an investigation of the feldstein-horioka puzzle for the association of southeast asian nations...

An Investigation of the Feldstein–Horioka Puzzle for theAssociation of Southeast Asian Nations Economies

Mu-Shun Wang*

Abstract

This article reviews the extensive literature onhow to respond to the Feldstein–Horiokapuzzle. This study quantitatively investigatesthe impact of instrumental variables on thecorrelation between domestic savings andinvestment. I consider two financial frictions:the economic system and the bank features.I find that the dynamic panel model withinstrumental variables produces a savings–investment correlation and that informationtechnology generates capital flows to ensurethat countries have an incentive to develop. Thefindings emphasise the potential benefits asso-ciated with developing prospects and capitalflow in an increasingly competitive emergingmarket.

1. Introduction

In their seminal article, Feldstein and Horioka(1980) provided evidence of an insignificantcorrelation between national savings andforeign direct investment (FDI). They exam-ined the results of cross-sectional regressionson savings and investment across 16 Organisa-tion for Economic Co-operation and Develop-ment (OECD) countries for the period of 1960–74. Based on this relationship, they found ahighly positive correlation between domesticinvestment and savings in these OECDcountries.

They proposed a method for assessing thedegree of capital mobility by measuring thecorrelation between savings and investment.They utilised the following cross-sectionalregression for the estimation:

I

Y

� �i

¼ aþ bS

Y

� �i

þ ei ð1Þ

where (I/Y)i and (S/Y)i are the investment andsavings rates of country i, respectively, b is thesavings retention coefficient and ei is the errorterm and independent and identically distributed(0. se2). For a small, open economy, wherecapital is perfectly mobile internationally, bshould be close to 0; if b equals 0, then there isno relationship between savings and invest-ment. Feldstein and Horioka (1980) supposedthat if b is large, then capital will be consideredto be immobile internationally. For example, ifb is equal to 1, then all additional savings go tofinancing domestic investment. This phenome-non is called the Feldstein–Horioka (F–H)puzzle.

Capital mobility is important because it hasimplications for single currency debates, tax

* Department of Banking and Finance, Kainan University,Taoyuan 33857 Taiwan; email <[email protected]>. The author would like to thank the anonymousreferees and the Editor, Professor Jensen, for their helpfulcomments, which have helped to improve the quality of thisarticle. The remaining errors and omissions are theresponsibility of the author alone.

The Australian Economic Review, vol. 46, no. 4, pp. 424–43

�C 2013 The University of Melbourne, Melbourne Institute of Applied Economic and Social ResearchPublished by Wiley Publishing Asia Pty Ltd

policies on capital and savings, whether growthis constrained by the domestic savings rate andif fiscal deficits will have large crowding-outeffects on private investment. Rao, Tamazianand Kumar (2010) argued that if capitalmobility is high, countries cannot pursueindependent monetary policies.

Recently, the empirical literature has shownseveral comprehensive studies based on theF–H puzzle (see Di Iorio and Fachin 2007;Ozmen 2007; Apergis and Tsoumas 2009;Fouquau, Harlin and Rabard 2009; Rao,Tamazian and Kumar 2010; Bangake andEggoh 2011). Feldstein and Horioka (1980)and Feldstein (1983) estimated the b-coefficient of national savings to be 0.887,with a standard error of 0.07. They showed thata high presence of capital mobility arises whenb has a value of 0, as domestic savings will givea high rate of return on interest rates elsewhere.The international mobility of capital across Ncountries in the panel is the main contributionconsidered in the relationship between invest-ment and national savings. However, manyfactors have been identified that clearly affectcapital mobility (Fouquau, Hurlin and Rabaud2009). Several studies utilising the correlationbetween savings and investment as an indicatorof capital mobility have emerged. Examples ofexogenous factors are population growth,productivity and other shocks (Obstfeld1986), as well as the presence of non-tradedconsumer goods (Murphy 1986; Wong 1990).

In addition to unobserved variables, in thisstudy, I tackle the cross-sectional dependenceissue from the outset, proposing a second panelgeneralisation based on the method developedby Bai and Ng (2004). Existing empiricalstudies on the F–H puzzle have used cross-sectional panel data and time series methods forestimation. Such studies have been carried outfor regional economies including the MiddleEast and North Africa (Ozmen 2007), a groupof 37 African countries (Bangake and Eggoh2011), the OECD countries (Feldstein andHorioka 1980; Tesar 1991; Coakley, Fuertesand Spagnolo 2001; Giannone and Lenza 2004;Katsimi and Moutos 2007; Fouquau, Hurlinand Rabaud 2009), the European Union (DiIorio and Fachin 2007; Telatar, Telatar and

Bolatoglu 2007) and the United States (Grier,Lin and Ye 2008).

This point has been taken into account byboth Banerjee and Carrion-i-Silvestre (2004)and Di Iorio and Fachin (2007), who appliedbreaks in cointegrated panel tests for a sampleof data from 14 European economies forthe period of 1960–2002. This current workrepresents an attempt to explain the influence ofthe F–H puzzle in Association of SoutheastAsian Nations (ASEAN) and ASEANþ3(Japan, Korea and China), using dynamic paneldata with cross-sectional dependence andstructural breaks. The ASEAN group iscomposed of several dynamic emerging econ-omies. Prior to the 1980s, most of the ASEANeconomies suffered from restricted internationalcapital mobility across countries, but after thisthey implemented a relatively flexibleexchange rate system and cautiously andprogressively started to promote capitalaccount liberation (Kim, Oh and Jeong 2005).The opening of policies for internal capital alsocontributed to massive capital flows to Asiancountries in the 1990s, which continued up tothe financial crisis in 1997. It was regionaleconomics, including financing and capitalflow that resurrected the ASEAN economiesafter the Asian Financial Crisis in 1997 and theso-called ‘financial tsunami’ in 2008. The long-term aim has been to integrate Asian economiesinto a single production base and expand themarket scale. The ASEAN and ASEANþ3economies have long enjoyed market-drivenintegration not only through trade but alsothrough FDI. The Asian Financial Crisis madeit clear that these countries needed to strengthentheir domestic financial sectors to efficientlymanage and absorb the flow of capital andfinancial development.

This article is distinct from other studies in afew respects. First, it applies the recentlydeveloped panel unit root method with cross-dependence to investigate the relationshipbetween national savings and investment inASEAN economies. Furthermore, the dynamicgeneralised method of moment (GMM) is usedwith panel data in order to deal with heteroge-neity problems and to conduct plausible tests.Second, this study measures the F–H puzzle for

Wang: An Investigation of the Feldstein–Horioka Puzzle for the ASEAN Economies 425

�C 2013 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research

the ASEAN and ASEANþ3 economies as agroup, rather than for individual countries.

The rest of the article is organised as follows:Section 2 briefly reviews the literature on theF–H puzzle. Section 3 explains the model andempirical methods: second general panel unitroot, structural break and dynamic GMM. I alsobriefly review the dynamic panel data modeland discuss the extended GMM estimator,which is available when these restrictionsare satisfied. The data and empirical resultsare presented and interpreted in Section 4 andthe final section contains the summary andconclusions.

