Survey

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

no text concepts found

Transcript

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. References Arestis, P., P. Demetriades and K. Luintel (2000), Financial Development and Economic Growth: The Role of Stock Markets, Journal of Money, Credit and Banking, 33(1), 16-41. Barro, R.J. (1991), Economic Growth in a Cross Section of Countries, Quarterly Journal of Economics, 106(2), 407-443. Beck, Thorsten, Ross Levine, and N.V. Loayza (2000) “Finance and the Sources of Growth,” Journal of Financial Economics, 58(2), 261-300. Bencivenga, V. and B.D. Smith (1991), Financial Intermediation and Endogenous Growth, Review of Economic Studies, 58(2), 195-209. Central Statistics Office, ncb.intnet.mu/cso.htm Charemza W. W. and D.F. Deadman. (1992). New directions in econometric practice. Cheltenham, England: Edward Elgar Publishing Limited Davidson, R and J. MacKinnon (1993), Estimation and Inference in Econometrics, New York, Oxford University Press. Engle, R. and C.W.J. Granger (1987), Cointegration and error Correction: Representation, Estimation and Testing, Econometrica, 55(2), 251-276. Feder G. (1983), On Exports and Economic Growth, Journal of Development Economics 12, 59-73. Goldsmith, R. W. (1969), Financial Structure and Development, New Haven: Yale University Press. Jung, Woo S. (1986), Financial Development and Economic Growth: International Evidence, Economic Development and Cultural Change, 34, pp. 333-344. King, R. and R. Levine (1993) Finance and Growth: Schumpeter Might be Right, Quarterly Journal of Economics, 108(3), 717-737. Levine, R. (1997), Financial Development and Growth: Views and Agenda, Journal of Economic Litereature, 35, 688-726. Levine, R. (2004) “Finance and Growth: Theory and Evidence,” NBER WP 10766. Levine, R. N. Loayza and T. Beck (2000), Financial Intermediation and Growth: Causality and Causes, World Bank, WPS 2059 Levine, R. and D. Renelt (1992), A Sensitivity Analysis of Cross country Growth Regressions, American Economic Review, 82(4), 942-63. Levine, R. and S. Zervos (1998), Stock Markets, Banks and Economic Growth, American Economic Review, 88(3), 537-558. McKinnon, R.I. (1973), Money and Capital in Economic Development. Washington D.C.:The Brookings Institution. Meade, J.E., et. al (1961), The Economics and Social Structure of Mauritius – Report to the Government of Mauritius, London: Methuen. Patrick, H. T. (1966), Financial Development and Economic Growth in Underdeveloped Countries, Economic Development and Cultural Change, 14(2), 174-189. Robinson, J. (1952), The Rate of Interest, and Other Essays, London: Macmillan. Rodrik, D. (1999a), The New Global Economy and Developing Countries: Making Openness Wor, Overseas Development Council, London. Rousseau, P.L. and P. Wachtel (1999), Equity Markets and Growth: Cross-Country Evidence on Timing and Outcomes, 1980-1995, Journal of Banking and finance, 24, 1933-1957. Sala-I-Martin, X.X. ((1997), I Just Ran Two Million Regressions, American Economic Review, AEA Research and Proceedings, 87 (2), 178-183. Schumpeter, J.A.(1912), The Theory of Economic Development, translated by Redvers Opie Cambridge, MA: Harvard University Press, 1934. Shaw, E. (1973), Financial Deepening in Economic Development, Oxford University Press, NY. UNDP Human Development Report 2003, www.undp.org/hdr2003/ Xu, Z. (2000), Financial Development, Investment, and Economic Growth, Economic Inquiry, 38, 331-44. 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%