62
occur as an impact of the strong domestic demand and expanded monetary supply.
Table 4.5 Unemployment Total
measured in percent of total labor force in National estimate
No Countries
Name 2009
2010 2011
2012 2013
1 China
4.3 4.1
4.1 4.1
4.1 2
Germany 7.7
7.1 5.9
5.4 5.3
3 Hong Kong
5.2 4.3
3.4 3.3
3.1 4
Indonesia 7.9
7.1 6.6
6.1 6.3
5 Ireland
12.0 13.9
14.6 14.7
12.0 6
Italy 7.8
8.4 8.4
10.7 12.2
7 Japan
5.0 5.0
4.5 4.3
4.0 8
Malaysia 3.7
3.4 3.1
3.0 3.1
9 Philippines
7.5 7.3
7.0 7.0
7.1 10
Portugal 9.5
10.8 12.7
15.6 16.3
11 South Korea
3.6 3.7
3.4 3.2
3.1 12
Spain 18.0
20.1 21.6
25.0 26.4
13 Thailand
1.5 1.0
0.7 0.7
0.7 14
Vietnam 2.3
2.3 2.0
1.8 4.4
Source: data processed Table 4.5 shows that the lowest unemployment rate reached by
Thailand in 2011, 2012, and 2013, that is 0.7. Thailand’s unemployment
rate is low along with the development of the investment flow in Thailand. Different from the US and the other developing countries, Thailand is rely
more on the labor intensive rather than the technological used. Thus, that makes the unemployment level on the Thailand is low.
Meanwhile, the highest unemployment rate reached by Spain in 2013, that is 26.4. Spain has the highest value of unemployment rate, as an
63
impact of the collapse of the countrys property bubble in 2008. Since the collapse in 2008 of a Spains labour-intensive property boom
, Spain’s unemployment rate continue to rise relentlessly. The unemployment rate
continues to rise relentlessly until 2013, and it reached the highest value in 2013. Neverthele
ss, Spain’s government has negotiated to find a way to reduce the number of unemployment in Spain.
Table 4.6 Import of Goods and ServicesGDP in percent
No Countries
Name 2009
2010 2011
2012 2013
1 China
22 26
26 25
24 2
Germany 38
42 45
46 45
3 Hong Kong
183 214
222 224
229 4
Indonesia 21
23 25
26 26
5 Ireland
74 81
81 84
86 6
Italy 24
29 30
29 28
7 Japan
12 14
16 17
20 8
Malaysia 71
76 75
75 73
9 Philippines
33 37
36 34
32 10
Portugal 35
39 40
39 40
11 South Korea
43 46
54 54
49 12
Spain 26
30 32
32 32
13 Thailand
58 64
72 74
70 14
Vietnam 73
80 84
77 90
Source: data processed Table 4.6 shows that the lowest importGDP value resulted by Japan in
2009, that is 12. The impact of the economic crisis on Japan has so far been relatively moderate, the Japanese trade has been badly hit. In
February 2009, indicate a 43 decrease in Japan volumes of imports. The
64
import decline was explained mainly by the declined volume of imported mineral fuels, which would in part reflect a drop in the price for crude oil.
Meanwhile, the highest importGDP value resulted by Hong Kong in 2013, that is 229. Hong Kong has the highest importGDP value because
Hong Kong has a free market economy, highly dependent on international trade and finance. Hong Kong has no tariffs on imported goods, and it
levies excise duties on four commodities, whether imported or produced locally: hard alcohol, tobacco, hydrocarbon oil, and methyl alcohol. Thus,
that what makes the Hong Kong GDPimport is high compared with the other countries.
