inter cia 2

International Economics
CIA – 2

Exploring the relationship between a nation's growth (GDP) and volume of
trade (Exports/Imports)

GDP

Course: Master of Economics (CBCS)
Submitted to: Prof. Manoj Morais
Submitted by: Ashish Goyal (2138353), Prerna Mendiratta (2182020), Mumal

Rathore (2182023), Animesh Jaiswal (2182024).

Abstract

India and Japan share strong cultural ties dating back to the 6th century when Buddhism was
introduced to Japan. Indian culture, filtered through Buddhism, significantly impacted Japanese
culture, which is the source of the Japanese people’s sense of closeness to India. Over the years,
both nations have built upon these values and created a partnership based on solid principles and
pragmatism. Today, India is the largest democracy in Asia, and Japan the most prosperous
democracy. Most importantly, both the economies are complementary to each other. Economic
relations between India and Japan have improved in recent years with the Comprehensive Economic
Partnership Agreement, 2011 (CEPA), which would further enhance the bilateral trade and
investment relations between the two Asian giants.

The modern states have carried on the positive legacy of the old association, which has been
strengthened by shared values of belief in democracy, individual freedom, and the rule of law. Over
the years, both the countries have built upon these values and created a partnership based on solid
principles and pragmatism. The bilateral relationship has become much more strategic due to the
changing Asian landscape.

In addition, Japan has also been a crucial source of support for India’s initiative to join the Asia-
Pacific Economic Co-operation (APEC) along with four international export control regimes (the
Nuclear Suppliers Group, Missile Technology Control Regime, Wassenaar Arrangement, and the
Australia Group), which is essential for India to fulfill it.

India and Japan’s economic cooperation has also grown over the past few years. Japan currently
holds the position of India’s fourth-largest investor.

Introduction

India (भारत): -

India is a country that occupies the more significant part of South Asia. Its capital is New Delhi,
built in the 20th century just south of the historic hub of Old Delhi to serve as India’s administrative
center. Its government is a constitutional republic representing a highly diverse population
consisting of thousands of ethnic groups and likely hundreds of languages. With roughly one-sixth
of the world’s total population, India is the second-most populous country after China.

It is known from archaeological evidence that a highly sophisticated urbanized culture—the Indus
civilization—dominated the north-western part of the subcontinent from about 2600 to 2000 BCE.
From that period on, India functioned as a virtually self-contained political and cultural arena,
which gave rise to a distinctive tradition that was associated primarily with Hinduism, the roots of
which can largely be traced to the Indus civilization. Other religions,
notably Buddhism and Jainism, originated in India—though their presence there is now relatively
small—and throughout the centuries, residents of the subcontinent developed a rich intellectual life
in mathematics, astronomy, architecture, literature, music, and the fine arts.

Primary sources of income

Agricultural Sector:

Once India’s primary source of revenue and income, Agriculture has since fallen to approximately
18.32% of the country’s GDP as of 2020. However, analysts have pointed out that this fall should
not be equated with a decrease in production. Instead, it reflects the significant increases in India’s
industrial and service outputs.

Today, India is the world's second-largest fruit producer and the leading global producer of lemons,
bananas, mangoes, papayas, and limes. While forestry is a relatively small contributor to the
country's GDP, it is a growing sector. It is responsible for producing fuel, wood-based panels, pulp
for paper, paper, and paperboard. An additional small percentage of India’s economy comes from
fishing and aquaculture, with shrimp, sardines, mackerel, and carp being bred and caught

Industrial Production:

Chemicals are big business in India; The petrochemical industry, which first entered the Indian
industrial scene in the 1970s, experienced rapid growth in the 1980s and 1990s.

In addition to chemicals, India produces an ample supply of the world’s pharmaceuticals and
billions of dollars worth of cars, motorcycles, tools, tractors, machinery, and forged steel.

India mines many gems and common minerals, including iron ore, bauxite, gold, asbestos,
uranium, limestone, and marble. For example, from 2019 to 2020, foodies mined 729 million tons
of coal (which was insufficient to meet the country’s coal needs). Oil and gas were extracted at
34.2 million metric tons and 32.9 billion cubic meters, respectively, from 2018 to 2019.

Information Technology (IT) and Business process Outsourcing:

Over the past 60 years, the service industry in India has increased from a fraction of the GDP to
approximately 55% between 2019 and 2020. India–with its high population of skilled, English-
speaking, and educated people–is an excellent place for doing business.

Among the leading services industries in the country are telecommunications, IT, and software,
and both domestic and international companies employ the workers, including Intel, Texas
Instruments, Yahoo, Meta -formerly Facebook, Google, and Microsoft.

Business process outsourcing (BPO) is a less significant but more well-known industry in India. It
is led by companies like American Express, IBM, Hewlett-Packard, and Dell. BPO is India's
fastest-growing segment of the ITES (Information Technology Enabled Services) industry, thanks
to economies of scale, cost advantages, risk mitigation, and competency.

Other Services:

Other parts of India’s service industry include electricity production and tourism. The country is
mainly dependent on oil, gas, and coal fossil fuels, but it is increasingly adding capacity to
produce hydroelectricity, wind, solar, and nuclear power.

In 2018, over 10 million foreign tourists visited India. In 2018, the estimated foreign exchange
earnings from tourism in India were $28.585 billion. The World Travel and Tourism Council
calculated that tourism generated 10.3% of India's GDP in 2019.

Medical tourism to India is also a growing sector. India's market for medical tourism is expected to
touch the $9 billion mark by 2020, according to a report released by the Federation of Indian
Chambers of Commerce and Industry (FICCI) and Ernst & Young. Medical tourism is popular in
India because of its low-cost healthcare and international standards compliance. Customers come
from all over the world for heart, hip, and plastic surgery procedures, and a small number of people
take advantage of India’s commercial surrogate facilities.

Currency

The Indian rupee (INR) is the currency of India. INR is the International Organization for
Standardization currency code for the Indian rupee, for which the currency symbol is ₹.

Coins:
Coins in India are issued in denominations of 50 paise, one rupee, two rupees, five rupees, and ten
rupees. A paise is 1/100th of a rupee. Coins worth 50 paise are called small coins, while coins
equal to or above one rupee are called rupee coins.

