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THE IMPACT OF MACROECONOMIC FACTORS ON NON-LIFE

INSURANCE CONSUMPTION IN THAILAND

Porntida Poontirakul

A Thesis Submitted in Partial

Fulfillment of the Requirements for the Degree of

Master of Science

(Insurance, Actuarial Science, and Risk Management )

School of Applied Statistics

National Institute of Development Administration

2012

ABSTRACT

Title of Thesis The Impact of Macroeconomic Factors on Non-life Insurance

Consumption in Thailand

Auther Miss Porntida Poontirakul

Degree Master of Science

(Insurance, Actuarial Science, and Risk Management)

Year 2012

Non-life insurance consumption in Thailand has increased significantly in the

past decade. Many factors have contributed to the development of non-life insurance

industry including macroeconomic factors. This research, therefore, aimed to study

the impact of macroeconomic factors on the increasing non-life insurance

consumption in Thailand. Twenty independent variables were gathered from eight

macroeconomic indices, which were published by the Bureau of Trade and Economic

Indices, i.e.: Consumer Price Index, Business Cycle Index, Inflation Cycle Index,

Export Business Situation Index, Consumer Confidence Index, Producer Price Index,

Construction Material Price Index, and Export and Import Price Index. They were

selected to be statistically examined for their potential impacts on non-life insurance

consumption, which was represented by the amount of all directly earned premium of

total non-life insurance consumption by all insurance companies in Thailand,

published on the website of the Office of Insurance Commission (OIC). The research

data was collected on a monthly basis for a 10 year period from 2002 to 2011.

Multiple Regression analysis was used as the research methodology. The result

suggested that four macroeconomic indices, i.e.: Coincident Index (from Business

Cycle Index), Employment Rate (from Export Business Situation Index), Consumer

Confidence Index, and Export Price Index, were found to have an impact on total non-

life insurance consumption in Thailand of around 84%. From this analysis, it can be

concluded that some macroeconomic factors have an impact on non-life insurance

consumption in Thailand.

ACKNOWLEDGEMENTS

I would like to take this opportunity to express my gratitude towards all those

who gave me the possibilities to complete the thesis. First and foremost, I would like

to express my deep appreciation to my advisor, Archan Preecha Vichitthamaros, for

the support during my study. His patient guidance helped me to work through the

project and complete it within the limited timeframe. Beside my advisor, I would like

to thank the thesis committee, Archan Duanpen Teerawanviwat, for her comments

and inspiration.

Moreover, I wish to express my sincere thanks to the Department of Applied

Statistics, National Institute of Development Administration, who provide the

financial support to the project and gave me this opportunity to explore my ability as a

researcher. In addition, I’d like to thank all my friends and fellows who helped me

during the study course.

Especially, I would like to give my special thanks to my parents whose patient

love and support enabled me to complete this work.

Porntida Poontirakul

March 2013

TABLE OF CONTENTS

Page

ABSTRACT iii

ACKNOWLEDGEMENTS iv

TABLE OF CONTENTS v

LIST OF TABLES viii

LIST OF FIGURES ix

CHAPTER 1 INTRODUCTION 1

1.1 Background Statement and Significance of the Study 1

1.2 Research Objective 5

1.3 Scope of the Study 5

1.4 Expected Benefits and Analysis 6

CHAPTER 2 LITERATURE REVIEW 7

2.1 Non-Life Insurance in Thailand 7

2.1.1 Fire Insurance 7

2.1.2 Automobile Insurance 8

2.1.3 Marine and Transportation Insurance 9

2.1.4 Miscellaneous Insurance 10

2.2 Non-Life Insurance Consumption in Thailand 10

2.2.1 Fire Insurance 12

2.2.2 Automobile Insurance 12

2.2.3 Marine and Transportation Insurance 13

2.2.4 Miscellaneous Insurance 15

2.3 The Theory of Business Economics 16

2.4 Macroeconomic Variables 17

2.4.1 Gross Domestic Product 17

2.4.2 Inflation 18

vi

2.5 Key Macroeconomic Indicators 20

CHAPTER 3 RESEARCH METHODOLOGY 22

3.1 Research Methodology Framework 22

3.2 Conceptual Framework 25

3.3 Population Sampling and Methodology 28

3.4 Research Variable 28

3.4.1 Dependent Variable 28

3.4.2 Independent Variable 28

3.5 Data Collection 32

3.6 Data Analysis 32

3.6.1 Statistical Models 32

3.6.2 Data Analysis Tool 34

3.6.3 Data Analysis Procedure 34

CHAPTER 4 RESULT OF THE ANALYSIS 36

4.1 Analysis Result of Total Non-life Insurance Consumption 36

4.1.1 Result of Correlation Analysis 36

4.1.2 Result of Stepwise Analysis 38

4.2 Analysis Result of Each Type of Insurance Consumption 41

4.2.1 Result of Correlation Analysis 42

4.2.2 Result of Stepwise Analysis 44

4.3 Summary 50

CHAPTER 5 RESEARCH CONCLUSION AND DISCUSSION 51

5.1 Research Conclusion 51

5.2 Research Discussion 56

5.3 Recommendation 58

5.3.1 Recommendation from the research 58

5.3.2 Recommendation for further research 59

BIBLIOGRAPHY 60

APPENDICES 65

Appendix A Descriptive Statistics 66

Appendix B Correlation Analysis 68

vii

Appendix C Stepwise Analysis 77

BIOGRAPHY 103

LIST OF TABLES

Tables Page

1.1 Direct Premium and Net Written Premium Comparison 3

2.1 Comparison of Key Economic Indicators Worldwide and Thailand 20

3.1 The Comparison of Non-Life Insurance Premium between the 26

Estimated by ThaiRe Research and Statistic Services and the

Actual Data

3.2 Summary of Variables and Their Definitions 31

4.1 Correlation Analysis between Total Non-life Insurance Consumption 37

in Thailand and the Actual Indices


in Thailand and the Percentage Changes and Growth Rates

4.3 Total Non-Life Insurance Consumption Stepwise Analysis 39


in Thailand and the Percentage Changes and Growth Rates

4.5 Automobile Insurance Consumption Stepwise Analysis 45

4.6 Marine and Transportation Insurance Consumption Stepwise Analysis 47

4.7 Miscellaneous Insurance Consumption Stepwise Analysis 49

4.8 Summary 51

LIST OF FIGURES

Figures Page

1.1 Insurance Penetrations in Asian Countries – Year 2010 2

2.1 Non-Life Insurance Consumption in Thailand Period 2001-2010 11

2.2 Direct Premium Proportion of Non-Life Insurance in Thailand 2010 11

2.3 Direct Premium and Net Written Premium of Fire Insurance in 12

Thailand during 2000-2010

2.4 Direct Premium and Net Written Premium of Motor Insurance 13

in Thailand during 2000-2010

2.5 Direct Premium and Net Written Premium of Marine and 14

Transportation Insurance in Thailand during 2000-2010

2.6 Direct Premium and Net Written Premium of Miscellaneous 15

Insurance in Thailand during 2000-2010

2.7 Factors Impact Business Strategy 16

2.8 The Circular-Flow Diagram 18

3.1 Research Methodology 24

3.2 Conceptual Framework 27

CHAPTER 1

INTRODUCTION

1.1 Background Statement and Significance of the Study

Insurance plays a vital role to both a nation’s economy and its societal

development because of its many benefits. The main advantage of insurance is its

utility to promote long-term financial stability and security of individuals and

businesses. In other words, it helps entities recover financial loss due to unexpected

perils such as floods, automotive collisions, earthquakes, and tsunamis. Moreover,

insurance is considered to be one of the essential financial services to an economic

system. (Brainard, 2008)

Although insurance has many benefits to whole societies, historical statistics

show that the consumption of insurance in Thailand is relatively low compared to that

of its international companions. This metric is represented by the amount of gross

premium written, also known as direct premium written, or the monetary consumption

value of non-life insurance.

The insurance penetration in Thailand, which is the ratio percentage of gross

insurance premium to gross domestic product, or GDP, was 4.3% for the entire

insurance industry, both life and non-life, in the year 2010. While it was 6.9% for the

world as a whole, total insurance premiums indicated 60% lower consumption

overall. For non-life insurance, the insurance penetration in 2010 was 1.7% in

Thailand and 2.9% for the overall population, or 71% lower consumption. Figure 1.1

shows the comparison of insurance penetration in the emerging Asian countries in

2010. According to the figure, South Korea had the highest insurance penetration in

non-life insurance while Bangladesh had the lowest. The insurance penetration

percentage of Thailand was similar to that of Hong Kong, Singapore, Malaysia and

China.

2

Figure 1.1 Insurance Penetrations in Asian Countries – Year 2010

Source: Insurance Regulatory and Development Authority of India, 2011.

The significance of insurance penetration is that it indicates the contributions

of insurance to a nation’s economic growth. Since GDP can be used as one of the

leading indicators of a nation’s economic growth, insurance is included in the

measurement of GDP. Insurance penetration, therefore, measures the proportion of

insurance sectors to a nation’s GDP. As per the information stated in figure 1.1, the

insurance penetration signifies that the amount of non-life insurance contribution to a

country’s economic growth was similar to that of Hong Kong, Malaysia and China.

3

Table 1.1 Direct Premium and Net Written Premium Comparison

Source: Office of Insurance Commission, 2012m.

According to the annual report of the OIC in December 2010, there were 70

active non-life insurance companies operating in Thailand. Of these, 59 companies

were domestic companies (legally registered in Thailand), 5 companies were foreign

branches, 5 companies were health insurance companies, and 1 company was a

reinsurance company. One company was withdrawn from the study due to financial

insolvency (Office of Insurance Commission, 2011).

The number of insurance companies is changing almost every year in the

recent past due mostly to insolvency reasons; however, the amount of premium and

the number of policies issued have been increasing significantly.

During the past decade, non-life insurance consumption in Thailand has

dramatically increased. In the year 2000, the non-life insurance gross premium was

THB 48,700 million whereas in the year 2010, it was THB 125,087 million, which

indicated a 157% increase for the entire non-life insurance industry. Of these

amounts, the net premium had increased from THB 37,277 million in the year 2000 to

THB 95,986 million, which equaled a 158% increase. In addition, the numbers of

policies also increased from 14,694 million to 37,609 million, a 156% increase.

Moreover, the total sum insured of all types of non-life insurance had increased from

THB 19,180 billion to THB 27,570 billion, or about 44%. Please note that these

figures exclude Thai Reinsurance Public Co., Ltd. From this data, it shows that the

consumption of non-life insurance had increased over the ten-year period.

Type of Insurance Policy

2000 2010 Growth Rate 2000 2010 Growth Rate

TOTAL INSURANCE 55,120 125,087 126.93% 38,990 95,986 146.18%

FIRE INSURANCE 7,818 7,867 0.63% 4,841 5,760 18.99%

AUTOMOBILE INSURANCE 31,999 74,614 133.18% 29,668 70,959 139.17%

Compulsory Insurance 7,669 11,175 45.72% 7,331 10,972 49.66%

Voluntary Insurance 24,331 63,439 160.74% 22,337 59,987 168.55%

MARINE INSURANCE 2,575 4,326 67.98% 1,232 2,417 96.26%

Hull Insurance 273 398 45.97% 51 116 129.12%

Cargo Insurance 2,303 3,928 70.59% 1,181 2,301 94.85%

MISCELLANEOUS INSURANCE 12,728 38,279 200.75% 3,249 16,850 418.60%

Direct Premium Net Written Premiums

4

Macroeconomics is the study of the economic system as a whole. The goal of

macroeconomic study is to explain the changes to a nation’s economy that affects

many households, firms, and markets simultaneously, such as the forces that drive

household consumption to increase, etc. Economists monitor and investigate the state

of the overall economy through macroeconomic factors, which are often called,

macroeconomic indicators. These include: Gross Domestic Product (GDP),

unemployment rates, investment, consumption, etc. Macroeconomic factors are

considered to impact industry and hence, can be used to measure a society’s overall

economic well-being. (Mankiw, 2008: 510-511 and Barro, 2008: 23)

As per the researcher’s literature review, it is currently lacking the integrated

analysis of macroeconomic factors that potentially impact non-life insurance

consumption in Thailand. Regarding life insurance consumption, Vichit

Wattanabunjongkul (2006) studied the factors affecting life insurance premiums.

However, most of the studied factors were not macroeconomic factors (inflation rate

was the only macroeconomic factor that was included). The result suggested that life

insurance premiums were negatively related to inflation rates. However, life insurance

characteristics are different from those of non-life insurance such as: coverages, terms

and conditions, coverage period, etc. (Rejda, 2008: 25). Therefore, the result did not

imply that it has similar impact to non-life insurance premium consumption.

Nevertheless, the ThaiRe Research and Statistic Services, which issues Thai

Non-Life Insurance Business Report each year for those individuals and businesses

that require such information, foresaw the highly volatile economy in Thailand, which

could change business strategic management in many industries including non-life

insurance. Therefore, they published an article titled, “Non-life Insurance Business

Trend Report 2009-2010” on “Insurance Journal issue no. 105” owned by the General

Insurance Association (GIA). The article provided estimates of non-life insurance

consumption in Thailand for the year 2009 and 2010 with the goal to anticipate the

consumption trend of the industry. It assumed that many economic factors were

related to the premium consumption. The actual results were different from the

estimated boundary around 1%, which were considered minimal. Therefore, this study

was conceptualized by using the study of ThaiRe as a model.

5

In conclusion, macroeconomics has impacts to the non-life insurance industry

similar to that of other industries (Zweifel and Eisen, 2012: 6). However, there are

many other factors that impact the state of a nation’s economy. Some may be

considered to affect non-life insurance operating performance while some may not.

This study, therefore, focuses on the study of macroeconomic factors that affect non-

life insurance performance. The result is expected to point out the impacting factors to

be used as key indicators to non-life insurance consumption trend.

1.2 Research Objective

To analyze the impact of macroeconomic factors on non-life insurance


1.3 Scope of the Study

1) This research studied non-life insurance consumption from a

macroeconomic viewpoint. Therefore, the researcher studied overall consumption for

the entire non-life insurance industry. Individual insurance companies were not be

analyzed.

2) The dependent variables for this research was total non-life insurance

consumption in Thailand which was gathered from the Office of Insurance

Commission (OIC) in Thailand.

3) The independent variables for this study were selected from the Bureau of

Trade and Economic Indices, Ministry of Commerce, Thailand, which were

comprised of 8 indices with a total of 20 variables. The selected indices were:

Consumer Price Index (CPI), Business Cycle Index (BCI), Inflation Cycle Index

(ICI), Export Business Situation Index (ESI), Consumer Confidence Index (CCI),

Producer Price Index (PPI), Construction Material Price Index (CMI) and Export and

Import Price Index (EPI). The collected variables included the actual indices, the

percentage changes from the previous month, and the six-month smoothed annualized

growth rates.

6

4) This research studied data for a 10 year period, from 2002 to 2011. The

data was collected on a monthly basis. Therefore, each variable has a total of 120

samples.

5) All data for this research was derived from secondary sources including:

published papers, thesis, research papers, books, journals, news, websites and other

related documents. Most of the data was derived from the Office of Insurance

Commission (OIC) and the Bureau of Trade and Economic Indices.

1.4 Expected Benefits and Applications

1) To understand the pattern of non-life insurance consumption in Thailand,

2) To be able to indicate the macroeconomic factors which impact non-life

insurance consumption in Thailand.

3) To understand the impact of macroeconomic factors on non-life insurance


CHAPTER 2

LITERATURE REVIEW

2.1 Non-Life Insurance in Thailand

Non-life insurance, also known as property and casualty insurance, refers to

the insurance coverage that is not life insurance but instead covers such things as

automobiles, buildings, and hulls. In addition, it also covers liability damages and

personal health (Rejda, 2008: 26). Non-life insurance usually provides coverage for

one year only, unlike life insurance which allows an insured to obtain insurance

coverage longer than one year. The policy will be renewed every single year, except

for some types of policy, e.g. Construction All Risk insurance (CAR) which is

nonrenewable.

There are many types of non-life insurance coverage; however, non-life

insurance in Thailand is classified into four main classifications by the Office of

Insurance Commission (OIC) as follows:

2.1.1 Fire Insurance

Fire insurance provides coverage for loss or damages to an insured property

arising directly from three main causes: fire, lighting, and explosion caused directly

by domestic gas usage. The insurance could also include other perils which are

specifically stated in the policy, e.g. water damage, electrical injury, flood, etc.

However, there are some exclusions under the policy, such as explosions following

the fire, earthquake, spontaneous combustion and others, as stated in the policy.

8

In Thailand, the main target customers for this policy are homeowners. Fire

insurance policies for homeowners provide coverage to properties for residential

useonly. It is corporately developed by Office of Insurance Commission (OIC) and

General Insurance Association (GIA) with the purpose of designing coverage

specifically for homeowners’ risks. This is because homeowners’ fire risk exposure is

considered lower than others, i.e. hotel, industrial, office. Moreover, there are six

main risks covered under fire insurance policies for homeowners which are: fire,

lightning, explosions, damages caused by vehicles or animals, damages caused by

aircrafts and water damage. The insured could also purchase additional coverage such

as: electrical injury, flood, strike and riots, etc. (Office of Insurance Commission,

2012c).

2.1.2 Automobile Insurance

Automobile insurance, sometimes called motor insurance, protects the insured

against any loss or damages of the insured’s vehicle. There are basically two types of

motor insurance policies in Thailand; compulsory and voluntary.

