carpentry 5,560 104
Electrical Repair 5,512 18,815
Dressraking 4,233 702
Tailoring 3,817 570
Shoework 3,813 699
Other Textile 3,709 87
Vending Foods 2,674 102
Knitting 2,417 539
Grass/Cane 2,045 1
Crocheting 1,355 32
Vending Earl Products 1,142 49
Traditional Healer 1,003 *
Wood Carving 607 26

 

* Not available
US$1.00 = Z$6.70

 

have a different impact on the two types of industries. This hypothesis can be
tested by estimating the entry model separately for high- and low—proﬁt industries.
Using the minimum taxable income of 284,801, ten of the 21 industries from
Table 5-5 fall into the high-proﬁt category, while the remaining industries can be

categorized as low-proﬁt industries.

104
Table 5-6 presents the results of the entry model for high- and low-proﬁt

industries. As predicted, the forces that drive entry are different for the two

categories. Beginning with barriers to may, capital is not a signiﬁcant barrier to

Table 5-6: Random effects entry model results: high- and low-proﬁt industries

Dependent variable: gross entry rate

 

 

 

 

High-profit industries Low-profit industries
Variable Coefficient t-statistic Coefficient t-statistic
Capital -0.60** -3.472 -0.63 -0.095
chapital 0.128-04** 3.425 -0.94B-03 -0.085
Workcap 1.70** 2.678 3.53 0.037
Expernc -1.92** -4.077 -1.49 -0.914
Lic -0.38** -3.335 -0.19 -0.998
Growthup 33.71 0.712 57.93 0.374
Wage *4.40** -2.895 -1.62* -l.844
Sqwage 0.02** 2.918 0.72E-02* 1.752
GDP -0.38 -1.001 -0.64** -2.895
Maize -1.40** -2.427 -1.42 -0.947
Urban 0.28** 2.476 0.56** 5.082
Constant 276.19** 3.485 118.35** 2.326
Regression
Statistics
No. of
observations 80 88
R-sguare .43 .32
F-statistic 4.64 3.67

 

* = significant at the a = 0.10 level
** = significant at the a = 0.05 level

entry for low-proﬁt industries. This result is compatible with the labor-supply
hypothesis that assumes proprietors choose industries that require low-capital

levels. Table 5-5 shows, for example, that the average cost of entry for low-proﬁt

105
industries is only 2328] compared to 287,947 for high-proﬁt industries. It is also

reasonable to assume that capital would be a barrier to entry for high-proﬁt
industries where higher levels of capital are necessary. This assumption is
supported by the results in Table 5-6.

The effect of working capital is also different for the two types of
industries. Low-proﬁt industries are not affected by working capital requirements
whereas entry into high-proﬁt industries is positively correlated with working
capital requirements. As suggested in the entry model that combined all
industries, this positive relationship could reﬂect the correlation between proﬁts
and working capital. Since working capital is positively correlated with proﬁts,
entry driven by proﬁts may be higher in industries characterized by greater
working capital requirements.

Experience was not a signiﬁcant determinant of entry in low-proﬁt
industries. Again, this supports the labor-supply hypothesis that proprietors are
driven into low-skill industries due to an excess supply of labor and limited
options. Experience is a barrier to entry, however, in high-proﬁt industries where
technical and marketing skills are necessary.

Licensing as a proxy for government regulations appears to be a signiﬁcant
barrier to entry in hi gh-proﬁt industries, however, low-proﬁt industries are not
affected according to the results. Although licensing may not affect entry in low-

proﬁt industries, licenses are required by a number of industries in this category.

106

The differences between the high- and low—proﬁt industries may, therefore, reﬂect
a higher level of enforcement within high-proﬁt industries. These results also
support the suggestion that proprietors that have the option of expanding into
proﬁtable industries may choose other options due to the penalties placed on
”visible” ﬁrms. Visible ﬁrms are those ﬁrms that are subject to government
regulation and taxes.

Wage rates are negatively correlated with entry in both high- and low-proﬁt
industries. Again, however, there is a turning point in both types of industries.
Initially, entry decreases as wage rates rise. After the turning point, wage rates
are positively correlated with entry. This turning point occurs, however, at a
lower wage in high-proﬁt industries. This may suggest that high-proﬁt industries
are driven more quickly by output demand if increasing wages lead to higher
demand for MSE products.

The growth rate of GDP was negatively related to entry in low-proﬁt
industries. Again, this supports the labor—supply hypothesis. As the macro
economy declines, more people turn to low-proﬁt industries as an alternative
means of income. In the high—proﬁt industries, GDP was not a signiﬁcant
determinant of entry. This is surprising since the output-demand hypothesis
suggests that higher levels of income should lead to an increase in demand for
MSE products. Entry in high-proﬁt industries would, therefore, expected to be

positively related to GDP growth. Nonetheless, the fact that GDP is not

107

signiﬁcant suggests that people do not turn to high-proﬁt industries as an
alternative source of income when the macro economy declines.

Surprisingly, the level of maize production within proprietor households is
not signiﬁcant in low-proﬁt industries, however, it is inversely related to entry in
high-proﬁt industries. There is no clear explanation. for this pattern.

The variable used as a proxy for the dominant regional location of an
industry was negatively related to entry in both types of industries. A higher
proportion of ﬁrms located in urban areas is therefore associated with higher entry
rates. Within high-proﬁt industries, this could reﬂect a larger market for MSE
products in urban areas. For lower-proﬁt industries, higher entry rates for
industries in urban areas could reﬂect an excess supply of labor in urban areas and
a lack of alternative income sources.

In addition to the variables in Table 5-6, other variables tested in the
general model that combined all industries were also included in the high- and
low-proﬁt entry models. These included market concentration, economies of
scale, education, the type of site, proﬁts, and growth in paid employment. None
of these variables were signiﬁcant determinants of entry in high- or low-proﬁt
industries.

In summary, the labor-supply hypothesis appears to be supported by the
results of the low-proﬁt entry model. The insigniﬁcance of capital expenditures

and experience indicate that proprietors with low capital and skills can easily enter

108

these industries as an alternative source of income. This is further supported by
the inverse relationship between GDP and the entry rate. As GDP declines, more
people turn to these industries as a source of income.

The results of the high-proﬁt entry model suggest that capital, experience,
and government regulations are barriers to entry. Furthermore, entry into these
industries is not driven by changes in GDP as in low-proﬁt industries. Although
these results do not necessarily support the output-demand hypothesis for high-
proﬁt industries, they do not coincide with the labor-supply hypothesis. Firms

within these industries do not appear to be driven by an excess supply of labor.

5.3 Beadisnttherandomsffectsexmmndel

Similar to the entry model, a random effects model was used to examine
factors that inﬂuence exit rates of MSES in Zimbabwe. The results of the model
are presented in Table 5-7. The dependent variable is the gross exit rate of ﬁrms
within a four-digit industry during time period t. Again, the coefﬁcients can be
interpreted as the partial derivative of the exit rate with respect to the associated
regressor.

Three exit models are presented in Table 5-7. The ﬁrst column represents
the results used in a general model that combines all industries. The last two
columns represent the results from the model using only hi gh- and low-proﬁt

industries, respectively. The deﬁnitions of hi gh- and low-proﬁt industries are the

109

Table 5-7: Results of the random effects exit model
—

Dependent variable: gross exit rate

 

 

 

 

All industries High-profit Low-profit
industries industries
Variable Coefficient Coefficient Coefficient
(t-ratio) (t-ratio) (t-ratio)
Workcap -0.01 -o.17 41.49**
(-0.07) (-0.62) (2.59)
Growthpd -0.77 -1.10 139.39
(-.43) (-0.50) (1.19)
Growthup -4.80 -39.56 62.58*
(-0.26) (-1.20) (1.72)
GDP -0.09 0.14 -0.30**
(-0.86) (1.29) (-2.67)
Urban 0.05 0.02 -0.15216**
(1.06) (0.49) (-2.67)
Entry 0.07** 0.03 0.10932**
(2.15) (0.89) (2.76)
Constant 1.64 8.85** 1.7835
(0.47) (2.00) (0.87)
No. of
observations 168 80 88
R-square .13 .20 .37
F-statistic 4.72 5.92 8.14

 

* = significant at a = 0.10
** = significant at a = 0.05

same as those used in the entry model.

Beginning with barriers to exit, only two factors were tested in the exit
models, capital expenditures and economies of scale. Neither of these variables
was signiﬁcant in the three models. This is not surprising given the low levels of
capital expenditures in Zimbabwean micro and small enterprise industries

compared to the studies conducted in the U.S. and Canada. Furthermore, Caves

110
and Porter (1976) identify durable and speciﬁc assets associated with high capital

expenditure that act as barriers to exit. These include large machinery and
inventories of inputs and ﬁnal products with limited salvage value. Again, capital
expenditures are relatively low in Zimbabwe. Furthermore, even larger ﬁrms did
not necessarily keep large inventories due to the lack of inputs and ﬁnal products
prior to the structural adjustment program. The insigniﬁcance of economies of
scale as an exit barrier can be explained by the lack of economies of scale in most
Zimbabwean industries studied here.

Working capital was hypothesized to be an exit-inducing factor. For
example, industries characterized by high levels of working capital may exhibit
higher levels of exit rates as ﬁrms struggle to survive. The results of the model
show that this was true only for low-proﬁt industries. This suggests that access to
working capital is not readily available to ﬁrms in low-proﬁt industries.

Industry growth as measured by growth in paid and unpaid employment
was not signiﬁcant in the general model or the high-proﬁt industry model. In
low-proﬁt industries, however, increases in unpaid employment were a signiﬁcant
exit-inducing factor. As mentioned earlier, previous studies have hypothesized
that employment growth, as measured by increases in paid and unpaid employment
combined, is a sign of proﬁtability. The results from this study show, however,
that the two types of employment growth may have very different effects on entry

and exit. In fact, unpaid employment growth was a negative determinant of entry

111

and a positive determinant of exit in low-proﬁt industries. These results indicate
that unpaid employment growth may be negatively correlated with proﬁts rather
than the positive correlation suggested by previous studies. This interpretation is
also supported in the employment growth model in Chapter Six that shows a
positive relationship between proﬁts and paid employment growth and an

insigniﬁcant relationship between proﬁts and unpaid employment growth.

Table 5-8: Results of the random effects net entry model
—

Dependent variable: net entry rate (entry minus exit) for low-

 

 

 

proﬁt industries
Variable Coefﬁcient t-ratio
Capital 2.45 0.40
chapital -0.005 -0.44
Workcap -55.26 —0.59
Expernc -l.73 -1.10
Lic -O.17 —0.86
Growthup 18.36 0.12
Wage -1.92** -l.97
Sqwage 0.009“ 1.93
GDP -0.37* -1.49
Maize -1.25 -0.82
Urban 0.63** 5.23
Constant 129.22“ 2.33
Regression Statistics

No. of observations 88

R-square .16

F—statistic 1.93

 

 

 

* = signiﬁcant at a =0T0, one—tailed test=
** = signiﬁcant at a = 0.05

112

Increases in GDP could have two opposite effects on exit rates. First, exit
rates could increase as more jobs become available in the formal sector.
Alternatively, GDP increases could lead to higher demand for MSE products and
thereby fewer exits. The results show that GDP is inversely related to exit in low-
proﬁt industries only. This supports the latter effect that an increase in GDP leads
to lower exit rates. Combining the results from the entry and exit models of low-
proﬁt industries, however, an increase in GDP ceterus pan'bus leads to a net
decrease in the expansion rate (entry rate minus exit rate) as illustrated in Table 5-
8. The overall effect of an increase in GDP is, therefore, a decrease in the
expansion rate of ﬁrms in low-proﬁt industries or an increase when GDP declines.
The inverse relationship between GDP and the expansion rate supports that labor-
supply hypothesis that low-proﬁt industries are driven by an excess supply of
labor.

The regional location was a signiﬁcant factor in the low-proﬁt industry
model. The inverse relationship between the proportion of ﬁrms in urban areas
and exit rates suggests that markets are larger in urban areas or that proprietors
have few alternatives. Because regional location was signiﬁcant only in low—proﬁt
industries, the latter conclusion is more likely.

