令和 3 年度
三重大学大学院

生物資源学研究科

修士論文

北太平洋の海水温と世界の経済格差の共変動
Global Connectivity between GDP Growth Rates andNorth Pacific Sea Surface Temperature andGlobal Economic Disparity

Weather and Climate Dynamics Division
Graduate school of Bioresources
Mie University

520M203
Akane Kato

Supervisor: Prof. Yoshihiro Tachibana

February 26, 2022

1

Abstract
Weather and climate affect the economy. Weather in one region can affect weather in anotherregion, such as the El Niño phenomenon. Similarly, the economy of one country can affect theeconomy of another. Therefore, it is necessary to study the relationship between economy andweather / climate from a global perspective. Although there are studies investigated therelationship between the El Niño / Southern Oscillation (ENSO) and the economy, the relationshipbetween teleconnections other than ENSO and the economy is uncertain. It is also unclear how the
relationship with teleconnection change depending on the economic situation of the country andindustry. The purpose of this study is to investigate the relationship between teleconnections otherthan ENSO and economy of individual countries from a global viewpoint by using GDP. In
addition, we examine GDP of developing countries, and GDP by industry. Result show that Pacific
Decadal Oscillation (PDO) has the relation with GDP growth rates and developing countries tend
to make profits whereas developed countries loss profits in La Niña years. Furthermore, for thelevel 1 countries, the service sector tends to be made more profits in the La Nina summer than in
the El Nino winter. Our findings highlight the importance of the relationship between GDP growth
rates and PDO. In addition, our paper shows that it is important to consider the relationship withthe teleconnection index by economic situation and industry.
2

Keywords
Gross Domestic Product (GDP), Teleconnection, Pacific Decadal Oscillation (PDO), Southern
Oscillation (SO), Correlation coefficient31. Introduction
It is widely known that the economic activity of a country depends on domestic weather
and climate as well as on government policy and diplomatic relations. For example, Jeffrey et al.,(2011) showed that the gross domestic product (GDP) fluctuates significantly linking with thenumber of summer / winter days and precipitation variation in the United States of America.
Florian Badorf et al., (2020) also indicated that the effect of weather on daily sales can be as highas 23.1% based on the store location and as high as 40.7% based on the sales theme in Germany.
These studies investigated the relationship of the economy of a single specific country withthe weather of the same country. It is important to investigate from a global perspective inconsidering the relationship between weather / climate and the economy, because both weather /climate and the economy have the characteristics that local phenomena have wide-range andremote effects. The economic condition of one country can affect those of others. For example, afinancial crisis triggered by the bankruptcy of Lehman Brothers Holdings, a major American bankin 2008, stagnated the economies of Japan, Europe, Africa, and other countries around the world.
A similar remote influence can be seen in the weather and climate. For instance, It is widely known
that EL Niño Southern Oscillation (ENSO), which originates in the tropical Pacific Ocean. ENSO
affects global weather and climate. This kind of atmospheric remote influence is referred to as a4

teleconnection. We should therefore consider teleconnections when we investigate the relationshipbetween weather / climate and economy. Dufrenot et al., (2021) investigated the effect of ENSO
on global commodity prices and indicated the existence of a direct link between weather anomalies
and commodity inflation. Moreover, Iizumi et al., (2014) showed that El Niño likely improves theglobal-mean soybean yield by 2.1 to 5.4%, but appears to change the yields of maize, rice andwheat by -4.3 to 0.8%. They also indicated that the global-mean yields of all four crops during La
Niña years tend to be below normal (-4.5 to 0.0%). However, further investigations are needed.
First, teleconnections other than ENSO might also affect the global-scale economy because global
weather and climate is never determined by ENSO only. Second, the relationship of the economy
with weather and climate might depends on the sectors of industry. i.e., agriculture, industry, andservice. As mentioned above, there are studies focusing on specific industries, but they did notview from a global scale. Because the data and analysis methods are different in each study, it isdifficult to summarize the global influence if we pick up the previous studies only. Third,developing countries might have different linkages with weather/climate from those of developed
countries, because economic vulnerability on the extreme weather depends on the countries.
Global North-South divide might be amplified by extreme weather and climate. We thus need totake these factors into consideration in order to globally understand the relationship between
5economy and weather / climate.
The purpose of this study is to reveal the existence of linkage between climatic
teleconnection patterns and the economies of individual countries from a global viewpoint byusing GDP. Since the climatic effect may depend on the economic level, we classify countries byusing GDP. We also investigate the dependency on the sectors of industries because the responseof the agriculture sector to the climate may differ from the other sectors. Present study uses GDP
because it is widely used as an economic indicator. Obviously, climate alone does not determine
the economy. Climatic effect is usually hidden by other primary factors. Thus, present study
focuses only on listing the significantly correlated countries with climate, without taking accountof the degrees of climatic effect compared with those of other factors.

6

2.