2. Literature Review

2.1 Determinants of the F–H Puzzle

Murphy (1986) argued that if a country is largeenough to affect the world interest rate, anincrease in national savings would trigger areduction in the world interest rate, andtherefore, increase domestic investment. Fur-thermore, larger countries are more diversifiedand do not need to borrow abroad in the event ofshock declines (Harberger 1980). Perfectcapital mobility and changes in national savingrates are primarily reflected in current accounts,not in investment (Dornbusch 1991). Krol(1996) argued that the F–H puzzle is related tothe estimation techniques and reported esti-mates of the savings–investment correlationbased on panel regressions. Krol’s findingsindicated much lower savings retention coef-ficients than those obtained by Feldstein andHorioka (1980). Blanchard and Giavazzi(2002) argued that financial market integrationis likely to lead both to a decrease in savingsand an increase in investment, and so, to a largercurrent account deficit.

Fouquau, Hurlin and Rabaud (2009) used thepanel smooth threshold regression model toestimate the F–Hpuzzle for 24 OECD countriesfor the period of 1960–2000. Their b-countryestimates ranged from 0.5 to 0.7 and theirconclusions are important for the currentextensive study. The findings that the nationalsavings and investment relation is non-linearand that the degree of openness, the size of the

country and the ratio of current account balanceto gross domestic product (GDP) have signifi-cant effects on the estimated b-value indicate acountry’s long-run account solvency constraintrather than an index of capital mobility(Coakley, Kulasi and Smith 1998).

Coakley, Kulasi and Smith (1998) providedan interpretation of the F–H coefficient asoffering a direct indication of a country’s long-run account solvency constraints rather than anindex of capital mobility. Corbin (2001)interpreted the F–H puzzle to find that a highestimated savings–investment coefficient maybe due less to low capital mobility than to theexistence of specific individual country effects.Apergis and Tsoumas (2009) concluded thatalthough the majority of empirical studiesfound evidence opposing the original strongresults for the F–H paradox, they found thiscorrelation to still exist in weaker form. Ozmen(2007) showed that, in a fixed exchange regime,the savings retention coefficient is higher thanin a flexible exchange rate regime and that legalprotection for investors and capital tends to bemore mobile internationally than in countrieswith weaker investor protection. Murphy(1986) argued that if a country is large enoughto affect the world interest rate, an increase innational savings would lead to a reduction inthe world interest rate, thereby increasingdomestic investment. Furthermore, largercountries are more diversified and do notneed to borrow from abroad in the event ofshock declines (Harberger 1980). Bai andZhang (2010) considered limited enforcementand limited spanning. They found both factorsto cause frictions that produce a savings–investment correlation and a volume of capitalflow close to solving the puzzle.

2.2 Developed and Developing Countries

Kao and Chiang (2001) and Ho (2002) applieddynamic ordinary least square (OLS) and fullymodified OLS estimators to non-stationarypanel data for 20 OECD countries. They foundthe saving–investment correlation b to be only0.12 less than for the F–H puzzle. Ozmen(2007) investigated whether the relation offinancial development and investment provides


426 The Australian Economic Review December 2013

evidence for the F–H puzzle and argued that thedomestic savings–investment relationship issupported by data for countries in the MiddleEast and North African region. The findingssupported the hypothesis that successful inter-national financial integration requires com-patible levels of financial intermediation.Therefore, I am curious whether the situationwould be similar in ASEAN and ASEANþ3countries. Christopoulos (2007) also exploresthe relationship in a sample of 13 OECDcountries over the period 1885–1992.

Bangake and Eggoh (2011) investigated theF–H coefficients for 37 African countries,confirming the existence of the F–H puzzle inless-developed countries. Their conclusionsindicate that FDI is relatively high in develop-ing countries (Wong 1990; Kasuga 2004;Adedeji and Thornton 2006). They includeddifferences in retention rate for oil-producingversus non-oil-producing nations and commonlaw countries, whose savings retention coef-ficients are relatively low compared to civil lawcountries. These findings could be explainedby missing variables, which drive both invest-ment and national savings and the economicapproaches that result in the F–H puzzle.

Kim and Beladi (2005) investigated thesavings–investment relationship by applyingthe FMOLS and DOLS panel cointegrationtechniques for 11 Asian countries. Coakley,Fuertes and Spagnolo (2001) used the group-mean OLS panel estimator to reassess the F–Hpuzzle in a non-stationary framework for asample of 12 OECD economies over the periodof 1980–2000. Their mean group estimates arenotably smaller than those obtained from theconventional cross-sectional estimator andare statistically insignificant.

2.3 Recent Research and Its Extension

Recent works on the F–H puzzle have studiedstructural breaks (Di Iorio and Fachin 2007;Fouquau, Hurlin and Rabaud 2009) and GMMsystems (Rao, Tamazian and Kumar 2010).Blundell and Bond (1998) proposed a system-GMM estimator that basically combines thefirst-differenced equations with the sameequation expressed in levels. Rao, Tamazian

and Kumar (2010) used a system-GMM toestimate the F–H puzzle for the OECDcountries and to test for structural breaks. Thesystem-GMM estimator assumes that thecorrelations between unobserved fixed effectsand independent variables remain constant overtime. Blundell and Bond (1998) argued that theinstruments used with the standard first-difference GMM estimator (endogenous vari-ables are auto-correlated) become less informa-tive in models where the variance of the fixedeffects is particularly high relative to thevariance of the transitory shocks. Bond(2002) explained that the estimates obtainedusing the first-differenced GMM estimator aresimilar to the system-GMM estimates.

The one-step and two-step GMM estimatorsare asymptotically equivalent to each otherfor the first differenced estimator. The one-step system estimator assumes homoscedasticerrors, while the two-step estimator uses thefirst-step errors to contract the heteroscedastic-ity consistent standard error. Due to the largenumber of instruments that is employed in thesystem estimators; however, the asymptoticstandard errors from the two-step panel estima-tor may be a poor guide for hypothesis testing insmall samples where over-fitting becomes aproblem; this is not a problem in the one-stepestimators.

Otherwise, the two-step estimator is moreefficient, which is always true for system-GMMestimators (Arrelano and Bond 1991). Unfor-tunately, Monte Carlo studies indicate that theefficiency gain is typically small and that thetwo-step GMM estimator has the disadvantageof converging to its asymptotic distributionrelatively slowly. In finite samples, asymptoticstandard errors associated with the two-stepGMM estimators can be seriously biaseddownwards, subsequently forming an unreli-able guide for inferences. Therefore, this studydescribes the results for the one-step GMMestimators with standard errors that are not onlyasymptotically robust to heteroscedasticity, buthave been found to be more reliable for finitesample inferences (Blundell and Bond 1998).This problem is exacerbated when the numberof instruments is equivalent to or larger than thenumber of cross-sectional units. This dilemma



biases both the standard errors and the Sargantest downwards, possibly resulting in a biasedasymptotic inference. The consistency of theGMM estimator depends on the validity of theassumption that the error terms do not exhibitserial correlation and on the validity of theinstruments. To address these issues, I use twospecification tests suggested by Arellano andBond (1991), Arellano and Bover (1995) andBlundell and Bond (1998). The first is a Sargantest for over-identifying restrictions, whichtests the overall validity of the instruments byanalysing the sample analogue of the momentconditions used in the estimation process. Thesecond test examines the hypothesis that theerror term ei,t is not serially correlated. I testwhether the differenced error term is second-order serially correlated (by construction, thedifferenced error term is probably first-orderserially correlated even if the original error termis not). Failure to reject the null hypotheses ofboth tests gives support to this study’s model.Blundell and Bond (1998) and Alonso-Borregoand Arellano (1999) show that, in the case ofpersistent explanatory variables, lagged levelsof these variables are weak instruments for theregression equation in differences. The internalinstruments, based on previous realisations ofthe explanatory variables, consider the potentialjoint endogeneity of the other regressors aswell. I assume that the explanatory variables areonly weakly exogenous, which means that theycan be affected by current and past realisationsof the growth rate, but must also be uncorrelatedwith future realisations of the error term.

