ECONOMIC GROWTH

GLOBALIZATION AND FINANCIAL DEVELOPMENT IMPACTS ON ECONOMIC GROWTH 1

Tsolmon Sodnomdavaa 2 Uyanga Gantumur 3 Dansranbavuu Lkhagvaa 1

Department of Economics and Business, Mandakh University, Mongolia tsolmon@mandakh.mn , 2 uyanga@mandakh.mn , 2 dansranbavuu@mandakh.mn

Abstract Recently, the development of globalization has become more important. Globalization is “an increase in the extent to which individuals and institutions transact or exchange with others based in nation states other than their own, or otherwise influence them through their economic and social behavior”(Centre For Economic Policy research, 2002). So, our study has a main aim to investigate how globalization and financial development affect economic growth. To estimate the impacts from globalization and financial development on economic growth, panel cointegration, FMOLS and generalized impulse response analysis are examined for panel data of 86 countries both developed and developing over the period 1993-2013. To compare the effects of globalization and financial development in different income-level countries, the full sample, covering eighty six countries, is divided into four income groups. The empirical results are shown as the followings. First, higher economic globalization decreases economic growth, but higher social globalization rises economic growth. Second, all coefficients of three indices of financial development are positive, implying higher financial development can increase economic growth. Third, comparing the effects of three indices of financial development on economic growth, rising domestic credit to private sector can cause higher effect than other two indices of financial development, domestic saving and M2. Fourth, the effects of economic globalization on economic growth are different in different income-level countries, higher economic globalization can decrease economic growth in the high and middle-high income groups, but there are converse effects in the middle and low income groups. However, the effects of social globalization on economic growth are positive in all of four income-level groups. Finally, all of three indices of financial development can cause positive effects on economic growth in the high and middlehigh income groups, but they have inconsistent effects on economic growth in the middle and low income groups.

Keywords: Globalization, Financial Development, Economic Growth, Panel Cointegration I. INTRODUCTION There are many studies have focused on the relationship between financial development and economic growth for a long time. The main question is that the economic growth can cause financial development or financial development can promote economic growth. This studies dates back to Schumpeter (1911), the seminal contribution of King and Levine (1993a, 1993b) reinstituted the interest of subject and gave a boost to develop academic researchers. There are abundant empirical analysis which addressed the relationship between financial development and economic growth (for instance, Levine, Loayza and Beck, 2000; Beck, et al., 2000).

Recently, the development of globalization has become more important lately. Globalization is “an increase in the extent to which individuals and institutions transact or exchange with others based in nation states other than their own, or otherwise influence them through their economic and social behavior”(Centre For Economic Policy research, 2002). Hence, more literature have been discussed the effects of globalization (including the impacts on economic growth, distributional consequences, government expenditure, financial markets, etc.) in different economies, and their finding show

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inconsistent outcomes and arguments. There some theory argues that globalization can be global beneficial to increase GDP, because free trade can improve economic growth. Economic globalization can cause some factors, such as scale economies, competition, and technological innovation, and further can improve economic growth in the long run(Bergh and Nilsson, 2014). On the contrary, Stiglitz (2004) debates that globalization could cause some harmful impacts on employment and bring about some risks. Nevertheless, globalization has increasingly affected the internationalization of different sectors.

The goal of this study is to examine the long-run relationship and the inter-temporal link among globalization, financial development, and economic growth. The organization of the paper is as follows: o In section 2 discuss the methodology o In section 3 discuss the data o In section 4 we examine the long-run relationship of globalization, financial development, and economic growth by using panel unit root tests, Pedroni’s panel cointegration test, and FMOLS. Furthermore, short term shocks on economic growth are examined by using generalized impulse response analysis (GIRF).

