Globalization, Financial Development, and Economic Growth: Evidence from panel cointegration tests and generalized impulse response analysis Author: Uyanga Gantumur, Tsolmon Sodnomdavaa, Dansranbavuu Email: uyanga@mandakh.mn , tsolmon@mandakh.mn , dansranbavuu@mandakh.mn Abstract This study investigates how globalization and financial development affect economic growth. To estimate the impacts from globalization and financial development on economic growth, panel cointegration, fully modified ordinary least square 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 religion countries, the full sample, covering eighty six countries, is divided into four religion groups. The empirical results are shown as the followings: economic and social globalization could increase economic growth only in Europe. As to the effects of financial development on economic growth in different areas, increasing M2/GDP could raise economic growth for most of areas, except for Africa and Middle East; higher domestic credit to private sector/GDP and gross domestic saving/GDP could increase economic growth for most of areas, except for Asia and Pacific. Keywords: Globalization, Financial Development, Economic Growth, Panel Cointegration
Introduction There are many literatures which have investigated the association between financial development and economic growth for a long time. 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 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. However, the existing literatures have not covered the index of globalization into the estimating effects of financial development on economic growth so far. To fill the gap of the existing empirical papers, this study set up an empirical model covering the index of globalization and financial development at the same time. Globalization has many different dimensions, but most of past literature only targets the
impacts of economic globalization. Except for the economic influence, many other factors can cause globalization. The paper uses the KOF Index of Globalization which covers three dimensions, including economic, political, and social globalization. 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:
In section 2 discuss the literature
In section 3 discuss the methodology
In section 4 discuss the data the empirical results , including examine the linkages among globalization, financial development, and economic growth by applying panel unit root tests, Pedroni’s panel cointegration test, and FMOLS. Furthermore, short term shocks on economic growth and examined by using generalized impulse response analysis (GIRF).
In section 5 is conclusion. 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) indicated that there is existence of mutual effects and important association between financial system and economic growth, and Goldsmith (1969) argued financial structure can cause key impacts on a country’s economic development. Hicks (1969) has suggested that financial development stimulates economic growth. Moreover, some other researchers, such as Braun and Raddatz (2007), Jung (1986), Roubini and Sala-I-Martin (1992), and King and Levine (1993a, 1993b), believed that the level of financial intermediation is a good indicator of economic growth and financial development is an important key to economic growth. However, the existing literature have not covered the index of globalization into the estimating impacts from financial development on economic growth so far, because they have just focused on the impacts of globalization on economic growth, or the impacts of financial development on economic growth. To fill the gap of the existing empirical papers, this study set up an empirical model covering the index of globalization and financial development at the same time, which can examine how globalization and financial development affect economic growth. Panel cointegration, FMOLS and generalized impulse response analysis are applied to test for panel data covering 86 countries from 1993 to 2013.
Research 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 (¿) N 1 t́= ∑ ¿ N i=1
(3)
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:
1
t ips=
N
√ N [ t́− ∑ E [ tit /❑i =0 ] ] N i=1
√
1 N
(4)
N
∑ Var [ tit /❑i =0 ] 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
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 y i,t is integrated of order 1. At the same time, the vector error process ε i,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
g t=A ∑ ¿
(10)
i
where gt is a m×1 vector of jointly determined endogenous variables, ɸ 1 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 wellbehaved disturbances with covariance ∑=Ϭ iʲ. 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+ɸ2Sn-2+……+ 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.
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.
Results The results of panel unit roots for four areas are presented in tables 8A and 8B., 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 for all areas. Hence, the results of the panel unit root tests for all variables confirm that all variables are I(1) for all areas.