Data and analysis methods
As economic indicators, we used 179-country GDP per capita spanning 57 years (1960 -2017).

The

data

are

availableatthewebsite

of

the

World

Bank:

https://data.worldbank.org/indicator/NY.GDP.PCAP.KD. GDP data are in constant 2010 U.S.
dollars. We can calculate total GDP from Eq. (1).

𝑌𝑌 = C＋I＋G＋(X − M)(1)where Y is total GDP, C denotes private consumption, I describes private investment, G isgovernment expenditure, X is export value, and M is import value. GDP per capita is calculatedby dividing the total GDP by the population of a country. The total GDP tends to increase as the
population increases, so even if the value is large, the people are not necessarily wealthy.
Accordingly, we can consider the GDP per capita to be more suitable as an index showing thestandard of living of the people than the total GDP. This study thus used the GDP per capita.
We calculated GDP growth rates of each country by the Eq. (2).

𝑔𝑔 =yt − yt−1
yt−1

7

(2)

where 𝑔𝑔 is GDP growth rate, yt denotes GDP per capita in a specific year, t, and yt−1 describes

GDP per capita in a year earlier. Year-to-year variations of the GDP growth rate in Brazil and South
Africa are shown in Figs. 1a and 1b as examples.
Additionally, we used data of percentage of individual sectors of industries, agriculture,industry, and services in GDP per capita. Agriculture includes forestry, hunting, fishing, crops, andlivestock production. Industry includes mining, manufacturing, construction, electricity, water,and gas. Services include value added in wholesale, retail trade (including hotels and restaurants),
transport, government, financial, professional and personal services for example, education, healthcare, and real estate. The World Bank supplies the GDP percentage of individual sectors ofindustries. We calculated the value of the three sectors of GDP of each country (1960 - 2017) using
Eq. (3).
𝑦𝑦𝐴𝐴 =

yt × PA100(3)where 𝑦𝑦𝐴𝐴 is GDP per capita of one field, yt denotes one country’s GDP per capita and PA describes

the rate of one sector of the three industry fields. After that, we calculated GDP growth rate by (1).
We classified all the countries into 4 levels by 2017 GDP. The classification method was based on
Hans Rosling et al. (2019). The countries with GDP 0 to less than 1460 dollars belong to Level
1(Fig. 2a). These countries are mainly on the African continent from latitude 30 degrees north to8

latitude 30 degrees south. The countries with GDP 1460 to less than 5840 dollars belong to Level
2 (Fig. 2b). The countries of this group are mainly distributed on the African Continent andSoutheast Asia. The countries with GDP 5840 to less than 11680 dollars belong to Level 3 (Fig.
2c). They are mainly located on the African continent and Eurasia Continent. The countries with
GDP of 11680 dollars or more dollars belong to Level 4 (Fig. 2d). This group’s countries aremainly on the Europa and the North America Continent and are subject to developed countries.
Some oil-producing countries belong to this group.
As for climatic teleconnection indexes, we used Pacific Decadal Oscillation (PDO) index(Nathan J. Mantua et al., 1997) and Southern Oscillation Index (SOI) (Zhang. Y. et al., 1997). We
focused on these teleconnections because they are known to have global climatic effects. PDO is
PDO is a major sea surface temperature fluctuation found in the North Pacific Ocean, with apattern of inverse sign deviations near the central region and along the North American coast.
When PDO index is positive in winter, it is known that the northwestern part of North America
tends to be warmer, and the southeastern United States tend to cooler than normal (Japan
Meteorological

Agencyathttps://www.data.jma.go.jp/gmd/kaiyou/data/db/climate/knowledge/pac/pacific_impact.html).
SOI is well known to as an ENSO signature; positive SOI indicates La Niña, and the negative is
9

EL Niño. The analysis period is from 1960-2017. Both indices were averaged over the threemonths of June, July, and August (JJA) and January, February and December (JFD) (Fig. 3 - 4).
From a climatological point of view, it is natural to use DJF mean value that is average from
December of a previous calendar year, January and February. GDP data is only available annually.
If we use DJF mean, it would crosse years and comparison between GDP and climate would
include the data of different year. We thus used the JFD mean climatic indices in correspondencewith GDP. To show the global distribution of linkage between GDP growth rates and theteleconnection indexes, we will display global maps of the countries that have significantly highenough correlation between the economic indicators and the teleconnection index in Results
section.
Since PDO has a linkage with SO to some degrees, there is possibility that the relationshipof PDO index with GDP growth rate might be owing to SO. For example, the correlation
coefficient between PDO and SOI in JJA is -0.40 and in JFD is -0.51. For this reason, we calculateda residual index by Eq. (4) to linearly exclude the SO influence,

