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Do Commercial Banks, Stock Market and Insurance Market Promote Economic Growth? An analysis of the Singapore Economy

Tan Khay Boon

School of Humanities and Social Studies Nanyang Technological University

50 Nanyang Avenue, Singapore 639798 Republic of Singapore

Tel: (65) 67904332 Fax: (65) 67911859 Email: mkbtan@ntu.edu.sg

Abstract This paper conducts within sample and out-of-sample causality tests between finance and growth in the Singapore economy. It uses bank loans, stock market capitalization value and insurance funds as financial indicators with real GDP per capita and real gross fixed capital formation per capita as growth indicators. The results show that the direction of causality is dependent on both financial and growth indicators. The loan market is largely demand following and the insurance market is supply leading. The stock market is demand following in the short term and supply leading in the long term. In addition to highlighting the benefits of using disaggregated financial data, the findings also demonstrate that causality patterns vary with indicators used and therefore emphasizes on the danger of very few and restrictive indicators in individual country studies. 1. Introduction The relationship between financial economic development and economic growth is highly controversial. The supply leading theories consider the development of financial sector as the precondition for economic growth, while the demand following theories consider financial development as merely responsive to economic growth. Understanding the relationship may allow appropriate government policies to be implemented which will facilitate economic development. Yet empirical evidence provided so far are inconclusive and contradictory. This study hopes to add on empirical evidence to resolve this controversy. Singapore is chosen because of its characteristics that may be able to shed more light in the relationship between finance and growth. Being a small and high growth economy without any natural resources, its growth is obtained through manufacturing and service sectors and these sectors are closely link to the financial sector. Moreover, the lack of internal monetary policy means there is no ad hoc change in monetary aggregates and the bank loan becomes a better indicator of financial deepening. Finally, it has a well-developed financial sector with many different financial indicators available. Section 2 provides a discussion of the theoretical foundation and the empirical evidence between financial development and economic growth. Section 3 brings in the empirical methodology of the research in this area and Section 4 explains the measurement and data sources in detail. Section 5 presents the empirical findings and Section 6 concludes.

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2. Theoretical Discussion and Empirical Evidence In the early days, Schumpeter (1911) had highlighted the importance of finance in promoting entrepreneurship and economic growth. Several models were built to incorporate the roles of financial development in economic growth. This includes the growth model by Solow (1956) which shows that the development of financial sector can encourage saving and lead to a higher output per worker. More recently, the endogenous growth models by Greenwood and Jovanovic (1990), Bencivenga and Smith (1991) and King and Levine (1993a) among others suggest that both the growth level and rate can be affected by financial development. Pagano (1993) summarizes their finding succinctly and explained that financial development promotes economic growth via three paths: channel more savings to investment, raise the marginal productivity of capital and encourage saving.1 However, there is also theory considers financial sector as following economic development. This theory, suggested initially by Robinson (1952), believe that as economic progresses, there will be increasing needs for sophisticated financial services and this lead to the development of financial sector2. There are also economists such as Lucas (1988) who believe that finance is not important at all. All these theories are feasible and hence this issue can only be resolved empirically. The exact relationship between finance and growth can only be resolved through empirical analysis. However, the evidence so far is inconclusive. Although King and Levine (1993b) use cross-sectional data on 80 countries to show finance causes growth, their methodology is questionable. Evidence from time series individual countries studies, such as by Jung (1986), Murinde and Eng (1994), Ahmed and Ansari (1998) and Fase and Abma (2003) provide evidence of finance causes growth in developing countries. Similarly, Wachtel and Rousseau (1996) and Rousseau and Wachtel (1998) also show finance causes growth in developed countries. However, the empirical work involving 16 countries provided by Demetriades and Hussein (1996) shows considerable evidence of bi-directional causality and some evidence of growth causes finance. As for stock market analysis, Levine (1991) and Levine and Zervos (1998) show that stock market positively predicts growth. But Harris (1997) studies find no evidence of stock market explains growth in per capita output. The conflicting results highlight the danger of using aggregated data and inappropriate financial indicators in performing causality test between finance and growth. Most of the studies use highly aggregated data, such as M3 or domestic credit which did not distinguish between bank loans, insurance funds and stock market funds. These data do not exhibit the pathways which finance affects growth. This is an omission in the existing work and it presents a gap worth covering.

