Financial liberalisation and industrial development in Malawi

Abstract

It has been suggested that financial liberalisation may be a key policy to promote industrialisation as it removes the credit access constraint on firms, especially small and medium ones. We investigate the effect of credit expansion in the wake of liberalisation on the structure of the industrial sectors in Malawi and find that, in contrast to the hypothesis above, it resulted in an increase in industrial concentration and a decrease in net firm entry, especially in sectors that are more finance dependent. The case of Malawi is interesting because financial liberalisation has been justified precisely as a means for industrial development and because the implementation of the policy has been regarded as relatively successful.

Full text

Financial liberalisation and industrial development in Malawi
Grant P. Kabango, Reserve Bank of Malawi,
and
Alberto Paloni, University of Glasgow, UK
March 2010
Abstract
It has been suggested that financial liberalisation may be a key policy to promote
industrialisation as it removes the credit access constraint on firms, especially small and
medium ones. We investigate the effect of credit expansion in the wake of liberalisation on
the structure of the industrial sectors in Malawi and find that, in contrast to the hypothesis
above, it resulted in an increase in industrial concentration and a decrease in net firm entry,
especially in sectors that are more finance dependent. The case of Malawi is interesting
because financial liberalisation has been justified precisely as a means for industrial
development and because the implementation of the policy has been regarded as relatively
successful.
JEL Classification: O16: Economic development: financial markets;
O55: Economywide country studies: Africa;
G20: Financial institutions and services: general

1. Introduction
The importance of developed and stable financial systems for the sustainability of
economic growth cannot be over-emphasised. Instead, whether financial sector development
can be an engine for growth in developing countries and whether policies for financial
development should have priority over other policies are still controversial matters. Rather
than concentrating on the cross-country aggregate relationship between financial development
and economic growth (which on the whole has produced few conclusive results) much of the
more recent research investigates the theoretical mechanisms through which financial
development may lead to faster growth.
One of these transmission channels centres on the ability of developed financial
market institutions to reduce the cost for firms of raising external finance. This may be the
result of greater availability of finance, due to competition among financial institutions, and
of the presence of better accounting and disclosure rules and better corporate governance
which help firms overcome adverse selection and moral hazard problems (Levine, 1997).
Rajan and Zingales (1998) provide support for the working of this channel with their finding
that, in countries where financial markets are more developed, industrial sectors that are more
dependent on external finance grow faster than less dependent ones. Moreover, in such
sectors, the establishment of new firms (which are more dependent on external finance than
existing firms) is a more important phenomenon than the growth in the average size of
establishments.1
More research into this area seems to be warranted on both theoretical and practical
grounds. The paper by Rajan and Zingales – as well as much of related research – compares
countries at different levels of financial development. In practice, however, policymakers
need information on the likely effects of financial liberalisation on the credit access constraint
1 This is an additional indirect channel through which financial development can boost economic growth since
new firms are more likely to make use of more productive innovative technologies.
1

experienced by firms. For example, in a liberalised regime, banks, which during financial
repression did not develop their risk assessment and management skills, may concentrate their
lending to well-established customers rather than financing new establishments (Caprio,
1994). Banking competition, which always accompanies financial liberalisation policies, may
not necessarily mitigate this problem since in a competitive environment the importance of
relationship lending may increase rather than decrease, because it allows lenders to
differentiate themselves from other lenders and gives them a competitive edge (Boot and
Thakor, 2000). Moreover, since small and medium firms are more informationally opaque
entities, the costs for banks of investing in informational capital to assess the prospects of
small and medium enterprises may not be worthwhile (Berger, Klapper and Udell, 2001).
These observations suggest that financial institutions may provide credit mostly to larger
clients with long-standing relationships thus perpetuating entry barriers especially for those
firms that are more external finance dependent. It is therefore theoretically ambiguous
whether financial liberalisation relaxes firms’ financing constraints and which industrial
sectors stand to gain from liberalisation or are more likely to be hurt by it.
This paper adopts a somewhat original approach to investigate the link between
financial liberalisation and industrial development. First, it analyses the impact of
liberalisation on the degree of industrial concentration and firm entry. If liberalisation boosts
financial institutions’ lending capacity and makes credit more widely available to firms, it will
encourage the creation of new firms. If their entry outweighs the expansion of existing firms –
as Rajan and Zingales and others find – the degree of industrial concentration should fall. If,
on the other hand, liberalisation privileges larger firms, industrial concentration will rise. It is
important to look at both concentration and firm entry since the promotion of
entrepreneurship could go hand in hand with stronger growth of existing firms. Second, this
paper uses panel data for a single country (where the cross-sectional units are the various
2

manufacturing sectors) rather than following a cross-country approach, which requires
demanding assumptions about the universality of the process being modelled. The country we
analyse is Malawi. We argue below that the experience of this country is meaningful for a
study on the liberalisation-industrial development relationship.
The structure of the paper is as follows. The next section reviews the literature on the
effect that financial system deepening may have on a country’s industrial structure. This
section provides the motivation for this study and for the choice of Malawi as the country of
analysis. Section 3 describes the empirical approach. Section 4 presents and discusses the
empirical results. Section 5 concludes.
2. Financial liberalisation and industrial structure
Three different strands of the literature are related to our paper. The first strand
highlights the heterogeneity of industrial sectors and suggests that financial development will
have different effects on the various sectors’ rates of growth depending on their
characteristics.
The seminal contribution in this strand is the paper by Rajan and Zingales cited above.
These authors adopt an original perspective, which has since inspired many other
contributions in the field and is based on the distinction between industrial sectors that require
external sources of finance to fund their investment plans and those which use internally
generated resources. This distinction, according to Rajan and Zingales, is not country specific
but arises from technological constraints which are always valid regardless of the institutional
context. In other words, the set up costs of a firm in a specific industry are to a large extent
determined by the industry-specific technological needs; firms in an industry which requires
costly technology would be more dependent on external finance than firms in an industry
where technology is less expensive. According to this reasoning, firms in tobacco or leather,
3

for example, would always be less dependent on external finance than firms in the
pharmaceutical or the computing sector. Hence, Rajan and Zingales maintain that this ranking
of industrial sectors in terms of their degree of dependence from external finance is likely to
be valid across countries.
Having made this distinction between sectors more or less dependent on external
finance, Rajan and Zingales go on to show that, in their sample of 36 industries in 41
developed and developing countries, greater financial development would disproportionally
benefit the firms in the sectors that are more external finance dependent. Using a slightly
different sample and approach, Beck and Levine (2002) find the same result. The explanation
is that, since financial repression tends to restrict access to bank credit and, therefore, firms
more dependent on external finance face a financial constraint that is especially severe
compared to firms that are less external finance dependent, the former type of firms would
gain much more than the latter from financial development and greater access to credit.
Hence, industrial sectors in which firms are more external finance dependent will grow faster
in countries with greater financial development. According to these studies, therefore,
financial development reduces the cost of external finance for firms. This effect, which is felt
primarily by firms more dependent on external finance, may go undetected in analyses that
concentrate exclusively on the search for average effects.
Rajan and Zingales also find that, in the sectors that are more external finance
dependent, industrial growth appears to be driven by the growth in the number of new firms
rather than the growth in the average size of existing establishments. Similarly, Beck and
Levine (2002) using industry-level data and Klapper, Laeven and Rajan (2006) using firm-
level data in a sample of European firms show that greater financial development is associated
with higher entry in more financial dependent industries. This finding is explained by the fact
that new firms depend more on external finance than established firms.
4

The literature has also analysed whether financial development may have different
effects on large or small firms. Using the insight in Kumar, Rajan and Zingales (1999) that
due to production technologies – including capital intensities and scale economies – firms in
each industrial sector will have an optimal size, Beck et al. (2008) find in their cross-country,
cross-industry study that industries where the optimal firm size is small grow faster in
economies with more developed financial systems.2 They speculate that financial
development lowers information and transaction costs, which are particularly high for small
firms and prevent their access to financial services. Small firms, therefore, are those that
benefit the most from financial liberalisation and development. Using firm-level data from the
World Business Environment Survey on obstacles to performance – such as restricted access
to finance, corruption and legal obstacles – perceived by firms in 74 developed and
developing countries, Beck, Demirguc-Kunt and Maksimovic (2005) conclude that small
firms are adversely affected by these obstacles much more significantly than large firms. They
suggest that financial development should be particularly beneficial for small firms.
Overall this strand of the literature provides favourable evidence that financial
development has a positive impact on industrial development by relaxing external finance
constraints on firms, encouraging firm entry and benefitting especially smaller firms. A
second strand of the literature looks at the impact of financial deepening on firms’ financing
constraints from a different perspective: it analyses whether financial deepening is associated
with reduced sensitivity of firms’ investment to the availability of internal funds and, unlike
the previous literature strand, investigates how developed financial systems compare to less
developed ones as well as the impact of financial liberalisation in a single country setting.
Empirical studies in this area have produced mixed results. These are partly explained by the
fact that the impact of liberalisation on firms would depend on whether large or small firms
2 While they find an insignificant correlation between the industrial sectors’ small firm share and external
finance dependence, Beck et al. (2008) confirm the finding by Rajan and Zingales (1998) that external finance
dependent sectors grow faster in countries that are more financially developed.
5