Banknotes:

Paper currency or banknotes are issued in 5, 10, 20, 50, 100, 500, and 2,000 rupees. On the reverse
side of paper rupees, denominations are printed in 15 languages, while sects are published in Hindi
and English on the front side.

The banknotes are updated frequently with new designs, including distinct differences from the old
Mahatma Gandhi Series of banknotes to the new ones of the same name. The notes include various
themes of India's rich heritage.

Below are the images of the current coins and banknotes, along with their denominations, that are
currently in circulation for the Indian rupee as listed on the website for the Reserve Bank of India.

Japan (日本国): -

Japan island country lying off the east coast of Asia. It consists of an incredible string of islands in a
northeast-southwest arc stretching approximately 1,500 miles (2,400 km) through the western
North Pacific Ocean. The country’s four main islands take up nearly the entire land area; from north
to south, these are Hokkaido (Hokkaidō), Honshu (Honshū), Shikoku, and Kyushu (Kyūshū).
Honshu is the largest of the four, followed by Hokkaido, Kyushu, and Shikoku. In addition, there
are numerous smaller islands, the major groups of which are the Ryukyu (Nansei) Islands (including
the island of Okinawa) to the south and west of Kyushu and the Izu, Bonin (Ogasawara),
and Volcano (Kazan) islands to the south and east of central Honshu. The national
capital, Tokyo (Tōkyō), in east-central Honshu, is one of the world’s most populous cities. The
Japanese landscape is rugged, with more than four-fifths of the land surface consisting of
mountains. There are many active and dormant volcanoes, including Mount Fuji (Fuji-san), Japan's
highest peak at 12,388 feet (3,776 meters). Heavy emphasis is placed on education, and Japan is
one of the world’s most literate countries. The agricultural regions are characterized by low
population densities and well-ordered rice fields and fruit orchards. In contrast, the industrial and
urbanized belt along the Pacific coast of Honshu is noted for its highly concentrated population,
heavy industrialization, and environmental pollution.

Japan is remarkable for its extraordinarily rapid economic growth rate in the 20th century, especially
in the first several decades after World War II. This growth was based on an unprecedented expansion
of industrial production and the development of an enormous domestic and aggressive export trade
policy. In terms of gross national product (GNP; or gross national income), a standard indicator of a
country’s wealth, Japan is the world’s second-largest economic power, ranking behind only
the United States. It has developed a highly diversified manufacturing and service economy and is
one of the world’s largest producers of motor vehicles, steel, and high-technology manufactured
goods (notably consumer electronics). The service sector has come to dominate the economy
regarding its overall proportion of the gross domestic product (GDP) and employment.

Primary sources of income

Fishery:

Japan ranked fourth in the world in 1996 in tonnage of fish caught. Japan captured 4,074,580 metric
tons of fish in 2005, down from 4,987,703 tons in 2000, 9,558,615 tons in 1990, 9,864,422 tons in
1980, 8,520,397 tons in 1970, 5,583,796 tons in 1960 and 2,881,855 tons in 1950.

In 2003, the total aquaculture production was predicted at 1,301,437 tonnes. In 2010, Japan's total
fisheries production was 4,762,469 fish. Offshore fisheries accounted for 50% of the nation's real
fish catches in the late 1980s, although they experienced repeated ups and downs.

Coastal fishing by small boats, set nets, or breeding techniques accounts for about one-third of the
industry's total production, while offshore fishing by medium-sized boats makes up for more than
half the entire production. Deep-sea fishing from larger vessels makes up the rest.

Among the many seafood species caught are sardines, skipjack tuna, crab, shrimp, salmon, pollock,
squid, clams, mackerel, sea bream, sauries, tuna and Japanese amberjack. Freshwater fishing,
including salmon, trout, eel hatcheries, and fish farms, takes up about 30% of Japan's fishing
industry. Among the nearly 300 fish species in the rivers of Japan are native varieties of catfish,
chub, herring, goby, and such freshwater crustaceans as crabs and crayfish. Marine and freshwater
aquaculture is conducted in all 47 prefectures in Japan.

Japan maintains one of the world's largest fishing fleets and accounts for nearly 15% of the global
catch, prompting some claims that Japan's fishing leads to depletion in fish stocks such as tuna.
Japan has also sparked controversy by supporting quasi-commercial whaling.

Industry:

Japanese manufacturing and industry are very diversified, with a variety of advanced industries that
are highly successful. Industry accounts for 30.1% (2017) of the nation's GDP.

The country's manufacturing output is the third highest in the world.
The industry is concentrated in several regions, with the Kantō region surrounding Tokyo (the part)
as well as the Kansai region surrounding Osaka (the Hanshin industrial region) and the Tōkai
region surrounding Nagoya (the Chūkyō–Tōkai industrial region), the leading industrial centers.
Other industrial centers include the southwestern part of Honshū and northern Shikoku around
the Seto Inland Sea (the Setouchi industrial region); and the northern part of Kyūshū (Kitakyūshū).
In addition, a long narrow belt of industrial centers called the Taiheiyō Belt is found between Tokyo
and Fukuoka, established by particular industries that have developed as mill towns.

Japan enjoys high technological development in many fields, including consumer
electronics, automobile manufacturing, semiconductor manufacturing, optical
fibers, optoelectronics, optical media, facsimile and copy machines, and fermentation processes
in food and biochemistry. However, many Japanese companies face emerging rivals from the
United States, South Korea, and China.

Automobile manufacturing:

Japan is the third-biggest.

Toyota is currently the world's largest carmaker, and the Japanese
carmakers Nissan, Honda, Suzuki, and Mazda are also counties some of the largest car makers in
the world.

Currency

The monetary unit of Japan. The yen’s symbol is ¥. The name yen derives from an ancient Chinese
round coins (yuan) term.

The word “yen” means “circle” or “round object.” The Meiji government officially adopted this
currency with the “New Currency Act” of 1871 in the hope of stabilistabilizingnetary situation of the
country at the time.

Current yen coins:

Currently, there are 1, 5, 10, 50, 100, and 500 yen coins in circulation since 2009.