A generational or turnover variable was also included in the exit model.

As suggested by Caves and Porter (1976), the entry variable represents the

”unknown underlying forces that speed the generational turnover of sellers

113

(p.56). " This variable was positively correlated with exit in the low—proﬁt
industry model and the general model that combined all industries. The fact that
entry was not correlated with exit in the hi gh-proﬁt industry model suggests an
irregular pattern of generational turnover in hi gh—proﬁt industries.

Proﬁts and wages were also included in the exit model, however, these
variables were not signiﬁcant determinants of exit. It was hypothesized that
proﬁts would be negatively correlated with exit, while wages were expected to be

positively related to exit.

CHAPTER SIX

RESULTS OF THE EMPLOYMENT GROWTH MODEL

This chapter begins with a review of MSE employment growth rates in
Zimbabwe. This is followed the results of an OLS employment growth model
based on three perspectives. In the ﬁrst case, paid, unpaid, and total employment
growth are compared in section 6.2.1. Section 6.2.2 then examines paid
employment growth in two time periods under decreasing and increasing GDP.
Similarly, unpaid employment grth is examined under decreasing and increasing

GDP in section 6.2.3.

6.1 MW

As discussed in the literature review, employment growth rates have been
more widely studied than entry or exit of MSEs in developing countries. In a
cross-country comparison, for example, Liedholm and Mead (forthcoming) show
that the average annual growth rate of MSES, deﬁned as the percentage change in
the number of employees since the ﬁrm started divided by the number of years the
ﬁrms has operated, is 11.3 percent for ﬁve countries in southern and eastern
Africa. Including Niger and the Dominican Republic, the average annual growth
rate is 11.4 percent. Within these countries, the growth rate ranges from 6.6

percent in Swaziland to 29 percent in Kenya.

114

115

Table 6—1: Average annual employment growth rates by industry,
Zimbabwe

 

 

Industry Growth Rate*
Manufacturing Total 1.0%
Dressmaking 1.3 %
Tailoring 0.4%
Knitting 0.6%
Other textile mfg 2.7%
Crocheting 0.7 %
Shoework and repairs 0.8 %
Grass/cane/bamboo work 1.9%
Carpentry 3.0%
Construction Total 18.2 %
Trade Total 4.4%
Vending foods 1.9%
Vending drinks 9.6%
Vending farm products 5.5 %
Vending garments 0.4 %
Vending other 3.1%
General trader/ dealer 5 .5 %
Tuckshop 5.2 %
Services Total 6.1%
Hairdresser/ Barber 6.1 %
Total 2.5 %

 

* The growth rate is calculated as the percentage change in the number of
employees added or subtracted since the start of the business divided by the
number of years the business has been in existence.

This table is based on all industries that had enough ﬁrms to represent at
least one percent of the sample. It includes only existing ﬁrms in 1993.

116

The average annual employment growth rates of ﬁrms in Zimbabwe are
illustrated in Table 6-1. The overall growth rate is low compared to previous
studies. This low growth rate could represent the impact of the drought in
1991/92. During this period, for example, the GDP fell by 6.6 percent. Despite
the low overall growth rate, some four-digit industries experienced higher growth
rates, such as construction, vending drinks, and hair dressing. Among the ISIC
one—digit industries, construction and services exhibited the highest growth rates
while manufacturing growth rates were the lowest.

While the results in Table 6—1 give some indication of the rate of
employment growth, they do not reveal the factors that drive employment growth.
This is explored in more detail below including a comparison of the results to

studies from other countries.

6.2 W
As mentioned above, several studies have examined employment growth

rates and the factors that drive ﬁrm growth in developing countries. Employment
growth is typically deﬁned in these studies as the percentage change in the number
of employees since the ﬁrm started divided by the number of years that the ﬁrm
has operated. While these studies provide valuable information on ﬁrm growth,

there are two limitations with this method that are described below.

117

First, the use of an annual average employment growth rate does not reveal
the exact time period of the growth. The inﬂuence of macroeconomic factors on
growth, therefore, cannot be determined since the time period for growth of
individual ﬁrms is not uniform. Furthermore, an accurate relationship between
growth and other factors cannot be estimated. For example, estimating growth
over the life of the ﬁrm as a function of the size of the ﬁrm when it started is
inaccurate since grth may have occurred in several stages. In this case, each
time that the ﬁrm added employees, it started as a different size. Similarly,
estimating the percentage change in the number of employees as a function of age
in the current period is inaccurate. Again, this is because the exact age or time
period when the growth occurred is unknown. A third example is the measure of
growth as a function of experience of the proprietor. If a ﬁrm has high growth
and it has operated for 20 years, the results may indicate that growth is high
because the proprietor has run the business for 20 years. The growth may have
occurred, however, in the ﬁrst year of the business.

The second limitation with the average annual employment growth rate
concerns the type of employment growth. As deﬁned above and in previous
studies, employment growth includes the combined change in the number of
proprietors, paid employees, unpaid employees, and trainees. The forces that
drive changes in these different types of employment may vary signiﬁcantly. As

mentioned in Chapter Four, for example, unpaid employees are often immediate

1 18
family members or distant relatives that may not have other employment

opportunities. Alternatively, paid employment growth may indicate proﬁtable
grth in the ﬁrm. In Zimbabwe, the majority of the change in the number of
employees occurred in unpaid and paid employment growth. For example, 47
percent of the change in the number of employees occurred in unpaid employment
growth and 43 percent occurred in paid employment. Only six percent of the total
change in employment wound in proprietors and 4 percent in trainees.

To address these limitations, employment grth will be examined from
several perspectives using ordinary least squares. First, growth in paid and unpaid
employment will be addressed separately within the same time period to determine
the factors that inﬂuence grth in different types of employment. These results
will be compared to the combination of all types of growth in the same time
period. Second, growth in paid employment will be examined in two time periods
to determine the impact of macroeconomic factors on paid employment growth.
Similarly, growth in unpaid employment growth will be examined in two time
periods to determine the impact of macroeconomic factors. In the ﬁrst time period
from August 1991 to August 1992, GDP was declining whereas in the second time
period, from September 1992 to September 1993, GDP was rising. More
speciﬁcally, the GDP growth rate changed from 4.1 percent in 1991 to -6.6 in

1992. In 1993, the GDP growth rate was 4.2 percent.

 

Table 6-2 illustrates the results of the employment growth model
from September 1992 to September 1993. The dependent variable in the ﬁrst two
equations is the absolute change in the number of paid and unpaid employees,
respectively. The third equation uses the absolute change in the number of paid
and unpaid employees combined from September 1992 to September 1993 as the
dependent variable. The coefﬁcients can be interpreted as the partial derivative of
the change in the number of employees with respect to the associated regressor,
holding all other variables constant. In contrast to Chapter Five, insigniﬁcant
variables were maintained in each estimation in order to compare the signiﬁcance
of variables across the different time periods and their effect (or lack of effect) on
different types of employment growth.

The F-statistic in all three equations rejects the null hypothesis that all
coefﬁcients are equal to zero. The adjusted R-square for the three equations is
.42, .02, and .33, for paid, unpaid, and total employment growth, respectively.
These correlation coefﬁcients suggest that a much higher proportion of the
variation in paid employment growth can be explained by the regressors than in
the unpaid employment growth equation. As mentioned earlier, unpaid
employment growth may result from personal favors to unemployed relatives and

thus may not reﬂect proﬁtable growth in the business. In these cases, typical

120

regressors in a growth model such as ﬁrm age, size, proﬁts, and location are not

likely to have any inﬂuence on the use of unpaid employees.

E'l ..

The ﬁrst set of regressors in the model represents proprietor
characteristics. Education, for example, is expected to be positively correlated
with employment growth. In all three equations, education was not a signiﬁcant
determinant of growth. McPherson (1992) found similar results in Swaziland and
Botswana where completion of secondary school did not affect employment
growth. Using a 1991 data set from Zimbabwe, however, McPherson did ﬁnd
that ﬁrms run by proprietors that completed secondary school had higher growth
rates than ﬁrms nm by proprietors with less than a secondary school education.
This difference may reﬂect several factors. First, McPherson used a much smaller
sample of 345 ﬁrms that were not randomly selected compared to the sample
3,613 ﬁrms that were randomly selected. Second, the deﬁnition of growth and
the time period differ in the two models. For example, McPherson measures
growth as the percentage change in employment over the life of the ﬁrm. The
results in Table 6-2, however, reﬂect growth in one year, which allows for the

control of macroeconomic variables.

1221

Table 6-2: OLS employment growth model results

 

 

Independent Dependent Variable
Variables Change in Paid Change in Unpaid Change in Paid and
Elployees Employees Unpaid Elployees
Coefficient Coefficient Coefficient
(t-statistic) (t-statistic) (t-statistic)
Constant -0.1OE-01 0.128-01 0.208-02
(-0.278) (0.513) (0.043)
Educ -0.17E-02 0.318-03 -0.14E-02
(-0.623) (0.171) (-0.410)
Expernc 0.408-03 -0.35£-03 0.608-04
(0.197) (-0.259) (0.023)
Othexp 0.258-03 -0.17E-04 0.238-03
(0.115) (-0.012) (0.086)
Hale -0.48E-01* 0.768-02 -0.4OE-01
(-l.888) (0.463) (-1.270)
Mixed 0.20 ** 0.23 ** 0.42 **
(3.407) (5.624) (5.640)
Married -0.338-02 -0.86E-02 -0.1ZE-01
(~0.168) (-0.672) (-0.482)
Ageper 0.60E-02** -0.23E-02 0.372-02
(2.064) (-1.225) (1.016)
Sizestpr 0.11 ** 0.26E-OI** 0.14 **
(6.947) (2.535) (6.872)
Agexsize -0.40E-01** -0.49E-02** -0.45E-01**
(15.923) (-2.977) (14.283)
Age2 -0.17r-03** 0.492-04 ~o.1zr~o3
(-2.080) (0.935) (-1.180)
Size2 -0.22£-01** -0.92E-03** -0.23£-01**
(30.894) (-2.022) (~25.766)
Agezxsize o.93r-03** 0.442-04 0.935-0344
(15.647) (1.135) (13.108)
Agexsize2 0.373-0244 0.123-03 0.333-02**
(29.695) (1.477) (24.525)
Agezxsizez -o.1or-03** 0.96E-06 -0.1or-03**
(23.846) (0.340) (-18.903)
Durly for
industry
oritted category:
crocheting
dressraking -0.46E-01* -0.64E-02 -0.53E-01
(-1.669) (-0.357) (-1.520)
Tailor -0.69E-01 -0.ZOE-Ol -0.88E-01*
(~1.579) (-0.695) (-l.624)
Knit -0.3SE-01 -0.39E-02 -0.39E-01
(-1.596) (-0.279) (-1.421)
Other textile 0.17B-01 ~0.21£-01 -0.33E-02
(0.407) (-0.747) (-0.061)

1212

Table 6-2 (cont’d)

 

Independent Dependent Var1able
Variables Change in Paid Change in Unpaid Change in Paid and
Biployees Elployees Unpaid Biployees
Shoe work -0.72E-01 0.27E-01 -0.44B-01
(-1.090) (0.640) (~0.540)
Grass/cane work ~0.13 ** 0.14E-01 -0.11 **
(-2.862) (0.505) (-2.028)
Carpentry 0.335-01 01711-01 0.266-01
(0.573) (-0.272) (0.317)
Construction -0.20 ** 0.11 ** -0.87E-01
(-3.073) (2.661) (-1.079)
Vending food -0.11£-01 0.538-01 0.42E-01
(~0.211) (1.522) (0.620)
Vending drinks 0.34E-01 -0.35E-01 -0.12£-02
(0.674) (-1.077) (-0.019)
Vending farl 0.13E-01 0.37E-01** 0.518-01
prod. (0.516) (2.219) (1.563)
Vending garlents -0.44E-01 0.28E-03 -0.44E-01
(-1.192) (0.012) (-0.948)
Vending other -0.36E-01 0.318-01 '0.SIE-02
(-1.028) (1.358) (-0.118)
General trader -1.43 ** -0.ll * -1.5 **
(-14.609) (-1.715) (-12.577)
Tuckshop -0.83E-01* -0.11 ** -0.19 **
(-1.729) (-3.490) (-3.192)
Hair dresser -0.68E-01 0.528-01 -0.16£-01
(-1.328) (1.577) (-0.245)
Bunny for urban -0.17B-01 0.1763-01 -0.SSE-02
location (-1.039) (1.088) (-0.268)
Bully for
location
onitted category:
hone
Harket -0.12E-01 -0.43E-02 -0.16E-01
(~0.282) (-0.157) (-0.307)
Shop 0.19 ** 0.23E-01 0.22 **
(4.413) (0.807) (3.949)
Road 0.26E-01 -0.81E-02 0.188-01
(0.836) (-0.397) (0.463)
Mobile 0.313-01 -0.3ZE-01 -0.63E-03
(0.897) (-1.413) (-0.015)
Industrial site 1.19 ** 0.14E-02 1.19 **
(4.257) (0.008) (3.410)
Other -0.11 -0.37E-01 -0.15
(-0.715) (-0.369) (-0.764)