II. LITERATURE REVIEW There are many papers have studied the interrelationship of financial development and economic growth over several past decades. The early studies of Gurley and Shaw (1955) elaborated that “there is mutual stimulation and mutual checks and balances relationship between financial system and economic growth”, and Goldsmith (1969) demonstrated “the important effect of financial structure on a country’s economic development“, and Hicks (1969) has suggested that financial development stimulates economic growth. Moreover, some other researchers, such as Braun and Raddatz (2007), Ranciere et al (2007), Jung (1986), Roubini and Sala-I-Martin (1992), and King and Levine (1993), believed that the level of financial intermediation is a good indicator of economic growth and financial development is an important key to economic growth.

Using the data of 80 countries for the period of 1960-1989, King and Levine (1993) examined the relationship between financial development and economic growth. In this study real per capita GDP growth was used as the indicator of growth, and the indicators of financial development included liquid liabilities which consist of currency held outside the banking system plus demand and interest bearing liabilities of banks and nonbank financial intermediaries (M3), the ratio of deposit money banks’ domestic assets to deposit money banks’ domestic assets plus central bank domestic assets, the ratio of claims on the nonfinancial private sector to total domestic credit (excluding credit to money banks), the ratio of claims on the nonfinancial private sector to GDP. It was concluded that financial development makes contribution to economic growth (King and Levine, 1993).

Many latest papers have applied the KOF Index of Globalization 1 to examine the effects of globalization on diverse topics, and most of these studies that examined the effects of globalization on economic growth were done after 2006. The main reason for that, most of the empirical works used the globalization index which is developed by Dreher (2006) 2. Dreher (2006) analyzed the relation between globalization and economic growth with panel data analysis technique by using the data of 123 countries from years 1970 to 2000. He found out that globalization affects the economic growth in a positive way.

Chang and Lee (2010) analyzed the connection between general globalization index and three sub-indices, economic, social, and political globalization indices, and the economic growth of 23 OECD Potrafke(2015) surveyed over 100 empirical papers which used the KOF indices to test the effects of globalization.

Some of them used financial integration, liberalizing, trade and financial receptivity variants, representing globalization.

1 2

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countries. The sample period is from 1970 and 2006, and the methodology is cointegration. The results of the analysis showed that there is a weak connection between variants and causality in short terms, but in long terms there is unidirectional connection from general, economic and social globalization to economic growth.

III. METHODOLOGY Several panel unit root tests are used to verify that all variables are non-stationarity of the series in panel data, including: the Levine-Lin-Chu test (LLC, Levine, & Lin-Chu, 2002), the Im-Pesaran-Shin test ( IPS,Im-Pesaran, & Shin, 2003) the Maddala and Wu-Fisher Augmented Dickey Fuller (ADF) test, and the Maddala and Wu-Fisher Phillips and Perron (PP) test (Maddala & Wu, 1999). The LLC is a homogeneous panel unit root test, while IPS, ADF and PP are panel heterogeneous unit root tests.

The LLC test is based on the principles of the conventional Augmented Dickey Fuller (ADF) test and explores the heterogeneity of intercepts across the members of the panel. It uses the equation (1): L

∑

∆xi,t = i + bxi,t-1 +

z∆xi,t-z + t

(1)

z=1

The IPS test is an extension of the LLC test, which relaxes the assumption of homogeneity, which is, allowing heterogeneity in the autoregressive coefficients for all the panel members. The basic equation for the panel unit root tests for IPS is as the quation (2) pi

∆yi,t = 0 + iYi,t-1 +

∑

i,j ∆yi,t-j + t,t

(2)

j=1

The IPS statistic is based on averaging individual Augmented Dickey-Fuller (ADF) statistics and it exhibited as follows: t iT (¿) (3) N 1 t́ = ∑ ¿ N i=1 Where tiT is the ADF-statistic for the unit root test and i based on the country-specific ADF regression.

