Exposure Growth in China and Indiairfrc.ntu.edu.sg/...ExposureGrowthinChinaandIndia.pdfExposure Growth in China and India Summary The Asia-Pacific region is currently the only region

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Exposure Growth in China and India

Shinichi Kamiya, Nanyang Technological University

George Zanjani, Georgia State University

K. Ramachandran, SBI General Insurance

Yunjie Sun, LMU Munich

31 March 2013

Insurance Risk and Finance Research Centre (IRFRC)

Nanyang Business School

Nanyang Technological University

Email: [email protected] / Website: www.irfrc.com

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Exposure Growth in China and India

Summary

The Asia-Pacific region is currently the only region that shows stable growth in insurance

business in recent years. The economy is growing even faster in China and India, in that the

real GDP growth rates of these countries are both 10.3% in 2010 (IMF). Considering that

recent economic growth and current low penetration rates (premiums as % of GDP) of

nonlife insurance in China and India (0.97% in China and 0.38% in India), it is reasonable to

predict that those countries will be the key driver of global nonlife insurance consumption

growth in the future.1

Regardless of increased understanding of those markets, little is known of the

consumption growth by lines of business such as motor, property, and liability. To gain

practically useful information, we forecast China’s and India’s insurance consumption

growth of nonlife total and three lines of business: motor, property, and liability insurance.

To achieve the objective, we first provide the overview the nonlife insurance market

in China and India (Section 1). Second, we review existing studies on nonlife insurance

consumption to identify potential determinants of insurance consumption (Section 2). Third,

we propose a new projection approach and forecast future insurance consumptions under base

economic scenarios in the next 20 years (Section 3). The sensitivity tests by changing

parameter assumptions are conducted. Unmodelled factors that potentially affect nonlife

insurance consumption are discussed in Section 4. Understanding the uncertainty of future

nonlife insurance market in China and India, we offer implications including severity from

systematic or extreme events (Section 5). Finally, we discuss the impact of financial crisis on

nonlife insurance consumption as one of potential factors that adversely affects nonlife

insurance consumption (Section 6).

Major findings of our projections are summarized as follows. First, under our base

scenario, nonlife insurance premium volume in 2020 is expected to be US$137 billion in

China and US$12 billion in India. The nonlife premium volume in 2030 will reach US$386

billion in China and US$20 billion in India. Within the nonlife premium, motor insurance

premium volume is expected to be 81% in China and US$7.4 billion 67% in India. Thus,

1 Note that nonlife (nonlife total) does not include health insurance throughout this report. It may be a custom to include health insurance to nonlife insurance when insurance are split into life and nonlife. We, however, do not include health insurance to nonlife category for the purpose of improving the accuracy of prediction (see Technical Notes).

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motor insurance is the major driver of nonlife insurance consumption growth in both

countries in the next 20 years.

Second, we identify that the key economic development indicators to support China’s

rapid growth of nonlife insurance market are (1) GDP growth; (2) investment; and (3) private

consumption. China’s growth of nonlife insurance consumption does not only depend on

GDP growth in the future but also the GDP growth is driven by investment rather than

consumptions. Thus, whether China maintains its high level investment is one of key

determinants of the nonlife insurance consumption growth.

Third, property premium growth is expected to be not large relative to motor

insurance in both countries. Therefore, catastrophe risk exposure is limited under the past

catastrophe loss scenarios. Yet, the impact on catastrophe exposure growth depends on the

type of cat event expected and whether cat events hit the region where exposure is highly

concentrated like the 2011 Thailand flood.

Finally, we evaluate the adverse effect of financial crisis on nonlife insurance

consumption and conclude that nonlife insurance growth could be set back by 15 years or

more if financial crisis occur in either country.

The results would provide insight into how the key markets grow, what are the

necessary factors that make it possible. Equipped with a better understanding of the

contributing factors on the market growth, insurance / reinsurance companies would be able

to optimally allocate resources in each line of business more timely manner.

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Table of Contents

1 Introduction 7

1.1 Economic Growth in China and India 8

1.2 Nonlife Insurance Markets in China and India 9

1.3 Country-Level Nonlife Insurance Consumption 11

2 Economic Development Indicators and Nonlife Insurance Consumption 21

2.1 Literature Review 22

2.2 Principal Economic Development Factors 24

2.3 Methodology 26

2.4 Model Estimation Results 32

2.5 Regressions with Price Variable 40

2.6 Regressions with All Economic Development Measures 42

2.7 Conclusion 45

3 Nonlife penetration forecasting for China and India 46

3.1 Literature Review 47

3.2 Insurance Consumption Models 52

3.3 Model Estimation Results 56

3.4 Projections for Explanatory Variables: Base Scenario 60

3.5 Base Scenario Projections of Non-life Insurance Consumption 64

3.6 Conclusion 77

4 Discussion on nonlife insurance consumption growth in China and India 78

4.1 Potential Sources of Nonlife Insurance Consumption Growth 79

4.2 Discussions on Non-life Insurance Consumption in China 81

4.3 Discussions on Non-life Insurance Consumption in India 92

4.4 Concluding Remarks 100

Appendix 4A The Historical Development of the U.S. Insurance Industry 101

5 Catastrophe Exposure in China and India 107

5.1 Recent Catastrophes 108

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5.2 Growth of Economic Damages and Insured Losses 110

5.3 Regional Penetration Rate in China 114

5.4 Catastrophe Insurance Programs 118

5.4 Reaction in India: Natural Catastrophic Perils Minimum Rate 119

6 Impact of Financial Crisis on Non-life Insurance Consumption 120

6.1 Introduction 121

6.2 Determinants of Insurance Consumption 126

6.3 Methodology 129

6.4 Estimation Results 134

6.5 Conclusion 149

7 Concluding Remarks 150

8 References 151

9 Acknowledgements 156

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Technical Notes

The following is a list of technical notes in this report.

1 All premiums and other values are converted into the most recent price level. As of

March 2012, the most recent price available in our data sources is 2010. Using the 2010

price level comes with some cost, as the local current price GDP or the GDP deflator for

15 countries are not reported, and these countries are dropped from the sample. Those

countries include Australia, Greenland, Kuwait, and Qatar.

2 To calculate each country’s 2010 GDP in U.S. dollars, we retrieve the country’s GDP in

current prices in local currency, the GDP deflator, and the currency exchange rate from

the World Bank Indicator. First, the current price GDP in local currency is divided by the

country’s GDP deflator to convert it into the 2010 price GDP, and then the 2010 price

GDP in local currency is divided by the 2010 currency exchange rate.

3 Similarly, the premium in local currency for each country and each year, obtained from

the Axco Global Statistics, is multiplied by the country’s CPI Index to convert it into

premium in 2010 local prices, and then the 2010 local price premium is divided by the

2010 currency exchange rate.

4 GDP, per capita GDP, premiums, and density are converted into US dollars by the

market exchange rate unless otherwise stated.

5 Our nonlife (nonlife total) premium does not include personal accident and healthcare

premiums.

6 Axco’s line of business categories differacross countries. For property insurance,

Property is separated from Construction and Engineering in some but not all countries.

We assume that property premiums do not include construction and engineering

premiums for countries without a separate premium, and we do not add construction and

engineering premiums to property premiums for countries with a separate construction

and engineering category.

7 Similarly in liability lines, Workers’ Compensation is separated from Liability in some

but not all countries. We add this premium to total liability premiums in countries where

it is reported. In addition, paid claim and loss ratio data for Workers’ Compensation are

unavailable in many countries, so we use the liability loss ratio data to represent the loss

ratio of the aggregated liability business.

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1. Introduction

This study is concerned with forecasting the development of the nonlife insurance markets in

China and India. We focus specifically on three lines of business: motor, property, and

liability insurance. The goals of this project are to: (1) identify the key determinants of

market growth; (2) present expectations regarding insurance premium volume penetration

under different scenarios in 5 years and 20 years time; (3) outline and discuss factors that are

not included in our modelling; and (4) consider additional implications associated with

systematic or extreme events.

