The Impact of Macroeconomic Factors on Non-life Insurance in Thailand

8/12/2019 The Impact of Macroeconomic Factors on Non-life Insurance in Thailand

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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 EconomicIndices, 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.


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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, Id 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


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TABLE OF CONTENTS

Page

ABSTRACT iiiACKNOWLEDGEMENTS iv

TABLE OF CONTENTS v

LIST OF TABLES viiiLIST 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 6CHAPTER 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


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vi2.5 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 565.3 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


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viiAppendix C Stepwise Analysis 77

BIOGRAPHY 103


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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

4.2 Correlation Analysis between Total Non-life Insurance Consumption 37

in Thailand and the Percentage Changes and Growth Rates

4.3 Total Non-Life Insurance Consumption Stepwise Analysis 394.4 Correlation Analysis between Total Non-life Insurance Consumption 44

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


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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 2000-20102.7 Factors Impact Business Strategy 16

2.8 The Circular-Flow Diagram 18

3.1 Research Methodology 24

3.2 Conceptual Framework 27


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CHAPTER 1

INTRODUCTION

1.1 Background Statement and Significance of the Study

Insurance plays a vital role to both a nations 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 thatof 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.


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2

Figure 1.1 Insurance Penetrations in Asian CountriesYear 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 nations economic growth. Since GDP can be used as one of the

leading indicators of a nations economic growth, insurance is included in the

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

insurance sectors to a nations GDP. As per the information stated in figure 1.1, the

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

countrys economic growth was similar to that of Hong Kong, Malaysia and China.


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3Table 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


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4Macroeconomics is the study of the economic system as a whole. The goal of

macroeconomic study is to explain the changes to a nations 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 societys overall

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

As per the researchers 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 BusinessTrend 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.


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5In 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 nations 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 werecomprised 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.


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64) 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 insuranceconsumption in Thailand.


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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-lifeinsurance 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.


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8In 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 insureds 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


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9amount 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 canbe 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).


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10Cargo 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 insurances 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.


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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.


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122.2.1 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 2.3Direct 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.


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13The 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 researchers 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


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14hull 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%,


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15annually. 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.


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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 companys

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.


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172.4 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.


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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


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19theory 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 nations economy

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


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202.5 Key Macroeconomic Indicators

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

Lets 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


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21In 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

Baumohls recommendation. Inflation rates, Balance of payments, interest rates, and

exchange rates are deemed major economic indicators.


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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 Thais 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 andunderstanding of the insurance industry regarding the rapidly changing economic

conditions that have an impact on Thais 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.


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23Quantitative 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.


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253.2 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.


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26Table 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


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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 eachindependent 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 todays economic world, factors are subject to change

simultaneously.

Consumer Price IndexBusiness Cycle IndexInflation Cycle Index

Export Business Situation IndexConsumer Confidence Index

Producer Price IndexConstruction Material Price Index

Export and Import Price Index

Non-Life Insurance ConsumptionFire Insurance

Direct PremiumAutomobile Insurance

Direct PremiumMarine and Transportation Insurance

Direct PremiumMiscellaneous Insurance

Direct Premium


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283.3 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.


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29The 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 opinionsregarding 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


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30opinions of export businesss 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 nations 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.


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31Table 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 IndexX4 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


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323.5 Data Collection

1)Non-life insurance consumption, which was represented by direct premiumwritten, 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 ofTrade and Economic Indices.

3.6 Data Analysis

3.6.1 Statistical Models

3.6.1.1 Pearsons Product Moment Correlation Coefficient ()

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 linearrelationship between total non-life insurance consumption and each independent

variable.

2)To prepare the variables to be used for Multiple LinearRegression 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 insuranceconsumption, 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.


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33In this study, the significance of the study was = 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 minimizingthe 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.


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34One 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 thegrowth 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


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35selected 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.


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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 inorder 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.


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37Table 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 -0.137X12 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*

Table 4.2 Correlation Analysis between Total Non-life Insurance Consumption in

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 -0.101X9 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


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38consumption 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), ConstructionMaterial 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


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39index), 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)


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40The 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 couldnt 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 consumerspurchase 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.


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41The 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 nations 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 nations

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. Theanalysis 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.


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42Table 4.4 Correlation Analysis between Total Non-life Insurance Consumption in

Thailand and the Percentage Changes and Growth Rates

VariablesFire 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 - LeadingInflation 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,


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43it 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 consumptionwas 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.


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444.2.2 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 X10were 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)


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45Table 4.5 Automobile Insurance Consumption Stepwise Analysis

Variables Coef Beta Coef P-value VIF

Constant -6758065 0.000

X19 Export Price Index 60049 0.817280 0.000 2.883

X3 Coincident Index 60070 0.233323 0.000 2.373

X13 Employment Rate -24805 -0.100462 0.016 1.481

X14 Consumer Confidence Index 11834 0.141802 0.008 2.439

Note: R2= 86.9%, Standard Error of Estimate = 467338

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 couldnt be the casebecause all variables involved in the

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

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, andEmployment Index had lower impact respectively.


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46Export Price index had the highest impact on automobile insurance

consumption in Thailand because of having the highest beta coefficient.

The Impact of Macroeconomic Factors on Non-life Insurance in Thailand

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