enjoyed preferential treatment in the pre-reform period and partly by the fact that small firms
face asymmetric information problems which are intrinsic to them and may not necessarily be
alleviated by financial deepening.
Love (2003), using firm-level data for 36 developed and developing countries from
the Worldscope database – which contains data on large, publicly traded firms – finds that
financial development effectively relaxed financing constraints on firms’ investment.
Moreover, her empirical analysis shows that financial development disproportionally
benefitted the smaller firms in the sample. Her hypothesis is that, compared with large firms,
small firms face greater informational asymmetries and tighter financing constraints in less
financially developed countries. Laeven (2003) uses the same approach as Love (2003) but
finds that, in his sample of 13 liberalising developing countries, financial liberalisation did not
reduce the financing constraints of all firms but only of small ones; for large firms, financing
constraints increased after liberalisation. The reason for this result is that, following
deregulation, large firms lost preferential access to directed credit. A number of other studies
also reach the conclusion that liberalisation relaxes the financing constraint especially for
small firms, see for example Gelos and Werner (2002) for Mexico; Harris, Schiantarelli, and
Siregar (1994) for Indonesia; Koo and Maeng (2005) for Korea; and Ghosh (2006) for India.
On the other hand, in the case of India, Bhaduri (2005) finds that while financial
liberalisation had no significant impact on the financial constraints faced by large firms those
on small firms intensified on two accounts, namely, the reduction in preferential credit
allocation in favour of small firms and asymmetric information problems, which are more
acute for small firms and have a greater impact on bank lending decisions in a deregulated
regime. Jaramillo, Schiantarelli and Weiss (1996) find that financial liberalisation in Ecuador
did not change the strength of the financial constraints on firms’ investment: small firms were
constrained both before and after liberalisation while large firms were not constrained in
6

either. Similarly, for the case of Chile, Hermes and Lensink (1998) report that reforms did not
improve small firms’ access to external finance. Such diversity of experience among
individual countries seems to suggest that cross-country studies could generate misleading
results.
The third strand of the literature related to this paper considers the effect of banking
sector structure – in particular banking competition – on the supply of credit. While financial
liberalisation in developing countries is accompanied by a relaxation of the rules governing
bank entry, this literature compares the strength of firms’ financing constraints in systems
where banking sectors have different degrees of concentration rather than how those
constraints are affected by regime changes where the banking sector becomes less
concentrated as a result of financial liberalisation and bank entry deregulation. Both
theoretically and empirically this literature gives rise to conflicting results.
A large part of this literature provides evidence that bank competition makes credit
more widely available to firms, benefitting in particular smaller ones and potential entrants.
More firm entry leads to lower average firm size and fosters industrial competition. Cetorelli
and Strahan (2006) show that increased bank competition in the U.S. has led to an increase in
the number of establishments and to a reduction in average establishment size especially in
sectors that are more financial dependent. Greater competition, however, appears to have had
no effect on large firms’ access to financial resources since these firms can also access
security markets. Cetorelli (2003) shows that, in U.S. regions with more banking
concentration, industrial sectors are characterised by less entry of new firms and delayed exit
of older firms. In a sample of 35 manufacturing sectors in 17 OECD countries, Cetorelli
(2001) finds that in countries with more concentrated banking systems the average firm size
in sectors that are more dependent on external finance is larger than in other sectors. In
countries where financial markets are more developed, this effect of bank concentration
7

remains significant but is much weaker, since access to alternative sources of finance reduces
the impact of the banking sector on the industrial structure. Using survey data from the World
Business Environment Survey, Beck, Demirguc-Kunt and Maksimovic (2004) report that
financing obstacles are greater in more concentrated banking systems, especially for small and
medium enterprises. This relation, however, is only found in low income countries and it
disappears with higher institutional and economic development.
By contrast, another part of this literature argues that bank concentration may provide
more credit for firms. While noting that greater bank concentration may lead to lower entry
and greater concentration in the industrial sector, Da Rin and Hellmann (2002) argue that
banks can play a catalytic role for industrialisation and growth only if they have a degree of
market power which allows them to earn sufficient profits. Using a sample of 41 developed
and developing countries, Cetorelli and Gambera (2001) find that, although bank
concentration lowers industry growth on average, its impact on the various industrial sectors
depends on their characteristics. In particular, industries that are more external finance
dependent (especially the younger firms in those sectors) have greater access to credit and
grow faster when the banking sector is concentrated. Moreover, in these sectors, bank
concentration leads to a faster growth in the number of establishments, thus rejecting the
argument that bank concentration may enhance industry concentration by providing credit
only to well-established clients. Petersen and Rajan (1995) find that young firms appear less
financially constrained when banking markets are more concentrated. While their empirical
analysis concentrates on small businesses in the U.S., the theoretical explanation for their
finding may apply to larger firms as well. They argue that bank competition hinders the
formation of close lending relationships between banks and firms and reduces the provision of
long-term finance. In a competitive environment it is more difficult for a bank to subsidise
new borrowers in earlier periods since it may not be able to claim a share of the rents in future
8

periods. The importance of relationships for the provision of banks’ finance to small U.S.
firms and the negative effect that bank competition may have on such provision are also
found in Cole (1998), Cole, Goldberg and White (2004) and, for major industrialised
countries, in Mayer (1988 and 1990). Petersen and Rajan (1995) conclude that in the early
stages of a country’s economic growth, when there are comparatively few established firms
and their access to finance is most important, it may be preferable to restrict interbank
competition. Aryeetey et al. (1997) report that financial liberalisation in some sub-Saharan
African countries and greater banking competition did not lead to any significant reduction in
the financial constraints for domestic firms, as banks concentrated their lending on large
manufacturing firms. Kariuki (1995) provides evidence from survey data that access to credit
by small and medium enterprises in Kenya declined after liberalisation.
However, as noted by Cetorelli (2001), the existence of a positive relationship
between bank concentration and access to credit by young firms generates theoretically
ambiguous implications for the extent of industrial competition. One possibility is that once a
bank has established lending relationships with an incumbent firm it would not be in the
interest of the bank to finance new entrants, since the increase in industrial competition would
reduce the profitability of the incumbent and of the bank itself. Thus, bank concentration can
be expected to lead to greater industrial concentration. By contrast, it is possible for bank
concentration to result in greater industrial competition if banks always foster entry on the
basis that new entrants are likely to be endowed with newer technologies and undertake
higher return projects which raise bank profitability.
In this paper we intend to examine whether financial liberalisation results in a
relaxation of the credit access constraint faced by firms and, in particular, by firms in more
external finance dependent sectors and small firms. Since the literature reviewed has yielded
somewhat conflicting finding, we regard this as a worthwhile endeavour. We treat this
9

research question as essentially empirical because, as mentioned in this literature review,
theoretical frameworks leading to opposite conclusions have already been formulated.
Our empirical approach has distinctive features. The first is that we attempt to capture
the effect of financial liberalisation on industrial concentration, rather than on sectoral value
added growth, investment or average firm size as is often the case. The existence of change
patterns determined by financial liberalisation in the degree of concentration of industrial
sectors gives information on the type of firms mostly affected by the liberalisation policy.
Interestingly, the three strands of the literature reviewed above use the results of analyses of
firm entry and firm size to draw implications for industrial concentration rather than analysing
industrial concentration directly.3 We also investigate whether financial liberalisation has an
effect on firm entry since an increase in industrial concentration may be consistent with a
tighter financial constraint on small firms or with its relaxation (if larger firms obtain
relatively greater access to credit). The former case would be reflected in fewer firm entries,
the latter in more.
The second feature of our empirical approach is that the analysis of concentration and
firm entry is performed in a panel data context for a single country, with the industrial sectors
as the cross-sectional units. Our literature review has highlighted that the impact of financial
deepening on firms’ financial constraints often depends on the characteristics of the pre-
liberalisation regime, especially whether large or small firms were granted preferential access
to credit during repression. Moreover, the strength of financing constraints has been found to
also vary according to the individual countries’ characteristics of the institutional context and
in particular of the legal system, the extent of corruption, and other factors, which, despite
significant research, are not accurately captured in a cross-country setting.
3 It is worth noting that the literature on industrial concentration, which we have not reviewed in this paper, has
by and large disregarded the role of financial liberalisation as one of its possible determinants.
10