Table 4.7 Current Account Balance in percent of GDP
No Countries
Name 2009
2010 2011
2012 2013
1 China
4.9 4.0
1.9 2.3
2.1 2
Germany 6.0
6.3 6.2
7.0 7.5
3 Hong Kong
9.9 7.0
5.6 1.7
2.9 4
Indonesia 2.0
0.7 0.2
-2.7 -3.3
5 Ireland
-2.2 1.1
1.3 4.4
6.6 6
Italy -1.9
-3.5 -3.1
-0.4 0.8
7 Japan
2.9 3.7
2.0 1.0
0.7 8
Malaysia 15.7
10.9 11.6
6.1 3.8
9 Philippines
5.6 4.5
3.1 2.8
3.5 10
Portugal -11.0
-10.6 -7.1
-2.1 0.5
11 South Korea
3.6 2.7
2.2 3.5
5.8 12
Spain -4.8
-4.5 -3.8
-1.1 0.7
13 Thailand
8.3 3.1
1.2 -0.4
0.7 14
Vietnam -6.2
-3.7 0.2
5.8 6.6
Source: data processed
65
Table 4.7 shows that the lowest current account balance reached by Portugal in 2009, that is -11. The imbalances have amplified the impact
of the crisis. The wage and price run-up occurred in Portugal during the boom, as a consequences its exports are less competitive on world markets
compare with the other Eurozone countries. As Portugal used the same currencies as other Eurozone countries, it cannot independently cut interest
rates or devalue their currency to stimulate growth. All these factors will make it harder to determine the policy independently to decrease the deficit
on the current account. Meanwhile, the highest current account balance achieved by Malaysia
in 2009. Since the Asian financial crisis until 2013 , εalaysia’s current
account balance has consistently recorded a surplus. The highest current account balance recorded in 2009, that is 15.7. Exports of goods have
outpaced imports, and have more than offset the deficit in the services and income accounts. The strong performanc
e of εalaysia’s exports was due to the broad diversification of products and the expansion of trade in new
markets. In particular, commodity exports, played an important role in contributing to the increase in the current account surplus.
66
2. Data Processing
Before conducting the panel data regression to test the hypothesis, firstly, the stationary and classic assumption test should have to do as the
requirement for conducting the panel data regression test. The next stage is determining the model that will be used in panel data regression method.
After all of the requirements have solved, thus the hypothesis testing is can be implemented.
a. Stationary Test
According to Granger and Newbold in Ariefianto 2012:124, the stationary test aimed to avoid the phenomena that known as spurious
regression. Spurious or nonsense regression is a phenomena where a regression equation has a good siginificance high R
2
even though there is no meaningful relationship between the two variables Gujarati,
2004:792. According to Gujarati 2004:815, we can reject the H
0 of
non- stationary
if the probability of α is 0.05. In this research, the stationary tested using Levin, Lin Chu t,
ADF, and PP unit root test
.
There are three approaches to test the stationary, they are: level, first difference, and second difference. If the
data is not stationer on the level, we can move to the first difference or the second difference test. The stationary test at level will be presented
in the following table:
67
Table 4.8 Stationary Test at level
No Variable
Method Levin, Lin Chu
t ADF
– Fisher Chi- square
PP – Fisher Chi-
square Statistic
Prob Statistic
Prob Statistic
Prob
1 CDS
-8.77487 0.0000
41.8714 0.0446
49.8817 0.0067
2 GDP
-33.6857 0.0000
103.793 0.0000
106.414 0.0000
3 INF
-5.66532 0.0000
47.3777 0.0125
61.3029 0.0003
4 UNEMP
-7.22625 0.0000
30.5704 0.2447
46.5753 0.0079
5 IMP
-16.9846 0.0000
68.8446 0.0000
84.9933 0.0000
6 CAB
-10.4601 0.0000
21.3734 0.8094
32.6039 0.2506
Source: data processed The requirement to reject the null hypothesis non-stationary is
statistic p- value should have to α 0.05. Based on the stationary test
on the level that served on table 4.8, it can be shown that the probability of ADF
– Fisher Chi-square for unemployment UNEMP variable is 0.2447, and the probability of ADF
– Fisher Chi-square and PP – Fisher Chi-square for current account balance CAB variables are 0.8094 and