Table 6-2 (cont’d)

1223

 

 

Independent Dependent Variable
Variables Change in Paid Change in Unpaid Change in Paid and
Employees Employees Unpaid Employees
Dummy for
linkages
omitted category:
consumer
Other 0.16E-01 -0.4ZB-01 -0.26E-01
(0.086) (-0.349) (-0.112)
Retail -0.16E-01 0.14 ** 0.12
(-0.161) (2.130) (0.975)
Wholesalers 0.11 0.11E-01 0.13
(0.278) (0.041) (0.244)
Export 0.46E-01 -0.24E-01 0.22E-01
(0.677) (-0.540) (0.262)
Manufacturer 0.17 -0.58£-01 0.11
(0.924) (-0.487) (0.487)
Profits 0.12E-04** 0.3OE-O6 0.128-04**
(26.955) (1.024) (22.098)
Capital 0.22E-04** 0.23E-06 0.228-04**
(9.698) (0.162) (7.844)
Credit 0.59E-05** -0.46B-06 0.54E-05**
(4.068) (-0.489) (3.001)
Ag income 0.14E-01 0.47E-03 0.14E-01
(1.356) (0.071) (1.122)
License -0.26E-01 -0.16E-01 -0.4ZE-01
(-1.233) (-1.164) (-1.593)
Reg O.76E-01* 0.4SE-Ol* 0.12 **
(1.917) (1.734) (2.432)
Title 0.108-01 0.56E-01** 0.66E-01**
(0.546) (4.692) (2.868)
Degrees of
Freedom 3,563 3,563 3,563
Adj. R-square .42 .02 .33
P-statistic 54.80 2.72 37.18

 

= significant at a = 0.10
** = significant at a = 0.05
—

124
The experience of the proprietor is also expected to be positively correlated

with employment growth. Experience in similar types of businesses and other
types of businesses, however, was not signiﬁcant determinants of growth. These
results contradict Jovanovic’s (1992) hypothesis that ﬁrms grow as managers gain
more experience and learn about their ﬁrm.

The gender of the proprietor is expected to inﬂuence employment growth
within the ﬁrm. Downing (1990) suggests, for example, that female proprietors
avoid risk and are therefore less likely to expand their businesses. Rather tlmn
investing in one business, Downing suggests that women diversify their activities
as a source of income security. In contrast, the results in Table 6-2 show that
female-owned ﬁrms actually exhibit higher paid employment growth rates than
male-owned ﬁrms. There is no signiﬁcant difference, however, in the growth of
unpaid employees between male- and female-owned ﬁrms. Combining both types
of employment growth in equation three, male- and female-owned ﬁrms do not
exhibit signiﬁcant differences in growth rates. McPherson (1992) found similar
results in Lesotho, Botswana, and Zimbabwe where the employment growth rates
of male- and female-owned ﬁrms did not differ. In South Africa and Swaziland,
however, McPherson found that female-owned ﬁrms did exhibit signiﬁcantly
lower growth rates. Liedholm and Mead (forthcoming) also found that female-
owned ﬁrms experienced signiﬁcantly lower growth rates in a regression that

combined growth rates from six countries in southern and eastern Africa.

125
In addition to male- and female-owned ﬁrms, 2 percent of the ﬁrms

examined were owned by both men and women. The results in Table 6—2 show
that these ﬁrms exhibit signiﬁcantly higher employment growth rates than female-
owned ﬁrms in all three equations. These results may reﬂect a stronger capital
base through the combination of several sources of income.

The marital status of the proprietor was also included as an explanatory
factor, however, it was not signiﬁcant. These results are similar to McPherson’s
study (1992) that found that marital status was not a signiﬁcant determinant of

grth in Swaziland or Botswana.

E' I . .
The second set of regressors in Table 6—2 relates to ﬁrm

characteristics. Beginning with age and size, several studies have shown an
inverse relationship between employment growth and the age and size of the ﬁrm.
For example, Cortes et al. , found a decrease in employment growth after ten years
of operation. Chuta (1990) also found an inverse relationship between growth and
age for all sectors in northern Nigeria. In the United States, Evans (1987) found a
negative relationship between growth and age in three-quarters of all industries and
a negative relationship between growth and size. McPherson (1992) also found a

negative relationship between growth and age in South Africa, Swaziland, and

126

Zimbabwe and a negative relationship between growth and size in South Aﬁica,
Swaziland, Lesotho, and Botswana.

Table 6-2 shows the results for the relationship between growth and ﬁrm
age. Following Evans (1987), Dunne, et a1. (1989) and McPherson (1992), a
complete set of interactive terms for age and size has been included. Taking the
partial derivative of growth with respect to age, as illustrated in Table 6-3, there is
a signiﬁcant negative relationship between growth and age in both paid and unpaid
employment. Regarding size, however, the relationship differs for paid and
unpaid employment. In the case of paid employment, size is inversely related to
employment growth. Alternatively, unpaid employment growth is not signiﬁcantly
related to the size of the ﬁrm. Combined, the two types of employment growth
show a signiﬁcant negative relationship between employment growth and size.
These results show that the combination of paid and unpaid employment in
equation 3 conceal information about the relationship between unpaid employment
andthesizeoftheﬁrm. Furthermore, theresultssupportthehypothesisthatthe
factors that drive unpaid employment changes differ from the factors that inﬂuence
changes in paid employment.

Industries are expected to exhibit different growth rates depending on
market demand and supply conditions. Using the predominant type of MSE in

1993, crocheting, as the base category, the results from the ﬁrst equation show

127

Table 6-3: Partial derivatives of growth with respect to age and size
_

 

 

Partial. Dependent Variable
derivatives Change in Change in Change in
Paid Unpaid Paid and
Employees Employees Unpaid
Employees
aGrowth/aAge -0.53 ** -0.31 ** -0.0l **
(-2.44) (-2.21) (-3.10)
aGrowth/BSize -0.11 ** -0.003 -0.11 **
(-11.32) (:0.53) (-9.33)

 

t-statistics are in parentheses

** a significant at a = .05

All partial derivatives were calculated at the mean value of
the variables age and size.

The formulas, based on age, are as follows:

aGrowth/aAge =
b(8)+(b(10)*size)+(2*b(ll)*age)+(2*b(l3)*age*size)+(b(l4)*sizez
)+(2*b(15)* age*sizez)

where:b(8) = the eighth beta coefficient, etc

t-statistic for aGrowth/aAge = (aGrowth/aAge)/((Var
aGrowth/aAge)‘) =

(varb(8)+(4*agez*varb( ll ) )+(4*age2*s i ze2*varb( 13) )+( size‘*
varb(14))+(4*age’*size‘*varb(15))+2*((size*cov(b8,b10))+(2*age*
cov(b8,bll))+(2*age*size*cov(b8,b13) )+(size2*cov(b8,bl4) )+(2*
age*size’*cov(b8,b15))+(size*2*age*cov(b10,bll ) )+(size*2*age*
size*cov(b10,bl3) )+(size*sizez"cov(b10,bl4) )+(size*2*age*size’*
cov(b10,b15))+(2*age*2*age*size*cov(bll,b13))+(2*age*size“=
cov(b11,b14))+(2*age*2*age*sizez*cov(bll ,b15))+(2*age*size*
sizez*cov(b13,bl4))+(2*age*size*2*age*size’+cov(b13,b15))+(size’
*2*age*size’*cov(bl4,b15)) ))"

128
that the dressmaking, grass/cane, construction, general trading, and tuckshop

industries exhibited lower growth in paid employment from September 1992 to
September 1993. In the second equation, the construction and vending farm
products industries exhibited higher growth in unpaid employment than crocheting,
whereas general trading and tuckshops exhibited lower growth in unpaid
employment. Combining the two types of employment growth in equation three,
the tailoring, grass/cane, general trading, and tuckshop industries exhibited
signiﬁcantly lower growth rates than crocheting. Again, these results show that
growth of paid and unpaid employment within industries differs. For example, the
construction industry experienced signiﬁcantly lower paid employment growth
rates and signiﬁcantly higher unpaid employment growth rates than crocheting.

By combining paid and unpaid employment in equation three, the results show that
employment changes in construction were not signiﬁcantly different from
crocheting.

Growth rates are also expected to differ by regional location. Urban-based
MSES, for example, should have higher growth rates because of a larger market
for MSE products. The results from Table 6—2, however, do not show any
signiﬁcant difference in the growth rates between urban and rural-based ﬁrms in
all three equations. McPherson (1992) found similar results for Zimbabwe using
an earlier data set. He also showed that urban-based MSES did not have higher

growth rates in Lesotho and Botswana, however, they did exhibit higher growth

129
rates in Swaziland. Liedholm and Mead (forthcoming) also showed that MSES in

rural towns and villages had signiﬁcantly lower growth rates in a model that
combined six countries in southern and eastern Africa.

MSES located in commercial or industrial shops are also expected to have
higher growth rates than those located at home, in markets, or on the roadside.
McPherson (1992) showed, for example, that commercial-based shops had higher
growth rates in Botswana, Swaziland, and Zimbabwe. Liedholm and Mead
(forthcoming) also showed similar results by combining six countries. The results
from this study show that paid employment growth was signiﬁcantly higher for
MSES located in commercial and industrial shops compared to home-based MSES.
Unpaid employment growth, however, did not vary by location. Combining both
paid and unpaid employment growth in equation three, commercial and industrial
shops displayed higher growth than home-based MSES.

Forward and backward linkages are expected to have a positive effect on
employment growth. For example, businesses that sell products to retailers,
wholesalers, or the export market may display higher employment growth rates.
The dummy variables that represent forward and backward linkages, however,
were not signiﬁcantly different from zero as a group in all three equations.

Many authors have suggested that proﬁts and sales should be positively
correlated with employment growth. As businesses become more proﬁtable, they

will expand by adding new employees. Parker and Aleke Dondo (1991), for

130

example, showed a positive correlation between sales and employment growth for
two manufacturing industries near Nairobi, Kenya. The results in Table 6—2 show
that this is true for paid employment growth. Proﬁts are a positive determinant of
paid employment growth. Equation 2 shows, however, that there is not signiﬁcant
relationship between proﬁts and unpaid employment growth. Combining paid and
unpaid employment growth in Equation 3, proﬁts are a signiﬁcant determinant of
employment growth. These results highlight the limitation of combining paid and
unpaid employment growth as a measure of success. Paid and unpaid employment
growth are driven by separate factors as illustrated by the results in Table 6-2.
Similar to proﬁts, higher initial investments are expected to lead to
employment growth. Again, the results show that capital investment is positively
correlated with paid employment growth, however, there is no signiﬁcant
relationship between investment and unpaid employment growth. Combining paid
and unpaid employment growth in Equation 3, which shows a positive relationship
between investment and total employment growth, conceals this information.
Many studies have suggested that credit is a primary constraint faced by
MSES. Liedholm and Mead (forthcoming) show, for example, that capital is
reported as one of the three leading problems of MSES in ﬁve African countries.
Furthermore, numerous assistance programs focus on credit as a means of
improving the MSE sector. The results in Table 6-2 show that credit is positively

correlated with the growth of paid employment. Unpaid employment growth in

131

Equation 2, however, is not correlated with the amount of credit received by the
business. These results suggest that paid employment growth within industries
may be a more important criteria to determine proﬁtability.

