Embed Size (px)
Citation preview
This article was downloaded by: [Moskow State Univ Bibliote]On: 10 December 2013, At: 01:17Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
Applied EconomicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/raec20
The interactions among foreign direct investment,economic growth, degree of openness andunemployment in TaiwanShu Chen Chang aa Department of Business Administration , National Formosa University , 64 Wen-Hua Road,Huwei, Yunlin, Taiwan E-mail:Published online: 30 Oct 2009.
To cite this article: Shu Chen Chang (2007) The interactions among foreign direct investment, economic growth, degree ofopenness and unemployment in Taiwan, Applied Economics, 39:13, 1647-1661, DOI: 10.1080/00036840600675612
To link to this article: http://dx.doi.org/10.1080/00036840600675612
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.
This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
Applied Economics, 2007, 39, 1647–1661
The interactions among foreign
direct investment, economic
growth, degree of openness and
unemployment in Taiwan
Shu Chen Chang
Department of Business Administration, National Formosa University,
64 Wen-Hua Road, Huwei, Yunlin, Taiwan,
E-mail: [email protected]
The vector autoregression method of variance decomposition and impulse
response function analysis are applied to analyse various relationships
among foreign direct investment (FDI), economic growth, unemployment
and degree of openness in Taiwan. The analysis results show that
these five variables have a long-run equilibrium relationship; however,
unemployment rate and FDI outflow have weak exogeneity. We also
found that there exist three unidirectional causalities from FDI outflow to
FDI inflow, from economic growth to degree of openness, and from
economic growth to unemployment in short-run. Furthermore, the shocks
of economic growth and degree of openness have positive effects on FDI
inflow. On the contrary, the shocks in economic growth and FDI inflow
have negative effects on unemployment rate.
I. Introduction
After economic growing rapidly in the 1980s,Taiwan’s gross domestic product (GDP) grew at anannual average rate of 6.5% in the period from 1990to 1997. During the Asian financial crisis devastatingmany countries, Taiwan’s economy was relativelyresilient and still grew 4.7% in 1998, which was thesecond highest growth rate in Asia. Since Taiwan’stotal trade and foreign direct investment (FDI) inpercentage of GDP are about 50% in recent years,Taiwan’s economy is gradually integrated into theworld economy successfully. Moreover, according tothe United Nation’s Conference on Trade andDevelopment (UNCTAD), markets opening andFDI, including inflows and outflows, stimulated thegrowth of an economy in the latest years. Therefore,FDI and markets opening play an important role inTaiwan’s economic development. The relation amongFDI, economic growth and trade, or the relation
between FDI and unemployment is a subject ofdebate all the time. Although recent researchesattempt to analyse the impacts of FDI on hostcountry’s economy and competitiveness of firms,empirical results show that the consequence isdifferent. Some studies indicate that FDI canstimulate the economic growth through spillovereffect such as new technologies, capital formation,human resources development (human skills andemployment) and expanded international trade andcreate employment. Others point out that FDI canoffset the economic growth and decrease domesticemployment.
Most empirical studies, however, have followed aproduction function model or foreign investmentfunction in examining the effect of FDI on the hostcountry. For example, Chen and Ku (2000), basedon Taiwan’s data, examine the effect of FDI on thegrowth performance of investing firms. They foundthat FDI is beneficial to domestic industries and
Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online � 2007 Taylor & Francis 1647http://www.tandf.co.uk/journalsDOI: 10.1080/00036840600675612
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
trade, but has no correlation with job creation athome country. They rarely considered the endogen-ous nature as well as the possible relationshipbetween FDI, trade and economic growth especiallyin a multi-variance framework. In other words, mostempirical studies did not investigate specific estimatesof the impacts and dynamic movements of FDI,markets opening and economic growth. Moreover, ahigher structural unemployment, resulting from theongoing transformation of Taiwan’s domestic eco-nomic structure which has been exacerbated after2001 by the sluggish world economic growth, hascontributed to an increase in unemployment. Somestudies found that overseas investment replaceddomestic employment in developing countries;however, the same result did not happen in developedcountries (Blomstrom et al., 1997). Therefore, it isworthwhile to investigate the strength of causalitybetween FDI, trade, economic growth as well asunemployment variables. Now, we use the variable ofdegree of openness, which is constructed by includingexports and imports of goods and services, tomeasure markets opening in order to capture thedegree of openness and free trading.
The objective of this article is to recognize possiblelinks among FDI inflow and outflow, economicgrowth, degree of openness and unemployment ratefor labour forces based on a systematic approachsuch as vector autoregression (VAR). Moreover, weseek causality between FDI, degree of openness,economic growth and unemployment rate variablesfrom the dynamic reaction per system to alterationsin the control variables. Hence, the main contribu-tions of this article are the following: (i) To determinewhether there exists a stationary long-run relation-ship among FDI, economic growth, degree of open-ness and unemployment rate and weak exogeneity ofall variables. (ii) To determine the causal relationshipamong FDI, economic growth, degree of opennessand unemployment rate. (iii) To find out the short-run dynamic relationship among FDI, economicgrowth, degree of openness and unemployment rate.(iv) To determine what types of economic governancewill be deleterious or beneficent to conditions foreconomic growth.
This article is based on quarterly time series dataover the period from 1981:1 to 2003:3 in Taiwan.Variance decomposition and impulse response func-tion analysis are applied to analyse dynamic inter-relationships between FDI and other economicvariables of interest in a VAR system, including ananalysis of the determinants of all variables inTaiwan. The rest of the article is organized as follows.The next section describes the general trend of FDI,degree of openness, economic growth and
unemployment rate of Taiwan. Section III providesa brief overview of recent literature. Section IVpresents the proposed framework and econometricmethodology. Section V describes the main empiricalresults found in this research. Some concludingremarks are provided in the last section.
II. FDI, Degree of Openness, EconomicGrowth and Unemployment in Taiwan
With the rise in land price and labour cost,the production costs of Taiwan’s traditionalindustry have been increased. Due to TaiwaneseGovernment’s gradual lifting of restrictions on over-seas investments as well as the rising NTD (NewTaiwan Dollar) exchange rate and wage since 1980,there occurred enormous supply of capital andcreated an environment for manufacturers to investoverseas. Hence, Taiwan’s traditional enterprises losttheir abilities to compete with producers in thedeveloping countries of Asia Pacific, and respondedby shifting their operations to lower-cost overseasareas. As Table 1 shows, the value of FDI outflowhas rapidly grown more than its inflow during theperiod from 1996 to 2003. With the outward flow ofinvestment, Taiwan has become a net exporter ofcapital. For Taiwan’s FDI outflow, outward FDI in1997 reached a peak of US$7.2 billion and netoutflow amounted to US$3.0 billion. Owing to thewarming up of Taiwan’s domestic investment duringthe period from 1998 to 1999, the FDI outflow fromTaiwan has moderated. Taiwan’s total outward FDIdeclined to US$5.3 billion and US$4.5 billion in theyears of 1998 and 1999, respectively.