We have chosen to concentrate on Malawi. This is not just due to the fact that one of
us is Malawian. Instead, the case of Malawi is interesting for two reasons. The first is that the
World Bank identified financial sector reforms as the key to growth and development of the
manufacturing sector in Malawi (World Bank, 1989).
Given its relatively small manufacturing sector, industrialisation has been an explicit
policy objective since the attainment of independence in 1964 but government efforts had in
general not been very successful. Beginning in 1981, Malawi embarked on wide-ranging
policy reforms with the support of various structural adjustment and sectoral adjustment loans
from the World Bank. Some of these policies specifically aimed to stimulate competition and
growth in the manufacturing sector, such as industrial licensing deregulation, industrial price
decontrol, abolition of exclusive monopoly rights, establishment of export processing zones,
corporate tax incentives and reduction in income tax, and broad-based privatisation of public
enterprises (World Bank, 1994, 1996). However, the performance of the manufacturing sector
fell short of expectations. It is in this context that the World Bank (1989) identified financial
sector underdevelopment as a continuing impediment to the growth and development of the
manufacturing sector. Consequently, after several financial sector studies, a wave of financial
sector reforms began.
While lending and deposit interest rates were deregulated in 1987 and 1988
respectively, it is in 1989 that new legislation deregulated entry of new banks into the
financial system and gave the central bank a supervisory role, including the assessment of
applications for entry by new institutions. The objective of this legislation was to reduce the
monopoly powers hitherto enjoyed by the dominant commercial banks and to promote
competition in the financial sector by introducing the market mechanism in the resource
allocation process and creating a competitive environment in saving mobilisation and
intermediation (Nissanke and Aryeetey, 1998; World Bank, 1991). These reforms were
11

expected to lead to the growth of small and medium enterprises by making credit more widely
available to them.
The second reason why the case of Malawi is interesting is that the implementation of
financial liberalisation in Malawi has been generally regarded as successful, especially in
improving financial system intermediation and competitiveness. In their assessment of 29
sub-Saharan African countries’ experiences with financial liberalisation, Reinhart and
Tokatlidis (2003) conclude that Malawi is in the small group of countries that register “more
advanced” progress in financial development following financial liberalisation. Chirwa (1998)
finds that financial liberalisation in Malawi has led to an increase in financial depth – a
finding corroborated by Aryeetey et al. (1997) and Nissanke and Aryeetey (1998) – and a
decline in monopoly power within the banking system. Analysing financial development in
38 sub-Saharan African countries, Gelbard and Pereira Leite (1999) report that Malawi has its
best performance in the area of “market structure and competitiveness of the financial
system.” It should also be noted that Malawi is one of a relatively small number of developing
countries where financial liberalisation has not been followed at some point by a banking or
financial crisis. Hence, the case of Malawi appears particularly suitable for an analysis of the
relationship between financial sector liberalisation and industrialisation.
3. Financial liberalisation and industrialisation: The empirical approach
The ultimate aim of this study is to investigate the impact of financial liberalisation on
industrial concentration and firm entry. We are also interested to ascertain whether financial
liberalisation has a greater impact on industrial sectors that are more external finance
dependent. To investigate these issues we adopt various approaches. Our initial analyses have
a temporal and a comparative nature. In particular, the temporal analysis is a before-after type
of analysis in which we analyse the behaviour of selected economic variables before and after
12

financial liberalisation. The comparative analysis is a control-group type of analysis in which
we compare the behaviour of industrial concentration and firm entry in different industrial
sectors, grouped according to their degree of external finance dependence. Later in the paper
we adopt a panel regression approach, in which we control for the effect of other variables,
besides financial liberalisation, on industrial concentration and firm entry.
3.1. The before-after approach
We use this approach to describe the evolution of selected variables during the period
of financial liberalisation and to evaluate the statistical significance of their changes compared
with the pre-liberalisation period. The variables measure various aspects of the Malawian
economy and of the industrial and financial sectors, including in particular industrial
concentration and net firm entry. In order to account for the short and the long-term effects of
liberalisation we split the post-reform period into two shorter periods.
We implement the before-after approach by running OLS regressions in which each
variable is regressed on time dummies for each reform period. The estimated model is:
(1)
t
k
kkt uy ++= ∑
τβγ
where y is the variable of interest;
γ
is the mean value of y in the pre-reform period;
τ
are
time dummies for each reform period k and u is the error term. The estimated coefficients β
represent the difference between the value of variable y in the reform period and its mean
value in the pre-reform period.
We set the lengths of the pre-reform, short- and long-run reform periods to 5 years
each. While this is arbitrary, it makes sense that the length of the period should be chosen so
that the values of the variables selected are not overly affected by the particular phase of the
business cycle. Given these lengths of pre- and reform periods, the overall time period t is
from to , where T denotes the first year of liberalisation. We take it to be 1989, 5−T9+T
13

corresponding to the deregulation of bank entry opening up the banking sector to
competition.4
It is a well-known weakness of the before-after approach that it attributes the entire
change in the variables of interest to the policy change under consideration – in this case,
financial liberalisation. Nonetheless, this approach gives information on whether financial
liberalisation is associated with significant changes compared with the pre-reform period.
3.2. The control group approach
As we are interested to investigate whether the effect of financial liberalisation on the
manufacturing sector may depend on the degree of financial dependence of firms in the
various sectors of activity, we divide the industrial sectors into three groups j – namely, high
finance dependent, medium finance dependent and low finance dependent sectors. To do so,
we first rank the sectors on the basis of our external finance dependence index (described
below in Section 3.4) and we then split the distribution at the 25th and 75th percentile.5 The
composition of the three groups is shown in Appendix A. We use the control group approach
to investigate whether changes in industrial concentration and net firm entry between the
period preceding financial liberalisation and the reform period differ according to the degree
of financial dependence of the industrial sectors. The low finance dependent sectors constitute
the control group, whose performance is compared with that of high and medium finance
dependent sectors. The estimated model is:
it
kk k
M
k
M
k
H
k
H
kkkjit uddy ++++= ∑∑
∑
τβτβτβγ
(2)
4 Chirwa and Mlachila (2004) also take 1989 as the beginning of the financial liberalisation period.
5 Since the classification of industrial sectors into the three groups is to some extent arbitrary, we have chosen to
let 50 percent of the sectors fall into the middle group in order to increase the separation between the high and
low dependence groups and reduce the likelihood that an eventual finding of different behaviour of these two
groups may be spurious and attributed to an incorrect classification of the sectors.
14

where i denotes the 20 sectors in Malawi’s manufacturing industry; j
γ
is the mean value of y
in the pre-reform period for group j; and are dummies for high and medium
dependent sectors respectively. The statistical significance of the estimated coefficients
and indicates whether the changes in variable y between the reform and the pre-reform
period for the medium and high financial dependent sectors are different from those of the
low finance dependent sectors.
H
dM
d
H
β
M
β
The main shortcoming of the control group approach is that the degree of financial
dependence is taken as the only characteristic differentiating the various industrial sectors, so
that differences in performance are attributed exclusively to the greater or smaller degree of
financial dependence, when performance (and, in particular, the degree of concentration and
net firm entry) is potentially affected by a host of other factors. We address this problem by
adopting a regression approach, as described in the next section. Nonetheless, despite this
shortcoming, the control group approach provides some information on whether more
financial dependent sectors do better than less financial dependent sector following financial
liberalisation.
3.3. The regression approach
By adopting the regression approach we address the shortcomings of both before-after
analyses (where it is assumed that determinants of industrial concentration and firm entry
other than financial liberalisation remain unchanged between the pre-reform and reform
periods) and control group analyses (where it is assumed that industrial sectors differ from
one another only for their degree of financial dependence). With the regression approach, we
account for the role of various determinants of industrial concentration and firm entry. The
estimated model is the following:
15

(3)
itti
m
mitmtiit uxyy ++++= ∑
−
τγβα
1,
where y is industrial concentration in some regressions and firm entry in others; x denotes a
set of m determinants of y including, in particular, financial liberalisation;
γ
is a sector-
specific fixed effect (e.g. initial efficiency); τ is a set of period dummies, which is included to
account for shocks common to all industrial sectors; u is the error term. The lagged level of y
is included as a regressor to account for persistence in industrial concentration and firm entry.
It can also be taken to represent factors that, although not modelled explicitly, may have
influenced concentration and entry in the previous period. The sample period is from 1970 to
2004. To filter out at least to some extent business cycle fluctuations we have grouped
observations into five-year periods.
The appropriate approach for the estimation of dynamic models with a short time
dimension such as (3) is the system generalised method of moments (sys-GMM) estimator
(Blundell and Bond, 1998). This approach allows for inertia in the dependent variable,
potential endogeneity of the explanatory variables, and unobserved sector-specific effects.6
The system comprises a difference equation, which is obtained by first-differencing (3),
together with a level equation, i.e. equation (3) itself. The instruments used in the estimation
are internal: in the difference equation, the instruments are levels of the series lagged two
periods or more while, in the equation in levels, the instruments are lagged first-differences of
the series.7
6 The model allows for weak exogeneity of the explanatory variables, which means that the current values of the
explanatory variables can be correlated with current and previous realisations of the error term but not with
future realisations. This implies that future unexpected shocks to industrial concentration and firm entry do not
influence current realisations of the determinants of concentration and firm entry.
7 Given that lagged levels are used as instruments in the differences specification, only the most recent difference
is used as instrument in the levels specification. Using the other lagged differences would result in redundant
moment conditions.
16