1 The sign of this relationship is ambiguous. The effect on saving rates on economic development can be positive or negative depends on risk sharing, household borrowing and interest rate effects. 2 Joan Robinson declares that "where enterprise leads finance follows" in her 1952 publication, p 86.

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3. Empirical Methodology The conventional method in solving the causality issue between finance and growth is using the Granger causality method on time series data. This method involves regressing growth or finance indicators with lagged finance indicator and lagged growth indicator and then apply F test in hypothesis testing. This method can test for finance causes growth; growth causes growth, bi-directional causality or no causality. The Granger causality method involves running the following two regression models: m n

Yt = Σ αiFt-i + Σ βjYt-j + U1t (1a) i=1 j=1 m n

Ft = Σ γiFt-i + Σ δjYt-j + U2t (1b) i=1 j=1 where Y is an indicator of economic development, F is an indicator of financial development and U1t and U2t are the disturbances which are assumed to be uncorrelated. In this framework, there are four possible cases: Case 1: Unidirectional causality from F to Y. This is indicated if Σαi ≠ 0 and Σδj = 0. This outcome supports the view of Schumpeter. Case 2: Unidirectional causality from Y to F. This is indicated if Σαi = 0 and Σδj ≠ 0. This outcome is consistent with the view of Robinson. Case 3: Bilateral causality. This is indicated if Σαi ≠ 0 and Σδj ≠ 0. This outcome supports both Schumpeter and Robinson. Case 4: Independence. This is indicated if Σαi = 0 and Σδj = 0. This is consistent with the view of Lucas. However, this type of causality test is only suitable if both data series are stationary. It the data series are non-stationary, then appropriate level of differencing must be done to make the series stationary before the test can be carry out. But Granger (1988) has pointed out that complication will arise if the two series are cointegrated. By differencing the series alone may actually lead to specification bias of the model that produce spurious results. The appropriate method is to convert the model into an error correction model (ECM) framework by including an error correction term. In the models below, α and β are the estimates from the cointegrating vector and the term in parenthesis is the error correction term. m n

∆Yt = µ + θ(Yt-1 - α - βFt-1 ) + Σ αi∆Ft-i + Σ βj∆Yt-j + Ut (2a) i=1 j=1

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m n ∆Ft = µ + φ(Yt-1 - α - βFt-1 ) + Σ γi∆Ft-i + Σ δj∆Yt-j + Ut (2b)

i=1 j=1 When Yt and Ft are cointegrated, Ft can Granger cause Yt in two ways. One is through the lagged short run dynamic terms Σαi ≠ 0 and this can be tested using F test. The other is through the lagged error correction term if θ ≠ 0 and this can be tested using using t-test. This link also represents the long run dynamics between finance and growth. Failure to include the error correction term with cointegrated process will result in models that are misspecified and the causality testing can lead to erroneous. The ECM based causality tests offer the additional advantage that the source of causation can be identified, in the form of either short run dynamics or long run disequilibrium adjustment. Two methods to test for cointegration are commonly used in applied research. The first method, suggested by Engle and Granger (1987), involves using Augmented Dickey Fuller (ADF) test on the residual series of a cointegrated model. If the series are cointegrated the residual series should not have unit root. The second method, suggested by Johansen (1988), is a multivariate maximum likelihood estimation technique. It involves estimating a vector error correction model (VECM) of the form:

∆Zt = µ + Γ1∆Zt-1+ Γ2∆Zt-2 + Γ3∆Zt-3 + … + Γp-1∆Zt-p+1 + Π Zt-p + εt where Zt is a nX1 vector of I(1) variables indicating financial development and economic growth, Γ1, Γ2, …, Γp and Π are nXn matrices of parameters to be estimated. Existence of cointegration implies that the matrix Π has non-zero rank r < n, equal to the number of linear combinations of the variables in Zt that are stationary. Two tests can be used to test for cointegration. One is the λtrace statistic while the other is the λmax statistic. The statistic tests the null hypothesis of at most r cointegration vectors against an alternative of at least r+1.3 Thus Granger causality test can be used on the level series (if series are stationary), on the differenced series (if series are non-stationary and non-cointegrated), formulated in an error correction model framework (if series are cointegarted) or using Johansen test. The problem with this methodology is the difficulty in obtaining the appropriate framework due to low power of unit root test and cointegration test, and also inability to detect causal effect due to low power of Granger causality test. It is not necessary to perform differencing on non-stationary series in order to test for causality. Sims et al. (1990) have shown that in a tri-variate system, if there is a single cointegrating relationship the Wald tests for Granger causality are asymptotically distributed as chi-square. Thus Granger causality test can be done on the level for all the three series. This is the method adopted by Rousseau (1999) in analyzing Japan and Bell and Rousseau (2001) in analyzing India. In both papers, VAR models involving a finance indicator, a growth indicator and a money supply indicator are used. However, 3 A full discussion of the test statistics is given in Johansen and Juselius (1990).