Due to the rising prices of land and labour,growing environmental concerns, and inadequacy ofpublic facilities, much of capital was shifted tooverseas investment by traditional manufacturers.Recently, Taiwan’s total outward FDI rose rapidlyfrom US$10.0 billion in 2002 to US$11.6 billion in2003. For Taiwan’s inward FDI, approved inwardinvestment rose gradually from US$2.4 billion in1996 to US$7.6 billion in 2000. Nevertheless, itdeclined back to US$5.1 billion in 2001 and US$3.2billion in 2002 because of the opening of the fixed-network market of telecommunications. Inwardinvestment rebounded to a normal level of US$3.5billion in 2003, still higher than the US$2.7 billionannual average for the period from 1992 to 1999.Taiwan’s main export commodities have moved fromtraditional labour intensive products to high-techproducts after 1980s. The share of informationand electronic products in total exports has soared
1648 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
in the 1990s. Taiwan also exports great amount ofmachinery and parts due to rapid Taiwanese overseasinvestments since the 1980s. As shown in Table 1, thevalue of export has rapidly grown during the periodfrom 1988 to 1998. With Taiwan’s currency apprecia-tion, exports declined significantly to US$110.5billion in 1998. Owing to global economic prosperityand the growing demand for electronics, informationand telecommunications products, Taiwan’s exportrebounded to US$121.5 billion and US$148.32 billionin 1999 and 2000, respectively. But it decreased toUS$122 billion (a decline of 17.16%) in 2001,rebounded to a normal level of US$130.6 billion (anincrease of 6.29%) in 2002, and rose again toUS$144.2 billion (an increase of 10.44%) in 2003.
For economic growth, an average annual economicgrowth rate reached 9.2% during the 1960s becauseTaiwan’s government put emphasis on promotinglabour-intensive and export-oriented industries. Withan economic change, developing capital-intensiveindustries and promoting the production of inter-mediate products were the major tasks for the
government in the 1970s. This strategy enabledTaiwan to effectively reduce importing intermediateproducts and upgrade Taiwan’s industrial structure,and an average annual economic growth rate rose to10.2% in the 1970s. The amount of trade surplusstarted to expand after the middle of the 1970s andbecame prominent since 1981. As Table 1 shows, withadopting economic liberalization and internationali-zation as the guiding policy of Taiwan’s economicdevelopment, Taiwan had become a net exporter ofcapital and trade surplus during the period from 1981to 1990, and an average annual economic growth rate7.67% was reached. Although Taiwan’s economy hasentered a more matured stage in recent years, apositive economic growth has been maintained. Theaverage annual economic growth rate was 2.61%from 2000 to 2003.
For unemployment, Taiwan’s labour forceparticipation rate has declined steadily over theyears, falling to 57.3% in 2002 from its peak of60.9% in 1987 because of the desire of young menand women to extend their education. As shown
Table 1. Outward FDI, inward FDI, GDP, exports and unemployment rate
Outward FDI Inward FDI GDP at current price Export at current price Unemployment rate
Value Growth Value Growth Value Growth Value Growth(US$1000) rate (%) (US$1000) rate (%) (US$ billion) rate (%) (US$ billion) rate (%) (%)
1981 10764 �74.44 395757 �15.07 48.2 6.16 22.61 14.13 1.361982 11632 8.06 380006 �3.98 48.5 3.55 22.20 �1.81 2.141983 10563 �9.19 404468 6.44 52.4 8.45 25.12 13.15 2.711984 39263 271.70 558741 38.14 59.1 10.60 30.45 21.22 2.451985 41334 5.27 702459 25.72 62.0 4.95 30.72 0.89 2.911986 56911 37.69 770380 9.67 75.4 11.64 39.86 29.75 2.661987 102751 80.55 1418796 84.17 101.5 12.74 53.67 34.65 1.971988 218736 112.87 1182538 �16.65 123.1 7.84 60.67 13.02 1.691989 930986 325.62 2418299 104.5 149.1 8.23 66.3 9.29 1.571990 1552206 66.72 2301772 �4.81 160.2 5.39 67.21 1.37 1.671991 1656030 6.68 1778419 �22.73 179.4 7.55 76.18 13.34 1.511992 1134251 �31.5 1461374 �17.82 212.2 7.49 81.47 6.95 1.511993 4829346 325.77 1213476 �16.96 224.3 7.01 85.09 4.44 1.451994 2578973 �46.59 1630717 34.38 244.3 7.11 93.05 9.35 1.561995 2449591 �5.01 2925340 79.38 264.9 6.42 111.66 20 1.791996 3394645 38.58 2460836 �15.87 279.6 6.1 115.94 3.83 2.61997 7228139 112.92 4266629 73.38 290.2 6.68 122.08 5.3 2.721998 5330923 �26.24 3738758 �12.37 267.2 4.57 110.58 �9.42 2.691999 4521793 �15.17 4231404 13.17 287.9 5.42 121.59 9.96 2.922000 7684204 69.93 7607739 79.79 309.4 5.86 148.32 21.98 2.992001 7175801 �6.61 5128529 �32.58 281.2 �2.18 122.87 �17.16 4.572002 10093104 40.65 3271747 �36.2 281.5 3.54 130.6 6.29 5.172003 11667372 15.59 3575656 9.29 286.2 3.24 144.24 10.44 4.99
Source: 1. Directorate-General of Budget, Accounting and Statistics (DGBAS), Executive Yuan, R.O.C., Quarterly NationalEconomic Trends, Taiwan Area, R.O.C., Feb. 2003.2. DGBAS, Statistical Abstract of National Income, Taiwan Area, R.O.C., 1951–2003, March 2003.3. The Central Bank of China, R.O.C., Financial Statistics Monthly and Balance of Payments Quarterly, Taiwan District,R.O.C., Feb. 2003.4. Ministry of Finance, R.O.C., Monthly Statistics of Exports and Imports, Taiwan Area, R.O.C., March 2003.
Interactions among FDI, economic growth, openness and unemployment 1649
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
in Table 1, unemployment rate remained between1.36 and 1.79% of the labour force over the periodfrom 1981 to 1995. However, unemployment rateshowed an increasing trend since the second half of1995 due to a short-term slack in the economy andlabour-market rigidity. A higher level of structuralunemployment has contributed to the rise in jobless-ness. The unemployment rate was 2.6% in 1996,2.99% in 2000, 4.57% in 2001, 5.17% in 2002 and4.99% in 2003. Recent available data seems toconfirm the seriousness of the unemploymentproblem in Taiwan.
III. Overview of Recent Literature
Previous researches on the impact of FDI oneconomic growth and exports were relatively inade-quate as compared to the empirical researches on themainstream determinants of economic growth. Theycan be roughly categorized into five approaches. Asto the first approach, researchers tested the maindeterminants of FDI using either time-series conceptof cointegration or panel data (Liu et al., 1997; Wangand Swain, 1997; Sun and Parikh, 2001). The extentof these studies indicated that FDI can be explainedby economic growth and external trade. Althoughthese studies implicitly assumed a one-way causalityfrom openness and economic growth to FDI, andthen estimated the impacts of FDI based on suchcausality, they usually ignored the endogenous natureof the growth process so that a single equation modelcannot deal with the simultaneity issue properly.
As to the second approach, some researchersexamined the relationship between FDI and exports(or FDI and economic growth), where the empiricalresults showed complementary (positive) and sub-stitutive (negative) effects. Other researchers (Zhangand Song, 2000; Hejazi and Safarian, 2001; Marchantet al., 2002) observed a positive direct link betweenFDI and economic growth. This result implies thateconomic growth is one important determinant forFDI inflow to a host country. However, Pfaffermayr(1994) found a positive effect of increased FDIoutflow on exports and a negative effect of exportshocks on FDI outflow. Gopinath et al. (1998) founda negative relationship between FDI and export witha positive direct link between FDI and economicgrowth. As to the third approach, the effects of tradeand FDI on economic growth were examined byBende-Nabende and Ford (1998), Wei et al. (2001),Bende-Nabende et al. (2001), Shan (2002) andBende-Nabende et al. (2003), where their empiricalresults were even contradictory to each other. Amongthese studies, parts of the empirical results showed
that exports and FDI influence economic growth.Furthermore, FDI has a positive effect on economicgrowth through its influence on the changes inindustrial techniques (Bende-Nabende and Ford,1998; Wei et al., 2001). However, Bende-Nabendeet al. (2003) found that FDI in some countries had anegative relation with economic growth and some-times does not even have a significant impact oneconomic growth. Although the second and thirdapproaches considered both possible directionalcausal relationships, they failed to assess the relation-ships among exports, FDI and economic growth.