Current yen banknotes:

The current series was issued in 2004 with 1,000, 2,000, 5,000, and 10,000 yen notes concerning
banknotes.

Comparative advantage

India and Japan have complementarity in their economic structures. Japan has high-end technology
in manufacturing, good working discipline, es, and developed infrastructures; however, the
declining population and aging society are significant challenges for Japan. On the other hand,
India has an aspirational young population and rich natural resources; it needs FDI, especially in the
manufacturing sector, and has vast infrastructure development. India’s prowess is in services,
Japan’s excellence is in manufacturing, and Japan’s surplus capital in investments. India’s large and
growing markets and the middle class complement both economies.
Economic relations between India and Japan have vast growth potential, given the apparent
complementarities between the two Asian economies. To analyse the comparative advantage of
India and Japan, the revealed comparative advantage (RCA) has been used to assess the country’s
export potential and the Revealed Import Dependence (RID) index to identify the commodities
which have import dependence on the partner countries.

Commodities Feasible for Trade between India and Japan

where RCA>1 (India) and RID>1 (Japan) India Japan
RID
Aggregate Description RCA 3.61791

1. Raw Materials Metalliferous Ores (23) 1.33941 1.86612
1.46257
2. Tropical Fruits, Vegetables (05) 1.78809 1.09633
Agriculture Coffee, Tea, Cocoa, Spices (07) 1.12396
Sugar (06) 1.57772

3. Animal Products Meat Preparations (01) 4.62386 2.22469
Fish Preparations (03) 1.73244 3.63971

4. Cereals etc Cereals, Preparations (04) 1.98029 2.51938
Oilseeds Nuts, Kernels (22) 1.62493 1.40333
Fixed Vegetable Oils (42) 1.98693 1.98701

5. Labour Intensive Clothing (84) 1.75803 2.66987
Manufactures Footwear (85) 1.00873 1.42694

6. Capital Intensive Leather &Leather Manufactures (61) 2.12711 1.16281
Manufactures Rubber Manufacturers (62) 1.01789 1.96922
Textile Yarn (65) 5.43238 1.96605
Iron and Steel (67) 3.26451 1.89623
Metal Manufactures (69) 1.12232 1.95164

7. Chemicals Chemical Elements (51) 3.09423 1.65545
Dyeing, Tanning, coloring (53) 1.57919 1.94743
Essential Oils (55) 1.66296 1.97786

Comparing the RCA of commodities in India with the RID of things in Japan will give a more
reliable picture of the export potential of the Indian goods in Japan and Vice versa. Suppose India’s
particular index is more significant than one for a specific commodity, and for the same thing, Japan
has a RID greater than one. In that case, such a commodity is considered to have a strong export
potential in that partner’s country.

India’s Revealed Comparative Advantage Index & Revealed Import Dependency Index:
The Revealed Comparative Advantage (RCA), computed for India, presented that India has a
comparative advantage concerning 20 commodities in the seven commodity categories except for
the commodity categories Machinery, Forest Products, where the RCA indices for all the
commodities under this category are lower than one.

India’s comparative advantage could be observed concerning Metalliferous Ores (23), Non-Ferrous
Metal (68), Fruits, Vegetables (05), Sugar (06), Coffee, Tea, Coca, spices (07), Meat preparations
(01), Fish Preparations (03), Cereals Preparations (04), Oilseeds Nuts, Kernels (22), Fixed
Vegetable Oils (42), Chemicals (84), Footwear (85), Leather & Leather Manufactures (61), Rubber
Manufacturers (62), Textile Yarn (65), Iron & Steel (67), Metal Manufactures (69), Chemical
Elements (51), Dyeing, Tanning, Colouring (53), Essential Oils (55) where the RCA index(average)
computed for them is greater than 1.
India’s comparative advantage could be seen in the commodity categories of agriculture and labour-
intensive product categories.
The Revealed Import Dependency Index (RID) could be observed in the product categories
Petroleum (33), Crude Fertilizers (27), Coke, Coal, Briquettes (32), Natural Manufactures Gas (34),
Non-Ferrous Metal (68), Pulp, Waster Paper (25), Paper, Paperboards (64), Fruits, Vegetables (05),
Crude Rubber (23), Fixed Vegetable Oils (42), Travel Good, Handbags (83), Textile Yarn (65), Iron
& Steel (67), Non-Electrical Machinery (71), Electrical Machinery(72), Chemical Elements (51),
Mineral Tar, Crude Chemicals (52), Dyeing, Tanning, Colouring (53), Fertilizers (56) and Chemical
Materials, n.e.s (59) where the RID index registered is greater than 1. The Revealed Import
Dependency concerning sophisticated and high-end commodities for 20 products under ten distinct
categories could be observed.
The above table also provides the commodities feasible for trade between India and Japan where
RCA of India > one and RID of Japan>1. Nineteen items were found possible for trade between the
two countries.

Japan’s Revealed Comparative Advantage & Revealed Import Dependency Index:
Japan’s Revealed Comparatives Advantage could be observed in Coke, Coal, Briquettes (32), Non-
Ferrous Metal (68), Pulp, Waste Paper (25), Paper, Paperboards (64), Postal Packs (91), Rubber
Manufacturers (62), Non-Electrical Machinery (71), Electrical Machinery (72), Transport
Equipment (73), Professional Goods (86), Chemical Elements (51), Mineral Tar, Crude Chemicals
(52), Chemical Materials, n.e.s. (59) concerning 13 commodities under six commodity categories
where the comparative advantage is greater than 1.
The Revealed Import Dependency Index (RID) could be observed concerning Petroleum (33),
Metalliferous Ores (23), Coke, Coal, Briquettes (32), Natural Manufactured Gas (34), Non-Ferrous
Metal (68), Wood, Lumber, Cork (24), Wood, Cork Manufactures (63), Sugar (06), Coffee, Tea,
Cocoa, Spices (07), Crude Rubber (23), Meat Preparations (01), Fish Preparations (03), Cereals
Preparations (04), Tobacco Manufactures (12), Oilseeds Nuts, Kernels(22), Fixed Vegetable Oils,

Fats (41), Travel Goods, Handbags (83), Clothing (84), Footwear (85), Leather & Leather
Manufactures (61), Rubber Manufactures (62), Textile Yarn (65), Iron & Steel (67), Metal
Manufactures (69), Professional Goods (86), Chemical Elements(51), Dyeing, Tanning, Colouring
(53) and Essential Oils (1.977) in 28 commodities under all the ten commodity categories of
Leamer’s, where the RID index computed is lower than 1.
Japan’s competitiveness could be observed in Machinery, Chemicals, and Capital production.
Intensive commodity categories. Import dependency could be kept under the labor-intensive
commodity categories and farm products.