Forecasting the development of any market is an inexact science, yet history does

provide a useful guide in doing so. We have access to relatively rich sources of data on

markets in other countries and can observe how insurance consumption has varied in those

countries according to myriad economic, legal, and political factors. While any country will

of course have idiosyncracies that may cause its own experience to deviate from patterns in

other countries, it turns out that much of the cross-country variation in insurance consumption

can be explained by observable factors. In particular, insurance consumption tracks rather

closely with economic output, although differences persist among countries according to

legal regimes, urbanization, education, and other factors. Thus, our approach to forecasting

insurance market development in China and India relies heavily on the assumption that such

development will follow a pattern similar to that observed in other countries with similar

characteristics in the past.

The rest of this report is organized as follows. In this section, we provide background on

the economies and nonlife insurance markets of China and India in the context of the global

economy. In Section 2, we describe the data, identify useful scaled measures of insurance

consumption, and identify potential determinants of insurance consumption across countries.

We estimate nonlife insurance penetration models to forecast long-term nonlife insurance

consumption in China and India in Section 3. We present projections of the various

economic, political, and social determinants of insurance consumption in China and India

over 20 year horizons under various scenarios. We discuss catastrophe exposures in Section 6.

In Section 7, we explore caveats to our analysis and provide more in-depth discussion of

particular institutional features and recent developments in China and India that could affect

the course of insurance market development in those countries. Among uncertain factors

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associated with nonlife insurance consumption, we discuss the impact of financial crises on

nonlife insurance consumption in Section 8. Section 9 concludes.

1.1. Economic Growth in China and India

Real GDP growth rates in China and India were both 10.3% in 2010 (IMF). Chinese GDP is

now the second largest in the world (See Figure 1.1).

According to China’s 12th Five-Year Plan (2011-2015), the GDP is expected to grow

annually at 7% on average.2 The Economist Intelligence Unit forecasts the annual average

GDP growth rates of 7.3% during 2011-2020 and 4.1% in the following decade, and predicts

that China will overtake the US and become the largest economy in the world by 2021.3

Similarly, the report forecasts India’s GDP growth rates at 7.6% during 2011-2020 and 5.5%

in the following decade.

(Source: World Bank Indicator)

Figure 1.1: 2010 World GDP (USD billion)

China and India have the world largest and the second largest populations (1.3 billion in

China and 1.2 billion in India). Together, these two countries account for about 40% of the

world population.

2 The GDP was 39.8 trillion yuan in 2010 and is expected GDP in 2015 is 55.8 trillion yuan 3 In terms of nominal GDP at market exchange rates.

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1.2. Nonlife Insurance Markets in China and India

Current penetration rates (premiums as % of GDP) are relatively low: 0.97% in China and

0.38% in India. However, the huge populations and rapid economic growth in both countries

suggest significant future insurance market potential. These countries may well be the key

drivers of global nonlife insurance consumption growth in the future.4

Today, China and India have not yet reached full potential in terms of insurance

consumption. In 2010, about 41% of world nonlife insurance premium came solely from the

US (see Figure 1.2). Also, the US market is dominant in property (48%) and liability (62%).

China appears in Figure 1.3 as the third largest country for motor insurance consumption with

US$44 billion in premiums, which amounts to 8.7% of total global premium volume. The

gaps between the size of the economy and the volume of nonlife insurance consumption

imply tremendous growth opportunities in both China and India.

(Source: Axco Global Statistics)

Figure 1.2: 2010 World Nonlife Insurance Premiums

4 Note that nonlife (nonlife total) does not include health insurance throughout this report. It may be a custom to include health insurance to nonlife insurance when insurance are split into life and nonlife. We, however, do not include health insurance to nonlife category for the purpose of improving the accuracy of prediction.

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(Source: Axco Global Statistics)

Figure 1.3: World Motor Insurance Premiums

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1.3. Country-Level Nonlife Insurance Consumption

In this section, we first describe the insurance market and economic data used to model

nonlife insurance consumption. Second, we introduce and discuss the three measures of

insurance consumption---total premium, density, and penetration.

1.3.1. Data

Country level gross written premiums and loss ratios, are taken from Axco Global Statistics.5

The data covers 129 countries, and the sample period is 23 years from 1988 to 2010.6 The

country-year premium data are matched with economic variables, such as GDP, obtained

from the World Bank.7 The matched data is our base data and contains 2654 country-year

observations.

1.3.2. Insurance Consumption Measures

Premium

Premium is the simplest measure of insurance consumption and is here defined by direct

written premium. Table 1.1 provides 2010 premiums for each line of business. China’s total

nonlife premium is US$58 billion, the 6th largest in the world. India’s nonlife premium

volumeof US$6.5 billion is much smaller. One notable difference between China and India is

found in the relative importance of motor insurance premium, which accounts for 77% of

total nonlife premium in China but only52% in India. The corresponding figure for the US is

41%.

5 Loss ratio is calculated in two ways depending on data availability. One is paid loss divided by written premium and another is incurred loss divided by earned premium. 6 Axco Global Statistics as of April 2012 has 2011 data for many countries, but we do not use it in our analysis because many of the matched economic variables are available only through 2010. 7 All variables used in our analyses are available at http://data.worldbank.org/indicator.

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Table 1.1: 2010 Nonlife Insurance Premiums

Country China India USA

Currency USD

(=6.77 CNY) CNY USD

(=45.7 INR) INR USD

GDP (billion) 5,926 (2) 40,120 1,722 (9) 78,756 14,587 (1)GDP per capita 4,428 (68) 29,978 1,471 (86) 67,259 47,198 (5)

Premium (billion) Nonlife 57.5 (6) 390 6.54 (24) 299 456 (1)Motor 44.4 (3) 300 3.37 (20) 154 187 (1)Property 6.78 (7) 45.9 0.92 (27) 41.8 140 (1)Liability 1.72 (10) 11.6 0.17 (35) 7.55 93.5 (1)

* Figures in parenthesis are ranking in 129 sample countries

Figures 1.4-1.7 plot nonlife insurance premiums and GDP on a log scale and indicate a linear

relationship between them. Thus, countries with larger GDP tend to have larger insurance

consumption. The relationship between premium and GDP can be observed in time-series

plots for China (red connected dots) and India (orange connected dots) in the plots. The time-

series plots indicate that insurance consumption in both countries has grown with GDP,

although the level of insurance consumption in both countries is low in relation to output

when compared with other countries. Green circles indicate OECD countries in the plots. In

contrast to China and India, they are located on the top of the plots in nonlife total, motor,

and property---indicating a level of insurance consumption that amounts to a high

proportionof GDP in comparison with other countries. Figure 1.7 shows great variation in the

level of liability premiums even among OECD countries.

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Figure 1.4: Plot of Nonlife Total Premium versus GDP

Figure 1.5: Plot of Motor Insurance Premium versus GDP

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Figure 1.6: Plot of Property Insurance Premium versus GDP

Figure 1.7: Plot of Liability Insurance Premium versus GDP

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Density

Density is defined as written premium divided by population, meaning per-capita insurance

consumption. Density naturally suggests determinant factors such as per capita income, per

capita wealth, and so on.

Table 1.2 shows that nonlife insurance densities are US$43 in China and US$5.6 in

India in 2010. It can be seen from the rankings that per capita insurance consumption in both

countries is small relative to the size of the economy and the volume of aggregate premium.

Table 1.2: 2010 Nonlife Insurance Densities

Country China India USA Currency USD CNY USD INR USD

Nonlife 43.0 (60) 291.1 5.6 (82) 255.3 1,475.4 (3)Motor 33.1 (53) 224.4 2.9 (79) 131.5 605.6 (2)Property 5.1 (65) 34.3 0.8 (87) 35.8 453.3 (4)Liability 1.3 (59) 8.4 0.1 (78) 6.4 302.6 (1)

* Figures in parenthesis are density ranking in 129 sample countries

Figures 1.8-1.11 illustrate approximately linear positive relationships between income

and density on a log scale. Countries with larger per capita income tend to have larger

insurance consumption per person. As above, the plots display green circles representing

OECD countries, which consist of many high income countries with high density for all lines.