𝑅𝑅 = PDO index − SOI × r(4)

where R describes the residual index, r denotes correlation coefficient between the PDO index and
10the SOI. The residual indexes are shown in Fig .4. The residual index is, by definition, independentof SOI.
To see the connectivity between GDP growth rates and the teleconnection indexes, we
calculated correlation coefficient of GDP growth rates of all the countries with PDO index, SOI,
and R. The number of the countries with statistically high enough correlation is not expected to belarge because many factors have influences on GDP of individual countries. Climate is notexpected to be a primary effect but a secondary one. To extract a hidden climatic effect on the GDP,following statistical procedure is performed. We first calculate the absolute value of the correlation
coefficients of all the countries in the four economic levels. Then we extract the countries thathave top 30 percentile in descending order of the absolute value in each economic level. The
number ratio of positively correlated countries to negative ones is calculated by using the extracted
countries in each economic level. The larger or the smaller the ratio, the stronger link between the
climate and the GDP. The reason for choosing 30 percentile is that the 30 percentile corresponds
to 10 or more samples since the number of the countries in each economic level is at least about
thirties.

11

3. Results
3.1. GDP and climate
First, we show relationship between GDP and PDO. The second row of Table 1 summarizes
the number ratio of positively correlated countries to negatively correlated countries in each GDP
level in the top 30 percentile countries with the absolute value of the correlation coefficient. Morethan 80% of the countries in the level 1, 2 and 3 are negatively correlated with PDO in JJA. GDP
response to the PDO index in the level 4 countries is in reverse to the level 1, 2 and 3 countries,e.g., 20.0% vs. 80.0% in the level 1 and 81.3% vs. 18.8% in the level 4. This indicates that thehighest economic level countries tend to make profits whereas lower-level countries lose profits
in the positive PDO years, and vice versa. Similar contrast is seen with less ratio when we use in
the JFD-mean PDO index. The relation of GDP with SOI is also significant in level 1 countries
both in JJA and JFD mean indices: 80 % countries of Level 1 raise the GDP in a La Niña year, i.e.,a positive SOI year. The connectivity of GDP with PDO is, on average, larger than with SOI.
Since PDO has some relation with ENSO, the countries with negative correlation with
PDO might be influenced by ENSO only. To see ENSO eliminated correlations, we use the residual
PDO index, in which ENSO signature is linearly excluded as explained in the Method section. The
results using the residual index of JJA is almost the same as those with PDO index with higher
12

ratio in the level 1 at 90:10. Therefore, connectivity of the GDP growth rates with PDO is not anapparent connection.
Table. 2 shows the lists of the number of countries that have significant correlation
coefficients with the climatic indices. The GDP growth rates of these countries significantly tie tothe climatic teleconnection. The names of the countries with significant coefficients are listed in
the appendix (Table. A.1). Figure 5a shows the map of significantly positively correlated countries
with PDO or R indices regardless of the season in the level 4 group. The map signifies that manyof level 4 countries are positively correlated with PDO. Figure 5b shows the same as Fig. 5a but
for the level 1 group with the significantly negative correlation.
3.2. Industry group and climate
Next, we show dependency on industry groups, i.e., primary, secondary, and third sectors.
Table 3 shows the same as in Table 1 but for the list classified by the industry groups. The mostoutstanding relation to the climatic teleconnections is the third sector of the level 4 countries, inwhich the positive/negative ratio for PDO index is 100:0. This indicates that many economicallyleading countries tends to make larger profits in the service sector in the positive PDO summer
than in the negative PDO, and vice versa. In contrast, the ratio in the level 1 countries is 30:70 in
the third sector. This see-saw like response between the level 1 and level 4 countries are the same13

as that of GDP growth rate shown in Table 1. This means that economically leading countries tends
to make larger profits while least developing countries make smaller profit in the service sector in
the year of positive PDO summer. The industry sector in the Level 1 countries also have similar
response to PDO: e.g., the ratio is 10:90 in PDO in JJA.
The primary sector, i.e., the agriculture, does not have statistically significant correlation
with both PDO and SOI overall. Dependency on the country level is also small. This result
noteworthy because it was expected that agriculture is the most vulnerable to climate variation of
all the industrial sectors. These results indicates that the contrasting response between level 1
countries and 4 countries is mainly owing to second and third industrial sectors. The contrast is
not owing to agriculture. Similar relation is also seen in SOI index for the economically leading
countries, i.e., the profits in the service sector of the leading countries are more in the El Nino
summer than in the La Nina summer. The ratio, 0:100 is seen in the industrial sector of level 1
countries. Contrasting response between level 1 and level 4 countries is also see in SOI withrelatively weaker connection than PDO. Again, agriculture section does not have significant ratio
overall.
Table. 4 shows the same as in Table 2 but for the classification by the industrial sectors.
Note that the countries of the secondary industry in level 4 are not always positively correlated14with climatic indices. The names of the countries with significant coefficients are listed in the
appendix (Table. A.2).
The relationship with residual index is resemble to PDO index. We thus consider that the
PDO variation excluding the influence of ENSO are still related to the service section in Level 4
countries.

