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Causality in Finance and Growth: The Case of a Small Open Economy
VINAY PRASANDJEET NUNDLALL
International Business School
Brandeis University
Waltham
MA 02452-9110
USA
ABSTRACT
This paper investigates causality between economic growth and financial development in Mauritius over the
period 1968 through to 2004. Using Engle and Granger error correction methodology with annual data, we
find that financial intermediation has been contributing to growth in Mauritius since independence. However,
the equity market has not had any impact on the economy during its relatively shorter life span. A channel of
growth from financial intermediation to the construction sector is identified. The study also finds that exports
also have had a significant impact on growth, lending support to the export led growth strategy adopted by the
authorities.
Introduction
The UNDP 2003 Human Development Index ranks Mauritius sixty second overall and third behind the
Seychelles and Libya among African countries. Based upon GDP per capita, Mauritius ranks third
among African countries, behind the Seychelles and Republic of South Africa. Mauritius is a small,
densely populated island of 1.2 million inhabitants living in an area of 1,860 square kilometers (720
square miles). The island does not have any natural mineral resources and has relied heavily on its
monocrop sugar sector for exports during most its life as an independent nation. Situated about 1,000
km (620 miles) off the eastern coasts of Africa in the Tropic of Capricorn, it is a victim of the vagaries
of the Indian Ocean’s tropical climate. However, its volcanic origin has endowed it with beautiful
sandy beaches and a calm blue lagoon which has made it a popular holiday resort for European and
South African tourists and made tourism an important sector of the economy.
Economic history teaches us that Mauritius was never destined to achieve economic success because,
as Meade reports in 1961, the island was a crucible waiting to explode due to ethnic tension. During
the 1960’s, the economy relied solely on sugar for exports, a sector that was prone to trade shocks (and
climactic conditions), while at the same time experiencing unbridled population growth. Meade
actually predicted that the then British colony would be caught in the Malthusian trap, and that the
scramble for jobs would create tension between the ‘underdogs’ who were descendants of Afican
slaves and Indian indentured labourers, and the wealthy Franco-Mauritian ‘top dogs’.
However, Mauritius never fell in the Malthusian trap and if anything, achieved the opposite by
developing an export processing zone, gradually diversifying away from sugar to textiles, tourism and
financial services, and perhaps pertinently, upholding a stable economic and political environment
after independence in 1968. The same, sadly, cannot be said for most Sub-Saharan African countries
post independence. Per capita income rose from US $1,000 in the early eighties to more than US
$4,000 in 2004. The annual growth rate has been about 5% over the past two decades which has
boosted the ranking of the country to the top of middle-income category economies.
In this study, we investigate some of the determinants of this growth, with special emphasis on the
finance sector. Barro’s (1991) seminal paper on economic growth has led to a spurt of creativity in the
empirical growth literature. Sala-I-Martin’s (1997) curiously titled “I just ran two million regressions”
points to the direction which research has taken in the field; a medley of economic and socio-political
variables have been tried in growth regressions. However, the majority of studies are cross-sectional
in nature, with the main determinants of growth identified as initial income level, investment rate,
secondary school enrollment rate and the rate of population growth [Levine and Renelt (1992)].
Unfortunately, there are not many case studies of countries using time series approaches. This paper
uses Mauritian annual data from 1968 to 2004 to estimate a growth regression applying time series
techniques. The purpose of the study is to identify factors that have contributed to growth over the
past 35 years in this small island economy with particular emphasis on the role of financial
intermediation. While we control for capital investment, human capital and exports (since export led
growth was a strategy explicitly adopted by the authorities), we find evidence of a positive
contribution by the financial sector in facilitating growth. The results also confirm that Mauritius has
experienced export led growth. Whilst the role of banks (financial intermediaries) has been significant
in assisting economic growth both in the long term and in the short term, the stock market is, on the
other hand, not important in defining growth at this stage of the countries development.
The paper is organized as follows; Section 1 reviews from the existing literature the role of financial
intermediation in an economy, Section 2 contains a description of the data and the methodology used,
Section 3 presents the results and a discussion of their implications and finally, Section 4 concludes.
Section 1 – Financial development and economic growth
Starting from the pioneering work by King and Levine (1993), Levine (1997), Levine and Zervos
(1998) and Levine, Loayza and Beck (1998), many studies have investigated and uncovered a positive
contribution of financial development on growth. The seed of this idea actually goes as far back as
Alexander Hamilton (1781, in Levine et al. (2000)) who argued that “banks were the happiest engines
that ever were invented” for spurring economic growth. Other early records are from Bagehot (1873,
in Levine et al. (2000)) and Schumpeter (1911), who postulated that technological innovations, an
important factor for growth, rely on external funds to come to fruition. If the economy has a financial
system, then banks can fund productive investments, and give innovators access to funding which
enables them to undertake projects. An illustration of this example at work is the Industrial
Revolution in England. Since England already had a functioning financial system, backed by an
established and credible legal system, the country progressed by channeling funds into its industries
during those crucial years. Schumpeter explains how banks can choose which firms or entrepreneurs
get to use society’s savings, hence positively influencing the path of economic development by
tweaking the allocation of savings.