As to the fourth approach, many studies(Yaghmaian and Ghorashi, 1995; Liu et al., 1997;Sun and Parikh, 2001; Liu et al. 2002; Bende-Nabendeet al., 2003; Dawson andHubbard, 2004) attempted toexamine the relationship between exports and eco-nomic growth, where either the expansion of exportswas caused by rapid economic growth or the higherexpansion of exports leads to a higher economicgrowth. They found that the relationship betweenexports and economic growth depends on the level ofdevelopment and the economic structure. In otherwords, exports expansion may not cause significanteffect on the economic growth at a very low level ofdevelopment. For the fifth approach, researchers(Scahaik and Groot, 1998; Sogner, 2001; Apergisand Rezitis, 2003) analysed the relationship betweenmovements of unemployment and economic growth,which is called Okun’s law. They found that theunemployment–economic growth relationship inimperfect competition economy and different periods,where structural changes occur, has a negativecorrelation and effect of different degrees throughtesting the structural stability.
As discussed above, previous studies indicated thatdecreasing unemployment could promote economicgrowth, and economic growth had a causal relation-ship with exports. In addition, the causal relation-ships between economic growth and FDI, as well asthe relationship between exports and FDI werefound. However, they did not consider the causallinks between FDI, exports, economic growth andunemployment based on a multivariate framework.Understanding the casual connections betweenthese phenomena is important for the developmentstrategies in developing countries. Most empiricalstudies generally applied the following two econo-metric approaches to testing the determinant of FDIor examining the effect of FDI and trade oneconomic growth. Some utilized cross-sectional datathat usually relies on traditional original least squares(OLS) regression, whereas others applied time seriestechnique such as simultaneous equation model,cointegration and Granger-causality procedure.
1650 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
Sims (1980) argued that a simultaneous equationmodel involves too many arbitrary decisions andsuggested VAR as an alternative approach.Moreover, Gujarati (1995) argued that VAR, whichrequires less a priori information, has the advantageof treating each variable under study as endogenouswhen economic theory cannot offer a priori informa-tion regarding the variables in the VAR model.Hence, VAR can be used to analyse dynamicrelationships among the economic variables ofinterest.
This article reports the nature of interactionsbetween economic growth, degree of openness,unemployment rate and FDI in Taiwan. The focusis on the dynamic effects of changes to particularvariables. Furthermore, this study uses cointegratingtest to determine whether there exists a long-runequilibrium relationship among variables and weakexogeneity, and multivariate Granger-causality test todetermine their causal direction in short-run betweenall five variables. Finally, this study uses an impulseresponse function and variance decomposition(together called ‘innovation accounting’) to examinethe short-run dynamic response of all series.
IV. The Framework and EconometricMethodology
Our system consists of five variables – foreign directinvestment inflow (IFDI), foreign direct investmentoutflow (OFDI), unemployment rate (RU), degree ofopenness (DOP) as well as the real GDP. The sampleperiod uses quarterly time series data for the periodfrom 1981:1 to 2003:3. Such quarterly data can beobtained from Taiwan Economic StatisticalDatabank System. Among quarterly time seriesdata, Taiwan’s outflow FDI was approved by theTaiwan government and quoted from the statistics onoverseas Chinese and foreign investment and outwardinvestment. The exact definitions for the variablesand data sources are provided in the Appendix.These variables are seasonally adjusted because timeseries of quarterly frequencies of this article exhibitcyclical movements that recur every quarter. Forseasonal adjustment procedures, this article adoptsX–11 procedure to remove seasonality and usesEViews-4.0 program to remove cyclical seasonalmovements from a series and extract the underlyingtrend component of the series. All data with seasonaladjustment are transformed to logarithmic form toachieve stationarity in variance.
Based on the discussion in the previous section, theVAR framework, where each variable is explained by
its own lagged values and the lagged values of othervariables, is adopted to analyse the dynamic inter-relationships among the economic variables of inter-est. That is, each variable is strongly influenced byother variables if forecast of each variable can beimproved by adding some own lagged and laggedvalues of other variables. The VAR of order p modelcan be expressed in matrix representation as follows:
lnYt ¼ �þ�1 lnYt�1 þ . . .þ�p lnYt�p þ �t;
t ¼ 1, . . . ,N ð1Þ
where
lnYt ¼
lnIFDIt
lnOFDIt
lnRUt
lnDOPt
lnGDPt
26666664
37777775
� ¼
�1
�2
�3
�4
�5
26666664
37777775
�i ¼
#11,i . . .#15,i
#21,i . . .#25,i
#31,i . . .#35,i
#41,i . . .#45,i
#51,i . . .#55,i
26666664
37777775
and
�1,t
�2,t
�3,t
�4,t
�5,t
26666664
37777775
Notice that � and �i(i¼ 1, . . . , p) are vector auto-regression parameters to be estimated, and �trepresents random errors with zero mean and finitevariance.
In the following, we present the econometricmethodology about unit root tests, cointegration testand impulse response and variance decompositions.
Unit root tests
In most applications, it is not known a priori whetherthe variables are stationary. Because a nonstationaryregressor invalidates many standard empirical results,economic time series for stationary properties isusually required before estimating the VAR modelin which statistical inferences are conducted. Toexamine the stationary properties of the economictime series, we employ the unit root test of augmentedDickey–Fuller (ADF) and KPSS (Kwiatkowski et al.,1992) tests for the logged levels and the first or higherorder differencing of all logged variables in order todetect stationary in the stochastic trend and todetermine the order of integration of the economictime series. The null hypothesis for ADF test(including a drift and a time trend) is nonstationaryseries, but that for KPSS test is stationary series. Theappropriate lag-length in the ADF regression isselected by minimizing the Akaike’s informationcriterion (AIC).
Interactions among FDI, economic growth, openness and unemployment 1651
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
Previous literature argued that the standard ADFtests are not appropriate for testing the stationary ofseries encountering structural changes. Perron (1989)found that the existence of structure changes tends tobias the finding from ADF tests, and he suggestedtesting the hypothesis that a given series has unitroot with an exogenous structure break whichoccurs at time point. Furthermore, Zivot andAndrew (1992) developed a unit root test withendogenous structure break, which has been regardedas a more suitable test for the order of integration ofseries. Their test models are expressed as thefollowing equations:
Model A: Yt ¼ �A1 þ �
A1 tþ �
A2DUtð�Þ þ �
AYt�1
þXkj¼1
�j�Yt�j þ "t ð2Þ
Model B: Yt ¼ �B1 þ �
B1 tþ �
B2 DTtð�Þ þ �
BYt�1
þXkj¼1
�j�Yt�j þ "t ð3Þ
Model C:Yt ¼ �C1 þ �
C1 tþ �
C2DUtð�Þ
þ �C2 DTtð�Þ þ �CYt�1
þXkj¼1
�j�Yt�j þ "t ð4Þ
where DTt(�)> t�T �, 0 otherwise, with �¼TB/Tand TB represents a breakpoint. DUt (�)¼ t�T� ift>T�, 0 otherwise. Thus, we take the structurebreak into account and use Zivot and Andrew (1992)model to test unit root. Since an appropriate modeland optimal lag lengths are crucial in interpreting theresults of the tests, we adopt the findings from theADF tests to select the model and the lag lengths.