The validity of the instruments, which is necessary for consistency of the GMM
estimator, is tested as usual by means of the Hansen-J test8 (which tests the exogeneity of the
instruments as a group) and the test for the absence of second-order serial correlation of the
errors in the difference equation (the presence of autocorrelation would indicate that some
lags of the dependent variable, which might be used as instruments, are in fact endogenous
and, hence, bad instruments).9
Since a high number of instruments can give rise to an over-fitting bias, we reduce
their number by combining them through addition into smaller sets (Roodman, 2009).10 We
report the results of system GMM estimation based on the two-step estimator with the finite-
sample correction proposed by Windmeijer (2005).11
3.4. Variable selection: concentration, net firm entry and financial variables
Industrial concentration in each sector i is measured by the log of the 3-firm
concentration ratio, which is the cumulative share of total value added of the three largest
firms in the sector:12
Concentrationi ∑
=
=
3
1j
ij VAVA
8 The Sargan’s statistic is a special case of Hansen’s J under the assumption of homoscedasticity.
9 This is equivalent to testing for first order serial correlation of the errors in levels. By construction, first-order
serial correlation is expected in the differenced error term even if the error term in levels is not autocorrelated.
10 This operation is performed by the “collapse” command in Stata. Collapsing turns the instrument count into a
linear function of the number of observations rather than quadratic.
11 While the two-step estimator is asymptotically more efficient than the one-step estimator, the estimated
standard errors are severely biased downwards in a finite sample. For this reason, researchers have traditionally
preferred to use the one-step estimator. However, Windmeijer (2005) derives a finite-sample correction of the
two-step covariance matrix which makes the two-step estimator more efficient than the one-step variant even in
a small sample.
12 Selecting the three largest firms as opposed to another number is a common, albeit arbitrary, choice.
Moreover, it should be considered that in Malawi industrial sectors are characterised by the presence of very few
producers and therefore concentration ratios above three firms – such as five firms, which is often used – would
equal 100 percent in some sectors. Another measure of concentration that is often used is the Hirschman-
Herfindahl Index; however, we did not have access to the firm-level data that are required for its calculation. In
any case, empirical studies have shown that the various concentration measures are highly correlated and provide
similar findings (see, for example Curry and George, 1983).
17

Firm entry data are not available. In this paper we use the growth rate of the number of
firms as a proxy for (net) firm entry, since this variable reflects the relative frequency of
entries and exits from the industry.13
To examine the role of financial development and liberalisation on industrial
concentration and firm entry we create a number of variables that we enter sequentially in our
regressions. The measure we use for financial development is the ratio of credit to the
manufacturing sector granted by commercial banks as a percentage of GDP. This ratio is
meant to capture an economy-wide effect of financial deepening on industry concentration
and firm entry. To investigate whether greater financial intermediation affects the various
industrial sectors differently, we generate a new variable by interacting the ratio of credit to
the manufacturing sector with a measure of external finance dependence of each sector. This
new variable is therefore sector specific and may be used to examine whether greater access
to credit is associated with a significant impact on industrial concentration and firm entry in
the sectors that are relatively more dependent on external finance. To test whether financial
reforms have altered the impact of credit access – especially in more finance dependent
sectors – on concentration and firm entry, we create further two variables by multiplying the
two financial variables just described – the manufacturing credit to GDP ratio and its
interaction with the indicator of external finance dependence – by the financial liberalisation
dummy. This equals one from 1989 onwards and zero otherwise. For robustness and to allow
for gradual reform implementation we also use an alternative financial liberalisation dummy,
which equals zero until 1988, one from 1989 to 1993 and two thereafter.14 This reflects the
fact that, while the major financial reforms – such as interest rate deregulation, the elimination
13 Sometimes this variable is included as a determinant of industrial concentration; other studies criticise such
inclusion since changes in the number of firms reflect the impact of economic forces which should have already
been taken into account into the concentration regression. We have decided to remove this variable from our
concentration regressions since its coefficient was never significant and its removal was inconsequential for the
other results.
14 Chirwa and Mlachila (2004) too date the consolidation of financial liberalisation in the period beginning in
1994.
18

of directed credit allocation, the overhaul of the legal framework for the financial sector,
which resulted in the restructuring of the existing institutions and facilitated the entry of new
financial institutions – took place in the 1989-93 period, the subsequent period saw a series of
follow-up reforms – such as the shift from direct to indirect, market-based instruments of
monetary control, and the adoption of a more market-determined exchange rate regime –
which served to consolidate the liberalisation process.
A measure of external finance dependence of the different industrial sectors was put
forward by Rajan and Zingales (1998). Using firm-level data and assuming that the supply of
credit is infinitely elastic so that access to external finance is demand driven, they define the
external finance dependence of firms as the share of capital expenditures that is not financed
through internal cash flows. This is computed as capital expenditures minus cash flow from
operations, divided by capital expenditures, where cash flow from operations is broadly
defined as the sum of cash flow from operations plus decreases in inventories, decreases in
receivables, and increases in payables. The median value of finance dependence of the firms
in a sector is then taken as the measure of external finance dependence in that sector.
Rajan and Zingales employ data from Standard and Poor’s Compustat for United
States firms and, as mentioned in Section 2, they assume that each sector’s degree of financial
dependence is mainly determined by structural/technological reasons and, therefore, the
ranking of sectors according to their financial dependence is valid in all countries. We are
doubtful of the universality of this ranking for at least two reasons. First, for strategic reasons
such as trade or food security, many developing countries support certain industries through
subsidies; as a result, these industries are less dependent on external finance than those
without state support. Second, capital expenditure data may underestimate the true level of
firms’ external finance dependence. With regard to Malawi and other sub-Saharan African
countries, Nissanke (2001) notes that the unstable and high-risk political and economic
19

environment has significantly affected not only the asset composition of the savings portfolio
held by private agents but also the composition of private investment in Africa, favouring
reversible and safe investments that have a self insurance character. Thus, borrowing is more
related to commerce and trading activities than long-term capital investments.
Von Furstenberg and Von Kalckreuth (2006) cast serious doubts on the empirical
validity of the Rajan and Zingales measure of external finance dependence. Firstly, using an
alternative source of data to that used by Rajan and Zingales, they find that the values of
external finance dependence obtained by Rajan and Zingales do not adequately represent
financing conditions in the various manufacturing sectors even in the United States, the
country from which they are derived.15 Secondly, Von Furstenberg and Von Kalckreuth fail
to find support for the assumption made by Rajan and Zingales that the differences between
the degrees of financial dependence can be attributed to structural/technological
characteristics intrinsic to the various sectors. These findings invalidate the ranking of sectors
proposed by Rajan and Zingales and its applicability to other countries.
We have therefore decided to construct an alternative measure of financial dependence
to that provided by Rajan and Zingales. Our measure is based on industry-level (three digit
ISIC) data on credit extended by the banking system for the period 1996-2002 – the length of
the period is dictated by data availability – and, unlike the Rajan and Zingales theoretical
indicator of financial dependence, it allows the ranking of Malawian industrial sectors
according to their actual dependence from external finance. Our external finance dependence
ratio is defined as the share of total capital expenditure (fixed plus working capital) that is not
financed through internal cash flows. In appendix 1 we compare the ranking of industrial
sectors according to their degree of financial dependence provided by Rajan and Zingales
with the ranking obtained by using our own index of financial dependence. We show that the
15 Their data are from the Bureau of Economic Analysis, U.S. Department of Commerce. They are relative to all
establishments in each sector. By contrast, the Campustat data used by Rajan and Zingales refer to the median
exchange-listed firm in each sector.
20

two rankings are statistically different. For robustness, the regressions are re-run with their
ranking.
3.5. Variable selection: the control variables in the industrial concentration regressions
The lagged concentration ratio is included in the industrial concentration regressions
to reflect the fact that adjustment towards equilibrium is likely to be gradual. The sign of the
expected coefficient, however, is not theoretically obvious a priori. Stigler (1952) argues that
leading firms in highly concentrated industries are likely to lose market shares over time since
their profits will encourage entry, which in turn will lower industrial concentration in the long
run. On the other hand, Bain (1966) contends that incumbent firms may set a low ‘limit price’
so that the resulting low profit rate would discourage potential entrants. This may even
increase industrial concentration if prices are so low that they drive small firms out of
business.
Industry growth – which we measure by the growth in the ratio of value added in each
sector to GDP – is likely to affect concentration negatively, since it provides potential profit
opportunities to new entrants.
Industry size – measured by the share of sectoral value added in total manufacturing
value added – may have a positive effect on entry decisions since a large size reflects the
sector’s potential to absorb additional capacity creation. If displacement – the phenomenon
whereby new entrants force incumbents’ exits – is important, concentration might fall.16
International trade is expected to be an important determinant of industry
concentration. It is all the more important to account for the role of international trade in our
study since financial reforms have been accompanied by the implementation of trade reforms.
We include two trade variables in our regressions because imports and exports may have
16 In high concentration industries, however, Shapiro and Khemani (1987) observe that, if entrants are small,
they are likely to replace other small incumbents thus leaving concentration broadly unchanged.
21

different effects. Import intensity – which we measure by the growth of the ratio of
manufactured imports to total imports – is a sign of greater competition from foreign
producers, which is likely to limit the oligopolistic power of existing firms and reduce
industrial concentration.17 Moreover, to the extent that the increase in manufactured imports
reflects greater availability of imported inputs for domestic producers, import liberalisation
may result in higher firm entry, which could also reduce concentration.
Greater export intensity – which is measured by the growth of the ratio of
manufactured exports to total exports – is associated with a larger market size for exporters. If
economies of scale are important and/or the costs of entering export activities are high, the
degree of industrial concentration is expected to rise.
3.6. Variable selection: the control variables in the net firm entry regressions
Lagged net firm entry is included to allow for gradual adjustment of entries and exits
in response to changes in their economic determinants. According to Geroski (1995) and
Cincera and Galgau (2005) firm entries and exits tend to come in waves. Similarly, Johnson
and Parker (1994) and Hannan and Freeman (1989) suggest that there may be a multiplier
effect, whereby entry causes future entry (and retards future exits) and/or exits cause future
exits (and retard future entry). Gort and Konakayama (1982) argue that the perceptions of
profit opportunities by entrants are positively related to the successful experience of those that
have operated in that market before.
Industry growth, defined as above, is an important factor in both entry and exit
decisions since it provides a picture of the opportunities available to those entering or staying
in the business.
17 Theoretically, foreign competition may result in greater concentration as it drives some producers out of
business. However, in our regressions, the coefficient of import intensity turned out to be negative.
22