On the other hand, Bencivenga and Smith (1991) warn that higher returns from more efficient
allocation of funds could depress savings rate and hence hamper growth. Lucas (1988) further
counters by saying that economists have badly over-stressed the contribution of the financial system.
Robinson (1952) too is skeptical of its influence on the economy, concluding that banks respond
passively to economic growth. Going way back in history, opponents to the banking system have been
found among leading people of the nation - President John Adams (1819, in Levine et al. (2000))
asserted that “banks harm the morality, tranquility, and even wealth” of nations.
Patrick (1966) and Goldsmith (1969) are among the earliest of modern writers who find a positive
correlation between financial development and growth. However, Patrick cautions that there is only
proof of correlation and not causality. Patrick actually sets up two relationships: causality can be
supply-leading or demand-following. Supply-leading means that development of financial institutions
services induces investment and growth. Demand-following says that the financial sector responds to
increasing demand for their services from a growing real economy.
In addition, Patrick also hypothesizes there are stages of development that will experience the different
causal relationships. That is, causality between finance and growth changes over time as the economy
develops. During the early stages, financial development spurs growth and innovation as it reallocates
funds from savers to modern sectors of the economy and encourages entrepreneurs to put their ideas
into practice. At higher development levels, the supply-leading force of financial development
gradually weakens.
demand-following.
Financial development responds increasingly to output growth, so we have
McKinnon (1973) and Shaw (1973) specifically address the supply-leading hypothesis and
recommend governments to liberalize their financial sector in order to spur growth. More recent
studies like Jung (1986) delve into the time series aspect of the problem. Using bivariate causality
tests to detect temporal patterns in causality, Jung does not find support of Patrick’s hypothesis. Xu
(2000) finds a negative relationship between bank-based financial development and growth in 14
middle and low income countries (mostly African), but finds significant positive long run effects of
financial development on growth in 27 other countries. Wachtel and Rousseau (2000) show that banks
and stock market development both explain growth. Arestis, Demetriades and Luintel (2000) use
quarterly data from five OECD countries and find that banks and stock markets both cause growth, but
that the effect of banks is larger.
This paper develops an error correction model and finds that while financial intermediation as proxied
by bank lending to the private sector is important for economic growth, the stock market is not
significant in explaining growth in a small developing economy. However, since the Stock Exchange
of Mauritius was only established in 1989, we have only 16 years of observations for carrying out tests
on the stock market’s importance. The result, even if not surprising due to the smallness of exchange,
cannot be generalized because of the length of the time series.
Section 2 – Data and Methodology
We analyze the effect of stock market and bank development on growth in Mauritius using annual data
from 1968 to 2004 - quarterly data of economic variables are not available. 1968 marks the year of
independence from British rule, and also the year when most socio-economic data collection started.
Data for this study has been extracted from the Central Statistical Office (CSO), and The International
Financial Statistics (IFS) webpage of the IMF. In what follows, we describe the indicators of stock
market development and bank development.
We use three measures of stock market development; market capitalization to GDP ratio, turnover
ratio and value of shares traded ratio. Market capitalization ratio is an indication of size and it is the
value of all listed shares divided by GDP. Total value traded to GDP is an indicator for activity or
liquidity and is defined as total shares traded on the exchange divided by GDP. The efficiency
indicator we use is turnover ratio, which is the value of total shares traded divided by market
capitalization. It measures the activity of a stock market relative to its size because it is important to
distinguish between a small stock market that is active (has high turnover ratio) and a large market that
is less liquid (and has a low turnover ratio). In theory, one should be careful in using the market
capitalization indicator as, if markets are efficient, market capitalization already reflects the discounted
future value of the economy. Hence, if causation is from economic growth to stock market, it is the
opposite that will be revealed.
Measuring bank development is more straightforward. We use activity which is claims on the private
sector made by deposit money banks divided by GDP. This measure excludes loans issued to public
enterprises and government, thus isolating loans given only to the private sector (which includes
corporations, various enterprises and households). A measure of liquidity, or financial depth in our
study, is currency plus demand and interest-bearing liabilities of banks and other intermediaries
divided by GDP. Financial depth is also a measure for the overall size of the financial sector.