Cointegation test
If a unit root has been confirmed for each series,temporal causality analysis follows directly fromEquation 1 (Geweke et al., 1983; Engle andGranger, 1987). The equation is used to test whetherthere exists some long-run equilibrium relationshipamong variables. The cointegration among variablesallows the estimation for the long-run equilibriumrelationship. We apply the maximum likelihoodprocedure using Johansen (1988) and Johansen andJuselius (1990) method to determine the presence of
cointegrating vector or the long-run equilibriumrelationship among the variables. By following theirmethod, the restricted VAR model with differenceand error correction derived from the cointegrationequations can be expressed as follows:
�lnYt ¼ �þ �1�lnYt�1 þ � � � þ �p�1�lnYt�pþ1
þ� lnYt�1 þ "t; t ¼ 1, . . . ,N ð5Þ
where
lnYt ¼
lnIFDIt
lnOFDIt
lnRUt
lnDOPt
lnGDPt
26666664
37777775
� ¼
�1
�2
�3
�4
�5
26666664
37777775
�i ¼
�11,i . . . �15,i
�21,i . . . �25,i
�31,i . . . �35,i
�41,i . . . �45,i
�51,i . . . �55,i
26666664
37777775
and "t ¼
"1,t
"2,t
"3,t
"4,t
"5,t
26666664
37777775
Notice that � is the difference operator, � and�i(i¼ 1, . . . , p) are vector autoregression parametersto be estimated and "t represents random errors withzero mean and finite variance. �i denotes adjustmentparameters, describing dynamic inter-relationships inthe short-run. Matrix � may be potentially reducedto rank � (� is the number of cointegrating relationsor the rank) and can be written as �¼��0, where �and �1 are 5� � matrices of full ranks. Then, matrix�¼ ��0 conveys information about the long-runrelationship between Yt vector, and the rank of � isthe number of linearly independent and stationarylinear combinations of variables. The rank of � fromunrestricted VAR is examined to determine whetherthe eigenvalues of � are significantly different fromzero using cointegrating tests.
If the rank of � is found to be five, the variables inlevels are stationary and an unrestricted VAR inlevels is appropriate (Sims et al., 1990). If �¼ 0, then�¼ 0. So none of the linear combinations arestationary and an unrestricted VAR in first-orderdifference is appropriate. When the rank of � is foundto be between zero and five, there exist � cointegra-tion vectors or � stationary linear combinations of Yt.Then, unrestricted VAR in levels will not exploit theinformation variable and, instead, a restricted VARin first-order difference with error-correction term
1Where � is the matrix of cointegrating vectors and � is known as the matrix of coefficients of error-correction term oradjustment coefficients in the restricted VAR model.
1652 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
is appropriate (Maddala, 1992). The inclusion oferror-correction terms in Equation 5 introduces anadditional channel through which cointegrating canbe detected. The error correction terms containinformation about weak exogeneity. If the loadingson the error correction terms in a particular equationare all zero, then the dependent variable of thatequation is weakly exogenous with respect to thematrix of cointegrating vectors. If they are differentfrom zero, there will be causality in both directions.However, if one of the elements is zero, there will beno long-run causation towards this variable in thesystem, and consequently, this variable will be weaklyexogenous in the system.
Impulse response and variance decompositions
Park and Philips (1989) and Sims et al. (1990) haverecently shown that the conventional asymptotictheory is not applicable to testing in levels VAR ifvariables are integrated or cointegrated. If eco-nomic variables of interest were I(1) (indicatingintegrated of order one) with noncointegrstion, thenthe conventional asymptotic theory is valid forhypothesis testing in first-order difference VAR.However, if economic variables of interest wereknown to be I(1) with full ranks, then all variablesare regarded as stationary and inter-relationship istested based on levels VAR. On the contrary, ifeconomic variables were known to be I(1) with onecointegrating vector, then the conventional asymp-totic theory is valid for hypothesis testing inrestricted VAR with difference and error correctionmodel (Maddala, 1992). Thus, the restricted VARmodel with an error-correction term for fiveendogenous variables in level and p lags in eachvariable can be expressed as follows:
�lnIFDI1,t ¼ �1 þXp
i¼1
�11,i�lnIFDIt�i
þXp
i¼1
�12,i�lnOFDIt�i
þXp
i¼1
�13,i�lnRUt�i
þXp
i¼1
�14,i�lnDOPt�i
þXp
i¼1
�15,i�lnGDPt�i
þ �1�t�1 þ "1,t
�lnOFDI2,t ¼ �2 þXp
i¼1
�21,i�lnIFDIt�i
þXp
i¼1
�22,i�lnOFDIt�i
þXp
i¼1
�23,i�lnRUt�i
þXp
i¼1
�24,i�lnDOPt�i
þXp
i¼1
�25,i�lnGDPt�i
þ �2�t�1 þ "2,t
�lnRU3,t ¼ �3 þXp
i¼1
�31,i�lnIFDIt�i
þXp
i¼1
�32,i�lnOFDIt�i
þXp
i¼1
�33,i�lnRUt�i
þXp
i¼1
�34,i�lnDOPt�i
þXp
i¼1
�35,i�lnGDPt�i
þ �3�t�1 þ "3,t
�lnDOP4,t ¼ �4 þXp
i¼1
�41,i�lnIFDIt�i
þXp
i¼1
�42,i�lnOFDIt�i
þXp
i¼1
�43,i�lnRUt�i
þXp
i¼1
�44,i�lnDOPt�i
þXp
i¼1
�45,i�lnGDPt�i
þ �4�t�1 þ "4,t
Interactions among FDI, economic growth, openness and unemployment 1653
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
�lnGDP5,t ¼ �5 þXp
i¼1
�51,i�lnIFDIt�i
þXp
i¼1
�52,i�lnOFDIt�i
þXp
i¼1
�53,i�lnRUt�i
þXp
i¼1
�54,i�lnDOPt�i
þXp
i¼1
�55,i�lnGDPt�i
þ �5�t�1 þ "5,t ð6Þ
where kt�1 is an error-correction term derived fromthe long-run cointegrating equation, and � is the firstdifference operator. The term "t represents randomerrors with zero mean and finite variance.
It is well known that the VAR estimate is verysensitive to the choice of lag length (i.e. p). So, thelikelihood ratio test is used to select the number oflags. Diagnostic tests including Lagrange multipliertest and white heteroskedasticity test are also used totest the residual serial correlation, and heteroskedas-ticity. The advantage of this procedure is to checkstatistical acceptability. However, the VAR modelbasically has no structure and impulse responseanalysis and variance decompositions of forecast-error may be heavily influenced by the choice of theorder in which the variables are shocked and thechoice of the number of lags. That is, the results ofimpulse responses analysis and variance decomposi-tions of forecast-error will be different if the order ofvariables correlating to each other in the VAR modelis different. Zhou (1996) also pointed out that as thereis more than one common trend in the models,different ordering of the variables may affect theresults of impulse responses and variance decomposi-tions if the common trends are not absolutelyuncorrelated. Following the practice of Sims (1980),the presumably exogenous variable is followed byrelatively endogenous variables. Therefore, the orderin which the variables are to be shocked is importantif it is assumed that the variables in the equationsare uncorrelated to each other. Then, we must realizethe causal ordering among all variables via weakexogeneity tests in order to determine the order of thevariables in the VAR model.