All legally registered motor vehicles, including motorcycles, in Thailand

(except state vehicles) must be insured under the Protection for Motor Vehicle

Accident Victims Act, enacted since 1992. The Act made provisions setting a Victims

Compensation Fund to protect all victims, i.e. drivers, passengers, pedestrians, and

cyclists, who get injured by motor accidents including the vehicle owners. However,

the insurance covers only bodily injury or death, excluding property damage, to the

victims of road accidents within the stated specified amounts. (Office of Insurance

Commission, 2012a). In addition, the insurance compensates on a “no-fault” basis,

which means if an accident occurred, there is no need to prove negligence.

Furthermore, all injured parties would get the same coverage and compensation.

Based on the Act provisions stated above, the number of compulsory motor insurance

consumption is dependent primarily on the number of motor vehicles registered, both

for private and public uses.

Moreover, the premium for compulsory motor insurance is stated as tariff rate,

which means the premium rate is fixed, i.e. the same type of vehicles pays the same

premium amount regardless of brand, vehicle prices, etc. For example, the premium

9

amount for all personal use sedans with not more than seven passenger seats,

regardless of brand, selling price, etc., is THB 600 per vehicle. The premium is

considered minimal compared to other types of policies. (Lawrence, 2004: 85).

Voluntary motor insurance, on the other hand, is not a requirement to vehicle

owners. The insurance coverage is classified into four sections: Third Party Bodily

Injury (TPBI), Third Party Property Damage (TPPD), Own Damage (OD) and Fire

and Theft (F&T). It could also be divided into three different types of policies ranging

from the broadest coverage to the least coverage. The broadest coverage policy, which

is also called, comprehensive motor insurance, covers all four coverage categories and

has the highest premium rate. The second provides coverage for third party liability

section (TPBI and TPPD) and the vehicle damaged (OD) caused by fire or theft only;

while the third provides coverage for third party liability (TPBI and TPPD) only and

has the lowest premium rate. However, the voluntary motor policy is considered to be

an excess policy; the claim amount will be paid on top of the compulsory insurance.

Moreover, the premium for voluntary insurance is also stated as tariff rate.

(Office of Insurance Commission, 2012a). However, there are many factors affecting

premium rate for this type of policy, e.g. type of vehicles, vehicle age, driver age,

occupation, experiences, etc.; therefore, premium rates are varied among individual

policies. (Lawrence, 2004: 84).

2.1.3 Marine and Transportation Insurance

This insurance is related to marine operation and is classified into two types:

Hull and Cargo. Hull insurance covers loss or damages to an insured hull arising from

various perils such as collision, stranding, windstorm, etc. The coverage for hull can

be chosen as an all risk or name perils policy.

There are basically three types of hull insurance. The first is an all-risks policy

which protects the insured hull caused by all perils that are not specifically excluded

in the policy. This policy has the highest premium rate among other types of marine

insurance. The second is “with average” (W.A.) which covers loss to the insured hull

for total loss and partial loss. The third is “free from particular average” (F.P.A.)

which protects the hull for total loss only. This provides the least coverage and has the

lowest premium rate. (Office of Insurance Commission, 2012i).

10

Cargo insurance, however, covers loss or damages to cargo during marine

transit. The causes of coverage is called, “The Institute Cargo Clause” (ICC) and is

divided into three clauses: ICC A, ICC B, and ICC C. Clause A is the broadest type of

coverage, an all risks policy, which covers loss or damages caused by all external

causes that are not specifically excluded in the policy. Clause B and C have lower

coverage; Clause B has lower coverage than Clause A, and Clause C has the least

coverage. (Office of Insurance Commission, 2012i).

2.1.4 Miscellaneous Insurance

This insurance protects broader perils than other types of policies in Thailand

because all other insurances, which are not included in the above three types of

insurance, are included under this category. The examples of miscellaneous insurance

are: all risk, burglary, money, public liability, construction all risk, etc. (Office of

Insurance Commission, 2012j).

2.2 Non-Life Insurance Consumption in Thailand

According to the statistical data presented by the Office of Insurance

Commission (OIC), non-life insurance consumption in Thailand had an average

growth rate of 9.96% per year in the past decade with an average direct premium of

THB 89,000 Million. Figure 2.1 shows the pattern of non-life insurance consumption,

comparing between direct premium and net written premium, from 2001 to 2010 in

Million Baht. The figure indicates that non-life insurance consumption had increased

continually with a relatively constant reinsurance costs and commission expenses,

causing net premium written to grow at a relatively constant rate.

As shown in figure 2.2, automobile insurance’s market share was about 60%

of all non-life insurance industry. The second largest market share was pertaining to

miscellaneous insurance with 27.97% proportion. The third largest share was fire

insurance, which made up about 9%. While, the least market share in non-life

insurance industry was marine coverage.

11

Figure 2.1 Non-Life Insurance Consumption in Thailand Period 2001-2010

Figure 2.2 Direct Premium Proportion of Non-Life Insurance in Thailand 2010


With the trend of increasing consumption of the non-life insurance industry in

Thailand, it is of most importance to understand the factors that affect insurance

consumption both internally and externally.

12

2.2.1 Fire Insurance

Fire insurance consumption in Thailand had been relatively stable during the

past decade. Figure 2.4 shows premium consumption for fire insurance in Thailand

during 2001-2010. The 10-year average direct premium of THB 7,530 million or

0.2% average growth per year. It had a 10-year average net written premium of THB

5,230 Million which equals approximately 1.51% growth per year. From the figure,

the amount of gross premium and net written premium has increased in a relatively

constant rate.

Figure 2.3 Direct Premium and Net Written Premium of Fire Insurance in Thailand

during 2000-2010


2.2.2 Automobile Insurance

Automobile insurance has an average direct premium of around THB 52,000

million and an average net written premium of around THB 50,000 million. Both

average growth rates are 9.78% and 9.81%, respectively. It seems that the direct and

net written premium have similar average growth. Overall, automobile insurance

consumption has continually increased, as shown in figure 2.5. Starting in 2000, the

average annual growth rate is roughly 9.8% for both direct and net written premium.

13

The trend line firmly suggests an increase in premium consumption for automobile

insurance in Thailand. In addition, as automobile insurance had the highest

consumption portion of all non-life insurance industry and a strong upward trend, it is

considered significant to non-life insurance industry.

Figure 2.4 Direct Premium and Net Written Premium of Motor Insurance in



2.2.3 Marine and Transportation Insurance

Hull insurance consumption is considered minimal with only 0.42% premium

consumption proportion of all non-life insurance industry in Thailand with. Also, it

has an average THB 361 million in direct premium and THB 90 million in net

premium written. The consumption factors are varied in the researcher’s opinion.

However, it had a 10-year average premium growth rate of around 13% per year

during the past decade.

For cargo insurance, a 10-year average direct premium of THB 3,212 million

with an average growth rate of 6.24% was identified. Additionally, it had a 10-year

average net written premium of THB 1,949 million with an average growth rate of

8.13%. It seems that cargo insurance had a higher industry contribution than that of

14

hull insurance. It had about 4% consumption contribution for non-life insurance

industry.

Figure 2.5 Direct Premium and Net Written Premium of Marine and Transportation

Insurance in Thailand during 2000-2010


Nonetheless, marine insurance had a 10-year average direct premium of THB

3,573 million with an average growth rate of 6.52% per year. Also, it had a 10-year

average net written premium of THB 2,039 million with an average growth rate of

8.27%, annually. From figure 2.6, the direct premium and net premium written of

marine insurance in Thailand during 2001-2010 had much fluctuation.

2.2.4 Miscellaneous Insurance

As the name suggests, miscellaneous insurance covers any insured risks that

are not categorized as the other three types of coverage. This categorization pertains

to the second largest consumption proportion of non-life insurance industry, which

makes up 28% of the industry. The 10-year average direct premium was THB 25,271

million with an average growth rate of 16.7% per year. The 10-year average net

written premium was THB 9,422 million with an average growth rate of 19.21%,

15

annually. However, as shown in figure 2.6, the gap between direct premium and net

written premium was wide during the study period. As the net written premium is the

premium after the deduction of reinsurance costs and other expenses, the figure

signifies that the costs of reinsurance and other administrative expenses had increased.

As the information from other policies stated above, it is assumed that the expenses

had been constant. This implies an increasing reinsurance portion for miscellaneous

insurance; which can also be implied that there is room to grow for miscellaneous

insurance for Thai non-life insurance industry.

Figure 2.6 Direct Premium and Net Written Premium of Miscellaneous Insurance in



2.3 The Theory of Business Economics

The theory of business economics is the study of the impact of economics on

business operations, both internally and externally. The theory helps identify the

economic factors influencing business strategies, operations and environments.

16

Figure 2.7 Factors Impact Business Strategy

Source: Harris, 2001: 11.

Figure 2.7 shows the factors affecting business strategy and hence, business

operations. As shown above, business economics is one of the main factors affecting

how a company constructs its business strategy. In other words, business economists

try to explain how an economy works and how it affects businesses. The figure

categorizes economics into three broad categories, which are: microeconomics,

macroeconomics and international trade. From the above figure, macroeconomics

impacts economic environment and hence, it impacts how an organization identifies

its business strategy. In other words, macroeconomics indirectly impacts a company’s

strategic management. (Harris, 2001: 10).

Since insurance follows a business model, the theory of business economics

could also be applied to insurance business. Therefore, the theory of business

economic indicates that insurance, being a business, is also affected by

macroeconomics.

17

2.4 Macroeconomic Variables

2.4.1 Gross Domestic Product (GDP)

Gross Domestic Product is defined as “the market value of all final goods and

services produced within a country in a given period of time”. (Mankiw, 2008: 510)

To better understand GDP, we will explore its several components. First, its value is

based on market price; or the prices that consumers are willing to pay for products or

services in the consumer market. The market price of each product that contributes to

the value of GDP should not be the same. Second, it includes the value of all final

goods and services produced. The valuation of GDP counts all final tangible goods

and intangible services. The calculation of GDP does not include unfinished goods

and services or their intermediate steps. Third, the products or services produced in a

country are counted within a given period of time, usually in one year. Each country

has its own GDP as one of its economic indicators.

The valuation of GDP measures two things: national income and national

expenditures. Figure 2.8 illustrates the flow diagram of national income and

expenditures among many parties. According to the figure, the income and

expenditures are circulated from one party to another through the purchase of goods

and services within a country. All flows of inputs and outputs measures the valuation

of GDP.

18

Figure 2.8 The Circular-Flow Diagram

Source: Mankiw, 2008: 25.

2.4.2 Inflation

Inflation is defined as “an increase in the overall level of prices in the

economy”. (Mankiw, 2008: 13). The concept presented by inflation is simply said that

the price of goods and services purchased today may not be the same in the near

future; it could be subject to change due to the level of prices and the value of money

has changed.

In order to understand inflation, the view of the quantity theory of money

should be first established. The theory states that “the quantity of money available

determines the price level and that the growth rate in the quantity of money available

determines the inflation rate.” (Mankiw, 2008: 667). Simply stated - the amount of

money available in the market determines its value. This can be explained by the

19

theory of supply and demand. Assuming money demand is constant, an increase in

money supply forces the value of money to a new level. (Mankiw, 2008: 668). When

the quantity of money supply is increased, which is regulated by the Federal

Government, or the Bank of Thailand (BOT) in the case of Thailand, it reduces the

value of money. When the value of money is reduced, it stimulates the increase in

price level and hence, generates inflation.

So, what constitutes the inflation in the real economic world? Barro (2008:

266) suggests factors that potentially affect inflation rate include: interest rate,

expected inflation rate, indexed bonds, consumer price index (CPI), money demand

and supply and money growth rate. Harris (2001: 284) suggests the factors causing

inflation rate include: non-monetary demand, monetary demand, money supply, wage

cost, monetary policy, fiscal policy and exchange rate management policy. In

summary, what causes inflation to shift is what causes the price index to rise to a new

level. Therefore, in order to measure the inflation rate, one must first measure the

price level of goods and services in an economy.

Mankiw (2008: 530) recommends the tool to be used to measure inflation rate

in an economy is called, “Consumer Price Index (CPI)”. It is defined as “a measure of

the overall cost of goods and services bought by a typical consumer”. The inflation

rate is then calculated as “the percentage change in the price index from the preceding

period”. There are other tools which can be used to measure inflation rate such as

Producer Price Index (PPI), GDP Deflator, etc. The current Thai economic situation

uses CPI as a leading indicator for inflation rate (BOT).

The inflation rate is one of the key leading indicators of a nation’s economy

because high inflation may pose several problems. By definition, inflation erodes

living standards as the price level shifts higher; consumers and businesses are required

to work harder and earn higher wages in order to maintain their living standard. While

it may promote economic growth, it creates uncertainty. Moreover, the impact to the

businesses sector is that it increases the cost of doing business. Even though the cost

of doing business could be shifted to consumers in the mean of raising product prices,

it could turn into a new type of cost, e.g. wage cost. Therefore, inflation rate could be

harmful to individuals and businesses.

20

2.5 Key Macroeconomic Indicators

Key economic indicators in this study are referred by different organizations.

Let’s first review the U.S. key economic indicators as recommended by Baumohl

(2008: 8-9) the chief global economist at The Economic Outlook Group, written in his

book titled, The Secrets of Economic Indicators. In the book, the author suggests that

real GDP and its components can be used to indicate 17% of U.S. economic health,

the highest contributor to the economy. Business fixed investment, ranked the second

contributor to U.S. economy, can be used to explain 17% of the economic situation,

similar to that of government spending. As GDP represents the amount of consumer

consumptions, as implied by the figure, the consumer consumption contributes the

highest proportion of U.S. economy comparing to other sectors i.e. business, and

government. In addition, the net exports, the amount of imports less the amount of

exports, have negative impacts to U.S. economy.

Table 2.1 Comparison of Key Economic Indicators Worldwide and Thailand

Worldwide Thailand

World Trade Volume Population

Economic Growth Rates GDP

Inflation Rates (CPI) Inflation Rates (CPI)

Balance on Current Account Balance of Payments

Unemployment Rate External Debts

Interest Rates Interest Rates

Exchange Rates Exchange Rates

World Grain Situation International Reserves

Internal Reserves

Exports

Imports

Key Macroeconomic Indicators

21

In summary, Baumohl (2008: 8-9) suggests five key indicators to track U.S.

economy which are: real GDP and its components, business fixed investments,

change in business inventories, government spending, and net exports. Each indicator

can be analyzed using many indexes and statistical data. The sign of price pressure,

the increase in price index, can also be analyzed by the amount of inflation, with

many key indicators.

In Thailand, the BOT recommends a number of key macroeconomic indicators

published on its website as shown in table 2.1, but there are in total 11 indicators that

are deemed significant to Thai economy.

Comparatively, most economic indicators of the BOT are the same as the

Baumohl’s recommendation. Inflation rates, Balance of payments, interest rates, and

exchange rates are deemed major economic indicators.

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Research Methodology Framework

The main objective of this study was to analyze the impact of macroeconomic

factors on non-life insurance consumption in Thailand. The main reason for this

objective was that macroeconomics is viewed as having an impact overall on

households, businesses, and governments. (Mankiw, 2008: 28). The analysis of such

an impact is deemed significant for Thai’s insurance industry to be aware of such a

potential impact.

The research problem for this study was to indicate one or more

macroeconomic factors that currently effect non-life insurance consumption in

Thailand. The answer for this question leads to the improvement of awareness and

understanding of the insurance industry regarding the rapidly changing economic

conditions that have an impact on Thai’s state of economy and hence, affects the

overall insurance industry.

The research was conceptualized by using the assumptions of the ThaiRe

Research and Statistic Services, which listed macroeconomic factors, which were

considered having an impact on the non-life insurance premium. They were also used

to estimate the premium in the year 2009 and 2010. These variables were assumed to

have an impact on non-life insurance consumption. Further review of related literature

assisted in formulating research problems. It was found that, in Thailand, the Bureau

of Trade and Economic Indices had provided the economic analysis as the key

economic indicator; after reviewing literatures, it was found that there was no

additional research studying the impact of key macroeconomic indices to non-life

insurance industry in Thailand. Therefore, this study was conceptualized using the

research published by ThaiRe.

23

Quantitative Research was used for this study. The variable data was collected

from secondary sources and analyzed using statistical software. The dependent and

independent variables for this study, namely non-life insurance consumption and

macroeconomic factors, are normally presented in terms of numerical value published

from the related organization, i.e. the Office of Insurance Commission (OIC) and the

Bureau of Trade and Economic Indices. Figure 3.1 summarizes the research

methodology for this study.

This research studied a time series data for a 10 year period as a sample size.

The macroeconomics focuses on the overall state rather than each individual unit.

Therefore, all non-life insurance consumptions from the whole industry in Thailand

were used to analyze the impact. Moreover, all the data was collected on a monthly

basis in order to obtain 120 samples for the analysis.

24

Figure 3.1 Research Methodology

Review Related Literatures

Formulating Research Problems and Objectives

Define Conceptual Framework

Data Collection

Macroeconomic Factors Gross Premium Written

Source:

Bank of Thailand (BOT)

Bureau of Economic Indices

Source:

Office of Insurance Commission (OIC)

General of Insurance Association(GIA)

Data Analysis

Macroeconomic Factors

(Independent Variables)

Non-Life Insurance Consumption

(Dependent Variables)

Correlation

Coefficient Analysis

Multiple Regression

Model Analysis Recommendation

Conclusion

25

3.2 Conceptual Framework

Macroeconomic factors were assumed to affect non-life insurance premiums

in the years 2009 and 2010. This was shown by ThaiRe Research and Statistic

Services, published in “Insurance Journal issue no. 105”, by the General of Insurance

Association (GIA), and was used as a model to construct the conceptual framework.