Profitability of an industry is a potent force encouraging industry entry. We measure
profitability by the price-cost margin, which is defined as total value added minus labour
costs divided by the sum of total value added and the cost of materials. It should be noted that,
empirically, regression analyses in this area have often uncovered a negative relationship
between net firm entry and profitability. This could be explained either by the incumbents’
oligopolistic power to effectively blockade entry and/or force competitors to exit (Duetsch,
1975; Shapiro and Khemani, 1987) or by the presence of strong displacement effects leading
to greater exits than entries in high profitability industries (Shapiro and Khemani, 1987).
The state of the economy (which we measure by real GDP growth) affects the
anticipated profitability of firms and, hence, impacts on their entry and exit. The expected
sign, however, is ambiguous. A favourable state of the economy may induce net entry but it is
equally possible that far-sighted entrepreneurs would see in a downturn in economic activity
the opportunities to enjoy lower labour and equipment costs or to exploit the creation of
attractive market niches following business failures (Highfield and Smiley, 1987; Storey,
1991).
The availability of imported inputs may be an important factor generating industry
entry. On the other hand, stronger competition from imports may force domestic firms out of
business. We use import intensity, defined as above, to control for these effects on net firm
entry.
Both in the industrial concentration and the net firm entry regressions we include time
dummies in order to allow for shocks affecting simultaneously all industries, such as changes
in the general economic policy environment.
23

4. Empirical results
Table 1 reports a summary of before-after and control group analyses. The financial
sector variables describe some of the effect of financial liberalisation. Credit to the private
sector and to the manufacturing sector grew very strongly compared to the pre-reform period;
the same can be said for the depth of banking sector intermediation and the share of
commercial banks’ assets in total bank assets. One should also note, however, the growing
holdings of government bonds by commercial banks as well as the increasingly large spread
between the Treasury bill rate and the bank lending rate.
Insert Table 1 here
The performance of the manufacturing sector in the reform period has been very
disappointing. The share of manufacturing value added to GDP even dropped relative to the
pre-reform period, though the drop is not statistically significant. Both the investment to GDP
ratio and the rate of growth of GDP registered a small, not statistically significant increase.
Industrial concentration is generally high in Malawi due to the dualistic structure of
the industrial sector with small-scale enterprises co-existing with a small number of relatively
large-scale modern plants, mostly dominated by public enterprises and multinational
affiliates. The micro, small and medium scale enterprises constitute a relatively small segment
of the industrial sector, both in absolute terms and in relation to the formal manufacturing
sector. The average concentration of industrial sectors increased very significantly following
financial reforms both in the short and the long run. The average figures, however, hide
important differences among the different industrial sectors. When sectors are classified in the
three categories of low, medium, and high external finance dependence, the group of sectors
with low dependence is the only one to record a fall in concentration, albeit small and not
significant in the long run. In the sectors with medium or high financial dependence,
concentration rises significantly.
24

Net firm entry fell significantly both in the short and the long-run post-reform period.
When the analysis accounts for the different degrees of sectoral dependence from external
finance the results show that the overall fall in firm entry is due to the performance of
medium and high dependence groups. In the low dependence group net firm entry increases.
The changes in industrial concentration and firm entry just described could, however,
have been determined by a host of factors other than financial deepening. With the regression
approach we attempt to control for these other factors. Regression results for industrial
concentration are reported in Table 2. The control variables, which are identified as
theoretical determinants of industrial concentration, turn out to be all significant at
conventional levels. Concentration adjusts sluggishly to the long-run equilibrium levels. The
positive sign of the lagged concentration coefficient may be taken as reflecting the
oligopolistic nature of industry in Malawi and the continuing privileged position of large
firms. The relative size of industrial sectors appears to have a moderately negative effect on
industrial concentration. Sectoral growth also reduces concentration. The increases in exports
and imports have respectively a positive and a negative effect on concentration. The former
reflects the fact that access to the export markets allows successful firms to enjoy economies
of scale and establish a dominant position in their sector of activity. In principle, the impact of
imports on concentration may be due to firm entry and expansion following greater
availability of imported inputs. However, in the case of Malawi, it is likely that the main
explanation for the drop in concentration, ceteris paribus, is the intensity of import
competition, which has led to the closure of major manufacturing enterprises, including those
in the export sector (this is the so-called Vanek-Reinert effect).18
Insert Table 2 here
18 The Vanek-Reinert effect strictly refers to industrial sectors rather than firms within sectors, though it can be
extended to the latter. It predicts that premature trade liberalisation will cause the most advanced sectors in the
least advanced country to die out first. The reason is that the importance of increasing returns in these sectors
implies that they are most vulnerable to the drop in volume caused by competition from abroad (Reinert, 2007)
25

The variables of interest for this paper are those reflecting access to finance. When this
is measured by the percentage of total credit given to the manufacturing sector, the finding in
column (1) of Table 2 is that an increase in the latter is associated with an increase in
concentration. This result suggests that credit tends to be made available first ad foremost to
the largest, well-known and possibly politically influent firms. It is, however, equally possible
that the estimated coefficient may be giving a distorted picture of the effects of greater access
to credit due to the fact that the RHS variable does not explicitly account for the effects of the
financial liberalisation policy started in the 1980s. We have generated, therefore, a new RHS
variable by interacting the percentage of total credit given to the manufacturing sector with
the financial liberalisation dummy. The results obtained by including this additional regressor,
which are reported in column (2), confirm that following financial liberalisation the increase
in credit to the manufacturing sector has been associated with greater industrial concentration.
This is, however, an average effect, which does not differentiate between industrial
sectors. In particular, we wish to test whether sectors that are more dependent on external
sources of finance are affected differently compared with less external finance dependent
sectors. We generate two extra variables: the first is obtained by interacting the percentage of
total credit given to the manufacturing sector with our measure of the degree of external
finance dependence of each industrial sector based on Malawian data. The second extra
variable is obtained by multiplying the new variable just described by the financial
liberalisation dummy. The regression results, which are reported in columns (3) and (4), show
that the rise in concentration following the increase in credit to manufacturing is greater in
sectors that are more external finance dependent and that this is especially the case after
financial liberalisation.
Results of net firm entry regressions are reported in Table 3. The choice of control
variables appears sound, as the selected variables are almost always significant. Firm entry
26

and exit adjust sluggishly to their long-run equilibrium levels. The positive sign of the
coefficient of the lagged dependent variable is consistent with the wave/multiplier view of
firm entry and exit dynamics. Sectoral growth stimulates entry; however, when this is
controlled for, industry profitability appears to be negatively related to net firm entry,
probably reflecting the incumbent firms’ oligopolistic behaviour. The regression results also
suggest that entrepreneurs are forward looking and able to identify profit opportunities which
may become available in an economic downturn. Greater availability of imports is positively
related to net firm entry.
Insert Table 3 here
The variables of interest in this paper are those that reflect firms’ access to finance.
The results show that greater access to finance does not result into larger net firm entry; its
negative impact on the latter is more pronounced after financial liberalisation and is felt
disproportionately in the industrial sectors that are more dependent on external finance. This
is consistent with the finding that industrial concentration rises after financial liberalisation
and especially so in the more financially dependent sectors.
Robustness checks
We conduct a series of robustness checks to assess the reliability of our results
particularly with regard to the role of access to finance. In the first experiment we replace our
0-1 financial liberalisation dummy with a graduated 0-1-2 dummy. The results are reported in
Tables 4 and 5.
Insert Table 4 here
Insert Table 5 here
The results are a confirmation of those reported in Tables 2 and 3. The size of the estimated
coefficients as well as their significance levels are similar to those in Tables 2 and 3. Indeed,
27