The measure for capital investment is gross domestic fixed capital formation from the national
accounts. Since statistics for labour force is only available as from 1976 onwards, we use population
as a proxy for labour. The correlation between population and labour force is 0.99 over the available
sample. Further, normalizing by population gives us a more interesting measure; GDP per capita as
opposed to GDP per labour. A measure of human capital is gross secondary enrolment, which is
available for the country. More pertinent measures, such as the level of education attained by
members of the workforce, are unfortunately not available for the sample we are looking at. Exports
are measured by exports of goods and services, as a share of GDP. Since tourism, an exported service,
is very important for Mauritius, we adopt exports and services as opposed to exports of goods. All
variables are measured in MRU, and deflated by CPI (base year 1992).
We start with the following aggregate production function:
Y  F K , L 
Yt  ( At Lt )1  K t H t
(1)
Where Y is real GDP, L is population, K is physical capital, H is human capital, and A is the level of
technological efficiency and economic efficiency. Economic efficiency includes economic and
institutional variables exports X and financial intermediation F. Normalizing with respect to L and
taking logs, we have
ln y   0  1 ln K   2 ln H   3 ln X   4 ln F
(2)
The model to be estimated is therefore:
ln yt   0  1 ln K t   2 ln H t   3 ln X t   4 ln Ft   t
(3)
All the coefficients are expected to have a positive sign and be significant. Controlling for K, H and X,
F is expected to be positive and significant.
In order to construct the error correction mechanism (ECM), we first need to test whether the series in
the model are all stationary and integrated of the same order. If they are all integrated of the same
order d (if they are I(d)), we check whether they all share a common stochastic trend - that is whether
they are co-integrated.
Following Engle and Granger (1987) breakthrough theory of co-integration, suppose two time series xt
and yt are related via the following relations:
yt  xt  u t
u t  c1  1u t 1   1,t
(4a)
yt  xt  vt
(4b)
vt  c2   2 vt 1   2,t
where δ ≠ η hence restricting both parameters from being equal to zero at the same time.
0 ≤ ρ1 ≤ 1
0 ≤ ρ2 ≤ 1
c1 and c2 are intercept terms
ε1,t and ε2,t are standard white noise error processes, mutually independent at all lags.
Equations (4a) and (4b) represent two distinct linear combinations of xt and yt that can be described by
AR(1) models. The interpretation of the two models however depend upon the values that ρ1 and ρ2
take. We have three relevant cases which will each imply a different interpretation of (4a) and (4b).
In the first case, where ρ1 = ρ2 =1, any linear combination of xt and yt is a random walk. Therefore
both xt and yt are non-stationary processes. Both series have a stochastic trend, and they do not share
this trend as no linear combination of xt and yt is itself stationary.
In the second case, both 0 ≤ ρi ≤ 1(for i = 1, 2). Then any linear combination such as (4a) and (4b)
above is a stationary AR(1) process, and xt and yt are individually stationary variables.
The third and most interesting case is when ρ1 = 1 and 0 ≤ ρ2 ≥ 1 (or vice-versa). There is then one
linear combination of xt and yt which is a stationary AR(1) process, while the other combination is a
random walk. Further, it means that even though individually xt and yt are I(1) time series, there is one
combination of these two which is stationary. In the language of Engle and Granger (1987), these two
time series are cointegrated. Cointegration implies that these series have a common stochastic trend –
in other words, they move together in unison, and any divergence between these two series is only
transitory.
Testing for cointegration is then quite straightforward. We first test that xt and yt are I(1). This is done
by applying the Augmented Dickey-Fuller (ADF) test on each process:
k
 1 y t    t  y t 1    i  1 y t i   t
(5)
i 1
The null hypothesis is ρ = 0, that is there is a unit root. However, the proper test statistic to use in the
ADF is not the t-statistic, but the τ-statistic. The number of differenced lags to be used is also
important as one should care about the degrees of freedom (especially in a small sample like the one
we have here). In this study, one lag happens to be sufficient.
So once xt and yt are found to be I(1), a linear combination of the two processes is run (consistent with
causality) and the residuals saved. Suppose we run
yt  ˆ  ˆxt  uˆt
(6a)
Then
uˆt  yt  ˆ  ˆxt
(6b)
ut could be I(1). However, in special circumstances where ut is I(0), (ie it is stationary and rarely drifts
away from zero) then the constant δ is such that the ‘bulk of the long run components of xt and yt
cancel out’. xt and yt and are said to be cointegrated with a cointegrating vector [1 -δ]’. Generally, if
the variables are I(d) and the errors are I(b), where b < d, then we have cointegration. Equation (6a)
is called the cointegrating equation.