The reason that the macroeconomic factors were based on such an assumption

was because the estimated premiums in the year 2009 and 2010 were close to the

actual data. Table 3.1 shows the comparison between the estimated and the actual

non-life premiums in the years 2009 and 2010. The estimate of 2009 and 2010 were

based on the actual premium in the year, 2008. The differences between the estimate

and the actual were around 1% in the year 2009 and around 9% in the year 2010,

which were considered relatively low. Based on this information, it signifies that the

macroeconomic factors used to estimate the premiums were having relationships with

non-life insurance premium. Therefore, the model was used to define conceptual

framework.

From the model above, it was found that macroeconomic factors should have a

relationship with non-life insurance industry. Therefore, these selected

macroeconomic factors were used as an assumption to select the independent

variables at the Bureau of Trade and Economic Indices.

26

Table 3.1 The Comparison of Non-Life Insurance Premium between the Estimate by

ThaiRe Research and Statistic Services and the Actual Data

Unit:Million Baht

Source: ThaiRe Research and Statistic Service, 2009: 12-16.

Estimate Actual Differences Estimate Actual Differences

Fire 7,709.00 7,749.00 40.00 8,027.00 7,867.00 -160.00

0.52% -1.99%

Marine and Transportation 3,652.00 3,633.00 -19.00 3,827.00 4,326.00 499.00

-0.52% 13.04%

Automobile 63,263.00 65,430.00 2,167.00 65,538.00 74,614.00 9,076.00

3.43% 13.85%

Miscellaneous 34,292.00 33,188.00 -1,104.00 37,646.00 38,279.00 633.00

-3.22% 1.68%

TOTAL 108,916.00 110,000.00 1,084.00 115,038.00 125,086.00 10,048.00

1.00% 8.73%

2009 2010Type of Insurance Policy

27

Figure 3.2 Conceptual Framework

Figure 3.2 shows the conceptual framework for the analysis of the impact of

macroeconomic factors to non-life insurance in Thailand. The independent variables

were assumed to have an impact on non-life insurance consumption. However, the

conceptual framework may have some limitations and may not be considered the most

perfect framework for the study because of the following reasons. First, the selected

macroeconomic factors may be correlated, which means, they were having a

relationship with each other which causes the statistical testing to be inaccurate. This

problem will be eliminated by analyzing Variance Inflation Factor (VIF) of each

independent variable in order to review the existing multicollinearity problem.

Second, the framework may not be applicable to future application. Due to the highly

dynamic state of today’s economic world, factors are subject to change

simultaneously.

Consumer Price Index

Business Cycle Index

Inflation Cycle Index

Export Business Situation Index

Consumer Confidence Index

Producer Price Index

Construction Material Price Index

Export and Import Price Index

Non-Life Insurance Consumption

Fire Insurance Direct Premium

Automobile Insurance Direct Premium

Marine and Transportation Insurance

Direct Premium

Miscellaneous Insurance Direct Premium

28

3.3 Population and Sampling Methodology

The population for this study was non-life insurance companies in Thailand. In

order to obtain the most relevant data, the researcher selected the most recent

completed data; therefore, the data in the period from 2002 to 2011 was selected, on a

monthly basis for this analysis. The number of active companies may vary each year

during the study period; however, this issue did not affect the study result because the

research studied from macroeconomics viewpoint.

3.4 Research Variable

3.4.1 Dependent Variable

In this study, the dependent variable represented non-life insurance

consumption in which the researcher defined as, the amount of insurance purchased in

the whole industry throughout the study period. Regardless of which company a

consumer purchased his insurance coverage from, his data had been gathered for this

analysis. Therefore, non-life insurance consumption was represented by the amount of

direct premium written by the whole non-life insurance industry.

Direct premium written is the original amount of premium received by an

insurer before making any adjustments for reinsurance costs and loss reserves. (Office

of Insurance Commission, 2012k). The direct premium represents the monetary

amount of consumer consumption for non-life insurance. This is referring to direct

premium to non-life insurance companies and health insurance companies. The study

does not include reinsurance premium.

3.4.2 Independent Variable

The independent variables, which were used to analyze the impact to non-life

insurance consumption in Thailand, were selected based on the assumptions set to

estimate non-life insurance premiums for the years 2009 and 2010 by the ThaiRe

Research and Statistical Services. They were determined to affect the state of Thai

economy.

29

The independent variables were classified into 8 main categories collected

based on macroeconomic indices published by the Bureau of Trade and Economic

Indices as follows:

3.4.2.1 Consumer Price Index

It is an index used to measure the change in price level of consumer

goods and services. In Thailand, the index does not include the measurement of raw

food and goods and services in energy sector. The actual index value and the

percentage change from the previous month were collected for this study.

3.4.2.2 Business Cycle Index

It is an index used to measure the cycle components of economic

variables. It reflected the recession and expansion of business cycle. The index could

be used to predict future state of economy because it reflects the real business cycle.

The coincident index value, the percentage change from the previous month, and the

six-month smoothed annualized growth rate were collected for this study.

3.4.2.3 Inflation Cycle index

It is classified as Reference Inflation Index and Leading Inflation

Index. Reference Inflation Index is a six-month smoothed annualized growth rate of

Consumer Price Index. While Leading Inflation Index was used to predict inflation

cycle in advance of three to six months. The actual Reference Inflation Index was

collected for this study. Moreover, the actual Leading Inflation Index, its percentage

change from the previous month, and the six-month smoothed annualized growth rate

were collected from this study.

3.4.2.4 Export Business Situation Index

It is measured by the survey of entrepreneurs for their opinions

regarding economic factors that could potentially impact their business. The index is

used as an early-warning system for short economic indicator. The index is classified

as Total Export, New Export Orders, Inventories, and Employment. Total Export

index is used to measure the amount of net exports; New Orders index refers to the

amount of new export orders; Inventories index refers to the amount of export

inventories remained at that period and; Employment index refers to the current

employment situation. Note that these indices are not based on facts but on the

30

opinions of export business’s entrepreneurs. These four indices were collected for this

study.

3.4.2.5 Consumer Confidence Index

It is an index used to measure the confidence of consumers toward the

state of economy. When consumers have high confidence over the nation’s economy,

they are likely to spend more and hence, the business situation could be better. The

actual index was collected for this study.

3.4.2.6 Producer Price Index

It is used to measure the change in price level of goods and services of

domestic producers. The actual index and the percentage change from the previous

month were collected for this study.

3.4.2.7 Construction Material Price Index

It is used to measure the change in price level of average construction

materials in Thailand. The actual index and the percentage change from the previous

month were collected for this study.

3.4.2.8 Export and Import Price Index

It is used to measure the change in price level of export price and

import price. Free on broad (F.O.B.) price was used for export price index; Cost,

Insurance, and Freight (C.I.F.) price was used for import price index. Both actual

indices were collected for this study.

The dependent and independent variables and their definitions for the model

are listed in table 3.2.

31

Table 3.2 Summary of Variables and Their Definitions

Dependent

Variables (Yi) Descriptions

Y Total Direct Premium Written of All Non-Life Insurance

Industry

X1 Consumer Price Index

X2 Consumer Price Index - Percent changed from previous month

X3 Business Cycle Index - Coincident Index

X4 Business Cycle Index - Six-month smoothed annualized growth

rate

X5 Business Cycle Index - Percent changed from previous month

X6 Inflation cycle index - Reference Inflation Index

X7 Inflation cycle index - Leading Inflation Index

X8 Inflation cycle index - Six-month smoothed annualized growth

rate

X9 Inflation cycle index - Percent changed from previous month

X10 Export Business Situation Index

X11 Export Business Situation Index - New Export Orders Index

X12 Export Business Situation Index - Inventories Index

X13 Export Business Situation Index - Employment Rate

X14 Consumer Confidence Index

X15 Producer Price Index

X16 Producer Price Index - Percent changed from previous month

X17 Construction Material Price Index

X18 Construction Material Price Index - Percent changed from

previous month

X19 Export Price Index

X20 Import Price Index

32

3.5 Data Collection

1) Non-life insurance consumption, which was represented by direct premium

written, was gathered from annual reports and historical data published on the Office

of Insurance Commission (OIC) website. Monthly data was collected.

2) The independent variables were collected from the website of the Bureau of

Trade and Economic Indices.

3.6 Data Analysis

3.6.1 Statistical Models

3.6.1.1 Pearson’s Product Moment Correlation Coefficient (ρ)

The coefficient of correlation was used to analyze the existence and the

strength of the relationship between the dependent variable and the

independent variables. The use of this model had two main objectives:

1) To analyze the existence and the strength of the linear

relationship between total non-life insurance consumption and each independent

variable.

2) To prepare the variables to be used for Multiple Linear

Regression Model (MLR) analysis because one of the assumptions of data to be used

for MLR analysis is that the independent variable has a linear relationship with the

dependent variables.

The calculation of the correlation coefficient was split into two

categories. First, each independent variable was paired with non-life insurance

consumption, the independent variable. The correlation coefficient of each pair was

calculated to analyze whether each individual variable has a linear relationship with

total non-life insurance consumption and also to measure the strength of the

relationship. Second, each individual independent variable was paired with each other

in order to the relationship among them.

The result of the correlation coefficient calculation was determined by

its statistical significance by computing p-value to test the hypothesis.

33

In this study, the significance of the study was α = 0.05. Therefore, the

p-value which was less than 0.05 is deemed significance and accepts the hypothesis

(H1).

In this study, the independent variables were classified into two main

categories. The first category was the actual indices which were referred to the actual

indices each month from each type of indices. The second category was the

percentage changes from the previous month and the six-month annualized growth

rates which were shown in a form of percentage. Since there were twenty variables in

this study, some of them were from the same sources. For example, the actual

consumer price index and its percentage change from the previous month were

collected for this study; the correlation analysis would help to select which type of

variable to be studied in the analysis.

3.6.1.2 Multiple Linear Regression Model (MLR)

Multiple Regression Analysis model involves the use of more than one

variable to predict the dependent variable. It is used to model the linear relationship

between one dependent variable and two or more independent variables.

Based on the review of literatures, it was assumed that there were

many economic factors affecting non-life insurance consumption. In order to analyze

the impact of so many variables, the model was deemed to be the most suitable model

for this study.

In addition, the analysis of the relationship between macroeconomic

factors and non-life insurance consumption used the Ordinary Least Square (OLS)

method to estimate the parameters in the Multiple Regression Model analysis. The

method of least square used to fit this relationship is typically by way of minimizing

the sum of the squared errors between the observed values and the value that would

be fitted under the assumed relationship in order to create a straight line equation

model. By this mean, the error of estimating the dependent variable is minimized.

In this study, stepwise analysis was used as a method of Multiple

Regression analysis. The stepwise procedure would select the variable into the model

using alpha to enter at 0.15 and alpha to remove at 0.15. The model with the highest r-

square would be selected for detail analysis whether it was at an acceptable level.

34

One issue of concern for the analysis using MLR is Multicollinearity,

in which the independent variables were highly intercorrelated to each other, which

caused the model to be inaccurate. In this study, the researcher was aware of the

multicollinearity problem and, therefore, used the Variance Inflation Factor (V.I.F.) to

analyze the multicollinearity problem of the model. If the VIF was higher than 4, the

variable would be eliminated from the model and the model would be retested.

In conclusion, this study used stepwise procedure as the main data

analysis. After getting an equation from stepwise analysis, each model was reviewed

and retested until the model was at an acceptable level.

3.6.2 Data Analysis Tool

This study used Minitab 16 Statistical Software to analyze the data and

hypothesis based on statistical models, as described above.

3.6.3 Data Analysis Procedure

The analysis process was listed as follows:

3.6.3.1 Step 1

All variables were analyzed using correlation matrix in order to review

their relationship with total non-life insurance consumption. Since the selected

independent variables consist of the actual indices, and their percentage changes from

the previous month and their annualized growth rates, it was highly necessary to

examine the correlations before further choosing the variables for regression analysis.

The purpose of using correlation analysis was to select the appropriate variables,

either the actual indices or the percentage changes from the previous month and the

growth rates, to review which type of data suits the model.

3.6.3.2 Step 2

Once the correlation among variables was analyzed, the result would

show that which type of data should be used for the model. The type of data of

selected variables was further studied in stepwise analysis.

3.6.3.3 Step 3

After getting the results from the stepwise analysis, the model with the

highest R-square was selected to further study. Correlation among variables of the

35

selected model was further analyzed. The variables which were not related to the

independent variable were removed from the model. Thereafter, the model would be

retested.

After retesting without using unrelated variables, the model would be

reviewed again to investigate whether there were any multicollinearity problems

among variables by examining Variance Inflation Factor (VIF). Ideally, VIF value

higher than 4 should be further investigated; VIF value higher than 10 is required

corrective action. Therefore, the VIF value around 4 was acceptable in the study. If

there was VIF value higher than 4 in the equation, the variable which had the highest

VIF would be eliminated from the analysis one at a time until an acceptable result was

obtained.

3.6.3.4 Step 4

The final equation was generated after all variables were concluded at

an acceptable level, i.e. p-value of each constant value and each independent variables

were less than 0.05, VIF of each independent variables were less than 4, p-value of f-

test in the analysis of variance was less than 0.05 and Durbin-Watson statistic was

close to 2. Standardized Coefficient was also calculated in order to review which of

the variables had the greatest effect on total non-life insurance consumption in

Thailand.

CHAPTER 4

RESULT OF THE ANALYSIS

In the study of the impact of macroeconomic factors on non-life insurance

consumption in Thailand, the data analysis will be presented in the following

sequences:

4.1 Analysis Result of Total Non-life Insurance Consumption

4.2 Analysis Result of Each Type of Insurance Consumption

4.3 Summary

4.1 Result of the Analysis of Total Non-life Insurance Consumption

4.1.1 Result of Correlation Analysis

Step 1 of data analysis was to investigate correlation among all variables in

order to examine the relationship between total non-life insurance consumption and

all indices. The result is shown in table 4.1 and 4.2.

Thirteen variables were the actual indices and seven variables were the

percentage changes from the previous month and the six-month smoothed annualized

growth rates. The correlation analysis was made in order to analyze which type of

data should be used for the study.

From the correlation analysis above, it was found that nine out of thirteen

actual indices, or approximately 70%, were related to total non-life insurance

consumption in Thailand, namely, X1 Consumer Price index, X3 Coincident index

(from Business Cycle index), X12 Inventories index (from Export Business Situation

index), X13 Employment rate (from Export Business Situation index), X14 Consumer

Confidence index, X15 Producer Price index, X17 Construction Material Price index,

X19 Export Price index, and X20 Import Price index.

37

Table 4.1 Correlation Analysis between Total Non-life Insurance Consumption in

Thailand and the Actual Indices

Variables Y

X1 Consumer Price Index 0.887*

X3 Business Cycle Index - Coincident Index 0.709*

X6 Inflation cycle index - Reference Inflation Index 0.171

X7 Inflation cycle index - Leading Inflation Index 0.029

X10 Export Business Situation Index -0.099

X11 Export Business Situation Index - New Export Orders Index -0.137

X12 Export Business Situation Index - Inventories Index -0.236*

X13 Export Business Situation Index - Employment Rate -0.263*

X14 Consumer Confidence Index -0.555*

X15 Producer Price Index 0.884*

X17 Construction Material Price Index 0.745*

X19 Export Price Index 0.905*

X20 Import Price Index 0.895*


Thailand and the Percentage Changes and Growth Rates

Variables Y

X2 Consumer Price Index - Percent changed from previous month -0.038

X4 Business Cycle Index - Six-month smoothed annualized growth rate -0.265*

X5 Business Cycle Index - Percent changed from previous month -0.01

X8 Inflation cycle index - Six-month smoothed annualized growth rate -0.101

X9 Inflation cycle index - Percent changed from previous month -0.063

X15 Producer Price Index - Percent changed from previous month -0.05

X17 Construction Material Price Index - Percent changed from previous

month

-0.059

Note: *p-value < 0.05

On the other hand, only one percentage changes and annualized growth rates,

or approximately 15%, was found to have no correlation with total non-life insurance

38

consumption in Thailand which was X4 Six-month smoothed annualized growth rate

of Business Cycle index.

From the correlation analysis of the actual indices, it was found that total non-

life insurance consumption had a positive relationship with Consumer Price Index

(X1), Coincidence Index (X3), Producer Price Index (X15), Construction Material

Index (X17), Export Price Index (X19), and Import Price Index (X20). If these

variables increase, the consumption would largely increase.

However, it was found that the insurance consumption was negatively related

to Inventories Index (from Export Business Situation index) (X12), Employment Rate

(from Export Business Situation index) (X13), and Consumer Confidence Index

(X14). If these variables increase, the consumption would decrease.

From the correlation analysis of the percentage changes and growth rates, it

was found that only the six-month smoothed annualized growth rate of Business

Cycle index was related to total non-life insurance consumption in Thailand. In

addition, it had a weak negative relationship which means that if the variable

increases, the insurance consumption would slightly decrease.