IPS shows that under the null hypothesis of non-stationary in panel data framework, the t́ statistic follows are standard normal distribution asymptotically. The standardized statistic tips is expressed as:

√ N [ t́− tips=

√

1 N

N

∑ E [ tit /❑i=0 ] ] i=1

(4)

N

1 ∑ Var [ tit /❑i=0 ] N i=1

Maddala and Wu (1999) argue that while Im et al.’s tests relax the assumption of homogeneity of the unit root across the units, several difficulties still remain. They suggest the use of a Fisher type test, which is based on combining the p-values, i of the test-statistic for a unit root in each cross-sectional unit. The MW test statistic  given by: N

¿−2 ∑ ln ❑i i=1

(5) 3.2 Panel Cointegration Test The concept of cointegration introduced by Granger is relevant to the problem of the determination of long-run relationships between variables. The basic idea that underpins cointegration is simple. If the

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difference between two non-stationary series is itself stationary, the two series are cointegrated. The implementation of Pedroni’s cointegration test requires estimating first the following long-run relationship: Yit = i + it + lixl,it + 2ix2,it + … + MixM,it + it (6) For i = 1,…,N ; t = 1,…,T ; m = 1,…,M , Where N refers to the numbers of individual members in the panel; T refers to the number of observation over time; M refers to the number of exogenous variables. The structure of estimated residuals is follows: ^ it =^ i ^ it−1 + u^ it (7) 3.3 Fully Modified Ordinary Least Square (FMOLS) Cointegration Estimation Having established the existence of cointegration, the next step is to estimate the associated long-run cointegration parameters. Although ordinary least squares (OLS) estimators of the cointegrated vectors are super-convergent, their distribution is asymptotically biased and depends on nuisance parameters associated with the presence of serial correlation in the data (Pedroni, 2000). Many problems that exist in time-series analysis may also arise in panel data analysis and tend to be more prevalent in the presence of heteroscedasticity (Kao & Chiang, 2000). For this reason, several estimators are proposed.

This study uses two panel cointegration estimators: the between-group fully modified OLS (FMOLS).

FMOLS provide consistent estimates of standard errors that can be used for inference. According to Kao and Chiang (2000), both FMOLS estimator have normal limiting properties.

FMOLS estimation shown as follows: Yi,t = i + x i,t +  i,t and, (8) x i,t = x i,t-1 + v i,t (9) Where, αi allows for the country specific fixed effects, β is a cointegrating vector if yi,t is integrated of order 1. At the same time, the vector error process εi,t = (µi,t , νi,t) is a stationary process.

3.4 Generelized impulse response analysis Variance decompositions identify the proportion of variance in one variable caused by the economic growth in other variables in a VAR system. Therefore, by employing variance decomposition techniques, a researcher can find the relative importance of a set of variables that affect a variance of another variable. Impulse responses can trace out the dynamic responses of a variable to economic growth in other variables in the system. Variance decompositions may be termed as out sample causality tests. Both analyses are employed using the moving average representation of the original VAR. The following brief technical discussion, based on Koop et al. (1996), Pesaran and Pesaran (1997), and Pesaran and Shin (1998), is rephrased from Cheung and Yeun (2002). Consider the following VAR representation for gt: ɸ gt −i+ ¿ t ρ (10) ¿= A ∑ ¿ i

where gt is a m×1 vector of jointly determined endogenous variables, ɸ1 trough ɸp are m×m matrices of coefficients to be estimated, A is a vector of constant, t is linear time trend, and t is an m×1 vector well-behaved disturbances with covariance ∑=Ϭiʲ. The generalized impulse response of gt+n with respect to a unit shock to jth variable at time t is represented by (sn∑ej)(Ϭij), where Sn=ɸ1Sn-1+ɸ2Sn2+……+ ɸpSn-p, n=1,2,…., S0=1, Sn=0 for n˃0, and ej is m×1 selection vector with unity as its jth element and zero elsewhere.

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IV.

