Finance and Growth in Mauritius-The Case of a Small Open Economy

Causality in Finance and Growth: The Case of a Small Open EconomyVINAY PRASANDJEET NUNDLALL International Business SchoolBrandeis UniversityWaltham MA 02452-9110USA ABSTRACT This paper i

Causality in Finance and Growth: The Case of a Small Open Economy

VINAY PRASANDJEET NUNDLALL

International Business SchoolBrandeis UniversityWaltham

MA 02452-9110USA

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.

The UNDP 2003 Human Development Index ranks Mauritius sixty second overall and third behind theSeychelles and Libya among African countries Based upon GDP per capita, Mauritius ranks thirdamong 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 (720square miles) The island does not have any natural mineral resources and has relied heavily on itsmonocrop 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 beautifulsandy beaches and a calm blue lagoon which has made it a popular holiday resort for European andSouth 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 Duringthe 1960’s, the economy relied solely on sugar for exports, a sector that was prone to trade shocks (andclimactic conditions), while at the same time experiencing unbridled population growth Meadeactually predicted that the then British colony would be caught in the Malthusian trap, and that thescramble for jobs would create tension between the ‘underdogs’ who were descendants of Aficanslaves 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 bydeveloping an export processing zone, gradually diversifying away from sugar to textiles, tourism andfinancial services, and perhaps pertinently, upholding a stable economic and political environmentafter independence in 1968 The same, sadly, cannot be said for most Sub-Saharan African countriespost 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 hasboosted 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 thefinance sector Barro’s (1991) seminal paper on economic growth has led to a spurt of creativity in theempirical 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-politicalvariables 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 paperuses Mauritian annual data from 1968 to 2004 to estimate a growth regression applying time seriestechniques The purpose of the study is to identify factors that have contributed to growth over thepast 35 years in this small island economy with particular emphasis on the role of financialintermediation While we control for capital investment, human capital and exports (since export ledgrowth was a strategy explicitly adopted by the authorities), we find evidence of a positivecontribution by the financial sector in facilitating growth The results also confirm that Mauritius hasexperienced 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 theother 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 financialintermediation 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 positivecontribution of financial development on growth The seed of this idea actually goes as far back asAlexander Hamilton (1781, in Levine et al (2000)) who argued that “banks were the happiest enginesthat 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, animportant factor for growth, rely on external funds to come to fruition If the economy has a financialsystem, then banks can fund productive investments, and give innovators access to funding whichenables them to undertake projects An illustration of this example at work is the IndustrialRevolution in England Since England already had a functioning financial system, backed by anestablished and credible legal system, the country progressed by channeling funds into its industriesduring those crucial years Schumpeter explains how banks can choose which firms or entrepreneursget to use society’s savings, hence positively influencing the path of economic development bytweaking the allocation of savings

On the other hand, Bencivenga and Smith (1991) warn that higher returns from more efficientallocation of funds could depress savings rate and hence hamper growth Lucas (1988) furthercounters 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 respondpassively to economic growth Going way back in history, opponents to the banking system have beenfound 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 positivecorrelation between financial development and growth However, Patrick cautions that there is onlyproof of correlation and not causality Patrick actually sets up two relationships: causality can besupply-leading or demand-following Supply-leading means that development of financial institutionsservices induces investment and growth Demand-following says that the financial sector responds toincreasing demand for their services from a growing real economy

In addition, Patrick also hypothesizes there are stages of development that will experience the differentcausal relationships That is, causality between finance and growth changes over time as the economydevelops During the early stages, financial development spurs growth and innovation as it reallocatesfunds from savers to modern sectors of the economy and encourages entrepreneurs to put their ideasinto practice At higher development levels, the supply-leading force of financial development

gradually weakens Financial development responds increasingly to output growth, so we havedemand-following

McKinnon (1973) and Shaw (1973) specifically address the supply-leading hypothesis andrecommend governments to liberalize their financial sector in order to spur growth More recentstudies like Jung (1986) delve into the time series aspect of the problem Using bivariate causalitytests 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 14middle and low income countries (mostly African), but finds significant positive long run effects offinancial development on growth in 27 other countries Wachtel and Rousseau (2000) show that banksand stock market development both explain growth Arestis, Demetriades and Luintel (2000) usequarterly data from five OECD countries and find that banks and stock markets both cause growth, butthat 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 notsignificant 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 datafrom 1968 to 2004 - quarterly data of economic variables are not available 1968 marks the year ofindependence 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 InternationalFinancial Statistics (IFS) webpage of the IMF In what follows, we describe the indicators of stockmarket development and bank development

We use three measures of stock market development; market capitalization to GDP ratio, turnoverratio and value of shares traded ratio Market capitalization ratio is an indication of size and it is thevalue of all listed shares divided by GDP Total value traded to GDP is an indicator for activity orliquidity and is defined as total shares traded on the exchange divided by GDP The efficiencyindicator we use is turnover ratio, which is the value of total shares traded divided by marketcapitalization It measures the activity of a stock market relative to its size because it is important todistinguish 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 marketcapitalization indicator as, if markets are efficient, market capitalization already reflects the discountedfuture value of the economy Hence, if causation is from economic growth to stock market, it is theopposite that will be revealed