Therefore, the analysis above showed that the actual indices should be used to

study the impact analysis to non-life insurance consumption in Thailand, instead of

the percentage changes and growth rates because they had a better relationship with

the dependent variable. Therefore, thirteen indices were used in the stepwise analysis

which were: Consumer Price Index (X1), Coincidence Index (X3), Reference

Inflation Index (X6), Leading Inflation Index (X7), Export Business Situation Index

(X10), New Export Order Index (X11), Inventories Index (X12), Employment Rate

(X13), Consumer Confidence Index (X14), Producer Price Index (X15), Construction

Material Index (X17), Export Price Index (X19) and Import Price Index (X20).

4.1.2 Result of Stepwise Analysis

In the stepwise analysis, eleven steps were recommended. Step 10 had the

highest R-square; therefore, it was selected for further analysis. There were eight

variables having an impact on total non-life insurance consumption in Thailand, i.e.

X3 Coincident index (from Business Cycle index), X7 Leading Inflation index (from

Inflation Cycle index), X11 New Export Order index (from Export Business Situation

39

index), X13 Employment Rate (from Export Business Situation index), X14

Consumer Confidence index, X17 Construction Material Price index, X19 Export

Price index, and X20 Import Price index. However, the model was unreliable because

of having a high multicollinearity problem. The problem was examined by reviewing

the Variance Inflation Factor (VIF). Correlation analysis was further required to

analyze the model. The result showed that X7 and X11 were found to have no

correlation with total non-life insurance consumption in Thailand; therefore, they

were removed from the model. After the model was retested, the VIFs were at the

high level. X20, which had the highest VIF, was removed from the model. After

retesting, X17 had the VIF higher than 4; therefore, X17 was removed from the

model. Finally, after removing 4 variables which were X7, X11, X17, and X20, the

model was at an acceptable level. (See more detail in Appendix C)

Table 4.3 Total Non-Life Insurance Consumption Stepwise Analysis

Variables Coef Beta Coef P-value VIF

Constant -8171741 0.000

X19 Export Price Index 107313 0.891359 0.000 2.883

X3 Coincident Index 67141 0.159157 0.006 2.373

X14 Consumer Confidence Index 26786 0.195884 0.001 2.439

X13 Employment Rate -46926 -0.115984 0.012 1.481

Note: R2 = 84.1%, Standard Error of Estimate = 842389

Table 4.3 shows the result of stepwise analysis of total non-life insurance

consumption in Thailand. Four variables were found to have an impact on total non-

life insurance consumption in Thailand, namely Export Price Index (X19), Coincident

Index (X3), Consumer Confidence Index (X14), and Employment Rate (X13). The

regression equation from the analysis was as follows:

Y = - 8171741 + 107313 (Export Price Index) + 67141 (Coincident Index) + 26786

(Consumer Confidence Index) - 46926 (Employment Index)

40

The model could be used to explain about 84.1% of total non-life insurance

consumption in Thailand. The standard error of the estimate was 842,389.

The intercept value was -8,171,741 which indicated that in the case that the

four variables were zero or no values, the total non-life insurance consumption would

be negative. However, this couldn’t be the case because all variables involved in the

economic activities; therefore, it is impossible that all four variables would be zero.

Of all five impacting variables, three variables were positively related to total

non-life insurance consumption in Thailand while one variable was negatively related.

The positively related variables were Export Price index, Coincident index, and

Consumer Confidence index. The negatively related variable was Employment Rate.

Export Price index had the highest impact on total non-life insurance

consumption in Thailand because of having the highest beta coefficient. It was

positively related to the insurance consumption which could be interpreted that if

Export Price index changes by one unit and other variables remain unchanged, total

non-life insurance consumption in Thailand would directly change by 107,313. As the

export price increases, business sectors need to increase their production and hence,

purchase more insurance coverage.

Consumer Confidence index was the second impacting variable of total non-

life insurance consumption in Thailand. It was positively related to the insurance

consumption. If the variable changes by one unit and other variables remain

unchanged, the insurance consumption would directly change by 26,786. As

consumers have more confidence in the state of economy, they are willing to make

more spending for goods and services. Insurance consumption is one of the increasing

expenditures of consumers when the economy is peak. For example, when consumers

purchase a new car, they also are required to purchase insurance coverage and the

insurance consumption would increase.

Coincident index was the third impacting variable that quantitatively effects

the changes in total non-life insurance consumption in Thailand. If the index changes

by one unit and other variables remain unchanged, the insurance consumption would

directly change by 67,141. This index reflects a real business cycle. As the economy

tends to be better, business sectors are willing to make more investment and need to

purchase more insurance coverage for their businesses.

41

The last impacting variable was Employment rate. This was the only variable

which had a negative impact on total non-life insurance consumption in Thailand. If

the index increases by one unit and the other variables remain unchanged, the

insurance consumption would decrease by 46,926. It was found from the correlation

analysis that the variables from Export Business Situation Index were negatively

correlated with total non-life insurance consumption in Thailand. The Export

Business Situation index included the actual index, New Export Order index,

Inventories index, and Employment rate. These indices were calculated by asking

export business entrepreneurs to submit an online survey about their attitudes toward

the nation’s economy. It was shown that the entrepreneurs believed that they would

purchase less insurance if the economic conditions were better; however, the

Coincident index suggested otherwise. In addition, the correlation with the Coincident

index showed that there was no significant relationship between the Coincident index

and the Employment rate (see more detail in Appexdix B). From the analysis, it could

be concluded that the attitudes of export business entrepreneurs toward the nation’s

economy were negatively related to the insurance consumption.

4.2 Result of the Analysis of Each Type of Insurance Consumption

In this study, the research aimed to find out the impact of macroeconomic

factors, namely the actual indices, on total non-life insurance consumption in

Thailand. It was found that four variables of the actual indices were having an impact

on the insurance consumption. In order to further analyze such impact, the actual

indices were further analyzed with each type of insurance policies in Thailand. The

analysis would show the impact of the actual indices on each type of insurance

policies and also show how did the impact of each type of insurance policies

contributed to the impact on total non-life insurance consumption in Thailand.

4.2.1 Result of Correlation Analysis

Before further analyze the impact of the independent variables on other types

of insurance policies in Thailand, correlation among variables were investigated and

is shown in table 4.4.

42


Thailand and the Percentage Changes and Growth Rates

Variables Fire Auto Marine Misc.

(Y2) (Y3) (Y4) (Y5)

X1 Consumer Price Index 0.001 0.903* 0.713* 0.762*

X3 Business Cycle Index - Coincident

Index

-0.102 0.757* 0.784* 0.559*

X6 Inflation cycle index - Reference

Inflation Index

-0.022 0.193* 0.360* 0.112

X7 Inflation cycle index - Leading

Inflation Index

0.051 0.032 0.092 0.013

X10 Export Business Situation Index 0.072 -0.061 0.061 -0.162

X11 Export Business Situation Index -

New Export Orders Index

0.023 -0.113 -0.031 -0.17


Inventories Index

0.108 -0.245* -0.076 -0.215*


Employment Index

0.057 -0.258* -0.088 -0.261*

X14 Consumer Confidence Index -0.042 -0.590* -0.438* -0.432*

X15 Producer Price Index 0.013 0.899* 0.733* 0.758*

X17 Construction Material Price Index -0.061 0.774* 0.704* 0.618*

X19 Export Price Index 0.003 0.918* 0.724* 0.782*

X20 Import Price Index 0.02 0.905* 0.711* 0.776*

Note: *p-value < 0.05

For fire insurance consumption in Thailand, it was found that the relationship

between fire insurance consumption and the actual indices was not significantly

correlated.

For automobile insurance consumption, ten variables were found to be

correlated with the consumption. It was positively related to Consumer Price Index

(X1), Coincidence Index (X3), Reference Inflation Index (X6), Producer Price Index

(X15), Construction Material Index (X17), Export Price Index (X19), and Import

Price Index (X20) that is if one of these variables increases and other variables remain

unchanged, the consumption of automobile insurance would also increase. However,

43

it was negatively related to Inventories Index (X12), Employment Index (X13), and

Consumer Confidence Index (X14) which means if one of these variables increases,

automobile insurance consumption in Thailand would decrease.

For marine and transportation insurance consumption, eight variables were

found to be correlated to the consumption. It was positively related to Consumer Price

Index (X1), Coincidence Index (X3), Reference Inflation Index (X6), Producer Price

Index (X15), Construction Material Index (X17), Export Price Index (X19), and

Import Price Index (X20) that is if one of these variables increases and other variables

remain unchanged, the consumption of marine and transportation insurance would

also increase. However, it was negatively related to Consumer Confidence Index

(X14) that is if one of the variable increases, the consumption of marine and

transportation insurance in Thailand would decrease.

For miscellaneous insurance consumption, nine variables were found to be

correlated with the consumption. It was positively related to Consumer Price Index

(X1), Coincidence Index (X3), Consumer Confidence Index (X14), Producer Price

Index (X15), Construction Material Index (X17), Export Price Index (X19), and

Import Price Index (X20) that is if one of these variables increases and other variables

remain unchanged, the miscellaneous insurance consumption would also increase.

However, it was negatively related to Inventories Index (X12), and Employment

Index (X13) which means if one of these variables increases, miscellaneous insurance

consumption in Thailand would decrease.

Of all the correlation analysis, there were seven variables which were common

correlated variables to the consumption of automobile insurance, marine and

transportation insurance, and miscellaneous insurance (fire insurance consumption

was ignored because no indices were found to be correlated with.), namely, X1

Consumer Price Index, X3 Coincident Index (from Business Cycle Index), X14

Consumer Confidence Index, X15 Producer Price Index, X17 Construction Material

Price Index, X19 Export Price Index, and X20 Import Price Index. All variables were

positively related to all three insurance consumption except X14 Consumer

Confidence Index which had a negative relationship with the insurance consumption.

44

4.2.2 Result of Stepwise Analysis

After the correlation among variables was analyzed, thirteen actual indices

were used to analyze in the stepwise analysis of each type of insurance policies in

order to examine the impact of the indices on each type of insurance policies. The

result was expected to show how the actual indices affect each type of insurance

policies and eventually contributed to the impact on total non-life insurance


4.2.2.1 Fire Insurance Consumption

No variables were found to have an impact on fire insurance

consumption in Thailand because all independent variables had no relationship with

fire insurance consumption during the past decade, at 0.05 significance level, as

shown in table 4.4. In the literature review, it was found that the consumption of fire

insurance in Thailand in the past decade was stable even though the economic was

swing. The research, therefore, concluded that macroeconomic indicators had no

impact on fire insurance consumption in Thailand.

4.2.2.2 Automobile Insurance Consumption

In the stepwise analysis, there were nine steps resulted. Step 9 was

selected because it had the highest r-square. In step 9, nine variables were suggested

to have an impact on automobile insurance consumption; however, the model was

unreliable because it had high multicollinearity problems, which were indicated by

having high variance inflation factors (VIF). Thereafter, correlation analysis between

automobile insurance and the independent variables were examined in order to

eliminate some unrelated variables from the model. Table 4.4 shows the correlation

analysis between the dependent variable and the independent variables. X7 and X10

were found to be unrelated to the consumption of automobile insurance in Thailand, at

0.05 level of significance; therefore, they were eliminated.

After retesting the model, the VIF showed high multicollinearity. X15

and X20 were having too high VIF; therefore, they were eliminated from the model.

Thereafter, further investigation was required. X17 was the problem; it had high

correlation with X19 and X3. Therefore, X17 was eliminated from the model. After

eliminating X7, X10, X15, X20, and X17, the final result was shown below. (See

more detail in Appendix C)

45

Table 4.5 Automobile Insurance Consumption Stepwise Analysis


Constant -6758065

0.000


X3 Coincident Index 60070 0.233323 0.000 2.373

X13 Employment Rate -24805 -0.100462 0.016 1.481



Table 4.6 showed the result of the analysis of automobile insurance

consumption in Thailand, four variables were found to have an impact on automobile

insurance in Thailand, namely, Export Price Index (X19), Coincident Index (X3),

Employment Index (X13) and Consumer Confidence Index (X14). The regression

equation was modeled as follows:

Y3 = - 6758065 + 60049 (Export Price Index) + 60070 (Coincident Index) - 24805

(Employment Index) + 11834 (Consumer Confidence Index)

The model was used to explain about 86.9% of automobile insurance

in Thailand, the model was reliable at 0.05 level of significance.

The constant value was -6,758,065 indicated that in the case that the

four variables were zero or no values, the automobile insurance consumption would

be negative. However, this couldn’t be the case because all variables involved in the


Three variables were positively related to automobile insurance

consumption in Thailand, which were Export Price index, Coincident index, and

Consumer Confidence index, while one variable was negatively related which was

Employment Rate. Export Price index had the highest impact on automobile insurance

consumption in Thailand while Coincident index, Consumer Confidence Index, and

Employment Index had lower impact respectively.

46

Export Price index had the highest impact on automobile insurance

consumption in Thailand because of having the highest beta coefficient. It was

positively related to the insurance consumption which could be interpreted that if

Export Price index changes by one unit and other variables remain unchanged,

automobile insurance consumption in Thailand would directly change by 60,049. As

the export price increases, the business tends to be good as the correlation between

Export Price index and Coincident index revealed positive relationship (see more

detail in Appendix B). Consumers have more willingness to purchase new vehicles

and hence, the consumption of automobile insurance would increase as a result.

Coincident index was the second impacting variable that quantitatively

effects the changes in automobile insurance consumption in Thailand. If the index

changes by one unit and other variables remain unchanged, the insurance

consumption would directly change by 60,070. As the economy tends to be good,

consumers are willing to make new purchases on their vehicles and need to purchase

insurance coverage for their automobile, and hence, automobile insurance

consumption in Thailand would increase as a result.

Consumer Confidence index was the third impacting variable of

automobile insurance consumption in Thailand. It was positively related to the

insurance consumption. If the variable changes by one unit and other variables remain

unchanged, the insurance consumption would directly change by 11,834. As

consumers have more confidence in the state of economy, they are willing to make

more spending for new vehicles and hence, automobile insurance consumption would

increase.

The last impacting variable was Employment rate. This was the only

variable which had a negative impact on total non-life insurance consumption in

Thailand. If the index increases by one unit and the other variables remain unchanged,

the insurance consumption would decrease by 24,805. The logic behind the negative

impact was the same as for total non-life insurance consumption in Thailand. It was

found that if the export business situation index increases, the insurance consumption

for automobile would be decreased.

47

4.2.3.3 Marine and Transportation Insurance (Y4)

In the stepwise analysis, there were six steps resulted. Step 6 was

chosen for further analyzed because it had the highest r-square. It was found that six

variables were having an impact on marine and transportation insurance consumption.

The model was not reliable because X7 was not significance at 0.05 significance

level. In addition, some variables were not related to marine and transportation

insurance consumption in Thailand which were: X7, X10 and X11. Therefore, the

model was retested without X7, X10 and X11. After eliminating X7, X10 and X11, it

was found that X14 was not significance to the model with p-value higher than 0.05.

Thereafter, the model was re-run without X14. Therefore, after eliminating X7, X10,

X11, and X14, the model was at an acceptable level. (See more detail in Appendix C)

Table 4.6 Marine and Transportation Insurance Consumption Stepwise Analysis


Constant -471058

0.000

X3 Coincident Index 6229.6 0.562750 0.000 1.974

X20 Import Price Index 1006.9 0.315431 0.000 1.974


Table 4.7 showed the result of the analysis of marine and

transportation insurance. Two variables were concluded to have an impact on marine

and transportation insurance in Thailand, namely, Coincidence Index (X3) and Import

Price Index (X20). The regression equation was as follows:

Y4 = - 471058 + 6230 (Coincident Index) + 1007 (Import Price Index)

The model was used to explain 66.6% of marine and transportation

insurance consumption. The standard error of the estimate was 31,819.

The constant value was -471,058 indicated that in the case that the two

variables were zero or no values, the marine and transportation insurance

48

consumption would be negative. However, this couldn’t be the case because all

variables involved in the economic activities; therefore, it is impossible that all four

variables would be zero.

Two variables were found to have positive impact on marine and

transportation insurance in Thailand.

The first impacting variable was Coincident index. It was positively

related to the consumption of marine and transportation insurance. If the variable

changes by one unit and the other variables remain unchanged, the insurance

consumption would directly change by 6,230. As the economy tends to be good, the

economy expenditures would be increased, leading to the increase in the insurance

consumption.

The second impacting variable was Import Price index. If the variable

changes by one unit and the other variables remain unchanged, the insurance

consumption would directly change by 1,007. As the import price increases, the

economy tends to be good and the consumption of goods and services would increase.

4.2.3.4 Miscellaneous Insurance (Y5)

There were four variables suggested by the stepwise analysis. Step 4

was selected for further analysis. It was found that four variables were having an

impact on miscellaneous insurance consumption. However, the model was not reliable

because X7 were not related to the consumption of miscellaneous insurance, as shown

in table 4.4. The model was retested after X7 was removed. Thereafter, the model was

not significance with X17 having the highest p-value and the highest VIF value;

thereafter, the model was re-run without X17. Therefore, after eliminating X7 and

X17, the model was significant at an acceptable level. (See more detail in Appendix

C)

Table 4.8 showed the result of the analysis of miscellaneous insurance

consumption in Thailand. Two variables were found to have an impact on

miscellaneous insurance in Thailand, namely, Export Price Index (X19) and

Consumer Confidence Index (X14). The regression model was as follow:

Y5 = - 2464641 + 45685 (Export Price Index) + 10739 (Consumer Confidence Index)

49

Table 4.7 Miscellaneous Insurance Consumption Stepwise Analysis


Constant -2464641 0.000




The model could be used to explain 63.1% of miscellaneous insurance

consumption. The standard error of the estimate was 530,545.

