the manufacturing sector in zimbabwe: first report on the ......university of zimbabwe, harare...
TRANSCRIPT
H II u ' m
Country Study Series
'. ,
The Manufacturing Sector in Zimbabwe: First Report on the Round II RPED Survey Data
Free University of Amsterdam, The Netherlands
University of Zimbabwe, Harare
October 1994
The views and interpretations expressed in this study are solely those of the authors. They I do not necessarily represent the views of the World Bank or its member countries and should not be attributed to the World Bank or its afftliated organizations. i
'----_______ .. _____________________________ ----1
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Table of Contents page
Introduction 1
" 2 Growth, Adjustment and Resource Allocation 5 2.1 Introduction 5 2.2 Changes in the capital stock, 1990-1993 6 2.3 Capacity utilization 1993-94 15 2.4 Sales 1990-1993 17 2.5 Employment 1990-1993 20 2.6 Expectations 22 2.7 Conclusions 24
3 Infrastructure and Regulations 29 3.1 Introduction 29 3.2 Infrastructure 29 3.3 Regulations 33
4 Determinants of self-employment in Zimbabwe 39 4.1 Introduction 39 4.2 Modelling the choice between self-employment and wage employment 40 4.3 Description of the data set 43 4.4 Empirical results 47 4.5 Conclusion 55
5 Finance: the Use of Credit and Finns' Indebtedness 57 5.1 Introduction 57 5.2 The use of credit 57 5.3 Sectoral changes in outstanding debt and vulnerability 64
6 Firm Dynamics 1990-1993 71 6.1' Introduction 71 6.2 Firm growth 72 6.3 Factors determining growth 75
1 Introduction
This is a progress report on the Zimbabwe industrial surveys conducted as part of the
Regional Programme on Enterprise Development (RPED). The report is based on the first
and second rounds of data collection, conducted in June-July 1993 and 1994 respectively.
The structure of the report is as follows. The next section briefly describes the original
(1993) sample of manufacturing firms and the changes in its composition in the second
round (1994). In Chapter 2 we consider changes in investment, employment and output. We
find some encouraging evidence: capacity utilisation is rising, exportables . are doing
relatively well in terms of investment and capacity utilisation, and firms' expectations about
sales are bullish. We find some evidence that structural adjustment is successful in the sense
that resources are shifting from importables to exportables. However, there are also some
signs that investment is constrained by risk, in particular uncertainty about fiscal policy.
Chapter 3 discusses the survey evidence on infrastructure and regulations. Notable findings
are that transport and electricity supply have improved' since last year, and that telephone
services have further deteriorated in the view of our respondents. The Round I survey
already indicated that deregulation had gone far, few firms reporting gove~ent regulations
as a source of problems. This is reinforced by the Round II results. Firms reported further
improvements, in particular with respect to labour regulations and the ease of access to
foreign exchange. In Chapter 4 we use the survey data to estimate determinants of
entrepreneurship. We test the conventional model where the choice between self employment
(entrepreneurship) and wage employment is determined by personal characteristics (including
age, experience and education) and individual-specific estimates of the expected income
differential between the two occupations. The results show a large difference between blacks
and others. a difference which requires further work. In Chapter S we consider two fmancial
issues. We first present evidence on the use of different forms of credit (fonnal sector loans,
overdraft facilities, infonnal loans and trade credit) and investigate whether there is evidence
of credit rationing. We conclude that very few firms can be considered as credit constrained.
The second issue is the exposure of the industrial sector to changes in interest rates. We
show that indebtedness has risen in the past year but that it is low (relative to sales). Finally,
in Chapter 6 we consider firm dynamics, updating the regression analysis of the growth of
firms in the previous report. A notable result is that exporting firms are growing
1
significantly and substantially faster than others in the period since 1991 when adjustment
started.
The Sample
Details about the selection of the sample have been presented in the final report on the first
round (Gunning (1994), Chapter 2). Here we present a brief summary and discuss how we
have replaced firms that, for some reason, could not be interviewed again.
Last year's survey covered 201 industrial enterprises. The survey was restricted to four
subsectors of manufacturing: food processing, textile and garments (including leather and
footwear), woodworking and furniture, and metalworking. A broad definition of
manufacturing was used: any kind of processing of raw materials or intermediates was
accepted. Millers, butchers, bottlers, spinners and weavers were all included. However,
tobacco processing and electronics were excluded.
For a firm in one of the subsectors to qualify for inclusion in the sample two conditions
needed to be satisfied. First, a minimum size of five (including owners-managers) was
imposed. Secondly, in order to qualify a firm should be able to make its own investment
decisions .. Totally dependent subsidiaries were therefore excluded. as were subsidiaries
without separate accounts.
The sample includes both registered formal sector enterprises and unregistered informal
ones. The sampling frame consists of a list of registered (formal sector) firms obtained from
the Central Statistical Office (COO) used for firms with at least SO employees. and a survey
of small:'scale and micro firms (most of them in the informal sector), used for smaller firms.
Sampling was done on the basis of firm size (in terms of employment) in such a way that
every worker had an equal probability of being drawn. Hence the probability of a firm to be
included in the sample is proportional to its size.
Ten firms included in last years' survey could not be interviewed again this year. These
firms are evenly spread over the four sectors that were distinguished in last year's survey. 1
In three cases this was because the firm went bankrupt, in one case the firm's owner was
seriously ill and unable to do business, in another case the firm was in the process of
moving to other premises. and in one case the firms' books were not available. The other
four firms refused to participate in the survey mainly for reasons of time. The sample's
IThuS. in both the food and die meral sector we had to fOld replacements for rwo firnls. while in the wood and meral sector we had to replace dlree fums eacb.
2
"
attrition rate is therefore 5 %. Note that the number of firms which went out of business in
the course of the year is very low (1.5%).
In addition to these ten fmns two of last year's firms. while still existing and willing to
participate. had (temporarily?) stopped operations. Both firms produced gannents. One firm
indicated that it was waiting for a loan for which it had applied, the other firm (a very small
informal firm) had had its equipment stolen. Although these firms have been interviewed
again in this year survey, the large number of questions that were non-applicable have led us
to decide to find replacements.
Hence 12 replacements were selected. Replacements were selected from the same sector
as the firm to be replaced, and of roughly similar size in terms of employment.
The total sample size of this year is 203: 191 of last year's firms, plus ten replacements
for firms that could not be re-interviewed. plus two replacements for firms that were
reinterviewd but had stopped producing. For one of these 203 firms, we have only been able
to re-interview some of last years workers. This leaves a sample of 202 firms with
information about other issues than workers.
Sometimes. notably in Chapter 2. we shall have to weigh the data in order to make
inferences at the sectoral level. In these cases we have omitted the two firms which were
again in the sample but for :which we had selected replacements because of the large number
of missing data. In these two cases we only use the replacements; including the two replaced
firms would bias the sample. As explained in the previous report, weighing the data
requires, inter alia, the use of so-called blow-up factors to correct for biases in the sampling
frame (for details see Gunning (1994), Chapter 2). A replacement therefore receives the
blow-up factor of the firm it replaces.
3
Reference
Gunning. l.W. (ed.). The Manufacturing Sector in Zimbabwe: Dynamics and Constraints. Free University. Amsterdam and University of Zimbabwe. Harare, 1994.
4
,
2 Growth, Adjusbnent and Resource Allocation
Jan Willem Gunning and Marc Pomp
2.1 lnbn)duction
In this Chapter we will use the survey data to assess changes in investment, capacity
utilization, employment, and output since 1990. The direction and magnitude of these . changes constitute a yardstick for measuring the success or failure of the structural
adjustment programme (known in Zimbabwe by the acronym ESAP). Apart from
documenting actual changes, we will briefly look at expectations about a number of variables
(namely sales, availability and cost of credit, and availability and cost of foreign exchange).
From these expectations we hope to learn something about the credibility of the adjustment
programme.
Implementation of ESAP started in 1991. The initial impact of ESAP was blurred by the
severe drought which hit the country in 1992; this had a large negative impact on the
economy. The drought made it difficult to separate the impact of ESAP from the impact of
the drought. However, it seems reasonable to assume that by now (1994) the effects of ESAP
should be clearly measurable.
Apart from the aggregate picture, we will look at differences between sectors in
investment and other variables. The sectors covered by the survey can be classified into
importables, exportables, and non-tradables. Since one of ESAP'S aims is to increase the
efficiency of the Zimbabwean economy, sectors in which the country has a comparative
advantage should grow while other sectors should shrink. Thus, successful adjustment
requires sectoral shifts in resource allocation.
This chapter is structured as follows. Sections 2 to 5 use the survey data to estimate
aggregate and sectoral indices for the capital stock, capacity utilization, employment. output,
and sales, respectively. There are at least three complications in using the survey for the
assessing sectoral or economywide changes. First, since we use a panel of firms that was
selected in 1993, we do not have information on the number of new firms that have been set
up since then. One possible impact of ESAP is that it has been attractive to stan new lines of
business. Indeed. a credible adjustment programme which leads to sharp changes in relative
prices is quite likely to create new business opportunities. Unfortunately, there is nothing we
s
can do about this but it should be kept in mind that our use of panel data introduces a bias:
structural change will be ~erestimated to the extent it occurs through finn foundation.
The second complication arises from the way in which the sample was selected (see
Chapter I, and Gunning 1994a). The sampling scheme was designed in such a manner as to
give each worker the same probability of being selected. This implies that a unit of capital
installed in a large finn Oarge in terms of employment) has a higher probability of being in
the sample than a unit of capital installed in a small finn. As a result, sample means do not
constitute unbiased estimates of sectoral or economywide averages of variables such as
investment, capacity utilization or output. In order to obtain such unbiased estimates,
observations need to be weighed, with the weighing scheme depending on the variable under
study. We will describe these weighing schemes below.
The third complication arises from the fact that we use fairly small samples to make
inferences at the sectoral level. Since this makes the results sensitive to possible outliers, we
will test the robustness of the findings by comparing weighted means with weighted
medians. A large difference between the two measures suggests that the weighted sample
mean is heavily affected by one or a few observations. In such cases, our findings should be
interpreted with extra caution.
Section 6 summarizes the survey data on the expectations of finn owners and managers.
and Section 7 concludes.
2.2 Changes in the capital stock, 1990-1993
In this section, changes in the capital stock are calculated, both at the aggregate level and at
the sectoral leveL The survey covers five sectors. namely food, wood, textiles. gannents and
metal. Gunning (1994b, p. 11) proposes to classify these sectors according to tradability in
the following manner2:
21n Gunning (1994a) textiles and garments were aggregated into one sector.
6
food
wood
textile
garments
metal
non-tradable
non-tradable/exportable
exportable
exportable
importable
The data allows us to assess whether this classification is correct. Table 1 shows for each
sector the average share of output exported, and the percentage of firms stating imports by
other firms as a major source of competition for their main products.3 According to our
sample, the most tradable sectors appear to be textiles (both as importable and exportable);
wood (exportable); and garments (exportable and importable).
Table 1: Tradability indicators: Percentage of output exported and percentage of firms stating imports as source of competition for principal products
Exports (" of Imports N output) (% of ftrms)
Food 9.4 28.3 46
Wood 22.7 7.4 27
Textiles 25.6 30.8 26
Garments 15.2 26.6 64 Metal 10.2 18.9 37
All sectors 15.3 23.5 200
Table 1 suggests some modifications to the above classification:
food: non-tradable/importable
wood non-tradable/exportable
textile
garments
metal
importable/exportable
importable/exportable
non-tradable/importable
lUnless indicated otherwise. all Tables in this Chapter are based on the RPED surveys.
7
One important aim of structural adjustment is to encourage resource shifts which favour
sectors producing exponables. According to Table I, this implies a relative increase in the
textiles, wood, and gannents sectors. Below we will assess whether this happened.
Table 2 shows for each of these sectors the percentage of finns investing in 1990-1993.
To begin with, note that a very large percentage of finns (90 percent) shows positive
investment levels in at least one of the years 1990-93 (Table 2).4 Funhennore. the
percentage of finns investing is higher in 1992 and 1993 than in the. earlier years; this is the
case in all five sectors. The percentage differs substantially between sectors. Two notable
findings are that the percentage is consistently highest in the food sector. while in the wood
sector the percentage rose very sharply in 1993.
Of course, this discrete classification of finns into investors and non-investors is very
rough since investment levels (or rates) will vary; below we will compute index figures that
take this into account. Nevenheless. the fact that a high and increasing number of finns do
invest is a positive sign.
Table 1: Iovesttnent 1990-1993 Number of firms reponing positive gross investment, percent of all fums
Food Wood Textiles Garmenu Metal Total
.1990 25 8 9 27 32 87 (61.0) (33.3) (36.0) (47.4) (56.3) (48.6)
1991 26 8 13 38 21 106 (63.4) (33.3) (52.0) (66.7) (65.6) (59.2)
1992 35 11 17 57 32 124 (85.4) (45.8) (68.0) (68.4) (68.8) (69.3)
1993 35 17 19 39 21 131 (85.4) (70.8) (76.0) (68.4) (65.6) (73.2)
1990-93 40 19 22 52 28 161 (97.6) (79.2) (88.0) (91.2) (87.5) (90.0) .
N (1993) 41 24 2S 57 32 179
Notes: 1. Sbown are weighted averages (see text). 2. The Table includes only firms with a complete investment record since 1990.
"10 Zimbabwe fmancial years often do not coincide with calendar years. For the purpose of aggregating the data we have allocated investment to the calendar year that has the greateSt overlap with the financial year. For example, if the (mancial year runs from April I 1993 to March 31 1994 (quite common in Zimbabwe), then we allocate all investtnent undertaken during the financial year to 1993.
8
Table 2 is based on a subset of 179 fmns for which we were able to reconstruct the
investment history since 1990. One might expect that this imparts an upward bias to our
investment data since large (and formal sector) firms are more likely to have a complete
record than small firms (which are more frequently in the informal sector). If firm size
affects investment (or other variables of interest), then this leads to biased results. Below we
present results obtained by weighing observations, and for this the sample had to be
restricted even further (to 129 firms). This is because constructing weights requires
additional information which is sometimes missing.
In order to assess whether the subsample of firms with a complete investment history is
biased towards large firms, we ran a probit (Table 3). The probit has as the dependent
variable a dummy variable indicating whether or not the firm belongs to the set of 179 (129)
firms with a complete investment history, and the size of the firm (in terms of employment)
as the explanatory variable (denoted as EMPl994). The coefficient on this variable is
insignificant and very small: a firm with 100 employees has a probability of being in the set
of 179 (129) firms with complete record that is only .80 (.31) percent higher than for a fitm
with ten employees. We conclude that the probability of being in the sample of 179 (129)
firms with complete records does not depend on size, so that we do not need to fear that the
estimates are biased. S
Table 3: Probit analysis: effect of firm size on availability of invesunent data
Intercept
EMP)994·
Notes: 1. N - 200.
CoeO"IClent (t-value)
21 incomplete
-1.18 (8.8)
-.00028 (.9)
71 incomplete
-.35 (3.S)
-.00008 (.S)
2. Dependent variable - 0 if invesunenl history is complete, 1 otherwise.
Of course, we are not only interested in whether firms have invested, but also in the
amounts invested. For this purpose, we calculate an index of the capital stock. based on
gross investment data. Ths index is constructed as follows. First, we need an estimate of the
S Another possible source of bias is that firms which did not invest in a given year have missing data for that year. In that case, our estimates will again be biased upward. However we cannot assess this possibility.
9
value of the opening capital stock, i.e. the capital stock in 1990. We do not have direct
observations on this variable.6 We therefore estimate each finn's opening capital stock on
31 December 1989 using the following fonnula:
93 1: (1-d)(93-t)~ (1)
t=9O
where:
KI990 = The economic value of the finn's capital stock (equipment) on December
31 1989, in 1994 prices.
KS = The reported sales value of the finn's equipment in 1994
'" = The ratio of the sales value and the replacement value of a newly
purchased piece of equipment.
It = Gross investment in year t in 1994 prices.
~ = A depreciation factor assumed to be equal to ten percent per year.