DATA

Our data of economic globalization and social globalization were obtained from KOF website, which offers data information for 122 countries annual time series data over the period from 1970-2013, but this information is not available for the whole period for each country. Hence, the empirical analysis of this paper is based on a panel of 86 countries over the period of 1993-2013 and the variables in natural logarithm. The data of economic globalization (EG) and social globalization (SG) were obtained from the website of KOF Index (introduced in 2002 and published in 2006). Three variables of financial development include domestic credit to private sector/GDP (DDPS), gross domestic savings/GDP (GDS), and M2/GDP (M2). All three financial development’s variables, government final consumption (EXPEND), and GDP per capita were obtained from the World Development Indicators published by the World Bank fiscal year 2018.

In this paper we divided the countries to four sub-income groups to give better picture of the long-run relationship among globalization, financial development, and economic growth. To estimate the relationship between high, upper middle, middle, and lower income group, we used the World Bank income classification for the current 2018 fiscal year dispensation of economies. The lower income economies cover the countries whose GNI per capita is $1,005 or less in 2016; the lower middle-income economies are those with GNI per capita between $1,006 and $3955 and we joined this two into one category. Also upper middle-income economies are those with a GNI per capita between $ 3956 and $12,235; high-income economies are those with a GNI per capita of $12,236 or more.

V. RESULT The results of panel unit roots for four income groups are presented in tables 1A and 1B., which show that all variables (EG, SG, GDP, EXPEND, GDS, DCPS, M2) in levels are I(I) because the null hypothesis with unit root is accepted but the results of first differences of all variables reject the null hypothesis . Hence, the results of the panel unit root tests for all variables confirm that all variables are I(1) for all four income groups.

Table1A. Panel unit root test for high and upper middle income groups High Income Upper Middle Income Vari LL IPS AD PP LL IPS AD PP ables C F S F Level EG 4.3 1.4 37. 40. 2.2 1.5 27. 51.

532 979 066 733 210 731 250 719 8 8 1 9 3 1 6 5 (1.0 (0.9 (0.9 (0.9 (0.9 (0.9 (0. (0.

000 329 854 585 868 422 996 406 ) ) ) ) ) ) 4) 5) SG 2.6 1.4 49. 71. 8.6 59. 49.

763 090 273 903 889 0.3 454 896 5 5 9 9 9 804 1 9 (0.9 (0.9 (0.7 (0.1 (1.0 1 (0. (0.

963 206 858 037 000 (0.3 169 477 ) ) ) ) 518 2) 5) ) GDP 0.3 1.6 29. 28. 2.6 2.5 36. 23.

689 774 095 313 028 017 751 452 5 2 2 1 1 4 8 9 (0.6 (0.9 (0.9 (0.9 (0.9 (0.9 (0. (0.

5

439 ) EXP 0.7 END 888 3 (0.7 849 )

533 ) 0.4 506 2 (0.6 739 )

995 ) 42.

217 4 (0.9 407 )

996 ) 61.

397 4 (0.3 553 )

954 ) 0.8 271 6 (0.7 959 )

GDS

0.4 762 8 (0.6 831 ) 0.4 886 4 (0.

687 5)

40.

406 4 (0.9 618 ) 48.

996 1 (0.7 941 )

57.

466 8 (0.4 951 ) 40.

967 0 (0.9 560 )

12.

869 8 (1.0 000 ) 3.6 081 1 (0.9 988 )

4.9 788 9 (1.0 000 ) DCP 4.4 S 058 1 (1.

000 0) M2

1.6 0.2 51. 58. 0.5 846 521 244 477 396 4 9 3 6 9 (0. (0. (0.7 (0.4 (0.7 954 599 227 577 053 0 6) ) ) ) First Difference EG 164 358 8.0 7.7 .86 .40 4.8 610 075 0 5 784 4 2 (0.0 (0.0 7 (0. (0. 000 000 (0.0 000 000 )** )** 000 0)* 0)* )** * * SG 130 366 4.4 5.7 .73 .95 9.3 441 513 9 8 145 4 7 (0.0 (0.0 2 (0.0 (0.0 000 000 (0.0 000 000 )** )** 000 )** )** )** GDP - 178 271 13. 8.6 .56 .11 8.0 752 201 6 2 411 7 3 (0.0 (0.0 2 (0.0 (0.0 000 000 (0.0 000 000 )** )** 000 )** )** )** EXP - 159 373 END 13. 7.5 .88 .32 8.4 041 425 7 8 673 0 2 (0.0 (0.0 3 (0.0 (0.0 000 000 (0.0 000 000 )** )** 000 )** )** )** GDS - 171 386 13. 8.2 .18 .97 7.0 804 207 2 1 277