The constant value was -2,464,641 indicated that in the case the two

variables were zero or no values, the miscellaneous insurance consumption would be

negative. However, this couldn’t be the case because all variables involved in the


The two variables were having an impact on miscellaneous insurance

consumption in Thailand with a positive relationship. In addition, the beta coefficient

suggested that Export Price index had higher impact on miscellaneous insurance

consumption in Thailand than that of Consumer Confidence index.

Export Price index was the first impacting variable on miscellaneous

insurance consumption in Thailand. It was positively related to the consumption of

miscellaneous insurance. If the variable changes by one unit and the other variables

remain unchanged, the insurance consumption would directly change by 45,685.

The second impacting variable was Consumer Confidence index. If the

variable changes by one unit and the other variables remain unchanged, the insurance

consumption would directly change by 10,739.

4.3 Summary

The summary of variables, which were found to have an impact on non-life

insurance consumption in Thailand, is shown in Table 4.9.

50

Table 4.8 Summary

Variables Total Fire Automobile Marine and

Transportation Miscellaneous

X1 Consumer Price Index

X3 Coincident Index

X6 Reference Inflation Index

X7 Leading Inflation Index

X10 Export Business Situation Index

X11 New Export Order Index

X12 Inventories Index

X13 Employment Index

X14 Consumer Confidence Index

X15 Producer Price Index

X17 Construction Material Price

Index



50

CHAPTER 5

CONCLUSION AND DISCUSSION

This research aimed to study the impact of macroeconomic factors on non-life

insurance consumption in Thailand from 2002 to 2011 by using multiple regression

analysis. The result was expected to identify which macroeconomic factors were

having an impact on such consumption. In addition, it was expected to be used as an

estimation tool in the future. This section is presented in the following sequence:

5.1 Conclusion

5.2 Discussion

5.3 Recommendation

5.1 Conclusion

Insurance is deemed significant to a country’s economy due to its contribution

to economic growth. As the state of economy around the world has changed rapidly, it

was significant to understand how the insurance business was impacted by economic

factors. This research, therefore, aimed to find out the potential impact of

macroeconomic factors on non-life insurance consumption in Thailand. The result

was expected to help non-life insurers be aware of such potential impacts and to be

used as a vital tool to predict future insurance consumption.

The research was mainly focused on the impact of macroeconomic factors on

non-life insurance consumption in Thailand as a whole. Therefore, the dependent

variable was total non-life insurance consumption in Thailand from 2002 to 2011. It

was collected from the Office of Insurance Commission (OIC).

The independent variables, or macroeconomic factors, were represented by

macroeconomic indices published by the Bureau of Trade and Economic Indices,

Thailand. They were classified into eight main classifications, namely: Consumer

52

Price Index, Business Cycle Index, Inflation Cycle Index, Export Business

Situation Index, Consumer Confidence Index, Producer Price Index, Construction

Material Price Index, and Export and Import Price Index. These variables were

gathered in the form of the actual index, the percentage change from the previous

month and six-month smoothed annualized growth rate. The data were collected on a

monthly basis from 2002 to 2011 and were analyzed by Multiple Linear Regression

(MLR). The independent variables were collected in the total of 20 different variables.

Of all twenty variables, thirteen variables were the actual indices, and seven variables

were the percentage changes from previous month and six-month smoothed

annualized growth rates.

Before further analyzing the data using the MLR technique, the correlation

coefficient among variables were examined. This process was used to analyze which

type of data was the most suitable for the analysis of non-life insurance consumption

in Thailand. As there were two types of independent variables collected which were

the actual index, and the percentage change from a previous month and six-month

smoothed annualized growth rate, the data were considered duplicate. Therefore, this

process helped select the type of independent variable which should be included in the

analysis, either the actual indices, or the percentage changes from the previous month

and the six-month smoothed annualized growth rate would be selected for the MLR.

After the correlation among variables was analyzed; it was found that nine out

of thirteen actual indices, or 70%, were having a relationship with total non-life

insurance consumption in Thailand whereas only one out of seven percentage changes

from previous month and six-month smoothed annualized growth rates, or 15%, were

having a relationship with total non-life insurance consumption in Thailand. From this

information, it was shown that the actual index was more suited to the model because

it had better relationships with non-life insurance consumption in Thailand. Therefore,

the actual indices were selected for further analysis.

Thereafter, the actual indices were used to analyze in the stepwise analysis in

order to study the impact on non-life insurance consumption in Thailand and also to

construct the estimated model used to predict future consumption. Thirteen actual

indices were included in the stepwise analysis, namely, Consumer Price index,

Coincident index (from Business Cycle index), Reference Inflation index (from

53

Inflation index), Leading Inflation index (from Inflation index), Export Business

Situation index, New Export Order index (from Export Business Situation index),

Inventories index (from Export Business Situation index), Employment Rate (from

Export Business Situation index), Consumer Confidence index, Producer Price index,

Construction Material Price index, Export Price index, and Import Price index. These

variables were used to analyze the impact on total non-life insurance consumption in

Thailand.

Thirteen variables were used to analyze in the stepwise analysis until an

acceptable result was obtained. In this study, the alpha was set at 0.05 significance

level; however, the alpha to enter and alpha to remove were both set at 0.15

significance level. After the analysis was obtained, the model which had the highest r-

square would be selected for further analysis. This is because it was found that some

of the parameters in each model should be rejected the null hypothesis. In order to

construct a reliable model, the model would be selected to get in-depth analysis.

In the analysis of total non-life insurance consumption in Thailand, eleven

steps were obtained from the stepwise analysis. Step 10, which had the highest r-

square, was selected. It contained eight variables. Since, the Variance Inflation Factor

(VIF) of some of the variables were higher than 4, the variables in the selected model

were re-reviewed the correlation among them. Leading Inflation index, New Export

Order index, Import Price index, and Construction Material index were eliminated

from the model because there were not related to total non-life insurance consumption

in Thailand and also had too high relationship with other variables causing the VIF to

be too high.

Therefore, it was found that four variables were having an impact on total non-

life insurance consumption in Thailand which were Coincident index (from Business

Cycle index), Employment Rate (from Export Business Situation index), Consumer

Confidence index, and Export Price index. The model could be used to estimate

around 84% of total non-life insurance consumption in Thailand with an estimated

standard error of 842,389.

Export Price index was the main contributor of total non-life insurance

consumption in Thailand. They had an almost perfect positive relationship with each

other. If the Export Price index increases which would be resulting in the increase in

54

export values, total non-life insurance consumption in Thailand would largely

increase due to higher demand for insurance. The increase in export price would

probably result in higher domestic productions for export and hence, increase the

overall demand of insurance purchased in Thailand.

The second highest contributor of total non-life insurance consumption in

Thailand was Consumer Confidence index. They had a weak positive relationship. If

Consumer Confidence index increases, total non-life insurance consumption in

Thailand would slightly increase. This finding shows that the perspective of

consumers toward the state of the nation’s economy affects the demand for non-life

insurance in Thailand.

The third highest contributor of total non-life insurance consumption in

Thailand was Coincident index. They had a weak positive relationship with each

other. If Coincident index increases, total non-life insurance consumption in Thailand

would slightly increase. Since Coincident index reflects real business cycles, it shows

that total insurance consumption and the business cycle in Thailand change in the

same direction.

The final contributor of total non-life insurance consumption in Thailand was

Employment rate from Export Business Situation index. They had weak negative

relationship. If the Employment rate decreases, total non-life insurance consumption

would increase. The Employment rate was measured by collecting survey data from

export business entrepreneurs. The survey asked those entrepreneurs their opinion on

the future export business situation. It is not, therefore, the actual employment ratio in

Thailand. In this case, if those entrepreneurs view that the business situation is getting

worse, and then they would probably hire lower number of personnel. However, this

information implies that the poor economic condition causes the entrepreneurs to

purchase more insurance coverage for themselves.

In summary, four variables were found to have an impact on total non-life

insurance consumption in Thailand, namely, Export Price index, Consumer

Confidence index, Coincidence index, and Employment rate.

In addition to the analysis above, all thirteen actual indices had also been used

to analyze the impact to each type of insurance policies in Thailand separately, i.e.

55

Fire insurance, Automobile insurance, Marine and Transportation insurance, and

Miscellaneous insurance, in order to analyze how the variables affect each policy.

For Fire insurance consumption, it was found that it was not impacted by the

variables in the study as the correlation analysis suggested that no actual indices were

related to fire insurance consumption. This finding is in line with the statistics of fire

insurance consumption in the past ten years. It was shown that fire insurance

consumption in the past ten years was stable; therefore, the economic condition did

not affect the consumption.

For Automobile insurance consumption, it was found that it was impacted by

the identical variables to total non-life insurance consumption which were Coincident

index (from Business Cycle index), Employment Rate (from Export Business

Situation index), Consumer Confidence index, and Export Price index. The model

could be used to estimate around 87% of total non-life insurance consumption in

Thailand with an estimated standard error of 467,338. As the statistics showed that

automobile insurance consumption had the highest market share in Thailand, about

60% of total non-life insurance consumption in Thailand was from automobile

insurance consumption, this finding showed that the variables which impacted

automobile insurance consumption would also have an impact on total non-life

insurance consumption in Thailand.

For Marine and Transportation insurance consumption, it was found that only

variables were found to have an impact which were Coincidence index (from

Business Cycle index), and Import Price index. The model could be used to estimate

approximately 66.6% of marine and transportation insurance consumption in Thailand

with an estimated standard error of 31,819. Coincident index was also the common

variable with total non-life insurance consumption. However, Import Price index was

not effect total non-life insurance consumption. It should be because Import Price

index used Cost, Insurance, and Freight (C.I.F.) for the calculation. The insurance

costs would be included in the import price. Therefore, when Import Price index

drops, the consumption for this insurance would also decrease.

For Miscellaneous insurance consumption, it was found that there were two

variables having an impact on the consumption, namely, Export Price index, and

Consumer Confidence index. Both variables also impacted the consumption of total

56

non-life insurance consumption in Thailand. The model could be used to estimate

approximately 63% of miscellaneous insurance consumption with an estimated

standard error of 530,545. Since miscellaneous insurance is the insurance coverage

for all other types of policies which are not fire, automobile, and marine and

transportation insurance, the finding showed that the increasing of the country’s

expenditures lead to the increase in the consumption of miscellaneous insurance.

In conclusion, some types of non-life insurance consumption were impacted

by common macroeconomic variables. First, coincidence index was one of the

common impacting variables. It affected automobile insurance consumption and

marine and transportation insurance consumption in Thailand. Second, export price

index was another common impacting factor. It affected automobile insurance

consumption and miscellaneous insurance consumption. Third, consumer confidence

index was found to have an impact on automobile insurance consumption and

miscellaneous insurance consumption. Regardless, these three common variables

were also having an impact on total non-life insurance consumption in Thailand and

could be the main contributors to non-life insurance industry.

5.2 Discussion

From the study, it was found that total non-life insurance consumption and

automobile insurance consumption had been impacted by common macroeconomic

factors which were: Export Price Index (X19), Coincident Index (X3), Consumer

Confidence Index (X14), and Employment Rate (X13). In addition, the estimates of

the four variables impacted both insurance consumption by more than 80%. Since

automobile insurance contributed the highest consumption proportion of all non-life

insurance industry, this could be the main cause of such a result.

When Export Price Index, Coincident Index, and Consumer Confidence Index

have increased, total non-life insurance consumption would also increase. The

increasing of these indices indicated a favorable economic performance; the business

sectors tend to make more investment; consumers tend to make more spending during

such economic status. Therefore, total non-life insurance consumption would

increase.

57

On the other hand, when Employment rate has increased, total non-life

insurance consumption in Thailand would decrease. From the correlation analysis, it

was found that all of the Export Business Situation indices were negatively related to

the consumption of total non-life insurance consumption in Thailand as same as of

automobile, marine and transportation, and miscellaneous insurance. As the indices

were calculated based on the opinion survey of export business entrepreneurs, it was

found that as the export business situation tends to be better; those entrepreneurs

believe that they would purchase less insurance coverage. However, this result was

the opinion of the entrepreneurs, not the result of the actual situation. In addition, it

was found that there was no correlation between the Export Business Situation indices

and the Coincident index which is the index that reflects the real business cycle in

Thailand. Therefore, the view of entrepreneurs suggested that if the employment ratio

of export business increases, they tend to purchase less insurance coverage.

Additionally, marine and transportation insurance consumption analysis found

that it was impacted by Coincidence Index (X3) and Import Price Index (X20) of

around 66%. Coincidence Index was also found to be a common impact of total non-

life insurance consumption. As these variables increase, marine and transportation

insurance consumption in Thailand would also increase.

Moreover, Export Price Index (X19) and Consumer Confidence Index (X14)

were found to have an impact on miscellaneous insurance consumption of around

63%; these two variables were also found to have an impact on total non-life

insurance consumption and automobile insurance consumption in Thailand. As these

variables increased, miscellaneous insurance consumption in Thailand would also

increase.

Nevertheless, fire insurance was not affected by any macroeconomic factors.

Since the consumption of fire insurance during the past decade remained unchanged

regardless of economic situation, the result was reliable. This should be caused by the

stability of insurance consumption in fire insurance industry. However, further

analysis to find out the cause of such a circumstance should be analyzed.

From the analysis, four variables were found to have an impact of more than

80% on overall non-life insurance consumption in Thailand, namely: Export Price

Index, Coincidence Index, Consumer Confidence Index and Employment Rate. From

58

these four variables, Export Price Index was contributed to the consumption of

automobile insurance and miscellaneous insurance; Coincidence Index was

contributed to the consumption of automobile insurance and marine and transportation

insurance; Consumer Confidence Index was contributed to the consumption of

automobile insurance and miscellaneous insurance; Employment rate was only

contributed to the consumption of automobile insurance in Thailand. From this

information, it was found that three out of four variables were contributed by

insurance classifications in Thailand and thus, they impacted on total industry

consumption.

5.3 Recommendation

5.3.1 Recommendation from the research

From the conclusion, it was recommended that some macroeconomic factors

were having an impact on non-life insurance consumption, namely, Coincident index,

Consumer Confidence index (from Business Cycle index), Employment rate (from

Export Business Situation index), and Export Price index. Some factors may

contribute greatly to the consumption while some factors may not. Non-life insurance

companies may use this conclusion to their benefits by having a better understanding

of how macroeconomic factors impact their business. It was shown in the analysis that

these four variables were contributed to over 80% of total non-life insurance

consumption in Thailand. Moreover, the model could also be used to predict future


In addition, it was found in the analysis that these four independent variables

also had an impact on other types of insurance coverage in Thailand. Automobile

insurance was greatly contributed by these four variables while marine and

transportation insurance consumption, and miscellaneous insurance consumption were

contributed by two variables each. Therefore, it can be concluded that variables which

contributed to each type of insurance consumption in Thailand would contribute to

total non-life insurance consumption in Thailand.

59

5.3.2 Recommendation for further research

In this analysis, there are some recommendations for further research. First,

additional variables should be collected for analysis. In this study, the indices

published by the Bureau of Trade and Economic Indices, were implied as

macroeconomic indicators and were used to indicate the state of economy. However,

some other variables should also be collected to analyze their potential impact on non-

life insurance consumption. As the selected factors were not having impacts on fire

insurance consumption at all, some other factors should be considered for study. The

example of variables include interest rates, the amount of land and building

transactions, the amount of new housing, the amount of vehicles produced, etc.

Second, a longer period should be considered. In this study, 10 year data was selected

on a monthly basis. However, a longer period may be able to provide a different

suggestion. Due to a limited access to publicly available data, the monthly data was

collected for this study. However, it is recommended that collecting data on an annual

basis should be better to study such analysis. Annual data should be better for the

analysis because there were some variation between months such as the consumption

in August may be higher than in September. Therefore, annual data could be better.

Moreover, the period should be a minimum of 50 years in order to analyze the

potential impact. It should be tested out longer period because it could potentially

show an impact in a long term basis. Third, the impact analysis could be compared

with other countries. For example, it was concluded that export price index impacted

total non-life insurance consumption in Thailand. The export price index should also

be used to analyze the potential impact on other countries’ non-life insurance

consumption in order to compare the impact of macroeconomic factors of Thailand to

other countries worldwide. Finally, business cycle should be considered and studied

in parallel with this analysis. In this study, it had not included business cycle, i.e.

peak, trough; the inclusion of such a situation could lead to different results. Even

though, in this study, there was a variable called “Business Cycle index” which was

used to represent a periodically actual business cycle on a monthly basis, it is

recommended that the analysis should be studied in parallel with the actual business

cycle.