the use of a perhaps more realistic graduated financial liberalisation dummy seems to have
improved the significance levels of some of the control variables. The results in Tables 4 and
5 confirm the earlier findings that the greater availability of credit following financial
liberalisation has resulted in an increase in industrial concentration and a decrease in net firm
entry. Moreover, both of these effects are stronger in industrial sectors that are more
dependent on external finance.
In the second experiment we replace our measure of external finance dependence,
which was based on Malawian banking system data, with the index of financial dependence
calculated by Rajan and Zingales. As discussed above, the two measures look at financial
dependence from two different perspectives. The measure by Rajan and Zingales uses a
‘theoretical’ perspective, i.e. the degree of individual industrial sectors’ financial dependence
if credit from external sources is used exclusively to finance capital expenditure as dictated by
the sector’s technological needs and if the supply of credit is infinitely elastic. By contrast, the
measure used in this paper represents the individual industrial sectors’ ‘actual’ demand for
external finance. In the Appendix we compare the rankings of industrial sectors according to
their degree of external finance dependence produced by the two measures. We show that the
rankings are statistically significantly different from one another. Nonetheless, for certain
sectors, their ranks are similar in the two classifications. It is thus interesting to evaluate the
sensitivity of the results reported in Tables 2 and 3 to the particular measure of financial
dependence used in the regressions. The results obtained when using the Rajan and Zingales
index are reported in Tables 6 and 7.
Insert Table 6 here
Insert Table 7 here
Interestingly, the results are similar to those reported in Tables 2 and 3. The significance
levels of the control variables are almost unchanged. Moreover, the coefficients of the
28

interaction terms where the Rajan and Zingales index is used have size and significance levels
analogous to those in Tables 2 and 3, confirming the findings that net firm entry falls and
industrial concentration rises after financial liberalisation and that these effects are stronger in
sectors that are more finance dependent.
In a third experiment we re-estimate the regressions for industrial concentration and
net firm entry using higher frequency data, in this case annual data, without averaging. On the
one hand, the averaging procedure is a standard approach to filter out business cycle effects;
on the other hand, however, this procedure may be a source of bias in the presence of
heterogeneity, for example due to the different impact of the business cycle on the different
industries. Some sectors may lead the cycle, other sectors may lag it.
The increase in the number of observations implies that system GMM may not be the
most suitable method of estimation, since typically its use is appropriate when the dataset has
a short time dimension. In this paper, we employ two estimators to gauge the robustness of
the results obtained through system-GMM estimation, namely, the Anderson-Hsiao IV-
estimator (which can be seen as a special case of the Arellano-Bond difference estimator) and
the fixed-effect estimator with industry-specific dummies.19 Judson and Owen (1999) find
that the Anderson-Hsiao estimator outperforms all others when T is larger than 20 while the
fixed-effect estimator is superior when T is larger than 30.
The estimation results are reported in Tables 8 (for the industrial concentration
regression) and 9 (for the net firm entry regression). Despite different data frequency and
methods of estimation, Tables 8 and 9 show that the significance levels of the estimated
coefficients are analogous to those of the coefficients reported in Tables 2 and 3. Of particular
interest is the confirmation that financial liberalisation is associated with an increase in
concentration, that such increase affects especially the sectors that are more dependent on
19 Although this estimator is biased when the lagged dependent variable is included among the regressors, the
size of the bias has been shown to drop sharply as T exceeds 20 or 30 (Judson and Owen, 1999).
29

external finance, and that financial liberalisation is also associated with a fall in net firm entry
in the financially dependent sectors.
Insert Table 8 here
Insert Table 9 here
Explanation of results
Our empirical results should not come as a surprise. While the perspective and the
empirical approach we have used in this paper are original, our results provide further support
for the argument made in Nissanke and Aryeetey (1998) and Nissanke (2001) that merely
changing policy from financial repression to financial liberalization has not fully addressed
the fundamental problems facing financial systems in sub-Saharan African countries,
including Malawi.
Financial liberalisation in Malawi has led to significant financial development, as seen
for example in the emergence of a large number of institutions with a mandate to finance
business activity. The country now has 11 registered commercial banks (compared to only
two during the pre-liberalization period), a stock exchange, two discount houses, and over
twenty non-bank financial institutions, in addition to insurance companies and foreign
exchange bureaux. Most of the commercial banks have also created specialized windows for
lending to small and medium enterprises.20 Moreover, over the years, non-governmental
organisations in partnerships with the public and private sectors have created over twenty
institutions with a mandate to lend to micro, small and medium enterprises. Yet, despite this
and the greater ability on the part of the banking sector to mobilise savings, access to finance
20 For instance, apart from serving large enterprises, National Bank established National Financial Services to
cater for the credit needs of small-scale entrepreneurs; Commercial Bank of Malawi, (now STANBIC), does the
same through Commercial Bank Financial Services; and INDE Bank extends credit to the small-scale sub-sector
through its two establishments, namely, INDE Fund and INDE Finance.
30

remains a problem for both large and small firms. Multiple factors contribute to give rise to
this outcome.
One is the scarcity of term lending by the commercial banks, which have been shown
to focus on the provision of short-term credit rather than long-term loans that are necessary
for industrial development (World Bank, 1991; Malawi NSO, 2000 and 2005). Even business
lending to ‘blue chip’ firms is in the form of short-term overdraft facilities. An important
explanation for this fact is the availability of high-interest government bonds, which remove
the incentives for banks to lend to businesses. Furthermore, traditional term lending
institutions, such as pension funds, are not yet fully developed.
Another reason for the persistence of constraints on firms’ access to credit is that
commercial banks tend to concentrate their lending on traditional and established customers
(often public enterprises and businesses with good cash flow, which usually are large and
modern), and avoid those that are new and without any record. Aryeetey, et al. (1997) report
that “in Malawi the small enterprise sector (fewer than 30 workers) received only 15.0 percent
of total loan volumes in 1992, while large enterprises received 63.0 percent of total loans
disbursed.” (pp.210-211). Typically banks find it easier and more profitable to deal with the
already established and large-scale enterprise segment of the market, which they consider to
involve minimal risk and where transaction costs are lower.
Micro and small enterprises remain credit constrained despite the establishment of
specialised lending institutions due to their inability to meet collateral requirements and their
informational opacity. Furthermore, most entrepreneurs within the micro and small-scale sub-
sector (firms up to 50 employees and consisting of approximately 744,000 income earning
activities) consider credit from the lending institutions to be too expensive.21 As a result, lack
21 A Malawi Government (2004) survey reveals that only 15.0 percent out of 606,000 business owners were able
to secure financial assistance between 2000 and 2003. Of these, only 35.0 percent received credit from the
micro-finance institutions. A previous survey (Malawi Government, 2000) reports that about one-third of
31

of access to finance is still the main obstacle to starting up an enterprise in Malawi. Micro-
finance institutions are unable to fill this funding gap, as their lending capacity is rather
limited. Most of these institutions were in fact created with donor seed capital and had no
mandate to collect savings. Moreover, they are generally affected by low repayment rates,
which erode their capital base. Similarly, the lending capacity of the informal financial sector
(moneylenders, savings collectors, traders, etc.) is grossly inadequate to meet entrepreneurs’
demand for credit. Furthermore, informal lenders do not provide medium and long-term credit
either, despite being necessary for enterprise growth (Chipeta and Mkandawire, 1996).
Lack of access to adequate finance also hinders the transformation from micro and
smaller enterprises to larger establishments. These enterprises use in fact their retained profits
to finance working capital required for their daily operations or to meet household needs,
rather than ploughing them back into expansion of capacity. As a result, despite its impressive
income potential, the micro and small enterprise sub-sector is stagnating and its ability to
grow and provide competition in the manufacturing sector continues to be inhibited.22 Over
the period 1996-2000, 78.0 percent of firms ended up contracting in size (both in terms of
capital and number of employees) and, since 1999, more enterprises have closed-down than
entered (World Bank, 2004).
5. Conclusions
This paper investigated the effects of financial liberalisation on industrial
concentration and firm net entry and found that liberalisation is associated with a significant
increase in concentration and a significant drop in firm entry. These effects are even stronger
enterprises did not even apply for any loan; of these, about 16.0 percent did not apply due to the rigorous
screening requirements. Furthermore, the high rejection rates tend to discourage any would-be applicants.
22 On average, this sub-sector generates an annual gross sales value of about 43.9 percent of Malawi’s GDP
(adjusted to 2000 prices) and total annual profits of 15.6 percent of GDP (World Bank, 2004, p. 8). This sub-
sector also employs over 1.7 million people, which account for about 38.0 percent of the country’s total
economically active population of 4.5 million.
32

in the industrial sectors that are more external finance dependent. These results indicate that
financial liberalisation, even if it results in greater supply of credit and a larger number of
lending institutions, does not remove financing constraints on firms, especially the micro,
small and medium enterprises. It is the large existing firms that benefit from a more liberal
financial regime.
These results say nothing about the desirability of financial liberalisation policies but
they do imply that liberalisation may fail to relax the credit access constraints on firms,
especially the small and medium ones. Hence, financial liberalisation may not be the key for
starting a country’s industrialisation effort. The development of productive capacities is
unlikely to take place without the leadership of the state in industrial, technological,
infrastructural and trade policies (UNCTAD, 2006). While, strictly speaking, this paper has
nothing to say about these, it suggests that finance, once the state has withdrawn from credit
allocation decisions, may not be an engine for industrial development.
The results of this paper hold for Malawi. Further research is required to establish the
extent to which they can be generalised to other countries. It is unlikely, however, that the
pooling restrictions required for cross-country studies hold across countries with different
characteristics. Cross-country regressions may not be an adequate approach for research in
this area. We are aware that our country-based study has also involved pooling of different
cross-sectional units. Even within a single country, industrial sectors have different
characteristics and further research is required to assess the sensitivity of our results to
industrial sector heterogeneity. The research agenda has only just begun.
33