6

938 ) 0.4 557 6 (0.3 243 ) 0.2 435 3 (0.5 962 ) 0.2 951 7 (0.3 839 ) 0.4 402 0 (0.6 701 )

918 6) 45.

481 3 (0.6 551 )

999 5) 52.

046 4 (0.3 942 )

38.

560 4 (0.8 329 ) 41.

116 9 (0.8 104 )

60.

257 0 (0.1 518 ) 39.

270 1 (0.8 628 )

6.1 958 0 (0.0 000 )**

129 .66 1 (0.0 000 )**

284 .84 3 (0.0 000 )**

7.3 998 2 (0.0 000 )** 6.5 236 8 (0.0 000 )** 6.4 234 6 (0.0 000 )** 7.1 119

147 .62 6 (0.0 000 )**

316 .78 2 (0.0 000 )**

132 .03 8 (0.0 000 )**

250 .96 5 (0.0 000 )**

127 .85 6 (0.0 000 )**

249 .14 0 (0.0 000 )**

45. 42.

576 351 1 8 (0.6 (0.7 513 704 ) )

144 319 .75 .28 2 9

6 (0.0 000 )** DCP S 8.5 663 8 (0.0 000 )** M2 10.

153 4 (0.0 000 )**

8 (0.0 000 )** 3.9 168 3 (0.0 000 )** 4.0 895 7 (0.0 000 )**

(0.0 (0.0 8 6 000 000 (0.0 (0.0 )** )** 000 000 )** )** 157 291 .83 .16 5.9 4.6 8 9 864 562 (0.0 (0.0 8 6 000 000 (0.0 (0.0 )** )** 000 000 )** )** 123 317 .75 .66 4.1 5.7 7 4 468 610 (0.0 (0.0 7 2 000 000 (0.0 (0.0 )** )** 000 000 )** )**

(0.0 (0.0 000 000 )** )** 109 .09 1 (0.0 000 )**

163 .54 5 (0.0 000 )**

119 .29 0 (0.0 000 )**

308 .94 3 (0.0 000 )**

Table1B. Panel unit root test for middle and low income groups Middle Income Low Income Va ria ble s

LL C

IPS

AD F

PP

EG

1.3 806 1 (0.9 163 ) SG 4.1 402 4 (1.0 000 ) G 0.7 DP 747 8 (0.7 808 )

0.4 225 9 (0.6 637 ) 0.7 935 9 (0.7 863 ) 1.1 271 0 (0.8 702 )

31.

960 9 (0.9 116 ) 42.

988 8 (0.5 149 ) 28.

281 1 (0.9 685 )

Level 42.

160 5 (0.5 507 ) 48.

571 6 (0.2 939 ) 12.

022 5 (1.0 000 )

EX 8.4 PE 422 ND 2 (1.0 000 ) G 7.2 DS 456 8 (1.0 000 )

0.0 190 7 (0.5 076 ) 0.7 585 4 (0.7 759 )

29.

284 3 (0.9 568 ) 33.

434 0 (0.8 768 )

52.

252 7 (0.1 840 ) 49.

318 9 (0.2 689 )

7

LL S

IPS AD F

PP

2.1 541 3 (0.9 844 ) 1.0 784 3 (0.8 596 ) 0.2 767 9 (0.3 910 ) 3.2 990 1 (0.9 995 ) 7.8 436 0 (1.0 000 )

1.1 439 7 (0.8 737 ) 1.4 659 7 (0.9 287 ) 1.6 582 8 (0.9 514 )

12.

421 9 (0.