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APPENDICES

APPENDIX A

DESCRIPTION STATISTICS

67

Mean

Standard

Error of

the Mean

Standard

Deviation Minimum Maximum

Y1 8,157,144.00 189,784.00 2,078,979.00 4,493,655.00 14,196,132.00

Y2 636,107.00 11,496.00 125,929.00 (265,443.00) 951,068.00

Y3 4,843,518.00 115,822.00 1,268,772.00 2,459,314.00 8,258,552.00

Y4 314,948.00 4,980.00 54,554.00 192,060.00 451,883.00

Y5 2,362,571.00 79,068.00 866,145.00 786,301.00 5,170,830.00

X1 98.30 0.81 8.92 84.70 113.31

X2 0.25 0.06 0.60 (3.00) 2.10

X3 110.05 0.45 4.93 97.00 119.10

X4 1.85 0.41 4.44 (14.60) 9.00

X5 0.15 0.14 1.50 (9.24) 7.13

X6 2.89 0.24 2.62 (4.30) 11.40

X7 99.90 0.24 2.63 92.00 104.80

X8 0.59 0.47 5.11 (11.50) 16.00

X9 0.05 0.10 1.04 (3.50) 2.90

X10 49.61 0.71 7.79 23.90 69.20

X11 49.73 0.68 7.45 19.80 64.90

X12 45.79 0.40 4.37 32.50 54.30

X13 52.04 0.47 5.14 35.10 60.40

X14 27.63 1.39 15.20 7.80 63.60

X15 108.98 1.68 18.45 80.70 139.30

X16 0.46 0.14 1.53 (6.50) 4.50

X17 105.95 1.18 12.94 79.00 142.40

X18 0.37 0.16 1.77 (7.40) 7.20

X19 99.17 1.58 17.27 72.20 128.40

X20 99.75 1.56 17.09 76.90 132.90

APPENDIX B

CORRELATION ANALYSIS

Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 X5 X6 X7 X8


X1 Index

Correlation 0.887 0.001 0.903 0.713 0.762 1.000 0.026 0.731 -0.343 -0.087 0.223 -0.005 -0.052

P-Value 0.000 0.993 0.000 0.000 0.000 0.775 0.000 0.000 0.346 0.014 0.954 0.574

X2 Percentage change from previous month

Correlation -0.038 -0.012 -0.016 0.001 -0.063 0.026 1.000 0.087 0.099 0.017 0.422 0.102 0.110

P-Value 0.680 0.894 0.859 0.989 0.496 0.775 0.346 0.282 0.857 0.000 0.270 0.230


X3 Index

Correlation 0.709 -0.102 0.757 0.784 0.559 0.731 0.087 1.000 0.024 0.084 0.535 0.156 -0.078

P-Value 0.000 0.266 0.000 0.000 0.000 0.000 0.346 0.795 0.362 0.000 0.088 0.395

X4 Six-month smoothed annualized growth rate

Correlation -0.265 0.082 -0.244 -0.129 -0.278 -0.343 0.099 0.024 1.000 0.469 0.282 0.735 0.596

P-Value 0.003 0.375 0.007 0.160 0.002 0.000 0.282 0.795 0.000 0.002 0.000 0.000


Correlation -0.010 0.174 0.025 -0.042 -0.075 -0.087 0.017 0.084 0.469 1.000 0.038 0.235 0.272

P-Value 0.918 0.058 0.785 0.646 0.413 0.346 0.857 0.362 0.000 0.679 0.010 0.003

Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 X5 X6 X7 X8



Correlation 0.171 -0.022 0.193 0.360 0.112 0.223 0.422 0.535 0.282 0.038 1.000 0.361 0.166

P-Value 0.061 0.808 0.034 0.000 0.223 0.014 0.000 0.000 0.002 0.679 0.000 0.069

X7 Lead Inflation Index

Correlation 0.029 0.051 0.032 0.092 0.013 -0.005 0.102 0.156 0.735 0.235 0.361 1.000 0.714

P-Value 0.749 0.583 0.728 0.315 0.891 0.954 0.270 0.088 0.000 0.010 0.000 0.000


Correlation -0.101 0.103 -0.102 -0.152 -0.096 -0.052 0.110 -0.078 0.596 0.272 0.166 0.714 1.000

P-Value 0.274 0.262 0.267 0.097 0.299 0.574 0.230 0.395 0.000 0.003 0.069 0.000


Correlation -0.063 0.184 -0.064 -0.214 -0.062 -0.069 0.159 -0.151 0.141 0.475 -0.033 0.158 0.429

P-Value 0.495 0.045 0.487 0.019 0.498 0.455 0.083 0.101 0.126 0.000 0.717 0.085 0.000


X10 Total Export Index

Correlation -0.099 0.072 -0.061 0.061 -0.162 -0.146 0.089 0.017 0.536 0.307 0.211 0.547 0.465

P-Value 0.283 0.436 0.505 0.510 0.077 0.113 0.335 0.850 0.000 0.001 0.021 0.000 0.000

Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 X5 X6 X7 X8

X11 New Orders Index

Correlation -0.137 0.023 -0.113 -0.031 -0.170 -0.205 0.241 0.002 0.632 0.315 0.283 0.647 0.541

P-Value 0.135 0.803 0.220 0.739 0.064 0.025 0.008 0.982 0.000 0.000 0.002 0.000 0.000


Correlation -0.236 0.108 -0.245 -0.076 -0.215 -0.287 0.017 -0.001 0.561 0.170 0.251 0.604 0.412

P-Value 0.009 0.242 0.007 0.407 0.018 0.001 0.851 0.988 0.000 0.063 0.006 0.000 0.000


Correlation -0.263 0.057 -0.258 -0.088 -0.261 -0.337 0.154 -0.039 0.698 0.229 0.248 0.701 0.447

P-Value 0.004 0.538 0.005 0.337 0.004 0.000 0.093 0.673 0.000 0.012 0.006 0.000 0.000


X14 Index

Correlation -0.555 -0.042 -0.590 -0.438 -0.432 -0.755 -0.069 -0.537 0.460 0.098 -0.164 0.332 0.038

P-Value 0.000 0.650 0.000 0.000 0.000 0.000 0.452 0.000 0.000 0.287 0.073 0.000 0.681


X15 Index

Correlation 0.884 0.013 0.899 0.733 0.758 0.994 0.027 0.752 -0.287 -0.077 0.268 0.049 -0.040

P-Value 0.000 0.892 0.000 0.000 0.000 0.000 0.766 0.000 0.001 0.404 0.003 0.599 0.665

Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 X5 X6 X7 X8


Correlation -0.050 0.054 -0.047 0.011 -0.053 -0.005 0.718 0.058 0.183 0.139 0.385 0.186 0.224

P-Value 0.591 0.561 0.611 0.904 0.569 0.956 0.000 0.527 0.046 0.129 0.000 0.042 0.014


X17 Index

Correlation 0.745 -0.061 0.774 0.704 0.618 0.894 0.025 0.738 -0.329 -0.098 0.384 -0.020 -0.125

P-Value 0.000 0.510 0.000 0.000 0.000 0.000 0.787 0.000 0.000 0.286 0.000 0.825 0.175


Correlation -0.059 0.066 -0.042 -0.109 -0.079 -0.050 0.536 -0.006 0.190 0.105 0.391 0.271 0.257

P-Value 0.520 0.477 0.653 0.234 0.390 0.584 0.000 0.947 0.038 0.253 0.000 0.003 0.005



Correlation 0.905 0.003 0.918 0.724 0.782 0.988 0.010 0.730 -0.266 -0.069 0.201 0.080 -0.024

P-Value 0.000 0.974 0.000 0.000 0.000 0.000 0.912 0.000 0.003 0.451 0.027 0.386 0.798

X20 Import Price

Index

Correlation 0.895 0.020 0.905 0.711 0.776 0.986 0.006 0.702 -0.274 -0.072 0.226 0.098 -0.011

P-Value 0.000 0.830 0.000 0.000 0.000 0.000 0.948 0.000 0.002 0.437 0.013 0.286 0.906

X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20


X1 Index

Correlation -0.069 -0.146 -0.205 -0.287 -0.337 -0.755 0.994 -0.005 0.894 -0.050 0.988 0.986

P-Value 0.455 0.113 0.025 0.001 0.000 0.000 0.000 0.956 0.000 0.584 0.000 0.000


Correlation 0.159 0.089 0.241 0.017 0.154 -0.069 0.027 0.718 0.025 0.536 0.010 0.006

P-Value 0.083 0.335 0.008 0.851 0.093 0.452 0.766 0.000 0.787 0.000 0.912 0.948


X3 Index

Correlation -0.151 0.017 0.002 -0.001 -0.039 -0.537 0.752 0.058 0.738 -0.006 0.730 0.702

P-Value 0.101 0.850 0.982 0.988 0.673 0.000 0.000 0.527 0.000 0.947 0.000 0.000


Correlation 0.141 0.536 0.632 0.561 0.698 0.460 -0.287 0.183 -0.329 0.190 -0.266 -0.274

P-Value 0.126 0.000 0.000 0.000 0.000 0.000 0.001 0.046 0.000 0.038 0.003 0.002


Correlation 0.475 0.307 0.315 0.170 0.229 0.098 -0.077 0.139 -0.098 0.105 -0.069 -0.072

P-Value 0.000 0.001 0.000 0.063 0.012 0.287 0.404 0.129 0.286 0.253 0.451 0.437

X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20



Correlation -0.033 0.211 0.283 0.251 0.248 -0.164 0.268 0.385 0.384 0.391 0.201 0.226

P-Value 0.717 0.021 0.002 0.006 0.006 0.073 0.003 0.000 0.000 0.000 0.027 0.013

X7 Lead Inflation Index

Correlation 0.158 0.547 0.647 0.604 0.701 0.332 0.049 0.186 -0.020 0.271 0.080 0.098

P-Value 0.085 0.000 0.000 0.000 0.000 0.000 0.599 0.042 0.825 0.003 0.386 0.286


Correlation 0.429 0.465 0.541 0.412 0.447 0.038 -0.040 0.224 -0.125 0.257 -0.024 -0.011

P-Value 0.000 0.000 0.000 0.000 0.000 0.681 0.665 0.014 0.175 0.005 0.798 0.906


Correlation 1.000 0.244 0.289 0.049 0.146 -0.027 -0.076 0.332 -0.136 0.308 -0.067 -0.067

P-Value 0.007 0.001 0.595 0.111 0.769 0.410 0.000 0.138 0.001 0.469 0.465


X10 Total Export Index

Correlation 0.244 1.000 0.915 0.453 0.734 0.252 -0.111 0.173 -0.188 0.243 -0.104 -0.102

P-Value 0.007 0.000 0.000 0.000 0.005 0.229 0.059 0.039 0.008 0.259 0.266

X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

X11 New Orders Index

Correlation 0.289 0.915 1.000 0.525 0.829 0.334 -0.166 0.313 -0.264 0.375 -0.148 -0.153

P-Value 0.001 0.000 0.000 0.000 0.000 0.069 0.000 0.004 0.000 0.106 0.095


Correlation 0.049 0.453 0.525 1.000 0.746 0.380 -0.239 0.061 -0.278 0.141 -0.240 -0.236

P-Value 0.595 0.000 0.000 0.000 0.000 0.008 0.506 0.002 0.124 0.008 0.010


Correlation 0.146 0.734 0.829 0.746 1.000 0.496 -0.288 0.206 -0.388 0.288 -0.267 -0.265

P-Value 0.111 0.000 0.000 0.000 0.000 0.001 0.024 0.000 0.001 0.003 0.003


X14 Index

Correlation -0.027 0.252 0.334 0.380 0.496 1.000 -0.732 -0.005 -0.706 0.073 -0.681 -0.670

P-Value 0.769 0.005 0.000 0.000 0.000 0.000 0.954 0.000 0.426 0.000 0.000


X15 Index

Correlation -0.076 -0.111 -0.166 -0.239 -0.288 -0.732 1.000 0.020 0.901 -0.055 0.991 0.987

P-Value 0.410 0.229 0.069 0.008 0.001 0.000 0.826 0.000 0.547 0.000 0.000

X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20


Correlation 0.332 0.173 0.313 0.061 0.206 -0.005 0.020 1.000 0.037 0.540 -0.008 -0.017

P-Value 0.000 0.059 0.000 0.506 0.024 0.954 0.826 0.691 0.000 0.930 0.857


X17 Index

Correlation -0.136 -0.188 -0.264 -0.278 -0.388 -0.706 0.901 0.037 1.000 0.003 0.868 0.875

P-Value 0.138 0.039 0.004 0.002 0.000 0.000 0.000 0.691 0.973 0.000 0.000


Correlation 0.308 0.243 0.375 0.141 0.288 0.073 -0.055 0.540 0.003 1.000 -0.056 -0.034

P-Value 0.001 0.008 0.000 0.124 0.001 0.426 0.547 0.000 0.973 0.546 0.714



Correlation -0.067 -0.104 -0.148 -0.240 -0.267 -0.681 0.991 -0.008 0.868 -0.056 1.000 0.992

P-Value 0.469 0.259 0.106 0.008 0.003 0.000 0.000 0.930 0.000 0.546 0.000


Correlation -0.067 -0.102 -0.153 -0.236 -0.265 -0.670 0.987 -0.017 0.875 -0.034 0.992 1.000

Value 0.465 0.266 0.095 0.010 0.003 0.000 0.000 0.857 0.000 0.714 0.000

APPENDIX C

STEPWISE ANALYSIS

78

1. Total Non-life Insurance Consumption Stepwise Analysis

1) Stepwise Regression Analysis

The result showed 11 steps. The step which had the highest r-square was

selected to review which is step 10.

Stepwise Regression: Y versus X1, X3, ... Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15 Response is Y1 on 13 predictors, with N = 120 Step 1 2 3 4 5 6 Constant -2647357 -2351997 -8327848 -1966877 -134279 856852 X19 108951 186706 203072 226174 190822 163500 T-Value 23.10 5.53 6.09 6.67 5.21 4.21 P-Value 0.000 0.000 0.000 0.000 0.000 0.000 X15 -73466 -102335 -126084 -79649 -36928 T-Value -2.32 -3.16 -3.81 -2.09 -0.84 P-Value 0.022 0.002 0.000 0.039 0.401 X3 68142 82370 81451 89407 T-Value 2.80 3.37 3.39 3.71 P-Value 0.006 0.001 0.001 0.000 X7 -76373 -115321 -123796 T-Value -2.50 -3.35 -3.61 P-Value 0.014 0.001 0.000 X14 21883 23486 T-Value 2.31 2.50 P-Value 0.022 0.014 X17 -28416 T-Value -1.93 P-Value 0.057 S 888300 872195 847727 829183 813914 804401 R-Sq 81.90 82.70 83.79 84.63 85.32 85.78 R-Sq(adj) 81.74 82.40 83.37 84.09 84.67 85.03 Mallows Cp 34.3 29.7 22.6 17.6 13.9 12.0 Step 7 8 9 10 11 Constant 1252576 1394498 -2200884 -836630 -1075038 X19 131682 67695 65230 54387 T-Value 14.00 1.74 1.69 1.41 P-Value 0.000 0.085 0.094 0.162 X15 T-Value P-Value X3 85692 103536 118199 124392 140872 T-Value 3.62 4.02 4.40 4.65 5.83 P-Value 0.000 0.000 0.000 0.000 0.000 X7 -126766 -141963 -89783 -107029 -116305 T-Value -3.72 -4.06 -1.95 -2.31 -2.53

79

P-Value 0.000 0.000 0.053 0.023 0.013 X14 27414 27188 27801 30289 29614 T-Value 3.37 3.37 3.47 3.78 3.68 P-Value 0.001 0.001 0.001 0.000 0.000 X17 -34718 -43817 -52774 -53483 -58454 T-Value -2.73 -3.20 -3.63 -3.72 -4.18 P-Value 0.007 0.002 0.000 0.000 0.000 X20 67448 69063 80047 134087 T-Value 1.69 1.75 2.03 14.25 P-Value 0.094 0.084 0.045 0.000 X13 -42576 -82140 -87580 T-Value -1.73 -2.58 -2.76 P-Value 0.087 0.011 0.007 X11 34630 38297 T-Value 1.93 2.15 P-Value 0.056 0.034 S 803383 796915 789992 780522 783942 R-Sq 85.69 86.05 86.41 86.85 86.62 R-Sq(adj) 85.07 85.31 85.56 85.90 85.78 Mallows Cp 10.8 9.9 8.8 7.2 7.1

2) Regression Analysis for Step 10

The regression analysis of step 10’s equation was shown below which

includes Coefficient, Standard Error of the coefficient, p-value, and Variance Inflation

Factor (VIF). The model was unreliable because X19 and X20 were having too high

VIF. In addition, X19, X7, X20, X13, and X11 had insignificance p-value.

Regression Analysis: Y versus X19, X3, X7, X14, X17, X20, X13, X11

The regression equation is Y1 = - 836630 + 54387 X19 + 124392 X3 - 107029 X7 + 30289 X14 - 53483 X17 + 80047 X20 - 82140 X13 + 34630 X11 Predictor Coef SE Coef T P VIF Constant -836630 3889422 -0.22 0.830 X19 54387 38616 1.41 0.162 86.858 X3 124392 26746 4.65 0.000 3.394 X7 -107029 46269 -2.31 0.023 2.898 X14 30289 8017 3.78 0.000 2.902 X17 -53483 14365 -3.72 0.000 6.755 X20 80047 39496 2.03 0.045 88.995 X13 -82140 31799 -2.58 0.011 5.215 X11 34630 17920 1.93 0.056 3.479 S = 780522 R-Sq = 86.9% R-Sq(adj) = 85.9% Analysis of Variance Source DF SS MS F P Regression 8 4.46714E+14 5.58392E+13 91.66 0.000

80

Residual Error 111 6.76228E+13 6.09214E+11 Total 119 5.14336E+14 Source DF Seq SS X19 1 4.21225E+14 X3 1 2.58598E+12 X7 1 1.48066E+12 X14 1 1.06425E+13 X17 1 4.82350E+12 X20 1 1.81518E+12 X13 1 1.86546E+12 X11 1 2.27497E+12 Unusual Observations Obs X19 Y1 Fit SE Fit Residual St Resid 10 75 7524257 5724157 176064 1800100 2.37R 23 81 5315005 6856180 235471 -1541175 -2.07R 84 106 10830860 9015144 276771 1815716 2.49R 108 125 13410632 11448027 171163 1962605 2.58R 120 127 14196132 12020187 296762 2175945 3.01R R denotes an observation with a large standardized residual. Durbin-Watson statistic = 2.19538

3) Correlation Analysis for Step 10

Correlation among the variables was reviewed. It showed that X7 and X11

were not related to Y1. Therefore, it was eliminated and the model was retested.