3. Data and Research Methodology

3.1 Second-Generation Panel Unit Root

Bai andNg (2004) andMoon and Perron (2004)recommended a panel unit root test with cross-sectional dependency based on defactoredpanels, which is also designed for the panelwithN/T converging to 0. As noted by Breitungand Pesaran (2008), time series are contempo-raneously correlated in many macroeconomicapplications using country or regional data.Cross-sectional dependence can arise due to avariety of factors, such as the omission of

observed common factors and spatial spillovereffects. Bai and Ng (2004) proposed the firsttest of the unit root null hypothesis, taking intoaccount the potential cross-sectional correla-tion. The problem consists of specification of aspecial form of the dependencies. Bai and Ngsuggested a rather simple approach, consider-ing the factor analytical model as follows:

Y i;t ¼ Di;t þ f0iGt þ ei;t ð2Þ

where Di,t is a polynomial time function oforder t, Gt is a vector of common factors and fiis a vector of factor loadings. Therefore, Y isdecomposed into a heterogeneous deterministiccomponentDi,t,Gt (G¼ w1t,…, wkt) and an errorterm ei,t, largely idiosyncratic. Bai and Ngsuggested separately testing for the presence ofa unit root in the common and individualcomponents (panel analysis of non-stationarityin the idiosyncratic components (PANIC)).

The idiosyncratic components are modelledas an AR(1) process:

ei;t ¼ diet�1 þ ei;t ð3Þ

where ei,t follows a mean 0, stationary andinvertible moving average process. In this setup, the goal of PANIC is to determine thenumber of non-stationary common factors wand to test whether di¼ 1 for i¼ 1, …, N. Baiand Ng (2004) proposed a principle componentestimator for the unobserved idiosyncraticcomponent, common factors and factorloading.

Pesaran (2007) utilised a different approach todeal with the unit root problem of cross-sectionaldependencies, including augmented Dickey–Fuller (ADF) regressions with the cross-sectionalaverage of lagged levels and first-differences ofthe individual series. The Pesaran test is based onthese individual cross-sectional ADF statistics,denoted by cross-sectional augmented Im,Pesaran and Shin (CIPS):

CIPS ¼ 1

N

XNi¼1

tiðN; TÞ tiðN; TÞ

K1 8 tiðN; TÞKitiðN ; TÞ 8 K1 < tiðN; TÞ < K2

K2 8 t1ðN ; TÞK2

8><>: ð4Þ



The important issues of the stationarity of thedata and the existence and estimation of acointegrating relationship over the long runhave recently been addressed based on theconsistency of new tools proposed for theanalysis of panel data. I apply panel integrationand cointegration techniques for analysis of thelong-run determinants of the financial develop-ment processes. Based on the integration andcointegration results, the long-run cointegratingproduction function is embedded within theshort-run vector error correction models toproduce consistent estimations.

The recently developed methodology pro-posed by Pedroni (2004) is employed todetermine whether a cointegration relationshipexists. The estimated slope coefficients arepermitted to vary across individual members ofthe panel whose statistics allow for heteroge-neous fixed effects and deterministic trends andalso for heterogeneous short-run dynamics. Thepanel cointegration test is based on within-group residual regression analysis, whichinvolves panel n statistics, panel r statistics,panel Phillips–Perron (pp) statistics and panelADF statistics. The panel cointegration test isbased on between-group residual regressionanalysis, which involves group r statistics,group pp statistics and group ADF statistics.Panel n statistics undergo upper-tailed tests,while all other statistics undergo the lower-tailed test. In both cases, the basic approach isfirst to estimate the hypothesised cointegratingrelationship separately for each panel memberand then to pool the resulting residuals toconduct the panel tests.

3.2 Structural Break

Nelson and Plosser (1982) offered debate aboutthe unit root hypothesis. They argued thatcurrent shocks only have a transient effect andthat long-run movement in the series isinvariant. Most macroeconomic series are notcharacterised by a unit root. Perron (1989)showed that the power to reject a unit rootdecreases when the stationary alternative is trueand a structural break is ignored.

Capital mobility is known to have increasedas a consequence of the worldwide flow towards

financial liberalisation. Any investigation of theexistence of this relationship must allow forbreaks. This point has been taken into accountby Banerjee and Carrion-i-Silvestre (2004) andDi Iorio and Fachin (2007), who applieddifferent panel cointegration tests that allowedfor breaks. Grier, Lin and Ye (2008) tested therelationship between national savings andinvestment in the United States with structuralbreaks from the first quarter of 1947 to the firstquarter of 2007. Their results show that nationalsavings remained stationary with two structuralbreaks, but the investment rate remainedstationary without a break. Rao, Tamazian andKumar (2010) investigated the existence ofstructural breaks around 1972 and 1992, usingthe system-GMM to explore the F–H puzzle.

I follow the method of Rao, Tamazian andKumar (2010), the Mancini-Griffoli and Pau-wels (2006) procedure, to construct a structuralbreak test. Rao, Tamazian and Kumar claimedthat this method had three main practical andtechnical advantages including not making anydistributional assumptions. The power of thetest remains high even when there are fewobservations after the time of the break and thetest requires very few regularity conditions.Above all, it remains asymptotically validdespite non-normal, heteroscedastic, auto-correlated errors and non-strictly exogenousregressors. I also reviewed the study of Lean,Narayan and Smyth (2011) regarding thestructural break associated with the AsianFinancial Crisis (which occurred in 1998).

3.3 Dynamic Panel Data with InstrumentVariables

To assess the relationship between nationalsavings (S) and investment (I) in the paneldataset, I use the GMMestimators developed fordynamic panel models by Holtz-Eakin, Neweyand Rosen (1988), Arellano and Bond (1991)and Arellano and Bover (1995). I utilise yearlydata for the period 1986–2010. The bivariatevector auto-regressions for a panel of Ncountries and T years can be written as follows:

Si;t � Si;t�1 ¼ a1Si;t�1 þ b1I i;t þ b01X 1;t

þ fi;t þ e1;i;t ð5Þ



I i;t � I i;t�1 ¼ a1I i;t�1 þ b1Si;t þ b01X 1;t

þ fi;t þ e1;i;t ð6Þ

where Si,t indicates national savings for countryi at time t, X represents the set of explanatoryvariables, including the mediating effects ofConsumer Price Index (CPI), inflation rate(IFL) and per capita growth rate, and h is acountry-specific fixed effect, a time effect anderror term with the subscripts i and t represent-ing the country and time period. The specifica-tion of equations (5) and (6) as a set of projectedequations implies that the error terms areorthogonal to the fixed and time effects, aswell as the lag values of the endogenousvariables.