Once the presence of cointegration and weakexogeneity of variables are confirmed, the VAR canbe estimated and the impulse responses and variance
decomposition of forecast-error can be found byinverting the VAR into a moving-average representa-tion.2 A moving average representation with adisturbance process is used to derive impulse responsefunctions and variance decompositions (Sims, 1980).Since the impulse response functions can be used totrace how each variable responds over time and isshocked by other variables of the system, it is apractical way to explore the response of a variableto a shock immediately or with various lags. Theforecast-error of variance decomposition analysisallows us to infer the proportion of the movementin sequence due to its own shocks vs. shocks to othervariables in the system. Hence the innovation canhave insight into short-run dynamic inter-relation-ships among FDI, economic growth, degree ofopenness and unemployment.
V. Empirical Results
In order to test the existence of stochastic nonsta-tionary in the series, we first examine the integrationorder of individual time series using ADF and KPSStests. The results of nonstationary tests for IFDI,OFDI, RU, DOP and GDP in the levels and firstdifference using the ADF and KPSS tests are shownin Table 2. This table shows that each time series isnonstationary in levels but is stationary in the firstdifference, which implies suggesting that all the timeseries are integrated of order one. The results of Zivotand Andrew (1992) tests are presented in Table 3which provides evidence for the existence of a unitroot when breaks are allowed. The plausible breaksfor the series occur at 1987Q1, 1990Q4, 1999Q4,1995Q4, and 1987Q1, respectively, for outflow FDI,inflow FDI, GDP, unemployment rate and the degreeof openness. All time series carry unit-root in the leveland reject the null of ‘nonstationarity’ in the firstdifference. This insures the I(1) type series for all timeseries considered.
Having confirmed the existence of unit roots for alltime series, we employ cointegration technique ofJohansen (1988) and Johansen and Juselius (1990) totest whether there exist a long-run relationship amongvariables. In the case of cointegration tests, the nullhypothesis can be detected by Johansen’s maximumlikelihood method. The r of time series is tested using�max and Trace statistics. Denoting the number ofcointegrating vectors by r0, �max and Trace tests arecalculated under the null hypothesis r¼ r0 (against
2 The procedures do not have the drawback of the orthogonalized innovations generated by Cholesky decomposition, wherethe ordering of the variables can significantly affect the results of impulse response.
1654 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
the alternative of r � r0). The test result for coin-tegrating rank is reported in Table 4. The resultshows that the null hypothesis with zero vs.one cointegrating vector is clearly rejected since�max(¼44.72) and Trace (¼95.48) are above thecritical values 33.46 and 68.52 at the 1% significancelevel, respectively. But in the null hypothesis with onevs. two cointegrating vectors, �max (¼24.31) andTrace (¼50.76) are below the critical values 27.07 and47.21, respectively. This implies that there existsexactly one significant cointegrating vector.Moreover, according to the estimates of the normal-ized cointegrating vector, OFDI, RU and DOPvariables have statistically significant positive effecton the FDI inflow. GDP has significantly negativeeffect on FDI inflow. Thus, the co-movement amongthe relevant time series confirms that a long-runequilibrium relationship exists.
Among these significant effects, the sign of theOFDI coefficient is not consonant with theoreticalexpectation. We infer the inconsonant result from theofficial data of FDI because the existing officialstatistics of FDI have discrepancy between theoutward FDI statistics reported by Taiwanesegovernment and the relevant statistics reported by
host countries. That is, small firms usually hide theirFDI information to avoid tax liability or the burdenof filing tax returns while large firms do not hidebecause their investments are often too large toescape the government’s capital controls (Chen andKu, 2000).
Having obtained cointegrating vectors, we testweak exogeneity of the variables in the cointegratedsystem. The test statistics is likelihood ratio test underthe null hypothesis of the existence of weak exogene-ity. The results for weak exogeneity tests under theassumption of rank of one are presented in Table 5.They indicate that null hypotheses that lnOFDIand lnRU are weakly exogenous to system are notrejected at the 5% significant level. However, nullhypotheses that ln IFDI, lnGDP and lnDOP areweakly exogenous to system are rejected at the 5%significant level, indicating that FDI inflow, eco-nomic growth and degree of openness are not weaklyexogeneous. Under a Sims recursive structure, weakexogeneity of FDI outflow and unemployment ratehave an important implication for the form of thelong-run impact matrix of the structural shocks.Thus, structural shocks to FDI outflow and unem-ployment rate have permanent effects on the levels ofall series whereas structural shocks to FDI inflow,economic growth and degree of openness have onlytemporal effects.
Having verified that the variables are cointegrated,a restricted VAR model, which employs all fivevariables in first difference, can be applied. Theoptimal lag length of restricted VAR is set to four bythe sequential modified likelihood ratio test, which isequivalent to two lags in levels VAR. In order toexamine whether the residuals of restricted VAR arefree from serial correlation and heteroskedasticity,the diagnostic checking on the residuals was per-formed. The estimated results for residual diagnosticstest are shown in Table 6. This table shows that allmodels pass through a battery of diagnostic tests.
Table 2. ADF and KPSS unit root tests
ADF(T) KPSS
Level Difference Level Difference
ln IFDI �2.341(2) �7.438��(3) 0.162��(6) 0.035(2)lnOFDI �1.725(8) �4.403��(7) 0.205��(7) 0.062(7)lnRU �1.484(2) �4.945��(1) 0.237��(7) 0.138(4)lnDOP �2.853(0) �5.795��(4) 0.194��(6) 0.031(2)lnGDP 2.188(6) �3.915��(7) 0.283��(7) 0.140(4)
Notes: T denotes the ADF statistics with trend and intercept.The number in parentheses indicates the selected lag order.Notation ** denotes significance at 5% levels.The critical values for ADF and KPSS are taken from MacKinnon(1991) and Kwiatkowski et al. (1992), respectively.
Table 3. Results of Zivot and Andrew (1992) tests for unit
roots with structural break
Breakpoint Level First difference
ln IFDI 1990Q4 �4.689(C) �10.400��(C)lnOFDI 1987Q1 �5.325(A) �6.487��(A)lnRU 1995Q4 �4.090(C) �5.787��(A)lnDOP 1987Q1 �4.470(C) �6.229��(C)lnGDP 1999Q4 �0.423(B) �6.008��(B)
Notes: The number in parentheses indicates the selectedmodel. The critical values for 1% levels are �5.34, �4.93and �5.57 for Model A, B and C, respectively, from Zivotand Andrew (1992).Notation ** denotes significance at 1% levels.
Interactions among FDI, economic growth, openness and unemployment 1655
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
That is, the computed LM test on one and 12 orderautocorrelation of the residuals can not reject the nullof white noise residuals of 5% significance levels.Then, it indicates that the residuals are white noise.The White heteroskedasticity test also cannot rejectthe null of no heteroskedasticity, indicating that thereis no evidence of residual heteroscedasticity effects insystems. So the residuals of VAR system are freefrom serial correlation and heteroscedasticity.