15

4. Discussion
Here we consider why the PDO index is systematically related to GDP in many remotecountries. PDO, which is the phenomena within a North Pacific Ocean, influences weather andclimate in other regions of the globe through climatic teleconnections. Similarly, the economy of
one country can affect the economy of another. This can be described as “economic
teleconnection”. At the same time, weather and climate in a specific region affect the economy of
the same region. Thus, climatic teleconnection and economic teleconnection acts as a chain
response. Climatic teleconnection driven by PDO has an influence on remote country`s climate
along with its economy, which further influences the economy of other countries through theeconomic teleconnection. The economy of the country that is located far from the North Pacific is
thus connected to PDO. The presence of the chain of climatic-economic teleconnection may
increase the significantly correlated counties with PDO.

There are many countries with low correlation coefficients although the values exceed thelevel of statistically significant. This implies that teleconnection is not primarily linked to theeconomy. We thus repeat that the climatic linkage with economy is not the primary but thesecondary. Besides, large countries, such as Russia the United States, and China have different
16tendencies from those of their economic categories. Although their different responses are quite
interesting, the consideration of these countries will be in the future.
Next, we compare the present results with a previous study. Iizumi et al., (2014) showedthat the global-mean yields of all four crops during La Niña years tend to decrease. Present study
shows that ratio of agriculture in level 1 countries is 70:30 for SOI, i.e., agriculture section of the
GDP in level 1 countries tends to be positive in La Niña years. Overall connection of primaryindustry in level 2, 3 and 4 with SOI does not arrive the level of significant correlation. This
discrepancy is partially owing to different the data and method; GDP versus the yields of fourcrops. Also, appropriate climatic conditions for a specific crop probably differ from others. The
data we use includes fisheries and dairy.

17

5. Conclusions
The conclusions obtained in our study are as follows: First, the climatic teleconnections of
PDO and ENSO have an influence upon economic variation in many remote countries over theworld. Second, the economic response to the climatic teleconnections depends on the level ofannual GDP per capita. GDP growth rates tend to decline in the poorest countries when PDO is
positive. To the contrary, in the economically leading countries, the GDP growth rates tend to
incline when PDO is positive. Therefore, the response of the poorest countries to the climatic
teleconnection is opposite to that of the leading countries, i.e., global North-South divide is
amplified by PDO. Third, PDO and ENSO have a strong linkage with the service sector of
economically high-ranking countries. It should be noted that even the service sector of economical
leading countries is influenced by climatic teleconnection. Our analyses did not show significant
linkage between the agriculture sector and the climatic indices, contrary to expectations.
Our findings would provide the international economics with the consideration of global
influence of climatic teleconnections. The present study implies the existence of a chain ofclimatic-economic teleconnection. Weather and climate in one region remotely affect weather andclimate in another region through climatic teleconnections. Similarly, the economy of one country
can affect the economy of another through economic teleconnection. Thus, these two kinds of18

teleconnection acts as a chain response. When we investigate the relationship between economy
and weather or climate, it is thus necessary to consider from a global perspective, i.e., considering
the chain of climatic-economic teleconnection.
However, problems remain. First, we have not fully shown the causal relationship between
teleconnection and GDP growth. Second, larger countries, e. g., the United States of America,
behave different from the other many countries of the same economic levels. These problems
should be explored in the future. This study focuses only on two climatic teleconnections. Other
teleconnections must be scoped in the next step. For example, North Atlantic Oscillation and Arctic
Oscillation is known to have a strong impact on the weather and climate of extratropical regionsof the Northern Hemisphere, such as Europe, East Asia, and North America. To anatomize thechain of climatic-economic teleconnections will deepen climatic effect on economy.

19

Acknowledgments.
I express gratitude to Professor Yoshihiro Tachibana, my supervisor. He taught me the basic
knowledge of the physics and detailed knowledge of the physical oceanography and theatmospheric dynamics. Graduate teachers and students at Mie University offered us fruitful advice.
I thank that very much.
A special gratitude I give to Dr. Yuta Ando and Dr. Satoru Kasuga for his continuous
support and very helpful discussions. Members of Weather and Climate Dynamics Division
provided some advice for my research. I would like to thank for them.
The Grid Analysis and Display System (GrADS) was used to draw the figures.