Formally, the auxiliary test regression for cointegration is
k
1uˆ t    uˆ t 1    i 1uˆ t i   t
(7)
i 1
So if ut is I(0), then it can be used in the dynamic regression below in what is known as the Granger
Representation Theorem:
k
l
i 1
i 1
1 y t   0  1 (uˆ t 1 )    2,i 1 y t i    3,i  1 xt i   t
(8)
β1 reflects the speed of adjustment towards equilibrium. Equation (8) is the Error Correction Model
(ECM), where generally, there is Granger causality if either β1 is significant, or the β2’s and the β3’s
are significant. The number of lags k and l to be included will be determined by the Akaike
Information Criterion (AIC). Fortunately, for this sample, one lag in each differenced variable gives
the most significant results and hence there is minimum loss of degrees of freedom.
Section 3 – Results
3.1 Financial Intermediation and Growth
We first present the results for financial intermediaries (or banks). The ADF tests for the levels of the
variables and the differenced variables are given in Table 1 below:
Table 1: ADF tests for levels and differences in variables
First Differences (1 )
Levels
Variable
Type
Rho
Tau
Pr < Tau
Rho
Tau
Pr < Tau
lnGDP
Zero Mean
0.26
2.72
0.9978
-10.86
-2.23**
0.0266
Single Mean
-1.33
-1.29
0.6233
-21.27
-3.16**
0.0310
Trend
-10.48
-2.26
0.4407
-22.90
-3.27*
0.0885
Zero Mean
0.37
1.23
0.9417
-9.59
-2.12**
0.0345
Single Mean
-4.14
-2.17
0.2218
-12.25
-2.39
0.1513
lnK
First Differences (1 )
Levels
Variable
lnH
lnEXP
lnACT
lnDEPTH
Type
Rho
Tau
Pr < Tau
Rho
Tau
Pr < Tau
Trend
-10.33
-2.40
0.3707
-14.12
-2.58
0.2891
Zero Mean
-1.01
-2.81
0.0062
-8.39
-1.97**
0.0482
Single Mean
-0.56
-0.54
0.8716
-21.52
-3.13**
0.0332
Trend
-8.33
-1.97
0.5973
-21.70
-3.08
0.1268
Zero Mean
-0.67
-0.68
0.4151
-26.08
3.47***
0.0010
Single Mean
-10.67
-2.32
0.1710
-26.21
-3.41**
0.0171
Trend
-14.17
-2.38
0.3808
-26.83
-3.41*
0.0670
Zero Mean
-0.88
-1.82
0.0655
-46.94
4.70***
<.0001
Single Mean
-0.80
-0.49
0.8805
-61.57
5.29***
0.0002
Trend
-8.32
-1.98
0.5948
-62.48
5.21***
0.0009
Zero Mean
-1.35
-2.01
0.0435
-22.49
3.84***
0.0003
Single Mean
-1.18
-0.74
0.8236
-28.11
4.08***
0.0030
Trend
-6.47
-1.70
0.7293
-27.71
-4.00**
0.0182
*significant at 10%, ** significant at 5%, ***Significant at 1%
As Table 1 shows, all the variables are I(1). The critical τ-statistics are not reported by the program but
the probability values are provided. Since the variables are all integrated of the same order, we should
check whether they are cointegrated. A Granger causality test is also carried out, and it is found that
causality only runs from Activity, Financial Depth and Exports to GDP at the 1% and 5% level of
significance. However, at 10%, we are not able to reject the null of no causality from GDP to Activity
(Table A in Appendix). We run two separate cointegration regressions, one with Activity and the
other with Financial Depth as measures of financial intermediation. Results for the Cointegration
regression for Activity are reported in Table 2 below:
Table 2: Cointegration Regression. Dependent variable is ln(GDP)
Variable
Parameter
Estimate
Standard
Error
t-Value
Pr > |t|
Intercept
6.241***
0.3055
20.43
<0.0001
lnK
0.274***
0.0405
6.76
<0.0001
lnH
1.120***
0.1273
8.80
<0.0001
lnEXP
0.329***
0.1044
3.15
0.0035
lnACT
0.176***
0.0543
3.24
0.0028
F-Value
666.621***
R2
0.988
Adj-R
0.987
Durbin-Watson D
1.742
Number Obs.
37
<0.0001
*** significant at 1%
The cointegration results show that all the independent variables have a long run impact on GDP. All
have the expected positive signs and are significant at the 1% significance level. Now an
extraordinary result of the theory of cointegration is that, in the presence of cointegration, the OLS
parameter estimates are superconsistent [Davidson and MacKinnon (1993)], as the asymptotic
variance fades very fast with the number of observations. In that sense then the estimates of the
coefficients give us the elasticity of the variables with respect to GDP. Human capital has the highest
elasticity at 1.12, which can be interpreted as a sign that policy towards providing quality education to
the population has been succesful. The elasticity for exports is 0.329, which shows that GDP responds
to exports. The variable of interest is the coefficient for Activity which at 0.176 means that a 1%
increase in Activity will lead to a 0.176% increase in GDP. However, we have a slight hint of serial
correlation at the 10% level, (but the test is passed at the 5% level) while some multicollinearity is also
present. So the elasticities from this regression are not robust to interpretation.