Several studies have suggested that rural households engage in numerous
income-generating activities. According to Haggblade, Hazell, and Brown (1987)
for example, diversiﬁed activities allow households to reduce risk by shifting
capital between activities. In particular, non-farm activities are often supported by
agricultural income. This diversiﬁcation may lead to lower proﬁts for each
activity as suggested by Kilby (1971). The results in Table 6-2 show agricultural
income used to support small businesses is not a signiﬁcant determinant of

employment grth in all three equations.

Qoxemmenuegulations

As discussed in Chapter Two, there are numerous government
regulations in Zimbabwe that may inhibit MSE growth. Licensing, registration,
and title deeds are three areas that are repeatedly cited as impediments to the MSE
sector. In the case of licensing and registration requirements, it has been
suggested that ﬁrms remain small in order to avoid government regulations and
taxes that are administered to licensed or registered ﬁrms. The results in Table 6-
2 show that license ownership is not a signiﬁcant determinant of employment

growth. Registration, however, does affect employment growth. In all three

132
equations, ﬁrms that are registered exhibit signiﬁcantly higher growth rates than

ﬁrms that are not registered. These results support the hypothesis that
unregistered ﬁrms may remain small to avoid government regulations.

Title deeds, or property rights, are expected to be positively related to
employment growth. For example, proprietors that own title deeds can use them
as collateral for loans or they may invest more in their property. In the case of
paid employment growth, title deeds were not a signiﬁcant determinant of
employment growth. Unpaid employment growth, however, was positively
affected by title dwds. In this case, ﬁrms that owned title deeds added more
unpaid employees. Combining both types of employment growth in Equation 3,

ﬁrms with title deeds experience higher employment growth.

 

The differences between the paid and unpaid employment growth

models are highlighted in Table 6-4. The numerous disparities indicate that paid
and unpaid employment growth are inﬂuenced by different factors. The most
important difference is the relationship between growth and proﬁts. Paid
employment growth is positively related to proﬁts whereas there is no signiﬁcant
relationship between unpaid employment growth and proﬁts. Because employment
growth is often used as a proxy for proﬁts or a measure of success, combining

paid and unpaid employment growth is misleading. A second important result is

133
the relationship between credit and employment growth. Paid employment grth

is positively related to credit, while unpaid employment growth is not signiﬁcantly
related to credit. Finally, the low correlation coefﬁcient of the unpaid
employment growth equation indicates that a very small proportion of the variation
in unpaid employment can be explained by common regressors used in
employment growth models. Again, this suggests that unpaid employment may be

inﬂuenced by non—market factors.

 

The previous section highlighted the differences between the factors
that inﬂuence growth in paid and unpaid employment in the same time period. It
did not, however, address the impact of macroeconomic factors on employment
growth. In the next two sections, employment growth is examined in two time
periods. The ﬁrst time period, from August 1991 to August 1992, represents a
period when GDP was decreasing from a 4.1 percent growth rate in 1991 to a —6.6
percent growth rate in 1992. The second time period, from September 1992 to
September 1993 represents a period when GDP was increasing from a -6.6 growth
rate in 1992 to a 4.2 percent growth rate in 1993. Paid employment growth is
examined ﬁrst followed by unpaid employment growth in the next section.

Table 6-5 shows the results of the OLS model of paid employment growth

in two time periods. During the time period covered in equation one, GDP was

1254

Table 6-4: Differences between paid and unpaid growth models

 

Variable/ ' Dependent Variable

 

 

 

 

 

 

 

 

 

 

, Statistic . , ,
i | Paid employment Unpaid employment Combined employment i
(_gr0wth_ _ _gr0wth 1 ,, .,- _growth ;
rgjusted ) .42 .02 .33
R l
i Felale- ) Higher growth U.S. (-) U.S. (+)
; owned 1
firms 5
K Size ) Negative U.S. (-) Negative
, Sector - ) Dressmaking ('l Construction (+) Tailor (-)
Crocheting 2 Grass/cane (-) Vend farm products (+) Grass/cane (-)
omitted 5 Construction (-) General trading ('l General trading H
i General trading (') TuckshoPS ('l Tuckshop (-)
) Tuckshop (-)
Commercial ) Higher growth N.S. (+) Higher growth
and .
Industrial
[1811005 1
u Profits 2 Positive N.S. (+) Positive
Investment Positive U.S. (+) Positive
Credit ) Positive U.S. (-) Positive
3 Title : U.S. (+) Higher growth with Higher growth with
Lamb_t !_ __“_. unenws

 

 

 

 

 

m.s. = not significant
+ or - indicates the sign of the coefficient that was not significant.

135
decreasing. In equation two, repeated from Table 6—2 above, GDP was increasing

during the time period covered. As before, the dependent variable is the absolute
change in the number of paid employees. The coefﬁcients can be interpreted as
the partial derivative of the change in the number of paid employees with respect
to the associated regressor and its quadratic and interactive terms, holding all other
variables constant.

The F—statistic in both equations rejects the null hypothesis that all
coefﬁcients are equal to zero. The adjusted R—square is .07 and .42 for equations
one and two, respectively. Before examining the individual regressors, it is
apparent that a much higher proportion of the variation in growth can be explained
by the regressors in equation two when GDP was rising. This may suggest that
employment growth is affected by more typical forces during economic growth.

Under poor economic conditions, patterns of growth may be less predictable.

E'l ..

The results in Table 6-5 show that education and experience do not
have an impact on paid employment growth in either time period. The difference
between the paid employment growth of male- and female-owned ﬁrms, however,
differed. Under decreasing GDP, there was no signiﬁcant difference between paid
employment growth of male- and female-owned ﬁrms. During the period of

increasing GDP, however, female-owned ﬁrms had signiﬁcantly higher growth

1363

Table 6-5: OLS employment growth model: paid employment

 

 

Independent Dependent Variable: Change in pa1d employees
Variables Time 1: Decreasing GDP Time 2: Increasing GDP
Coefficient Coefficient
(t-statistic) (t-statistic)
Constant 0.41E-02 -O.1OB-01
(0.175) (-0.278)
Educ -O.16£-02 -0.17E-02
(-0.335) (-0.623)
prernc -0.86H-03 0.416-03
(-0.639) (0.197)
Othexp 0.128-02 0.258-03
(0.929) (0.115)
Male ~0.17E-01 -0.48E-01*
(-1.038) (-1.888)
Mixed 0.67B-01* 0.20 **
(1.720) (3.407)
Married 0.518-02 -O.33E-02
(0.402) (-0.168)
Ageper -0.36E-03 0.6OE-02**
(-0.191) (2.064)
Sizestpr -0.118-01 0.11 **
(-1.018) (6.947)
Agexsize -0.51£-02** -0.4OH-01**
(-2.857) (15.923)
Age2 0.102-04 -0.17E-03**
(0.202) (-2.080)
Size2 -0.15E-02** -O.228-01**
(-2.222) (-30.894)
Agezxsize 0.14E-03** 0.93E-03**
(2.521) (15.647)
Agexsize2 0.47r-03** 0.37E-02**
(3.749) (29 695)
Aqezxsize2 -o.21r-04** -o.1or-03**
(-3.436) (-23.846)
Industry dummy
onitted category:
crocheting
Dressmaking -0.10E-01 -0.46E-01*
(-0.585) (-1.669)
Tailor -0.7OE-02 -0.69B-01
(-0.266) (-1.579)
Knit 0.638-02 -0.35E-01
(0.472) (-1.596)

1397

Table 6-5 (cont’d)

 

Ihdgpendent Depeident Variihle: Change in paid enployees
Variables Time 1: Decreasing GDP Time 2: Increasing GDP
Other textile -0.73E-02 0.17H-01
(-0.288) (0.407)
Shoe work 0.41E-01 -0.7ZE-01
(0.983) (-1.090)
Grass/cane work 0.328-03 -0.13 **
(0.012) (-2.862)
Carpentry 0.208-01 0.388-01
(0.499) (0.573)
Construction 0.518-02 -0.20 **
(0.120) (-3.073)
Vending food -0.18£-01 -0.118-01
(-0.493) (-0.211)
Vending drinks 0.298-02 0.34E-01
(0.080) (0.674)
Vending farm -0.14£-01 0.13E-01
prod. (-0.813) (0.516)
Vending garments -0.54H-01** -0.44£-01
(-2.304) (-1.192)
Vending other -0.14H-01 -0.36H-01
(-0.585) (-1.028)
General trader -0.39 ** -l.43 **
(-5.269) (14.609)
Tuckshop -0.51£-02 -0.83E-01*
(-0.152) (-1.729)
Hair dresser 0.26E-01 -0.68£-01
(0.828) (-1.328)
Dummy for urban 0.248-02 -0.17E-01
location (0.232) (-1.039)
Dummy for
location
omitted category:
home
Market 0.278-01 -0.IZE-01
(1.009) (-0.282)
Shop 0.13 ** 0.19 **
(4.916) (4.413)
Road -0.18E-01 0.26B-01
(-0.337) (0.836)
Mobile 0.4lB-Ol* 0.318-01
(1.795) (0.397)
Industrial site 0.76E-01 1.1932**
(0.459) (4.257)

Table 6—5 (cont’d)

1238

 

 

Independent Dependent Variable: Change in paid employees
Variables Time 1: Decreasing GDP Time 2: Increasing GDP
other 0.15E-02 -0.11
(0.017) (-0.715)
Dummy for
linkages
omitted category:
consumer
Other -0.69E-02 0.168-01
(-0.051) (0.086)
Retail -0.10E-01 -0.16E-01
(-0.l67) (-0.161)
Wholesalers 0.15E-01 0.11
(0.066) (0.278)
Export -0.34E-02 0.46E-01
(-0.084) (0.677)
Manufacturer -0.11E-01 0.17
(-0.094) (0.924)
Profits 0.17E-05** 0.12E-04**
(4.426) (26.955)
Capital 0.36E-05 0.22E-O4**
(1.035) (9.698)
Credit 0.81E-05** 0.59E-05**
(9.226) (4.068)
Ag income 0.38E-02 0.14E-01
(0.591) (1.356)
License -0.36E-02 -0.26E-01
(-0.278) (-1.238)
Reg 0.60E-01** 0.76E-01*
(2.512) (1.917)
Title 0.218-01* 0.108-01
(1.804) (0.546)
Degrees of
Freedom 2,936 3,563
Adj. R-square .07 .42
F-statistic 5.70 54.80

 

* = significant at a = 0.10

*4 = significant at a = 0.05
_

139
rates than male-owned ﬁrms. This suggests that female-owned ﬁrms do not grow

more slowly than male-owned ﬁrms as reported in many studies. One explanation
may be, however, that men leave the MSE sector for jobs in the formal sector
when the economy improves. If this is true, female-owned ﬁrms may have the
opportunity to expand as the number of MSES decrease. Firms owned jointly by
men and women experienced signiﬁcantly higher growth rates than female—owned
ﬁrms in both time periods. Finally, the marital status of the proprietor did not

have a signiﬁcant effect on paid employment growth rates in either time period.

I? l . .

Theageandsizeoftheﬁrrnareexpectedtobeinverselyrelatedto
growth as discussed earlier. Taking the partial derivative, as illustrated in Table
6-6, size was negatively related to paid employment growth in both time periods at
the mean, however, the effect of age differed. Dining the decreasing GDP
period, age was not a signiﬁcant determinant of paid employment growth. In
contrast, age was a negative determinant of paid employment growth during the
period of increasing GDP. Again, this may suggest that many hypotheses related
to growth are relevant under good economic conditions. Under poor economic

conditions, a different set of factors may inﬂuence growth.

140
Table 6—6: Partial derivatives of growth with respect to age and size: paid
employment in two time periods
w

 

 

Partial Dependent Variable: Change in paid
derivatives employees
Time 1: Decreasing Time 2: Increasing
GDP GDP
aGrowth/aAge -0.001 -0.0l **
(-0.87) (-2.44)
aGrowth/aSize -0.04 ** -0.ll **
(—6.11) (-11 32)

 

t-statistics are in parentheses

** = significant at a = .05

All partial derivatives were calculated at the mean value of
the variables age and size.