According to this fonnula, the opening capital stock (Le. the economic value of the capital
stock in 1990) equals its sales value in 1994 minus net additions to the capital stock since
1990, allowing for the difference between the economic value of the capital stock and its
sales value (I-£).7 We apply a depreciation rate (d) to allow for wear and tear and economic
obsolescence due to e.g. changing relative prices since 1990. Admittedly the assumption of a
fixed and constant d is quite restrictive: changing relative prices will imply that economic
obsolescence differs between sectors. But we have no way of measuring this.
~or do we think it would be possible to ask finn owners I managers directly about the value of the opening capital stock.
'Note that equation I estimates the value of the opening capital stock as the difference between two tenns. namely the economic value of the 1994 capital stock <Ke). and the economic value of net additions to the capital stock since 1989 (Ie). This implies that if the difference between KE and IE is small. then measurement error will have a big impact on the estimate for the opening capital stock. For example. suppose that ~ equals 110 and that IE equals 99. Then KI289 equals 11. Now suppose that we are able 10 measure IE correctly but that KE is measured with error, eg. KE = 100 (an error of about nine percent). Then the estimate for the Kl989 equals ). which differs from its true value by more than an order of magnitude. In this special case measurement error leads to a very large underestimate of the opening capital stock, implying that small amounts of investment are suffICient to generate a very high value for the index of the capital stock in 199()..1993. This argument may explain an outlier reported below.
10
The parameter p. captures the difference between the economic value and the sales valu.e
of a piece of equipment. Because of transaction costs and informational imperfections. the
sales value of a piece of equipment will be lower than its economic value. We assume a
value of p. of .7. and we will test the sensitivity of the results to this assumption.
In equation 1 investment (It) is measured in 1994 prices. We have data on the actual
investment outlays in the year in which the investment took place. so we have to inflate
these figures to arrive at the 1994 values. Unfortunately. there is no separate price index for
capital goods in Zimbabwe. We therefore use the CPI in order to inflate historical prices of
capital goods (i.e., our investment data) to 1994 prices. The CPI series used is presented in
Table 4.
Table 4: CPt index. 1990-100
1990 100.0
1991 123.3
1992 1~2
1993 223.6
Source: IMf. International financial Statistics
Having converted the investment data to 1994 prices we construct an (unweighted) average
(unweighted) index of the capital stock. Table 5 shows the results.
Table S:. Changes in capital stock. 1989·1993, gross. unweighted means
Food Wood Textiles Garments Metal Total
1989 100 100 100 100 100 100 1990 116.9 124.7 117.7 109.1 122.2 116.6 1991 137.5 136.1 288.6 168.5 152.5 170.9 1992 159.0 151.0 314.3 191.7 172.4 192.3 1993 187.0 351.0 348.4 220.6 186.1 236.7
N 29 17 18 40 2S 129
Notes: 1. The Table refers to the capital stock at the ~nd of each year. 2. Shown are weighted averages (see text).
The Table shows substantial gross investment in our sample in all five sectors, with apparent
investment boorns in the wood and textiles sectors. However, this is partly caused by a few
11
observations for which we estimated a very small opening capital stock. This could very
well be due to measurement error. For reasons that will be explained below, we will weigh
observations by the opening capital stock, and this will partly eliminate'these investment
booms (the observations causing the boom in Table 5 have a small share in the opening
capital stock).
The findings reponed in Table 5 need not hold at the sectoral or economywide level. As
argued in the introduction, the sample is biased towards large firms in the sense that a dollar
worth of equipment in a large firm has a larger probability of being in the sample then a
dollar of equipment in a small firm. In order to restore representativeness of the sample for
the Zimbabwean capital stock, we will weigh the data. If investment rates vary with firm
size, then weighing the data wiH affect the results. The weighting scheme employed is given
by the following formula:
where:
bi =
Ii = K1989i = K 1989tot =
(2)
A blow-up factor needed to ensure that each worker has the same
probability of being selected.s
Employment in 1991.
The firm's capital stock at the end of 1989.
Weighted sum of the 1989 capital stock of the firms in the sample.
The last variable - the weighted sum of the 1989 capital stock - is calculated as follows:
(3)
It should be noted that some firms receive very large weights, a consequence of which is
that a small number of observations has a very large impact on the outcomes. This skewness
in the distribution of weights is particularly severe in this case where we weigh with inter
8See last year's report for an explanation of the blow-up factor (Gunning. 1994, p. 4).
12
"
alia a finn's share in the initial capital stock. According to our estimates of tbe opening
capital stock. these shares simply do vary a lot.
Finally. these sectoral or aggregate investment rates can be converted into indices
showing the change in the capital stock for each sector or for all sectors in the survey
combined. Table 6 shows the results. The Table also shows the estimated share of each
sector in tbe total opening capital stock, defined as the total in all five sectors. This is
calculated using the following estimate of the opening capital stock in sector j:
= E (b/lj)'KI989j j
(4)
where tbe summation is over the finns in the sample that belong to sector j. Dividing this by
K1989tot yields the reported estimates. Clearly, the food sector dominates, accounting for
over 40% of the capital stock. followed by metal. textiles, garments and wood.
The next entry in Table 6 is P, the percentage of N largest firms acounting for half the
sample weight. This statistic gives an idea of the extent to which the outcomes are
detennined by a few observation receiving a large weight. A small value for P implies iliat a
small number of firms has a large impact of the outcome of the calculations. Clearly, this
problem is quite severe for the overall picture (P=7.8).
Table 6: Cbanges in capital stock, 1989-1993, gross, weighted means (weighted medians in parentheses). index. 31 December 1989-100
Food Wood Textiles Garments Metal Total
1989 100 100 100 100 100 100
1990 107.5 120.6 13l.7 113.7 103.2 109.9 (103.2) (100.0) (100.0) (107.9) (100.0) (101.8)
1991 134.5 131.3 244.1 135.6 114.1 139.5 (142.2) (100.0) (100.0) (109.3) (108.4) (109.3)
1992 148.3 154.1 265.4 148.9 121.5 152.4 (142.4) (104.6) (118.5) (128.1) (142.7) (123.2)
1993 155.9 165.5 293.7 154.4 123.7 160.5 (169.6) (116.2) (128.9) (135.8) (102.4) (128.9)
K'2!2j1K 1989tot • 41.7 4.7 10.6 10.6 32.4 100
P 10.3 17.6 22.2 17.5 16.0 7.8
N 29 17 18 40 2S 129
Notes: • Estimated share of sector i in total opening capital stock, in percent. P: Number of largest (1fIIlS accounting for half the sample weight, as a percentage of all finns in the
sector.
13
A comparison of Tables 5 and 6 shows that as a result of weighting the data the (gross)
1993 index of the capital stock falls substantially all sectors. The overall 1993 index falls
from 236.7 to 160.5. This may be due to the fact that firms with few employees (which
receive a greater weight as a consequence of our weighting procedure) had lower investment
rates; another explanation is that firms with a small opening capital stock had higher
investment rates.
Before commenting further on the results of Table 6, we want to assess their robustness.
To that end, Table 6 also shows (in parentheses) the weighted median.9
A comparison of the weighted mean and the weighted median shows that the two
methods yield very substantial differences, with the notable exception of the large food
sector. Overall, the weighted median estimator in 1993 is more than 30 points lower than
the 1993 weighted mean. The difference is particularly striking in the textiles sector.
We also did a sensitivity analysis for different values of p., the ratio of the sales value
and the replacement value of a newly purchased piece of equipment. Recall that Tables 5
and 6 are based on a value of p. of .7. We recalculated the Tables using values of p. of .6
and .8, respectively. The results (not shown) indicate that raising (lowering) p. by .1 leads to
an increase (decrease) in the 1993 index of gross investment of about ten percent. This
applies both at the sectoral and at the overall level.
Keeping this in mind, Table 6 supports the following conclusions. First, the overall
investment performance appears to have been reasonably satisfactory: an increase of over SO
percent over a period of five years will be more than sufficient to cover depreciation. In
fact. assuming a depreciation rate of ten percent per year, and applying this to both the
opening capital stock and to subsequent additions, yields a net increase in the capital stock of
about ten percent. 'However, this is conditional on p.=.7; with p.=.6, the conclusion would
be that the capital stock has remained roughly constant.
Second. the results differ markedly between sectors. In particular. the textiles sector has
experienced something like an investment boom in 1991. We have no explanation for this.
9we could also have reponed appropriately weighted standard errors. However, a larae standard error does not necessarily imply outliers. A large difference between the median and the mean does. The weighted mediaD is calculated as follows. First we sort observations in order of increasing growth rate of the capital stock. Next we construct a new variable, namely the cumulative sum of Ibe weights Wi (defined by equation 2). Third, we rmd the observation for which this cumulative sum equals (is closest to) .• 5. The median growth rate is then the growth rate corresponding to this observation (which need not be the same in each year).
14
"
The metal sector, on the other hand, was the worst performer in terms of investment. In net
terms, the. sector must have experienced a substantial decline in production capacity.
Third, with respect to the timing of investment, 1991 shows large increases in the food,
textiles and to a smaller extent the garments sectors, while 1992 shows jumps in the wood
sector. Overal11991 shows the largest increase according to the weighted means, while 1992
shows the largest increase according to the weighted medians. That 1992 shows a large
increase in investment is surprising in view of the drought. The limited amount of
investment undertalcen in 1993 may well have to do with high real interest rates (current and
expected), which are largely attributable to the government deficit.
2.3 Capadty utilization 1993-94
The data allows us to calculate capacity utilization rates for two years, 1993 and 1994.
Tables 7 and 8 show the results. 10 Table 7 shows unweighted means, Table 8 weighted
means and weighted medians, where we have employed the weighting scheme given by
formula 2 above. Note that the number of observations for which we have all the data
needed to calculate capacity utilization and the associated weights differs slightly from the
number of observations in the previous Section.
Comparing the unweighted and the weighted averages (Tables 7 and 8) shows that
weighting the observations leads to a fall in the average over all five sectors in 1994, but at
the sectoral level there are large differences. The difference is most striking for metal where
the unweighted data indicate an increase in capacity utilization of more than seven percent
while the weighted data show a fall of 9.5 percent. In the garments sector the pattern is
similar but less dramatic: unweighted the data indicate an increase in capacity utilization of
five percent. weighted they show a fall of more than four percent. This indicates that large
firms, which are overrepresented in the sample (in the sense that a unit of capital in a large
firm has a higher probability of being in the sample), have experienced rising capacity
utilization while small firms saw the reverse happening.
The weighted medians (also reported in Table 8) show a pattern that is quite different
from the weighted mean, indicating that outliers have a large impact on the overall picture.
lOrhe survey aslced by what percentage production could be increased given the existing capital stock and given the current mode of operation (e.g .• one or two shifts per day). Call this percentage X. Then the rate of capacity utilization is given by IOO/(lOO+X).
15
However, a few robust conclusions emerge from Table 8. First, 1994 capacity utilization is
highest in the wood and textiles sectors; in the wood sector capacity utilization has increased
substantially between 1993 and 1994. Note that we have classified these sectors as
exportables, so that this is an encouraging sign. Second, the food sector has had a very low
rate of capacity utilization in 1993 and is recovering slightly in 1994 although the rate of
capacity utilization is still low in 1994.
One might expect that a high rate of capacity utilization stimulates investment in
equipment. This is however hard to test using these Tables, since there is two way causality:
a high (expected) rate of capacity utilization may stimulate investment which may then lower
capacity utilization. Still. it is interesting to note that the textiles sector experienced both
relatively high investment and high rates of capacity utilization, while the reverse applies to
the metal sector. In future analysis we will explore the link between capacity utilization and
investment econometrically.
Table 7: Capacity utilization. percent. 1993-1994. unweighted means
Food
1993 (June) 5S.5
1994 (June) 60.0
N 29
Wood
60.3
73.1
16
Textiles
65.7
72.1
17
Garments
69.5
74.5
39
Metal
59.8
67.1
23
Note: The Table refers to capacity utilization rates in mid-1993 and 1994. respectively.
Table 8i Capacity utilization. percent, 1993-1994. weighted means (weighted median in parentheses)
Food Wood Textiles Garments Metal
1993 53.4 57.0 76.2 72.9 71.S (June) (50.0) (57.1) {71.4} (76.9) (66.7)
1994 63.4 75.S 74.S 68.7 62.0 (June) (60.2) (80.0) (SO.O) (75.2) (66.7)
N 29 16 17 39 23
16
Total
63.4
69.2
124
Total
63.9 (59.9)
65.3 (66.7)
124
•
2.4 Sales 1990-1993
Tables 9 and 10 show changes in sales, using the three descriptive statistics also used in the
previous sections (means, weighted means, and weighted medians). However, the weighting
scheme is different: we now weigh in order to reflect shares in opening sales rather than
opening capital stock. Again, a result of this weighting scheme is that a small number of
firms receive fairly large weights but the problem is less severe than in the case of the
capital stock.
Both Tables show an index of sales in current prices. Taking into account the fairly high
inflation rates (presented in Table 4 above) would seem to suggest that sales volumes fell
substantially in both 1993 and 1994. However, below we will present data on the change in
sales volumes between 1993 and 1994 indicating that sales have remained flat at worst. But
first we discuss our findings on sales in current prices (Tables 9 and 10).
Weighing the data again has important consequences for the outcomes, and the effect is
particularly striking in the metal sector. Here the unweighted data show a flat series until
1993, and a 43 percent increase in sales in 1994. However, the weighted series shows a
very different picture: now sales in the metal sector fall by a quarter in 1992, followed by a
slight increase (in nominal terms: in real terms this would imply a fall). The weighted
. median series differs sharply from both the unweighted and the weighted mean: now there is
a jump in sales in 1992, followed by a slight decline. This difference between the weighted
mean and the weighted median is caused by a very large firm which experienced a very
large drop in sales in 1992.
Other large differences between mean and median are present in the wood sector in
1993, and in the textiles sector in 1992 and 1993. Again this is due to influential
observations. For the textiles sector, it is plausible that the weighted median gives a better
impression of what happens at the sectoral level, especially in the light of the evidence in the
previous Section which showed that capacity utilization went up recently in the textiles
sector.
17
Table 9: Sales value, 1991-100, unweighted means
Food Wood TextHes Garments Metal Total
1991 100 100 100 100 100 100
1992 123.8 119.8 165.5 134.9 130.4 134.2
1993 160.4 161.3 149.1 136.2 107.9 148.6
1994 178.3 199.8 160.5 171.0 151.0 166.6
SlS· 47.0 1.7 16.8 18.0 14.9 100.0
N 33 18 19 47 28 145
Note: '" Unweighted share of sector i in 1991 sales (percent).
Table 10: Sales value, 1991-100, weighted means and medians
Food Wood TextUes Gannents Metal Total
1991 100 100 100 100 100 100
1992 110.8 101.1 179.2 140.4 78.1 121.3 (114.4) (101.6) (130.6) (140.0) (137.5) (128.6)
1993 139.1 142.7 150.3 145.9 79.8 136.5 (161.2) (96.0) (130.3) (137.5) (136.5) (144.8)
1994 156.5 175.8 153.6 176.4 85.6 152.0 (177.8) (108.9) (150.0) (170.0) (123.0) (159.1)
SlS· 43.6 3.8 17.6 18.6 16.5 100
P 18.8 33.3 21.1 17.4 22.2 12.7
N 33 18 19 47 28 145
Notes: '" Weighted share of sector i in 1991 sales (percent). P: Number of largest firms accounting for half the sample weight (N). as a percentage of all firms in
lite sector.
We now tum to the change in sales volumes in the final year (the only year for which we
have data). It should be pointed out that the volume data are rather rough, since they are
based on an infonnal assessment of the respondent. Thus. it was left to respondents
themselves to aggregate different products together in order to produce an overall estimate
of the increase in sales volumes.
The results are shown in Tables 11 and 12. According to the unweighted data (Table 11)
the garments sector performs very well. and sales volumes in the wood and food sector are
also quite satisfactory. However. firms in the metal and textiles sector show stagnating or
even declining sales volumes.