Table 1 Panel unit root test for different areas (America/Caribbean and Europe) America/Caribbean IPS ADF
Variables
LLC
EG
2.35209 (0.9907 3.13755 (0.9991) 13.8358 1.0000) 19.4132 (1.0000) 17.5512 (1.0000) 6.71487 (1.0000) 7.15152 (1.0000)
-0.15589 (0.4381) 0.50444 (0.6930) 2.52500 (0.9942) 1.61463 (0.9468) -0.29691 (0.3833) -0.20721 (0.4179 0.30723 (0.6207)
-4.57941 (0.0000)** -4.24842 (0.0000)** -4.61153 (0.0000)** -5.44273 (0.0000)** -4.61416 (0.0000)** -4.44546 (0.0000)** -4.74900 (0.0000)**
-7.39095 (0.0000)** -5.86879 (0.0000)** -4.45998 (0.0000)** -6.68410 (0.0000)** -5.17053 (0.0000)** -4.94319 (0.0000)** -4.21305 (0.0000)**
SG GDP EXPEND GDS DCPS M2
EG SG GDP EXPEND GDS DCPS M2
Europe
PP Level 32.1523 48.2434 (0.8069) (0.1740) 29.8688 23.7118 (0.8788) (0.9809) 16.2476 18.7196 (0.9997) (0.9983) 21.1146 34.7830 (0.9939) (0.7038) 28.5981 40.3195 (0.8654) (0.4561) 27.2577 38.0685 (0.9377) (0.5575) 28.0195 46.2636 (0.9231) (0.2295) First Difference 131.260 192.846 (0.0000)** (0.0000)** 104.521 182.777 (0.0000)** (0.0000)** 90.0205 161.271 (0.0000)** (0.0000)** 117.286 220.218 (0.0000)** (0.0000)** 96.1897 213.869 (0.0000)** (0.0000)** 94.8031 166.631 (0.0000)** (0.0000)** 113.607 236.605 (0.0000)** (0.0000)**
LLS
IPS
ADF
PP
2.67894 (0.9963) 2.44831 (0.9928) 1.02558 (0.8475) 5.08469 (1.0000) 7.53803 (1.0000) 8.02326 (1.0000) 7.57490 (1.0000)
1.47821 (0.9303) 1.24079 (0.8927) 0.73133 (0.7677) -0.72633 (0.2338) 1.10413 (0.8652) 0.65769 (0.7446) 0.09610 (0.5383)
22.2990 (0.9800) 32.1056 (0.7380) 23.7426 (0.9658) 40.0050 (0.3812) 22.1731 (0.9810) 19.8891 (0.9932) 32.1148 (0.5603)
29.2616 (0.8446) 39.9870 (0.3820) 17.6756 (0.9980) 38.4055 (0.4511) 30.5665 (0.7989) 18.0468 (0.9975) 47.9968 (0.1283)
-9.01488 (0.0000)** -4.92204 (0.0000)** -9.40140 (0.0000)** -10.5043 (0.0000)** -8.30155 (0.0000)** -4.47517 (0.0000)** -6.74811 (0.0000)**
-5.76111 (0.0000)** -5.53176 (0.0000)** -6.29446 (0.0000)** -6.87078 (0.0000)** -7.36915 (0.0000)** -4.45454 (0.0000)** -5.16938 (0.0000)**
103.938 (0.0000)** 96.0699 (0.0000)** 106.535 (0.0000)** 114.278 (0.0000)** 120.625 (0.0000)** 85.3837 (0.0000)** 97.7088 (0.0000)**
271.280 (0.0000)** 226.749 (0.0000)** 172.445 (0.0000)** 190.409 (0.0000)** 274.843 (0.0000)** 128.138 (0.0000)** 173.158 (0.0000)**
Table2 Panel unit root test for different areas (Asia/Pacific and Africa/Middle East) Asia/Pacific Variables
LLC
IPS
ADF
Africa/Middle East PP
LLS
IPS
ADF
PP
29.7889 (0.9897) 23.7658 (0.9994)
38.945 (0.871 49.729 (0.484
29.6019 (0.9904) 36.0961 (0.8968)
44.068 (0.709 62.434 (0.111
17.7080 (1.0000) 37.7089 (0.8996)
53.260 (0.349 50.320 (0.460
Level EG SG GDP EXPEND GDS DCPS
3.26854 (0.9995) 0.02578 (0.5103)
0.72519 (0.7658) 0.72513 (0.7647)
26.5466 (0.9827) 41.1114 (0.5962)
34.6271 (0.8433) 42.1706 (0.5503)
1.38962 (0.9177) 0.11035 (0.5439)
4.17231 (1.0000) -0.65854 (0.2551)
20.6115 (0.9990) 42.8459 (0.5211)
10.5603 (1.0000) 49.1855 (0.2733)
1.46535 (0.9286) 5.75285 (1.0000)
-0.58081 (0.2807) 0.5930 (0.7234)
43.2678 (0.5029) 32.8739 (0.8909)
44.7061 (0.4420) 32.2204 (0.9060)
1.91964 (0.9725)
1.61808 (0.9472)
0.28809 (0.6134) 1.01443 (0.8448)
3.20619 (0.9993) 0.78678 (0.7843)
2.33551 (0.9902) 8.50618 (1.0000) 2.43176 (0.9925)
0.49284 (0.6889) 2.12215 (0.9831) 1.10791 (0.8660)
M2
4.4134 (1.0000)
0.2522 (0.5996)
34.1834 (0.8563)
41.2134 (0.5917)
2.80530 (0.9975)
0.01235 (0.5049)
41.6150 (0.7947)
54.302 (0.313
First Difference EG
-5.4465 (0.0000)**
-5.26876 (0.0000)**
104.121 (0.0000)**
259.523 (0.0000)**
-4.53674 (0.0000)**
-7.12674 (0.0000)**
140.277 (0.0000)**
334.08 (0.0000)
SG
-4.19938 (0.0000)** -9.71759 (0.0000)** -4.60992 (0.0000)** -4.75235 (0.0000)** -5.46316 (0.0000)**
-5.22645 (0.0000)** -9.1106 (0.0000)** -6.21675 (0.0000)** -6.61726 (0.0000)** -5.28399 (0.0000)**
104.341 (0.0000)** 159.329 (0.0000)** 118.123 (0.0000)** 122.353 (0.0000)** 103.075 (0.0000)**
210.732 (0.0000)** 239.420 (0.0000)** 235.737 (0.0000)** 289.338 (0.0000)** 189.069 (0.0000)**
4.30075 (0.0000)** -5.37020 (0.0000)** -7.42367 (0.0000)** -6.59130 (0.0000)** -4.50698 (0.0000)**
-7.37887 (0.0000)** -7.74706 (0.0000)** -9.09450 (0.0000)** -8.49131 (0.0000)** -6.84601 (0.0000)**
148.223 (0.0000)** 155.099 (0.0000)** 172.022 (0.0000)** 166.077 (0.0000)** 139.577 (0.0000)**
317.75 (0.0000) 246.88 (0.0000) 298.36 (0.0000) 317.23 (0.0000) 229.09 (0.0000)
-8.85558 (0.0000)**
-8.73831 (0.0000)**
160.188 (0.0000)**
269.134 (0.0000)**
-6.66377 (0.0000)**
-7.58852 (0.0000)**
149.786 (0.0000)**
282.02 (0.0000)
GDP EXPEND GDS DCPS M2
Table 3
Pedroni’s (1999) panel conitegration test for different areas
Panel v-Statistic Panel rho-Statistic Panel PP-Statistic
America/Caribbean Statistic 7.468189*** 4.467651 -2.728804***
P-value 0.0000 1.0000 0.0032
Europe Statistic 6.479994*** 5.576859 -6.398485***
P-value 0.0000 1.0000 0.0000
Panel ADF-Statistic Group rho-Statistic