The formulas to calculate the partial derivatives and the t-
statistic can be found in Table 6-3.

”

Industry growth rates are expected to differ depending on market supply
and demand conditions as mentioned earlier. Using crocheting as the base
category, only vending of garments and general trading exhibited signiﬁcantly
lower growth rates under decreasing GDP. Under increasing GDP, dressmaking,
grass/ cane work, construction, general trading, and the tuckshop industries
experienced lower growth rates. These results indicate that growth rates within
the same industry vary with economic conditions.

The regional location was not signiﬁcantly related to growth rates in either
time period. Firms located in shops, however, exhibited signiﬁcantly higher
growth rates than home-based ﬁrms in both time periods. In the decreasing GDP

period, mobile MSES also experienced higher growth rates than home-based

141
MSES. In the increasing GDP period, ﬁrms in industrial sites experienced higher

growth rates than home-based ﬁrms.

Forward and backward linkages were expected to be positively related to
growth as described earlier. In both time periods, however, linkages were not
signiﬁcantly related to paid employment growth.

Proﬁts, investment, and the amount of credit received by a ﬁrm are all
expected to be positively related to employment growth. Dming the period of
decreasing GDP, proﬁts and the amount of credit received were positively related
to growth; however, investment was not a signiﬁcant determinant of growth.
During the period of increasing GDP, all three variables were positively related to
employment growth. This suggests that under poor economic conditions, the level
of initial investment in the ﬁrm may not be relevant. Instead, market conditions
or demand may play a stronger role in determining employment growth.

The use of agricultural income to support a business may lead to lower
proﬁts and possibly lower employment growth as suggested by Kilby (1971). The
results in Table 6—5 show, however, that agricultural income used to support the

business did not affect employment growth in either time period.

142

WWW

Licensing, registration, and title deeds were three types of
regulations cited as impediments to MSE activity in Zimbabwe. In the case of
licensing, ﬁrms that owned licenses did not experience e
signiﬁcantly different growth rates than ﬁrms without licenses in either period.
Registered ﬁrms, however did exhibit higher grth rates than non-registered
ﬁrms in both periods. Ownership of title deeds led to higher employment growth
under decreasing GDP, however, it was not a signiﬁcant factor during the period
of rising GDP. This may suggest that ﬁrms that own titled deeds are more likely

to succeed under poor economic conditions.

 

The differences between the two OLS models of paid employment
growth in two time periods are summarized in Table 6—7. First, the adjusted R-
square under increasing GDP suggests that a much higher proportion of
employment growth can be explained by the regressors used in the model. Among
proprietor characteristics, only the inﬂuence of gender was different between the
two periods. Female-owned ﬁrms appeared to have higher growth rates during the
period of increasing GDP than male-owned ﬁrms. Among ﬁrm characteristics,
age was no longer signiﬁcant under decreasing GDP. These results contradict

many previous studies and may indicate that age in combination with high levels

143

of GDP is negatively related to growth. Under poor economic conditions, age
does not appear to affect growth. Industries also experienced different growth
patterns during the two time periods. This suggests that growth is not uniform
across time periods. Instead, economic conditions will affect growth within
industries. The initial level of investment also affects growth differently in the
two time periods. Under poor economic conditions, investment is not signiﬁcantly
related to growth. During the period of increasing GDP, investment was
positively related to growth. These results suggest that high levels of investment
will not affect ﬁrm growth under poor economic conditions. Finally, title deed
ownership led to higher growth during decreasing GDP, but had no impact during
increasing GDP.

Overall, macroeconomic conditions do affect paid employment growth
patterns. Analyses of growth over the life of the ﬁrm, therefore do not accurately
portray the inﬂuence of individual regressors. Growth should be examined within

speciﬁed time periods whenever possible.

 

This section reviews two OLS estimations of impaid employment

growth in Table 6-8. The same two periods used for the paid employment growth
model are used again. The ﬁrst period represents decreasing GDP while the

second period represents increasing GDP. The dependent variable is the absolute

144

Table 6—7: Differences between paid employment growth models in two

time periods

(_ ,—

ll ,, 1.
;Variable/

._. ---._ __*_ "—T' __.__..__._ _._ .

 

 

 

 

 

 

 

_ I Macroeconomic conditions of paid employnent model

) Statistic ) _ , i
a (Decrease GDP __ __D___________1_________1___-_____1 ___1__1
' Adjusted R2 ( .07 .42 )
Female-owned firms ‘ 14.5. (+) Higher growth )
Firm Age i N.S. (-) Negative

Industries ) Vending garments (-) Dressmaking (-)

Crocheting omitted *

General Trading (-)

Grass/cane work (-)
Construction (-)
General trading (-)
Tuckshop (-)

 

 

 

 

Location - home ) Mobile (+) Industrial sites (+)
omitted i Commercial shops (+) Commercial shops (+)
Initial investlent ) 11.8. (+) Positive
Title deeds ) Higher growth N.S. (+)

 

 

+a4imnna“aannntmvann;mataaasnnnam.

change in the number of unpaid employees. The coefﬁcients can be interpreted as

the partial derivative of the change in the number of unpaid employees with

respect to the associated regressor and its quadratic or interactive terms, holding

all other variables constant.

The F—statistic in both equations rejects the null hypothesis that all

coefﬁcients are equal to zero. The adjusted R-square is .08 and .02 for equations

one and two, respectively. These low correlation coefﬁcients suggest that a very

low proportion of the variation in the number of unpaid employees can be

explained by the regressors in the equations. Again, this may suggest that unpaid

145
employment is driven by a different set of factors than paid employment growth.

Personal reasons for hiring unpaid employees, which are not easily measured, may

be more important determinants of unpaid employment changes.

2.] ..

The results in Table 6—8 show that education and experience in similar
types of businesses do not affect unpaid employment growth in either time period.
Experience in other types of businesses, however, was positively related to unpaid
employment growth in equation one. The gender of the proprietor did not affect
employment growth in either time period, however, married proprietors had
signiﬁcantly higher growth in unpaid employees than unmarried proprietors dtning

the period of decreasing GDP.

13' l . .
The age of the ﬁrm was negatively related to unpaid employment
growth in both time periods as illustrated by the partial derivatives in Table 6-9.
The size of the ﬁrm, however, was negatively related to growth only during the
period of decreasing GDP. In equation two, size was not a signiﬁcant determinant
of unpaid employment growth.
Although some of the individual coefﬁcients for the industry dummy

variables have signiﬁcant t-statistics in Equation 1, the dummy variables

1445
Table 6-8: OLS employment growth model: unpaid employment growth

 

 

Iidzpendeni Variables Dependent Variable: Change in unpaid empioyment
Tile 1: Decreasing GDP Tiie 2: Increasing GDP
Coefficient Coefficient
(t-statistic) (t-statistic)
Constant -0.37E-01* 0.12E-01
(-1.689) ( 0.513)
Educ 0.69E-03 0.31E-03
(0.424) ( 0.171)
Expernc 0.538-03 -0.35E-03
(0.425) (-0.259)
Othexp 0.27r-02** -0.17r-04
(2.244) (-0.012)
Male 0.23E-01 0.76E-02
(1.522) ( 0.463)
Mixed -0.47E-01 0.22 **
(-1.302) ( 5.624)
Married 0.21E-01* -0.86E-02
(1.793) (-0.672)
Ageper -0.85E-03 -O.23E-02
(-0.494) (-1.225)
Sizestpr -0.SOE-01** 0.26E-01**
(-5.074) ( 2.535)
Agexsize -0.6ZE-02** -0.49E-02**
(-3.755) (-2.977)
Age2 0.493-05 0.493-04
(0.104) ( 0.935)
size2 0.27r-ozit -0.92£-03**
(4.429) (-2.022)
Agezxsize 0.132-03tt 0.44r-04
(2.558) ( 1.135)
rqexsize2 -0.19E-03* 0.122-03
(-1.630) ( 1.477)
Aqezxsize2 0.123-04** 0.96E-06
(2.097) ( 0.340)
Dummy for industry oritted
category: crocheting
Dressmaking -0.36E-01** -0.64E-02
(-2.243) (-0.357)
Tailor -0.25E-01 -0.20E-01
(-1.047) (-O.695)
Knit -O.13E-01 -0.39E-02
(-1.027) (~0.279)
Other textile -O.13E-01 -0.ZlE-01
(-0.575) (-0.747)
Shoe work -O.3GE-01 0.27E-01
(-o.942) ( 0.640)

14F7

Table 6-8 (cont’d)

Independent Variables Dependent Variable: Change in unpaid employment

 

Time 1: Decreasing GDP Tile 2: Increasing GDP

Grass/cane work 0.65E-02 0.14E-01
(0.266) ( 0.505)
Carpentry -0.115-01 -0.1zr-01
(-0.302) (-0.272)
Construction 0.67E-01* 0.11 **
(1.711) ( 2.661)
Vending food -0.13r-01 0.535-01
(-0 395) ( 1.522)
Vending drinks -0.28E-01 -0.35E-01
(-0.838) (-1.077)
Vending farm prod. 0.76E-02 0.37E-01**
(0.480) ( 2.219)
Vending garlents -0.79E-02 0.288-03
(-0.366) ( 0.012)
Vending other 0.27E-02 0.31E-01
(0.124) ( 1.358)
General trader -0.86E-01 -0.11 *
(-1.257) (-1.715)
Tuckshop 0.35E-01 -0.11 **
(1.119) (-3.490)
Hair dresser 0.59E-02 0.52E-01
(0.202) ( 1.577)
Dummy for urban location 0.14E-01 0.12E-01
(1.434) ( 1.088)
Dummy for location
omitted category: home
Market 0.37E-01 -0.43E-02
(1.517) (-0.157)
Shop 0.53E-01** 0.23E-01
(2.077) ( 0.307)
Road 0.23E-01 -0.81E-02
(1.123) (-0.397)
Mobile 0.20E-01 -0.32E-01
(0.952) (-1.413)
Industrial site 0.918-01 0.148-02
(0.596) ( 0.008)
Other 0.16E-01 -0.37E-01
(0.193) (-0.369)
Dummy for linkages
omitted category: consuner
Other -0.89E-02 -0.42E-01
(-0.07l) (-0.349)
Retail 0.29E-01 0.14 **
(0.520) ( 2.130)
Wholesalers 0.578-01 0.1lE-01
(0.263) ( 0.041)

Table 6-8 (cont’d)
Independent Variables

148

Dependent Var1able: Change 1n unpaid employment

 

Time 1: Decreasing GDP

Time 2: Increasing GDP

 

Export -0.73E-02 -0.24E-01
(-0.193) (-0.540)
Manufacturer -O.66E-01 -0.58E-01
(-0.607) (-0.437)
Profits -0.99E-06** 0.30E-06
(-2.817) ( 1.024)
Capital 0.62E-05** 0.23E-06
(1.933) ( 0.162)
Credit 0.26E-05** -0.46E-06
(3.220) (-0.439)
Ag incone 0.65E-02 0.47E-03
(1.093) ( 0.071)
License -0.16E-01 -0.16E-01
(-1.290) (-1.164)

Reg 0.62E-01** 0.45E-01*
(2.335) ( 1.734)

Title 0.12E-01 0.56E-01**
(1.139) (4.692)
Degrees of FreedOI 2,936 3,563
Adj. R-square .08 .02
P-statistic 6.44 2.72

 

* = significant at a = 0.10
** = significant at a = 0.05

 

representing industry were not jointly signiﬁcantly different from zero. Under

increasing GDP, construction and vending farm products had higher unpaid

employment growth while general trading and tuckshops had lower unpaid

employment growth than the omitted category during the period of increasing

(iI)I’.

F -tests of the groups of dummies for linkages and location showed that

they were not jointly signiﬁcantly different from zero in either time period.

Similarly, the regional location did not have a signiﬁcant effect on changes in

unpaid employment levels.