These conclusions change when we look at weighted data (Table 12). Now we find that
according to weighted mean sales in the metal sector decline, while in the other sectors,
18
•
again especially in the garments sector, sales volumes rise. However, weighted medians
paint a picture that is much less positive: now sales volumes appear to decline in botllthe
food and the metal sectors, while in the other three sectors they are completely flat.
These fmdings are very remarkable in view of the sales values in Tables 9 and 10, which
suggested a fall in overall sales volumes in 1994. According to the data in Table 12, the
worst case (represented by the weighted mean) shows that sales volumes have been flat.
Note that the rising rates of capacity utilization reported in the previous Section also
contradict falling sales volumes (unless stocks of final products would accumulate, but this is
not the case). We conclude that deflating sales values by the CPI gives an overly pessimistic
picture of sales volumes. Possibly, the CPI overestimates the price of domestically produced
goods because of increases in the relative price of imported goods. But without further data
we cannot test this hypothesis.
The volume changes in Table 12 have been combined with the value data in Table 10 to
calculate implied sectoral inflation rates. The results of this caluculation are shown in Table
13. The Table shows inflation rates based on weighted means and on weighted medians. The
first procedure gives an overall inflation rate of 4.6 percent, which is unrealistically low
given that the CPI rose by more than a third (Table 4), The second procedure gives. an
overall inflation rate of 9.9 percent, which is closer to a plausible inflation rate but still on
the low side. At the sectoral level, the difference betwen the two procedures is even more
striking. Weighted means show rising prices for all sectors except textiles; weighted means
show price increases for all sectors except metals.
We conclude from these findings that inflation rates do indeed appear to differ
substantially between sectors, so that the use of one single deflator for all sectors would be
misleading. Unfortunately the data do not allow us to estimate sectoral inflation rates.
Table 11: Sales volume, percentage increase 1993·94, unweighled means
Food Wood Textiles Garments Metal Total
Percent 9.0 9.9 ·1.2 22.1 2.4 10.7 increase
N 33 18 19 47 28 145
19
Table 12: Sales volume. percentage increase 1993-94, weighted means and medians
Percent increase
N
Food
8.0 (-7.S)
33
Wood
5.1 (0.0)
18
TextJles
6.4 (0.0)
19
Garments
15.1 (0.0)
47
Table 13: Implied inflation rates, 1993-94, weighted means and medians
Food Wood Textile.s Garments
Percent 4.5 18.1 -4.2 5.2 (17.8) (13.4) (15.1) (23.6)
N 33 18 19 41
2.S Employment 1990--1993
Metal
. -5.5 (-5.0)
28
Metal
12.8 (-4.9)
28
Total
6.8 (0.0)
145
Total
4.6 (9.9)
14S
The final variable we consider is employment. The appropriate weighing is now by the
. blow-up factor (see above). Since blow-up factors do not differ very much between firms,
the problem of a small number of firms receiving large weights is less severe in this case
(except for textiles). Surprisingly (in view of the results in previous Sections), the weighted
picrure hardly differs from this except in 1993. The weighted total is much lower than the
unweighted tital due to the fact that the textiles sector receives a relatively large weight.
Tables 14 and 15 present the three summary statistics that we have employed throughout the
Chapter. The unweighted sample means show large differences between sectors, with
substantial employment growth in the wood sector and a substantial decline in the metals
sector. Garments present an intermediate case: after a sharp increase in employment in
1992, the sector has experienced modest employment cuts in 1993 and 1994.11
lIThese findings differ substantially from the rlDdings reponed in Risseeuw, 1994, and in Gunning, 1994b. To summarize Risseeuw's findings in his own words: "Total employment fell with more than 10 percent in these two years [ .• ]. Especially the textile industry (-11.1,",) and the food processing industry (-9.S,",) experienced a massive decline, although woodworking firms (-2.2,",) and metalworking (-6.6'"') received their share as well.· (Risseeuw, 1994. p. 41). One possible explanation for the discrepancy between these results and the findings reponed here is the difference in the sample size: Risseeuw includes 188 ftrmS, while we restrict our sample to the 164 firms with a complete series of employment data for 1991-1994 according to the Wave n dataset. Why our sample is so much smaller is not clear but potentially serious, and we will address this in future work. A
20
Turning to the weighted medians (also shown in Table 15) the picture changes
dramatically. The overall employment figure now is almost completely flat, and the sectoral
series are much more ironed out compared to the weighted means series. Again. thiS
suggests that a small number of influential observations has a large impact on the results.
However. a robust result is that ESAP has not led to a fall in employment. with the exception
of the metal sector. In view of the fall in real tenos of sales over the period reported in the
previous Section this is a rather surprising result. One possible explanation is that firms find
it difficult to lay-off redundant employees; another is that firms are optimistic about the
future and hoard labour to avoid losing firm-specific skills.
Table 14: Employment, 1991 = 100, unweighted means
Food Wood TextlJes Garments Metal Total
1991 100 100 100 100 100 100
1992 104.,5 107.6 104.3 117.8 107.,5 108.9
1993 96.9 11,5.3 87.,5 115.3 99.6 100.6 1994 101.4 126 . .5 102.1 109.8 8.5.6 103.0
MjlM' 23.8 4.0 30.0 29.2 13.1 100
N 39 20 22 ,51 32 164
Note: * MjlM: Unweighted share of sector i in 1991 employment (percent). Shown are unweighted averages (see text).
second possible explanation is that some respondents made corrections to last year's data, but we find it hard to believe that this explains much of the difference.
21
Table 15: Employment, 1991"" 100, weighted means
Food Wood Textiles Garments Metal Total
1991 100 100 100 100 100 100
1992 106.6 107.9 106.9 1 19.2 109.2 109.7 (102.1) (100.0) (100.0) (106.2) (100.0) (100.4)
1993 96.2 115.0 85.9 114.4 105.6 94.7 (100.0) (100.0) (97.2) (100.0) (100.0) (100.0)
1994 99.1 126.4 103.3 110.3 85.9 103.5 (101.9) (115.0) (100.0) (103.5) (94.6) (100.0)
M/M* 19.8 8.0 31.0 26.9 J4.3 100
P 38.5 5S.0 13.6 33.3 46.9 20.7
N 39 20 22 SI 32 164
Notes: *M/M: Weighted share of sector i in 1991 employment (percem). P: Number of largest firms accounting for half the sample weight 00. as a percentage of all
fmns in the sector.
2.6 Expectations
The survey asked firm owners/managers about their one-year-ahead and three-years-ahead
expectations of sales; availability and cost of credit; and availability and cost of foreign
exchange. Tables 16 - 20 summarize the replies. Turning first to sales (Table 16). it is
remarkable that expectations are quite optimistic in all five sectors. Of course at a lower
level of aggregation we would find product categories about which expectations are
pessimistic, but apparently the share of such products in the total is rather low. Table 16
also shows that medium term expectations (three years ahead) are more favourable than
short term expectations (one year ahead).
Expectations about access to credit and access to foreign exchange (Tables 17 and 19) are
also fairly optimistic, although here optimism is less widespread than is the case with respect
to future sales. Nor is there much differences between short term and medium term
expectations with respect to access to credit and foreign exchange. It should be noted that
access is already fairly easy, especially with respect to foreign exchange, so that there may
simply be little scope for improvement here (see Chapter 3).
Expectations about interest rates (Table 18) are fairly evenly split between optimisic and
pessimistic. Interest rates depend on inflationary expectations which in a country like
22
Zimbabwe depend to a large extent on the government deficit. Thus, an expected decline in
interest rates would imply optimism about the government's ability to reduce the deficit and
vice versa. The fmding that expectations about interest rates are not overwhelmingly
optimistic or pessimistic would then imply uncertainty about the government's success on
this score. Such uncertainty may well be a disincentive for investment.
Table 16: Sales expectations, one and three years ahead Percentage of all fums
One year &bead Three years ahead
higher lower higber lower N
Food 75.6 15.6 89.1 8.7 45
Wood 88.9 0.0 96.2 0.0 27
Textiles 66.7 7.4 8U 11.1 27
Garments 69.4 16.1 82.5 12.3 62
Metal 77.1 2.9 87.9 6.1 35
All sectors 74.5 10.2 86.8 8.5 196
Table 17: Expected access to credit, one and three years ahead Percentage of all firms
One year &bead Three years ahead
hlgber lower higher lower N
Food 65.0 5.0 64.1 7.7 40
Wood 58.3 16.7 50.0 1&.2 24
Textiles 53.& 3.8 64.0 4.0 26
Garments 49.2 22.0 56.4 1&.2 59 Metal 48.6 14.3 60.1 9.1 35
All sectors 54.3 13.6 59.2 12.1 184
Table 18: Expected cost of credit. one and three years ahead Percentage of all firms
One year &bead Three years &bead
bigher lower blgber lower N
Food 26.8 51.2 27.5 67.5 40 Wood 6&.2 31.2 63.6 36.4 2
Textiles 52.2 26.1 57.1 28.6 21
Garments 49.2 32.2 37.S 51.8 56
Metal S1.4 22.9 47.1 44.1 34
All sectors 47.2 33.9 42.8 49.1 173
23
Table 19: Expected access to foreign exchange, one and three years ahead Percentage of all finns
One year ahead Three years abead
bigher lower bigher lower N
Food 66.7 7.7 71.0 10.5 38
Wood 50.0 22.2 53.3 20.0 IS
Textiles 59.1 4.5 45.5 27.3 22
Garments 62.1 8.6 69.8 1.3 53
Metal 62.9 14.3 63.6 18.2 33
All sectors 61.6 10.4 64.0 15.5 161
Table 20: Expected exchange rate, US$IZ$, one and three years ahead Percentage of all finns
One year abead Three years ahead
higber lower higher lower N
Food 83.3 7.1 90.0 5.0 40
Wood 72.0 28.0 72.0 28.0 2S
Textiles 85.0 10.0 85.7 14.3 21
Garments 68.3 18.3 71.4 23.2 56
Metal 68.6 22.9 67.6 29.4 34
All sectors 74.2 17.0 76.7 19.9 176
2.7 Conclusions
Do the findings presented in this Chapter imply that ESAP is a success? In order to answer
this, it is useful to draw together in a single Table our rmdings on changes in resource
allocation (Table 21), Recall from Section 2 that the estimates for the capital stock are
extremely sensitive to the assumptions made; the estimates in Table 21 constitute our best
point estimates, but these will have a large margin of error.
The Table shows that all sectors haye shown positive gross investment, with the highest
average investment rate in the textiles sector. However, with the exception of the textiles
sector it is not likely that investment has been sufficient to compensate for depreciation of
24
the existing capital stock. In other words, net investment has in all likelihood been low .. In
the metal sector, net investment has probably even been negative.
Note also that when we look at the median rather than the mean, the 1993 index is much
lower in all sectors except the food sector.
Turning to employment, we found that employment in both the wood and the garments
sectors has grown substantially. On the other hand, the metal sector has witnessed a large
decrease in empoyment.
The conclusion of these findings is that metal, one of the two sectors that we have
classified as imponable (the other is food) is shrinking both in terms of capital stock and in
terms of employment. On the other hand, the sector that we have labelled exponable,
namley wood, shows a substantal increase in employment but probably a stagnating capital
stock. For garments and textiles, sectors producing both imponables and exponables, the
evidence is also mixed: in garments we find an inrease in employment and a stagnating
capital stock, while for ~extiles we find the reverse.
All in all, there is some evidence that ESAP is producing desirable resource shifts (from
imponables to exportables), but the evidence is not overwhelming. On this score, then, the
. verdict on ESAP is cautiously positive.
Table 21: Summary fmdings on factor allocation Weighted means and median
Index 31 Dec:ember 1993
(198'''100)
Food ISH (169.6)
Wood 16S.S (116.2)
Textiles 293.7 (128.9)
Garments 154.4 (13S.8)
Metal 123.7 (102.4)
All sectors 160.S (128.9)
Index Mid-19M
(1991-100)
99.1 (101.9)
126.4 (1IS.0)
103.3 (100.0)
llO.) (l03.S)
8S.9 (94.6)
103.5 (100.0)
The Chapter has documented a number of other changes that are also relevant to judging
ESAP'S success or failure. We classify these into positive and negative fmdings:
25
Positive:
• Capacity utilization rising
• Exportables do fairly well
in lenns of investment and capacity utilization
Expecled recovery in sales
Negative:
Uncertainty about future interest raleS,
possibly indicating uncertainty abotlt
fISCal policy
So there are a number of positive signs, which might be cause for optimism. Against this a
pessimist might argue that what we have called positive findings mainly constitutes a
recovery from the drought. and that the~e are also less positive signs. In particular he might
point out that uncertainty about government poJicy is holding back investment.
Of course, there is only so much one can learn from simple crosstabulations. The next
task before us is to explore the hypotheses suggested in this Chapter econometrically.
26
References
Gunning, l.W. (ed.), The Manufacturing Sector in Zimbabwe: Dynamics and Constraints, Free University, Amsterdam and University of Zimbabwe. Harare. 1994a.
Gunning. J.W., The Manufacturing Sector in Zimbabwe: Survey Evidence on Growth Adjustment and Vulnerability. mimeo, 1994b.
Risseeuw, P., Firm Growth in Zimbabwe 1981-1993, Chapter 4 in Gunning, 1994a.
27
28
3 Infrastructure and Regulations
Jan Bade.
3.1 Introduction
The RPED surveys include a large number of questions on the extent to which firms perceive
government regulations and the provision of infrastructure as problematic.
In the Round II survey many of these question were repeated. In most cases firms were both
asked to indicate whether they considered the current situation a problem and al~o whether
there had been a change since last year. The main results are summarised in this chapter. in
section 2 for infrastructure and in section 3 for regulations.
3.2 Infrastructure
The government of Zimbabwe bas stressed that the budget cuts associated with structural
adjustment should not lead to a deterioration of infrastructure. The government aims at a
better pricing mechanism for public utilities, and efficiency improvement and privatisation of
monopolistic public enterprises such as the National Railways of Zimbabwe, Air Zimbabwe.
the PTC (post and telecommunication) and ZESA (electricity).
In the 1993 survey entrepreneurs were asked about obstacles in their business
environment. They were asked to indicate on a one to five scale the severity of problems
presented to them; one indicating there was no problem. five that there was a serious
problem. They were also asked to list their most serious problems with the provision of
infrastructure. Telephone services. electricity and transponation for workers emerged as the
most serious problems.
This year we asked whether there had been any improvement or deterioration in the
provision of a number of infrastructure items. We shall first present the results and then
compare them with last year's information. Table 1 presents an overview of the answers
given by all the firms (202) in 1994.
29
Table 1: Changes in Infrastructure
improvement no cbange deterioration
electricity 105 71 11
water 49 118 21 freight transport 55 98 20
workers transport 64 82 31 roads 33 118 34 telephones 36 58 91 air/sea ports 19 97 12 waste disposal 13 158 12 security 30 99 59
Hence according to the survey the provision of electricity, water ,and transport has
improved, the quality of roads, ports and waste disposal facilities has remained the same and
telephones services and security got worse. The provision of electricity (hydroelectric
power) and water improved largely because the effect of the great drought was over. Freight
transport improved because it became easier to import trucks. Transport for workers
!mproved because it became easier to import all kinds of motor vehicles, including
minibuses 'and because ZUPCO lost its monopoly position. Problems with roads refer to
congestion rather than the quality of the surface. Telephone services deteriorated on
average. 12 The firms that reported an improvement in telephone services were mostly
situated in Harare, where new numbers were introduced. But in Bulawayo the situation got
worse. 9n1y 2 out of S2 Bulawayan firms reported improvement, as compared to 26 out of
104 in Harare and 8 out of 29 in the rest of the country. Complaints about air/sea ports
concentrated on delays caused by customs. Waste disposal is a non-issue. Security certainly
not. Due to the drought a large number of deprived people came to the cities, which had a
negative effect on security.
In order to be able to compare the above with last year's information, we looked at two
categories. The first category consists of firms which indicated that a certain item was a
problem (as 1 was no problem and S a big problem we took 3 or higher). The second
category consists of firms which identified an item as the most serious infrastructure
problem. Of course we confmed ourselves to the 190 firms that took part in both rounds.