China appears to be migrating toward the high income country zone in nonlife total and

motor insurance. In particular, the pace of increase in China’s motor density is rapid.

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Figure 1.8: Plot of Nonlife Density versus GDP per capita

Figure 1.9: Plot of Motor Density versus GDP per capita

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Figure 1.10: Plot of Property Density versus GDP per capita

Figure 1.11: Plot of Liability Density versus GDP per capita

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Penetration

Penetration is defined as written premium divided by GDP and is arguably the most

commonly used measure of insurance consumption. This proportional measure has an

inherent advantage in that it is insulated from currency exchange rates and price levels.

However, this advantage comes with a cost of instability, because there is time lag between

the change of GDP and the change of insurance consumption (See Section 9).

Table 1.3 illustrates nonlife insurance penetration in China, India, and USA for comparative

purposes. The US nonlife total penetration is 3.1%, the 2nd highest penetration. China’s

nonlife penetration is about 1%. China’s motor insurance penetration is 59% of the US motor

penetration figure. This again illustrates the significant influence of motor insurance in the

Chinese market.

Table 1.3: 2010 Nonlife Insurance Penetrations (%)

Country China India USA

Nonlife 0.97 (56) 0.38 (92) 3.12 (2) Motor 0.75 (36) 0.20 (84) 1.28 (4) Property 0.11 (77) 0.05 (93) 0.96 (3) Liability 0.03 (66) 0.01 (83) 0.64 (1)

* Figures in parenthesis are penetration ranking in 129 sample countries

Figures 1.12-1.15 show the relationship between nonlife insurance penetration and per-capita

GDP and show that while insurance penetration is not linearly related to income, the

association does appear to be positive. A number of researchers have modeled country

insurance penetration as an S-shape growth curve in terms of per capita GDP (Enz, 2000;

Zheng et al., 2008; Zheng et al., 2009). As can be seen in the figures, premium and density

measures track closely with GDP per capita, be GDP per capita tends to correlate less

strongly with penetration.

The growth curve of nonlife penetration varies between lines. Nonlife total (Figure

1.12) and motor insurance (Figure 1.13) are different from property insurance (Figure 1.14)

and liability insurance (Figure 1.15) in that the former exhibit positive correlation between

penetration and income throughout income ranges, while the connection between penetration

and income seems weaker in the latter lines, especially in the low and middle income ranges

(<US$10,000).

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Figure 1.12: Plot of Nonlife Penetration versus GDP per capita

Figure 1.13: Plot of Motor Penetration versus GDP per capita

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Figure 1.14: Plot of Property Penetration versus GDP per capita

Figure 1.15: Plot of Liability Penetration versus GDP per capita

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2. Economic Development Determinants of Nonlife Insurance

Consumption8

In this section, we investigate the relationship between macroeconomic development factors

and nonlife insurance consumption. Specifically, we investigate whether primary components

of GDP (investment, private consumption, government consumption, exports and imports)

and private credit are good predictors of nonlife insurance consumption measures (premium,

density and penetration) by lines.

The results of this section are summarized as follows. First, we find that private credit

tends to be a better predictor of nonlife insurance consumption than GDP, while the

relationship depends on lines and insurance consumption measures. In particular, compared to

GDP, private credit substantially improves the model fit for property insurance and liability

insurance consumption. Second, we also find that the aggregate economic output measure,

GDP (per capita GDP), is negatively associated with property and liability penetration when

components of GDP are included in the model. Third, controlling for country heterogeneity

and time effects is critical to understanding the relationship between those economic

development factors and nonlife insurance consumption.

This section serves as a preparation of our projections in Section 3. In Section 2.1, we

review related literature and discuss our hypotheses. The estimation models and the results are

discussed in Section 2.2 and Section 2.3, respectively.

8 Working paper: Kamiya (2013)

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2.1. Literature Review

Researchers have identified factors associated with insurance consumption. Outreville (2012)

reviews 85 empirical papers that study the relationship between economic development and

insurance consumption with various objectives. He finds that most of the studies use gross

domestic product (GDP) or GDP per capita as a proxy of personal disposable income to test

the relationship. Some exceptions are studies that use permanent income (Outreville, 1980,

1985; Beck and Webb, 2003) and studies that use GNP per capita (Browne and Kim, 1993;

Brown et al., 2000). Regardless of the difference in income measures, previous international

insurance consumption studies are unanimous in using disposable income to capture the

relationship between economic development and insurance consumption (See Outreville,

2012).

Such a bias has some support in the theoretical literature. The theoretical relationship

between income and life insurance consumption is justified by the life-cycle model proposed

by Yaari (1964, 1965). Under the consumer’s dynamic lifetime optimization problem,

purchasing life insurance can be optimal when a risk-averse consumer has bequest motives

and the price of life insurance is not too high. The demand for life insurance depends on

factors including wealth, the expected income stream over a lifetime, expected interest rates,

and the price of a life policy. The theoretical relationship between insurance demand and the

present value of all future disposable personal income justifies examining relationship

between life insurance density and GDP per capita (where the latter is taken as a proxy for

disposable income).

The demand for nonlife insurance, such as property insurance and liability insurance,

is often theoretically described by one-period optimal coverage models starting with Mossin

(1968) and Smith (1968). A risk-averse individual facing potential loss of wealth may

optimally purchase coverage to smooth wealth across possible states. These models identify a

set of factors similar to those in the life model, such as wealth, potential loss, risk aversion,

the cost (price) of a policy. Hence, the theoretical demand for nonlife insurance may also be

correlated with GDP (per capita) since nonlife insurance is used to provide coverage against

losses to wealth which are expected to be closely related with income level.

However, the connection between nonlife insurance consumption and GDP per capita

is not direct because nonlife insurance does not insure personal income. Furthermore, a large

portion, more than 50% in the US for instance, of nonlife business comes from corporate

purchases. The corporate demand for insurance requires a different theoretical explanation in

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cases where investors can diversify idiosyncratic risk away. Mayers and Smith (1982, 1987)

list factors associated with the corporate demand for costly insurance including (1) taxes, (2)

contracting costs, (3) claim management service, and (4) the impact of financing policy on

the firm’s investment. Even though corporate decisions are made by risk-averse managers

due to, for instance, their career concerns, the level of income may not necessarily be the best

proxy determinant of corporate demand for nonlife insurance.

In summary, the theoretical literature does not clearly link GDP per capita with

nonlife insurance, despite thethe extensive use of GDP per capita in modeling nonlife

insurance consumption. in what follows, we argue that GDP per capita may not necessarily

be the best predictor of nonlife insurance consumption. Specifically, we argue that other

economic variables may serve as better proxies for the theoretical determinants of nonlife

insurance demand and, more importantly, that these variables perform better as predictors of

nonlife insurance consumption empirically.

This section extends the international nonlife insurance consumption literature in

several directions. The most relevant area is insurance demand studies which identify

determinants of insurance consumption from the demand side (for instance, Browne et al,

2000). To our knowledge, this is the first study that considers a comprehensive set of

economic development factors in attempting to explain the relationship between economic

development and nonlife insurance consumption.

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2.2. Principal Economic Development Factors

Under the demand approach, GDP can be decomposed into the components: GDP =

C+I+G+(X-M) where C represents private consumption, I represents gross investment, G

represents government consumption, X represents exports, and M represents imports. It seems

likely that each of these components is associated with nonlife insurance consumption in

different ways.

For instance, private consumption, also known as household final consumption

expenditure in the national accounts, covers all purchases made by consumers: consumption

goods, durable goods (including motor vehicles), spending on health, and so on. It does not,

however, include households’ purchases of dwellings. Private consumption may align with

overall nonlife insurance consumption---not because the national account includes nonlife

insurance expenditures, but because the account reflects the economic activity of households

and corporations.