149

Table 6—9: Partial derivatives of growth with respect to age and size: unpaid
employment in two time periods
“

 

 

Partial Dependent Variable: Change in paid
derivatives employees
Time 1: Decreasing Time 2: Increasing
GDP GDP
acrowth/aAge -0.22 * —0.31 **
(~l.68) (-2.21)
aGrowth/BSize -0.83 ** -0.003
(-l4.74) (-0.53)

 

t-statistics are in parentheses

* = significant at a = .10

** = significant at a = .05

All partial derivatives were calculated at the mean value of
the variables age and size.

The formulas to calculate the partial derivatives and the t-
statistic can be found in Table 6-3.

—

WM

As mentioned earlier, proﬁts are expected to be positively correlated
with employment growth. During the period of decreasing GDP, however, proﬁts
were negatively correlated with growth in unpaid employment. In the period of
increasing GDP, proﬁts were not a signiﬁcant determinant of unpaid employment
growth. Compared to paid employment growth, which was positively related to
proﬁts in both periods, these results suggest that there is no positive correlation
between proﬁts and unpaid employment growth. Unpaid employment should,

therefore, not be automatically categorized as a sign of success.

150

Initial investments and credit are also expected to be positively related to
employment growth. During the period of decreasing GDP, both investment and
credit were positively related to growth. Dming the period of increasing GDP,
however, investment and credit were not signiﬁcant determinants of unpaid
employment growth.

Agricultural income used to support the MSE is expected to result in lower
proﬁts and possibly lower employment growth rates as mentioned earlier. In both
periods, the use of agricultural income to support MSES was not a signiﬁcant

determinant of unpaid employment growth.

ﬁoyemmenttegruations

As reviewed above, licensing, registration, and title deeds are
expected to affect employment growth of small ﬁrms. The results in Table 6—8
show that license ownership does not have a signiﬁcant impact on growth of
unpaid employment. Firms that are registered, however, had higher unpaid
employment growth rates in both periods. Finally, title deed ownership is not
signiﬁcantly related to unpaid employment growth during the period of decreasing
GDP, however, ﬁrms with title deeds experienced higher growth in unpaid

employment during the period of increasing GDP.

 

The differences between the two OLS models of unpaid employment
growth in two time periods are summarized in Table 6-10. As mentioned earlier,
the low R-squares for the two equations suggest that the factors that affect unpaid
employment growth are not closely tied to typical market forces examined in
employment growth models. Personal reasons for hiring unpaid employees, which
are difﬁcult to measure, may provide a stronger explanation of the factors that
inﬂuence growth in this area. Overall, the most interesting results from the
unpaid employment growth models is the relationship between proﬁts and unpaid
employment growth. During decreasing GDP, proﬁts are negatively correlated
with unpaid employment growth and insigniﬁcantly related to growth during the
period of rising GDP. Again, these results suggest that unpaid employment

growth may not be a sign of a successful business.

1152
Table 6—10: Differences in unpaid employment growth models in two
tinnezrmeriowls ‘

 
  
 
  
 
 

  

 

 

   

Variable/
Statistic

 
 

 

  

 

Adjusted R2

 

 

   
   

 

 

 

Other experience Positive

Married Higher growth N.S. (-)

Size Negative N.S. (-)
Industries - N.S. Construction (+)

Crocheting omitted Vending Farm Products (+)

General Trading (-)

 

 

I
l
i
l
)
I
)
I
1
)
1
I

 

 

Tuckshops H
H Profit Negative N.S. (+)
Initial investment Positive N.S. (+)
Credit Positive N.S. (-)
Title deed ownership ) N.S. (+) Higher growth

 

 

 

+ or - indicates the sigh of the variable that was not significant.

 

CHAPTER SEVEN

SUMMARY AND IMPLICATIONS

This chapter presents a brief review of the objectives and a summary of the
entry, exit, and growth model results in Sections 7.1 and 7.2. This is followed by
the implications of the results for policy in Section 7.3 and the implications for
research methods in Section 7.4. Finally, Section 7.5 suggests issues for further

research based on the ﬁndings of this study.

7.1 Qbiestixes

Micro and small enterprises represent an important source of income and
employment in Zimbabwe. Twenty-nine percent of the working-age population is
engaged in MSE activities and one out of every three households owns or operates
an MSE. Because of their importance as a source of income, the Government of
Zimbabwe has called for an "enabling” environment for the MSE sector. In order
to provide that environment, however, some basic questions need to be answered.
For example, what drives the entry and exit of MSEs? What factors inﬂuence
employment growth within MSES. Are MSES a sustainable source of employment
creation? Based on these questions, this research had three objectives: (1) to

measure MSE entry, exit, and employment growth; (2) to examine the factors that

153

154

inﬂuence MSE entry and exit; and (3) to explore the factors that inﬂuence

employment growth within MSES.

7.2 Summatmarlts

7 .2.1 W

Firm entry rates were signiﬁcantly higher in Zimbabwe compared to

studies of entry in other developing countries and the United Sates. These higher
entry rates could reﬂect a more accurate measurement of entry through the use of
both existing enterprise questionnaires and surveys administered to ﬁrms that had
folded prior to the survey. Firm exit rates in Zimbabwe were comparable to
previous studies.

There was a signiﬁcant positive correlation between entry and exit rates
within speciﬁc industries. This suggests that high entry rates do not necessarily
imply proﬁtable industries. Instead, it may signal an industry with high tin-hover
and possibly low barriers to entry and exit.

Using industrial organization theory as a framework, entry was ﬁrst
examined by combining all industries in a random effects model that estimated the
signiﬁcance of barriers to entry and entry-inducing factors. Although several of
the factors were signiﬁcant, the wide range of skills, proﬁts, access to capital, and
growth patterns exhibited by Zimbabwean ﬁrms called for separate analyses to

determine if the factors that affect entry were the same for all groups. This was

155
achieved by dividing the industries into two groups, high— and low-proﬁt

industries.

Among high-proﬁt industries, capital, experience, and licensing were
signiﬁcant barriers to entry. These same factors, however, were not signiﬁcant
barriers to entry in low-proﬁt industries. These results support both the labor—
supply and output-demand hypotheses of ﬁrm entry. As described earlier, the
labor-supply hypothesis suggests that ﬁrm entry is driven by an excess supply of
labor and therefore entry should be characterized by low costs and low proﬁts. As
shown in the low-proﬁt industries, capital and experience are not barriers to entry.
Alternatively, if entry is driven by demand for MSE output, then entry may be
characterized by higher capital and skill requirements. Again, this was true in the
case of higher-proﬁt industries where capital and experience were barriers to
entry.

Among entry-inducing factors, wage was negatively correlated with entry
in both high- and low-proﬁt industries and exhibited a turning point. As wage
rates increased, entry rates declined initially suggesting that people turn to the
MSE sector as a source of income. At higher wage rates, however, entry
increase. This tmning point occurs at a lower wage rate in higher-proﬁt industries
and could imply that higher-proﬁt industries respond more quickly to output-

demand generated by increased wages.

156
The relationship between GDP and entry differed between high— and low-

proﬁt industries. In the high proﬁt industries, there was no signiﬁcant relationship
between entry and GDP. Among low-proﬁt industries, as GDP increased, entry
declined. Again this supports the labor-supply hypothesis. As the economy
declines, more people m to the low-proﬁt industries with low barriers to entry as
a source of alternative income.

The exit model was also estimated separately for high— and low—proﬁt
industries to determine if the same set of forces affected exit in the two categories
of industries. Growth in unpaid employment in low-proﬁt industries is associated
with higher exit rates, however, it was not signiﬁcant in high-proﬁt industries.
This suggests that growth in unpaid employment is not a sign of increased sales or
proﬁts. GDP was not a signiﬁcant factor in high-proﬁt industries, however, it
was negatively correlated with exit in low-proﬁt industries. Combining these
resultswiththeentrymodel, anincreaseinGDPleadstoanoveralldecreasein
the expansion rate (entry rate minus exit rate). Again, this supports the labor-
supply hypothesis in low-proﬁt industries. The exit model also showed that exit
rates were positively related to entry rates in low-proﬁt industries, however, the
relationship was not signiﬁcant in high-proﬁt industries. This suggests a higher

tm'nover in low-proﬁt industries.

157

7.2.2 Wallis
The employment growth rate of Zimbabwean MSES is low

compared to other developing countries. Nonetheless, there is a wide rage of
growth rates among the four-digit industries. The factors that drive these growth
rates were examined with an ordinary least squares estimation. Unlike previous
studies, however, where the dependent variable was the average annual growth
rate, the absolute growth for a given time period was examined. This new
measurement of growth addressed two limitations. First, the annual average
employment growth rate does not reveal the exact time period of growth. The
inﬂuence of macroeconomic factors on growth, therefore, cannot be determined
since the time period for growth of individual ﬁrms is not uniform. Also, the
exact relationship between growth and other factors, such as age, cannot be
determined since the age when growth occurred is unknown. Second, previous
studies measured employment growth as the combined change in four types of
employees: proprietors, paid workers, unpaid workers, and trainees. Growth in
proprietors and trainees is minimal while growth in paid and unpaid workers
represent 90 percent of all change in employment in Zimbabwe. The factors that
drive paid and unpaid employment may vary signiﬁcantly and therefore should be
examined separately as done in this study.

Comparing paid and unpaid employment growth in the same time period,

the results showed signiﬁcant differences between the paid and unpaid employment

158
growth models. The most important difference was the relationship between

growth and proﬁts. Paid employment growth is positively related to proﬁts
whereas there is no signiﬁcant relationship between unpaid employment growth
and proﬁts. Because employment growth is often used as a proxy for proﬁts or a
measure of success, combining paid and unpaid employment growth is misleading.
The relationship between credit and employment growth also differed in the two
models. Paid employment growth was positively related to the amount of credit
received by the ﬁrms, however, unpaid employment growth was not signiﬁcantly
related to credit. Finally, the low correlation coefﬁcient of the unpaid
employment growth model suggests that unpaid employment is not driven by
typical factors used in employment growth models. Instead, non-market forces
may play a role.

Comparing paid employment growth during periods of increasing and
decreasing GDP showed that female-owned ﬁrms had higher employment growth
during increasing GDP than male-owned ﬁrms. Also, age was no longer a
signiﬁcant determinant under decreasing GDP. This result contradicts many
previous studies that indicate that age is negatively related to growth. Industries
also experienced different growth patterns during the two time periods suggesting
that growth within industries is affected by macroeconomic conditions. Overall,

the differences in the two estimations showed that macroeconomic conditions do

159
affect paid employment growth patterns. Analyses of growth over the life of the

ﬁrms, therefore, do not accurately portray the inﬂuence of individual regressors.
Comparing unpaid employment growth during periods of increasing and
decreasing GDP showed extremely low correlation coefﬁcients in both time
periods. Again, this suggests that the factors that affect unpaid employment
growth are not closely tied to typical market forces. This is supported by the
relationship between proﬁt and unpaid employment growth in the two time
periods. During decreasing GDP, proﬁts are negatively correlated with unpaid
employment growth and insigniﬁcantly related to growth during the period of
increasing GDP. Again, these results suggest that unpaid employment growth may

not be a sign of a successful business.

7.3 Imnlrcatrnnaftmmlrcx
7.3.1 Entrannrlemtmlicrumnlicatinns

Numerous organizations exist in Zimbabwe to assist the micro and
small enterprise sector. These include the Business Extension and Advisory
Program (BESA), the Canadian Executive Service Organization (CESO), the
Environmental Development Agency (EN DA), the Friedrich Naumann
Foundation, the Indigenous Business Development Center (IBDC), the Improve
Your Business (IYB) program from the International Labor Organization, the

Small Enterprise Development Company (SEDCO), and Zambuko Trust. Despite

 

160

their efforts, the level of assistance is low compared to the MSE sector, which
employs close to one-third of the working-age population. The use of these
resomces should, therefore, be carefully planned to have the greatest impact.

How to achieve the greatest impact, however, is not obvious. Some authors argue
for a poverty alleviation strategy that targets the poorest microentrepreneurs to lift
them out of poverty or to subsidize the meager earnings of the household. Others
argue for a growth-oriented strategy that targets ﬁrms with the potential to grow
and contribute to economic growth. According to this argument, economic growth
will create a greater number of jobs and more sustainable jobs that will ultimately
assist the poorest households. Finally, some organizations do not have any target.
Instead, assistance is provided to all types of ﬁrms.