REFERENCES
Gilles Dufrénot William Ginn, amd Marc Pourroy (2021), The Effect of ENSO Shocks on
Commodity Prices: A Multi-Time Scale Approach, AMSE Working Papers, WP 2021-Nr30
Florian Badorf, KaiHoberg (2020), The impact of daily weather on retail sales: An empirical study
in brick-and-mortar stores, Journal of Retailing and Consumer Services, 52, 101921
Hans Rosling, Anna Rosling Ronnlund, and Ola Rosling (2019), Factfulness: Ten Reasons We're
Wrong about the World--And Why Things Are Better Than You Think, Flatiron Books, 352p.
20

Japan Meteorological Agency, Impact of decades to decades of fluctuations in sea surface
temperatureonatmospheric

flow

and

weather,

2021,

from

https://www.data.jma.go.jp/gmd/kaiyou/data/db/climate/knowledge/pac/pacific_impact.html
Jeffrey K. Lazo, Megan Lawson, Peter H. Larsen, and Donald M. Waldman, (2011), U.S.
Economic Sensitivity to Weather Variability, Bulletin of the American Meteorological Society,292, 709-720.
Nathan J. Mantua, Steven R. Hare, Yuan Zhang, John M. Wallace, and Robert C. Francis (1997),
A Pacific interdecadal climate oscillation with impacts on salmon production, Bulletin of theAmerican Meteorological Society, 78, 1069–1079.
The World Bank, GDP per capita (constant 2010 US$), November ,2019, fromhttps://data.worldbank.org/indicator/NY.GDP.PCAP.KD The World Bank,
The World Bank, Agriculture, forestry, and fishing, value added (% of GDP), August ,2020, fromhttps://data.worldbank.org/indicator/NV.AGR.TOTL.ZS
Toshichika Iizumi, Jing-Jia Luo, Andrew J. Challinor, Gen Sakurai, Masayuki Yokozawa,
Hirofumi Sakuma, Molly E. Brown & Toshio Yamagata (2017), Impacts of El Nin˜o Southern
Oscillation on the global yields of major crops, Nature Communications, 5, 3712
Yuan Zhang, John M. Wallace, and David S. Battisti (1997), ENSO-like interdecadal variability,21

J. Climate, 10, 1004–1020.

22

Fig. 1. GDP growth rate of (a) Brazil (%) and (b) South Africa (%). The vertical axis is GDP
growth rate, and the horizontal axis is the year.

23

Fig. 2. Distribution map of countries in (a) Level 1, (b) Level2, (c) Level3, and (d) Level4
24

Fig. 3. PDO Index in (a) JJA, and (b) JFD. The vertical axis is PDO index (-), and the horizontal
axis is the year.

25

Fig. 4. SOI in (a) JJA, and (b) JFD. The vertical axis is SOI (-) and the horizontal axis is the year.

26

PDO index

Table. 1. Positive / negative ratio of the top 30% of countries with the highest correlation
coefficient between GDP growth rate and teleconnection indexes of each level.
correlationLevel
teleconnection averaged
coefficient
index
month
Level1
Level2
Level3
Level4
sign
plus
20.0%
20.0%
10.0%
81.3%
JJA
minus
80.0%
80.0%
90.0%
18.8%
JFD

SOI

JJA
JFD

R

JJA
JFD

plus

30.0%

40.0%

50.0%

81.3%

minus

70.0%

60.0%

50.0%

18.8%

plus

80.0%

66.7%

70.0%

37.5%

minus

20.0%

33.3%

30.0%

62.5%

plus

80.0%

83.3%

60.0%

52.6%

minus

20.0%

16.7%

40.0%

47.4%

plus

10.0%

20.0%

10.0%

81.3%

minus
plus

90.0%
60.0%

80.0%
53.3%

90.0%
60.0%

18.8%
75.0%

minus

40.0%

46.7%

40.0%

25.0%

27

teleconnectionindex

averagedmonth

PDO index

Table. 2. The list of the number of countries with significant correlation coefficient in Table 1

JJA
JFD

SOI

JJA
JFD

R

JJA
JFD

correlation coefficient
sign

Level
Level1 Level2 Level3 Level4

plus

2

1

1

5

minus

4

9

7

3

plus

2

1

3

9

minus

2

7

8

6

plus

5

5

2

None

minus

1

1

1

None

plus

3

9

10

1

minus

None

1

1

2

plus

None

2

None

4

minus

3

6

8

3

plus

2

3

4

10

minus

3

3

5

7

28

Fig. 5. The map of countries (a) for the level 4 group with the significantly positively correlation,and (b) for the level 1 group with the significantly negative correlation with PDO or R indicesregardless of the season.

29

JJA
JFD
JJA

SOI

agriculture

PDO index

Table. 3. Positive / negative ratio of the top 30% of countries with the highest correlation
coefficient between GDP growth rate and teleconnection indexes of each level classified by theindustry groups.
Levelcorrelation
teleconnection
averagedsectorcoefficient
index
month
Level1
Level2 Level3
Level4
sign