Gravity Model

A- Gravity model explanation

• Gravity model of trade that use Newton's gravity theory of Newton in physics mainly represented the
alternate trade determinants.

Newton's law is used to estimate and calculate the relationship between objects. The gravity model uses this
same idea to predict the relationship between places. Instead of gravitational pull, however, we're interested
in the degree of interaction between cities, towns, or regions.
So how does this work? Newton's law of gravity predicts that bodies which are larger and closer will exert
more force. Our main variables are size and distance. In the gravity model of human geography, we can use
these same variables. Size is measured in population, and distance can be measured using any metric. The
idea in the gravity model is the same as in Newton's law. The larger and closer two places are, the more
influence they'll have on each other.
As a result, the relationship between places reflects both variables of size and distance. Interaction is
proportional to the size of each place. As the population increases, the interaction increases. However, this
also means that interaction is inversely proportional to the distance between them. As the distance gets
larger, the expected interaction decreases.

In its traditional form, it predicts bilateral trade flows based on the economic sizes(often using GDP
measurement) and distance between two units.

The model was first used by Jan Tinbergen in 1962.

The basic theoretical model for trade between two countries (i and j)t ake the form.

Fij= G* Mi * Mj / Dij. ………..(1)

Where,

F= is trade flow,
M =is the Economic Mass (or GDP) of each Country
D =is the distance
G =is a constant.

Taking logarithm, we can convert the equation (1) into a linear form for econometrics analysis where G
becomes alpha.

In (Bilateral Trade Flow) = a + B In (GDP Country 1) + B In (GDP Country 2) - B In (Distance) + E

The model often includes variables to account for income level (GDP per capita), price levels, language
relationships, tariffs and colonial history (whether the country 1 ever colonized country 2 or vice versa).

The gravity model estimates the pattern of international trade using the gravity model countries with similar
levels of income have been shown to trade more.

B- Practical application

EXAMPLE:

If we compare the bond between the New York and Los Angeles metropolitan areas, we first multiply their
1998 populations (20,124,377 and 15,781,273, respectively) to get 317,588,287,391,921 and then we divide
that number by the distance (2462 miles) squared (6,061,444). The result is 52,394,823. We can shorten our
math by reducing the numbers to the millions place: 20.12 times 15.78 equals 317.5 and then divide by 6
with a result of 52.9.
Now, let's try two metropolitan areas a bit closer: El Paso (Texas) and Tucson (Arizona). We multiply their
populations (703,127 and 790,755) to get 556,001,190,885 and then we divide that number by the distance
(263 miles) squared (69,169) and the result is 8,038,300. Therefore, the bond between New York and Los
Angeles is greater than that of El Paso and Tucson.
How about El Paso and Los Angeles? They're 712 miles apart, 2.7 times farther than El Paso and Tucson!
Well, Los Angeles is so large that it provides a huge gravitational force for El Paso. Their relative force is
21,888,491, a surprising 2.7 times greater than the gravitational force between El Paso and Tucson.
While the gravity model was created to anticipate migration between cities (and we can expect that more
people migrate between LA and NYC than between El Paso and Tucson), it can also be used to anticipate
the traffic between two places, the number of telephone calls, the transportation of goods and mail, and other
types of movement between places. The gravity model can also be used to compare the gravitational
attraction between two continents, two countries, two states, two counties, or even two neighborhoods
within the same city.
Some prefer to use the functional distance between cities instead of the actual distance. The functional
distance can be the driving distance or can even be flight time between cities.

The gravity model was expanded by William J. Reilly in 1931 into Reilly's law of retail gravitation to
calculate the breaking point between two places where customers will be drawn to one or another of two
competing commercial centers.
Opponents of the gravity model explain that it can not be confirmed scientifically, that it's only based on
observation. They also state that the gravity model is an unfair method of predicting movement because it's
biased toward historic ties and toward the largest population centers. Thus, it can be used to perpetuate the
status quo.

Data Analysis

JAPAN

The trend values are increasing in Japan for GDP, exports, imports because over the past 3 decades Japan’s
economy has increased due to more development .

Line graph and scatter plot -- it shows increasing trend for imports, exports and trend values

trend lines

6000
5000
4000
3000
2000
1000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

TREND GDP TREND IMPORTS TREND EXPORTS

Scatter plot

6000 5 10 15 20 25 30 35
5000 TREND GDP TREND IMPORTS TREND EXPORTS
4000
3000
2000
1000

0
0

India

India has seen the rise in its exports, imports and GDP in past 3 decades because of that the trend values are
also increasing in India. It also shows increasing trend for India’s export, import, GDP.