I also remove the fixed effects by differenc-ing. Since the least dummy variable estimator isknown to produce biased results in a datasetwith a small time dimension, I adopt a linearinstrumental variable technique that uses thepredetermined lags of the system variables asinstruments to exploit a potentially large set ofover-identifying restrictions and deliver con-sistent coefficient estimates. The bivariatevector auto-regressions that I actually estimateare formulated as follows:

DSi;t ¼Xkj¼1

at;jDSi;t�j þXkj¼1

bt;jDI t;j

þXkj¼1

b0DX i;t�j þ Dfi;t þ De1;i;t ð7Þ

DI i;t ¼Xkj¼1

at;jDI i;t�j þXkj¼1

bt;jDSt;j

þXkj¼1

b0DX i;t�j þ Dfi;t þ De1;i;t ð8Þ

whereD denotes thefirst difference operator andthe errors from the transformed equations satisfythe conditions of orthogonality. I use thedynamic panel GMM technique to addresspotential endogeneity in the data. The estimatoruses lagged differences of the explanatoryvariables as instruments. They are deemed validinstruments under the assumption that thecorrelation between De and the levels of the

explanatory variables are constant over time.The advantage of the dynamic panel datatechnique is that it overcomes the shortcomingsof a purely cross-sectional regression in whichthe unobserved country-specific effect is part ofthe error term. Therefore, possible results are abiased coefficient estimate. I control for thepresence of unobserved country-specific effects.Since the lagged dependent variable is includedin the explanatory variables, the country-specific variable is certainly correlated withlagged explanatory variables (Beck, Levine andLoayza 2000). The nature of the transition pathsstands in contrast to the cross-sectionalapproach in that it can clearly explain whethercausality arises from the F–H puzzle relation.

3.4 Explanatory Variables

3.4.1 Inflation Rate

The IFL was measured as the average annualgrowth rate of the CPI in each 5-year period,where deflationary episodes were filtered. Thisallowed explicit examination of the directeffects of price inflation or growth. Manystudies have emphasised that the level ofinflation affects the relationship betweenfinancial development and economic growth;for example, inflation distorts the activities ofthe financial market to aggravate the problem ofasymmetric information (Boyd, Levine andSmith 2001). When this problem exists in thefinancial market, the IFL changes the loanbehaviour of lenders, which in turn will have animpact on the selection of loan contracts,further affecting the influence of inflation oneconomic growth (Bose 2002). In particular,under the situation of low inflation, it is easy toestablish positive relations between financialdevelopment and economic growth (Huybensand Smith 1999; Huang 2003). Azariadis andSmith (1996) found high inflation to corre-spond to a low economic growth rate. Shen andLee (2006), Shen and Lin (2009) and Yilmaz-kuday (2011) all collected samples fromOECDcountries and then studied the completedsamples using the threshold effect or quintileregression to examine its effects (Lin andChen 2010).



3.4.2 Internet Users and Mobile DeviceUsers

I use Internet users (INTNT) as a variable torepresent information technology in this explo-ration of its relation to the finance–investmentnexus. Banks make use of a bank-ownedinfrastructure (built on top of public telecom-munication lines) to deliver telephone-bankingservices. Personal computer (PC) bankingrequires the customer to have a PC, the skillsneeded to use it and a modem to access the bankserver through the phone line. Internet usage isdefined as the total number of Internet usersin thousands; mobile cellular subscriptions(MOB_CEL) are defined as the number ofusers in thousands.

3.4.3 National Savings

I refer specifically to the ratio of nationalsavings to GDP (NAS), where national savingsincludes all deposit-type assets and is presumedto relate to the extent and intensity of inter-mediary activity.

3.4.4 Investment Ratio

The capital formation of a country can beformulated as (It – It – 1)/It – 1, where t¼ 1, …T¼ time (INV). The other control variableshave per capita growth rates to control for thesize of the economy. The economic systemdummy variable (ECDUM) is 1 if the countriesare dominated by the agricultural sector, or isotherwise 0; MOHDUM is 1 if the countries aredominated by ethnic Muslims, or is otherwise0; and COMDUM is 1 if the countries are underCommunist rule, or is otherwise 0. I alsoconsider the importance of the financial sectorand the development of the banking industrywhen designing a bank composition ratio(BKCOM%) as a proxy for their differencesin the use of technology. The BKCOM% isformulated as follows:

ST BK

INT BK� 100% ð9Þ

where ST_BK indicates state banks andINT_BK indicates international banks.

4. Discussion of Results

4.1 Descriptive Statistics

This investigation of the NAS–INV relation-ship includes both cross-sectional and dynamicelements. The dataset was used to constructpanel data from 13 countries covering theperiod of 1986–2008 from country-by-countryobservations from the World Bank’s WorldDevelopment Indicators. The annual per capitagrowth rates were between –1.487 per cent and1.979 per cent. The IFL ranged from 0.864 percent in Japan to 46.41 per cent in China. TheNAS was between 4.353 per cent and 48.448per cent in the ASEANþ3 nations. If onlyASEAN economies were included, the mini-mum IFL was found inMalaysia, the maximumIFL in Vietnam. Malaysia also had the smallestNAS and Brunei the largest; the maximumInternet usage was found in the Philippines andthe minimum in Myanmar.

Mobile devices have become increasinglypopular since 1986, although some countrieshad zero users at the beginning of this period.Descriptive statistics for the explanatory vari-ables are shown in Table 1. As can be seen,there is substantial cross-country variation inINV, with the maximum values being 46.953per cent (ASEAN) and 48.448 per cent(ASEANþ3). The minimum values are14.764 per cent (ASEAN) and 4.353 per cent(ASEANþ3) and the standard deviation isbetween 2.215 and 4.158. There is a consider-able variation in INTNT across countries,ranging from a low of 15.882 in Vietnam to ahigh of 25.1 in China. The standard deviationshows that the dispersions of INTNT, IFL andNAS are high across the ASEAN economiesand that INTNT, MOB_CEL and NAS are highacross the ASEANþ3 and all countries in theAsian sample.

In order to construct the dummy variablesfor ECDUM, MOHDUM and COMDUM, Ialso utilise information from each country’swebsite, <http://www.ASEAN.org>, and theTaiwan ASEAN Studies Center, Taipei.