JFD

R

JJA

JJA
JFD
JJA

SOI

industry

PDO index

JFD

JFD

R

JJA

PDO index

JJA
JFD

SOI

services

JFD

JJA

plus

50.0%

47.4%

45.5%

38.9%

minus
plus

50.0%
33.3%

52.6%
53.3%

54.5%
60.0%

61.1%
43.8%

minus

66.7%

46.7%

40.0%

56.3%

plus

70.0%

73.3%

40.0%

62.5%

minus

30.0%

26.7%

60.0%

37.5%

plus

63.6%

52.9%

69.2%

55.0%

minus

36.4%

47.1%

30.8%

45.0%

plus

60.0%

46.7%

30.0%

37.5%

minus

40.0%

53.3%

70.0%

62.5%

plus

50.0%

60.0%

90.0%

62.5%

minus

50.0%

40.0%

10.0%

37.5%

plus

10.0%

40.0%

20.0%

62.5%

minus
plus

90.0%
30.0%

60.0%
26.7%

80.0%
40.0%

37.5%
68.8%

minus

70.0%

73.3%

60.0%

31.3%

plus

70.0%

60.0%

60.0%

56.3%

minus

30.0%

40.0%

40.0%

43.8%

plus

100.0%

86.7%

64.3%

61.1%

minus

0.0%

13.3%

35.7%

38.9%

plus

10.0%

33.3%

12.5%

71.4%

minus

90.0%

66.7%

87.5%

28.6%

plus

40.0%

33.3%

37.5%

71.4%

minus

60.0%

66.7%

62.5%

28.6%

plus

30.0%

57.1%

66.7%

100.0%

minus
plus

70.0%
45.5%

42.9%
50.0%

33.3%
60.0%

0.0%
93.8%

minus

54.5%

50.0%

40.0%

6.3%

plus

60.0%

35.7%

11.1%

12.5%

minus

40.0%

64.3%

88.9%

87.5%

30

JFD

R

JJA
JFD

plus

60.0%

57.1%

33.3%

5.9%

minus

40.0%

42.9%

66.7%

94.1%

plus

20.0%

61.5%

66.7%

75.0%

minus

80.0%

38.5%

33.3%

25.0%

plus

50.0%

60.0%

33.3%

75.0%

minus

50.0%

40.0%

66.7%

25.0%

31

teleconnectionindex

averaged
month
JJA
JFD
JJA

SOI

agriculture

sector

PDO index

Table. 4. The list of the number of countries with significant correlation coefficient in Table 3

JFD

R

JJA

JJA
JFD
JJA

SOI

industry

PDO index

JFD

JFD

R

JJA

services

PDO index

JFD
JJA
JFD

SOI

JJA

R

JFD
JJA

correlation
coefficient sign

Level
Level1 Level2 Level3 Level4

plus

1

3

3

4

minus

3

6

1

4

plus

2

2

2

3

minus

1

3

None

1

plus

4

6

2

1

minus

None

2

2

None

plus

5

6

6

3

minus

2

5

4

3

plus

1

2

1

2

minus

1

5

1

4

plus

1

2

2

2

minus

2

1

None

1

plus

1

2

None

8

minus

6

5

5

6

plus

2

2

2

12

minus

4

8

6

7

plus

6

4

1

1

minus

1

1

2

5

plus

4

5

5

7

minus

None

1

3

3

plus

None

3

None

9

minus

4

7

6

6

plus

2

3

3

13

minus

5

7

5

7

plus

None

2

5

14

minus

3

3

1

1

plus

1

2

2

9

minus

4

2

4

1

plus

2

1

2

None

minus

1

3

3

6

plus

1

3

None

1

minus

1

None

3

6

plus

None

3

3

13

32

JFD

minus

3

4

1

1

plus

5

4

2

8

minus

3

5

5

2

33

Appendix
Table. A.1 The list of countries with significant correlation coefficient in Table 1
telecoaveraged
nnection
month
index

correlation
coefficient
sign

Level
Level1
Ethiopia,
Liberia

plus

JJA
minus

Lesotho,Niger,
SierraLeone,Togo

PDO index

Ethiopia,
Nepal

plus

JFD
Niger,
Sierra
Leone

SOI

minus

JJA

plus

Congo
Dem.
Rep.,
Gambia,Guinea,Niger,
RwandaLevel2
BhutanLevel3

Antigua andBarbuda,Croatia,
Iceland,
Ireland,
Slovenia
Bolivia,
Brazil,France, Saudi
Ecuador, Fiji, Nauru,
Arabia,Georgia, Lao, Panama,
Trinidad andMongolia,
Russia, South Tobago
Morocco,Africa,Ukraine,Suriname,
Uzbekistan
Turkmenistan
India
Bulgaria,
Antigua andChina,
Barbuda,Dominica
Barbados,
Croatia,
Czech
Republic,
Hungary,
Ireland,
SlovakRepublic,
Slovenia,
UnitedKingdom
Belize,Botswana,
Austria,
Bolivia,
Brazil,Belgium,
Ecuador,Gabon,
Brunei,
Guatemala,Grenada,France,Lao, Nigeria, Iran,
Israel, Saudi
Tunisia
Malaysia,
ArabiaPanama,Suriname
Cote d'Ivoire, Gabon,
Ecuador, Fiji, Turkmenistan
Paraguay,
Swaziland
None