To detect cointegration, we check that the saved residuals or equilibrium errors are I(0). Table 3
summarizes the results of the ADF test of stationarity:
Table 3 ADF tests for residuals from Cointegrating Regression
Variable
Type
Rho
Tau
Pr < Tau
ε(Error)
Zero Mean
-18.57***
-3.09
0.0029
Single Mean
-18.65**
-3.06
0.0385
Trend
-19.98*
-3.22
0.0978
*significant at 10%, ** significant at 5%, ***Significant at 1%
It looks like the errors are stationary at conventional levels (even though if we assume a time trend in
the error process, it is not stationary at the 5% significance, but stationary at the 10% level). Therefore
we have that the time series are cointegrated with a cointegration vector
[1 -6.241 -0.274 -1.12 -0.329 -0.176]’.
Thus we can construct an ECM, which takes the form:
 1 ln GDPt 
k
l
i 1
i 1
 0  1 (uˆ t 1 )    2,i  1 ln GDPt i    3,i  1 ln K t i
m
n
p
i 1
i 1
i 1
(9)
   4  1 ln H t 1    5  1 ln EXPt i    6  1 ln ACTt i  t
Based on the AIC and also paying attention to degrees of freedom, it is found that one lag in each
independent growth variable (i.e. k = l = m = n = p = 1) is sufficient to give us the best model.
Results are reported in Table 4 below.
The null hypothesis with regards to the lagged error correction variable, EC(t-1), is that its coefficient
should lie between -1 and 0. The estimated coefficient is -0.46, which is significant at the 10% level.
The significant error correction means that there is a long run relationship between the variables, and
that any disequilibrium in the previous period is partially corrected in the present period – to be
precise, 46% of the previous year’s disequilibrium is corrected in the subsequent year. In other words,
it takes a little bit more than two years for any disequilibrium to be corrected.
Table 5: Results for ECM with Activity of banks. Dependent variable is ΔGDP(t)
Variable
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
0.0260
0.0164
1.58
0.1244
*-0.4643
0.2616
-1.77
0.0868
0.0529
0.2092
0.25
0.8022
ΔK(t-1)
**0.1812
0.0882
2.06
0.0493
ΔH(t-1)
-0.0117
0.4619
-0.03
0.9800
ΔEXP(t-1)
0.1810
0.1101
1.64
0.1112
ΔACT(t-1)
***0.3082
0.1156
2.67
0.0126
F-Value
***5.4000
Intercept
EC(t-1)
ΔGDP(t-1)
R2
0.5364
Adj-R2
0.4371
Durbin-Watson
D
2.1290
AIC
Number Obs.
0.0008
-186.1470
35
*significant at 10%, ** significant at 5%, ***Significant at 1%
The coefficients on lagged growth in capital stock and lagged growth in Activity are significant at the
5% and 10% levels respectively, while that on lagged growth of Exports is barely significant at the
10% level. The coefficient on lagged growth in human capital is not significant at any acceptable
level, probably reflecting the fact that changes in human capital in the short run does not affect growth,
but that the relationship is tenable only in the long run. Furthermore, cross sectional studies have also
shown that the proxy for human capital that we have used here, secondary school enrolment, is not
always significant in predicting growth.
The ECM therefore shows evidence that financial intermediation has been an important factor in
determining growth in Mauritius since independence. The cointegration result shows that there is a
long run relationship between bank activity and GDP.
As a supplemental test, we also include dummy variables for trade liberalization and financial
liberalization in the ECM regressions for more rigorous control. However, the dummy variables are
not significant, while the coefficients on growth rates for Activity, Capital and Exports do not change
significantly in size. (Results not reported). With regards to trade, this does not mean that trade
liberalization (circa 1983) in Mauritius was not successful. It may well be that the phasing in of the
policies happened over time. Rodrik (1999a) calls this ‘heterodox opening’; the authorities segmented
the export sector from the rest of the economy, raising returns in the export sector and preventing
domestic resources from being diverted into the (relatively inefficient) import sector. Financial
Liberalization occurred in 1992 when credit rationing was abolished. Again, this was not
implemented overnight, but phased in step by step. In 1992, credit ceilings were lifted on loans to
priority sectors, and by 1993, credit ceilings had disappeared.
We further run the same models replacing Activity by Financial Depth as an independent variable. In
the first step, we detect cointegration, and we run an ECM in the second step. For the sake of brevity,
we present only the ECM results in Table 5. The results are not qualitatively different. The error
correction term is significant at nearly the 5% level, and again lagged growth in capital is significant at
the 5% level. Exports however are not significant at conventional levels, while the financial
intermediary indicator is significant at the 10% level.