The existence of a long-run relationship amongvariables implies causal relationship among thesevariables at least in one direction. Then, we applyGranger-causality tests to find the causal direction inshort-run between all five variables. From the system,the Granger-causality test, examining by testingwhere all the coefficients of lagged endogenous
variables are different from zero based on therestricted VAR model with first difference, isperformed with Wald test which is a block-exogeneitytest (multivariate version of the Granger-causalitytest). The results for Granger-causality tests arepresented in Table 7. This table shows that the nullhypothesis of Granger noncausality from OFI toIFDI is rejected at the 5% significant level; however,no evidence of reverse causality is found, indicatingthat a one-way causal relationship exists from FDIoutflow to FDI inflow in the short-run. Likewise, thenull hypotheses of Granger noncausality from GDPto RU and from GDP to DOP are rejected at the 5%significant level. Evidence of reverse causality is notfound. Therefore, there exists a one-way causalrelationship from economic growth to unemploymentrate and degree of openness in the short-run. Thehypothesis that DOP does not Granger-cause IFDIhas been not rejected at the 5% significant level andno evidence of reverse causality has been found,indicating that no causal relationship exists fromdegree of openness to FDI inflow in the short-run.In addition, the null hypotheses of ‘GDP does notGranger cause OFDI’ and ‘RU does not Grangercause OFDI’ have not been rejected at the 5%significant level, and no evidence of reverse causalityis found. Thus, there are no causal relationships fromboth economic growth and unemployment rate toFDI outflow in short-run. Therefore, the results show
Table 4. Results of the cointegration tests (VAR lag^ 2)
Eigenvalue H0 H1 Trace test �max testCritical value 5%(Trace)
Critical value5% (�max)
0.398 r¼ 0 r� 1 95.48*** 44.72*** 68.52 33.460.241 r� 1 r� 2 50.76 24.31 47.21 27.070.150 r� 2 r� 3 26.44 14.38 29.68 20.970.116 r� 3 r� 4 12.05 10.92 15.41 14.070.012 r� 4 r� 5 1.13 1.13 3.76 3.76
IFDI GDP DOP RU OFDIEstimated cointegrating vectors 1 5.749 (1.106) �24.079 (3.608) �1.24 (0.612)2 �0.100 (0.129)
Notes: Notation *** denotes significance at 1% levels and r indicates the number of cointegrating vectors.The optimal lag in the cointegrating test was selected by the likelihood ratio test.Numbers in parentheses are the standard error value.
Table 5. Results of weak exogeneity tests
Likelihood ratio test Probability
ln IFDI is weakly exogenous to system 6.021 0.014�
lnGDP is weakly exogenous to system 5.167 0.023�
lnDOP is weakly exogenous to system 18.756 0.000�
lnOFDI is weakly exogenous to system 1.418 0.233lnRU is weakly exogenous to system 0.241 0.622
Note: Notation � denotes significance at the 5% level.
Table 6. Residual diagnostics of autoregressive models
Statistic value P-value
Residual serial correlationLM test
LM(1) 24.886 0.468LM(12) 33.261 0.124
Residual heteroskedasticity test 276.363 0.053
Notes: Residual serial correlation and heteroskedasticityare estimated by Lagrange Multiplier test (i.e. LM test) andWhite heteroskedasticity test, respectively.
1656 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
that there exists a one-way causality from FDIoutflow to FDI inflow. Economic growth hasunidirectional causality to degree of openness and aunidirectional causality also exists from economicgrowth to unemployment rate in the short-run.
Granger-causality tests can only be interpreted aswithin-sample tests and provide little evidence for thedynamic properties of the system in short-run (Masihand Masih, 1995). Next, we consider the dynamicinteractions between FDI inflow, economic growth,degree of openness, FDI outflow and unemploymentrate under the restriction that FDI outflow andunemployment rate are weakly exogenous. A betterunderstanding of the dynamic inter-relationshipsamong the variables is provided by impulse responsefunctions and variance decompositions of forecast-error, which can trace out the time path of variousshocks and determine how each variable respondsover time to shocks on the future states of a dynamicsystem. The recursive causal ordering is orderedas lnRU, lnOFDI, lnGDP, ln IFDI and lnDOPin the VAR model. The estimated results forvariance decomposition based on VAR modelwith first difference are reported in Table 8.As this table shows, the reported numbers indicatethe percentages of the forecast error in each variablethat can be attributed to innovations in othervariables at three different time horizons: firstperiod (1 quarter), fourth period (1 year) and eighthperiod (2 years).
As we can see from part A of Table 8, theinnovation of IFDI growth is accounted for a large
percentage of the forecast error variance by its owninnovations (70.19%, up to eight periods). Withregard to IFDI, changes in OFDI explain nearly13.13%, and 8.30, 6.01 and 2.34% are explained byGDP, RU and DOP, respectively, after the eighthperiod. In other words, the innovation in the IFDIgrowth is mainly explained by its own variation,OFDI, GDP, RU and DOP, respectively. In part B ofTable 8, the own shock of OFDI growth is accountedfor nearly 98.89% of the forecast variance in the firstperiod. OFDI growth variations in the eighth periodare accounted the most by its own changes (77.18%)while approximately 9.73% are attributed to GDP,7.94% to RU, 3.30% to DOP and 1.84% to IFDI,respectively. Consequently, among the five variables,the innovation of OFDI growth is mainly explainedby its own variation, GDP, RU, DOP and IFDI.
Furthermore, as we can see from part C of Table 8,the GDP growth change variability is explainedmostly by its own innovation (89.68%) in the firstquarter. However, this proportion on the forecasterror variance decreases to 80.33% in the fourthperiod and 75.08% in the eighth period. Moreover,14.53, 4.07, 3.81 and 2.48% of the forecast errorvariance are explained by RU, IFDI, DOP andOFDI, respectively, in eighth period. In other words,the innovation in the GDP growth is mainlyexplained by its own variation, RU, IFDI, DOPand OFDI.
Part D of Table 8 shows that the innovationof DOP growth is mainly explained by its ownchanges (76.49%), RU (12.80%) and GDP (9.47%)
Table 7. Results of Granger causality tests
Chi-square Statistic P-value
�lnOFDI does not Granger-cause �ln IFDI 4.041 0.044�
�ln IFDI does not Granger-cause �lnOFDI 0.270 0.603�lnRU does not Granger-cause �ln IFDI 0.100 0.750�ln IFDI does not Granger-cause �lnRU 2.549 0.110�lnDOP does not Granger-cause �ln IFDI 0.042 0.830�ln IFDI does not Granger-cause �lnDOP 1.621 0.202�lnGDP does not Granger-cause �ln IFDI 0.000 0.990�ln IFDI does not Granger-cause �lnGDP 3.387 0.065�lnRU does not Granger-cause �lnOFDI 1.133 0.287�lnOFDI does not Granger-cause �lnRU 0.002 0.959�lnDOP does not Granger-cause �lnOFDI 0.000 0.983�lnOFDI does not Granger-cause �lnDOP 1.320 0.250�lnGDP does not Granger-cause �lnOFDI 1.715 0.190�lnOFDI does not Granger-cause �lnGDP 1.179 0.277�lnDOP does not Granger-cause �lnRU 0.219 0.639�lnRU does not Granger-cause �lnDOP 1.167 0.280�lnGDP does not Granger-cause �lnRU 20.078 0.000�
�lnRU does not Granger-cause �lnGDP 1.778 0.182�lnGDP does not Granger-cause �lnDOP 14.413 0.000�
�lnDOP does not Granger-cause �lnGDP 0.560 0.454
Note: Notation � denotes significance at the 5% level.
Interactions among FDI, economic growth, openness and unemployment 1657
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
in the first period. However, the proportion of itsown innovation decreases to 57.50% in fourth period,while 24.06% is explained by GDP, 12.31% by RU,4.30% by IFDI and 1.81% by OFDI, respectively.After 2 years (the eighth period), 51.93% of varianceis explained by its own past values followed by GDP(27.55%), RU (12.66%), IFDI (4.40%) and OFDI(3.44%). Therefore, the innovation in the DOPgrowth is mainly explained by its own past values,GDP, RU, IFDI and OFDI. The implication is thatdegree of openness is more sensitive to GDP growththan other economic variables. The GDP growth isthe prerequisite for the expansion of Taiwan’s trade.Moreover, in part E of Table 8, the RU growthvariability is attributed to shocks by itself (100%) inthe first period. However, the innovations of RUgrowth are accounted the most by its own changes(61.53%) while approximately 28.99% are attributedto GDP, 4.18% to OFDI, 3.33% to DOP and 1.94%to IFDI, respectively, in the eighth period. In otherwords, the innovation in the RU growth rate ismainly explained by its own past values and GDP.
