Causality in Finance and Growth: The Case of a Small Open Economy
VINAY PRASANDJEET NUNDLALL
InternationalBusinessSchool
BrandeisUniversity
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 inMauritius since independence. However, the equity market has not had any impact on the economy during its relatively shorter lifespan. A channel of growth from financial intermediation to the construction sector is identified. The study also finds that exports also havehad 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 Libyaamong 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 inhabitantsliving 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 totextiles, 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 decadeswhich 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,withthe 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 identifyfactors 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 roleof 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, andeven wealth” of nations.
Patrick (1966) and Goldsmith (1969) are among the earliest of modern writers whofind 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 institutionsservices induces investment and growth. Demand-following says that the financial sector responds to increasing demand for their services froma growing real economy.
In addition, Patrick also hypothesizes there are stages of development that will experience the different causal relationships. That is,causality betweenfinance 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. Financial development responds increasingly to output growth, so we have demand-following.
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 aggregateproduction function:
(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
(2)
The model to be estimated is therefore:
(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 xtand ytare related via the following relations:
(4a)
(4b)
where δ ≠ ηhence 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 andε2,t are standard white noise error processes, mutually independent at all lags.
Equations (4a) and (4b) represent two distinct linear combinations of xtand ytthat 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 xtand ytis a random walk. Therefore both xtand ytare non-stationary processes. Both series have a stochastic trend, and they do not share this trend as no linear combination of xtand ytis 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 xtand yt are individually stationary variables.
The third and most interesting case is when ρ1 = 1and0 ≤ ρ2 ≥ 1 (or vice-versa). There is then one linear combination of xtand ytwhich is a stationary AR(1) process, while the other combination is a random walk. Further, it means that even though individually xtand ytare 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 xtand ytare I(1). This is done by applying the Augmented Dickey-Fuller (ADF) test on each process:
(5)
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 xtand 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
(6a)
Then
(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 xtand yt cancel out’. xtand 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
(7)
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:
(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 andGrowth
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
Levels / First DifferencesVariable / 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
Trend / -10.33 / -2.40 / 0.3707 / -14.12 / -2.58 / 0.2891
lnH / 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
lnEXP / 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
lnACT / 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
lnDEPTH / 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%