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Toda and Phillips (1993) have doubted the usefulness of unrestricted levels Vector autoregressions as it is valid only asymptotically and hence subject to many uncertainties. They suggest the use of Johansen-type ECM which is more valid. In a different perspective, Masih and Masih (1998) suggest the use variance decompositions (VDC) and impulse response functions (IRF) to unearth deeper insights of Granger causality test. The problem with the VECM, F- and t-tests is that they are within-sample causality tests. These tests do not allow us to gauge the relative strength of the Granger-causal chain among the variables beyond the sample period. The VDCs and IRFs can be considered as out-of sample causality tests. The VDC involves partitioning the variance of the forecast error of a certain variable into proportions attributable to innovations in each variable in the system including its own. The IRF maps out the dynamic response path of a variable due to a one-period standard deviation shock to another variable. Thus in the analysis of VDC if a large portion of the forecast error of the growth variable is explained by the finance variable, this provides further evidence of finance cause growth. Similarly, using IRF if the responses of the growth variable to shocks in the finance variables are positive and strong, it is more likely that finance causes growth. 4. Measurement and Data Sources This paper investigates the relationship between financial development and economic growth by using individual country time series data. This is also in line with the approach by Arestis and Demetriades (1997) and Wachtel and Rousseau (1996). Singapore is chosen because of its well-developed financial sector and its impressive economic growth rate and it will be interesting to establish the causal effect between the two. Although Masih and Masih (1996) recommend using annual data in this type of research, quarterly data is used in this paper due to its relatively short history of development and most of the required data are available on a quarterly basis. Since there are different functions performed by financial sector and they influence growth differently4, it is necessary to use different financial indicators and growth indicators to study the linkage between finance and growth. The indicators used to measure financial development and economic growth will have significant effect on the outcome of research. Based on the model by Pagano (1993), the growth indicators should reflect an improvement in productivity, an increase in investment or an increase in saving rate. Since the saving rate has an ambiguous effect on growth rate and the measurement of total factor productivity tend to be unreliable, the seasonal adjusted real gross domestic product per capita is used to approximate these two effects. The increase in investment

4 The survey by Levine (1997) highlights that financial sector performs five important roles that can promote economic development. They are (i) facilitating the trading, hedging, diversifying and pooling of risk, (ii) allocating resources; (iii) monitoring managers and exert corporate control, (iv) mobilizing savings and (v) facilitating the exchange of goods and services.

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can be captured by an increase in real gross fixed capital formation per capita and this is the second growth indicator. The data for both series are available from March 1985 to September 2002. The problem of using aggregate financial data is that it will not explain how finance affects growth. In this paper, three types of financial indicators are used to represent three financial markets. The first type is the ratio of commercial bank loans to nominal gross domestic product. This indicator will capture the functions of commercial banks and the data is available from March 1985 to September 20025. The second indicator is to represent the development of the stock market. It is proxied by the ratio of market capitalization of the Singapore stock market to nominal GDP and the data runs from December 1987 to September 20026. The final financial indicator is to represent the development of the insurance market and this is proxied by the total insurance funds.7 The data runs from March 1991 to September 2002. 5. Empirical Results The investigation begins with testing for unit root on all the growth and financial development series. Using ADF test, the null hypothesis is there is unit root and the alternative hypothesis is the absence of unit root. Tests on all the series are conducted on the level as well as the first difference. A visual inspection on the series is done to decide on the inclusion of intercept and time trend in the ADF test. Table 1 below shows the results of unit root test. The ADF test does not reject the null of a unit root for the data in levels and rejects the null for each of the differenced series. The findings imply that it is reasonable to model all of the relevant variables as non-stationary. Table 1 : ADF Test for Unit Root Data Series Level 1st differences Real gross domestic product per capita (GDP) -0.96 -7.06* Real gross fixed capital formation per capita (GFCF) -0.87 -10.93* Ratio of bank loans to nominal GDP (BANK) -1.01 -3.92* Ratio of stock market capitalization to nominal GDP (STOCK) -2.35 -7.80* Insurance funds (INSUR) -1.83 -5.26* A* denote rejections of unit root at 1% level. All variables are in log, with the exception of BANK and STOCK. As Singapore is a small open economy, whatever effects between real and financial sector tends to reflect rapidly in the data. Thus a maximum of 8 lags is considered in the Johansen test for cointegration. Table 2 below shows the results of cointegration test. Rejectionn of the null hypothesis of no cointegration (r=0) coupled with a failure to reject