12We still beard stories about managers who had to drive into town to pay a visit instead of making a telephone calls. At the time of the interview there was no telephone connection between Bulawayo and Harare for more than two days. except via London.
30
The opinion of a firms that identified a certain type of infrastructure as a problem last
year is probably more informative than the opinion of a firm without a problem. Table· 2
shows e.g. that of the 93 firms that had problems with electricity last year, 62 (67%) have
now reported an improvement in supply 13 • Complaints now concentrate on the price of
electricity, which, although still low relative to international levels, has increased
substantially. Workers transport is the other item where more than 50% of the firms which
reported a problem in 1993 reported improvement. This improvement is mainly confined to
Harare, where, as we already mentioned, zUPCO's monopoly was abolished and minibuses
were introduced. In Bulawayo 44 % of last year's complaining firms reported deterioration
and only 16% thought that transport had improved. Considering that "no change" and
"deterioration" imply that there is still a problem, workers transport is still one of the major
problems. Although we asked the respondents about the provision of transport for workers,
some of them may have answered that the situation has deteriorated because their workers
could no longer afford the bus, even though bus services had improved. Prices of bus fares
increased substantially over the last year. Telephones and security are the other items that
still cause problems. Especially in Bulawayo telecommunication and security got worse. In
Harare a number of firms got more and new (better accessible) telephone numbers. But from
Table 3 we can conclude that at least 65% of the firms that participated in both rounds still
. have problems with telephones. To this percentage we should add those that have still
problems, despite the fact that the situation has improved (part of the 29).
Table 1: Changes (1993-94) according 10 finns that had problems in 1993
improvement no change deterioration
electricily 62 24 7
water 10 IS S
freight transport 20 21 9
workers transport 44 32 17
roads 1 18 11
telephones 29 44 19
air/sea ports 4 14 1
waste disposal 1 19 S securily 13 3S 36
13 Note that when a firm reports an improvement we do not know whether there is still a problem. We did not ask about the current situation.
31
All firms were asked last year what their single biggest problem was. Of the 190 firms that
were also visited this year, 176 had answered the question last year. One had answered that
lack of space was the biggest problem. With respect to the 175 others, the following table
lists their opinions on how their biggest problem has changed.
Table 3: Changes (1993-94) to biggest problems
improvement no change deterioration
electricity 16 7 4
water S 7 0
freight transport 8 3 1 workers transport 12 8 4
roads 1 2 2 telephones 11 21 40
air/sea ports 0 2 0
waste disposal 0 0 0 security 1 6 2
Again we see that transport and the provision of electricity and water are improving.
whereas telephones and security are on average deteriorating. From the overall fact that 34 %
report an improvement as compared to 36% 'no change' and 30% 'deterioration', no
conclusions can be drawn.
To be able to make a general statement all firms were asked to sununarize their opinion
on infrastructure, including items not specifically asked. About half of the firms,,:all
infrastructure a problem now. We checked whether firm size, age or sector was related to
having'infrastructure problems, but that was not the case. Things have changed though.
Bulawayo is now leading the score with 56 %, whereas last year firms in Bulawayo had on
average less problems than those in Harare and the rest of the country.
The next Table gives insight in the extent of substitution of public facilities by own
provision. The fmt column shows how many firms acquired an item or started an activity.
The second column shows how many of these firms considered the corresponding public
facility a problem last year. Again this table includes only firms that took part in both
rounds. If we look at all firms in second round, one of the twelve new firms acquired four
items: freight transport, workers transport, waste disposal and security.
32
Table 4: Own provision of infrastructure
own problem last year provision
generator (eleclricity) 8 6
borehole. cistern (water) 16 3
freight Iranspon 21 3
workers transpon 10 6
roads 3 0
CB. Walkie-talkie. Radio (telephones) 10 8
(un)loading labour (airports and customs) 7 1
waste disposal 16 3
security 31 18
The Table shows that 6 out of the 8 companies that acquired a generator had indicated last
year that the provision of electricity was a problem. There also appears to be a relation
between the perception of a problem and own provision for workers' transport, communica
tion equipment, and security.
It is not easy to assess the overall effect of infrastructure on the cost of doing business is
and how this is changing. The clearest results from the surveys are that many finns report
improvements in transport (both freight transport and transport for workers) and in
electricity supply but that telephone services have further deteriorated. Telephones continue
to be the major obstacle in day to day business.
3.3 Regulations
Like in 1993 the 1994 survey included a large number of questions on regulations. Last year
the general conclusion14 was that 'Regulations are now relatively unimportant. This is a
remarkable success of ESAP: even a few years ago price controls and foreign exchange
regulations. to name just two, would have been identified as major problems'. The following
Tables give a comprehensive overview of the regulations we asked about. It shows the
opinion of the finns that participated in both rounds and answered the three relevant
questions on how the effect of the regulation on firm operation has changed in the last year
14 see Conclusion in Gunning (1994) ed.
33
(improved. no change or deteriorated) and whether this regulation is considered a problem
(moderate-problem or wors~) both last year and this year.
Table 5: Regulations: Changes and number of finns that used to have and still have problems
restrictions with respect to improvement no change deterioration problem '93 problem '94
joint ventures 18 30 10 4 profit repatriation 23 24 0 21 8 forex for business travel 102 19 1 SO 6
foreign loans 38 16 0 IS 6
payment of fees to non-residents 23 18 0 12 7
payment of technology licenses and 23 16 0 IS 6 royalties
The number of firms that answered these questions varies a lot. That is because some of the
questions only apply to foreign owned firms or firms that hire expatriates. Nevertheless it is
clear that things have only improved and last year's conclusion has been reconfirmed. The
government has relaxed the limitations on direct foreign investment. In a recent meeting
with the British Confederation of Industries, President Mugabe even promised 100%
repatriation of profits in the future. Foreign exchange, for business travel or any other
purpose, is now available to anyone who can afford it, whereas last year it was still
rationed. Access to foreign loans has improved, in the sense that there is no rationing any
more. But since there is no real forward market, the risk of devaluation has prevented a lot
of firms to engage in off-shore banking. Indeed many respondents complained about the
exchange rate risk. Foreign banks still do not get permission to open a branch in Zimbabwe.
A lot of complaints were ventilated on the lengthy procedures that are needed to get loans
from the International Finance Corporation or the African Development Bank. By the time
some firms got the loan, prices of investment goods had tripled because of the devaluation
of the Zimbabwe dollar. Payment of fees, licenses and royalties is not an issue any more.
since foreign exchange is not rationed any more.
34
Table 6: Labour regulations: Changes and number of fmns chat used to have and still have problems
obstacles to temporary reduction of Improvement no cbange deterioration problem '93 problem '94 production
trade union rules regarding lay-offs 43 92 S 53 43 government rules S8 89 4 82 49 high fmancial cost of lay-offs 23 106 10 68 42
Table 6 summarises the survey results on whether firms see labour regulations as a problem
in case they would want to reduce employment temporarily. Clearly, many firms see
improvements, although a lot of firms still consider them problematic. There are two
different processes that playa role. First, the government has made it easier to retrench
people. Second, most firms nowadays employ a substantial part of their labour force on a
contract base. Often the managers answered that laying off people was possible if you
followed the rules. Of course they thought the rules ,were a nuisance and that the costs
involved were too high. The Table illustrates this. In 1993 more firms were reorganizing
and reducing their labour force than in 1994, so in combination with the changing govern
ment policy and the already mentioned precautionary hiring on a contract basis, we find that
labour regulations cause less problems.
While the results in Table 6 apply to lay-offs only, the survey also included a general
question on labour regulations. In response to this question 65% of the firms indicated that
the situation had not changed; 31 % thought' it had improved, and only 4% said it had . . deteriorated. Nevettheless 48 firms rated labour regulations as a moderate to severe
problem. Wage costs have improved according to 15% of the Round II panicipants and
deteriorated according to 31 %. Wage costs are seen as a moderate to severe problem by 51
firms.
As in 1993 we asked about government restrictions on selling the enterprise or
transferring assets and problems related to the legal process of bankruptcy or liquidation
(Table 7). The first question actually asks about the transfer only, because, as far as the
respondents know. there are no restrictions on selling. The major complaint is about capital
gains tax. Foreign owned companies have problems with transferring assets abroad. Of the
18 firms that perceived problems 6 were foreign owned by Zimbabwean definition, i.e.
more than 35% foreign owned. This should be compared with 32 out of 190 firms that
patticipated in both rounds. The legal process of bankruptcy or liquidation is probably time
3S
consuming and it may be costly for those firms that are not a limited liability enterprise (lay
off benefits e.g.), but otherwise there are regulations preventing a liquidation.
Table 7: Regulations affecting selling or closing down: changes and number of finns tIlat used to have and still have problems
obstacles improvement no change deterioration problem '93 problem '94
government restrictions on seUing 20 78 0 34 18 tile enterprise or transferring assets
tile legal process of bankruptcy or 4 68 3 22 17 liquidation
The results for other regulations are shown in Table 8. We cannot directly compare the
results between the two Rounds because the questions were changed. In 1994 the question
was whether a regulation presented a problem while last year this was asked only in the
context of finn expansion. Hence a finn which saw a regulation as problematic but not. in
the sense of hampering expansion would report a problem in 1993 but not in 1994 even if it
perceived no change. Nevertheless the answers provide some insight in the change and
severity of certain problems.
Table 8: Regulation categories: Changes and number of rmns witll problems
Item Improvement DO change deterioration problem '94
ownership regulations 39 95 3 12
tax regulations 94 62 18 lOS government restrictions on activities 29 101 3 8 gaining investment benefits 31 72 38 45
diffICUlties in obtaining licenses 76 83 6 13 corruption 14 95 66 74 price controls 38 44 2 3
Ownership regulations consist mainly of regulations on foreign ownership. Since the
definition of a foreign owned finn has been relaxed and other items that have to do with
foreign ownership,. such as repatriation of profits, transfer of assets and getting bank loans
are also improving, it is not surprising that 28 % of the respondents report an improvement.
Another item that was mentioned was the 'positive action' policy by the government. Under
36
this policy public construction projects up to a certain amount are allocated to indigenous
firms only. White owned firms obviously had difficulties with this policy.
Tax regulations have improved in the sense that the company tax rate has been reduced.
On the other hand a surtax on services was introduced, which explains why some firms
report a deterioration. Asking whether taxes were a problem was an amusing part of the
interview. The government restricts activities in certain areas and requires licenses. For
example. it is not allowed to use electric machinery for industrial purposes in domestic
areas. Enforcement of these rules has recently been relaxed. The enforcement of zoning
restrictions (restricting heavy metal, light industry, chemical industries and tanneries to
specific areas) seems to have been relaxed.
Since there are no explicit investment benefits in Zimbabwe, many respondents
interpreted this question as if we asked about tax deductions for investment. In Zimbabwe
these are caUed Special Initial allowances. A few years ago firms could deduct 100% of
investment expenditure in the first year. Tben this changed into deduction in three years
according to a 50%, 25%. 25% scheme. Last year this was again changed, allowing
deduction of 25 % in 4 consecutive years. So, respondents that thought about these Special
Initial Allowances have reported a deterioration. Those respondents that thought about
investment regulations in general probably reported an improvement, since most of the
capital goods are now on OGlL (Open General Import License) and the Zimbabwe Investment
Centre (ZIC) does not any more cause the one year delay that used to be standard. For many
investments ZIC'S permission is now just a formality.
Since foreign exchange allocation is no longer related to export licenses, many exporting
respondents answered that the situation with respect to obtaining licenses had improved.
Corruption, although not a 'regulation category'. causes many problems and is getting
worse. There were many stories about competitors that got orders by bribing civil servants
or about civil servants that had asked for 'assistance to pay school fees' in return for a
license or an other service.
Price controls have been abolished in Zimbabwe. so one would not expect any problems.
But the price of cotton is still regulated. The price of sugar is a monopoly price set by the
Zimbabwe Sugar Refineries, a pa'rastatal. And the price of maize is largely determined by
the Agricultural Marketing Board. In general however price setting is free and firms do not
need permission to change their prices.
37
In sununary, last year's optimistic conclusion is confinned: regulations are no longer a
serious issue for Zimbabwean industries.
38
4 Determinants of self-employment in Zimbabwe
Hans Hoogeveen
4.1 Introduction
Since the Zimbabwean government embarked in 1991 on a program of deregulation and
liberalisation, self-employment has been receiving a great deal of attention.
Entrepreneurship, and especially the development of black entrepreneurship, is a key issue
in the process of revitalizing the economy and restoring economic growth. To this end
measures to promote self-employment, such as soft loans for indigenous enterprises have
been implemented.
Despite this renewed interest in entrepreneurship, relatively little is known about the
determinants of self-employment. In this chapter we investigate the hypothesis that the main
driving force in the choice between self employment and wage employment is the difference
in expected earnings between the two occupations. Thereby the focus is on factors
determining the individual's human capital (education and experience) and other personal
characteristics (gender and racial origin). Using an endogenous switching model we estimate
two earnings functions (one for the self-employed, and one for employees) and use the
difference in the predicted earnings in the choice equation. This equation also contains
variables capturing the main human capital and personal characteristics so that their effect is
measured in two ways: directly in the choice decision and indirectly via their role in the
determination of the difference in expected earnings.
On the role of education on the decision to become self-employed there is evidence that
does not contradict the hypothesis that those with higher education are more likely to form
their own firm than those with lower educational attainment (Boswell, 1972; Gudgin et ai.,
1979 cited in Rees and Shah 1986), but this is contradicted by Howell (1972) and Brockhaus
and Nord (1979) who find that people with an academic education have a very low
probability of becoming entrepreneurs. With respect to race or tribe, Rasmussen (1992)
finds that in Zimbabwe belonging to a powerful minority in casu the white business
community facilitates self-employment. while Shapero and Sokol (1982) point at the high
presence of Indians in the West-African business community. of Gujeratis in East Africa or
of !bos in Nigeria. Belonging to a minority appears to contribute to self-employment when
feelings of internal solidarity and mutual trust exist Gender plays a role in the labour
39
market participation (Appleton et al., 1990; Behnnan and Wolfe, 1983) and is likely to also
influence the choice for self-employment.
To our knowledge this is the third study which takes the earnings differential between
self and paid employment as the starting point for an analysis of the determinants of self
employment. It is the first using Zimbabwean data. Rees and Shah (1986), in a study for the
UK, found that the probability of self-employment depends positively on the earnings
difference with paid employment. This result was not confirmed by De Wit (1991) in an
analysis of self-employment in the Netherlands.
The organization of this chapter is as follows. In section two the model is presented. We
follow a two stage structural probit method in which the choice of self-employment is
dependent on the differences between the predicted earnings for self and paid employment.
In section three the data set is described followed by a presentation of the empirical results
in section four. Here earnings functions and the results of the choice equations are
presented, for the whole sample and, separately, for blacks Zimbabweans. We also
investigate whether factors such as family background, barriers to entry or liquidity
constraints affect the decision to become self-employed. Section five presents some
conclusions.
4.2 Modelling the choice between self-employment and wage employment
The model presented in the following section consists of three' equations: an equation to
describe the choice between self employment and wage employment and two earnings
equations, one for each of the two occupations. Individuals base their decision to become
self-employed upon their evaluation of the earnings differential between the two positions.
This decision can be described in the following way:
(1)
where Cj is an unobservable continuous variable that indicates the choice of individual i: if
C j > 0 the individual opts for self-employment. otherwise for wage employment. The
earnings from self-employment are indicated by 'lrj. those from wage employment by Wi' ~ is
a parameter to be estimated. In the absence of labour market rigidities so that workers can
switch between activities in response to earnings discrepancies. 8 is expected to be positive.
40
Zj 'is a vector of characteristics expected to influence the individual's preference for a certain
option: 'Y is the vector of coefficients to be estimated and Ej is a normally distributed error
term.