The results from the two models confirm Patrick’s supply-leading hypothesis. Development in the
financial intermediation services leads growth. GDP per capita was about MRU 1,200 in 1968
meaning the economy was at a very early stage of development. This is further support for the
hypothesis that in the early stages of development, causality in the growth-financial development
relationship is supply-leading.
Table 5: Results for ECM with Financial Deepening. Dependent variable is ΔGDP(t)
Variable
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
0.0238
0.0188
1.27
0.2150
EC(t-1)
*-0.5908
0.2995
-1.97
0.0585
ΔGDP(t-1)
0.1332
0.2479
0.54
0.5952
ΔK(t-1)
**0.2018
0.0949
2.13
0.0424
ΔH(t-1)
-0.1962
0.4586
-0.43
0.6721
ΔEXP(t-1)
-0.0789
0.1065
-0.74
0.4649
ΔACT(t-1)
*0.3313
0.1882
1.76
0.0892
F-Value
***4.2400
R2
0.4759
Adj-R2
0.3635
Durbin-Watson
D
1.8540
AIC
-184.3170
Number Obs.
35
0.0037
*significant at 10%, ** significant at 5%, ***Significant at 1%
3.2 The role of the Stock Market in the Economy
The Stock Market of Mauritius (SEM) was established in early 1989. Table 6a below gives a
summary of its evolution over the period 1989-2004. The Market Index is a value weighted index of
all companies listed on the market, with base period July 1989 (=100). The index has climbed
sharply over the past year. Number of listed companies is still very low, but has increased
progressively from 6 to 40. The other measures, market capitalization, volume traded and annual
turnover, all show rapid growth. However, absolute measures do not give a very good indication of
how important the stock market is. In order to get a better idea of how the market is doing relative to
the economy, we need to examine the size, activity and efficiency ratios.
Table 6a: SEM indicators over the period 1989-2004
Number
of Market
Market
Companies
Capitalization
Year
Index
Listed
(MRU)
Annual
Traded
Volume Annual Turnover
(MRU)
1989
117.34
6
1,437,079,336
613,505
14,259,850
1992
183.18
21
6,598,876,234
8,721,139
158,600,402
1995
344.44
39
27,817,756,013
60,675,964
1,232,581,768
1998
465.6
42
45,335,829,298
98,859,087
2,556,083,730
2001
340.92
40
32,147,404,156
139,068,367
3,292,410,159
2004
710.77
40
67,033,922,981
146,358,080
2,819,024,443
Table 6b below reports the market ratios that give a better indication of how the market is doing over
the period 1989-2004, relative to the economy.
Table 6b: SEM indicators over the period 1989-2004
Number
of
Year
Companies
Market Cap. to Turnover to Market Value Traded to
Listed
GDP (%)
Cap.(%)
GDP (%)
1989
6
4.32
0.99
0.04
1992
21
13.30
2.40
0.32
1995
39
39.58
4.43
1.75
1998
42
45.53
5.64
2.57
2001
40
24.33
10.24
2.49
2004
40
38.33
4.21
1.61
By all means, the stock market is still very small, as market capitalization ratio is still well below the
50% mark. The world’s big stock markets usually have a market cap to GDP ratio greater than 150%.
The other ratios of liquidity and efficiency confirm that this is not a very liquid market. Another
important piece of information is perhaps the fact that the number of stock broking companies since
1990 has remained intact at 11, while the number of registered stockbrokers working in the sector has
also stayed put at 32 over the past six years. Standard and Poor’s classifies this market as a ‘frontier
market’.
Given the smallness of the stock market, we should not expect that it has been a contributor to growth
over its short lifetime. In results which we do not report here, we run Engle and Granger model over
the smaller sample of 1989-2004, using in turn each of the three ratios, and we do not find any
significant impact of the stock market on growth. In order to keep degrees of freedom, we drop the
human capital variable, and keep the financial deepening variable to control for liquidity by financial
intermediaries. All three ratios are I(1), but none of them are found to Granger cause GDP. For all
three regressions, the stock market indicator is insignificant in both cointegration equation and ECM.
Therefore we conclude that the stock market has not had any impact in generating economic growth in
the country.
Section 3.3 – The Johansen Approach
The Johansen procedure offers an alternative framework to examine causality and long run
relationship between growth and financial intermediation. This procedure is based on a Vector AutoRegression (VAR) model and is generally considered superior to the Engle-Granger representation. It
is generally accepted that when there are more than two I(1) variables, the Engle-Granger
representation may be inefficient as the variables can be sensitive to the choice of left-hand side
endogenous variable. Further, with n (where n > 2) I(1) variables, we may have n – 1 roots, in which
case the single equation representation is not efficient. With 5 variables in our model, the Johansen
procedure can account for up to 4 cointegrating vectors while treating all the variables as exogenous.