Table 2 displays the correlation matrix forthe explanatory variables. The correlationmatrices shown in Table 2 are computed for



Tab

le1Descriptive

Statistics

fortheVariables

for1986–2011

(%)

ASE

ANa

ASE

ANþ3

(Japan,Korea

andChina)

Minimum

Maximum

Average

Deviatio

nSkew

ness

Kurtosis

Minimum

Maximum

Average

Deviatio

nSkew

ness

Kurtosis

Per

capita

grow

thrate

–14.870

19.690

4.650

5.410

–0.108

0.463

–1.342

1.979

2.420

5.318

0.162

0.313

INVb(%

ofGDPc)

4.350

43.640

23.440

8.760

–0.310

–0.586

4.350

48.450

25.700

9.320

0.140

–0.690

INTNTd

0.000

71.000

7.380

15.882

2.570

5.807

0.000

83.000

12.760

21.716

3.013

5.251

MOB_C

ELe

0.000

17.716

1.987

3.501

2.007

3.465

0.000

206.000

28.610

42.896

1.720

2.591

NASf

(%of

GDP)

0.000

16.520

9.210

3.623

–0.354

–0.566

1.610

20.750

10.200

4.020

0.099

0.147

IFLg

0.864

46.410

15.560

24.640

7.294

56.864

0.019

41.100

14.079

3.664

9.209

93.060

BKCOM%

h0.000

22.456

6.983

4.456

0.157

0.137

0.000

22.456

7.725

3.844

–0.315

0.632

ECDUM

i0.000

1.000

0.463

0.519

0.000

1.000

0.500

0.505

MOHDUM

j0.000

1.000

0.441

0.222

0.000

1.000

0.167

0.389

COMDUM

k0.000

1.000

0.500

0.333

0.000

1.000

0.333

0.493

Notes:(a)ASEAN

denotesAssociatio

nof

SoutheastAsian

Nations.

(b)IN

Vistheinvestmentratio

,formulated

as(It–I t–1)/I t–1.

(c)Percentageof

GDPisthegrossdomestic

productgrow

thrate.

(d)IN

TNTindicatesInternet

users,calculated

bytotalnumberof

Internet

usersin

thousands.

(e)MOB_C

ELindicatesmobile

cellu

larsubscriptio

ns,d

efinedas

thenumberof

usersin

thousands.

(f)NASindicatesnatio

nalsavings,which

issavingsdividedby

GDP.

(g)IFLdenotestheinflationrate.

(h)BKCOM%

iscalculated

bySK

BK

INT

BK�100%

.(i)ECDUM

denotesthat

theeconom

icsystem

dummyvariable

forcountriesthat

aredominated

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ralsector

is1;

otherw

ise,itis0.

(j)MOHDUM

denotesthat

theeconom

icsystem

dummyvariable

forcountriesthat

aredominated

byethnic

Muslim

sis1;

otherw

ise,itis0.

(k)COMDUM

denotesthat

theeconom

icsystem

dummyvariable

forcountriesthat

have

Com

munistrule

is1;

otherw

ise,itis0.

Sources:Dataarefrom

theWorld

Bank’sWorld

DevelopmentIndicators

andeach

country’swebsite

fortheperiod

of1986–2011.



the same periods. The expected signs of thecorrelation coefficients between growth andexplanatory variables are consistent with theforgoing discussion. Almost all variables arepositively correlated with each other, exceptfor IFL, which is negatively correlated with allthe variables, implying possible distortionaryeffects of positive price changes in all thetransmission channels in the economy. ForINV, there is an insignificant correlation withIFL, ECDUM and MOHDUM. The INV issignificantly positively correlated with INTNT,MOB_CEL and NAS. I note that while there isa significant correlation of GDP growth ratewith NAS, it is not significantly correlated withINV, INTNT, MOB_CEL, IFL, ECDUM,BKCOM% and MOHDUM. Beck and Levine

(2002) reported a correlation coefficient of0.664 for averages of these market- and bank-based measures from 1990 to 1995 across asection of 115 countries. Table 2 shows asignificant correlation between NAS and INVfor data that involve 13 countries for 25 years inthis study.

4.2 Panel Unit Root

As can be seen from Table 3, I identify non-stationarity in the data using the Bai and Ng(2004) PANIC and Pesaran (2007)methodologywith structural breaks. I am unable to reject thenull hypothesis of the unit root for both theindividual unit root process and common unitroot process for this panel time series. Since the

Table 2 Pearson’s Correlation Coefficient

B C D E F G H I Ja

Per capita growth –0.100b –0.004 0.013 0.047***c –0.082 0.024 0.047 –0.012 0.000rate (A) 0.461 0.897 0.691 0.010 0.815 0.461 0.136 0.700 0.990

INVd (B) 1.000 0.217*** 0.202*** 0.354*** –0.014 –0.020*** –0.037 –0.020 0.067*0.000 0.000 0.000 0.661 0.000 0.240 0.712 0.034

INTNTe (C) 1.000 0.871*** 0.526*** –0.090** 0.026 –0.024 0.020 –0.0320.000 0.000 0.020 0.599 0.440 0.590 0.310

MOB_CELf (D) 1.000 0.525*** –0.100* 0.543*** 0.004 0.040 –0.0100.000 0.046 0.000 0.910 0.230 0.710

NASg (E) 1.000 –0.200*** 0.062 0.058 0.100** 0.180**0.000 0.202 0.020 0.002 0.000

IFLh (F) 1.000 –0.250*** –0.024 –0.230** –0.110**0.000 0.460 0.000 0.740

BKCOM%i (G) 1.000 0.065* 0.426 0.0330.041 0.000 0.303

ECDUMj (H) 1.000 0.012 0.0450.695 0.160

MOHDUMk (I) 1.000 0.0250.422

Notes: (a) COMDUM denotes economic system dummy variable for countries that have Communist rule is 1; otherwise,it is 0.(b) Coefficients are listed in the first row and the p-values are reported in the second row. The data for both are for 25 years forthe 13 countries in this study.(c) *, ** and *** denote significance at the 10 per cent, 5 per cent and 1 per cent levels, respectively.(d) INV is the investment ratio, formulated as (It – It – 1)/It – 1.(e) INTNT indicates Internet users, calculated by total number of Internet users in thousands.(f) MOB_CEL indicates mobile cellular subscriptions, defined as the number of users in thousands.(g) NAS indicates national savings, which is savings divided by gross domestic product.(h) IFL denotes the inflation rate.(i) BKCOM% is calculated by SK BK

INT BK � 100%.(j) ECDUM denotes economic system dummy variable for countries that are dominated by the agricultural sector is 1;otherwise, it is 0.(k) MOHDUMdenotes economic system dummy variable for countries that are dominated by ethnicMuslims is 1; otherwise,it is 0.



Table 3 Panel Unit Root with Cross-Sectional Independence and Dependencea

Variable

1986–97 1998–2008

No deterministic(level)

First-leveldifference

No deterministic(level)

First-leveldifference

NASb INVc NAS INV NAS INV NAS INV

Assuming individual unit rootprocessd (only ASEANe)IPSf GLM –2.250 –2.520 –9.810*g –9.520* –1.790 –1.700 –3.820* –2.590*

(0.239)h (0.211) (0.000) (0.000) (0.146) (0.105) (0.000) (0.006)Maddala–Wu ADFi-Fisher testj 46.500 59.600 426.500* 359.600* 39.800 54.100 462.100* 347.100*

(0.878) (0.689) (0.000) (0.000) (0.913) (0.611) (0.000) (0.000)Maddala–Wu’ PPk-Fisher test 43.300 36.400 430.300* 380.000* 40.800 52.100 347.100* 283.100*

(0.432) (0.588) (0.000) (0.000) (0.487) (0.387) (0.000) (0.000)Assuming common unit rootprocess (only ASEAN)l

Breitung –5.365 –4.566 –8.911* –8.190* –3.933 –2.514 –8.291* –8.347*(0.187) (0.106) (0.000) (0.000) (0.298) (0.335) (0.000) (0.000)