Correlations: Y, X19, X3, X7, X14, X17, X20, X13, X11

Y1 X19 X3 X7 X14 X17 X20 X13 X19 0.905 0.000 X3 0.709 0.730 0.000 0.000 X7 0.029 0.080 0.156 0.749 0.386 0.088 X14 -0.555 -0.681 -0.537 0.332 0.000 0.000 0.000 0.000 X17 0.745 0.868 0.738 -0.020 -0.706 0.000 0.000 0.000 0.825 0.000 X20 0.895 0.992 0.702 0.098 -0.670 0.875 0.000 0.000 0.000 0.286 0.000 0.000 X13 -0.263 -0.267 -0.039 0.701 0.496 -0.388 -0.265 0.004 0.003 0.673 0.000 0.000 0.000 0.003 X11 -0.137 -0.148 0.002 0.647 0.334 -0.264 -0.153 0.829 0.135 0.106 0.982 0.000 0.000 0.004 0.095 0.000

81

Cell Contents: Pearson correlation P-Value

4) Retest of Regression Analysis without X7 and X11

The model was retested without X7 and X11. The result showed that X20 had

insignificance p-value and the highest VIF; therefore, it was eliminated and the model

was retested.

Regression Analysis: Y versus X19, X3, X14, X17, X20, X13 The regression equation is Y = - 8462006 + 74127 X19 + 117291 X3 + 22453 X14 - 55820 X17 + 55277 X20 - 74181 X13 Predictor Coef SE Coef T P VIF Constant -8462006 2199001 -3.85 0.000 X19 74127 38876 1.91 0.059 83.846 X3 117291 27205 4.31 0.000 3.344 X14 22453 7620 2.95 0.004 2.497 X17 -55820 14630 -3.82 0.000 6.672 X20 55277 39408 1.40 0.163 84.379 X13 -74181 18799 -3.95 0.000 1.736 S = 799781 R-Sq = 85.9% R-Sq(adj) = 85.2% Analysis of Variance Source DF SS MS F P Regression 6 4.42056E+14 7.36760E+13 115.18 0.000 Residual Error 113 7.22804E+13 6.39649E+11 Total 119 5.14336E+14 Source DF Seq SS X19 1 4.21225E+14 X3 1 2.58598E+12 X14 1 4.24609E+12 X17 1 3.75527E+12 X20 1 2.83862E+11 X13 1 9.95946E+12 Unusual Observations Obs X19 Y1 Fit SE Fit Residual St Resid 10 75 7524257 5783992 179020 1740265 2.23R 60 95 9822185 8234331 144350 1587854 2.02R 61 96 9284126 7645677 115798 1638449 2.07R 78 114 8995252 8524303 336130 470949 0.65 X 83 108 7768979 9482379 259815 -1713400 -2.27R 84 106 10830860 9157267 243071 1673593 2.20R 108 125 13410632 11430186 174525 1980446 2.54R 120 127 14196132 11586884 250372 2609248 3.44R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage.

82

Durbin-Watson statistic = 2.26436

5) Retest of Regression Analysis without X20

In this study, it was set in the methodology that the appropriate VIF should be

less than 4. The result showed below that X17 had high VIF; therefore, the model was

retested without X17.

Regression Analysis: Y versus X19, X3, X14, X17, X13 The regression equation is Y = - 7686350 + 127137 X19 + 102025 X3 + 23337 X14 - 47545 X17 - 69178 X13 Predictor Coef SE Coef T P VIF Constant -7686350 2137351 -3.60 0.000 X19 127137 9156 13.89 0.000 4.612 X3 102025 25038 4.07 0.000 2.809 X14 23337 7626 3.06 0.003 2.480 X17 -47545 13445 -3.54 0.001 5.587 X13 -69178 18536 -3.73 0.000 1.674 S = 803167 R-Sq = 85.7% R-Sq(adj) = 85.1% Analysis of Variance Source DF SS MS F P Regression 5 4.40798E+14 8.81595E+13 136.66 0.000 Residual Error 114 7.35389E+13 6.45078E+11 Total 119 5.14336E+14 Source DF Seq SS X19 1 4.21225E+14 X3 1 2.58598E+12 X14 1 4.24609E+12 X17 1 3.75527E+12 X13 1 8.98478E+12 Unusual Observations Obs X19 Y1 Fit SE Fit Residual St Resid 10 75 7524257 5672963 161250 1851294 2.35R 60 95 9822185 8195541 142276 1626644 2.06R 78 114 8995252 8524756 337553 470496 0.65 X 79 115 8633958 8830731 330929 -196773 -0.27 X 83 108 7768979 9470763 260783 -1701784 -2.24R 84 106 10830860 9205311 241664 1625549 2.12R 108 125 13410632 11477206 172000 1933426 2.46R 120 127 14196132 11317428 161254 2878704 3.66R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.22333

83

Standardized Regression Coefficients for Y Row Predictors StdCoef 1 X19 1.05603 2 X3 0.24185 3 X14 0.17066 4 X17 -0.29604 5 X13 -0.17098

6) Final Result

After the model was rerun without X17, it was reliable and could be used to

predict Y1, total non-life insurance consumption.

Regression Analysis: Y versus X19, X3, X14, X13 The regression equation is Y = - 8171741 + 107313 X19 + 67141 X3 + 26786 X14 - 46926 X13 Predictor Coef SE Coef T P VIF Constant -8171741 2237098 -3.65 0.000 X19 107313 7593 14.13 0.000 2.883 X3 67141 24137 2.78 0.006 2.373 X14 26786 7933 3.38 0.001 2.439 X13 -46926 18287 -2.57 0.012 1.481 S = 842389 R-Sq = 84.1% R-Sq(adj) = 83.6% Analysis of Variance Source DF SS MS F P Regression 4 4.32730E+14 1.08183E+14 152.45 0.000 Residual Error 115 8.16062E+13 7.09619E+11 Total 119 5.14336E+14 Source DF Seq SS X19 1 4.21225E+14 X3 1 2.58598E+12 X14 1 4.24609E+12 X13 1 4.67272E+12 Unusual Observations Obs X19 Y1 Fit SE Fit Residual St Resid 10 75 7524257 5612765 168179 1911492 2.32R 48 91 9439869 7749275 170501 1690594 2.05R 60 95 9822185 8084657 145555 1737528 2.09R 84 106 10830860 9039287 248637 1791573 2.23R 108 125 13410632 11207263 161661 2203369 2.67R 120 127 14196132 11357445 168712 2838687 3.44R R denotes an observation with a large standardized residual.

84

Durbin-Watson statistic = 2.00938 Standardized Regression Coefficients for Y Row Predictors StdCoef 1 X19 0.891359 2 X3 0.159157 3 X14 0.195884 4 X13 -0.115984

85

2. Automobile Insurance Consumption Stepwise Analysis


In step 1, automobile insurance consumption was tested in a stepwise

regression analysis in order to find the optimum variables for further analysis. The

analysis showed 9 steps in which step 9 had the maximum r-square and hence, it was

chosen for further analyze.

Stepwise Regression: Y3 versus X1, X3, ...

Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15 Response is Y3 on 13 predictors, with N = 120 Step 1 2 3 4 5 6 Constant -1843155 -6162452 -7255356 -3055983 -1411416 -1219298 X19 67427 57368 121100 136352 140145 105458 T-Value 25.09 15.36 6.72 7.54 7.85 4.73 P-Value 0.000 0.000 0.000 0.000 0.000 0.000 X3 48314 63380 72773 71620 86445 T-Value 3.69 4.82 5.58 5.58 6.23 P-Value 0.000 0.000 0.000 0.000 0.000 X15 -63181 -78860 -81343 -101513 T-Value -3.61 -4.47 -4.68 -5.39 P-Value 0.000 0.000 0.000 0.000 X7 -50420 -73507 -90374 T-Value -3.09 -3.84 -4.54 P-Value 0.002 0.000 0.000 X10 13767 15614 T-Value 2.21 2.54 P-Value 0.029 0.012 X20 54211 T-Value 2.50 P-Value 0.014 S 506150 481051 458114 442060 434817 425131 R-Sq 84.22 85.87 87.29 88.27 88.75 89.34 R-Sq(adj) 84.09 85.62 86.96 87.86 88.26 88.77 Mallows Cp 58.5 42.3 28.5 19.7 16.4 11.9 Step 7 8 9 Constant -2984343 -3449887 -3050306 X19 108841 87759 78699 T-Value 4.93 3.66 3.19 P-Value 0.000 0.000 0.002 X3 91990 101954 101268 T-Value 6.59 6.99 6.98 P-Value 0.000 0.000 0.000 X15 -106133 -86731 -67936

86

T-Value -5.67 -4.20 -2.81 P-Value 0.000 0.000 0.006 X7 -63744 -55126 -63198 T-Value -2.70 -2.33 -2.62 P-Value 0.008 0.022 0.010 X10 24406 24987 25974 T-Value 3.27 3.40 3.54 P-Value 0.001 0.001 0.001 X20 52209 61255 55546 T-Value 2.44 2.84 2.55 P-Value 0.016 0.005 0.012 X13 -30247 -41612 -45641 T-Value -2.03 -2.65 -2.88 P-Value 0.045 0.009 0.005 X17 -17513 -18832 T-Value -2.07 -2.23 P-Value 0.040 0.028 X14 7332 T-Value 1.48 P-Value 0.141 S 419405 413352 411143 R-Sq 89.72 90.10 90.29 R-Sq(adj) 89.07 89.39 89.50 Mallows Cp 9.7 7.5 7.3

2) Regression Analysis for Step 9

Nine variables were found to have an impact on automobile insurance

consumption. However, from the regression analysis below, it was found that some

variables were having too high VIF. Also, some variables including the constant value

were having insignificance p-value.

Regression Analysis: Y3 versus X19, X3, X15, X7, X10, X20, X13, X17, X14

The regression equation is Y3 = - 3050306 + 78699 X19 + 101268 X3 - 67936 X15 - 63198 X7 + 25974 X10 + 55546 X20 - 45641 X13 - 18832 X17 + 7332 X14 Predictor Coef SE Coef T P VIF Constant -3050306 2017061 -1.51 0.133 X19 78699 24648 3.19 0.002 127.530 X3 101268 14506 6.98 0.000 3.598 X15 -67936 24160 -2.81 0.006 139.886 X7 -63198 24141 -2.62 0.010 2.843 X10 25974 7346 3.54 0.001 2.303 X20 55546 21774 2.55 0.012 97.479 X13 -45641 15848 -2.88 0.005 4.669 X17 -18832 8444 -2.23 0.028 8.410 X14 7332 4948 1.48 0.141 3.983

87

S = 411143 R-Sq = 90.3% R-Sq(adj) = 89.5% Analysis of Variance Source DF SS MS F P Regression 9 1.72970E+14 1.92189E+13 113.70 0.000 Residual Error 110 1.85942E+13 1.69039E+11 Total 119 1.91564E+14 Source DF Seq SS X19 1 1.61334E+14 X3 1 3.15522E+12 X15 1 2.73023E+12 X7 1 1.87181E+12 X10 1 9.19439E+11 X20 1 1.13029E+12 X13 1 7.22390E+11 X17 1 7.35356E+11 X14 1 3.71217E+11 Unusual Observations Obs X19 Y3 Fit SE Fit Residual St Resid 57 95 3483126 4737240 97119 -1254114 -3.14R 60 95 5841214 5029280 86809 811934 2.02R 84 106 6381806 5592363 158007 789443 2.08R 108 125 7960075 6899711 97305 1060364 2.65R 111 128 7893778 7004365 109047 889413 2.24R 120 127 8258552 7163647 148208 1094905 2.86R R denotes an observation with a large standardized residual. Durbin-Watson statistic = 1.91939

3) Correlation Analysis for Step 9

Correlation analysis was investigated. It was found that X7 and X10 had no

relationship with the insurance consumption; therefore, it was eliminated from the

model.

Correlations: Y3, X19, X3, X15, X7, X10, X20, X13, X17, X14

Y3 X19 X3 X15 X7 X10 X20 X13 X17 X19 0.918 0.000 X3 0.757 0.730 0.000 0.000 X15 0.899 0.991 0.752 0.000 0.000 0.000 X7 0.032 0.080 0.156 0.049 0.728 0.386 0.088 0.599

88

X10 -0.061 -0.104 0.017 -0.111 0.547 0.505 0.259 0.850 0.229 0.000 X20 0.905 0.992 0.702 0.987 0.098 -0.102 0.000 0.000 0.000 0.000 0.286 0.266 X13 -0.258 -0.267 -0.039 -0.288 0.701 0.734 -0.265 0.005 0.003 0.673 0.001 0.000 0.000 0.003 X17 0.774 0.868 0.738 0.901 -0.020 -0.188 0.875 -0.388 0.000 0.000 0.000 0.000 0.825 0.039 0.000 0.000 X14 -0.590 -0.681 -0.537 -0.732 0.332 0.252 -0.670 0.496 -0.706 0.000 0.000 0.000 0.000 0.000 0.005 0.000 0.000 0.000 Cell Contents: Pearson correlation P-Value

4) Regression Analysis without X7 and X10

After the retest, the result showed that X15 had the highest VIF; therefore, it

was eliminated and the model was rerun.

Regression Analysis: Y3 versus X19, X3, X15, X20, X13, X17, X14

The regression equation is Y3 = - 7367640 + 80111 X19 + 93146 X3 - 55842 X15 + 40143 X20 - 34996 X13 - 20462 X17 + 3119 X14 Predictor Coef SE Coef T P VIF Constant -7367640 1232683 -5.98 0.000 X19 80111 26372 3.04 0.003 127.453 X3 93146 15382 6.06 0.000 3.532 X15 -55842 25619 -2.18 0.031 137.313 X20 40143 22759 1.76 0.080 92.973 X13 -34996 10492 -3.34 0.001 1.786 X17 -20462 8925 -2.29 0.024 8.203 X14 3119 5143 0.61 0.545 3.757 S = 440039 R-Sq = 88.7% R-Sq(adj) = 88.0% Analysis of Variance Source DF SS MS F P Regression 7 1.69877E+14 2.42681E+13 125.33 0.000 Residual Error 112 2.16870E+13 1.93634E+11 Total 119 1.91564E+14 Source DF Seq SS X19 1 1.61334E+14 X3 1 3.15522E+12 X15 1 2.73023E+12 X20 1 1.91032E+11 X13 1 1.43273E+12 X17 1 9.62696E+11 X14 1 71244978358 Unusual Observations

89

Obs X19 Y3 Fit SE Fit Residual St Resid 57 95 3483126 4698940 91964 -1215814 -2.83R 60 95 5841214 4939603 79782 901611 2.08R 108 125 7960075 6896422 104134 1063653 2.49R 111 128 7893778 6925295 101108 968483 2.26R 120 127 8258552 6850281 137775 1408271 3.37R R denotes an observation with a large standardized residual. Durbin-Watson statistic = 2.22979

5) Regression Analysis without X7

After the retest, the result showed that X15 had the highest VIF; therefore, it


Regression Analysis: Y3 versus X19, X3, X20, X13, X17, X14

The regression equation is Y3 = - 6853327 + 46487 X19 + 85417 X3 + 25060 X20 - 38827 X13 - 28866 X17 + 9611 X14 Predictor Coef SE Coef T P VIF Constant -6853327 1229807 -5.57 0.000 X19 46487 21742 2.14 0.035 83.846 X3 85417 15214 5.61 0.000 3.344 X20 25060 22039 1.14 0.258 84.379 X13 -38827 10514 -3.69 0.000 1.736 X17 -28866 8182 -3.53 0.001 6.672 X14 9611 4262 2.26 0.026 2.497 S = 447283 R-Sq = 88.2% R-Sq(adj) = 87.6% Analysis of Variance Source DF SS MS F P Regression 6 1.68957E+14 2.81595E+13 140.75 0.000 Residual Error 113 2.26070E+13 2.00062E+11 Total 119 1.91564E+14 Source DF Seq SS X19 1 1.61334E+14 X3 1 3.15522E+12 X20 1 21445989436 X13 1 3.69625E+11 X17 1 3.05922E+12 X14 1 1.01768E+12 Unusual Observations Obs X19 Y3 Fit SE Fit Residual St Resid 57 95 3483126 4641158 89510 -1158032 -2.64R 60 95 5841214 4923071 80729 918143 2.09R 78 114 5471666 5194964 187983 276702 0.68 X 84 106 6381806 5411860 135939 969946 2.28R

90

108 125 7960075 6808596 97604 1151479 2.64R 111 128 7893778 6877447 100321 1016331 2.33R 120 127 8258552 6845021 140022 1413531 3.33R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.21303


After the retest, it was shown that X17 was having too high VIF. (It was set

that the optimum VIF should be less than 4.) Therefore, X17 was eliminated from the

analysis.