900 8) 9.2 147 6 (0.

980 3) 8.3 641 6 (0.

989 2)

24.

514 4 (0.

220 6) 10.

915 2 (0.

948 4) 26.

186 0 (0.

159 8)

0.3 809 7 (0.6 484 ) 0.5 591 0 (0.7 120 )

12.

142 1 (0.9 111 ) 11.

051 5 (0.9 449 )

8.6 043 7 (0.9 871 ) 25.

747 9 (0.1 743 )

DC 11.

PS 286 9 (1.

000 0) M2 9.8 019 4 (1.

000 0)

0.9 877 1 (0.

838 4) 0.2 773 5 (0.

609 2)

23. 31. 5.8 504 867 743 6 1 6 (0.9 (0.9 (1.0 952 136 000 ) ) ) 36. 40. 4.3 730 014 595 3 9 2 (0.8 (0.6 (1.0 082 431 000 ) ) ) First Difference

EG

6.7 822 6 (0.

000 0)* * 7.1 043 7 (0.0 000 )** 6.3 324 8 (0.0 000 )** 6.0 141 3 (0.0 000 )** 6.7 391 4 (0.0 000 )** 5.1 093 3 (0.0 000 )** 5.9 513 9

SG

G DP

EX PE ND

G DS

DC PS

M2

5.0 376 2 (0.

000 0)* * 6.8 586 1 (0.0 000 )** 8.1 918 8 (0.0 000 )** 5.7 348 9 (0.0 000 )** 4.1 375 6 (0.0 000 )** 4.6 098 8 (0.0 000 )** 4.8 317 4

2.1 293 7 (1.0 000 ) 10.

969 2 (0.9 470 )

25.

622 7 (0.1 786 ) 23.

799 6 (0.2 513 )

125 .61 9 (0.0 000 )**

229 69. .35 8.4 5.6 451 0 497 512 4 (0.0 8 7 (0.0 000 (0.0 (0.0 000 )** 000 000 )** )** )**

167 .95 3 (0.0 000 )**

132 .61 8 (0.0 000 )**

247 74. .98 6.2 4.0 404 7 091 591 1 (0.0 2 1 (0.0 000 (0.0 (0.0 000 )** 000 000 )** )** )** 182 70. .95 6.4 5.9 281 7 639 459 5 (0.0 1 7 (0.0 000 (0.0 (0.0 000 )** 000 000 )** )** )** 228 90. .35 4.5 7.7 335 0 932 464 6 (0.0 0 7 (0.0 000 (0.0 (0.0 000 )** 000 000 )** )** )** 300 65. .81 4.1 5.4 495 2 668 670 0 (0.0 1 4 (0.0 000 (0.0 (0.0 000 )** 000 000 )** )** )** 145 70. .28 7.6 6.0 899 6 180 074 8 (0.0 1 4 (0.0 000 (0.0 (0.0 000 )** 000 000 )** )** )** 235 69. .99 7.3 5.8 704 8 475 585 2 (0.0 7 5 (0.0

105 .81 3 (0.0 000 )**

121 .57 5 (0.0 000 )** 114 .03 5 (0.0 000 )** 123 .81 6 (0.0 000 )** 101 .86 1 (0.0 000 )** 115 .02 1 (0.0

8

2.7 040 7 (0.9 966 ) 0.7 952 5 (0.7 868 )

118 .18 9 (0.0 000 )** 169 .95 6 (0.0 000 )** 148 .36 0 (0.0 000 )** 123 .05 8 (0.0 000 )** 149 .56 4 (0.0

(0.0 (0.0 000 000 (0.0 (0.0 000 000 000 000 )** )** 000 000 )** )** )** )** )** )** In Table 2, most of the results of the Pedroni’s test confirm that the null of no cointegration can be rejected at the 5% significant level. There are some exceptional cases, such as Panel rho-statistic, Group rho-statistic, and Group ADF-Statistic in all four income group, and Panel ADF-Statistic in the low income group. However, for all of four income groups, there are at least three statistics of the Pedroni’s test to reject the null of no cointegration at the 5% significant level, indicating the presence of a longrun equilibrium relationship among globalization, financial development, and economic growth.