Government consumption is another primary component of GDP. This expenditure is

categorized into “individual” and “collective” consumption expenditure. The former is

expenditure for the benefit of households and mainly covers public education and public

healthcare. The latter comprises expenditure on general administration, safety and order,

defense, and so on (see for instance, Lequiller and Blades, 2006). In contrast to private

consumption, government consumption may not be a direct driver for nonlife insurance

consumption because government consumption expenditure tends to negatively affect the

growth of per capita GDP (see, Afonso and Furceri, 2010), and government enterprises may

be less likely to purchase commercial insurance than private enterprises. On the other hand,

the account may positively affect the demand for nonlife insurance because education, which

is one of primary factor of this account, tends to be positively associated with insurance

consumption (Browne et al., 2000).

Macroeconomic-level consumption is supplemented with external supply (imports)

and external demand (exports). The net external balance of goods and services (exports minus

imports) measure domestic economic activities in addition to private consumption. In cases

where the growth of the domestic economy is underpinned by exports, the net external

balance could have positive effects on nonlife insurance demand.

Investment, also known as gross capital formation, represents the purchase of

machinery and buildings and the creation of stocks. This account measures total expenditures

on products intended to be used for future production, and a substantial part of this consists of

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housing purchases. Therefore, we may expect investment to have positive effects on nonlife

insurance consumption, possibly with time lag. In particular, investment may increase

property and liability insurance consumption because fixed capital may require property

coverage and because uncertain business investment needs liability coverage to reduce the

risk level.

There are studies that motivate us to investigate the separate components of GDP.

Among those is the literature about the impact of risk on investment (see, for instance,

Pindyck, 1991; Pindyck and Solimano, 1993), which suggests that the impact of risk on

investment could be large because 1) investment expenditures tend to be irreversible-sunk

costs and 2) firms usually have flexibility over the timing of their investments. Therefore,

investment expenditures may be good indicators of private risk-taking through business and

thus closely connected to the corporate demand for nonlife insurance. If so, it seems likely

that demand for commercial insurance and other types of insurance connected to durable

goods may be closely tied to a nation’s investment level--- connection that will be missed if if

we consider only an aggregate output measure.

Another macroeconomic factor considered in our analysis is credit because credit

cycles have often coincided with cycles in economic activity (see, for instance, IMF 2000;

Mendoza and Terrones, 2008). For instance, using micro data, Mendoza and Terrones (2008)

show a strong relationship between credit to the private sector (private credit) and firm-level

measures of firm values, external financing, and leverage. Their findings are consistent with

previous studies (e.g., Borio et al., 1994; Hofmann, 2001) and suggest a positive relationship

between private credit and corporate demand for nonlife insurance. In addition, the volume

of private credit may be indicative of the development of the financial sector in a country,

which could correlate with wider availability and acceptance of insurance.

Furthermore, the theory of financial acceleration explains that, due to information

asymmetry, firm and household borrowing capacity is constrained and depends on net worth.

Because borrowers’ net worth is procyclical, borrowing capacity positively affects economic

activity and feeds back into net worth. This implies that that credit amplifies business cycle

fluctuations and economic enterprise (see, Bernanke et al. (1998) and Kiyotaki and Moore

(1997), for the theoretical models). These macroeconomic theories combined together

suggest that credit, assets price, investment, and nonlife insurance consumption are expected

to be closely related.

The remainder of this section is organized as follows. Section 2.3 describes our

country-level nonlife insurance consumption data and models. In Section 2.4, we discuss

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estimation results. A summary of our findings and a discussion of limitations in our work are

contained in Section 2.5.

2.3. Methodology

We first describe insurance market and economic data used to model nonlife insurance

consumption. Then we define our proxies for the potential determinants of nonlife insurance

consumption. Finally, we describe models employed in our analyses.

2.3.1. Data

As explained in Section 1.3, country-level insurance market-related data---gross written

premiums and loss ratios---are taken from Axco Global Statistics. The premium data cover

129 countries over 23 years from 1988 to 2010. The country-year premium data are matched

with macroeconomic variables such as GDP retrieved from the World Development

Indicators. The matched data make up our base data and contain 2654 country-year

observations. Table 2.1 shows the list of variables.

To clean our data, we exclude observations from our sample if: (1) the country

population is less than 1 million, or (2) the GDP deflator (and CPI index) is less than 0.1

(2010 price = 1). The first screen reduces the impact of highly volatile insurance consumption

measures in small countries and also eliminates outliers such as the Cayman Islands (property

insurance density = US$2,255 in 2010) and Luxembourg (nonlife insurance density =

US$6,083 in 2010). The second screen excludes observations in which consumption

measures are distorted by an extremely high inflation rate. These two sample selection

screens reduce our sample size from 2654 to about 2000 for nonlife aggregate consumption.

The sample is further reduced for certain lines of business because many countries do not

report the premium data for the property and liability insurance lines.

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Table 2.1: Summary Statistics of Variables

Variable Obs. MeanStandard Deviation Median Minimum Maximum

Dependent Variables

Premium nonlife (million 2010 USD) 2,077 9,025 40,487 522 0.60 515,787

Premium motor (million 2010 USD) 1,995 4,372 18,237 219 0.24 220,230

Premium property (million 2010 USD) 2,051 2,275 10,798 124 0.32 143,780

Premium liability (million 2010 USD) 1,398 1,811 10,639 73.4 0.00 127,088

Density nonlife (2010 USD) 2,077 232 380 50.2 0.04 1,957

Density motor (2010 USD) 1,995 109 166 25.7 0.03 752

Density property (2010 USD) 2,051 64.8 118 10.5 0.04 719

Density liability (2010 USD) 1,398 37.4 67.7 4.53 0.00 428

Penetration nonlife (%) 2,111 1.25 0.79 1.10 0.02 4.29

Penetration motor (%) 2,029 0.60 0.40 0.54 0.00 1.82

Penetration property (%) 2,077 0.33 0.28 0.23 0.00 2.57

Penetration liability (%) 1,424 0.14 0.16 0.07 0.00 0.92

Macroeconomic Variables

GDP (million 2010 USD) 2,624 389,397 1,286,404 36,411 406 14,678,533

GDP per capita (2010 USD) 2,624 10,822 15,410 3,682 148 88,163

Investment (% of GDP) 2,593 22.2 6.75 21.7 2.65 61.5

Private consumption (% of GDP) 2,586 67.2 14.5 66.4 25.3 152

Government consumption (% of GDP) 2,586 15.3 5.78 14.3 2.98 43.0

Exports (% of GDP) 2,632 37.3 26.8 30.9 3.21 241

Imports (% of GDP) 2,632 41.9 25.1 36.2 4.63 219

Private credit (% of GDP) 2,592 46.1 45.4 27.9 0.82 284

Control Variables

Education (%) 1,868 28.6 23.5 24.5 0.20 104

Urban population (%) 2,656 53.0 23.1 54.9 5.64 100

Price nonlife 1,895 2.77 10.76 1.86 -61.7 322.6

Price motor 1,820 1.99 7.01 1.54 0.44 294

Price property 1,870 3.44 4.81 2.31 -49.0 90.1

Price liability 1,297 10.3 70.33 2.56 -556 1,429

28

A. Economic Development Measures (EDM)

As discussed, we investigate six economic development variables in addition to GDP. First,

by decomposing GDP into its components, we separately identify investment, private

consumption, government consumption, exports, and imports. Furthermore, private credit is

considered as an additional candidate measure of economic development. All proxies are

identified in the World Development Indicators, and the definitions of those measures are

provided in Table 2.2.

Table 2.2: Economic development measures

Table 2.3 shows the correlation coefficients between those economic development measures.

The upper panel of the table shows the correlation between GDP, and the middle panel shows

the correlation between per-capita measures. As expected both panels indicate that measures

Variable The World Development Indicators

Investment Gross capital formation:

Outlays on additions to the fixed assets of the economy plus net changes in the level of inventories

Private consumption Household final consumption expenditure (formerly private consumption):

The market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households

Government consumption General government final consumption expenditure:

All government current expenditures for purchases of goods and services (including compensation of employees).