Trend lines

Trend gdp Trend export Trend import

3000

2500

2000

1500

1000

500

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

-500

3000 Scatter plot
2500
2000 Trend gdp Trend export Trend import
1500
1000 5 10 15 20 25 30 35

500
0
0

-500

Calculation and Analysis

1- Japan GDP and Per capita:-

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness

Statistic Statistic Statistic Statistic Statistic Statistic

Gdp in billion 31 $3,055 $6,203 $4,685.07 $714.501 -.181
Per capita 31 $24,823 $48,603 $36,977.29 $5,418.608 -.115
Valid N (listwise) 31

Skewness
Std. Error

.421
.421

Correlations GDP in billion Per capita
.999**
Pearson Correlation 1 .000
Sig. (2-tailed) 31
GDP in billion N 31 1
Per capita Pearson Correlation .999**
Sig. (2-tailed) 31
N .000
31

**. Correlation is significant at the 0.01 level (2-tailed).

Interpretation - According to the data and Pearson's correlation there is high correlation in per capita and gdp with
0.999 for Japan and for India it is also same that is 0.999. The reason behind this high correlation is that GDP increase
with increase in per capita income

Frequencies Statistics Per capita
GDP in billion
Valid
N 31 31
0 0
Missing
GDP in billion Valid Percent Cumulative Percent
Frequency Table Percent

Frequency

Valid $3,055 1 3.2 3.2 3.2
$3,133 1 3.2 3.2 6.5
$3,584 1 3.2 3.2 9.7
$3,909 1 3.2 3.2 12.9
$4,033 1 3.2 3.2 16.1
$4,115 1 3.2 3.2 19.4
$4,304 1 3.2 3.2 22.6
$4,389 1 3.2 3.2 25.8
$4,415 1 3.2 3.2 29.0
$4,446 1 3.2 3.2 32.3
$4,454 1 3.2 3.2 35.5
$4,515 1 3.2 3.2 38.7
$4,530 1 3.2 3.2 41.9
$4,562 1 3.2 3.2 45.2
$4,755 1 3.2 3.2 48.4
$4,815 1 3.2 3.2 51.6
$4,834 1 3.2 3.2 54.8
$4,850 1 3.2 3.2 58.1
$4,867 1 3.2 3.2 61.3
$4,888 1 3.2 3.2 64.5
$4,907 1 3.2 3.2 67.7

$4,923 1 3.2 3.2 71.0
$4,955 1 3.2 3.2 74.2
$5,038 1 3.2 3.2 77.4
$5,065 1 3.2 3.2 80.6
$5,156 1 3.2 3.2 83.9
$5,231 1 3.2 3.2 87.1
$5,449 1 3.2 3.2 90.3
$5,700 1 3.2 3.2 93.5
$6,157 1 3.2 3.2 96.8
$6,203 1 3.2 3.2 100.0
Total 31 100.0 100.0

Frequency Per capita Valid Percent Cumulative Percent
Percent

$24,823 1 3.2 3.2 3.2
$25,371
$28,915 1 3.2 3.2 6.5
$31,415
$31,903 1 3.2 3.2 9.7
$32,289
$33,846 1 3.2 3.2 12.9
$34,524
$34,808 1 3.2 3.2 16.1
$35,022
$35,275 1 3.2 3.2 19.4
$35,434
$35,682 1 3.2 3.2 22.6
$36,027
Valid

1 3.2 3.2 25.8

1 3.2 3.2 29.0

1 3.2 3.2 32.3

1 3.2 3.2 35.5

1 3.2 3.2 38.7

1 3.2 3.2 41.9

1 3.2 3.2 45.2

$37,218 1 3.2 3.2 48.4
$37,689 1 3.2 3.2 51.6
$38,109 1 3.2 3.2 54.8
$38,387 1 3.2 3.2 58.1
$38,437 1 3.2 3.2 61.3
$38,532 1 3.2 3.2 64.5
$38,762 1 3.2 3.2 67.7
$39,159 1 3.2 3.2 71.0
$39,200 1 3.2 3.2 74.2
$39,339 1 3.2 3.2 77.4
$40,113 1 3.2 3.2 80.6
$40,454 1 3.2 3.2 83.9
$40,855 1 3.2 3.2 87.1
$43,429 1 3.2 3.2 90.3
$44,508 1 3.2 3.2 93.5
$48,168 1 3.2 3.2 96.8
$48,603 1 3.2 3.2 100.0
Total 31 100.0 100.0

Histogram

2- India GDP and Per capita: -

Correlations

GDP in billions Per capita
.999**
Pearson Correlation 1 .000
Sig. (2-tailed) 30
GDP in billions N 30 1
Per capita Pearson Correlation .999**
Sig. (2-tailed) 30
N .000
30

**. Correlation is significant at the 0.01 level (2-tailed).

Interpretation - According to the data and Pearson's correlation
there is high correlation in per capita and gdp with 0.999 for
Japan and for India it is also same that is 0.999. The reason
behind this high correlation is that GDP increase with increase in
per capita income

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness
Statistic
Statistic Statistic Statistic Statistic Statistic

GDP In billions 30 $270 $2,870 $1,199.48 $873.078 .587
Per capita $613.289 .501
Valid N (listwise) 30 $301 $2,101 $970.20

30

Skewness Frequencies percapita
Std. Error
Statistics
.427 gdpinbillions
.427
30 30
Valid 0 0
N

Missing

Frequency GDP in billions Valid Percent Cumulative Percent
Percent

Valid $270 1 3.3 3.3 3.3
$279 1 3.3 3.3 6.7
$288 1 3.3 3.3 10.0
$327 1 3.3 3.3 13.3
$360 1 3.3 3.3 16.7
$393 1 3.3 3.3 20.0
$416 1 3.3 3.3 23.3
$421 1 3.3 3.3 26.7

$459 1 3.3 3.3 30.0
$468 1 3.3 3.3 33.3
$485 1 3.3 3.3 36.7
$515 1 3.3 3.3 40.0
$608 1 3.3 3.3 43.3
$709 1 3.3 3.3 46.7
$820 1 3.3 3.3 50.0
$940 1 3.3 3.3 53.3
$1,199 1 3.3 3.3 56.7
$1,217 1 3.3 3.3 60.0
$1,342 1 3.3 3.3 63.3
$1,676 1 3.3 3.3 66.7
$1,823 1 3.3 3.3 70.0
$1,828 1 3.3 3.3 73.3
$1,857 1 3.3 3.3 76.7
$2,039 1 3.3 3.3 80.0
$2,104 1 3.3 3.3 83.3
$2,295 1 3.3 3.3 86.7
$2,623 1 3.3 3.3 90.0
$2,651 1 3.3 3.3 93.3
$2,701 1 3.3 3.3 96.7
$2,870 1 3.3 3.3 100.0
Total 30 100.0 100.0