The results from study suggest that low-proﬁt industries are a less
sustainable form of employment creation than high—proﬁt industries. The lack of
capital and skill requirements lead to high entry and exit rates. They are also
inﬂuenced by changes in macroeconomic conditions. As the economy improves,
more people leave these low-proﬁt industries in search of better employment.
Resom'ces devoted to these industries may, therefore, have a low return in the long
run. Alternatively, high-proﬁt industries with low exit rates, higher capital and
skill requirements, and less sensitivity to macroeconomic conditions, may have a

higher economic return.

161

Developing an assistance strategy with the economic return in mind may
not require a choice between entrepreneurs within the two categories of high- and
low-proﬁt industries. Instead, high-proﬁt industries could be supported while
entrepreneurs in low—proﬁt industries could be directed toward more dynamic
industries. This could be achieved by addressing barriers to entry within high-
proﬁt industries and assistance to entrepreneurs from low-proﬁt industries.
Licensing, for example, was a signiﬁcant barrier to entry in high-proﬁt industries.
The licensing system should therefore be examined to determine if it is necessary
to maintain this regulation. Removal of this constraint could help entrepreneurs
from low-proﬁt industries to make a transition to a higher-proﬁt industry. Capital
constraints, identiﬁed as a barrier in high-proﬁt industries, could also be alleviated
by credit programs. Finally, experience as a barrier to entry could be addressed
through training or apprentice programs to assist with a transition. This transition
does not imply a change from the ”informal to formal sector” as frequently
described in past literatm'e. Instead, the transition from crocheting to dressmaking
representsasigniﬁcantincreaseinskillsandmorethanaZOOpercentincreasein
proﬁts. Furthermore, all three types of assistance would apply to both cmrent
entrepreneurs within the high-proﬁt industries and potential entreprenem's from the
low-proﬁt industries.

Although removal of regulations would help all potential entrepreneurs, the

resources are not available to provide credit and training programs to all

162

entrepreneurs in the low-proﬁt industries to make a transition. Entrepreneurs
should, therefore, be screened for potential success. Some insights for the
methods of screening can be gleaned from the growth model results and their

implications for policy described below.

 

The results from the employment growth model showed that
increases in paid employment were a sign of success. Paid employment increases
were positively correlated with proﬁts, investment, and credit received by the
business. In contrast, increases in unpaid employment were negatively related to
proﬁts during a period of decreasing GDP and not signiﬁcantly related to proﬁts
during an increase in GDP. These results suggest that paid employment growth is
one criteria that can be used to evaluate the potential success of a business.

Using paid employment increases as a criteria or goal, proprietor, ﬁrm,
andmgulatorychmctaisﬁcscanbeexammedmdetermmemenecessarystepsto
support paid employment increases or to assess a ﬁrm’s potential for an assistance
program. Using the period of increasing GDP, education was not a signiﬁcant
factor leading to paid employment increases. Furthermore, education was not a
barrier to entry in high-proﬁt industries. These combined results suggest that

education may not be a key factor in the success of a business.

163
Although experience was not signiﬁcantly related to paid employment

growth, it was a barrier to entry in high-proﬁt industries. Again, this barrier
could be addressed through training or apprentice programs.

Female proprietors had signiﬁcantly higher paid employment growth than
male proprietors under good economic conditions. This suggests that female-
owned enterprises are not less dynamic as often implied by development literature.
Based on these results, female-owned ﬁrms should be given more consideration by
assistance programs. Furthermore, female entrepreneurs make up the bulk of the
MSE sector, representing 71 percent of all MSE proprietors.

Considering the location, MSEs located in commercial shops and industrial
sites had signiﬁcantly higher paid employment growth rates than MSEs located at
the home. Assistance programs should therefore support shop-based MSEs and
the transition from home-based to shop-based MSEs. Government regulations
could also support this transition by examining zoning laws which inhibit
development of new shops. For example, shops are only allowed in commercial
areas where rents are high. Many entrepreneurs cannot afford these rents.
Furthermore, many of the markets for MSE products are in residential areas. In
many cases, however, home-based MSEs are illegal and forced to remain small to
avoid police harassment. Again, these laws should be reviewed.

Initial investment and credit were positively related to paid employment

growth. This suggests that credit programs have a positive impact on MSE growth

164
and should be supported. More credit programs could be developed through loan

guarantee programs that operate through commercial banks or Non-Government
Organizations.

Finally, registered ﬁrms had signiﬁcantly higher paid employment growth
than unregistered ﬁrms. Despite this success among registered ﬁrms, only 6.7
percent of all ﬁrms in Zimbabwe are registered. This low proportion reﬂects the
current incentive structure, which encourages registration among large ﬁrms while
discouraging registration among small ﬁrms. For example, the advantages of
registration include limited liability, access to credit, subcontracting possibilities,
and access to foreign exchange prior to the economic reform program. All of
these incentives attract large-scale ﬁrms. A one—person enterprise, such as
crocheter operating from the home, may not require limited liability or access to
credit. In addition to the lack of incentives for small-scale ﬁrms, there are also
numerous disincentives. For example, the registration process can take from two
to eight months and requires large fees and preparation of ﬁnancial statements.
Overall, it appears that registration is a proxy for more sophisticated ﬁrms with
larger markets. Simpliﬁcation of registration procedures may, therefore, not lead
to paid employment growth among the majority of smaller ﬁrms in less dynamic
industries. Nonetheless, for the minority of ﬁrms with the potential to become
registered ﬁrms, registration procedures could be reviewed to ensure that

registration is not a barrier to entry.

 

There are several implications for research methods that can be
drawn from this study. First, an accurate measure of entry and exit requires gross
measures rather net measures. As discussed in the literature review, net entry
measures simply subtract the number of existing in period t-l from the number of
ﬁrms existing in period t. This measure does not reveal the actual turnover
between the two time periods. By using both an existing enterprise questionnaire
and a questionnaire administered to MSEs that folded prior to the survey, gross
measures of ﬁrm entry can be obtained. Studies that use only existing enterprises
will most likely underestimate ﬁrm entry.

Entry and exit models typically combine all industries to examine barriers
to entry and entry-inducing factors. While this may be appropriate for studies of
U.S. and Canadian ﬁrms, the results from this study showed that the forces that
drive entry and exit differ between high- and low-proﬁt industries. Although the
range of proﬁts between the two groups is not large, the capital and skill levels
may vary signiﬁcantly. For policy-making purposes, therefore, disaggregation of
high- and low-proﬁt industries offers a clearer view of how to promote high-proﬁt

industries.

 

Employment growth measures in previous studies used employment
changes over the life of the ﬁrm. This type of measurement does not reveal the
exact time period of growth and therefore it cannot be linked to macroeconomic
conditions. Furthermore, accurate relationships between growth and other factors,
such as size or age, cannot be measured since the exact time of growth is
unknown. By adding retrospective questions or repeating surveys, growth within
speciﬁc time periods can be measured. As illustrated by the results of this study,
this is a better method to measure the impact of different factors on employment
growth. For example, the factors that signiﬁcantly affected growth of paid
employment varied between the two time periods when macroeconomic conditions
differed.

The study of employment changes in previous studies combined changes in
proprietors, trainees, and paid and unpaid employees. As illustrated by the results
in this study, this type of measm‘ement is misleading. The factors that drive paid
and unpaid employment changes differ signiﬁcantly. It is important, therefore, to
list the number of each type of employee separately on the questionnaire for each

time period under consideration, including the number of each type at start-up.

167
7.5 MW

There are several areas for future research that arise from this study. First,
in the area of entry and exit, only traditional market forces were examined.
Numerous studies have revealed, however, that personal reasons play a strong role
in the entry and particularly exit of micro and small enterprises in developing
countries. For example, old age, health, and family responsibilities are often cited
as reasons for exit. This type of information could be explored in greater depth to
examine more closely the causes of exit.

Gender issues that also play an important role in entry and exit are not
examined here. Child care responsibilities, for example, may interfere with
women’s business activities unlike their male counterparts. A separate analyses
could be undertaken to examine entry and exit of female- and male-owned ﬁrms,
similar to the high- and low-proﬁt distinction. This may reveal more information
on how to assist male and female proprietors appropriately.

The proﬁt measures developed for this survey provided rough
approximations of ﬁrm-level proﬁts. Further reﬁnements of these measures are
needed to more accurately measure proﬁt. Furthermore, proﬁts should be
measuredinseveral timeperiodsforthesamesetofﬁrmstodeterminehow
proﬁts change under changing economic conditions. This would require repeated

surveys with proﬁt questions in all time periods.

168

As mentioned earlier, the results showed that traditional market forces may
not inﬂuence changes in unpaid employment. This needs to be examined further
to determine the reasons for use of unpaid employees.

A comparison of paid employment growth under decreasing and increasing
GDP showed that a much greater proportion of the variation in employment
growth was explained by the regressors during the period of increasing GDP. A
closer examination of paid employment growth during periods of low economic
growth could, therefore, be undertaken. The most appropriate assistance policies
or programs, for example, may vary depending upon the current economic
conditions.

Because registered ﬁrms exhibited higher paid employment growth, these
ﬁrms should be studied to determine the characteristics that led to employment
growth. Also, the registration process, which has been described as cumbersome,
could be examined with the assistance of these ﬁrms.

Finally, the minimum wage of a domestic worker was used as the
opportunity cost of jobs in the formal sector. While many MSE proprietors could
not expect jobs above this level, proprietors from industries with higher skill
requirements may compare the proﬁts from running their own business to working
for someone else in the same industry. In this case, wage rates within the industry

would be more appropriate. While these are often hard to acquire, wage rates

169

could be determined from those businesses that have paid employees and used for
further analysis.

The results from this study provided new information about the entry, exit,
and growth of micro and small enterprises in Zimbabwe. In particular, the factors
that drive high- and low-proﬁt ﬁrms were highlighted. Furthermore, the factors
that affect paid and unpaid employment growth under good and poor economic
conditions were examined. Based on these results traditional forms of support
such as credit and training were suggested combined with a closer examination of
how to effectively allocate scarce resources. A review of government regulations
is also necessary to provide a supportive environment that will help the MSE

sector play a productive role in the economic growth of Zimbabwe.

 

BIBLIOGRAPHY

BIBLIOGRAPHY

Acs, Zoltan, and David Audretsch. I989. ”Small-Firm Entry in U.S.
Manufacturing." Economica. May 1989, pp. 255—66.

Anderson, Dennis and Farida Khambata. 1981. ”Small Enterprises and
Development Policy in the Philippines: A Case Study. " World Bank
Working Paper No. 468. July.

Bain, Joe S. 1959. W. New York: John Wiley and Sons.

Baldwin, John R. and Paul K. Gorecki. 1991. ”Firm Entry and Exit in the
Canadian Manufacturing Sector, 1970-1982.” Canadian Journal of
Economics. XXIV, No. 2. May.

Brock, William A. ,and David S. Evans. 1986. IheEconmnijSmall
MWWMSW New York:

Holmes and Meier.

Caves, Richard. 1982. -
Prentice-Hall.

 

Caves, Richard, and Michael E. Porter. 1976. "Barriers to Exit. Chapter in

 

Masson, and P. David Qualls. ‘ Cambridge, MA: Ballinger Publishing
Company.

Chuta, Enyinna. 1990. "A Nigerian Study of Firm Dynamics.” Michigan State
University International Development Paper No. 38.

170

171

Chuta, Enyinna, and Carl Liedholm. 1985. Employment and Growth in Small-
Scale Industry: Empirical Evidence and Policy Assessment from Sierra
Leone. St. Martin’s Press.

Cortes, Mariluz, Albert Berry, and Ashfaq Ishaq. 1987. Success in Small and
Medium-Scale Enterprises: The Evidence from Colombia. Oxford
University Press.

Daniels, Lisa. 1994. "Changes in the Small-Scale Enterprise Sector from 1991 to
1993: Results of a Second Nationwide Survey in Zimbabwe. " GEMINI
Technical Report No. 71. Bethesda, MD:Development Alternatives, Inc.