Exports Exports of goods and services:

The value of all goods and other market services provided to the rest of the world.

Imports Imports of goods and services:

The value of all goods and other market services received from the rest of the world.

Private credit Domestic credit to private sector:

Financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment.

29

are highly correlated in dollar terms. The bottom panel shows the relationship between GDP

per capita and other variabless (all of which are measured asa percentage of GDP, and

indicates, for instance, that high income countries tend to have a lower proportion of private

consumption in GDP per capita. A high correlation is found between GDP per capita and

private consumption in the percent of GDP. Although not reported here, GDP per capita and

other measures per capita are almost perfectly correlated in the log scale.

Table 2.3: Pearson Correlation Coefficients between Economic Development Measures

B. Insurance Regulation and Distribution Costs

Insurance regulation may raise costs through a variety of channels. For example, regulators

may impose trade barriers to protect their local insurance industry, and the exclusion of

foreign insurers in a country may reduce competition and thus raise prices. Such a

protectionist policy could thus result in lower insurance consumption. Countries may also

differ in terms of financial infrastructure, which could lead to differences in insurance

InvestmentPrivate

consumptionGovernment consumption Exports Imports Private credit

GDP 0.959 0.993 0.991 0.838 0.908 0.966

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

Investment per capita

Private consumption

per capita

Government consumption

per capitaExports

per capitaImports

per capitaPrivate credit

per capita

GDP per capita 0.978 0.986 0.958 0.733 0.728 0.889

(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

InvestmentPrivate

consumptionGovernment consumption Exports Imports Private credit

(% of GDP) (% of GDP) (% of GDP) (% of GDP) (% of GDP) (% of GDP)

GDP 0.005 -0.145 0.101 -0.141 -0.210 0.474

(0.817) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

GDP per capita 0.019 -0.497 0.392 0.214 0.040 0.704

(0.327) (<.0001) (<.0001) (<.0001) (0.041) (<.0001)

30

production costs. Whatever the reason, basic economic theory predicts that that higher

insurance prices will be negatively related to property-liability insurance consumption.

Browne et al. (2000) use foreign firm market share as a proxy for the price of

insurance and find a negative relationship with motor insurance but the opposite relationship

with liability insurance in their fixed-effect models. We instead use a direct measure of

insurance price defined as one over the loss ratio.

C. Other Factors

There are many other factors associated with nonlife insurance consumption. For instance,

Browne et al. (2000) use education and urbanization as explanatory variables in addition to

income measures for motor and liability insurance density. We do not include those variables

because education and urbanization measures tend to be highly correlated with at least one of

the economic development measures. Note that, as discussed below, we include country

fixed-effects in estimation models. Therefore, country-specific factors that are constant over

the sample period will be fully reflected in the fixed-effects, so that the estimation results are

will not be biased by hidden country-specific constantss.

2.3.2. Models

A. Premium Models

The linear relationship between GDP and nonlife aggregate premium on a log scale as

observed in Figures 1.4-1.7 suggests a log-linear model. Although not reported here, the

linear relationship holds for the individual lines of business: motor, property and liability

premium. To control for heterogeneity among countries, we consider the following fixed-

effects model:

(1) Log Premium Log EDM Log Insurance Price

where , and represent a constant, an indicator variable for year t, and a fixed-effect for

country i. EDM denotes the economic development measure, which includes GDP as well as

its components. The disturbance term, , is assumed to be normally distributed. All

regression models assume variance components which allow for intertemporal correlation

and heteroscedasticity. Therefore, reported p-values are robust to heteroscedasticity.

31

B. Density Models

In Figures 1.8-1.11, we observe the linear relationship between GDP per capita and insurance

density on a log scale, which suggests a log-linear specification. Again, we confirm that the

linear relationship holds for motor, property, and liability density as well. Following premium

the models above, we consider the following specification:

(2) Log Density

Log EDM per capita Log Insurance Price

The difference from the premium model is that premium and EDM are replaced by density

and EDM per capita.

C. Penetration Models

As suggested by previous studies and as observed in Figures 1.12-1.15, the relationship

between GDP per capita and insurance penetration is not well represented by a linear model.

We apply the following logistic model specification:

(3) Log Log EDM per capita Log Insurance Price

where represents penetration for country i in year t. . For the penetration models, one

could also consider EDM per capita instead of the natural logarithm of EDM per capita as an

explanatory variable. Yet, in our estimations, the log-scaled EDM per capita always fit better

than non log-scaled EDM per capita. Therefore, we employ the natural logarithm of EDM.

We also considered the EDM lagged by one year. We found that the lagged terms did

not change the results materially and tended to produce slightly lower fit statistics. Therefore,

the results are not reported in this paper.

32

2.4. Model Estimation Results

2.4.1. Regressions with Only Economic Development Measures

First, we run regressions economic development measures as the only explanatory variables,

and the estimation results are reported as a benchmark.

A. Premium Models

The parameter estimation results are reported in Tables 2.4-2.7 for nonlife aggregate

premium, motor premium, property premium and liability premium, respectively. Table 2.4

shows that all measures are statistically significant. For instance, the coefficient on the

logarithm of GDP is 1.444, which is an elasticity figure implying a 1.4 percent change in

nonlife premium for a one percent change in GDP. The private consumption measure

indicates an elasticity close to one. In contrast, the elasticity is lowest for the private credit

measure. At the bottom of the table, model fit statistics in the form of the Akaike Information

Criterion (AIC) are reported. The smallest AIC indicates the best fit, and it is found that the

model with private credit shows the best fit for nonlife aggregate premium models, although

the differences in the fit statistics between the private credit model and the GDP model are

negligible.

Table 2.5 shows the results for motor premium. The GDP elasticity of motor

insurance premiums is 1.624, which is even higher than the total nonlife premium elasticity.

Again, the private consumption measure indicates that an elasticity is closely one. The lowest

is observed in the private credit model. The best fit is found with the GDP model.

Table 2.6 and Table 2.7 summarize the estimation results for property insurance

premiums and liability insurance premiums. Both results are consistent with the results for

nonlife aggregate premiums in Table 2.4 in that the highest elasticity is found in the GDP

model, the lowest is found in the private credit model, and the best fit is found with the

private credit model. This is consistent with our hypothesis that property and liability

insurance consumption is closely associated with private credit. Noteworthy differences can

be found in the property premium model in Table 2.6. The elasticity found for GDP, 0.935, is

the highest of the measures tested in the property models but notably smaller than the

elasticities foundin other lines. The lowest elasticity is found for government consumption.

Another finding is a substantial improvement of model fit with the private credit model,

which implies that private credit’s association with property premiums is much stronger than

than GDP’s association.

33

Table 2.4: Estimation Results - Nonlife Premium Model

* for AIC indicates the best fit model.

ParameterGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model

Private Credit Model

1.444(<.0001)

0.581(<.0001)

0.961(<.0001)

0.591(<.0001)

0.429(<.0001)

0.488(<.0001)

0.397(<.0001)

Intercept Yes Yes Yes Yes Yes Yes Yes

Year dummy Yes Yes Yes Yes Yes Yes Yes

Country fixed-effects Yes Yes Yes Yes Yes Yes Yes

Observation 2077 2031 2028 2027 2058 2058 2047

Number of Countries 126 125 125 125 126 126 126

AIC 436 624 790 792 880 897 433*

LOG(private credit)

LOG(GDP)

LOG(Investment)

LOG(Private consumption)

LOG(Government consumption)

LOG(Exports)

LOG(Imports)

34

Table 2.5: Estimation Results - Motor Premium Model


ParameterGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


1.624(<.0001)

0.563(<.0001)

1.020(<.0001)

0.558(<.0001)

0.403(<.0001)

0.453(<.0001)

0.409(<.0001)




Observation 1995 1949 1946 1945 1976 1976 1969


AIC 778* 1056 1117 1175 1225 1229 874

LOG(private credit)

LOG(GDP)

LOG(Investment)



LOG(Exports)

LOG(Imports)