LCCm t� d –1.117 –1.111 –8.271* –8.224* –2.499 –3.291 –9.253* –8.186*(0.226) (0.234) (0.000) (0.000) (0.206) (0.201) (0.000) (0.000)

Hadri consistent Z 3.875* 3.227* 5.998* 4.981* 3.374* 2.945* 5.887* 4.887*(0.000) (0.001) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000)

Only ASEANn ASEANþ3 (Japan, Korea and China)

1986–97 1998–2008 1986–97 1998–2008

NAS INV NAS INV NAS INV NAS INV

Common factors and cross-dependenceo

Pesaran (2007) CIPSp –3.119 –2.499 –1.967 –3.242 –3.615 –3.198 –5.188 –2.803(0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000)

Bai and Ng (2004) Pce 4.585 4.377 4.228 2.298 3.248 4.616 3.291 4.075

(0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000)

Notes: (a) Results are based on equations (3) and (4).(b) NAS indicates national savings, which is savings divided by gross domestic product.(c) INV is the investment ratio, formulated as (It – It – 1)/It – 1.(d) Hypothesis of IPS, ADF-Fisher and PP-Fisher tests is related to the individual unit root process.(e) ASEAN denotes Association of Southeast Asian Nations.(f) IPS denotes the univariate test that is Im, Pesaran and Shin’s (2003) panel unit root test.(g) * Significance at the 1 per cent level.(h) Values in parentheses denote p-value.(i) ADF denotes augmented Dickey–Fuller.(j) Probabilities for Fisher’s tests are computed using an asymptotic x2 distribution. All other tests assume asymptoticnormality.(k) PP denotes Phillips–Perron.(l) Hypothesis of LCC, Breitung and Hadri is related to the unit root and assumes a common unit root process.(m) LCC denotes Levin, Lin and Chu’s (2002) panel unit root test.(l) Annual data on savings and investment for 10 ASEAN economies and Korea, Japan and China from 1986 to 2008 (N¼ 10or 13; T¼ 22) are used.(n) For the common factor unit root test, I reject the null hypothesis of a unit root for cross-sectional independence (criticalvalue of the Pesaran test is –2.20 per cent) and I reject the null hypothesis of the unit root for cross-sectional independence(critical value of the Pesaran test is 1.65 per cent). All lag lengths are selected using the Akaike information criterion and thenumber of common factors is selected using Aznar and Salvador’s (2002) and Bai and Ng’s (2004) information criteria.Newey–West bandwidth selection is carried out using the Bartlett kernel.(o) CIPS denotes the cross-sectional dependence test that was proposed by Pesaran (2007).(p) Values in parentheses denote p-value, significance at the 1 per cent level.



individual components, NAS and INV, are non-stationary, I can apply the panel cointegrationtest proposed by Pedroni (2004). All panel testsused are based on the null hypothesis of thepresence of a unit root in the series, withthe exception of Hadri’s (2000) test, where thehypothesis is that the series are stationary. Thistest assumes cross-sectional independenceamong panel units, except for common timeeffects, but allows for heterogeneity in the formof individual deterministic effects and hetero-geneous serial correlation structure of the errorterms. Pesaran (2007) proposes an alternativebased on the Im, Pesaran and Shin (IPS) test,which presents critical values for the CIPS testbecause the CIPS test controls for cross-sectional correlations, which are robust to thepresence of serially correlated errors in theregressions. The results of the CIPS tests andthe IPS test with time effects fail to reject thenull hypothesis for the panel unit root for any ofthe variables.

This result is not surprising if we bear inmind that the time series are constructed usingthe ASEAN and ASEANþ3 data. Therefore,what are being captured with this commonfactor are NAS and INV that are common to allthe time series, which turns out to be non-

stationary. All the CIPS and Pce statistics reject

the null hypothesis of a panel unit root at the5 per cent significance level.

4.3 Panel Cointegration Test and DynamicPanel Data

Pedroni (2004) proposed a residual-based testfor the null cointegration for dynamic panelswith multiple regressors, which are permitted tobe heterogeneous across individuals. I considerthe use of seven residual-based panel cointe-gration statistics. Three within-group and threebetween-group tests are used to check whetherthe panel data are cointegrated. Under thealternative hypothesis, the panel n-statisticdiverges to positive infinity and the right tailof the normal distribution is used to reject thenull hypothesis of no cointegration. For theremaining six statistics, the left tail of thenormal distribution is used to reject the nullhypothesis.

Table 4 shows the outcomes of cointegrationtests between the NAS and the INV rates. Theresults of the within-group test show thatthe null hypothesis of no cointegration can berejected at the 1 per cent significance level. Thebetween-group test results reject the null

Table 4 Panel Cointegration Test

Variable

1986–97 1998–2008

Nodeterministic trend

Deterministicintercept and trend

Nodeterministic trend

Deterministicintercept and trend

Panel n 3.8700***a –1.500 1.770** 1.610***(0.0001)b (0.934) (0.038) (0.000)

Panel r –9.3100*** –7.470*** –3.780*** –2.070***(0.0000) (0.000) (0.001) (0.000)

Panel PPc –8.4300*** –10.640*** –4.500*** –1.930***(0.0000) (0.000) (0.000) (0.000)

Panel ADFd –1.7100** –1.140** –2.080** –2.650***(0.0440) (0.028) (0.018) (0.000)

Group r –5.8700*** –4.150*** 0.740 –4.280***(0.0000) (0.000) (0.770) (0.000)

Group PP –7.9500*** –9.770*** –3.850*** –2.100***(0.0000) (0.000) (0.000) (0.000)

Group ADF 0.0300 0.550* –0.490 –0.310**(0.5120) (0.071) (0.310) (0.050)

Notes: (a) *, ** and *** denote significance at the 10 per cent, 5 per cent and 1 per cent levels, respectively.(b) Values in parentheses denote p-value.(c) PP denotes Phillips–Perron.(d) ADF denotes augmented Dickey–Fuller.



hypothesis of no cointegration at the 5 per centsignificance level. With the exception of group-ADF (not significant over the 5 per cent level)and Panel n (insignificant over the 1 per centlevel for the deterministic intercept and trend),the NAS and INV rates appear to be cointe-grated at a reasonable significance level.

I next examine the size and direction ofdynamic relationships between NAS and INVwithGMMpath data. Both Tables 5 and 6 reportsimple AR(1) and AR(2) specifications for thetwo series with instrumental variables. Both twoseries are found to be highly persistent. Eventhough second-generation level estimates areused, none appears to have panel unit root. Thevalidity of using the lagged t – 1 as instrumentsin the first-differenced equations is clearly notrejected by the results of the Sargen tests ofover-identifying restrictions, used to examinethe overall validity of the instruments bycomparing the moment conditions with theirsample analogue. When the p-value is large, thetest of common factor restrictions is easilypassed, as shown in these first-differencedGMM results. Thus, I do not reject the nullhypothesis that the instruments are appropriate.