Frequency Per capita Valid Percent Cumulative Percent
Percent

Valid $301 1 3.3 3.3 3.3
$303 1 3.3 3.3 6.7

$317 1 3.3 3.3 10.0
$346 1 3.3 3.3 13.3
$374 1 3.3 3.3 16.7
$400 1 3.3 3.3 20.0
$413 1 3.3 3.3 23.3
$415 1 3.3 3.3 26.7
$442 1 3.3 3.3 30.0
$443 1 3.3 3.3 33.3
$452 1 3.3 3.3 36.7
$471 1 3.3 3.3 40.0
$547 1 3.3 3.3 43.3
$628 1 3.3 3.3 46.7
$715 1 3.3 3.3 50.0
$807 1 3.3 3.3 53.3
$999 1 3.3 3.3 56.7
$1,028 1 3.3 3.3 60.0
$1,102 1 3.3 3.3 63.3
$1,358 1 3.3 3.3 66.7
$1,444 1 3.3 3.3 70.0
$1,450 1 3.3 3.3 73.3
$1,458 1 3.3 3.3 76.7
$1,574 1 3.3 3.3 80.0
$1,606 1 3.3 3.3 83.3
$1,733 1 3.3 3.3 86.7
$1,901 1 3.3 3.3 90.0
$1,981 1 3.3 3.3 93.3
$1,997 1 3.3 3.3 96.7
$2,101 1 3.3 3.3 100.0
Total 30 100.0 100.0

Histogram

3- India and Japan’s Export $ imports: -

Correlations

India export in billions India import in billions Japan import in
billions

Pearson Correlation 1 .996** .940**
Sig. (2-tailed)
India export in billions N .000 .000
India import in billions Pearson Correlation
Japan import in billions Sig. (2-tailed) 32 32 31
Japan export in billions N .996** 1 .937**
Pearson Correlation
Sig. (2-tailed) .000 32 .000
N 32 .937** 31
Pearson Correlation 1
Sig. (2-tailed) .940** .000
N .000 31 31
31 .978**
.944**
.941**

.000 .000 .000
31 31 31

India export in billions Pearson Correlation Japan export in billions
India import in billions Sig. (2-tailed) .941
Japan import in billions N .000
Japan export in billions Pearson Correlation 31
Sig. (2-tailed)
N .944**
Pearson Correlation .000
Sig. (2-tailed) 31
N
Pearson Correlation .978**
Sig. (2-tailed) .000
31
1**

N 31

**. Correlation is significant at the 0.01 level (2-tailed).

Interpretation- Here also India import and export highly correlated with 0.996 and for Japan import and export it is 0.941, However
according to data for Japan import and India export it is 0.940 and for Japan export and India Import it is again 0.944. Both are
almost same upto 2 decimal point.

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness

Statistic Statistic Statistic Statistic Statistic Statistic

India import in billions 32 $22.94 $639.01 $255.2672 $226.79057 .405
India export in billions 32 $20.77 $538.64 $221.0141 $194.33858 .413
Japan import in billions 31 $285.00 $998.18 $590.0223 $243.24505 .310
Japan export in billions 31 $308.91 $919.00 $624.2994 $209.17822 .070
Valid N (listwise) 31

Skewness
Std. Error

.414
.414
.421
.421

Frequencies

Statistics

India export in billions India import in billions Japan export in billions Japan import in billions

Valid 32 32 31 31
N 0011

Missing

Frequency Table

India export in billions

Frequency Percent Valid Percent Cumulative Percent

Valid $20.77 1 3.1 3.1 3.1
$22.64 1 3.1 3.1 6.3
$22.94 1 3.1 3.1 9.4
$25.49 1 3.1 3.1 12.5
$27.47 1 3.1 3.1 15.6
$32.36 1 3.1 3.1 18.8
$39.07 1 3.1 3.1 21.9
$40.80 1 3.1 3.1 25.0
$44.46 1 3.1 3.1 28.1
$46.43 1 3.1 3.1 31.3
$52.54 1 3.1 3.1 34.4
$60.88 1 3.1 3.1 37.5
$60.96 1 3.1 3.1 40.6
$73.45 1 3.1 3.1 43.8
$90.84 1 3.1 3.1 46.9
$126.65 1 3.1 3.1 50.0
$160.84 1 3.1 3.1 53.1
$199.97 1 3.1 3.1 56.3
$253.08 1 3.1 3.1 59.4
$273.75 1 3.1 3.1 62.5
$288.90 1 3.1 3.1 65.6
$375.35 1 3.1 3.1 68.8
$416.79 1 3.1 3.1 71.9
$439.64 1 3.1 3.1 75.0

$447.38 1 3.1 3.1 78.1
$448.40 1 3.1 3.1 81.3
$468.35 1 3.1 3.1 84.4
$472.18 1 3.1 3.1 87.5
$474.15 1 3.1 3.1 90.6
$498.26 1 3.1 3.1 93.8
$529.02 1 3.1 3.1 96.9
$538.64 1 3.1 3.1 100.0
Total 32 100.0 100.0

India import in billions

Frequency Percent Valid Percent Cumulative Percent

Valid $22.94 1 3.1 3.1 3.1
$24.13 1 3.1 3.1 6.3
$27.13 1 3.1 3.1 9.4
$27.42 1 3.1 3.1 12.5
$27.64 1 3.1 3.1 15.6
$33.35 1 3.1 3.1 18.8
$43.32 1 3.1 3.1 21.9
$45.36 1 3.1 3.1 25.0
$49.61 1 3.1 3.1 28.1
$53.43 1 3.1 3.1 31.3
$61.31 1 3.1 3.1 34.4
$65.12 1 3.1 3.1 37.5
$65.22 1 3.1 3.1 40.6
$78.50 1 3.1 3.1 43.8
$95.07 1 3.1 3.1 46.9
$139.31 1 3.1 3.1 50.0