35

Table 2.6: Estimation Results - Property Premium Model


ParameterGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


0.935(<.0001)

0.353(<.0001)

0.664(<.0001)

0.290(0.002)

0.402(<.0001)

0.350(<.0001)

0.347(<.0001)




Observation 2051 2005 2002 2001 2032 2032 2021


AIC 1385 1281 1291 1342 1414 1472 1057*

LOG(private credit)

LOG(GDP)

LOG(Investment)



LOG(Exports)

LOG(Imports)

36

Table 2.7: Estimation Results - Liability Premium Model


ParameterGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


1.361(<.0001)

0.371(0.016)

0.674(0.042)

0.610(0.003)

0.574(<.0001)

0.392(0.020)

0.306(0.009)




Observation 1398 1380 1377 1376 1380 1380 1374


AIC 1882 1917 1923 1894 1887 1931 1821*

LOG(private credit)

LOG(GDP)

LOG(Investment)



LOG(Exports)

LOG(Imports)

37

B. Density Models

Estimation results for the density models are largely consistent with those of the premium

models. Therefore, the results are summarized in Table 2.8, which compactly reports the

coefficients of economic development measures per capita in the same line followed by p-

value and the fit statistics. Starting with the top panel and moving downward, estimation

results for nonlife, motor, property, and liability density models are reported. We observe that

private credit offers the best fit for thenonlife aggregate density, property density, and

liability density models.

Table 2.8: Estimation Results - Density Models

Nonlife Density ModelGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


1.467 0.598 1.001 0.616 0.448 0.512 0.407(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

AIC 423 646 790 807 895 905 416*

Motor Density ModelGDP

ModelInvest Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


1.690 0.596 1.096 0.605 0.441 0.500 0.425(<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001)

AIC 744* 1087 1114 1198 1252 1250 878

Property Density ModelGDP

ModelInvest Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


0.755 0.318 0.539 0.247 0.366 0.306 0.340(0.002) (<.0001) (0.001) (0.008) (<.0001) (0.001) (<.0001)

AIC 1373 1242 1264 1301 1364 1418 957*

Liability Density ModelGDP

ModelInvest Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


1.492 0.428 0.865 0.693 0.632 0.497 0.338(<.0001) (0.009) (0.015) (0.001) (<.0001) (0.004) (0.004)

AIC 1873 1934 1928 1902 1893 1942 1830*

Log (EDM per capita)




38


C. Penetration Models

Estimation results for the penetration models differ from those obtained from the premium

models and density models. Table 2.9 summarizes the results. Starting with the top panel and

moving downward, the results for total nonlife, motor, property, and liability penetration

models are reported. For nonlife penetration, all measures except for exports and imports are

statistically significant, and private credit per capita is the best predictor. For motor

penetration, government consumption, exports, and imports are statistically insignificant, and

the best predictor is investment. For property penetration, GDP per capita, private

consumption, exports, and imports are all statistically significant with a negative sign, and

private consumption is the best predictor. The statistically significant negative signs indicate

that penetration decreases as themeasures increase after controlling for country specific

effects. In liability penetration models, only investment and private credit are statistically

significant, and investment is the best predictor..

Overall, the private credit measure outperforms the GDP measure in nonlife aggregate

insurance consumption models. The finding that credit measure and investment measures

tend to offer the best fits for property and liability insurance consumption models generally

supports our initial hypotheses. On the other hand, the difference in the fit statistics between

the GDP model and the best predictor model are usually small (although there are several

exceptions), suggesting that GDP is a reasonably good predictor of nonlife insurance

consumption.

39

Table 2.9: Estimation Results - Penetration Models


Nonlife Penetration ModelGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


LOG(EDM per capita) 0.589 0.335 0.534 0.336 0.040 0.073 0.254(<.0001) (<.0001) (0.007) (0.057) (0.741) (0.611) (<.0001)

AIC 2621 2475 2618 2720 2851 2844 2454*

Motor Penetration ModelGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


LOG(EDM per capita) 0.770 0.410 0.630 0.410 0.082 0.146 0.201(0.001) (<.0001) (0.084) (0.149) (0.602) (0.414) (0.004)

AIC 2841 2659* 2896 2989 3102 3089 2932

Property Penetration ModelGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


LOG(EDM per capita) -0.397 -0.062 -0.364 -0.220 -0.210 -0.182 0.043(0.028) (0.489) (0.070) (0.190) (0.003) (0.021) (0.461)

AIC 2795 2763 2694* 2708 2743 2786 2824

Liability Penetration ModelGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


LOG(EDM per capita) 0.329 0.403 0.021 0.163 -0.143 -0.038 0.168(0.222) (0.001) (0.942) (0.480) (0.393) (0.811) (0.065)

AIC 3173 2996* 3069 3061 3076 3092 3091

40

2.5. Regressions with Price Variable

2.5.1. Premium Models and Density Models

Including the price of insurance as an explanatory variable slightly changes relative

performance of the various models and has little overall effect on the coefficient estimates on

the EDM variables because the insurance price variables tend not to be statistically

significant. As an example, the estimation results for the nonlife aggregate premium model

are presented in Table 2.10. When the insurance price variables are introduced in the

premium models and density models, the best fit in the nonlife aggregate and motor insurance

models is found when using GDP per capita, and the best fit in property and liability

insurance continues to be found with private credit.

2.5.2. Penetration Models

Including price of insurance here does not affect the best predictors: private credit for nonlife,

investment for motor, private consumption for property, and investment for liability. The

parameter estimates are consistent with models without insurance price variables and are not

reported here. In contrast to premium models and density models, insurance price variables

tend to be highly significant in penetration models. The variables are positive and statistically

significant in all nonlife and property penetration models and are negative and significant in

all liability penetration models.

41

Table 2.10: Estimation Results – Nonlife Premium Models with Insurance Price

Variable


ParameterGDP

ModelInvest. Model

Private Cons. Model

Gov. Cons. Model

Exports Model

Imports Model


1.445(<.0001)

0.553(<.0001)

0.881(<.0001)

0.488(<.0001)

0.499(<.0001)

0.466(<.0001)

0.382(<.0001)

LOG(Price nonlife) 0.003 -0.004 -0.016 -0.008 -0.015 -0.013 0.002(0.912) (0.878) (0.585) (0.799) (0.594) (0.649) (0.919)




Observation 1864 1824 1821 1820 1845 1845 1837


AIC 239* 478 629 674 579 674 278

LOG(private credit)

LOG(GDP)

LOG(Investment)



LOG(Exports)

LOG(Imports)

42

2.6. Regressions with All Economic Development Measures

2.6.1. Premium Models and Density Models

Here we evaluate the marginal contribution of each economic development measure by

including all economic development measures Equation (1). To avoid multicollinearity,

measures except GDP are expressed as a percent of GDP (See Table 2.3 for the correlation

between GDP and other variables when measured as the percent of GDP). Thus, we estimate

the marginal effects associated with the various components of economic development. The

import measure is excluded from all models due to its high correlation with the export

measure.

The results for premium models are reported in Table 2.11. Each column of the table

represents the parameter estimate for nonlife aggregate, motor, property and liability. The

dependent variable is the natural logarithm of premium.

For nonlife aggregate premiums and property premiums, GDP, exports, and private

credit are statistically significant with positive sign. In the motor premium model, GDP and

private credit are positive and significant. In the liability premium model, GDP and private

consumption are significant, but private consumption shows a negative sign. The fit statistics

for nonlife can be compared with those for the nonlife model in Table 2.10. The model fit

improves substantially when controlling forthe composition of GDP and also private credit.

We also use this approach for the density models but need to remove private credit

measure from the model due to its high correlation with GDP per capita. The results are not

reported here, but the additional measures without private credit do not produce significant

improvement in the fit statistics. This suggests that a large portion of the improvement in the

model fit can be attributed to the private credit measure.