As noted in Section 1, this work investigatesthe relationship between NAS and INV. Theresults in Table 5 show a statistically andeconomically significant relation between theinstrument components of an economic system.The results of pure cross-country regressionsusing the policy implication of information setare reported in the last four columns of Table 5.The ECDUM is significantly negatively relatedto NAS and INV in the long run at 1 per centsignificance. In general, the non-agriculturalsectors absorb investment funds better than theagricultural sector. In this study’s sample, onlyIndonesia and Malaysia have significant num-bers of Muslims; therefore, the results revealthis to be negatively related to INV andpositively related to NAS. I explain thisdifference as arising from customs that areinclined towards savings and averse to the risksof investment. I find that for the Communistcountries including China in the ASEANþ3group, the results show a significantly positiverelation to INV, but a significantly negativerelation to NAS is found in the ASEAN

economies (full periods and 1986–97). Thepositive relation to NAS in the ASEANþ3 isfurther proof of the blossoming of China.

I found that NAS affects INV and isinfluenced by country size. As can be seen inTable 5, NAS has a positive statisticallysignificant and an economically small coeffi-cient, between 1.5 per cent and 23 per cent. Theresults are lower than for the b-coefficient of theF–H puzzle and Fouquou, Hurlin and Rabaud(2009), but lend support to the findings of Kaoand Chiang (2000) and Ho (2002). In contrast,the INV is significantly positively related toNAS, with coefficients of 77.5 per cent and 18per cent for the ASEAN and ASEANþ3datasets, respectively. The dynamic panelestimates also indicate that bank systemdevelopment has a large economic impact onNAS and INV. The BKCOM% is significantlypositively related to ASEAN’s investmentbefore the 1997 Asian crisis, but negativelyrelated in the ASEANþ3 region. It is likely thatthere was less investment in some Communistcountries that had more state banks. Moreinternational banks developed in China, Koreaand Japan during this period. Comparingstructural breaks at different stages, I find aninsignificant relationship between BKCOM%and INV in Table 5; however, BKCOM% isnegatively related to ASEAN’s NAS andpositively related to ASEANþ3’s NAS. I thinkthis is the case in the last three countriesbecause of the higher business activity ofinternational and private banks, whereas theASEAN countries are inclined to depend on thestate banking system.

The results in Table 6 show that MOB_CELshave had a large, significant impact on NAS, butnot on INV in the ASEAN economies and thatthe relation is different if the break point is 1998,with a negative relation before 1998 and after1997. The IFL obtained using the two-stepestimator is significant and positive in allregressions. These results suggest that the IFLalso influences the willingness to invest or save;for example, residents might spendmore moneyon consumption or choose to hedge their bets ortransfer money abroad. The relationship is notsignificant for the ASEANþ3 data. Table 6shows an interesting result: the lower the



Table 5 Generalised Method of Moment Dynamic Panel Data (dependent variable: investment)

Group

ASEANa ASEANþ3 (Japan, Korea and China)

1986–2008 1986–97 1998–2008 1986–2008 1986–97 1998–2008

Per capita growth rate 0.044***b 0.005*** 0.036** –1.130 0.910*** 0.007(3.420)c (4.698) (2.434) (–0.314) (2.702) (0.102)

Constant 0.070* –0.030 0.011 –0.010 0.090 –0.005(1.957) (–0.393) (0.110) (–0.474) (0.885) (–0.055)

INVd (lag 1) –0.020 –0.240*** –0.120 0.040 –0.199*** 0.152***(–0.260) (–3.776) (–1.400) (0.968) (–2.610) (3.248)

INTNTe 0.392* 0.982*** –0.608*** –0.060* 1.040*** –0.041(1.940) (3.457) (–2.570) (–1.823) (8.732) (–0.110)

MOB_CELf–0.300 0.368 –0.799 0.150*** –0.398*** 0.148***(–1.320) (1.110) (–1.420) (4.840) (–8.010) (3.533)

NATIONAL SAVINGS 0.015*** 0.016 0.050*** 0.230** 0.004* 0.020***(2.420) (1.578) (3.070) (2.551) (1.851) (2.969)

IFLg–0.004*** –0.035* 0.003** –0.001*** –0.002*** 0.018(–2.689) (1.730) (–2.000) (–3.027) (–3.622) (0.550)

ECDUMh–0.724*** –5.003*** –0.575*** –1.190*** –1.920*** –0.896(–4.903) (–3.186) (–4.193) (–3.321) (–4.764) (–1.442)

MOHDUMi–0.631*** –1.718** –0.475*** –2.510*** –1.820*** –4.105***(–3.110) (–2.249) (–3.767) (–6.414) (–3.179) (–3.689)

COMDUMj–1.923*** –0.441*** –2.268 2.080*** 1.970*** 3.760***(–2.313) (–3.312) (–1.110) (4.667) (4.891) (3.062)

BKCOM%k 0.043 0.425*** 0.024 –0.010 –0.040*** 0.0210(0.674) (4.318) (0.320) (–0.326) (–2.370) (0.373)

AR(1)l –2.693 –2.379 –2.367 –3.174 –2.801 –2.907p-value (0.007) (0.020) (0.020) (0.002) (0.005) (0.004)AR(2)l –1.956 –1.449 –1.712 –2.215 –1.515 –2.085p-value (0.050) (0.147) (0.087) (0.027) (0.129) (0.037)Sargan over-identification testm 155.020 55.750 48.100 258.900 58.490 68.940

(0.462) (0.300) (0.765) (0.303) (0.314) (0.346)Wald (joint) test 198.600 370.790 938.160 590.860

(0.000) (0.000) (0.000) (0.000)ARCH Lagrange multiplier testn 0.033 54.180*** 34.220*** 83.024*** 31.180*** 78.130p-value (1.369) (0.001) (0.000) (0.000) (0.000) (0.000)Structural breako Break point¼ 1997 Break point¼ 1997

Coefficient¼ 1.252*** Coefficient¼ 0.018***t-ratio¼ 3.769 t-ratio¼ 6.399

Notes: (a) ASEAN denotes Association of Southeast Asian Nations.(b) *, ** and *** denote significance at the 1 per cent, 5 per cent and 10 per cent levels, respectively.(c) t-values are given in the parentheses.(d) INV is the investment ratio, formulated as (It – It – 1)/It – 1.(e) INTNT indicates Internet users, calculated by total number of Internet users in thousands.(f) MOB_CEL indicates mobile cellular subscriptions, defined as the number of users in thousands.(g) IFL denotes the inflation rate.(h) ECDUM denotes economic system dummy variable for countries that are dominated by the agricultural sector is 1;otherwise, it is 0.(i) MOHDUM denotes that the economic system dummy variable for countries that are dominated by ethnic Muslims is 1;otherwise, it is 0.(j) COMDUM denotes that the economic system dummy variable for countries that have Communist rule is 1; otherwise,it is 0.(k) BKCOM% is calculated by SK BK

INT BK � 100%. I consider the importance of the financial sector and the development of thebanking industry to design this bank composition ratio as a proxy for differences in the use of technology.(l) AR(1) and AR(2) are tests for first-order and second-order serial correlation in the first-differenced residuals,asymptotically distributed as N(0,1), under the null hypothesis of no serial correlation.(m) Sargan is a test for over-identifying restrictions, asymptotically distributed as x2-value under the null hypothesis ofinstrumental validity, with the degrees of freedom reported in parentheses.(n) The null hypothesis of the autoregressive conditional heteroscedastic (ARCH) Lagrangemultiplier test has noARCHeffect.(o) The structural break point is found with Chow’s test and the time is set at 1997.