The earnings functions represent the expectations of earnings at the time the choice is
made. It is assumed that the expectations of earnings are unbiased and that the error terms
(",Si and ",Pi) are normally distributed with zero means. The earnings functions are expressed
as follows:
(2)
(3)
where as and BP are the coefficients to be estimated, and Xi the vector of observable
characteristics of individual i influencing his or her earnings. The vector Xi is identical for
both equations: we assume that profit earnings and wage income are determined by the same
human capital factors.
The model described above is an endogenous switching model and it can be estimated by
maximizing the likelihood function that corresponds to (1), (2) and (3) together, the FlML
approach. However, because the data set only contains observations on wages when
information on earnings of self-employment are missing and vice versa, this involves time
consuming calculations and programming. Therefore we opt for another method of
estimation, the so called two stage structural probit method. which allows us to corrett for
the possible bias resulting from self-selection. This bias arises from the consideration that
individuals in "a subsample of say the self-employed may behave differently from the others
in that they have a comparative advantage of being self·employment and hence choose that
status. Therefore observations on earnings of one group may not tell us what the other group
would earn in the same situation. Since the choice equation (1) allocates individuals to
occupations according to the greatest earnings, the actual observed earnings in each class are
truncated non random samples, and we need to correct for that. A procedure to do this is
provided for in Maddala (1983),
First of all the reduced form equation is derived by substituting (2) and (3) into (1):
(4)
41
where vector Yj contains all the variables of Zj and Xj' and 0: is a vector of estimable
parameters and ttj • Ej + 0 (pSj ~ p.Pj). Now. in terms of the disturbances of the earnings
equations (2) and (3). p.Sj and p.Pj are correlated with Ej. It can be shown (Maddala. 1983. p.
224) that the expected values of the errors in the earnings equations are non-zero:
E(pSj I Cj > 0) ¢ 0 (self-employment) and
E(p.Pj I Cj S 0) ¢ 0 (wage employment).
The correction factor needed to compensate for the non-zero expected values from the
earnings equation error terms is provided for by the inverse Mills ratio,
).,Sj. 4>(0: Vi) ~(O:Yi)
and ).,Pj • cb(aY j)
[1-~(O:Yj)l
where q, is the density function of the standardized nonnal distribution and ~ is its
cumulative distribution function. These values are used as additional explanatory variables in
the earnings equations. If we assume that the error terms p.Sj. p.Pi and E·j have a trivariate
nonnal distribution with mean vector zero then the earnings equations can be written as:
(S)
w· = BPX· + 0: ).,P. + "P •. I I P I (6)
where
DS = COV(ps,E·) and Dp = COy (PP,E·) and with"sj • p.Sj + Ds).,Sj and "Pj • p.Pj + Dp)"Pj ,
of which the conditional means are zero:
42
E("sj I Cj > 0) = 0 (self-employment) and
E(vPj I Cj :s; 0) = 0 (wage employment).
Now, using the estimated values for ex, XSj and XPj can be calculated after which equations
(5) and (6) can be estimated by OLS, yielding consistent estimates for 85, 6P• Us and up1S •
Because the vectors Zj and Xi are specified in such a way that more than one variable in
Xi is not contained in Zi' the choice equation (1) is overidentified and must be estimated in a
final run. Using the estimated of 'lI'j and Wi' equation (1) can be estimated by the probit ML
method.
4.3 Description of the data set
The data set used for this chapter is derived from the first two rounds (June/July 1993 and
1994) of the Regional Program on Enterprise Development (RPED) data set. To qualify for
inclusion in the estimations all private owners (including chairmen of cooperatives) for
whom the personal history was recorded are incorporated 16. The employees included are
those interviewed in the second wave. Thus specified, the survey data identify 137 self
employed and 637 wage employees. As a result of missing data, 13 self-employed
individuals and 44 employees had to be dropped, leaving us with 124 entrepreneurs and 593
employees. Descriptive statistics of the variables used in the estimations in the estimations
can be found in Table 1.
Education is measured on a continuous scale as the number of grades or forms
completed, based on the worker's or employer's reported school level and highest grade or
form. Vocational training was assumed to add one year of schooling, polytechnic two and
university three years 17. On average self-employed individuals are higher educated than
employees (12.4 years for self-employed versus 9.5 for employees), who themselves are
much higher educated than the average Zimbabwean who received on average 2.9 years of
ISNote lhat the error tenus .,fI and .,. are heteroscedastic. They can be evaluated with estimators proposed by Lee and Trost (1978. p. 362). yielding more efficient estimates for 8'. 81'. (7. and up' However, this would still not produce fully satisfactory results because in the final run the choice equation is estimated making use of the estimated variables (1t'j and wi>. As de Wit (1991. p. 131) notes this might result in an underestimation of the standard error of the parameter &. Since it was decided not to use the FIML approach but an already inferior (but quicker) method and following Rees and Shah (1986). we refrain from further corrections.
16m order 10 find out if the inclusion of formerly unemployed and self~mpJoyed individuals in the sample entails any seJection bias. all estimations have also been done excluding this group of 37 entrepreneurs. The results of these estimations are essentially identical to the results presented here. and the restriction on the previous occupation was dropped.
t7With regard to the continuous variables for education and experience we follow the defmilions as presented by Velenchik (p. 98 in Gunning. 1994).
43
education (UNDP. 1993). Table one also presents dummy variables to represent the highest
level of education achieved. It shows that in comparison to wage employees, a small
percentage of the self-employed have primary school as their highest level of education
while quite a few obtained vocational training or attended university. In the estimations the
dummy variables on education are not used; they are presented for information purposes
only.
An educational dummy which is included in the estimations is the dummy for
apprenticeship. In our sample 12% of the entrepreneurs and 6% of the employees was a
former apprentice.
Experience is measured as potential experience, or the number of years since the
individual left school. This is equivalent in spirit to the Mincerian formulation of Experience
= Age - Schooling - 6, but more appropriate to the Zimbabwean context. where many
individuals start school after the age of six, and repetition of grades implies that the number
of grades completed may understate the number of years spent in school. On average the
self-employed had 26.9 years of experience, against 19.3 years for employees. The
difference in experience is also found in the differences in age between self-employed and
employees. The average entrepreneur is 46.6 years old, the average employee 37.0. In the
estimations the age variable has not been used in view of the high correlation between age,
years of education and years of experience.
Females are under-represented in both the labour force and amongst the self-employed.
but no indication is found of gender based differences in the self-employment participation
rate. JUst under 17 % of the employees in the sample are women; for the self-employed this
figure is slightly over 16 % .
With regard to the racial background of the individuals in the sample, virtually all
employees are black Zimbabweans (96%). This is clearly not the case for the self-employed
of whom only 38 % is black. Most entrepreneurs (42 %) have a European background, and
16% are Asians. The high presence of individuals from the European and Asian minorities
in Zimbabwe amongst the entrepreneurs, confirms the findings of Shapero and Sokol (1982)
with regard to minorities and self-employment.
The earnings variable may well be measured with considerable error in the case of
employers. For employees the data are probably much more reliable: the best informed
person (the employee himself) provided the answer. The only problem that might occur is
44
that someone does not want to reveal hislher income, most likely leading to missing data
instead of wrong information.
Table 1: Unweighted descriptive statistics of variables used in the estimations'"
Variables self1!mployed employees
Earnings per hour 98.2 2SS.S 6.29 8.09
Education Years of Education 12.4 3.S 9.S 2.7
No education <'I> 0.8 1.7
Primary school <'I> 12.2 42.7
Secondary school <%) 28.S SO.7
Vocational training (%) 34.1 2.8
University <'I) 24.4 2.0
Apprenticeship (%) 12.1 6.1
Experience Years of Experience 26.9 13.2 19.3 10.8
Personal Characteristics Age 46.6 12.1 37.0 9.9
Female (%) 16.1 16.8
Racial Background African ('I) 38.7 96.0
Asian (%) 16.1 2.9
European (%) 41.9 1.2
Other (%) 3.2 0.0
Observations (II} 124 593
.. For continuous variables the mean is given followed by the standard deviation. For dummies the percentage of the individuals with the characteristic is given.
Detennining the earnings for tbe self-employed is more problematic. First of all, data
problems are much likelier to have occurred because respondents were unclear about the
questions, because the entrepreneurs did not want to reveal their profits accurately, had
inaccurate or no data and needed to estimate them by hean. In some cases not the best
informed person (general or financial manager) provided the data. A second problem is that
the yearly income of self-employed not only consists of net profits but also includes material
and immaterial (goodwill) capital accumulation. Net profits therefore provide an
45
underestimation of total entrepreneurial income. The volatility of profits provide a third
reason for caution. A fourth reason is related to the sampling procedure which resulted that
virtUally all entrepreneurs in the sample (95 %) have run their business for three or more
years. Since half of the newly established business in Zimbabwe go out of operation before
the end of the third year (Mead et al., 1993, p. ii) there is an upward bias in our profits
measure.
Nevertheless we took net annual profits (before tax, after depreciation) as the earnings
measure for self-employed, averaged over the two survey years. In the estimations, total
yearly earnings have been divided by 2200, the (rounded) mean number of hours worked
per year by employees.
Table 1 shows that the earnings for self-employed are on average more than fifteen times
higher than those for wage employees. However, self employment is, of course, riskier.
Note that the standard deviation of income in self employment is very high.
The sizable difference in earnings by job status might be the result from the measurement
of profits, but it can also point towards the existence of entry barriers to self-employment. If
this is the case, the parameter 8 will be affected, as it can only be expected to be positive in
the absence of labour market rigidities.
In view of the (possibly) problematic determination of earnings function for self-
. employed we also performed estimations without inclusion of such a measure. To this end
the expected profits were set to constant (thus loosing the possibility of individual
characteristics influencing expected profits). In that case equation (1) can be written as
follows:
(1 a)
where c is a constant.
This leaves the basic model intact, except that the earnings equation (2) for the self
employed no longer needs to be estimated and that the choice equation (1) no longer depends
on the expected difference in earnings but solely on the expected employee income. Since
the second model is an unnested version of the first, an evaluation of the predictive power of
both models will have to determine whether the results suffer from the weakness of the
profit figures.
46
4.4 Empirical results
The theoretical model states that the observable variables in the choice equations are proxies
for all variables other than income that influence the choice between self-employment and
wage employment. Before deciding what variables to include in the choice equation. note
that an independent influence of the earnings differential is only observable if at least one
variable present in the earnings equations is not present in the choice equations.
In the estimations only variables are included which are explicitly associated to the
individual: human capital determinants (education and experience) and dummies for race and
gender. Sector dummies and variables indicating where the individual is located are left out
on the presumption that individuals have an unconditional choice with regard to their job
status. Other variables like whether the individual is married or has self-employed parents
could not be included, because this information was collected for entrepreneurs but not for
employees.
Variables incorporated in the earnings equations are: years education. years of education
squared. years of experience and dummies for apprenticeship, gender and race. Velenchik
(in Gunning. 1994) estimated employee earnings functions. with similar independent
variables. We re-estimate her equations using both wave I and wave n data and taking
yearly earnings instead of the log of hourly earning as the earnings measure. Other variables
used by Velenchik and yielding explanatory power like firm size and function could not be
included in our analysis, because it is impossible to determine which function or firm size a
presently self-employed individual would chose when opting for wage employment.
Variables included in the choice equation are: years of education, years of education
squared, years of experience and dummies for gender and race. By including these variables
in the choice equation we allow for a separate influence of these variables on the choice
decision, other than via the earnings differential. This allows us to compare our results to
those of other authors with regard the effects of education or belonging to a minority. It also
enables us to take a look at evidence of discrimination by gender and race.
At least one variable present in the earnings equations has to be left out in the choice
equation. Velenchik showed that apprenticeship does have a contributing factor to wages
(having been an apprentice provides a wage increase of nearly 100% beyond the wage
achieved by the academic education (p. 103». There is no a priori reason to assume that
47
apprenticeship will also effect the choice decision other than via the wage differential. This
dwruny was therefore omitt~ in the estimations of the choice equations.
The estimated earnings equations are presented in Table 2. The estimation results for the
employee earnings function are standard with regard to the positive effect of experience and
education on earnings and in accordance with the results of Velenchik. Apprenticeship raises
the wage level, as expected. Evidence of discrimination by race and gender is also found.
Especially being a black Zimbabwean has a considerable negative effect on the individuals
wage. It lowers the expected employee income by 74 per cent. Being a woman also has a
negative impact on the employee's income. Note that we do not establish whether the
discrimination found is the result of otherwise identical individuals in identical jobs being
paid less, or that certain categories of workers are excluded from the higher paying jobs.
The explanatory power of the wage function is satisfactory in view of tbe number and
kind of variables used. In the employee earnings equation, no evidence is found of selection
bias: the X-coefficient is not significant.
The results for the self-employed earnings equation are less satisfactory. The explanatory
power of the equation is low, and an F-test does not reject at the 5 % significance level the
constrained model in which all the coefficients except the intercept are set equal to zero.
This weak result provides a rationale for the estimation of choice equation (1a) which does
not include the earnings differential between self and paid employment but predicted wages
only.
The effect of education on profits is not clear, and experience even has a negative effect
on profits, possibly a reflection of the uncompetitive environment which existed in
Zimbabwe before the economic reform program. With all kinds of tnonopolies and licences
settled entrepreneurs were ensured of an easy share of the market. It is plausible that this
lead to a class of self-employed individuals who are experienced but who can not perform in
a competitive environment.
With regard to the selection bias, the X-coefficient is significant for the profit function.
Thus the average earnings of individuals with given characteristics who have chosen to be
self-employed are higher than what self-employment earnings would be for those with the
same characteristics who chose to be employees. The hypothesis that those who have chosen
to be self-employed have a comparative advantage in that occupation is confirmed.
The estimated choice equations are presented in Table 3. The coefficient ~ is small and
insignificant at the 5 % level in both models. The sign and the significance of the variable ~
48
in the basic model proved sensitive to the inclusion or omission of variables. The 0 becomes
positive but remains insignificant when the dummy for gender is replaced by that for appren
ticeship, and is positive and significant when the dummy for black, Zimbabweans is
excluded. An estimation ran with the earnings differential as the sole variable together with
the intercept showed that the earnings differential yields some explanatory power in the
choice equation: the 0 was positive and significant at the 1 % significance level, but, in
contrast to the presented estimation, no self-employed individuals were identified. This leads
to the conclusion that the earnings differential certainly does not play a major role in the
decision on self-employment.
Table 2: Earnings equations·
Variables
Intercept
Years of Education
Years of Education2
Years of Experience
D·Black Zimbabweans
D·Apprenticeship
D·Gender18
Lambda
Statistics
Observations (#)
R2
Adjusted Rl
self-employed
932.5
1099.3
-363.7
-271.7
507.0
-57.5
·71.8
-605.8
104
0.106
0.041
1.00
1.66 -2.15·
-2.23·
1.65 ..(J.67
-1.00
-2.08·
31.42
-27.84
9.33
3.27
-16.00
3.79
-3.13
-1.94
574
0.345
0.336
employees
4.86·
-4.66·
5.41·
3.74·
-4.21·
3.18*
-4.07·
..(J.4S
• OLS estimates. The dependent variable is the yearly earnings divided by 2200. Continuous independent variables have been taken as logarithms. T-statistics are given following the coefficients. An asterisk indicates significance at the S % level.
The contributions of the other variables to the choice equation are significant. A higher
number of years spent in school and more experience increase the probability of becoming
self-employed. Being black has a negative influence and a gender effect was not established.
The predictive power of the choice equation is fairly high: the model predicts self
employment in 76 cases, 63 times correctly, 12 times incorrectly. Note that the model
l8In all cases where a gender dummy is used. 0 indicates a man and I a woman.
49
underestimates the number of entrepreneurs: the predicted number is 75, the actual number
is considerably higher (124).
The choice equation which includes predicted wages instead of the earnings differential
(limited mode) predicts only slightly worse: 62 of the 124 entrepreneurs are identified. The
limited model suffers from multicollinearity. This explains the insignificant values of most of
the variables. An estimation ran with the expected wage as the sole variable together with
the intercept showed that predicted wages yield a positive and . significant (at the 1 %
significance level) influence on the choice decision.