The system of equation estimated is also known as a Vector Error Correction Model (VECM).
Charemza and Deadman (1992) make a case for using the procedure even for single equation
modelling (i.e. when we have only one root, or cointegrating vector) as a complementary tool.
There are two tests based on maximum likelihood method for finding the number of cointegrating
vectors, both due to Johansen and Juselius (1988, 1990). These are the l-max statistic and the trace
statistic. The tests progressively check in sequence the hypothesis that r = 0 (no cointegrating vectors),
then r ≤ 1, then r ≤ 2, etc. until a hypothesis cannot be rejected. If r ≤ r* - 1 is rejected but r ≤ r* is
not rejected, then there is evidence of r* cointegrating vectors.
Table 7a: Johansen Trace test for Cointegration Rank
Cointegration Rank Test using Trace
H0:
Rank=r
H1:
Rank>r
Eigenvalue
Trace
5% Critical
Value
0
0
0.6287
72.9656
68.68
1
1
0.4475
38.2862
47.21
2
2
0.2709
17.5226
29.38
3
3
0.1656
6.4656
15.34
4
4
0.0037
0.1294
3.84
Tables 7a and 7b show the results from the Johansen maximum likelihood test on the cointegrating
regression in levels (Equation (5))
Table 7b: Johansen Maximum Eigenvalue test for Cointegration Rank
Cointegration Rank Test Using Maximum Eigenvalue
H0:
Rank=r
H1:
Rank=r+1
Eigenvalue
Maximum
5% Critical
Value
0
1
0.6287
34.6794
33.46
1
2
0.4475
20.7636
27.07
2
3
0.2709
11.0571
20.97
3
4
0.1656
6.3362
14.07
4
5
0.0037
0.1294
3.76
The tests use a VAR(2) model with intercept and no linear trend. Both the trace and l-max tests
consistently reveal that we have a unique root in our model. With only one cointegrating vector, we
can plausibly estimate a single equation instead of a system of equation.
The single equation estimated the VECM gives a cointegrating vector
[1 - 5.65 - 0.35 - 0.62 - 0.26 - 0.34]’
or, in equation form:
ln GDPt  5.65  0.35 ln K t  0.62 ln H t  0.26 ln EXPt  0.34 ln ACTt
Note that since the estimation methods for the VECM and the single equation ECM are different, the
coefficients are not directly comparable.
Coefficients for the Single Equation estimation from the VECM are given in Table 8 below. α the
coefficient of EC(t-1), the error correction term, is -0.6451. A test for weak exogeneity, which is a test
for the null hypothesis that α = 0, is rejected at the 5% significance level (Chi-Squared value of 6.08, P
value of 0.0137). α is negative and significant which implies that the long run relationship is an
equilibrium relationship. The test for weak exogeneity also implies that financial activity causes GDP.
The number of lags in log differences of the dependent variables used in the VECM is three. The
results indicate that in the short run, banking activity has quite a big impact after one year (significant
at 10% significance level) as the coefficient (elasticity) is 0.22. The slightly lesser impact is discerned
after two years, as the elasticity drops to 0.17 but the significance is again marginal. After three years,
the impact is substantially higher at 0.34, and highly significant.
Table 8: VECM(3) estimations. Dependent variable is ΔGDP(t)
Parameter
Estimate
EC(t-1)
**-0.6451
Standard Error
t Value
Pr > |t|
0.0137
ΔGDP(t-1)
*0.5188
0.2761
1.88
0.0847
ΔK(t-1)
-0.1906
0.1400
-1.36
0.1981
ΔH(t-1)
-0.6664
0.5298
-1.26
0.2324
ΔEXP(t-1)
0.0132
0.1123
0.12
0.9087
ΔACT(t-1)
*0.2209
0.1151
1.92
0.0791
ΔGDP(t-2)
*0.5891
0.3226
1.83
0.0928
ΔK(t-2)
*-0.2541
0.1391
-1.83
0.0927
ΔH(t-2)
0.0517
0.6315
0.08
0.9361
ΔEXP(t-2)
**0.2628
0.1090
2.41
0.0328
ΔACT(t-2)
*0.1749
0.0939
1.86
0.0869
ΔGDP(t-3)
0.2295
0.2757
0.83
0.4214
ΔK(t-3)
-0.0925
0.0782
-1.18
0.2600
ΔH(t-3)
-0.2351
0.3260
-0.72
0.4847
ΔEXP(t-3)
**0.3596
0.1514
2.38
0.0350
ΔACT(t-3)
***0.3436
0.1032
3.33
0.0060
*significant at 10%, ** significant at 5%, ***Significant at 1%
Section 3.3 – Identifying some Channels
In this subsection, we attempt to identify some channels through which financial intermediation could
lead to growth. Data on the number of non-residential building permits issued is available from 19762004. Data is also available for the total floor area for each year. In what follows we posit that if
loans from banks are used to invest in these construction projects (whether it is a factory, warehouse,
or administrative offices), we should be able to detect the impact in a regression. However, since
Foreign Direct Investment (FDI) has also been flowing in the country, especially in the manufacturing
sector and tourism sector, it would be interesting to include FDI in the model as well. Results are
reported in Table 9 below.