43

Table 2.11: Estimation Results – Premium Models with All Economic Development

Measures


We also expand the penetration models by including all economic development measures

except for imports and private credit. The results are summarized in Table 2.12. The

parameter estimates show that GDP per capita is significant in all lines of business but most

of the additional measures are statistically insignificant. Note that GDP per capita is

significant with negative sign. These negative signs imply that property and liability

penetration decrease as income increases if other things are held constant. We find that

investment is positive and significant in the liability model. This is consistent with our

hypothesis. The fit statistics in Table 2.12 can be compared with those in Table 2.9. The fit

statistics improve substantially by including additional economic development measures even

without the private credit measure.

Parameter Nonlife Motor Property Liability

LOG(GDP) 1.177 1.455 0.742 1.139

(<.0001) (<.0001) (0.001) (0.001)

LOG(Investment; % of GDP) 0.105 0.121 0.010 -0.120

(0.114) (0.184) (0.933) (0.309)

LOG(Private consumption; % of GDP) -0.202 -0.334 -0.094 -0.662

(0.161) (0.117) (0.760) (0.035)

LOG(Government consumption; % of GDP) 0.016 0.001 -0.048 0.013

(0.863) (0.995) (0.571) (0.934)

LOG(Exports; % of GDP) 0.110 -0.073 0.228 0.183

(0.066) (0.279) (0.010) (0.192)

LOG(private credit; % of GDP) 0.203 0.178 0.240 0.067

(0.001) (0.004) (<.0001) (0.552)

LOG(Price of insurance) 0.013 0.015 0.035 0.016

(0.533) (0.823) (0.009) (0.655)

Intercept Yes Yes Yes Yes

Year dummy Yes Yes Yes Yes

Country fixed-effects Yes Yes Yes Yes

Observation 1791 1722 1762 1217

Number of Countries 117 111 114 98

AIC 15 561 511 1455

44

Table 2.12: Estimation Results – Penetration Models with All Economic Development

Measures


LOG(GDP per capita) 0.486 0.501 -0.484 -0.349

(0.047) (0.041) (0.023) (0.097)

LOG(Investment; % of GDP) 0.096 0.163 0.102 0.637

(0.380) (0.169) (0.512) (<.0001)

LOG(Private consumption; % of GDP) -0.345 -0.631 -0.089 -0.714

(0.198) (0.033) (0.804) (0.144)

LOG(Government consumption; % of GDP) -0.001 0.041 -0.042 0.221

(0.994) (0.845) (0.805) (0.428)

LOG(Exports; % of GDP) -0.180 -0.124 -0.140 -0.097

(0.094) (0.146) (0.203) (0.399)

LOG(Price of insurance) 0.070 0.155 0.098 -0.041

(0.017) (0.250) (0.001) (0.166)

Intercept Yes Yes Yes Yes

Year dummy Yes Yes Yes Yes

Country fixed-effects Yes Yes Yes Yes

Observation 1320 1264 1305 933

Number of Countries 114 111 113 93

AIC 1950 2173 2121 2293

45

2.7. Conclusion

In this section, we evaluate components of GDP and private credit as candidate predictors of

nonlife insurance consumption, as the academic literature is largely silent on the issue. The

economic growth literature suggests a close relationship between credit, assets price, and

investment, which boosts the fluctuation of economic output. The relevance of business cycle

to the corporate demand for property insurance and liability insurance is obvious, but credit

expansion and contraction are also directly related to the personal demand for property

insurance. For instance, coverage demand for personal housing and motor vehicles is

expected to fluctuate with credit. Therefore, we hypothesize that private credit and

investment are important variables for forecasting property and liability insurance

consumption. In addition, we hypothesize that private consumption also should align with

nonlife insurance consumption. We test these hypotheses.

Test results show that our hypothesis about the relationship between credit and

property/liability insurance consumption are generally supported when modeling the three

insurance consumption measures: premium, density, and penetration. Especially when each

economic development measure is the sole predictor of the models, private credit tends to

dominate GDP in terms of model fit for nonlife aggregate, property, and liability insurance,

but not motor insurance. These results highlight that importance of investigating the

relationship between economic development factors and individual lines of business. In

addition, further break down of economic development factors relevant to corporate and

household activities may not only improve predictive power but also contribute to our

understanding of the relationship between economic activities and nonlife insurance

consumption.

Another finding is that the estimation results for penetration are quite distinct from

those for premium and density. In some sense, he different results are expected because of

the distinct modeling approach: Penetration is a proportional measure and is estimated by a

nonlinear model. In particular, GDP per capita proves to be negatively associated with both

property and liability penetration especially when additional economic development

measures are included as explanatory variables. This suggests that the consumption growth of

those lines is slower than the economic growth. This result suggests that controlling for

country heterogeneity and time factors are indispensable to understand the relationship

between economic growth and nonlife insurance consumption.

46

3. Estimating Long-Term Growth of Non-Life Insurance Consumption:

The Case of China and India9

Applying the results obtained in Section 2, we propose a new methodology for forecasting the

long-term development of the non-life insurance markets. We focus specifically on three lines

of business---motor, property, and liability insurance---in China and India. The goals of this

project are: (1) to identify the key determinants of market growth, 2) to outline and discuss

critical assumptions as to how those determinants will develop, (3) to present forecasts of

insurance premium volume by each line of business in 5 years and 20 years time and (4) to

offer sensitivity tests by changing parameter assumptions.

As noted in Section 1, the gaps between the size of the economies in China and India

and the respective volumes of non-life insurance consumption suggest tremendous growth

potential. Whether the economies and insurance sectors in these prominent developing

countries grow as expected is becoming an increasingly significant matter for both the

industry and academics. Despite the huge market potential, relatively little has been written on

projections for nonlife insurance consumption in China and India, and even less on the market

growth in different lines of business. These are especially important issues for insurance and

reinsurance companies contemplating strategy in the Asia-Pacific region.

This section is organized as follows. Section 3.1 provides a review of the related

literature. In Section 3.2, we describe the methodology. Model estimation results are reported

in Section 3.3. Our base scenario is discussed in Section 3.4, and the base scenario projections

with sensitivity tests are reported in Section 3.5. Section 3.6 concludes.

9 Working Paper: Kamiya, Zanjani and Sun (2013)

47

3.1. Literature Review

The size of the insurance sector represents a significant portion of the world economy. In

2010, about 7% of global GDP was spent to purchase insurance (the direct premium written

by insurance companies reached US$4,340 billion).10 Going forward, developing countries

such as China and India are expected to be driving forces of global insurance market growth

in the next decades (See Section 1). These heady expectations are eay to understand. China,

with 1.3 billion people, and India, with 1.2 billion are the largest countries on Earth and

together account for 40% of the world population. Despite the huge market potential,

relatively little has been written on projections for nonlife insurance consumption in China

and India.11

Our study contributes to the literature of country-level long-term insurance market

growth projections. Studies in this area include Enz, 2000, Zheng et al., 2008, and Zheng et

al., 2009). These studies model life and non-life insurance market growth by fitting logistic

growth curves to insurance premium penetration solely as a function of country GDP. Our

methodology is similar to these studies in that we model insurance premium penetration by

fitting logistic models but do not rely solely on GDP.

Another related area of the literature consists of studies which identify determinants

of insurance consumption from the demand side.12 The literature provides a number of

determinants of both life and non-life insurance demand (e.g., Truett and Truett, 1990;

Browne and Kim, 1993; Outreville, 1996; Browne et al., 2000; Ward and Zurbruegg, 2002).

In particular, our study is similar to Browne et al. (2010), who identify a set of factors

associated with motor insurance and liability insurance. Yet, our study is distinct from the

previous studies because of the focus on macroeconomic determinants of nonlife insurance

consumption.

One of our contributions to the literature lies in identifying the effects of financial

crises on non-life insurance consumption. We rely on the IMF working paper by Laeven and

Valencia (2008), who identify all systemically important banking crises for the period 1970

to 2007.

Further, our study is also related to the recent studies that examine the effect of the

insurance growth on economic growth (e.g., Ward and Zurbruegg, 2000; Zeits, 2003; Hussels

et al., 2005). Insurance sector growth is expected to influence the development of financial

10 Data are taken from Axco Global Statistics. 11 Zheng et al. (2008) is the only exception. 12 Outreville (2012) provide excellent survey of the relationship between insurance and economic development.