$183.74 1 3.1 3.1 53.1
$229.96 1 3.1 3.1 56.3
$302.80 1 3.1 3.1 59.4
$347.18 1 3.1 3.1 62.5
$350.93 1 3.1 3.1 65.6
$449.97 1 3.1 3.1 68.8
$465.10 1 3.1 3.1 71.9
$480.17 1 3.1 3.1 75.0
$482.45 1 3.1 3.1 78.1
$527.56 1 3.1 3.1 81.3
$529.24 1 3.1 3.1 84.4
$566.67 1 3.1 3.1 87.5
$571.31 1 3.1 3.1 90.6
$582.02 1 3.1 3.1 93.8
$601.58 1 3.1 3.1 96.9
$639.01 1 3.1 3.1 100.0
Total 32 100.0 100.0

Japan export in billions

Frequency Percent Valid Percent Cumulative Percent

Valid $308.91 1 3.1 3.2 3.2
$320.17 1 3.1 3.2 6.5
$350.77 1 3.1 3.2 9.7
$378.84 1 3.1 3.2 12.9
$404.34 1 3.1 3.2 16.1
$424.40 1 3.1 3.2 19.4
$440.21 1 3.1 3.2 22.6
$441.81 1 3.1 3.2 25.8

$453.41 1 3.1 3.2 29.0
$454.01 1 3.1 3.2 32.3
$458.19 1 3.1 3.2 35.5
$465.70 1 3.1 3.2 38.7
$488.88 1 3.1 3.2 41.9
$517.44 1 3.1 3.2 45.2
$519.27 1 3.1 3.2 48.4
$624.63 1 3.1 3.2 51.6
$655.02 1 3.1 3.2 54.8
$666.35 1 3.1 3.2 58.1
$719.10 1 3.1 3.2 61.3
$773.03 1 3.1 3.2 64.5
$789.87 1 3.1 3.2 67.7
$800.72 1 3.1 3.2 71.0
$820.55 1 3.1 3.2 74.2
$850.78 1 3.1 3.2 77.4
$857.11 1 3.1 3.2 80.6
$863.96 1 3.1 3.2 83.9
$877.81 1 3.1 3.2 87.1
$888.89 1 3.1 3.2 90.3
$902.24 1 3.1 3.2 93.5
$917.87 1 3.1 3.2 96.8
$919.00 1 3.1 3.2 100.0
Total 31 96.9 100.0
System 1 3.1
Missing 32 100.0
Total

Japan import in billions

Frequency Percent Valid Percent Cumulative Percent

Valid $285.00 1 3.1 3.2 3.2
$295.00 1 3.1 3.2 6.5
$297.07 1 3.1 3.2 9.7
$298.54 1 3.1 3.2 12.9
$308.94 1 3.1 3.2 16.1
$348.44 1 3.1 3.2 19.4
$351.13 1 3.1 3.2 22.6
$383.09 1 3.1 3.2 25.8
$397.82 1 3.1 3.2 29.0
$411.81 1 3.1 3.2 32.3
$419.04 1 3.1 3.2 35.5
$420.00 1 3.1 3.2 38.7
$437.23 1 3.1 3.2 41.9
$442.07 1 3.1 3.2 45.2
$449.42 1 3.1 3.2 48.4
$527.28 1 3.1 3.2 51.6
$594.57 1 3.1 3.2 54.8
$626.20 1 3.1 3.2 58.1
$655.04 1 3.1 3.2 61.3
$704.41 1 3.1 3.2 64.5
$751.94 1 3.1 3.2 67.7
$773.86 1 3.1 3.2 71.0
$791.42 1 3.1 3.2 74.2

$818.68 1 3.1 3.2 77.4
$855.18 1 3.1 3.2 80.6
$880.23 1 3.1 3.2 83.9
$906.31 1 3.1 3.2 87.1
$940.00 1 3.1 3.2 90.3
$952.44 1 3.1 3.2 93.5
$970.35 1 3.1 3.2 96.8
$998.18 1 3.1 3.2 100.0
Total 31 96.9 100.0
System 1 3.1
Missing 32 100.0
Total
Histogram



Hypothesis Testing

JAPAN

Descriptives

Mean Statistic Std. Error
$4,685.0677 $128.32820
95% Confidence Interval for Mean $4,422.9866
Lower Bound $4,947.1489
5% Trimmed Mean Upper Bound $4,690.6568
Median $4,815.1700
GDP Variance 510511.967
Std. Deviation $714.50120
Minimum $3,054.91

IMPORTS Maximum Lower Bound $6,203.21 .421
EXPORTS Range Upper Bound $3,148.30 .821
Interquartile Range $43.68810
Skewness Lower Bound $648.43
Kurtosis Upper Bound -.181 .421
Mean .836 .821
$37.56952
95% Confidence Interval for Mean $590.0223
$500.7993 .421
5% Trimmed Mean $679.2453 .821
Median $584.6440
Variance $527.2800 Sig.
Std. Deviation 59168.154 .290
Minimum $243.24505 .006
Maximum .005
Range $285.00
Interquartile Range $998.18
Skewness $713.18
Kurtosis $435.59
Mean
.310
95% Confidence Interval for Mean -1.459
$624.2994
5% Trimmed Mean $547.5722
Median $701.0265
Variance $625.2490
Std. Deviation $624.6300
Minimum 43755.529
Maximum $209.17822
Range $308.91
Interquartile Range $919.00
Skewness $610.09
Kurtosis $408.97

.070
-1.620

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk
df
Statistic df Sig. Statistic

GDP .114 31 .200* .960 31
31 .002 .895 31
IMPORTS .202 31 .015 .893 31

EXPORTS .176

*. This is a lower bound of the true significance.

a. Lilliefors Significance Correction

GDP
Skewness/Std. error of skewness = -0.429
Kurtosis/Std. error of Kurtosis = 1.01

Here the distribution is negatively skewed. Also, the skewness value is acceptable. For Kurtosis we have the
value 1.01 > 1, it means the distribution is highly peaked and since 1.01 < 2 it is also acceptable. Thus we
have a normal distribution.
Another strong test for normality is the Shapiro Wilk Test. Here the p-value is 0.290 > 0.05, thus we accept
the null hypothesis and our data is normally distributed.

IMPORTS
Skewness/Std. error of Skewness = 0.73
Kurtosis/Std. error of Kurtosis = -1.77
Here we have positively skewed data and highly negative kurtosis, i.e. the graph is flat and not peaked.
We have the Shapiro Wilk Test p-value as 0.006 < 0.05. The data is not normally distributed.