Table 9: Effect of Financial Intermediation on New Buildings. Dependent variable is ln(Mean Area)
Variable
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
***2.6421
0.9348
2.83
0.0101
lnAREA(t-1)
**0.4156
0.1972
2.11
0.0472
Activity
*1.2022
0.6874
1.75
0.0949
FDI
2.4113
5.1471
0.47
0.6443
DEXP
0.0663
0.6084
0.11
0.9143
Tradelib
**0.4497
0.2068
2.17
0.0413
F-Value
***19.500
R2
0.8228
Adj-R2
0.7806
Durbin-Watson D
2.2610
Number of Obs.
27
<0.0001
*significant at 10%, ** significant at 5%, ***Significant at 1%
The dependent variable is the log of total floor area divided by number of permits issued. The
results give some support for the role of financial intermediation, which is positive and significant at
the 10% significance level. However, they do not provided any concrete proof that funds for FDI
has been diverted in new investment. Further, the trade liberalization dummy is also significant at
the 5% level reflecting the fact that after the trade liberalization phase, many small manufacturing
factories (mainly textiles) were started.
We further evaluate whether financial intermediation has had an influence on private consumption.
It is conceivable that consumers borrow from banks to spend on durable goods. The dependent
variable used is growth in private consumption for a smaller sample of observations that is available
for the period 1976-2004. Control variables added in this equation include lagged growth in private
consumption, economic growth and a financial liberalization dummy. Unfortunately, as is evident
from Table 8, we do not find an impact of financial intermediation on consumption.
Table 10: Impact of financial intermediation on private consumption. Dependent
Growth in private consumption
Variable
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
-0.0082
0.02015
-0.41
0.6861
lagdcons
***0.2965
0.08811
3.37
0.0028
growth
***0.6116
0.06527
9.37
<0.0001
Activity
0.0343
0.06860
0.50
0.6218
finlib
-0.0066
0.01962
-0.34
0.7378
F-Value
***27.5600
R2
0.8336
Adj-R2
0.8034
Durbin-Watson D
2.6510
Number of Obs.
27
variable is
<0.0001
Section 4 – Conclusion
Using data over the period 1968 through to 2004, we have found evidence that financial
intermediation has been an important contributor to growth in Mauritius. The methodology used,
which is the Engle-Granger representation, enables us to assess both long run and short run
relationships. Incidentally, exports are also found to be important for growth. This is evidence of
the success of the authorities in implementing an export oriented strategy to improve the economy.
Financial intermediation has been a facilitator during the process. One possible channel, the use of
bank loans in the construction sector, has been detected. However, we have not been able to
conclusively show that consumption has been affected by bank loans. Furthermore, the stock
market has not had any impact on economic growth yet, possibly both because of its smallness and
because of the short period of time it has been in operation.
On the other hand, the small size of the sample, which has 37 observations, may be a slight cause
for concern even though 30 data points are usually sufficient to run these types of tests.
Furthermore, the channels through which financial intermediation influence growth have not been
completely exploited. An agenda for further progress in this research may be to collect data on
corporate firms that are publicly listed on the stock exchange, and then testing for the effect of bank
lending on firms’ growth. Such a study would cover only the period 1989-2004, as data on firms
are not publicly available before that time. A further strategy would be to use available data on the
countries of the surrounding region, for example Sub-Saharan Africa and to do a panel study of the
effects of financial intermediation and on economic growth.
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Appendix A – Granger Causality Tests
Granger-Causality Test
Optimal Lags
Chi-Square
P>Chi-Square
GDP does not cause Financial Activity
1
***6.58
0.01
GDP does not cause Financial Deepening
1
***10.73
0.00
GDP does not cause Market Capitalization
1
0.04
0.84
GDP does not cause Turnover Ratio
1
1.74
0.18
GDP does not cause Traded Value Ratio
1
0.48
0.49
Financial Activity does not cause GDP
1
*2.93
0.08
Financial Deepening does not cause GDP
1
0.63
0.42
Market Capitalization does not cause GDP
1
0.08
0.78
Turnover Ratio does not cause GDP
1
0.96
0.32
Traded Value Ratio does not cause GDP
1
0.00
0.94
*significant at 10%, ** significant at 5%, ***Significant at 1%