When we check the predictive power of this extremely limited choice model we find that
it identified 61 out of the 124 self-employed individuals correctly. We may therefore
conclude that the variables determining employee earnings (experience, education, gender
and racial origin) are also the determinants for the decision to become self-employed. Higher
educated, experienced, non-black individuals are the most likely persons to earn higher
wages and to become self.employed.
Despite the convincing results, the limifed role of the earnings differential on the decision
to become self-employed is unsatisfactory. It might be that the earnings differential plays a
minor role, so that the coefficient is hard to measure empirically. This is in contrast with the
observation that 23 % of the entrepreneurs indicated that higher income opportunities was
one of the reasons to become self-employed; other major reasons were example of parents
(15%) and the wish to be self.employed (31 %). It is also possible that the earnings functions
(and especially the profit function) have been determined insufficiently. A third reason might
be that entry barriers hamper the proper functioning of the labour market, so that the settled
entrepreneurs can protect themselves and their profits against new entrants.
Further examination of the results reveals that of the 76 non-black self-employed
individuals in the sample 63 were predicted correctly, while of the 48 black entrepreneurs
not even one was identified. In other words according to this model racial origin is the
determining factor for the choice between self.employment and wage employment. This
explains why predicted wages are such a good estimator in the limited choice equation. In
our sample, non-blacks are significantly higher educated, have more experience and do not
suffer from racial discrimination. They therefore have significantly higher predicted wages.
50
Table 3: Choice equations*
Variables
Intercept
Earnings differential
Years of Education
Years of Education2
Years of Experience
D-Black Zimbabweans
D-Gender
Statistics Observations (II)
Entrepreneurs
Predicted Correctly
Predicted Wrongly
basic model
~.94
-2.44e4
-2.34
0.83
0.70
-1.46
4.87e-2
717
124
63
12
~.81
~.16
-2.23*
3.17*
5.02· -6.40·
~.21
-1.56
1.91e-2
-1.81 0.66
0.67
-1.20
-7.8Se-3
717
124
62
12
limited model
~.66
0.29
~.92
1.06 3.14·
-1.20
~.03
• Probit estimations of the probability of choosing for entrepreneurship. Years of education and years of experience have been taken as logarithms. T -statistics are provided following the coefficients. An asterisk indicates significance at the S% level.
Table 4: Unweighted differences between black and non-black Zimbabweans
Variables
Years of Education
Years of Experience
Predicted Wage
Results for black Zimbabweans
Non-African
13.7
19.6
12.8
Black Zimbabweans
9.2
19.6
5.6
F -statistic
200
40
400
Two reasons exist, to re-estirnate the model for black Zimbabweans only. First of all, the
data set was composed in such a way that relatively few blacks are included among the self
employed and even fewer non-blacks amongst the employees. This skewed racial distribution
by employment status possibly portrays the present Zimbabwean society correcdy, but it
causes concern on generality of the results. Since the promotion of indigenous
entrepreneurship is a major policy goal, a re-estimation of the model with blacks
Zimbabweans is justified. A secdnd reason for a separate estimation is that by limiting
ourselves to black Zimbabweans the huge differences in earnings between self employed and
paid employees (see Table 1) are reduced to more realistic proportions. This is expected to
have an effect on the impact of the earnings differential on the choice equation. By restrict-
SI
ing the observations to those for blacks only the average hourly earnings19 for employees
become Z$ 5.54 for employees and Z$ 49.5 for self-employed.
The variables used are identical to the ones presented previously. Evidently the dummy
for black Zimbabweans is no longer used.
The earnings equations are presented in Table S. The results for the employee earnings
function are comparable to the ones presented in Table 2. Higher education and more
experience contribute to the explanation of employee income. Contrary to the employee . earnings function for the whole sample no significant evidence is found of gender
discrimination.
Table 5: Earnings equations (black Zimbabweans only)"
Variables
Intercept
Years of Education
Years of Education2
Years of Experience
D-Apprenliceship
D..Qender
Lambda
Statistics Observations (#) R2
Adjusted R2
self-4lmpJoyed
-331.2
-727.2
242.7
155.0
-34.7
171.0
39
0.181
0.056
-0.50
-1.53 1.92
1.66 -0.34
1.l2
13.5
-29.1
9.8
3.6
4.4
·1.6
-3.2
SSI
0.224
0.215
employees
3.03·
-4.52"
4.98"
3.01"
3.82"
-LS8
-0.42
• OLS estimates. The dependent variable is the yearly earnings divided by 2200. Continuous independent variables have been taken as logarithms. T-slatistics are given following the coefficients. An asterisk indicates signifJCaJlce at the '" level.
The fit of the self-employed earnings function remains problematic. In contrast to the
previous self-employed earnings function, multicollinearity is a problem. For this reason the
gender dummy is not included in the estimation of the profit function. Despite this all
coefficients remain insignificant at the 5 % significance level. An F·test does not reject at the
5 % level the constrained model in which all coefficients except the intercept are set equal to
zero.
l'Estimated as before as annual earnings 12200.
52
No evidence of a selection bias was found, neither for the employee earnings function
nor for the self-employed earnings function. This latter result is interesting since it indicates
that the average earnings of individuals with given characteristics who have chosen to be
self-employed are not higher or lower than what profit earnings would be for those with the
same characteristics who chose to be employees and vice versa. The hypothesis that those
who have chosen to be self-employed have a comparative advantage at it can not be
confirmed for black Zimbabweans.
Table 6: Choice equations (black: Zimbabweans only)-
Variables
Intercept
Earnings differential rep. Wage
Years of Education
Years of Educationl
Years of Experience
D-Gender
Statistics . Observations (#)
Entrepreneurs
Predicted Correcdy
Predicted Wrongly
basic: model
-5.07
-8.0Ie-]
0.59
-6.71e-2
0.46
0.88
617
48
o o
-2.56-
-1.21
0.2]
-0.09
1.64
2.07-
-2.69
:].66e-2
-3.17
1.12
0.86
0.34
617
48
o o
limited model
-1.60 -0.47
-1.33
1.46
3.08-
1.32
* Probit estimations of the probability of chOOSing for entrepreneurship. Years of education and years of experience have been taken as logarithms. T-statisties are provided following the coefficients. An asterisk indicates significance at the 5'-' level.
The estimated choice equations are presented in Table 6. Again, the c5-c0efficients are
insignificant in both equations. The predictive power of the model is absolutely poor as both
the basic and the limited model do not identify even one self-employed individual. This can
only lead to one conclusion: for black Zimbabweans, the decision to become self-employed
is not Significantly determined by the factors specified in the model.
Other explanatory factors for self-employment
The economic literature distinguishes several other factors determining the decision to
become self-employed. The most important are: entrepreneurial ability (including animal
spirits), risk. capital constraints and other entry barriers. Entrepreneurial ability is hard to
53
measure; to the extent that entrepreneurial skills can be passed on from one generation to
another the importance of entrepreneurial ability is shown by the fact that half of the entre
preneurs in the sample had at least one self-employed parent. Attitude towards risk could
provide another explanatory factor to choice of job status. However, occupational choice
theories suggested by Johnson (1978), Jovanovic (1979) and Miller (1984)20, imply that
individuals will try riskier occupations such as self-employment when they are younger.
However, our data suggest the opposite: experience (and hence age) increase the probability
of becoming self-employed. It might be true that entrepreneurship is not an option for
younger workers because they had less time to build up the capital needed to start a business
and, with being liquidity constraint have difficulty to borrow sufficient start-up funds. Evans
(1989) fmds for the United States that:"Most individuals who enter self-employment face a
binding liquidity constraint and as a result use a suboptimal amount of capital to start up
their business" (p. 810). Although Ter Wengel and Mumbengegwi (in Gunning 1994)
showed that (most) firms in Zimbabwe are not credit constrained, there is no reason to
assume that capital constraints are not at play in the start-up phase. The survey data show
that personal savings playa critical role in business formation: on average three quartefZ1
of the capital required for the initial investment comes from this source. The need for own
savings is further underlined by the fact that at three per cent of the total start-up capital,
hardly any money was borrowed from formal sources.
The requirement of own savings to become self-employed. will not be so much a problem
if the total amount needed is small. This appears not to be the case. In the sample 18 firms
have been established in or after 1990. The (unweighted) average amount required was ZS
510,000: one to two times the lifetime salary of the average private sector employee. But
businesses are also started with less money. Six of the 18 firms were started at a cost below
ZS 7000 or the equivalent of a year's income. Two firms were started with even less than a
month salary, proving that also with little funds a business can be started in Zimbabwe.
Still, in most cases the required funds to start a businesses are substantial, especially whim
we take into account that even a relatively small sum of ZS 7000 will be difficult to save
from a meagre industrial wage.
20All cited in Evans. 1989, p. 809.
21 All figures in this section are unweighted.
S4
'In view of Zimbabwe's recent colonial past, in which capital accumulation by blacks was
frustrated, it might be true that capital constraints are more important for blacks than for
others. For those businesses started in the past four years such a restraint is not. found as no
significant difference in the amount of the start-up capital was established.
However the frequency of black Zimbabweans buying their business is at 6% much lower
than the 40% for non-blacks. This does point towards a racial difference in the possession of
liquidities and or assets, Finally, the fact that the average black Zimbabwean earns three to
four times less than the average non-black Zimbabwean presents a clear obstacle to the
propensity to accumulate.
4.5 Conclusion
This chapter has explored a number of issues concerning determinants of self-employment.
The starting point was the hypothesis that the main determinant is the expected income
differential between wage employment and self-employment.
Our results suggest that this hypothesis must be rejected for Zimbabwe. We find that the
probability of self-employment is positively related to expected wages. Since the expected
wage increases with the individual's education, his experience and his being non-black. the
choice for self-employment is basically explained by these personal characteristics.
With regard to black Zimbabweans, no effect of the earnings differential could be estab
lished. Also no convincing evidence was found of positive effects of higher education and
more experience on the probability of choosing to become self-employed. The absence of
selection bias in the estimations of predicted earnings indicates that black entrepreneurs have
no competitive advantage in their job in comparison to employees and vice versa: for black
Zimbabweans self-employment is the outcome of a random process.
Education and experience do not yield much predictive power other than via their effect
on predicted wages. The fact that non-black Zimbabweans are generally higher educated and
more experienced, makes it difficult to separate the potential effects of education and
experience from the racial factor.
55
References
Appleton, Simon, Paul Collier and Paul Horsnell, 1990, 'Gender, Education and Employment in Cote d'ivoire', Social Dimensions of Structural Adjustment in Sub-Saharan Africa; working paper no. 8, Washington DC, The World Bank.
Behrman, Jere R. and Barbara L. Wolfe, 1983, 'Labor force participation and earnings determinants for women in the special conditions of developing countries', Journal of Development Economics, vol. 15, pp. 259-288.
Evans, David S. and Boyan Jovanovic, 1989, 'An Estimated Model of Entrepreneurial Choice under Liquidity Constraints', Journal of Political Economy, vol. 97, no. 41, pp 808-827.
Gunning, J.W. (ed.), 1994, 'The manufacturing sector in Zimbabwe: Dynamics and constraints', RPED Report, Free University, Amsterdam and University of Zimbabwe, Harare.
Maddala, G.S., 1983, limited-dependent and qualitative variables in econometrics, Cambridge University Press, Cambridge.
Mead, D.C., H.O. Mukwenha and L. Reed, 1993, 'Growth and Transformation among Small Enterprises in Zimbabwe', working paper University of Zimbabwe/GEMINI.
Rasmussen, J., 1992, 'The local entrepreneurial milieu. Entrepreneurial networks in small Zimbabwean towns', Research report (79), Roskilde University. Copenhagen, Denmark
Rees, Hedley and Anup Shah, 1986, 'An empirical analysis of self-employment in the U.K.', Journal of Applied Econometrics, vol. 1, pp. 95-108. .
Shapero, A. and L. Sokol, 'The social dimensions of entrepreneurship', In: Kent, C.A., D. Sexton, L. Vespar and C. Englewood (eds.), 1982, The Encyclopedia of Entrepreneurship, Prentice Hall, New York.
UNDP, Human development repon 1993, 1993, Oxford University Press, New York, USA.
Vijverberg, Wim P.M., 1991, 'Measuring Income from Family Enterprises with Household Surveys', The Living Standards Measurement Study working paper no 84, Washington DC, The World Bank.
Wit, Gemt de, 1991, Determinants of self-employment, Amsterdam, The Netherlands.
56
5 Finance: the Use of Credit and Finns' Indebtedness
Jan Bade
5.1 Introduction
The RPED surveys provide very detailed financial information. In this chapter we use these
data to address two questions. First, in section 2. what sources of credit are used and
whether there is evidence of rationing. The data show that the surveyed firms rely heavily
on trade credit and that there are few firms which can be described as credit constrained.
This is true even if the analysis is confined to formal sector loans. If overdrafts, informal
borrowing and trade credit are also considered only a handful of firms appear to be
constrained.
In recent years interest rates in Zimbabwe have risen sharply. The survey data may be
used to investigate how vulnerable the industrial sector is to these changes. We do this by
comparing debt/sales ratios between sectors and over time. While in some sectors vulnerabil
ity. thus measured. has actually increased between the two survey rounds. there appears to
be no cause for concern: in our sample indebtedness is very low relative to sales.
5.2 The use of credit
The survey provides information on three forms of credit: credit from formal financial
institutions (both overdrafts and loans). credit from informal sources, and trade credit. In
this section we shall discuss these in tum. We will both consider the use made of the
different forms of credit and survey evidence whether firms are credit constrained.
Credit from fonnal fmandal institutions
Credit from formal financial institution is given in the form of an overdraft or' a loan. In
Zimbabwe banks prefer overdraft facilities. because of the flexible interest rates. Loans are
mostly shon-term (no longer than 5 years). Last. year we asked more details about bank
loans and found that the average maturity of bank loans was 3.5 years. For both loans and
overdraft facilities collateral requirements are very strict.
Of the 202 firms in the sample 132 had an overdraft facility. Of the 70 firms that did
not, 37 indicated that they expected that they could get an overdraft if they wanted while 11
57
finns did not know whether they could get an overdraft. We treated them as if they can not
get one and included them in the 33 firms that do not have and cannot get an overdraft.
Table 1 shows the answers about access to overdraft facilities by firm size.
Table 1: Overdraft facilities and firm size
small medium large very large total
no overdraft. cannot get one 20 9 2 2 33 no overdraft. can get one 14 15 7 1 37 overdraft. at maximum 0 10 11 13 34 overdraft below maximum 6 32 28 32 98 total 40 66 48 48 202
As the table shows, small firms (10 or less employees) are not likely to have an overdraft
facility. Only 15 percent of the small firms have an overdraft. For medium sized (11 - 1(0),
large (101 - 250) and very large (> 250) firms, these percentages are 64, 81 and 94 percent
respectively. Of the 98 firms that could expand their overdraft, 35 did not use the facility at
-all. The average amount due by firms with an overdraft was Z$ 3.1 million (131
observations due to one refusal to reveal the amount). The average due by firms that actually
used their overdraft at the time of the interview (131 - 3S = 96 observations) was ZS 4.2
million. The total amount outstanding on overdrafts was Z$ 410 million.
Table 2: Firms with loans from formal institutions by f1l'lll size
smaU medium Jarp very large total
bank loans 3 4 9 22 38
loans from non-bank fmancial institutions 0 14 12 14 40
loans from government programs. etc. 0 0 3 4 7
loans from foreign banks 0 0 2 11 13 other loans from formal institutions 1 3 3 8 any of these loans 4 20 23 30 77
Table 2 presents data on loans from formal financial institutions. A total of 77 firms got loans
from formal financial institutions. On average the outstanding balance wa. Z$ 13.3 million (74
firms). The total amount outstanding is about Z$ 1 billion. Again we see that larger firms make more
use of formal credit. It is interesting to note thet 11 of the 77 firm. did not have an overdraft facility.