34

China

Level4

minusplus
JFD

Liberia

Jamaica

Congo
Dem.
Rep.,
KyrgyzRepublic,
UgandaEcuador,Georgia,
Ghana, Lao,
Mongolia,
Morocco,Ukraine,Uzbekistan,
ZambiaMarshallIslands

minus

None

plus

None

minus

Niger,
SierraLeone,
Togo

JJA

R

Ethiopia,
Nepal

plus
JFD

minus

Malawi,
Niger,
Sierra
Leone

Palau

None
Trinidad andTobagoBelarus,Brazil, Cuba,
Gabon,
Kazakhstan,Peru, Russia,
South Africa,
Suriname,
Turkmenistan
China
Iceland,UnitedKingdom
Bhutan,Antigua andBosnia andBarbuda,Herzegovina
None
Croatia,
Iceland,
Slovenia
Bolivia,
Brazil,France, Saudi
Ecuador, Lao, Namibia,
Arabia,Mongolia,
Panama,Trinidad andMorocco,Peru, Russia, Tobago
Nicaragua
South Africa,
Suriname,
Turkmenistan
Armenia,
Bulgaria,
Antigua andIndia, Samoa Dominica,
Barbuda,Grenada,Austria,
Romania
Barbados,
Croatia,
Czech
Republic,
Hungary,
Ireland,
Poland,SlovakRepublic,
Slovenia
Guatemala,
Botswana,
Belgium,
Nigeria,
Brazil,Brunei,
Tunisia
Gabon, Iran, France,
Malaysia
Israel, Japan,
Saudi Arabia,
Seychelles

35

Table. A.2 The list of countries with significant correlation coefficient in Table 3
sectorteleconnection index

averagedmonth

correlation
coefficient
sign

Level
Level1
Benin

plus
Burundi,SierraLeone,
Tajikistan

JJA
PDO index

minus

Benin,
Congo
Rep.
Sierra
Leone

plus
JFD

Level3

Level4Lao,
Samoa,
TimorLeste
Albania,
Armenia,
Azerbaijan,
Morocco,Philippines, Ukraine
Bhutan,India

Costa Rica,
Equatorial,
Gabon

Bahrain,
Malta,
Oman,
Seychelles
Bahamas,Czech
Republic,
Germany,
Iceland

Armenia,
MarshallIslands,
Philippines
Burundi,Albania,
Cambodia, Cote
Sierrad'Ivoire,
Leone,
Georgia,
Tajikistan India,
Paraguay,
VanuatuLao, Papua
None
New
GuineaKyrgyzArmenia,
Republic, Guyana,
Nepal,
Marshall
Senegal,Islands,
SierraPhilippines
Leone,, Ukraine,
Tajikistan Vietnam

minus

agriculture

Level2

plus
JJA

SOI

minus

plus
JFD

Benin,
Congo
Rep.

minus

36

Bhutan,Iraq,
Jordan,Samoa,
TimorLeste

Cuba

Costa Rica,
Gabon

None
Belarus,
MexicoEquatorial,
Gabon
Cuba,
Kazakhstan,
Montenegro,Romania,
Russia,South
AfricaColombia,
Equatorial,
Gabon, Iran

Brunei,
Norway,Oman
Saudi
ArabiaGermany

None
Czech
Republic,
Germany,
Saudi
ArabiaBrunei,
Norway,
Oman

plus
JJA

Benin

Egypt,
Samoa

SierraLeone

Albania,
Kazakhstan
Armenia,
Azerbaijan,
Morocco,Philippines

Benin

Bhutan,India

SierraLeone,
Yemen
Ethiopia

Philippines

R

minus

plus
JFD

minus

Costa Rica,
Gabon

Oman,
Seychelles
Spain
Bahamas,Czech
Republic,
Iceland,UnitedKingdom
Oman,
SwedenNone

New
Zealand

Bosnia andHerzegovina, Sudan

plus

None

JJA

PDO index

minus

industry

Costa Rica

Lesotho,Liberia,
Niger,
Senegal,SierraLeone,
Togo
Ethiopia,
Nepal

Bolivia,
Ecuador,Egypt,
Nicaragua,
UkraineColombia,
Equatorial,
Kazakhstan, Namibia,
PanamaIndia,
Jamaica