Table2. Pedroni’s (1999) panel cointegration test for different income groups High Income Statistic 4.638870*** 6.999507 -4.671842*** -4.091655***

Panel v-Statistic Panel rho-Statistic Panel PP-Statistic Panel ADFStatistic Group rho8.376018 Statistic Group PP-Statistic -9.316881*** Group ADF-1.021698 Statistic Middle Income Statistic Panel v-Statistic 4.955135*** Panel rho-Statistic 6.289272 Panel PP-Statistic -5.494773*** Panel ADF-3.727362*** Statistic Group rho7.397527 Statistic Group PP-Statistic -7.107710*** Group ADF-1.496346* Statistic

Probability 0.0000 1.0000 0.0000 0.0000

Upper Middle Income Statistic Probability 4.610088*** 0.0000 5.707204 1.0000 -3.612134*** 0.0002 -2.360437*** 0.0091

1.0000

7.803213

1.0000

0.0000 0.1535

-2.658060** 0.698600

0.0039 0.2424

Probability 0.0000 1.0000 0.0000 0.0001

Low Income Statistic 3.566512*** 3.318423 -1.997788** 6.297904

Probability 0.0002 0.9995 0.0229 1.0000

1.0000

4.443526

10000

0.0000 0.0673

-4.140175*** 7.645260

0.0000 1.0000

Having confirmed the existence of cointegration of income groups, the next step is to estimate the long-run cointegration’s parameters of all variables for four income group by FMOLS. The estimated results are reported in Table 7, several discussions can be presented as follows.

First, comparing the coefficients of economic globalization in four income groups, it is -3.7895 for the high income countries, -0.1367 for the upper middle income countries, implying higher economic globalization reduces economic growth in the high and upper middle income countries, but this impact is insignificant in the later. Conversely, the coefficient of economic globalization is 6.653 for the middle income countries and 4.2475 for low income countries, which means that increasing free movement of goods, capital, services, technology and information will cause higher economic growth in the middle income and low income countries. In addition the (Economic Globalization: Trends, Risks and Risk Prevention, 2000) disclosed that: “Economic globalization refers to the increasing interdependence of world economies as a result of the growing scale of cross-border trade of commodities and services, flow of international capital and wide and rapid spread of technologies. It reflects the continuing expansion and mutual integration of market frontiers, and is an irreversible trend 9

for the economic development in the whole world at the turn of the millennium.” However, economic globalization causes different effects on economic growth for different income countries.

Second, comparing the coefficient of social globalization in four income groups, it is 2.5731 for the high income countries, 4.7380 for the upper middle income countries, 0.6865 for the middle income countries, and 2.7997 for the low income countries, which means that the social globalization has a positive impact on economic growth for all four income groups, but it is insignificant for the middle income group. Growing the social dimension of globalization to raise impact of globalization on employment, working conditions, and income, and it will be more closely in touch with people all around the world, which will cause positive effect on economic growth.

Table3. FMOLS estimates for different income groups Test

Variables

FMOLS

Test FMOLS

EG SG DCPS EXPEND GDS M2 Variables EG SG DCPS EXPEND GDS M2

High Income Coefficient -3.785966*** 2.573109** 7.144931*** -3.538092*** 6.019163*** 4.596800*** Middle Income Coefficient ***

6.653004 0.686564 0.527861 -0.212027 -2.204423** 2.065931***

Probability

Upper Middle Income Coefficient Probability -0.136715 0.8913 *** 4.738083 0.0000 5.095363*** 0.0000 -0.212479 0.8318 1.343218 0.1799 ** 2.232976 0.0260 Lower Income Coefficient Probability