Five of them also did not think they could get one. Twenty of the remaining 66 (with an overdraft)
58
were at the maximum of their overdraft facility. There is a good explanation for this. Interest rates on
overdrafts have risen sharply in recent years. In 1990 it was 10%. By the end of 1992 it was 30%.
and it has continued at that high level. Obviously interest rates on loans lag behind22• The high
interest rate on overdrafts explains why so many firms (981 do not use their overdraft facility up to
its maximum.
To investigate further whether firms are credit constrained. we looked at the question whether.
firms expected to be able to get a loan. Twenty firms with loans did not expect that they could get
an additional loan. For 12 of these 20 we expected that, because they also did not expect to get an
overdraft (3) or made maximum use of their overdraft (9). But the other 8 could borrow more on
overdraft. but did not expect to get a loan. This probably reflects the banks' preference for the
greater flexibility of overdrafts. Table 3 shows the expectations of getting a loan in relation to access
to overdraft facilities.
Table 3: Overdraft facilities and expectations of gening a loan
expects to get a loan?
yes no tolal
no overdraft. cannot gel one 16 11 33
no overdraft. can get one 31 6 31 overdraft. at maximum 18 16 34 overdraft below maximum 81 17 98 total 146 56 202
These are remarkable results. Half of the firms that we would have called credit constrained
on the basis of their overdraft (rows 1 and 3), expect that they can get a loan. It is also
remarkable that of the 20 small firms without an overdraft, who do not expect that they get
an overdraft, 8 expect that they could get a loan. We expected that these eight would then be
large firms, but when we checked that, it turned out that among this subgroup (no overdraft.
cannot get one) there is no additional size effect on expectations. Another very remarkable
finding is that black entrepreneurs have different expectations. We find that in general 50%
of the black entrepreneurs expect that they can get a loan as compared to 80 % of the other
entrepreneurs. This difference could of course be due to the fact that black owned firms are
often small, so that it is in fact a size effect that we measure instead of a racial effect. But
this difference occurs in all four size classes which we distinguish. Last year's analysis
22 Last year the average interest rate on bank loans was 20%.
S9
established that banks do not discrirninare23, so it seems that we have found a peculiar
influence of racial origin on expectations. Further analysis is of course needed to exclude
any other explanatory factors that may be correlated with race.
Table 3 shows that of the 56 firms that do not expect to be able to get a loan from
formal financial institutions 33 (17 plus 16) can be considered credit constrained (the other
22 firms are not at the limit of their overdrafts or do not use overdrafts at all). This is
confirmed by other information in the survey. Of the 17 firms which do not have an
overdraft and do not expect to get either an overdraft facility or a loan. 13 have cash flow
problems. Six of these 17 had applied for a loan, but none of them had received a loan24;
the others had not applied for various reasons2S• but mostly because they did not think
they would get one. Of the 16 firms that made complete use of their overdraft facility, only
one had not had cash flow problems in the last year and had thus not applied for a loan.
because he did not need a loan. Six of the remaining 15 had applied for a loan and 3 had
actually received one26.
Hence we conclude that at most 33 firms describe themselves as credit constrained with
respect to the formal financial sector. But in 5 cases the constraint is not binding in the sense
that the firms had no cash flow problems27• In five other cases the constraint is not
binding in the sense that the respondents indicated that they did not need a loan. Only in 8
cases can we be sure that the firms are credit constrained because a loan application was
refused.
Informal credit
If the fact that a finn cannot get or expand credit from the formal financial sector is not a
binding constraint, there are two possible explanations. The fllSt one is that the firm does
13 See C. Mumbengegwi and l. ter Wengel in l.W. Gunning (ed.), 'The Manufacturing Sector in Zimbabwe: Dynamics and CODSb'aints', 1994. They conclude that, controlling for fum characteristics such as age, size and sector, black entrepreneuR are mor~ likely to apply for loans and to obtain diem.
l4 In fact one application was approved. but the fum had not taken the loan. It is surprising that this fum now does not expect to be able to get a loan.
2S Reasons for not applying were: did not think they would get one (6), did not want to incur debt (2). thought the interest was too high (2). did not know how to apply (I).
26 The other nine firms either did not need one (5). or did not want to incur (more) debt (2). or did not think they would get one (1). or thought that a loan was too risky (1).
27 The constraint could, of course, be binding if the firm wants to invest or expand.
60
not need credit at all. The second explanation is that the finn is able to get credit from other
sources. We shall now look at these other sources. In this section we investigate the e;ttent
of informal borrowing. In the next section we shall look at trade credit (credit from
suppliers and clients) as a source of finance.
Informal sources of credit include friends, relatives, moneylenders. and savings groups,
but also suppliers, clients, parent companies and other enterprises. Trade credit was
explicitly excluded from our definition. Table 4 gives an overview of the number of firms
that made use of theSe sources relative to finn size.
Table 4: Firms with infonnal loans by source and firm size
smaD medium large very large total
relatives and friends 2 3 1 0 6
moneylenders 0 0 0
infonnal groups 2 0 0 3
suppliers 0 0 0 2 2
clients 1 0 0 0
oilier enterprises. holding companies or shareholders, 2 6 11 14 33 and parent companies
" In total 40 firms made use of informal loans. The bulk of infonnal credit was given by
"other enterprises". Mostly these were loans by parent and holding companies or other
enterprises owned by the same owner. The average total amount borrowed (sometimes from
different sources) per firms was Z$ 6.2 million (40 observations). The average interest was
20% per annum (21 observations), with a maximum of 43% and a minimum of 2.4%28.
Last year we found exactly the same percentage as an average on bank loans (32
observations)29. Of the 40 firms that borrow informally, 19 expect that they cannot get a
formal loan. And 12 of these 19 were among the 33 that we had identified as formal credit
constrained; 9 with an overdraft, but at its maximum and 3 without an overdraft. So the
28 Note llIat 20" is far below llIe market rate of 30". However. the average was calculated on the basis of only 21 of the 40 fterns. In all but one cases the source was another company.
29 See 'Regional Programme on Enterprise Development: First Report on the Zimbabwe Survey'. Amsterdam. 1993.
61
informal market is relatively small and definitely not (only) the result of "financial
repression" 30.
We asked whether the firms expected to be able to borrow from informal sources if they
needed money. It turned out that 40% of the respondents expected that they could borrow
from these sources. There were no differences between the firm size categories. Of the 33
companies that we had identified as formal credit constrained, 18 indicated that they can
borrow additionally from informal sources; 8 without an overdraft facility and 10 with
maximum use of their overdraft. That leaves us with 15 firms that cannot borrow additional
money if they wanted. Again we assume that expectations are correct. Of these 15 firms 9
are without an overdraft and 6 with maximum use of their overdraft. Among these 15. two
indicated that they did not need a loan and three indicated that they had not had any cash
flow problems in the last year. Of the remaining ten (6 without overdraft), two had informal
loans and another two had a bank loan.
We conclude that if we informal credit is taken into account then the number of credit
constrained firms declines to 15. In 10 of these 15 cases the constraint could be binding.
Trade credit
The final source that we look at is trade credit. Trade credit is an important source of credit.
For those firms that purchased on credit. the average outstanding balance due to suppliers
was ZS 5.3 million (151 firms). Firms that asked for prepayments, got an average amount of
Z$ 0.3 million in advance (35 observations). That means that the firms in our sample
borrow in total ZS 810.8 million from their trading partners. This is double the amount they
got as overdrafts and almost the same as the total of loans. So trade credit comes second as
a source of finance when we look at the total amount involved. When we look at the number
of firms involved, trade credit comes first (162 firms), overdrafts second (96 firms). formal
loans third (74 firms) and informal loans fourth (40 firms).
If we compare getting credit with giving credit. the firms in the sample all together give
more trade credit to their customers than they receive from their clients. This is not
surprising if one realizes that prepayments are rare and that value has been added to the raw
materials and intermediate inputs purchased from suppliers. However. if we compare this
year with last year, we see that the difference is declining. We looked at firms that took part
30 See McKinnon, R.I. (1973), Money and Copillli in Economic Developmelll, Washington, DC, Brookings Institution, where informal markets are described as the result of fonnal credit market imperfections.
62
in both rounds. Last year the outstanding balance of their customers (or suppliers in the case
of prepayments) was on average Z$ 1.58 million more than outstanding balance with their
suppliers (or again customers in case of prepayments). This year it was onl~ Z$ 80,000. The
average balance due to finns went up from Z$ 3.86 million to Z$ 4.32 million, whereas the
average balance owed to the finns who participated in both rounds went up from ZS 2.27
million to Z$ 4.24 million. So finns have reduced their net indebtedness, presumably in
response to the recent interest rate increases.
We defined a firm as a net creditor if the amount owed was less than the amount due
with respect to purchases and deliveries: Table 5 presents an overview of all finns by size.
Table 5: Trade credit balance by finn size
small medium large very 'large total
net lrade creditor 16 27 16 73 132 no trade credit 7 4 3 5 19
net trade debtor 10 6 16 19 51
total 33 37 35 97 202
So most of the finns in our sample are net creditors. But if we look at the 15 finns that
cannot borrow additionally from formal or informal sources only three of them are net
creditors. Seven finns are net debtors and the other five had a zero balance at the moment of
the interview. We did not ask whether the finns thought that they could get more credit . . from their suppliers or extend the tenns. Neither did we ask whether they thought they
could get more prepayments. But we did ask about the ease of access to suppliers and clients
credit in general.
The 9 firms without overdraft report that it is difficult (1 ), very difficult (1) or
impossible (7) to get supplier credit. Among the seven that answered impossible were six
with cash flow problems. But on the other hand six of these nine firms without overdraft,
the one with the bank: loan not included,. report that getting prepayments from their clients is
not a problem.
Only one of the six finns with an overdraft reported that getting trade credit from sup
pliers is a problem, the others say that it is easy (3) or even very easy (2). But for these six
firms getting prepayments turns out to be impossible (5) or very difficult (1).
63
The conclusion of this section on trade credit is that 12 of the 15 firms that could not get
credit from formal fmancial institutions and could not get informal loans, can still easily get
credit from either suppliers or clients. Therefore we cannot call them credit constrained in a
strict sense. The three that are left do not have an overdraft. The first of these three has a
bank loan and had an overdraft facility last year, so there is probably a good reason for all
possible creditors to refuse credit in that case. The second of the three is a member of a
collective self finance scheme, but his loan application did not fulfil the requirements. And
the third is a recent staner, leasing all equipment and renting a building on insecure and
exploitive shon tenn contracts.
In summary, we found that for 15 firms it is probably difficult to borrow additionally, as
they have to extend their trade credit or push for more prepayments. But five of them
indicate that they do not need additional funds. Of the remaining ten only three firms cannot
borrow additional money at all. This suggests that credit constraints are unimportant for the
firms in our sample. How imponant is this conclusion? Recall that the firms in our sample
are in general more than three years old.and have thus proven that they can survive. Last
year we found for staning firms it is very difficult to get credit. Own savings proved to be
the most imponant source of stan-up capital. In 52% of the cases it was the only source. On
average (per finn) 66 % of stan-up capital came from own savings. But again, for existing
. firms access to credit is probably not so imponant. Despite the fact that 27 % of the firms in
our sample caIl access to credit one of the three major problems. we could only fInd a few
firms where access to credit was a real problem. We got the impression that it is actually
cheap credit that they want. Finally, we found that black entrepreneurs have lower expecta
tions when it comes to getting a loan from formal institutions. As there is no proof of
discrimination by the financial sector. this calls for more research.
5.3 Sectoral changes in outstanding debt and vulnerability
Since the stan of ESAP interest rates have gone up sharply. We would expect that this has
affected borrowing. On the other hand investment has increased, as it became easier to
impon capital goods. In this section we shall compare outstanding debts between sectors and
between the two survey rounds. We shall also look at the relative debts of firms by relating
them to sales.
64
Table 6 gives an overview of the average debt of finns in Round L 31 For reasons of
comparison we confined ourselves to the 190 finns that participated in both rounds. Table 6
presents the average balances of reported in Round II.
Table 6: Outstanding balances, round 1 (in 1994 prices, unweigbted) (average amounts per firm, in ZS million)
sector ,rmns overdrafts formal loans informal loans trade credit total debt
food 46 2.94 5.54 -0.10 -0.29 8.09
wood 23 0.10 0.72 0.39 -0.59 0.62
textile 87 2.65 4.58 0.36 -3.14 4.45
metal 34 1.66 0.68 0.16 -2.10 0.39
total 190 2.23 3.65 0.22 -1.95 4.14
Table 7: Outstanding balances, round 2 (unweigbted) (average amounts per firm. in Z$ million)
sector # firms overdrafts formal loans Informal loans trade credit total debt
food 46 1.68 8.06 0.65 1.81 12.21
wood 23 0.21 0.72 1.80 -1.00 1.73
textile 87 3.11 6.47 0.40 ·1.12 8.86
metal 34 1.33 0.34 0.48 0.64 2.80
total 190 2.09 5.06 0.65 -0.08 7.72
Clearly there are remarkable differences. If we look at total indebtedness the average has
increased in all sectors. Average total indebtedness of all finns almost dOUbled. If we look
only at formal debt the increase was 20%. That is quite surprising given the high interest
rates. The increase in formal credit was only in the form of loans. Average overdraft
actually decreased slightly. Only the finns in the metal sector decreased their formal sector
debt. Note however that the relative increase in total debt was the highest in the metal
sector, due to a sharp reduction in the net creditor position with respect to trade credit. In
all sectors there was an increase in the average amount of informal loans, although the
increase was very small in the textile sector. The finns in the food sector even changed on
average from being net lenders to net borrowers. Also with respect to trade credit there are
31We used the consumer price indices of March 1993 and March 1994 to present the 1993 debt data. We took March because mostly the information on loans and overdrafts was based on the last completed fmancial records. Most of the fmanciaJ years close at the end of March.
65
considerable changes. The averages in Tables 6 and 7 reflect the average balances of
payments due to suppliers and prepayments made by customers minus the amounts owed to
the firm by customers and prepayments made to suppliers. In other words the average of
creditors minus debtors. Last year the firms in all sectors were on average creditors. This
year we observe that the firms in the wood sector increased on average their net lending
position. But in the other sectors we see a decrease, with even a change in net position from
creditor to debtor in the food and metal sector . . To be able to make more general statements about sectoral changes, we weighted the
firms in our sample. In our sample large firm are more than proportionally present, because
of our sampling method. By applying weights we have corrected for that. So in our
weighted sample each firm in Zimbabwe had an equal probability of being in the sample.
This is somewhat dangerous since a large weight was given to a small number of
particularly smaller firms in the sample. These were also the firms that often gave less
accurate information, because they lack proper bookkeeping. So errors in the data may have
been given more weight. Nevertheless, without weighting no general conclusions can be
drawn: unweighted sample averages are of no particular interest.
Table 8: Outstanding balances, round 1 (in 1994 prices, weighted) (average amounts per finn, in Z$ million)
sector , I1rms overdrafts tormal loans inrormal loans trade credit total debt
food 46 0.16 0.14 ~.Ol ~.09 0.20
wood 23 0.05 0.10 0.Q3 ~.10 0.08
textile 87 0.09 0.12 0.11 -0.13 0.19 metal 34 0.10 0.11 0.05 -0.23 0.03
total 190 0.10 0.12 0.06 -0.13 O.IS
Table 9: Outstanding balances, round 2 (weighted) (average amounts per ftrm. in Z$ million)
sector , I1rms overdrafts formal loans inrormal loans trade credit total debt
food 46 0.10 0.14 0.03 0.00 0.26
wood 23 0.06 0.08 0.16 ~.13 0.16
textile 87 0.14 0.13 0.11 ~.06 0.32
metal 34 0.08 0.04 0.10 -0.10 0.12
total 190 0.11 0.11 0.10 -0.06 0.25
66
Weighted averages are shown in Tables 8 and 9. The general picture remains by and lar~e
the same. Total debt in the four sectors has increased. The weighted averages give an
increase of 67% as compared to 86% for the unweighted averages. Those are incredible
increases given the high interest rates. The largest relative increase in debt has still taken
place in the metal sector, albeit that the very high growth factor of seven in the unweighted
case has now come down to four. Formal sector debt in the metal sector has also declined in
the weighted sample. In the textile sector total debt increased by 68 %, in the wood sector it
doubled, and in the food sector it increased by 30%. So far weighted and unweighted figures
tell more or less the same story, although the weighted averages are of course a lot smaller,
because we gave a larger weight to small finns. But there are also differences between the
weighted and the unweighted sample. Total formal debt in the weighted case e.g. does not
increase by 20%, but remains the same. So we conclude that despite the fact that the
average firm in our sample increased its formal sector debt by 20%, the average firm in
Zimbabwe has not incr~ed that debt at all (in real tenns). The increase in debt in the
weighted sample therefore comes from increased informal lending and a change in trade
credit balances. Note that in the weighted sample average net trade credit balances do not
change sign. This change in trade credit positions is not surprising. Last year we found that
most of th~ trade credit carries no interest and without any foregone discount32. It seemed
that charging interest or giving payment incentives by means of discounts was not standard
practice. So giving trade credit is costly and receiving it is rewarding. Given the high inter
est rates one should expect that many finns are delaying their payments and chasing their
debtors, Many respondents told us that doing exactly that was an important part of their
financial management job.