Regression Analysis: Y3 versus X19, X3, X13, X17, X14 The regression equation is Y3 = - 6501674 + 70520 X19 + 78496 X3 - 36559 X13 - 25114 X17 + 10012 X14 Predictor Coef SE Coef T P VIF Constant -6501674 1191816 -5.46 0.000 X19 70520 5106 13.81 0.000 4.612 X3 78496 13962 5.62 0.000 2.809 X13 -36559 10336 -3.54 0.001 1.674 X17 -25114 7497 -3.35 0.001 5.587 X14 10012 4252 2.35 0.020 2.480 S = 447857 R-Sq = 88.1% R-Sq(adj) = 87.5% Analysis of Variance Source DF SS MS F P Regression 5 1.68698E+14 3.37397E+13 168.21 0.000 Residual Error 114 2.28657E+13 2.00576E+11 Total 119 1.91564E+14 Source DF Seq SS X19 1 1.61334E+14 X3 1 3.15522E+12 X13 1 3.79257E+11 X17 1 2.71815E+12 X14 1 1.11188E+12 Unusual Observations Obs X19 Y3 Fit SE Fit Residual St Resid 57 95 3483126 4585471 75021 -1102345 -2.50R 60 95 5841214 4905485 79335 935729 2.12R 78 114 5471666 5195169 188225 276497 0.68 X 79 115 5088495 5394027 184531 -305532 -0.75 X 84 106 6381806 5433642 134755 948164 2.22R 108 125 7960075 6829914 95910 1130161 2.58R 111 128 7893778 6854820 98454 1038958 2.38R 120 127 8258552 6722860 89917 1535692 3.50R

91

R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.19016 Standardized Regression Coefficients for Y3 Row Predictors StdCoef 1 X19 0.959804 2 X3 0.304894 3 X13 -0.148065 4 X17 -0.256228 5 X14 0.119973

7) Final Result

After the retest, the model was reliable with 4 variables used to predict Y3.

Regression Analysis: Y3 versus X19, X3, X13, X14

The regression equation is Y3 = - 6758065 + 60049 X19 + 60070 X3 - 24805 X13 + 11834 X14 Predictor Coef SE Coef T P VIF Constant -6758065 1241091 -5.45 0.000 X19 60049 4212 14.26 0.000 2.883 X3 60070 13391 4.49 0.000 2.373 X13 -24805 10145 -2.45 0.016 1.481 X14 11834 4401 2.69 0.008 2.439 S = 467338 R-Sq = 86.9% R-Sq(adj) = 86.4% Analysis of Variance Source DF SS MS F P Regression 4 1.66447E+14 4.16118E+13 190.53 0.000 Residual Error 115 2.51166E+13 2.18405E+11 Total 119 1.91564E+14 Source DF Seq SS X19 1 1.61334E+14 X3 1 3.15522E+12 X13 1 3.79257E+11 X14 1 1.57915E+12 Unusual Observations Obs X19 Y3 Fit SE Fit Residual St Resid

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57 95 3483126 4561299 77921 -1078173 -2.34R 60 95 5841214 4846914 80750 994300 2.16R 80 115 4921338 5954563 72453 -1033225 -2.24R 84 106 6381806 5345945 137938 1035861 2.32R 108 125 7960075 6687325 89686 1272750 2.77R 111 128 7893778 6775413 99714 1118365 2.45R 120 127 8258552 6743998 93597 1514554 3.31R R denotes an observation with a large standardized residual. Durbin-Watson statistic = 2.00053 Standardized Regression Coefficients for Y3 Row Predictors StdCoef 1 X19 0.817280 2 X3 0.233323 3 X13 -0.100462 4 X14 0.141802

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3. Marine and Transportation Insurance Consumption Stepwise Analysis


Six steps were found from the stepwise regression analysis of Marine and

Transportation Insurance Consumption. Step 6 was chosen to further analyze because

of having the highest r-square.

Stepwise Regression: Y4 versus X1, X3, ... Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15 Response is Y4 on 13 predictors, with N = 120 Step 1 2 3 4 5 6 Constant -640506 -471058 -532807 -340800 -205326 -338982 X3 8682 6230 6433 6810 6701 6855 T-Value 13.73 7.49 7.76 8.10 8.14 8.48 P-Value 0.000 0.000 0.000 0.000 0.000 0.000 X20 1007 1264 1429 1560 1424 T-Value 4.20 4.66 5.08 5.58 5.09 P-Value 0.000 0.000 0.000 0.000 0.000 X14 500 839 887 889 T-Value 1.94 2.73 2.94 3.01 P-Value 0.055 0.007 0.004 0.003 X7 -2596 -4524 -2895 T-Value -1.96 -3.01 -1.79 P-Value 0.053 0.003 0.077 X10 1102 2980 T-Value 2.52 3.36 P-Value 0.013 0.001 X11 -2528 T-Value -2.42 P-Value 0.017 S 33988 31819 31450 31072 30372 29747 R-Sq 61.51 66.55 67.60 68.65 70.31 71.77 R-Sq(adj) 61.19 65.98 66.77 67.56 69.00 70.27 Mallows Cp 32.2 14.8 12.7 10.7 6.3 2.7

2) Regression Analysis of Step 6

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Six variables from step 6 were then analyzed in more detail. The result showed

below that the model was unreliable because some variables were having

insignificance p-value.

Regression Analysis: Y4 versus X3, X20, X14, X7, X10, X11

The regression equation is Y4 = - 338982 + 6855 X3 + 1424 X20 + 889 X14 - 2895 X7 + 2980 X10 - 2528 X11 Predictor Coef SE Coef T P VIF Constant -338982 143790 -2.36 0.020 X3 6855.0 808.6 8.48 0.000 2.136 X20 1424.3 279.6 5.09 0.000 3.070 X14 889.3 295.2 3.01 0.003 2.709 X7 -2895 1620 -1.79 0.077 2.446 X10 2979.9 887.0 3.36 0.001 6.414 X11 -2528 1046 -2.42 0.017 8.160 S = 29747.0 R-Sq = 71.8% R-Sq(adj) = 70.3% Analysis of Variance Source DF SS MS F P Regression 6 2.54174E+11 42362305644 47.87 0.000 Residual Error 113 99991583927 884881274 Total 119 3.54165E+11 Source DF Seq SS X3 1 2.17854E+11 X20 1 17854955472 X14 1 3723996344 X7 1 3704909546 X10 1 5864648549 X11 1 5171294942 Unusual Observations Obs X3 Y4 Fit SE Fit Residual St Resid 29 108 349118 286673 5405 62445 2.13R 38 110 401497 314249 8807 87248 3.07R 40 112 229594 289071 7961 -59477 -2.08R 82 112 427531 356469 9820 71062 2.53R 97 113 405529 338142 5923 67387 2.31R 109 116 436207 367164 6735 69043 2.38R 115 117 358059 416642 9163 -58583 -2.07R R denotes an observation with a large standardized residual. Durbin-Watson statistic = 2.29523

3) Correlation Analysis of Step 6

From below analysis, it was found that X7, X10, and X11 were not related to

the insurance consumption; therefore, they were eliminated from the model.

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Correlations: Y4, X3, X20, X14, X7, X10, X11 Y4 X3 X20 X14 X7 X10 X3 0.784 0.000 X20 0.711 0.702 0.000 0.000 X14 -0.438 -0.537 -0.670 0.000 0.000 0.000 X7 0.092 0.156 0.098 0.332 0.315 0.088 0.286 0.000 X10 0.061 0.017 -0.102 0.252 0.547 0.510 0.850 0.266 0.005 0.000 X11 -0.031 0.002 -0.153 0.334 0.647 0.915 0.739 0.982 0.095 0.000 0.000 0.000 Cell Contents: Pearson correlation P-Value

4) Regression Analysis after eliminating X7, X10, and X11

After the model was retested, it was found that X14 was insignificance to the

model; therefore, the model was retested.

Regression Analysis: Y4 versus X3, X20, X14 The regression equation is Y4 = - 532807 + 6433 X3 + 1264 X20 + 500 X14 Predictor Coef SE Coef T P VIF Constant -532807 82195 -6.48 0.000 X3 6432.6 828.5 7.76 0.000 2.006 X20 1263.6 271.4 4.66 0.000 2.589 X14 499.6 257.5 1.94 0.055 1.844 S = 31449.5 R-Sq = 67.6% R-Sq(adj) = 66.8% Analysis of Variance Source DF SS MS F P Regression 3 2.39433E+11 79810993609 80.69 0.000 Residual Error 116 1.14732E+11 989072732 Total 119 3.54165E+11 Source DF Seq SS X3 1 2.17854E+11 X20 1 17854955472

96

X14 1 3723996344 Unusual Observations Obs X3 Y4 Fit SE Fit Residual St Resid 38 110 401497 310842 6179 90655 2.94R 40 112 229594 309666 5356 -80072 -2.58R 82 112 427531 335688 4181 91843 2.95R 100 114 277444 357646 4213 -80202 -2.57R 119 107 355073 329450 10465 25623 0.86 X R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.48130

5) Final Result

After the model was retested, the model was reliable and could be used to

predict marine and transportation insurance consumption.

Regression Analysis: Y4 versus X3, X20

The regression equation is Y4 = - 471058 + 6230 X3 + 1007 X20 Predictor Coef SE Coef T P VIF Constant -471058 76675 -6.14 0.000 X3 6229.6 831.5 7.49 0.000 1.974 X20 1006.9 239.8 4.20 0.000 1.974 S = 31819.0 R-Sq = 66.6% R-Sq(adj) = 66.0% Analysis of Variance Source DF SS MS F P Regression 2 2.35709E+11 1.17854E+11 116.41 0.000 Residual Error 117 1.18456E+11 1012448148 Total 119 3.54165E+11 Source DF Seq SS X3 1 2.17854E+11 X20 1 17854955472 Unusual Observations Obs X3 Y4 Fit SE Fit Residual St Resid 29 108 349118 285092 4143 64026 2.03R 38 110 401497 302340 4408 99157 3.15R 40 112 229594 313918 4945 -84324 -2.68R 82 112 427531 340413 3438 87118 2.75R 100 114 277444 358353 4246 -80909 -2.57R 118 108 381250 335576 9472 45674 1.50 X 119 107 355073 326333 10463 28740 0.96 X

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R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.42821 Standardized Regression Coefficients for Y4 Row Predictors StdCoef 1 X3 0.562750 2 X20 0.315431

98

4. Miscellaneous Insurance Consumption Stepwise Analysis


In the analysis of the insurance consumption, four steps were found from the

stepwise regression analysis. Step 4 was found to have the highest r-square and was

used to further analysis in detail.

Stepwise Regression: Y5 versus X1, X3, ... Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15 Response is Y5 on 13 predictors, with N = 120 Step 1 2 3 4 Constant -1528899 -2464641 2490148 3231819 X19 39241 45685 50598 57178 T-Value 13.64 11.87 12.09 9.66 P-Value 0.000 0.000 0.000 0.000 X14 10739 17785 15642 T-Value 2.46 3.54 3.02 P-Value 0.015 0.001 0.003 X7 -56424 -56975 T-Value -2.65 -2.69 P-Value 0.009 0.008 X17 -12082 T-Value -1.56 P-Value 0.121 S 541748 530545 517414 514218 R-Sq 61.21 63.11 65.21 65.94 R-Sq(adj) 60.88 62.48 64.31 64.75 Mallows Cp 13.8 9.5 4.4 4.0

2) Regression Analysis of Step 4

Four variables from Step 4 were further analyzed below.

Regression Analysis: Y5 versus X19, X14, X7, X17 The regression equation is Y5 = 3231819 + 57178 X19 + 15642 X14 - 56975 X7 - 12082 X17 Predictor Coef SE Coef T P VIF Constant 3231819 1973291 1.64 0.104 X19 57178 5917 9.66 0.000 4.698 X14 15642 5178 3.02 0.003 2.790 X7 -56975 21177 -2.69 0.008 1.399

99

X17 -12082 7724 -1.56 0.121 4.500 S = 514218 R-Sq = 65.9% R-Sq(adj) = 64.8% Analysis of Variance Source DF SS MS F P Regression 4 5.88663E+13 1.47166E+13 55.66 0.000 Residual Error 115 3.04083E+13 2.64420E+11 Total 119 8.92746E+13 Source DF Seq SS X19 1 5.46427E+13 X14 1 1.69905E+12 X7 1 1.87762E+12 X17 1 6.46942E+11 Unusual Observations Obs X19 Y5 Fit SE Fit Residual St Resid 10 75 3728546 1568261 96243 2160285 4.28R 22 81 3043253 2018916 132387 1024337 2.06R 34 85 2889075 1705720 90909 1183355 2.34R 70 102 3336764 2261739 72342 1075025 2.11R 78 114 2497546 2500738 197838 -3192 -0.01 X 79 115 2488976 2488606 206101 370 0.00 X 118 128 5170830 3832353 130596 1338477 2.69R 120 127 4863495 3634546 104635 1228949 2.44R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.31942

3) Correlation Analysis of Step 4

From the analysis, X7 was found to have no impact on Y5; therefore, it was

eliminated from the model.

Correlations: Y5, X19, X14, X7, X17

Y5 X19 X14 X7 X19 0.782 0.000 X14 -0.432 -0.681 0.000 0.000 X7 0.013 0.080 0.332 0.891 0.386 0.000 X17 0.618 0.868 -0.706 -0.020 0.000 0.000 0.000 0.825 Cell Contents: Pearson correlation

100

P-Value


After the model was retest, X17 was found to be insignificance. Therefore, it


Regression Analysis: Y5 versus X19, X14, X17

The regression equation is Y5 = - 1791095 + 52030 X19 + 8591 X14 - 11737 X17 Predictor Coef SE Coef T P VIF Constant -1791095 655852 -2.73 0.007 X19 52030 5748 9.05 0.000 4.207 X14 8591 4585 1.87 0.063 2.075 X17 -11737 7928 -1.48 0.141 4.498 S = 527863 R-Sq = 63.8% R-Sq(adj) = 62.9% Analysis of Variance Source DF SS MS F P Regression 3 5.69524E+13 1.89841E+13 68.13 0.000 Residual Error 116 3.23222E+13 2.78640E+11 Total 119 8.92746E+13 Source DF Seq SS X19 1 5.46427E+13 X14 1 1.69905E+12 X17 1 6.10673E+11 Unusual Observations Obs X19 Y5 Fit SE Fit Residual St Resid 10 75 3728546 1559815 98744 2168731 4.18R 22 81 3043253 1920110 130566 1123143 2.20R 34 85 2889075 1811526 84137 1077549 2.07R 77 113 2155457 2543117 179951 -387660 -0.78 X 78 114 2497546 2569688 201376 -72142 -0.15 X 79 115 2488976 2598413 207380 -109437 -0.23 X 80 115 2549971 2667941 182082 -117970 -0.24 X 84 106 3526803 2465789 78335 1061014 2.03R 101 120 2152068 3218810 86509 -1066742 -2.05R 118 128 5170830 3632060 110145 1538770 2.98R 120 127 4863495 3543609 101652 1319886 2.55R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Durbin-Watson statistic = 2.23112

101

5) Final Result

After the rerun, the model was found reliable and could be used to predict

miscellaneous insurance consumption.

Regression Analysis: Y5 versus X19, X14

The regression equation is Y5 = - 2464641 + 45685 X19 + 10739 X14 Predictor Coef SE Coef T P VIF Constant -2464641 474779 -5.19 0.000 X19 45685 3848 11.87 0.000 1.867 X14 10739 4371 2.46 0.015 1.867 S = 530545 R-Sq = 63.1% R-Sq(adj) = 62.5% Analysis of Variance Source DF SS MS F P Regression 2 5.63418E+13 2.81709E+13 100.08 0.000 Residual Error 117 3.29329E+13 2.81478E+11 Total 119 8.92746E+13 Source DF Seq SS X19 1 5.46427E+13 X14 1 1.69905E+12 Unusual Observations Obs X19 Y5 Fit SE Fit Residual St Resid 10 75 3728546 1533305 97600 2195241 4.21R 22 81 3043253 1937112 130720 1106141 2.15R 84 106 3526803 2469197 78699 1057606 2.02R 118 128 5170830 3629451 110690 1541379 2.97R 120 127 4863495 3555844 101831 1307651 2.51R R denotes an observation with a large standardized residual. Durbin-Watson statistic = 2.18653 Standardized Regression Coefficients for Y5 Row Predictors StdCoef 1 X19 0.910818 2 X14 0.188508

BIOGRAPHY

Name Porntida Poontirakul

ACADEMIC BACKGROUND Bachelor Degree of

Business Administration

(Property and Casualty Insurance),

Assumption University, Thailand

2008

PRESENT POSITION Insurance Analyst,

PTT Exploration and Production Public

Company Limited

EXPERIENCES Insurance Servicer,

Aon (Thailand) Limited

AWARDS AND GRANTS “Full Tuition Fees” Scholarships

Assumption University, Bangkok,

2005-2008

“Magna Cum Laude”,

Honour Award,

Assumption University, Bangkok,

2008

“Full Scholarship”,

National Institute of Development

Administration, Bangkok,

2010-2012

the impact of macroeconomic factors on non-life …libdcms.nida.ac.th/thesis6/2012/b179819.pdfduring...

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