5 The data for bank loan are available monthly and hence aggregate of three months data is used. 6 The stock market capitalization is available monthly. Thus the data on the month of March, June, September and December are used. 7 The insurance fund is not normalized with nominal GDP because of its small value.

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the null of one cointgerating vector (r=1) provides evidence of a single long-run relationship in a given system. Table 2: Johansen Test for Cointegration

Trace Maximum eigenvalue System r=0 r=1 r=0 r=1

GDP and BANK (k=2) 19.01** 3.07 15.94** 3.07 GFCF and BANK (k=1) 20.47** 2.11 18.37** 2.11 GDP and STOCK (k=8) 21.34* 7.25 14.09** 7.25 GFCF and STOCK (k=2) 16.55** 4.34 12.21 4.34 GDP and INSUR (k=8) 15.97** 0.78 15.19** 0.78 GFCF and INSUR (k=6) 16.10** 4.81 11.28 4.81 The term k is the lag at which the levels terms enter the test regressions. A * and ** denote rejection of the null hypothesis of no cointegration at the 1% and 10% respectively. Out of the six systems considered, the results show the presence of cointegartion in four systems conclusively. The other two systems, GFCF and STOCK and GFCF and INSUR have ambiguous results. While the trace test indicates the presence of cointegration, the maximum eigenvalue test shows otherwise. In view of the low power of cointegration test, these two systems are also treated as cointegrated. Thus the VECM models are constructed (equation 2a and 2b) for the six systems. In all cases, the lag length is selected using AIC criteria. 5.1 Bank Loans and Economic Growth In Singapore, the commercial banks are the most important sources of finance for enterprises. The regression results between GDP, GFCF and BANK are shown below. The t statistics values are shown in parenthesis. ∆GDPt = 0.008 + 0.206∆GDPt-1 + 0.003∆BANKt-1 -0.0007(GDPt-1 -5.35 - 0.31BANKt-1) (3.21) (1.45) (0.613) (-0.11) (-4.24) ∆BANKt = 0.13 - 10.46∆GDPt-1 - 0.08∆BANKt-1 + 0.61(GDPt-1 -5.35 - 0.31BANKt-1) (1.73) (-2.6) (0.59) (3.39) (-4.24) ∆GFCFt = 0.006 - 0.41∆GFCFt-1 - 0.007∆BANKt-1 + 0.05(GFCFt-1 -3.8 - 0.36BANKt-1) (0.92) (-3.33) (-0.51) (2.74) (-5.35) ∆BANKt = 0.01 + 0.55∆GFCFt-1 + 0.07∆BANKt-1 + 0.45(GFCFt-1 -3.8 - 0.36BANKt-1) (0.21) (0.51) (0.58) (2.59) (-5.35) The results clearly indicate the direction of causality is from BANK to GDP in both the short run and the long run. But there is a long run bi-directional causality between GFCF and BANK. The IRF also shows that BANK response vigorously to one standard deviation shock in the GDP and GFCF.