It may be noted that the change in trade credit is most significant in the food and metal
sector which suffered most from the drought in 1992: The drought could have caused an
increase in trade credit in these sectors. If credit was given proportionally all the way down
to the end-user. this implies negative balances for the manufacturing finns, because of value
added. We then expect a reverse of this process now that times have improved.
We now consider differences between finns in vulnerability to changes in interest rates.
We measure vulnerability by the ratio of outstanding debt to sales (Tables 10 and 11).
Because of missing data our sample was reduced to 177 finns. Table 10 gives total
32 See Bade, J. and R. Chifamba, 'Transaction costs and institutional environment', in Gunning. l.W. (ed.) (1994), 'The manufacturing sector in Zimbabwe: dynamics and constraints',
67
debt/sales ratios for both rounds, weighted and unweighted. Table 11 does the same but only
for formal sector debt.
Table 10: Vulnerability: ratios of lOcal net debt and sales
unweighted weighted
sector I firms round 1 round :z # rU'ms round 1 round ::
food 40 0.05 0.06 40 0.007 O.OlS wood 23 0.05 0.12 23 0.038 0.003
textile 80 0.14 0.22 80 -0.017 0.022
metal 34 0.08 0.08 34 0.018 0.107
lOtal 177 0.10 0.15 177 0.002 0.034
Table 11: Vulnerability: ratios of formal sector net debt and sales
unweighted weighted
sector Irlm'lS round 1 round 2 # rlm'lS round 1 round :z food 40 0.06 0.06 40 0.011 0.014
wood 23 0.08 0.08 23 0.055 0.044
textile 80 0.17 0.22 80 0.045 0.054
metal 34 0.15 0.07 34 0.052 0.046
total 177 0.13 0.14 177 0.040 0.042
Table 10 shows that weighing has an enormoUs impact. In the textile sector a number of . .
finns had lent more than they had borrowed. These firms were given so much weight that
the average debt/sales rate became negative. If we look at the change in vulnerability per
sector, we see that in the unweighted sample the rates have increased in all sectors except
metal. In the weighted sample, however, the metal sector has become more vulnerable, as
the debt/sales rate has increased by a factor six. On the other hand we find that the debt rate
has decreased in the wood sector according to our weighted sample from 3.8 % to 0.3 %,
whereas the unweighted sample indicated an increase from 5 % to 12 %. In the weighted
sample the average debt rate in the food sector doubles, whereas in the unweighted sample it
increases by only 25% (figures in the table are rounded).
In Table 11 we exclude trade credit and informal lending. This makes little. difference.
Debt/sales ratios do not change in the food and wood sector, increase in textile and decrease
in metal. In the weighted case, the absolute changes are very small. If anything can be
concluded from the relative changes, it confirms what we found when we looked at total
68
debt in the weighted sample. Rates go up in the food and textile sector and they go down in
the wood sector. In the metal sector change in trade credit disturbs the picture. While the
weighted total debt rate increases by a factor six, the formal debt rate even decreases
slightly.
What can be concluded from this? First that the firms in the sectors producing tradable
goods (wood and textiles) have become more vulnerable. As large firm are overrepresented
in our sample many of these firms do in fact export and compete on the international
market. They may have invested, now that capital goods have become available. In the non
tradable sectors (food and metal) we see no change in vulnerability for the firms in our
sample. The wood sector has become less vulnerable, and the metal sector more vulnerable,
whereas the vulnerability of the textile and food sector has hardly changed. But indebtedness
is small (relative to sales) so that vulnerability to interest rate increases does not appear to be
a very serious issue.
69
70
6 Firm Dynamics 1990-1993
Peter Risseeuw
6.1 Introduction
Since Schumpeter (1935), economists agree that a high rate of firm dynamics (entries and
exits) is a healthy sign. High entry rates indicate low entry barriers and an open competitive
environment in which there is space for innovations and new entrepreneurship. High exit
rates are not supposed to be a problem: lost jobs in firms that disappear are repl~ by new
jobs in new firms.
In our previous report we concluded that economic circumstances in Zimbabwe after
independence were not dynamic at all.33 Entry barriers were high, both costs for entry and
exit were found to be prohibitively high, with as a result not much of dynamics in the
Schumpeterian sense, but a remarkable high rate of tradability of existing firms. We
abstained from a formal analysis of firms dynamics '(i.e., entry and survival analysis)
because of lack of data, but from the analysis of growth patterns we could deduce that
incumbent (usually pre-UDI, white owned) firms had major competitive adv~tages.
Although the enhancement of dynamics and a competitive environment is a goal of ESAP,
it will not come as a surprise that the second wave of data does not yield much indication of
a more dynamic economy yet. Given the state of the economy in 1993 (especially in the tex
tile industry things were looking badly), the researchers were expecting a significant
proportion of the firms in the sample to have quit existence/or economic reasons.
This expectation is not confirmed. 197 of the 201 firms in the original sample were still
in existence (although some of them had been taken over) at the time of the second Round
(June-July 1994). Of the nine firms that quit the sample. only four quit the market as well
(with one other firm in the process of liquidation. but still willing and able to provide data
on the fiscal year 1993/94). The new management of another firm. that had been taken over,
did not want to cooperate, one firm was virtually out-of-business because of illness of the
owner, and three firms were too busy (I) for an interview. Four or five firms out of a
sample of 201 is obviously too narrow a base to build a logit analysis on survival upon.
33 'Firm Growth in Zimbabwe 1981·1993" in Gunning. J.W. (ed.) The lNJIIU/oauring stetor in Zimbabwe: Dynamics and constraints, (chapter 4. pp. 27-47),
71
Some finns in the sample known to be in financial difficulties were taken over (just)
before bankruptcy. Though this no proof, it is an indication that entry- and exit barriers are
still in existence, and that the continuation of an existing firm, albeit as such not viable at
all, is still cheaper than letting it go bankrupt and starting a new one.
Since the analysis of firm dynamics with the current dataset is still impossible (no
substantial exit from the sample, while the sample design yields no possibility for systematic
analysis of entry), we will stick again to the analysis of growth and decline. Since we
associate growth and decline with success and failure, this may give us information which
finns -and in which markets- do well under ESAP, and which finns get into difficulties,
and therefore are candidates for being taken over, instead of going bankrupt.
6.2 Finn growth
Since in the second wave no new information about the period between 1981 and 1991 was
gathered, for an analysis of growth patterns in this decade we refer to our previous
report.34 In the current analysis we will examine the period 1991-1994 in more detail.
Obviously t. this is a very short period and one characterised by dramatic economic changes
(the drought and the program of structural adjustment) so that we do not expect results
which are satisfactory in a statistical sense.
The analysis of firm growth starts with firm size. In chapter 2 some statistics on average
employment growth in the period under study were presented. In that chapter the unit of
analysis' was the sector and hence the survey data were weighed. In the current analysis, the
unit of analysis is the individual firm: we try to explain factors enhancing or hampering
growth at the micro-level. That is why the statistics shown in this chapter can differ from the
statistics elsewhere in this report.
Let us first look at some statistics of average firm size of the finns in the panel for
various subsamples. For a review of the classification variables and the corresponding
theoretical considerations we refer to our report on Round I. Table 1 shows average firm
34 An extended analysis of growth and decline in both the eighties and the nineties, malting use of other ('new') variables from the side of labour. technology and fmance is planned to be carried out in a next version of this analysis.
72
size 1990, 1991, 1992 and 1993.35 (Note that data collected in 1994 are classified as
1993.)
Table 1: Mean Size 1990-1993
number mean significance-level
1990 1991 1m 1993 1990 1991 1992 1993
All firms 164 310 338 312 319 by sector
• food 39 398 325 354 326
• wood 5 76 113 103 III
• furniture 15 109 97 112 127
• textile 22 692 682 578 604 ••• •• • •• • garments 43 226 263 255 215
• leather 8 660 677 664 583 • · metal 32 205 218 205 175
by period of founding
• before 1965 60 623 598 596 570 ••• • •• •• * • •• · 1965-1980 48 280 310 278 280
· 1980·1990 42 59 69 66 63 ••• ••• ••• • •• · after 1990 14 28 40 38 40 • • •• by gender of the owner
• male 89 198 224 206 203
· female 19 21 20 17 23 by race owner(s)
· black 43 67 57 53 52 ••• ••• • •• ••• · Asian 21 76 82 76 86 • • · European 66 400 439 410 408 • • by location
• Harare 91 418 400 391 358 • • · Bulawayo 47 207 231 219 224
• other areas 26 235 238 215 189 by type of firm
• cooperative 13 94 107 100 11S
· entrepreneurial 74 85 84 79 80 ••• ••• • •• ••• • pan of group 19 412 537 536 490
• subsidiary 58 672 612 587 574 ••• ••• ••• •••
Significance-level of the F-statistic of an ANOVA-test of the given subsample vs. all other firms.
SignifICance-levels: ••• 99" ••• 95". • 90"
35 1990 refers to the end of the fISCal year 1990/1991 (usually March 30. 1991) etc. This may look a bit illogical when using variables with a stock nature like employment. We use this notation to remain consistent with the other chapters in this repan.
73
Table 1 is, like all results in this chapter, based on the 164 finns in the sample that could
provide us with data on their employment level in all of those four years. This does not alter
the 'survival' bias, because existence in 1994 remains the first criterion to be included in the
sample anyway.
A first conclusion is that systematic differences in size through this period are stable,
although in some cases the differences are only just significant. This does not imply that
growth patterns need to be the same. In Table 2 we compare growth rates for the various
subsamples. As we explained in the previous report. the measurement of size in tenns of
employment at a fixed moment in time can lead to measurement error. and thus to biased
statistics. We correct for this by averaging the value at different points in time. In the
current analysis. the intention is to shed light on factors influencing firm growth in the early
nineties, after the start of ESAP. That is why we have chosen to use two points for
measurement: average employment in 1990 and 1991 versus average employment in 1992
and 1993.
Table 2 shows average growth rates for the various subsamples. We recognize the
common finding of decreasing growth rates by firm age, but also that very few bivariate
relations are significant in a statistical way. 'Metal work'is the only sector with a significant
average decline rate. 'Textile' (Le. basic textiles, spinning and weaving), a sector supposed
to be both sensitive to climatological fluctuations and export possibilities, shows an
extremely stable pattern. The differences in growth rates by gender and races can largely be
explained by differences in size. As far as type of firm is concerned, subsidiaries (both
domestic and foreign) on the average have decreased in size. This is remarkable, because
these firms are supposed to have the strongest endurance in bad times. On the other hand is
the distance between owners and labour force larger in such finns, which may lower moral
barriers to layoff workers.
74
Table 1: Mean growth rates 1990191-1992193
All firms by sector
• food <wood
• furniture • textile • garments • leather
• metal
by period of founding
• before 1965
· 1965-1980
• 1980-1990
• after 1990 by gender owner
• male • female by race owncr(s) • black . Asian
· European by location • Harare • Bulawayo • other areas by type of firm · cooperative • entrepreneurial • part of group • subsidiary
mean growth significauce-Ievel
0.004
0.021
0.011
0.035
-0.001
0.019
-0.020
-0.043
-0.021
0.001
0.019
0.075
0.018
0.003
0.029
0.048
-0.010
0.007
-0.014
0.027
-0.019
0.020
0.040
-0.023
.*
*
•
Signiftcance-Ievel of the P-statistic of an ANOVA-test of the given subsample vs. all other firms. Signiftcance-Ievels: *** 99'1 ••• 95'1. * 91 'I
6.3 Factors detennining growth
To measure the influence of all variables together, we estimate a growth relation like we
specified in our previous report (equation 4.3, p. 37). Table 3 shows the results of this
75
regression. The results of the regression with all variables included is presented in Table 4.
In Table 3 only the results o~ the remaining significant variables are presented. 36
Table 3: OLS resulls growth 1990/91-1992193 (n-I64)
coeff T sig
constant 0.211 1.93 --size .0.500 -1.68 --age .0.081 -2.06 .-. size-age 0.012 1.43 .-D metal .0.054 -2.30 ••• D entrepreneurial 0.071 1.57 • D pan of group 0.106 1.81 •• D subsidiary 0.070 1.33 • D expon 1992193 0.100 2.65 ••• D expon 1992193 and 1993194 .0.059 -1.54 ••
Mean of dependent variable .0.004
Adjusted R2 0.099
A variable name beginning with 'D' denotes a dummy Significance levels: _.- 99", •• 95", • 90"
.The fit of the equation is (unsurprisingly) poor. But a few remarkable conclusions can be
drawn from Table 3. The collUDon findings of declining growth rates with size and age are
confinned. This is remarkable because in our analysis on growth patterns in the eighties no
relation between finn age and growth was found. Hence the more recent data suggest that
the Zimbabwean economy is becoming "more normal". As was indicated in Table 2. firms
in the metal sector are decreasing very rapidly.
The dummies defining type of finn (entrepreneurial, part of group and subsidiary) all get
significant positive coefficients, which implies that cooperatives must have a significant
negative one. Note however that firms which are part of some informal kind of group (often
family-related) get the highest and most significant coefficient. In our fonner report we
already mentioned that informal networking is a major asset for Zimbabwean firms.
The most important conclusion is on exports. Exports in the fiscal year 1992/93 are an
important engine for growth: the regression suggests that firms which exported had a growth
36 White's heteroscedasticity-consistent robust standard errors were both used in the process of variable elimination and presented beret
76
rate which was 10 percentage points higher than that of other finns. It is very remarkable,
that is effect is for 60 percent cancelled out by exports (by the same finns) in the next year.
This may partially be a matter of causality: exports enhance growth, and not the other way
round. Nevertheless, taking the estimated coefficients of the two dununy variables together a
substantial effect remains: the growth rate of finns which exported in both years was 4
percentage points higher than that of non-exporting finns.
Table 4: Full estimation results
toefl' T
constant 0.307 2.01
size -0.058 -1.17
size2 0.001 0.36 age -0.113 ·1.85
agel 0.008 0.67 size .. age 0.009 0.S9 ))..member of group 0.090 L34
D-subsidiary 0.060 0.97
D-male entrepreneurial 0.029 0.66
D-female entrepreneurial 0.024 0.28 D-entrepreneurial .. age 0.010 0.53
D-black owned -0.027 -0.63 D-bulawayo -0.017 -0.65 D-other areas -0.354 -LOS
D-woodworking -0.084 -0.93 D-fumiture 0.028 0.67 D-lextile -O.02S -0.60 D-garments -0.015 -0.47 D-Ieather -0.037 -0.70 D-metal -0.072 -2.64 D-exporting 92 0.091 2.05 D-exponing 92 and 93 -0.054 -1.33 D-foreign owned -0.054 -0.97 D-exporting 92 .. D-for. owned 0.083 1.38
Mean dependent variable 0.004 Adjusted R2 0.049
Variables names beginning with 'D-' denote dummy-variables. T-vaJues are White's heteroscedasticity-consistent standard errors.
77