0.2506. It means that the unemployment and current account balance variables are conceive of stationary problems, thus, these two variables
are not stationer at level. To solve the stationary problem, the stationary test have to
continued and move to the first difference test. The following table is shows the result of stationary test at first difference:
68
Table 4.9 Stationary Test at 1
st
difference
No Variable
Method Levin, Lin Chu
t ADF
– Fisher Chi- square
PP – Fisher Chi-
square Statistic
prob Statistic
Prob Statistic
Prob 1
CDS -8.64109 0.0000 65.4128 0.0001 63.8797 0.0001
2 GDP
-24.8321 0.0000 101.795 0.0000 131.585 0.0000 3
INF -13.8433 0.0000 33.3787 0.2221 46.0403 0.0173
4 UNEMP -9.77855 0.0000 26.3421 0.4444 29.5021 0.2887
5 IMP
-22.5145 0.0000 85.9519 0.0000 108.829 0.0000 6
CAB -8.64352 0.0000 43.1794 0.0334 51.3706 0.0045
Source: data processed The requirement to reject the null hypothesis non-stationary is
statistic p- value should have to α 0.05. Based on the stationary test
at the 1
st
difference that served on table 4.9, it shows that the probability of ADF
– Fisher Chi-square for inflation INF variable is 0.2221 and the probability of ADF
– Fisher Chi-square and PP – Fisher Chi-square for unemployment UNEMP variables are 0.4444 and
0.2887. It means that the inflation and unemployment variables are conceive of stationary problems, which is in these two variables are not
stationer at the 1
st
difference. Since the 1
st
difference test for unit root test cannot solve the stationary problem as well, thus, the stationary test have to continued
and move to second difference test. The following table is shows the result of stationary test at second difference:
69
Table 4.10 Stationary Test at 2
nd
difference
No Variable
Method Levin, Lin Chu
t ADF
– Fisher Chi- square
PP – Fisher Chi-
square Statistic
Prob Statistic
Prob Statistic
Prob 1
CDS -7.48963 0.0000 115.056 0.0000 108.643 0.0000
2 GDP
-38.6959 0.0000 129.664 0.0000 129.138 0.0000 3
INF -7.24232 0.0000 67.8396 0.0000 69.6795 0.0000
4 UNEMP -14.7336 0.0000 59.5434 0.0002 58.4277 0.0003
5 IMP
-16.0537 0.0000 103.095 0.0000 108.306 0.0000 6
CAB -11.2954 0.0000 89.5322 0.0000 88.0058 0.0000
Source: processed data The requirement to reject the null hypothesis non-stationary is
statistic p- value should have to α 0.05. Based on the stationary test
at the 2
nd
difference that served on table 4.10, it shows that all of the variables meet the criteria for reject the null hypothesis. Since all of the
variable does not have a stationary problem, thus the research can be continued to assumption classic test.
b. Classic Assumption Test
1 Normality Test
In eviews, the most commonly used test to detect the normality problem is Jarque
–Bera test. The hypothesis for Jarque–Bera test for normality is:
H = residuals are normally distributed
H
1
= residuals are not normally distributed
70
The requirement to reject the H is, H
is rejected if the JB value is chi square table, or H
is rejected if the probability of the Jarque
–Bera is α 0.05. The result of JB test can be seen in the following figure:
Figure 4.2 The Result of Jarque
–Bera Test for Normality
Based on the figure 4.2, the result of JB test shows that the value of Jarque
–Bera is 358.45 that is more than chi square table, that is 89.γ9, and the probability of JB is 0.00, that is less than the α 0.05.
thus, it can be concluded that the residuals is conceive of the normality problem.
According to Wooldridge in Ariefianto 2012:148, one of the advantage of using the panel data is, it becomes robust toward the
violation of the Gauss Markov assumption, which are: heteroscedasticity and normality. Since, this research is using the
5 10
15 20
25
-300 -200
-100 100
200 300
400 500
600 700
Series: Standardized Residuals Sample 2009 2013
Observations 70
Mean 1.14e-14
Median -22.61085
Maximum 694.1472
Minimum -253.3945
Std. Dev. 136.5552
Skewness 2.514476
Kurtosis 12.87978
Jarque-Bera 358.4595 Probability
0.000000