Measuring bank development is more straightforward We use activity which is claims on the privatesector made by deposit money banks divided by GDP This measure excludes loans issued to publicenterprises and government, thus isolating loans given only to the private sector (which includescorporations, 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 intermediariesdivided 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 nationalaccounts 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 availablesample Further, normalizing by population gives us a more interesting measure; GDP per capita asopposed to GDP per labour A measure of human capital is gross secondary enrolment, which isavailable for the country More pertinent measures, such as the level of education attained bymembers of the workforce, are unfortunately not available for the sample we are looking at Exportsare 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 Allvariables are measured in MRU, and deflated by CPI (base year 1992)

We start with the following aggregate production function:

F X

H K

ln  0 1  2  3  4 (2)

The model to be estimated is therefore:

t t t

t t

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 inthe model are all stationary and integrated of the same order If they are all integrated of the sameorder d (if they are I(d)), we check whether they all share a common stochastic trend - that is whetherthey are co-integrated

Following Engle and Granger (1987) breakthrough theory of co-integration, suppose two time series x t and y t are related via the following relations:

t t

t t

c 1 and c 2 are intercept terms

ε 1,t and ε 2,t are standard white noise error processes, mutually independent at all lags

Equations (4a) and (4b) represent two distinct linear combinations of x t and y t that can be described by AR(1) models The interpretation of the two models however depend upon the values that ρ 1 and ρ 2

take We have three relevant cases which will each imply a different interpretation of (4a) and (4b)

In the first case, where ρ 1 = ρ 2 =1, any linear combination of x t and y t is a random walk Therefore both

x t and y t are non-stationary processes Both series have a stochastic trend, and they do not share this trend as no linear combination of x t and y t is itself stationary.

In the second case, both 0 ≤ ρ i ≤ 1(for i = 1, 2) Then any linear combination such as (4a) and (4b) above is a stationary AR(1) process, and x t and y t are individually stationary variables.

The third and most interesting case is when ρ 1 = 1 and 0 ≤ ρ 2 ≥ 1 (or vice-versa) There is then one linear combination of x t and y t which is a stationary AR(1) process, while the other combination is a random walk Further, it means that even though individually x t and y t are I(1) time series, there is one

combination of these two which is stationary In the language of Engle and Granger (1987), these twotime 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 onlytransitory

Testing for cointegration is then quite straightforward We first test that x t and y t are I(1) This is done

by applying the Augmented Dickey-Fuller (ADF) test on each process:

t k

i i t i t

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 alsoimportant 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 x t and y t 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

t t

Then

x y

u t could be I(1) However, in special circumstances where u t 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 x t and y t cancel out’ x t and y t 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

u

1 1 1

So if u t is I(0), then it can be used in the dynamic regression below in what is known as the GrangerRepresentation Theorem:

t l

i

i t i i

t k

i i t

1 , 2 1

1 0

β 1 reflects the speed of adjustment towards equilibrium Equation (8) is the Error Correction Model

(ECM), where generally, there is Granger causality if either β 1 is significant, or the β 2 ’s and the β 3’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 givesthe 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 thevariables and the differenced variables are given in Table 1 below:

Table 1: ADF tests for levels and differences in variables

Levels First Differences (1)

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

lnK 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

Levels First Differences (1)

Variable Type Rho Tau Pr < Tau Rho Tau Pr < Tau

*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 butthe probability values are provided Since the variables are all integrated of the same order, we shouldcheck whether they are cointegrated A Granger causality test is also carried out, and it is found thatcausality only runs from Activity, Financial Depth and Exports to GDP at the 1% and 5% level ofsignificance 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 Cointegrationregression for Activity are reported in Table 2 below:

Table 2: Cointegration Regression Dependent variable is ln(GDP)

Estimate

Standard Error

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 serialcorrelation at the 10% level, (but the test is passed at the 5% level) while some multicollinearity is alsopresent 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 3summarizes the results of the ADF test of stationarity:

Table 3 ADF tests for residuals from Cointegrating Regression

ε(Error)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 inthe 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:

t n

i

p

i

i t i

t t

i t k

i i t

t

ACT EXP

H

K GDP

5 1

1

1

4

1 1 , 3 1

1 , 2 1

1

0

1

lnln

ln

lnln

)

ˆ

ln

(9)

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 coefficientshould 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, andthat any disequilibrium in the previous period is partially corrected in the present period – to beprecise, 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)GDP(Error)t)

*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 the5% and 10% levels respectively, while that on lagged growth of Exports is barely significant at the10% level The coefficient on lagged growth in human capital is not significant at any acceptablelevel, 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 alsoshown that the proxy for human capital that we have used here, secondary school enrolment, is notalways significant in predicting growth

The ECM therefore shows evidence that financial intermediation has been an important factor indetermining growth in Mauritius since independence The cointegration result shows that there is along run relationship between bank activity and GDP