5.842674 6.120357
1.0000 1.0000
-3.460833*** 7.531664
0.0003 1.0000
Group PP-Statistic Group ADF-Statistic
-4.893621*** 4.809739
0.0000 1.0000
-11.43579*** -2.712008***
0.0000 0.0033
Asia/Pacific Statistic
Africa/Middle East P-value
***
Panel v-Statistic Panel rho-Statistic
5.249240 7.462216
Panel PP-Statistic
-3.935136***
Panel ADF-Statistic
-5.079678
Group rho-Statistic
7.649051
Group PP-Statistic
-9.179678
Group ADF-Statistic
-0.748384
***
***
Statistic
P-value
0.0000 1.0000
7.236846 6.018184
***
0.0000 1.0000
0.0000
-3.244179***
0.0006
0.0000
0.499397
0.6913
1.0000
7.579713
0.0000
-4.077139
0.2271
-0.238051
1.0000 ***
0.0000 0.4059
Note: ***, ** and * mean significance at 1%, 5% and 10%, respectively.
In Table 3, most of the results of the Pedroni’s test confirm that the null of no cointegration can be rejected at the 5% significant level. For the America/Caribbean countries at 5% significant level, three statistics are significant; five statistics are significant for the European countries; four statistics are significant for the Asia/Pacific countries; three statistics are significant for the Africa/Middle East countries. However, for all four areas, 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 long-run equilibrium relationship among globalization, financial development, and economic growth for all four areas. The FMOLS estimated results for four areas are reported in table 10, several discussions can be presented as follows. First, comparing the coefficients of economic globalization in four areas, the EG’s coefficients are positive for Asia/Pacific (1.05) and Africa/Middle East (0.86), but they are negative for America/Caribbean (-0.83) and Europe (-3.91), in other words, higher economic globalization can rise economic growth for Asia/Pacific and Africa/Middle East but will reduce economic growth for America/Caribbean and Europe. Moreover, comparing the economic globalization between America/Caribbean (-0.83), Europe (-3.91), Asia/Pacific (1.05) and Africa/Middle East (0.86) countries, the coefficient of Asia/Pacific countries higher than other three sub-region groups, which means that increasing economic interdependence of national economies of Asian and Pacific region across the world through a rapid increase in cross-border movement of goods, service, capital and technology. However, the effect of economic globalization on economic growth is significant only in Europe at the 5% levels. Second, comparing the coefficient of social globalization in four areas, America/Caribbean (0.72), Europe (1.86), Asia/Pacific (-0.76) and Africa/Middle East (-0.74) countries, only the coefficient of European countries is significant and higher than other sub-region groups at the 10% levels, which adumbrating that increasing social globalization could cause higher economic growth only in Europe. Hence, Boockmann and Dreher (2003) point out that means of information and communication may prove important since they relay information about economic success in other countries, which can cause important effects in most of areas, except for Europe. Third, as to the coefficients of three variables of financial development, the M2’s coefficients are significantly positive for most of areas at the 5% level, except for Africa and Middle East areas, implying increasing M2/GDP could rise economic growth most of countries except for the counties in Africa and Middle East. The DCPS’s coefficients are significantly positive for most of areas at the 5% level, except for Asia and Pacific areas, displaying higher domestic credit to private sector/GDP could increase economic growth, but this effect is insignificant for the countries in Asia and Pacific. The GDS’s coefficients are significantly positive for most of areas at the 10% level,
except for Asia and Pacific area. Moreover, except for Asia and Pacific, the domestic credit to private sector/GDP’s coefficient is the highest in most of areas, showing that rising domestic credit to private sector/GDP is more efficient way to raise economic growth for most of the countries except for the Asian and Pacific countries. For instance, a 1% increase in domestic credit to private sector can increase economic growth by 3.95% for America/Caribbean countries, 6.93% for European countries, and 4.90% for Africa/Middle East countries. As to three financial development’s variables for the Asian and Pacific countries, only the coefficient of M2/GDP is significantly positive, hence, only increasing M2 is effectively to raise economic growth. Finally, the coefficient of EXPEND, for the America/Caribbean group is -0.74, for European group is 0.74, for Asia/Pacific group is -4.64, and for the Africa/Middle East group -11.53, which attesting that increasing government consumption can increase economic growth only in the European countries.