The conclusion of the variance decompositionanalysis is that FDI inflow is sensitive to changes inFDI outflow and GDP, while the former is moresensitive than the latter. GDP is also sensitive tochanges in FDI outflow. This can lend to the supportthat FDI inflow and economic growth have causalrelationship and strong effect of ‘economic growthled FDI inflow’. Moreover, degree of openness is verysensitive to changes in GDP and unemployment rate,
but not conversely. Unemployment rate is verysensitive to changes in GDP and FDI outflow, andvice versa, which does lend support that risingeconomic growth and FDI outflow can obviouslyaffect unemployment rate for labour force.
To examine the sensitivity of the estimated impulseresponse functions with respect to the imposition ofthese cointegration restrictions, we derived accumu-lated impulse response from the VAR model withfirst difference. The accumulated impulse responsesare plotted in Fig. 1 and the dynamic responses areobtained from a time interval with eight periods.Figure 1 shows that, among these self-responses, allvariables have permanent effects by their owninnovations. That is, the variables return to theirprevious equilibrium value of zero after some periodif no further shocks occur. Part A of Fig. 1 shows theresponse of IFDI to the five variables disturbance.The shocks of an impulse in GDP, RU and DOP areapproximately positive effects on IFDI variable.However, the impact of OFDI is almost negativeeffect and only positive effect at second, fifth andseventh periods. The results can be expected as apositive shock in rapid real GDP growth. Degree ofopenness and the unemployment rate can temporarilyinduce an increase in foreign investment. Thus,inward FDI is encouraged by the economic growthand employment of the work force but the part ofincreased effect is offset by FDI outflow. The findingsdescribed above are consistent with the so called‘market size hypothesis’ (Moore, 1993), emphasizing
Table 8. Variance decomposition percentage analysis
Period SE �ln IFDI �lnOFDI �lnGDP �lnDOP �lnRU
(A) Variance decomposition percentage of �ln IFDI1 0.53 92.58 2.39 4.10 0.00 0.914 0.69 83.63 6.79 4.54 1.73 3.288 0.79 70.19 13.13 8.30 2.34 6.01
(B) Variance decomposition percentage of �lnOFDI1 0.49 0.00 98.89 0.00 0.00 1.104 0.62 1.66 79.28 9.91 2.53 6.598 0.65 1.84 77.18 9.73 3.30 7.94
(C) Variance decomposition percentage of �lnGDP1 0.00 0.00 0.10 89.68 0.00 10.204 0.01 4.08 0.92 80.33 0.28 13.868 0.01 4.07 2.48 75.08 3.81 14.53
(D) Variance decomposition percentage of �lnDOP1 0.02 0.99 0.22 9.47 76.49 12.804 0.03 4.30 1.81 24.06 57.50 12.318 0.03 4.40 3.44 27.55 51.93 12.66
(E) Variance decomposition percentage of �lnRU1 0.07 0.00 0.00 0.00 0.00 100.004 0.09 1.59 0.58 29.98 0.89 66.948 0.09 1.94 4.18 28.99 3.33 61.53
Note: The trends are ordered as [RU, OFDI, GDP, IFDI, DOP].
1658 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
that increase per capita income will attract hugeinward FDI. Moreover, it can be found that theresponse of IFDI to a shock to GDP, and RUvariables slowly tends to revert back to original levelsafter the eighth period.
The response of OFDI to five variables disturbanceis shown in part B of Fig. 1. The responses of OFDIto a shock to GDP and IFDI have positive effectsover all periods. The impact of RU also has the mostpositive effect on OFDI and only negative effect forthe third and fourth periods. But DOP has negativeeffects for all period. The results show that FDIoutflow increases in response to a shock to real GDPgrowth and FDI inflow but decreases in response to ashock to degree of openness. Moreover, OFDI alsobelongs to a slow dynamic adjustment for a shock toGDP and DOP. The response of GDP to fivevariables disturbance is shown in part C of Fig. 1.The impulse in GDP has obvious positive effect onIFDI for all periods but negative effects on RU.Moreover, the impulses of OFDI and DOP havealmost positive effects on GDP; but the former shockhas negative effect for the third and fifth periods andthe latter shock has negative effect after fourthperiod. The graph shows that the responses of GDP
to a shock to OFDI, DOP and IFDI belong to fastdynamic adjustment. We confirm that degree ofopenness and FDI inflow are expected to generatenonconvex economic growth by encouraging theexpansion of trade and foreign investment. Inaddition, according to Solow’s growth theory,employment for labour force with skill can furtherpromote economic growth and this has been verifiedby Taiwan’s economy model. Okun’s law stating thatreducing unemployment for labour force can pro-mote further economic growth is then verified.
The response of DOP to five variables disturbanceis shown in part D of Fig. 1. The IFDI and RUshocks have an immediate negative impact on DOP;but IFDI shock becomes positive in the next period.The impacts of OFDI and GDP have positive effecton DOP; but the former shock has negative effect forthe fifth period. That is, a positive shock in OFDIand GDP can induce an increase in DOP but GDPshock is higher than OFDI. Therefore, we observedthe features of ‘GDP led trade’ and ‘FDI led trade’.The response of RU to five variables disturbance isshown in part E of Fig. 1. A shock to GDP and IFDIappears to have negative effects on RU. Theseresults can be explained by the fact that the growth
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8
lnIFDIlnOFDIlnGDP
lnDOPlnRU
(A) Accumulated response of lnIFDI to CholeskyOne S.D. Innovations
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8
lnIFDIlnOFDIlnGDP
lnDOPlnRU
(B) Accumulated response of ln OFDI to Choleskyone S.D. Innovations
−0.008
−0.004
0.000
0.004
0.008
0.012
0.016
1 2 3 4 5 6 7 8
lnIFDIlnOFDIlnGDP
lnDOPlnRU
(C) Accumulated response of lnGDP to Choleskyone S.D. Innovations
−0.02
−0.01
0.00
0.01
0.02
0.03
1 2 3 4 5 6 7 8
lnIFDIlnOFDIlnGDP
lnDOPlnRU
(D) Accumulated response of lnDOP to Choleskyone S.D. Innovations
−0.12
−0.08
−0.04
0.00
0.04
0.08
0.12
1 2 3 4 5 6 7 8
lnIFDIlnOFDIlnGDP
lnDOPlnRU
(E) Accumulated response of in RU to Choleskyone S.D. Innovations
Fig. 1. Accumulated impluse responses (first difference) based on VAR(4) process
Interactions among FDI, economic growth, openness and unemployment 1659
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
of GDP and FDI inflow tend to improve domesticunemployment rate and result in increasing employ-ment for labour force with skills. In addition, theresponses of RU to a shock in OFDI and DOP arealmost positive effects on RU, but the latter shockbecomes negative effect after the fourth period.
VI. Conclusions
This research applied the VAR technique of variancedecomposition and impulse response function analy-sis to analyse various inter-relationships betweenFDI, degree of openness, unemployment rate andGDP variables in the case of Taiwan from the periodof 1981 : 1 to 2003 : 3. It was found that these fivevariables have one cointegration, which indicates thatthey have a long-run equilibrium relationship. Wealso found that unemployment rate and FDI outflowhave weak exogeneity. In addition, there also existunidirectional causalities from FDI outflow to FDIinflow, from economic growth to degree of openness,and from economic growth to unemployment inshort-run. However, there is no causal direction fromeither economic growth or unemployment rate toFDI outflow in short-run.