Table 1: Before-after and control group results
a) Before-after – Equation (1)
Pre-reform levels (γ coefficient) and changes in the reform period with respect to
the pre-reform period (β coefficients)
Reform period
Variable Pre-reform
period Short run Long run
Lending rate (%) 17.3
(2.526)
4.3***
(1.167)
7.1
(10.310)
Private sector credit to
total domestic credit (%)
20.6
(1.764)
0.7
(2.141)
10.6***
(6.251)
Manufacturing sector
credit to GDP (%)
4.1
(0.215)
6.1***
(2.056)
15.3***
(5.562)
Liquid liabilities to GDP
(%)
10.9
(3.554)
11.3***
(3.578)
22.0***
(3.904)
Commercial bank assets
to total bank assets (%)
45.1
(1.773)
8.5**
(6.163)
26.2***
(4.320)
Spread Treasury Bill and
lending rate (%)
-7.1
(1.429)
12.4**
(9.821)
23.8***
(3.758)
Government bond
holdings to deposits (%)
6.1
(0.829)
18.1**
(12.072)
43.6***
(4.241)
Government bold
holdings to loans (%)
17.3
(0.581)
18.1**
(12.844)
52.8***
(9.405)
GDP growth (%) 2.9
(2.254)
0.5
(6.939)
1.0
(9.393)
Investment to GDP (%) 16.5
(3.825)
4.1
(3.595)
0.3
(7.241)
Manufacturing value
added to GDP (%)
19.3
(8.743)
-3.3
(16.781)
-3.4
(7.669)
Industrial concentration 63.1
(12.3)
24.6***
(4.1)
34.4***
(3.3)
Net firm entry 4.7
(8.0)
-4.7***
(0.0)
-20.6*
(15.1)
b) Control group – Equation (2)
Changes in the reform period with respect to the pre-reform period using the low
finance dependent group as the control group (β, βH and βM coefficients)
Reform period
Variable
Degree of
financial
dependence
Short run Long run
Industrial concentration Low -0.080*
(0.049)
-0.050
(0.048)
Medium
0.130**
(0.062)
0.255**
(0.060)
High 0.100
(0.072)
0.218***
(0.070)
Net firm entry Low 0.367***
(0.124)
0.338***
(0.124)
Medium
-0.368**
(0.157)
-0.344**
(0.157)
High -0.172
(0.184)
-0.353**
(0.183)
34

Table 2: Regression results: Industrial concentration
Economy-wide effects Sector-specific effects
Not
accounting
for Financial
Liberalization
Accounting
for Financial
Liberalization
Not
accounting
for Financial
Liberalization
Accounting
for Financial
Liberalization
(1) (2) (3) (4)
Lagged concentration 0.548***
(0.161)
0.548***
(0.161)
0.573***
(0.096)
0.379*
(0.200)
Industry size -0.059*
(0.031)
-0.059*
(0.031)
-0.059**
(0.028)
-0.051
(0.039)
Industry growth -0.235*
(0.136)
-0.235*
(0.136)
-0.174**
(0.079)
-0.127**
(0.058)
Export intensity 0.159***
(0.048)
0.078*
(0.042)
0.178***
(0.036)
0.201***
(0.052)
Import intensity -0.025***
(0.004)
-0.013**
(0.004)
-0.017**
(0.006)
0.009
(0.007)
FIN 0.339***
(0.082)
0.026
(0.087)
0.076
(0.131)
-0.054
(0.158)
FIN*FL 0.225**
(0.089)
FIN*ED
0.012**
(0.004)
0.011**
(0.005)
FIN*ED*FL
0.120***
(0.018)
F Test 52.51
(0.000)
52.51
(0.000)
313.88
(0.000)
130.29
(0.000)
Hansen J-Test 9.45
(0.397)
9.45
(0.397)
6.39
(0.700)
9.70
(0.375)
Test for AR (1) errors -2.52
(0.012)
-2.52
(0.012)
-2.73
(0.006)
-1.83
(0.068)
Test for AR (2) errors -1.09
(0.275)
-1.09
(0.275)
-1.26
(0.206)
-0.74
(0.458)
No. of Industries 20 20 20 20
No. of Observations 120 120 120 120
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the test
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
35

Table 3: Regression results: Net firm entry
Economy-wide effects Sector-specific effects
Not
accounting
for Financial
Liberalization
Accounting
for Financial
Liberalization
Not
accounting
for Financial
Liberalization
Accounting
for Financial
Liberalization
(1) (2) (3) (4)
Lagged net firm entry 0.507***
(0.166)
0.507**
(0.166)
0.541***
(0.122)
0.431**
(0.182)
Profitability -0.139**
(0.059)
-0.139**
(0.059)
-0.142**
(0.068)
-0.109
(0.097)
State of the economy -0.006***
(0.002)
-0.003*
(0.001)
-0.007***
(0.001)
-0.009***
(0.002)
Industry growth 0.192*
(0.105)
0.192*
(0.105)
0.147*
(0.078)
0.090*
(0.044)
Import intensity 0.023***
(0.004)
0.013***
(0.003)
0.015**
(0.006)
-0.005
(0.007)
FIN -0.286***
(0.066)
-0.013
(0.063)
-0.057
(0.124)
0.003
(0.128)
FIN*FL -0.196**
(0.067)
FIN*ED
-0.010**
(0.004)
-0.009**
(0.004)
FIN*ED*FL
-0.103***
(0.014)
F Test 73.12
(0.000)
73.12
(0.000)
278.05
(0.000)
201.06
(0.000)
Hansen J-Test 7.04
(0.633)
7.04
(0.633)
6.96
(0.641)
8.52
(0.483)
Test for AR (1) errors -2.52
(0.012)
-2.52
(0.012)
-2.70
(0.007)
-2.02
(0.044)
Test for AR (2) errors -1.30
(0.194)
-1.30
(0.194)
-1.22
(0.221)
-0.93
(0.350)
No. of Industries 20 20 20 20
No. of Observations 120 120 120 120
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the test
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
36

Table 4: Regression results with a graduated financial liberalisation dummy:
Industrial concentration
Economy-wide
effects Sector-specific
effects
(1) (2)
Lagged concentration 0.548***
(0.161)
0.573***
(0.096)
Industry size -0.059*
(0.031)
-0.059**
(0.028)
Industry growth -0.235*
(0.136)
-0.174**
(0.079)
Export intensity 0.078*
(0.042)
0.602***
(0.146)
Import intensity -0.013***
(0.004)
0.028**
(0.011)
FIN -0.069
(0.118)
0.776**
(0.334)
FIN*FL 0.095**
(0.038)
FIN*ED 0.012***
(0.004)
FIN*ED*FL 0.014***
(0.004)
F Test 52.51
(0.000)
313.88
(0.000)
Hansen J-Test 9.45
(0.397)
6.39
(0.700)
Test for AR (1) errors -2.52
(0.012)
-2.73
(0.006)
Test for AR (2) errors -1.09
(0.275)
-1.26
(0.206)
No. of Industries 20 20
No. of Observations 120 120
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the test
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
37

Table 5: Regression results with a graduated financial liberalisation dummy:
Net firm entry
Economy-wide
effects Sector-specific
effects
(1) (2)
Lagged net firm entry 0.507***
(0.166)
0.541***
(0.122)
Profitability -0.139**
(0.059)
-0.142**
(0.068)
State of the economy -0.002*
(0.001)
-0.024***
(0.007)
Industry growth 0.192*
(0.105)
0.147*
(0.078)
Import intensity 0.013***
(0.003)
-0.021**
(0.010)
FIN 0.070
(0.085)
-0.622*
(0.323)
FIN*FL -0.083***
(0.028)
FIN*ED -0.010**
(0.004)
FIN*ED*FL -0.011**
(0.004)
F Test 73.12
(0.000)
278.05
(0.000)
Hansen J-Test 7.04
(0.633)
6.96
(0.641)
Test for AR (1) errors -2.52
(0.012)
-2.70
(0.007)
Test for AR (2) errors -1.30
(0.194)
-1.22
(0.221)
No. of Industries 20 20
No. of Observations 120 120
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the test
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
38