Table4 FMOLS estimates for different areas Test
Variables
FMOLS
EG SG DCPS EXPEND GDS M2 Variables
Test FMOLS
America/Caribbean Coefficient P-value -0.833154 0.4053 0.728184 0.4670 3.954648*** 0.0001 -0.744762 0.4569 1.818480* 0.0698 3.125576*** 0.0019 Asia/Pacific Coefficient P-value 1.054318 0.2924 -0.726423 0.4680 0.539874 0.5896 -4.649152*** 0.0000 1.403172 0.1614 2.275693** 0.0234
EG SG DCPS EXPEND GDS M2
Europe Coefficient P-value -3.915644*** 0.0001 1.866334* 0.0629 6.930693*** 0.0000 0.766209 0.4441 4.998365*** 0.0000 2.890476*** 0.0041 Africa/Middle East Coefficient P-value 0.860490 0.3900 -0.742432 0.4582 *** 4.905879 0.0000 -11.53454*** 0.0000 3.765930*** 0.0002 0.197981 0.8431
The results of GIRF Response to Cholesky One S.D. Innovations S.E. Response±to2 Cholesky One S.D. Innovations ± 2 S.E. Response of GDP to GDS
Response of GDP to ECONOMIC_GLOBALIZATION
3,000
3,000
2,000
2,000
1,000
1,000
0
0 -1,000
-1,000 2
4
6
8
2
10
4
6
8
10
Response to Cholesky One S.D. Innovations 2 Cholesky S.E. Response±to One S.D. Innovations ± 2 S.E. Response of GDP to DCPS
Response of GDP to SOCIAL_GLOBALIZATION
3,000
3,000
2,000
2,000
1,000
1,000
0
0
-1,000
-1,000
2
4
6
8
10
2
4
6
8
10
Response to One S.D. Innovations ± 2 S.E. Response to Cholesky One S.D. Innovations ± 2Cholesky S.E. Response of GDP to M2
Response of GDP to EXPEND 3,000
3,000
2,000
2,000
1,000
1,000
0
0 -1,000
-1,000 2
4
6
8
10
2
4
6
8
10
Fig.11 illustrates the effect from all independent variables on GDP over ten years after the initial the shock of each variable in 86 countries (the full sample). As Fig. 1, EG curve shows economic growth could 1 In the figures, the solid line is the impulse response curve, and the interval between two dashed line means the confidence interval at the 95% confidence levels.
change from economic globalization and the effect continue to the tenth year, but SG curve interpreting that floats from seventh year upward 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 then remains stable, which means that EXPEND negatively correlated with economic growth. Moreover, the effect of the response of gross domestic savings (GDS) causes small effect on economic growth, and the effect of t M2 on GDP is a downward trend. In light of the significances of all variables, only three variables, DCPS, Economic Globalization, and Social Globalization, 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 tenth year. The response of economic globalization has maximum response than other variables. Conclusion
This study uses panel cointegration and impulse response test to investiage the impacts from globalization and financial development on economic growth for 86 countries’ data from 1993 to 2013.Some findings of this work are summarized as the followings: First, such as for Australian/Caribbean countries like Australia, social globalization is not good way to increase the economic growth, second, for Asian countries, such as for Mongolia, the best option of raising economic growth is increasing economic globalization. Third, to raise economic growth, increasing economic globalization and M2 are better tools in European countries rather than in America, Asia and African countries. Increasing domestic credit to private sector and gross domestic savings are good options of all countries except in Asian countries.
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