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5.2 Stock Market and Economic Growth The stock market provides an alternative source of funds for companies. The regression results between GDP, GFCF and STOCK are shown below. ∆GDPt =0.01- 0.02∆GDPt-1 + 0.002∆STOCKt-1 -0.02(GDPt-1 -7.97 - 0.15STOCKt-1) (3.75) (-0.11) (0.74) (-1.35) (-3.88) ∆STOCKt = 0.03 - 1.29∆GDPt-1 - 0.07∆STOCKt-1 + 1.17(GDPt-1 -7.97 - 0.15STOCKt-1) (0.24) (-6.73) (0.45) (1.79) (-3.88) ∆GFCFt = 0.012 - 0.31∆GFCFt-1 - 0.001∆STOCKt-1 - 0.09(GFCFt-1 -6.9 - 0.17STOCKt-1) (1.58) (-2.51) (-0.16) (2.39) (-3.62) ∆STOCKt = 0.02+0.25∆GFCFt-1 + 0.04∆STOCKt-1 +0.82(GFCFt-1 -6.9 - 0.17STOCKt-1) (0.14) (0.14) (0.27) (1.59) (-3.62) The results shown that there is a short term causality effect from GDP to STOCK but no long term disaggregate adjustment between GDP and STOCK. However, there is a long term causality effect from STOCK to GFCF. The IRF shows that the reaction of STOCK on a standard deviation shock in GDP and GFCF is stronger than vice versa.

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5.3 Insurance Market and Economic Growth The role of insurance market is mainly risk hedging. With sufficient hedging, a firm may be more willing in committing investment projects that are more risky but returns are higher. The regression results between GDP, GFCF and INSUR are shown below. ∆GDPt =0.007+ 0.14∆GDPt-1 + 0.019∆INSURt-1 -0.113(GDPt-1 -7.38 - 0.15INSURt-1) (1.2) (0.16) (0.18) (-2.17) (-4.0) ∆INSURt = 0.04 + 0.08∆GDPt-1 + 0.17∆INSURt-1 - 0.03(GDPt-1 -7.38 - 0.15INSURt-1) (4.42) (0.35) (1.06) (-0.41) (-4.0) ∆GFCFt = -0.001 - 0.34∆GFCFt-1 + 0.14∆INSURt-1 - 0.03(GFCFt-1 -16 + 0.83INSURt-1) (-0.03) (-2.28) (0.41) (2.05) (1.67) ∆INSURt = 0.04 - 0.01∆GFCFt-1 + 0.18∆INSURt-1 - 0.005(GFCFt-1 -16 + 0.83INSURt-1) (4.56) (-0.19) (1.15) (-0.83) (1.67) The results show that there is a long term causality effect from INSUR to GDP and also both a short and long term effects from INSUR to GFCF. It seems that only the insurance market show conclusive evidence of finance causes growth. The IRF shows that GDP and GFCF respond positively to a one standard deviation shock in INSUR but the respond the other way round is much weaker.

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6. Summary and Concluding Remarks The main purpose of this paper is to discern the causal relationship (in the Granger sense) between financial indicators and growth indicators in the Singapore economy. The methodology involves using ADF test for unit roots and Johansen's test for cointegration, followed by either unrestricted vector autoregression (if cointegration is absent) or vector error-correction model (if cointegration is present). The impulse response functions are also constructed to further capture the out-of-sample Granger causality. The evidence suggests that in the presence of cointegration, the causality usually occurs in the long run dynamics (the lagged error correction term). There are fewer cases where causality solely occurs in the short run dynamics. This suggests that the relationship between finance and growth is more likely to be a long term one. Thus it is important to determine whether unrestricted VAR or VECM framework should be used in the causality studies. The evidence from the IRF is largely consistent with the within sample Granger causality test, thus enhances the conclusions obtained from the former. This study did support the role of insurance market and stock market (over the long term) in promoting economic growth. But it is more likely that the commercial banks responsive passively to economic development to the need of enterprises, as predicted by Robinson (1952). This contrasts with the evidences obtained by Murinde and Eng (1994) and also by Fase and Abma (2003) which support the supply leading role. The differences could be attributed to the use of disaggregated financial data since our methodology is quite similar. This highlights the value of using suitable disaggregated data in the study of financial development and economic growth as suggested by Pagano (1993). Another obvious outcome from the research is that the results are very much dependent on the indicator used. If only one type of growth or financial indicator is used in the study, the conclusion could be totally different. This also highlights the danger of deriving conclusions based on very few indicators in multi-countries study. As different countries have different institutional characteristics and policies, the pathway which finance and growth affecting each other could be different. One should analyze the institutional characteristics of each country and select the appropriate financial indicators in a multi-countries study.

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