Daniels, Lisa and Yacob Fisseha. 1992. "Micro- and Small-Scale Enterprises in
Botswana: Results of a Nationwide Survey.” Washington, DC: GEMINI
Technical Report No. 46. Bethesda, MD:Development Alternatives, Inc.

Daniels, Lisa and Austin Ngwira. 1992. "Results of a Nation-Wide Survey on
Micro, Small, and Medium Enterprises in Malawi.” GEMINI Technical
Report No.53. Bethesda, MD: Development Alternatives, Inc.

Deb, NC. and M. Hossain. 1984. "Demand for Rural Industries Products in
Bangladesh. " The Bangladesh Development Studies, Volume XII, No.
1982.

Downing, Jeanne. 1990. "Gender and the Growth of Microenterprises.” GEMINI
Working Paper No. 5. Bethesda, MD:Development Alternatives, Inc.

Duetsch, Larry L. 1984. "Entry and the Extent of Multiplant Operations.” Journal
of Industrial Economics. June. pp. 477-87.

Duetsch, Larry L. 1975. ”Structm'e, Performance, and the Net Role of Entry into
Manufacturing Industries. " Southern Economic Journal, January. pp. 450-
456.

Dunne, Timothy, Mark J. Roberts, and Larry Samuelson. 1989. ”The Growth and
Failure of U.S. Manufacturing Plants. " Quarterly Journal of Economics,
vol. 104, No. 4.

Dunne, Timothy, Mark J. Roberts, and Larry Samuelson. 1988. ”Patterns of Firm
Entry and Exit in U.S. Manufacturing Industries." Rand Journal of
Economics 19. pp. 495-515.

172

Environmental and Developmental Activities (EN DA). 1990. ”Women in the
Informal Sector: A Zimbabwean Study." Harare, ZimbabwezENDA.

Evans, David S. 1987. "The Relationship Between Firm Growth, Size, and Age:
Estimates for 100 Manufacturing Industries. " The Journal of Industrial
Economics. Volume XXXV. No. 4. PP. 567-581.

Fisseha, Yacob. 1991. “Small-Scale Enterprises in Lesotho: Summary of a
Country-Wide Survey. " GEMINI Technical Report No. 14. Bethesda, MD:
Development Alternatives, Inc.

Fisseha, Yacob and Michael A. McPherson. 1991. "A Country-wide Study of
Small-Scale Enterprises in Swaziland.” GEMINI Technical Report No. 24.
Bethesda, MD:Development Alternatives, Inc.

Frishman, Alan. 1988. "The Survival and Disappearance of Small Scale
Enterprises in Urban Kano, 1973-1980.” Draft paper.

Gibrat, R. 1931, Les Inégalités Economiques. Paris.

Gorecki, Paul K. 1975. ”The Determinants of Entry by New and Diversifying
Enterprises in the U.K. Manufactming Sector, 1958-1963: Some Tentative
Results.” Applied Econometrics. pp. 139-47.

Government of Zimbabwe. 1991. "Zimbabwe. A Framework for Economic
Reform (1991-1995)." Harare, Zimbabwe.

Greene, William H. 1993. Econometric Analysis. New York: Macmillan
Publishing Company.

Haggblade, Steve, Peter Hazell, and James Brown. 1987. Fann-Nonfann Linkages
in Rural Sub-Saharan Africa: Empirical Evidence and Policy Implications.
Agricultme and Rmal Development Discussion Paper No. 67. Washington,
DC: World Bank.

Hall, Bronwyn. 1987. "The Relationship between Firm Size and Firm Growth in
the US Manufacturing Sector. " Journal of Industrial Economics, Volume
XXXV. NO. 4, pp 583-606.

Hamilton, R.T. 1985. "Interindustry Variation in Gross Entry Rates of
’Independent’ and ’Dependent’ Businesses.” Applied Economics. Vol 17,
pp. 271-280.

 

173

Harbin, Nancy. 1993. ”Govemment Regulations and the Costs of Compliance for
Small-Scale Metal-Working Enterprises in Zimbabwe. " Draft Report.
Harare, Zimbabwe: Environmental and Developmental Activities (ENDA).

Hause, John C., and Gunnar Du Rietz. 1984. ”Entry, Industry Growth, and the
Microdymmics of Industry Supply. " Journal of Political Economy. Vol.
92. No. 4. pp. 733-757.

Hazell, Peter and Alisa Roell. 1983. ”Rural Growth Linkages: Household
Expenditure Patterns in Malaysia and Nigeria. " International Food Policy
Research Institute Research Report #41. Washington, DC.

Hess, Richard. 1993. "Cost of Business Compliance. Small Scale Garment and
Textile Sector.” Paper presented at the Conference on Deregulation, 16-18
May, 1993, Nyanga, Zimbabwe.

Highﬁeld, Richard and Robert Smiley. 1987. ”New Business Starts and economic
Activity: An Empirical Investigation. " International Journal of Industrial
Organizations. March. pp. 51-66.

Hsiao, C. 1986. Analysis of Panel Data. Cambridge, England: Cambridge
University Press.

Human Resorn‘ces (PVT) Ltd. 1990. " Small Business Needs Survey for the
Friedrich Naumann Foundation. "

ILO/SATEP. 1989. "The Promotion of Economic Development and Equity in
Zimbabwe. " Report to the Government of Zimbabwe.

IMANI Development. 1990. 'Impediments Confronting the Informal Sector
Enterprise In Zimbabwe. " Harare, Zimbabwe: IMANI.

Jassat, EM. and K.O. Jirira. 1987. ”Industrial Development in Zimbabwe: The
Case of Women in Manufacturing Activities. " Zimbabwe Institute of
Development Studies.

Jovanovic, Boyan. 1982. "Selection and the Evolution of Industry. " Econometrica,
Vol. 50, No. 3.

Judge, GeorgeG., W. E. Grifﬁths, R. CarterHill, HelmutLutkepohl, and

Tsoung—Chao Lee. 1985.1?helhennran112ractrcenﬂicnnomemcs. New
York: John Wiley and Sons.

174

Judge, George G, W. E. Grifﬁths, R. Carter Hill, Helmut Lutkepohl, and

Tsoung-Chao Lee. 1931me
Econometrics. New York: John Wiley and Sons.

Kanbur, S. 1979. ”Of Risk Taking and the Personal Distribution of Income.”
Journal of Political Economy. Vol. 87. No. 4. August. pp. 769-797.

Khemani, RS and Daniel Shapiro. 1986. "The Determinants of New Plant Entry
in Canada.” Applied Economics. November. pp. 1243-57.

Kihlstrom R. and 1.]. Laffont. 1979. ”General Equilibrium Entrepreneurial Theory
of Firm Formation Based on Risk Aversion.” Journal of Political
Economy. Vol 59. No. 4. August. pp. 719-748.

Kilby. Peter. 1971. W. New York:
The Free Press.

King R.P. and Derek Byerlee. 1978. "Factor Intensities and Locational Linkages
of Rural Consumption Patterns in Sierra Leone." American Journal of
Agricultural Economics. Vol. 60. No. 2. pp. 197-206.

Konrad Adenauer Foundation. 1988. ”Small Scale Enterprise Development: An
Assessment of Progress and Strategies for Growth.” National Seminar
Series held at Ranche House College, July 26—29, 1988.

Liedholm, Carl. 1990. "The Dynamics of Small-Scale Industry in Aﬁica and the
Role of Policy.” GEMINI Working Paper No. 2. Bethesda, MD:
Development Alternatives, Inc.

Liedholm, Carl, and Donald Mead. Forthcoming. ”Growth and Dynamics of
Micro and Small Enterprises: A Comparative Analysis of Southern and
Eastern Africa and Other Economies. "

Liedholm, Carl and Donald Mead. 1993. "The Structure and Growth of
Microenterprises in Southern and Eastern Africa; Evidence from Recent
Sin-veys. GEMINI Working Paper No. 36. Bethesda, MD: Development
Alternatives Inc.

Little, Ian M. ,Dipak Mazumdar, and John M. Page, Jr. 1987. Small

 

W. Oxford University Press.

175

Lucas, RE. 1978. "On the Size Distribution of Business Firms." Bell Journal of
Economics 9. August. pp. 508-523.

MacDonald, James M. 1986. "Entry and Exit on the Competitive Fringe.”
Southern Economic Journal. Vol. 52. pp. 640—652.

Mansﬁeld, E. 1962. ”Entry, Gibrat’s Law, Innovation and the Growth of Firms. "
American Economic Review. 52. pp 1023-50.

Marion, Bruce. 1986. The Organization and Performance of the U. S. Food
System. U.S.D.A., University of Wisconsin-Madison. Lexington,
Massachusetts: Lexington Books.

Mason, Edward S. 1939. "Price and Production Policies of Large-Scale
Enterprise. " American Economic Review, Supplement 29. March. pp. 61-
74.

McPherson, Michael A. 1992. "Growth and Survival of Small Southern African
Firms. " Dissertation submitted to the Department of Economics, Michigan
State University.

McPherson, Michael. 1991. "Micro and Small-Scale Enterprises in Zimbabwe:
Results of a Country-Wide Survey. " GEMINI Technical Report 25.
Bethesda, Md:Development Alternatives, Inc.

McPherson, Michael and Joan Parker. 1992. "A Manual for Conducting a
Baseline Survey of Micro and Small Enterprises.“ GEMINI Technical
Note. Bethesda, MD:Development Alternatives, Inc.

Mead, Donald C., and Peter Kunjeku. 1993. ”Business Linkages and Enterprise
Development in Zimbabwe. " GEMINI Technical Report No. 55. Bethesda,
MD: Development Alternatives, Inc.

Nag, A. 1980. "Small Industries: Aspects of Their Mortality.” The Economic
Times. Oct. 6, Delhi, India.

Orr, Dale. 1974. "The Determinants of Entry: A Study of the Canadian
Manufacturing Industries." Review of Economics and Statistics. pp. 58-67.

Pack, Howard. 1993. "Productivity and Industrial Development in Sub-Saharan
Africa.” World Development. Vol. 21. No. 1. pp. 1-16.

176

Pakes, Arier and Richard Ericson. 1987 . ”Empirical Implications of Alternative
Models of Firm Dynamics.” University of Wisconsin, Social Systems
Research Institute.

Parker, Joan and C. Aleke Dondo. 1991. ”Kenya: Kibera’s Small Enterprise
Secto -- Baseline Survey Report.” GEMINI Working Paper No. 17.
Bethesda, MD:Development Alternatives, Inc.

Phillips, Bruce D. and Bruce A. Kirchhoff. 1988. ”Analysis of New Firm
Survival and Growth. " Paper presented at the Babson Entrepreneurship
Research Conference, Calgary, Canada, May.

Price Waterhouse. 1986. African Investment Corporation International (AIC): An
Assessment of the Private Sector in Zimbabwe. Harare, Zimbabwe.

Robinson, Peter B. 1991. ”Small-Scale Industry in the Context of Structural
Adjustment. " Notes for a Meeting of Researchers and Donor Agencies,
24/4/91. Zimconsult.

Saito, Katrine, and Meine Pieter Van Dijk. 1990. ”The Informal Sector in
Zimbabwe: The Role of Women. " Harare, Zimbabwe: Environmental and
Development Activities.

Scherer, RM. 1980. .
Rand McNally.

 

Shaffer, James D. 1980. ”Food System Organization and Performance: Towards a
Conceptual Framework. " American Journal of Agricultural Economics,
May. pp. 310-18.

Tabex. 1987. Encyclopedia Zimbabwe. Harare, Zimbabwe: Quest Publishing
Private Limited.

UNIDO. 1988. ”Human Resources in Zimbabwe’s Development. The Current and
Prospective Contribution of Women. "

Varian, HalR. 1987. ‘ .e ' ' -
York: W. W. Norton and Company.

 

USAID. 1990. Country Training Strategy and Private Sector Training Needs
Assessment. Harare, Zimbabwe: USAID.

177

USAID. 1992. Study of Monopolies and Competition Policy in Zimbabwe.
Harare, Zimbabwe: USAID.

Zimconsult. 1992. "Support to Small-Scale Industries and the Enhancement of
Indigenous Ownership in Zimbabwe. " Vienna, Austria: UNIDO.