Bulgaria,
Libya

Liberia,
Niger,
Senegal,SierraLeone

Bolivia,
Ecuador,Egypt,
Indonesia,
Iraq,
Micronesia
Fed. Sts.,

Costa Rica,
Equatorial,
Guinea,Gabon,
Iran,
Malaysia

plus

JFD

minus

37

Andorra,Belgium,
Croatia,
Hungary,
Ireland,
Luxembourg, Portugal,
Slovenia
Bahamas,France,Kuwait,Qatar,
Singapore,
South
Korea
Andorra,AntiguaandBarbuda,Croatia,
Finland,
Greece,Hungary,
Ireland,
Italy,
Japan,
Luxembourg, Portugal,
Slovenia
Brunei,France,Kuwait,Norway,Qatar,
Singapore,
UnitedNigeria,
Tunisia

plus
JJA

Congo
Dem.
Rep.,
Guinea,Haiti,
Malawi,
Niger,
RwandaNepal

Ecuador,Fiji,
Morocco,Nigeria

Dominican
Republic

Uruguay

Vietnam

Peru,
Suriname

Burkina
Faso,
KyrgyzRepublic,
Togo,
UgandaEcuador,Georgia,
Guyana,Morocco,Nigeria

Brazil,Colombia,
Equatorial,
Gabon,
RussiaAndorra,Croatia,
Hungary,
Ireland,
LatviaAustralia,
Kuwait,Netherlands, Norway,
Oman,
TrinidadandTobago,UnitedArab
Emirates
Andorra,Japan,
LuxembourgAndorra,Belgium,
Croatia,
Finland,
Hungary,Japan,
Luxembourg, Portugal,
Slovenia
Bahamas,France,Kuwait,Singapore,
South
Korea,
Sweden

SOI

minus

plus

Arab
Emirates

JFD

R

minus

None

plus

None

minus

Liberia,
Niger,
Senegal,Togo

Papua New China,
GuineaGrenada,Palau
Bosnia andHerzegovina, India,
Sudan

None

JJA

38

Albania,
Bolivia,
Ecuador,Egypt,
Guatemala,Nicaragua,
UkraineColombia,
Equatorial,
Kazakhstan, Namibia,
Panama,RussiaEthiopia,
Nepal

Georgia,
India,
Jamaica

Bulgaria,
Libya,
Maldives

Liberia,
Malawi,
Niger,
Senegal,SierraLeone

Bolivia,
Ecuador,Egypt,
Indonesia,
Iraq,
Micronesia
Fed. Sts.,
Tunisia

Costa Rica,
Equatorial,
Gabon,
Iran,
Malaysia

Bosnia andHerzegovina, Cape
Verde

China,
Kazakhstan, Malaysia,
Palau,
Serbiaplus

JFD

PDO index

services

minus

plus

None

JJA

Afghanista Fiji,n, Sierra
Mongolia,
Leone,
Morocco
Togo

minus

39

MexicoAndorra,AntiguaandBarbuda,Croatia,
Czech
Republic,
Finland,
Greece,Hungary,
Ireland,
Italy,
Japan,
Portugal,Slovenia,
SwitzerlandBrunei,France,Kuwait,Qatar,
Singapore,
Sri Lanka,
UnitedArab
Emirates
Andorra,AntiguaandBarbuda,Bahamas,Bahrain,
Canada,Croatia,
Czech
Republic,
Estonia,
Greece,Iceland,
Ireland,
Kuwait,Malta,
SlovakRepublic
FrancePakistan

India, Iraq

China,
Palau

Afghanista
n, GuineaBissau,
Uganda,Yemen
Gambia,Zimbabwe
BurundiFiji,
Guatemala

Botswana,
Brazil,Mexico,PanamaUgandaGeorgia,
Mongolia,
Uzbekistan

plus
JFD

minus

plus

Brazil,Gabon
Angola,China,
Lao, Sudan Palau,
Serbia

minus

SOI

JJA

Fiji

plus

Pakistan
JFD

R

minus

JJA

None

plus

None

40

Bosnia andHerzegovina, Cape
Verde,
Samoa

None
China,
Colombia,
Turkmenistan

China,
Kazakhstan, Serbia

Andorra,AntiguaandBarbuda,Brunei,
Croatia,
Czech
Republic,
Hungary,
Ireland,
Kuwait,UnitedKingdom
FranceNone
Andorra,Bahrain,
Canada,Kuwait,TrinidadandTobago,UnitedArab
Emirates
BarbadosAndorra,Bahrain,Brunei,
Canada,Norway,UnitedStatesAndorra,AntiguaandBarbuda,Bahamas,Bahrain,
Canada,Croatia,
Czech
Republic,

Greece,
Iceland,
Ireland,
Malta,
Portugal,
SlovakRepublicminus

Afghanistan, Sierra
Leone,
Togo
Bangladesh, Kyrgyz
Republic,
Nepal,
Tajikistan,Fiji,
Mongolia,
Morocco,ZambiaGeorgia,
India, Iraq,
Samoa

PanamaFranceChina,
Palau

Afghanista
n, GuineaBissau,
Yemen

Belize,Ecuador,Fiji,
Kiribati,
Papua New
GuineaBotswana,
Brazil,Colombia,
Mexico,PanamaAndorra,AntiguaandBarbuda,Croatia,
Czech
Republic,
Hungary,
Ireland,
Kuwait,UnitedKingdom
France,Venezuela

plus
JFD

minus

41