EXPORTS
Skewness/Std. error of Skewness = 0.16
Kurtosis/Std. error of Kurtosis = -1.97
Here again we have a positively skewed data and highly negative kurtosis (-1.97 is very close to -2), giving
us a very flat graph.
Again the p-value here is 0.005 < 0.05. The data is not normal.

INDIA

Descriptives

Mean Lower Bound Statistic Std. Error
Upper Bound $1,092.7594 $152.05771
95% Confidence Interval for Mean $782.2161
GDP $1,403.3026 .421
IMPORTS 5% Trimmed Mean $1,043.9437 .821
Median $709.1500 $40.70846
Variance 716767.927
Std. Deviation $846.62148
Minimum $246.00
Maximum $2,870.50
Range $2,624.50
Interquartile Range $1,434.74
Skewness .760
Kurtosis -.807
Mean $247.9387

95% Confidence Interval for Mean Lower Bound $164.8010 .421
Upper Bound $331.0765 .821
5% Trimmed Mean $239.4269 $34.46437
Median Lower Bound $139.3100
Variance Upper Bound 51372.531 .421
Std. Deviation $226.65509 .821
EXPORTS Minimum
Maximum $22.94 Sig.
Range $639.01
Interquartile Range $616.07
Skewness $434.81
Kurtosis
Mean .488
-1.507
95% Confidence Interval for Mean $212.8484
$142.4628
5% Trimmed Mean $283.2340
Median $205.5727
Variance $126.6500
Std. Deviation 36821.573
Minimum $191.88948
Maximum $20.77
Range $538.64
Interquartile Range $517.87
Skewness $398.84
Kurtosis
.499
Tests of Normality -1.490

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df

GDP .204 31 .002 .854 31 .001
31 .000 .824 31 .000
IMPORTS .234 31 .000 .825 31 .000

EXPORTS .221

a. Lilliefors Significance Correction

Here the findings are as follows:

GDP
Skewness/Std. error of skewness = 1.80
Kurtosis/Std. error of Kurtosis = -0.98
The distribution is positively skewed and a negative kurtosis value indicates distribution to be flat and have
thin tails.

p-value = 0.001 < 0.05 (Not normal)

IMPORTS
Skewness/Std. error of skewness = 1.15
Kurtosis/Std. error of kurtosis = -1.83
The distribution is positively skewed and a negative kurtosis value indicates distribution to be flat and have
thin tails.

p-value = 0.000 < 0.05 (Not normal)

EXPORTS
Skewness/Std. error of skewness = 1.18
Kurtosis/Std. error of kurtosis = -1.81
The distribution is positively skewed and a negative kurtosis value indicates distribution to be flat and have
thin tails.

p-value = 0.000 < 0.05 (Not normal)

We’ll now check that whether there exists a significant difference in the volume of trade
between the two nations.

• For that we either take the combined imports or the exports of the countries.
• We then check for its normality. Here we take the exports.

Descriptives

Mean Lower Bound Statistic Std. Error
Upper Bound $418.5739 $36.51002
95% Confidence Interval for Mean $345.5676
EXPORTS $491.5802 .304
5% Trimmed Mean $413.1663 .599
Median $441.0100
Variance 82644.877
Std. Deviation
Minimum $287.48022
Maximum $20.77
Range
Interquartile Range $919.00
Skewness $898.23
Kurtosis $514.53

.175
-1.036

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk
df
Statistic df Sig. Statistic Sig.

EXPORTS .115 62 .041 .923 62 .001

a. Lilliefors Significance Correction

By running the Shapiro-Wilk normality test we get p-value as 0.001 < 0.05. The data is not normally
distributed.

• We, thus, perform a non-parametric test. We compare exports of both countries for each year by
performing Wilcoxon Signed Ranks Test.

Our null hypothesis basically proposes that no statistical significance exists in a set of given observations.
Only when the p-value > 0.05, we accept the null hypothesis. Here our p-value is zero, which strongly
proves that there is a complete statistical significance between our given set of observations. Thus proving
that there exists a significant difference in the volume of trade between Japan (high GDP) & India (medium
GDP).

Analysts look to reject the null hypothesis because doing so is a strong conclusion. This requires
strong evidence in the form of an observed difference that is too large to be explained solely by
chance. Failing to reject the null hypothesis—that the results are explainable by chance alone—is a
weak conclusion because it allows that factors other than chance may be at work but may not be
strong enough for the statistical test to detect them.

Discussion and conclusion

From the given study we have studied the changes in trade pattern for the country and between the countries.
India being a developing economy has showed a steady continuous increase in be it GDP, imports or exports

because of rising population and demand. Japan on the other hand has a stable constant GDP over time
because it is already a highly developed or we can say mature economy. The population pyramids of both

the countries also prove this fact.

Talking through the perspective of gravity model, we know that countries with similar income levels and
close distance trade more. India with Japan does the trade but since the countries have significant trade
difference and income difference, and this we showed also with our findings, the two doesn’t trade that
intensively. The way India trades with its other neighbouring South East Asian counterparts, both because of

the distance and similar economy levels, it fails to do the same with the Japan on the same level.

There are common areas and commodities in which India and Japan trade extensively. Both benefit from it.
But according to me, India is not gaining to its full extent. India is yet to take advantage of the sector for

which Japan is most commonly known; its IT sector. India can achieve specialization in its labour by taking
use of Japan’s technology.

References

Japan Inc. Definition (investopedia.com)
The Fundamentals of How India Makes Its Money (investopedia.com)
The Top Indicators for India's Economy (investopedia.com)
Japan GDP - 2021 Data - 2022 Forecast - 1960-2020 Historical - Chart - News (tradingeconomics.com)
Japan GDP 1986-2026 | Statista
India GDP - 2021 Data - 2022 Forecast - 1960-2020 Historical - Chart - News (tradingeconomics.com)
India GDP Annual Growth Rate - 2022 Data - 2023 Forecast - 1951-2021 Historical (tradingeconomics.com)
https://www.thoughtco.com/what-is-the-gravity-model-408887
0606 inside-pdf (unescap.org)