Table 6: Regression results with the Rajan and Zingales index: Industrial concentration
Sector-specific effects
Not
accounting
for Financial
Liberalization
Accounting
for Financial
Liberalization
(1) (2)
Lagged concentration 0.548***
(0.161)
0.573***
(0.113)
Industry size -0.059*
(0.031)
-0.062**
(0.025)
Industry growth -0.235*
(0.136)
-0.170**
(0.083)
Export intensity 0.137***
(0.044)
0.083**
(0.037)
Import intensity -0.081**
(0.025)
0.015*
(0.058)
FIN 0.049
(0.080)
-0.008
(0.056)
FIN*ED 0.011**
(0.004)
-0.003
(0.009)
FIN*ED*FL 0.018**
(0.008)
F Test 52.51
(0.000)
99.75
(0.000)
Hansen J-Test 9.45
(0.397)
7.19
(0.617)
Test for AR (1) errors -2.52
(0.012)
-2.64
(0.008)
Test for AR (2) errors -1.09
(0.275)
-1.49
(0.137)
No. of Industries 20 20
No. of Observations 120 120
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the test
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
39

Table 7: Regression results with the Rajan and Zingales index: Net firm entry
Sector-specific effects
Not
accounting
for Financial
Liberalization
Accounting
for Financial
Liberalization
(1) (2)
Lagged net firm entry 0.507***
(0.166)
0.547***
(0.140)
Profitability -0.139**
(0.059)
-0.154**
(0.064)
State of the economy -0.005***
(0.002)
-0.004**
(0.002)
Industry growth 0.192*
(0.105)
0.147
(0.089)
Import intensity 0.071***
(0.019)
-0.009
(0.060)
FIN -0.033
(0.058)
-0.002
(0.056)
FIN*ED -0.010***
(0.003)
0.003
(0.009)
FIN*ED*FL -0.014*
(0.008)
F Test 73.12
(0.000)
123.91
(0.000)
Hansen J-Test 7.04
(0.633)
8.00
(0.534)
Test for AR (1) errors -2.52
(0.012)
-2.65
(0.008)
Test for AR (2) errors -1.30
(0.194)
-1.45
(0.147)
No. of Industries 20 20
No. of Observations 120 120
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the test
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
40

Table 8: Regression results with yearly data (1970-2004): Industrial concentration.
Economy-wide effects Sector-specific effects
Not accounting for
Financial Liberalization
Accounting for
Financial Liberalization Not accounting for
Financial Liberalization
Accounting for
Financial Liberalization
Fixed
Effects
Anderson-
Hsiao
Fixed
Effects
Anderson-
Hsiao
Fixed
Effects
Anderson-
Hsiao
Fixed
Effects
Anderson-
Hsiao
(1) (2) (3) (4) (5) (6) (7) (8)
Lagged concentration 0.632***
(0.031)
0.671***
(0.042)
0.617***
(0.031)
0.657***
(0.040)
0.581***
(0.031)
0.618***
(0.040)
0.573***
(0.031)
0.608***
(0.040)
Industry size 0.267**
(0.105)
0.265*
(0.146)
0.245**
(0.104)
0.241*
(0.146)
0.242**
(0.101)
0.233*
(0.141)
0.269***
(0.102)
0.268**
(0.136)
Industry growth -0.040***
(0.007)
-0.037***
(0.008)
-0.045***
(0.007)
-0.042***
(0.008)
-0.033***
(0.007)
-0.030***
(0.008)
-0.037***
(0.007)
-0.035***
(0.008)
Export intensity 0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
Import intensity -0.004**
(0.002)
-0.004
(0.002)
-0.005**
(0.002)
-0.005**
(0.002)
-0.004**
(0.002)
-0.005**
(0.002)
-0.008
(0.003)
-0.008**
(0.003)
FIN 0.003***
(0.001)
0.003*
(0.002)
0.003**
(0.001)
0.002
(0.001)
-0.001
(0.001)
-0.001
(0.001)
-0.001
(0.001)
-0.001
(0.001)
FIN*FL
0.005***
(0.002)
0.005**
(0.002)
FIN*ED
0.006***
(0.001)
0.006***
(0.001)
0.005***
(0.001)
0.005***
(0.001)
FIN*ED*FL 0.022**
(0.011)
0.022***
(0.013)
F Test 173.05
(0.000)
- 158.64
(0.000)
-
171.04
(0.000)
-
156.67
(0.000)
-
R-squared 0.71 - 0.71 - 0.72 - 0.73 -
No. of Industries 20 20 20 20 20 20 20 20
No. of Observations 680 680 680 680 680 680 680 680
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the F-
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
41

Table 9: Regression results obtained with yearly data (1970-2004): Net firm entry
Economy-wide effects Sector-specific effects
Not accounting for
Financial Liberalization
Accounting for
Financial Liberalization Not accounting for
Financial Liberalization
Accounting for
Financial Liberalization
Fixed
Effects
Anderson-
Hsiao
Fixed
Effects
Anderson-
Hsiao
Fixed
Effects
Anderson-
Hsiao
Fixed
Effects
Anderson-
Hsiao
(1) (2) (3) (4) (5) (6) (7) (8)
Lagged net firm
entry
0.428***
(0.034)
0.453***
(0.037)
0.431***
(0.035)
0.455***
(0.038)
0.424***
(0.034)
0.448***
(0.037)
0.419***
(0.034)
0.443***
(0.038)
Profitability -0.132**
(0.043)
-0.132**
(0.052)
-0.134**
(0.043)
-0.134**
(0.052)
-0.120**
(0.043)
-0.118**
(0.054)
-0.119**
(0.043)
-0.118**
(0.054)
State of the economy -0.005
(0.007)
-0.004
(0.008)
-0.005
(0.007)
-0.004
(0.008)
-0.007
(0.007)
-0.007
(0.009)
-0.007
(0.007)
-0.007
(0.009)
Industry growth 0.140***
(0.016)
0.136***
(0.019)
0.141***
(0.016)
0.138***
(0.020)
0.148***
(0.016)
0.156***
(0.021)
0.159***
(0.017)
0.156***
(0.021)
Import intensity 0.011**
(0.005)
0.011*
(0.006)
0.011**
(0.005)
0.012*
(0.006)
0.011**
(0.005)
0.022**
(0.009)
0.022***
(0.007)
0.022**
(0.009)
FIN -0.001
(0.003)
-0.001
(0.004)
-0.001
(0.003)
-0.001
(0.004)
-0.004
(0.004)
-0.005
(0.004)
-0.005
(0.004)
-0.005
(0.004)
FIN*FL -0.003
(0.004)
-0.003
(0.005)
FIN*ED 0.005**
(0.002)
0.007**
(0.003)
0.008***
(0.003)
0.007**
(0.004)
FIN*ED*FL -0.079***
(0.027)
-0.078**
(0.037)
F Test 41.71
(0.000)
-
37.53
(0.000)
-
36.50
(0.000)
-
35.82
(0.000)
-
R-squared 0.37 - 0.37 - 0.37 - 0.38 -
No. of Industries 20 20 20 20 20 20 20 20
No. of Observations 680 680 680 680 680 680 680 680
Note: Significant at the 1% ***, 5%**, and 10%*. Robust Standard Errors are in parentheses under the respective
estimated coefficients. In the lower part of the table, the numbers in parentheses under the values of the F-
statistics are probability values. FIN is credit to the manufacturing sector as a ratio of GDP. FL is the financial
liberalisation dummy. ED is the measure of external finance dependence.
42

Appendix
The rankings of industrial sectors according to their degree of external finance dependence
The table below presents the rankings obtained by using the index of finance dependence
formulated by Rajan and Zingales (1998) and the index that we calculated on the basis of
Malawian industry-level (three digit ISIC) data. We evaluated the similarity between the two
rankings by means of the Spearman Rank-Order Correlation Coefficient. This has a value of
0.0436, on the basis of which we cannot reject the null hypothesis that there is no association
between the two rankings.
Alternative measures and rankings of manufacturing industries’ external finance dependence
Rajan and Zingales Kabango and Paloni
Manufacturing
Sub-Sector Dependence
Ratio Rank
Dependence
Ratio Rank
Tobacco manufacturing -0.45 1 1.53 20
Leather -0.14 2 0.15 4
Footwear -0.08 3 0.37 9
Clothing and Apparel 0.03 4 0.43 10
Non-Metal Products 0.06 5 0.22 5
Beverages 0.08 6 1.18 16
Food 0.14 7 0.10 1
Paper Products 0.17 8 1.32 18
Textiles 0.19 9 0.59 13
Printing and Publishing 0.20 10 1.01 15
Rubber 0.23 11 0.13 3
Furniture 0.24 12 0.34 8
Fabricated Metal 0.24 12 0.26 6
Industrial Chemicals 0.25 14 1.26 17
Wood and Sawmill 0.28 15 0.11 2
Transport Equipment 0.36 16 1.42 19
Machinery – General 0.60 17 0.31 7
Other Chemicals 0.75 18 0.93 14
Machinery – Electrical 0.95 19 0.53 11
Plastic Products 1.14 20 0.55 12
To classify the industrial sectors as low, medium and high external finance dependent we
proceed as follows: sectors with the lowest 25 percent of the finance dependence ratios
constitute the low finance dependent group (5 sectors); the highest 25 percent are the high
finance dependent group (5 sectors); the remaining 50 per cent (10 sectors) are the middle
group.
43

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