Furthermore, the shocks of economic growth anddegree of openness have positive effects on FDIinflow. The shocks of FDI inflow and outflow havealmost positive effects on economic growth; how-ever, the shock of unemployment rate has negativeeffect on economic growth. We also found that theshocks of inward and outward FDI have no obviouseffects on economic growth. The shock in FDIinflow has an indistinct impact on degree ofopenness. The shocks in economic growth andFDI inflow decrease the unemployment rate. Thisresult means that rapid economic growth and FDIinflow, accompanied by higher per capita income,usually increase output growth. Thus, domesticfirms and foreign multinational corporations willdemand more labour force with skills to createproducts. Hence, economic growth can promotefuture employment growth for labour force based onnew Keynesian theory of the output-inflation trade-off. Furthermore, the shock of economic growth haspositive effect on degree of openness. The impact ofdegree of openness has positive effect on unemploy-ment rate but this shock is small in economicgrowth. Thus, an appropriate development strategysuch as providing incentives for economic growthcan lead to expansion of trading. As far as Taiwanis concerned, we suggest that appropriate economicgovernance such as offering disciplined work force
with modern technical skills can attract foreign
investment.
References
Apergis, N. and Rezitis, A. (2003) An examination ofOkun’s law: evidence from regional areas in Greece,Applied Economics, 35, 1147–51.
Blomstrom, M., Fors, G. and Lipsey, R. (1997) Foreigndirect investment and employment: homecountryexperience in the United States and Sweden,Economic Journal, 107, 1789–97.
Bende-Nabende, A. and Ford, J. L. (1998) FDI, policyadjustments and endogenous growth, multiplier effectsform a small dynamic model for Taiwan, 1959–1995,World Development, 26, 1315–30.
Bende-Nabende, A., Ford, J. L. and Slater, J. (2001) FDI,regional economic integration and endogenousgrowth, some evidence from Southeast Asia, PacificEconomic Review, 6, 383–99.
Bende-Nabende, A., Ford, J. L., Santoso, B. and Sen, S.(2003) The interaction between FDI, output and thespillover variables: co-integration and VAR analysesfor APEC, 1965–1999, Applied Economics Letters, 10,165–72.
Chen, T. J. and Ku, Y. H. (2000) The effect of foreign directinvestment on firm growth: the case of Taiwan’smanufactures, Japan and the World Economy, 12,153–72.
Dawson, P. J. and Hubbard, L. J. (2004) Exports andeconomic growth in central and east Europeancountries during transition, Applied Economics, 36,1819–24.
Engle, R. F. and Granger, C. W. J. (1987) cointegrationand error correction: representation, estimation, test-ing, Econometrica, 55, 410–21.
Geweke, J., Meese, R. and Dent, W. (1983) Comparingalternative tests of causality in temporal systems;analytic results and experimental evidence, Journal ofEconomics, 21, 161–94.
Gonzalo, J. (1994) Five alternative methods of estimatinglong-run equilibrium relationships, Journal ofEconometrics, 60, 203–33.
Gopinath, M., Pick, D. and Vasavada, U. (1998)Exchange rate effects on the relationship betweenFDI and trade in the U.S. food processing industry,American Journal of Agricultural Economics, 80,1073–79.
Gujarati, D. (1995) Basic Econometric, 3rd edn., McGraw-Hill, New York, pp. 749–50.
Hejazi, W. and Safarian, A. E. (2001) The complementarilybetween U.S. foreign direct investment stock andtrade, Atlantic Economic Journal, 29, 420–37.
Johansen, S. (1988) Statistical analysis of cointegratingvectors, Journal of Economic Dynamics and Control,12, 231–54.
Johansen, S. and Juselius, K. (1990) Maximumlikelihood estimation and inferences on cointegration -with applications to the demand for money,Oxford Bulletin of Economics and Statistics, 52,169–210.
1660 S. C. Chang
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. andShin, Y. (1992) Testing the null hypothesis ofstationary against the alternative of a unit root,Journal of Econometrics, 54, 159–78.
Liu, X., Song, H. and Romilly, P. (1997) An empiricalinvestigation of the causal relationship between open-ness and economic growth in China, AppliedEconomics, 29, 1679–87.
Liu, X., Burridge, P. and Sinclair, P. (2002) Relationshipsbetween economic growth, foreign direct investmentand trade: evidence from China, Applied Economics,34, 1433–40.
MacKinnon, J. G. (1991) Critical Values for cointegrationtests, Long-Run Economic Relationship: Readings inCointegration, Oxford University Press, Oxford,pp. 267–76.
Maddala, G. S. (1992) Introduction to Econometrics,2nd edn., Prentice-Hall, London, pp. 596–7.
Marchant, M., Cornell, D. and Koo, W. (2002)International trade and foreign direct investment:substitutes or complements, Journal of Agriculturaland Applied Economics, 34, 289–302.
Masih, A. M. and Masih, R. (1995) Temporal causality andthe dynamic interactions among macroeconomicactivity within a multivariate cointegrated system:evidence from Singapore and Korea,Weltwirtschaflisches Archiv, 131, 265–85.
Moore, M. O. (1993) Determinants of Germanmanufacturing direct investment: 1980–1988,Weltwirtschaftliches Archiv, 129, 120–37.
Pfaffermayr, D. (1994) Foreign investment and exports:a time series approach, Applied Economics, 26, 337–51.
Park, J. Y. and Phillips, P. C. B. (1989) Statistical inferencein regressions with integrated processes: part 2,Econometric Theory, 5, 59–132.
Perron, P. (1989) The great crash, the oil price shock,and the unit root hypothesis, Econometrica, 57,1361–401.
Schaik, V. and Groot, D. (1998) Unemployment andendogenous growth, Labour, 12, 189–219.
Sims, C. A. (1980) Macroeconomics and reality,Econometrica, 48, 1–48.
Sims, C. A., Stock, J. H. and Watson, M. W. (1990)Inference in linear time series models with some unitroots, Econometrica, 58, 113–44.
Sogner, L. (2001) Okun’s law does the Austrian unemploy-ment-GDP relationship exhibit structural breaks,Empirical Economics, 26, 553–64.
Sun, H. and Parikh, A. (2001) Exports, inward foreigndirect investment (FDI) and regional economic growthin China, Regional Studies, 35, 187–96.
Wang, Z. Q. and Swain, N. (1997) Determinants of inflowof foreign direct investment in Hungary and China:time-series approach, Journal of InternationalDevelopment, 9, 695–726.
Wei, Y., Liu, X., Song, H. and Romilly, P. (2001)Endogenous innovation growth theory and regionalincome convergence in China, Journal of InternationalDevelopment, 13, 153–68.
Yaghmaian, B. and Ghorashi, R. (1995) Export perfor-mance and development: an emprical analysis,American economist, 39, 37–46.
Zhang, K. and Song, S. (2000) Promoting exports the roleof inward FDI in China, China Economic Review, 11,385–96.
Zivot, E. and Andrew, D. W. K. (1992) Further evidence onthe great crash, the oil-price shock, and the unit-roothypothesis, Journal of Business and EconomicStatistics, 10, 251–70.
Appendix
IFDI and ODFI: real foreign direct invest-ment inflow and outflow deflated by the GDPdeflator.
DOP: denoting that values of goods and services forreal exports and imports divided by real GDP (basedon 1996).GDP: real gross domestic product (based on 1996).RU: unemployment rate (%).
Interactions among FDI, economic growth, openness and unemployment 1661
Dow
nloa
ded
by [
Mos
kow
Sta
te U
niv
Bib
liote
] at
01:
17 1
0 D
ecem
ber
2013