Table 6 Generalised Method of Moment Dynamic Panel Data (dependent variable: national savings)

Group

ASEANa ASEANþ3 (Japan, Korea and China)

1986–2008 1986–97 1998–2008 1986–2008 1986–97 1998–2008

Per capita growth rate –0.624 –0.002***b –0.001*** 0.003*** 0.0040*** 0.002***(–1.357)c (–2.833) (–4.302) (8.168) (11.1700) (3.133)

Constant –0.220*** –0.076 –0.532 0.070 0.6100*** –0.040(–2.000) (–0.147) (–1.496) (0.335) (10.1500) (–0.048)

NASd (lag 1) 0.295*** 0.092* 0.270*** –0.085*** –0.0850 0.006(5.835) (1.867) (4.641) (–2.914) (–1.2600) (0.110)

INTNTe 3.789*** –0.690 0.647*** –0.760*** –6.5600*** –0.390(2.875) (–0.516) (3.239) (–4.980) (–1.6900) (–1.380)

MOB_CELf 4.714*** 0.918*** 0.413*** 0.531*** 0.4660 0.620***(5.460) (3.552) (2.632) (6.214) (0.2960) (4.910)

INVg 0.590*** 0.210*** 0.775* 0.180** 0.2200*** 0.490***(4.613) (3.140) (1.844) (2.359) (2.5680) (3.584)

IFLh–0.063*** –0.020** –0.248*** –0.001 –0.0060 –0.750(–3.842) (–2.093) (–3.267) (–0.253) (–1.3210) (–1.500)

ECDUMi–0.130*** –0.764*** –0.218*** –3.700*** –0.2020*** –0.335***(–3.831) (–4.007) (–3.772) (–9.820) (–3.5390) (–4.642)

MOHDUMj 0.722*** 0.665* 0.956*** 0.740*** 0.1650*** 0.458(1.779) (1.688) (4.682) (4.483) (2.7810) (0.530)

COMDUMk–0.371** –0.493*** 0.195*** 0.920*** –0.2890*** 0.408***(–2.730) (–3.350) (3.811) (2.976) (–4.8510) (5.816)

BKCOM%l 1.670*** 0.794** 0.399*** –0.280*** –2.4960*** –0.404***(3.426) (2.507) (3.410) (–5.630) (–4.2380) (–3.045)

AR(1)m –2.636 –2.481 –2.595 –3.799 –2.9320 –2.873p-value (0.008) (0.013) (0.009) (0.001) (0.0030) (0.004)AR(2)m 1.571 –0.320 2.401 –3.016 –1.5230 –3.568p-value (0.116) (0.749) (0.016) (0.003) (0.1280) (0.004)Sargan over-identification testn 130.400 45.930 50.590 256.200 48.5000 60.120

(0.926) (0.853) (0.490) (0.397) (0.6856) (0.648)Wald (joint) test 4,362.600 1,572.3500 3,349.520

(0.000) (0.0000) (0.000)ARCH Lagrange multiplier testo 0.625 0.478*** 0.609*** 0.415*** 0.0560*** 0.176***p-value (4.040) (2.080) (4.070) (3.229) (6.7300) (3.017)Structural breakp Break point¼ 1997 Break point¼ 1997

Coefficient¼ 0.665*** Coefficient¼ 0.534***t-ratio¼ 3.378 t-ratio¼ 10.72

Notes: (a) ASEAN denotes Association of Southeast Asian Nations.(b) *, ** and *** denote significance at the 1 per cent, 5 per cent and 10 per cent levels, respectively.(c) t-values are given in the parentheses.(d) NAS indicates national savings, which is savings divided by gross domestic product.(e) INTNT indicates Internet users, calculated by total number of Internet users in thousands.(f) MOB_CEL indicates mobile cellular subscriptions, defined as the number of users in thousands.(g) INV is the investment ratio, formulated as (It – It – 1)/It – 1.(h) IFL denotes the inflation rate.(i) ECDUM denotes economic system dummy variable for countries that are dominated by the agricultural sector is 1;otherwise, it is 0.(j) MOHDUM denotes that the economic system dummy variable for countries that are dominated by ethnic Muslims is 1;otherwise, it is 0.(k) COMDUM denotes that the economic system dummy variable for countries that have Communist rule is 1; otherwise,it is 0.(l) BKCOM% is calculated by SK BK

INT BK � 100%. I consider the importance of the financial sector and the development of thebanking industry to design this bank composition ratio as a proxy for differences in the use of technology.(m) AR(1) and AR(2) are tests for first-order and second-order serial correlations in the first-differenced residuals,asymptotically distributed as N(0,1) under the null of no serial correlation.(n) The Sargan test for over-identifying restrictions is carried out, showing an asymptotic distribution as x2-value under thenull hypothesis for instrumental validity, with the degrees of freedom reported in parentheses.(o) The null hypothesis of the autoregressive conditional heteroscedastic (ARCH) Lagrangemultiplier test shows noARCH effect.(p) The structural break point is found with Chow’s test and the time is set at 1997.



economic growth, the higher the NAS acrossASEAN economies throughout the period, theparadox of thrift. To sum up, I show that theinstrumental variables have the greatest influ-

ence on the NAS–INV nexus. The resultssuggest significantly moderate factors in therelation between NAS and INV. Figure 1 alsoshows the specifics of the heteroscedasticity in

Figure 1 The System Residual of the Association of Southeast Asian Nationsþ 3 Savings–Investment Correlation

Continued



the ASEAN and ASEANþ3 data between NASand INV. Some countries have a smooth savingscurve, but some countries have volatile savingswith investment curves.

5. Conclusions and Suggestions

This study investigates the issue of cross-sectionaldependence in the panel unit root tests of the F–Hpuzzle. In light of the findings, I find that factorssuch as the information technology, the type ofeconomic system and banking sector and the size

of the country have significant effects on theestimates of the b-coefficient. My empiricalresults also fill out the system-GMM estimatesdiscussed by Rao, Tamazian and Kumar (2010).Serial correlation is used to estimate and todevelop a structural break test in order to betterunderstand the effects during the Asian crisis.

The econometric techniques utilised in thiswork offer significant improvement over exist-ing studies in relation to evaluating the linksbetween savings, investment and informationtechnology. By using instrumental variables to

Figure 1 Continued



extract the exogenous component for interpret-ing the F–H puzzle, I control for biases incurredby the reverse effect, unobserved factors andthe cross-dependence problem, thereby avoid-ing the informational loss or consistencydefault implied by using the initial values. Itis found that the savings and investment rates inthe dynamic panel data are non-stationary andheteroscedastic with cross-sectional depen-dence. Further improvements in FDI may bedifficult as ASEAN is an emerging marketwithout a stable financial system. Financialreforms may play an influential role inimproving international capital mobility. Withthis finding, and in line with Kumar, Webberand Fargher (2012), I argue that ASEAN couldeffectively adopt policies that focus on increas-ing information technology investment andderegulation through increasing nationalsavings.

First version received July 2012;final version accepted May 2013 (Eds).

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an investigation of the feldstein-horioka puzzle for the association of southeast asian nations...

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