0.0000 0.4928 0.5979 0.8322 0.0281 0.0395

4.247523*** 2.799746*** 4.841490*** 0.385128 -1.160315 -1.702467*

Probability 0.0002 0.0104 0.0000 0.0004 0.0000 0.0000

0.0000 0.0057 0.0000 0.7006 0.2474 0.0904

Third, as to the coefficients of three variables of financial development, the M2’s coefficients are significantly positive for most of income groups at the 5% level, except for the low income group, implying increasing M2/GDP could rise economic growth most of countries except for the low income countries. The DCPS’s coefficients are positive for all income groups, displaying higher domestic credit to private sector/GDP could increase economic growth, but this effect is insignificant for the middle income countries. The GDS’s coefficients are positive for the high and upper middle income groups, but they are negative for the middle and low income groups, in other words, the effect of GDS on economic growth is converse between two higher income groups and two lower income groups. Furthermore, the coefficient’s value of three financial development’s variables are the highest for the high income group, showing that financial development can cause the largest effects on economic growth for the high income countries.

Finally, except for the high income countries, the coefficient of government consumption (EXPEND) is insignificant, which implies that increasing government consumption is not the efficient way to improve economic growth.

Generalized impulse response analysis (GIRF)

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To discuss the impact of each financial development variables (EG, SG, GDS, EXPEND, DCPS M2) on unanticipated changes in economic growth (GDP), the generalized impulse response analysis Response Generalized One S.D. Innovations 2 S.E. (GIRF) of PEARSON and±to2 SHIN (1988) is used± in this section.

Response to Generalized One S.D. Innovations S.E. Response of ECONOMIC_GLOBALIZATION to GDP 3

Response of SOCIAL_GLOBALIZATION to GDP 3

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Response to Generalized One S.D. Innovations S.E. Response to± 2Generalized One S.D. Innovations ± 2 S.E. Response of DCPS to GDP

Response of EXPEND to GDP

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One S.D. Innovations ± 2 S.E. Response to Generalized One S.D.Response Innovationsto± Generalized 2 S.E. Response of M2 to GDP

Response of GDS to GDP 8

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Fig.13 illustrates the effect from all independent variables on GDP over 8-year period after the beginning of the shock in 86 countries (the full sample). As Fig. 1, EG curve depicts economic growth could response from economic globalization and the effect continue to the eighth year, but SG curve interpreting that floats upward from the beginning of the shock to the fourth year but from fourth year declining and stabilizing.

Furthermore, the curve of DCPS floats upward from the beginning of the shock. On the other hand, the curve of EXPEND shows a downward trend and decreases to minimum (-0.4), and then remains stable, which means that EXPEND negatively correlated with economic growth. Moreover, the effect of the response of gross domestic savings (GDS) makes a little contribution to economic growth, and the effect of t M2 on GDP is a downward trend and increases rapidly from third year.

In light of the significances of all variables, only three variables, DCPS, EXPEND, and M2, can cause significant impacts on GDP at the 5% levels. M2 causes significantly negative impact within two years, and other two variables, and EXPEND causes significantly negative impact within five years, and GDS bring about significantly positive impact from the second year to the seventh year.

VI. CONCLUSION This study applies panel cointegration and impulse response test to examine the impacts from globalization and financial development on economic growth for the panel data of 86 countries over 1993-2013. Our main findings are as follows:

3 In the figures, the abscissa means time interval after a shock, while the ordinate means the level of economic growth. The solid line is the impulse response curve, and the dashed line is the confidence interval curves at confidence level of 5%.

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First, the effects of economic globalization on economic growth are different in different incomelevel countries, higher economic globalization can decrease economic growth in the high and middlehigh income groups, but there are converse effects in the middle and low income groups. Moreover, all of three indices of financial development can cause positive effects on economic growth in the high and middle-high income groups, but they have inconsistent effects on economic growth in the middle and low income groups.

Finally, from the results of impulse response function, it is shown that domestic credit to private sector and M2 can make a larger contribution to economic growth, while government expenditure and gross domestic savings can make a little contribution to economic growth in a full sample analysis.

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