48

intermediation in an economy and to contribute to economic development. However, for our

purposes, we ignore the endogenous effect of insurance growth on economic growth because

the role of financial intermediation by non-life insurance sector is less important than life

insurance sector due to the smaller asset holdings.


Previous research models the growth of a country’s insurance penetration by an S-shape

growth curve in terms of per capita GDP.13 In particular, Enz (2000) proposes a logistic

function with three parameters14:

(4)

where i and t stands for a country and year, respectively. C1, C2, and C3 are parameters to be

estimated where the minimum penetration is 1/ ; the maximum penetration is 1/ ;

and the inflection point is found at ln ln /ln . In the case of 1 ,

penetration increases with per-capita GDP, and Enz (2000) confirms in a pooled cross-

sectional model that non-life insurance penetration increases with GDP per capita by the

estimated parameter, 0.86.15

The S-shape growth curve is intuitively appealing for several reasons. First, as noted

above, the empirical connection between penetration and income does not appear to be a

linear one. Second, the specification allows the growth rate of penetration to change as the

income level changes. Third, penetration hits a saturation point at high income levels, as

seems to be suggested by the historical record in developed countries.

The results from applying this model to our data are shown in Table 3.1. All of the

estimated parameters are statistically significant at a 1% significance level. The maximum

penetrations are identified as 2.2% for non-life total, 1% for motor insurance, 0.55% for

property insurance, and 0.28% for liability insurance. Inflection points vary significantly by

line of business. Considering that 2010 GDP per capita is US$4,428 for China and US$1,471

for India, China is expected to experience a decline in the steepness of the S-curve for

13 See Enz (2000), Zheng et al. (2008), Zheng et al. (2009), and Sigma (2004). 14 The same model is adopted by Zheng et al. (2008) and Zheng et al. (2009) as well. 15 Similar results are obtained by other studies: 0.82 by Zheng et al (2008) and 0.85 by Zheng et al (2009) for non-life insurance.

49

Nonlife, Motor, and Property insurance, meaning that future economic growth may produce

less insurance market growth than in the past.16 However, as suggested in Figure 3.1, the

model fit does not appear to be very good.

Table 3.1: Penetration Logistic Model Parameter Estimates


C1 45.7 104 183 355 C2 99.5 253 504 3,887 C3 0.87 0.79 0.80 0.80

Minimum Penetration (%) 0.69 0.28 0.15 0.02 Maximum Penetration (%) 2.19 0.96 0.55 0.28

Income at Inflection Point (USD in 2010 price) 5,545 3,716 4,531 10,724

Figure 3.1: Non-life Insurance Penetration Logistic Model Fit

16 The major discrepancy between our results and existing studies in aggregate nonlife penetration can be explained by our exclusion of personal accident and healthcare premiums from nonlife premium.

50

The lack of fit may be partly attributable to the model’s failure to include other

covariates that may capture important measurable differences across countries. For example,

Figure 3.2 plots nonlife insurance penetration in high-income countries over time. It is

apparent that repeated observations are correlated and that the level of penetration thus varies

across countries. In other words, the model implicitly assumes that there are no country

specific factors associated with insurance penetration: Chinese penetration growth is modeled

to behave as US penetration growth regardless of differences in social, cultural, and legal

systems.

Figure 3.2: Time-Series Plot of Non-life Penetration in High Income Countries

Enz (2000) and Zheng et al. (2008, 2009) also observe both the deviation of observed

penetration from the predicted penetration and the heterogeneity across countries. To solve

the problems, Zheng et al, (2008) introduce a new concept called “Benchmark Ratio of

Insurance Penetration” (BRIP). The BRIP is defined by the ratio of actual penetration divided

by the world average penetration at a country’s income level, calculated by the model (4). If

BRIP is less (greater) than one, the country’s penetration is below (above) the world average.

They assume that the BRIP follows a second-order autoregressive model.17 In this setting,

the AR(2) specification, in the absence of augmentation with additional explanatory variables

17 The AR(2) model is as follows: ln .

51

and country fixed effects, essentially models a country’s deviation from the world average as

being transitory.

In this study we do not assume independence of repeated observations but allow for

serial correlation and heterogeneity across countries through country-specific parameters and

variance components. To verify the need for country-specific effects, we run a pooling test

with the null hypothesis of all country-specific effects being identical. The test rejects the

null hypothesis of homogeneity and supports heterogeneity across countries at a 1%

significance level for all business lines.18

18 For instance, nonlife penetration indicates that F-ratio is 57.2, df1 is 110 and df2 is 2552. The p-value is less than 0.001 and the null hypothesis is therefore rejected.

52

3.2. Insurance Consumption Models

We use a two-step approach to forecasting insurance consumption. In the first step,

penetration rates for motor, property and liability insurance are separately predicted. Second,

to predict non-life total premium, the business line shares for each line are predicted. The

non-life total premium is estimated by the sum of the predicted business line premiums

divided by the sum of the predicted business line shares. In this section, we first discuss our

penetration models for each line of business, and then discuss the business line share models.

3.2.1. Business Line Penetration Models

Applying the results in Section 2, we use Equation (3) to identify statistically significant

variables to predict business line penetration. 19 We start with a benchmark model by

assuming independence among observations with a pooled cross-sectional model and

augment the model by adding country fixed-effects:

(5) Log Log GDP per capita

China Log GDP per capita India Log GDP per capita

Log EDM % Log Insurance Price LOG Year

where , , , and represents business line (motor, property and liability) penetration,

an intercept, country fixed-effects and a normally distributed error term, respectively. The

subscripts i and t represent country and year, respectively. Further, the EDM % is a vector of

economic development measures as a percent of GDP including investment, private

consumption, government consumption,and exports.

Thus, our logistic model is different from Enz’s logistic model in that our model

includes country-specific slope for GDP per capita and does not solely rely on per capita

GDP. Furthermore, we add country fixed-effects to control for heterogeneity among

countries. Introducing country fixed-effects has another advantage in that hidden time-

constant factors associated with insurance consumption are absorbed by the fixed-effect.

Thus, this model is robust to hidden time-constant variables. Including the country-specific

19 The logistic model has been widely used for non-binary dependent variables and frequently for dependent variables measured as percentages. As a technical note, the dependent variable in the logistic model can be interpreted with GDP being the number of trials and premium being the number of successes. Thus, the denominator and the numerator of the penetration measure are separately specified in the regression procedure.

53

slope for GDP per capita and the country fixed-effects improves the model fit, and our

models do not need to rely on additional assumptions regarding the behavior of error terms.

3.2.2. Market Share Models

The aforementioned models predict the level of insurance consumption by line of

business. Here we discuss the second step in the forecasting process: modeling the share of

each business line for the purpose of aggregating predicted premiums. To predict the market

share of business lines we consider a logistic model similar to Equation (5) with the

dependent variable being the share of a business line defined by the ratio of business line

premium (motor premium, property premium, or liability premium) to nonlife aggregate

premium:

(6) Log Log GDP per capita Log GDP per capita

Log EDM % LOG Insurance Price LOG Year

where represents a business line share in country i and year t.. A quadratic term for GDP

per capita is introduced but a country-specific slope for GDP per capita is excluded. The

fixed-effects model is also investigated by adding country fixed-effects to control for

heterogeneity among countries.

Figures 3.3-3.5 show the relationship between the market share of each line of

business and per capita GDP. Figure 3.3 indicates that both China’s and India’s motor

insurance share has increased over recent years, with China’s motor insurance share currently

at about 80%. In contrast, property insurance share in both countries has tended to decrease

over recent years despite economic growth (see Figure 3.4). Liability insurance shares in

China and India are very small and have appeared insensitive to economic growth in the past.

Thus, the relationship between the business line shares and income varies across lines.

54

Figure 3.3: Plot of Motor Insurance Share versus per capita GDP

Figure 3.4: Plot of Property Insurance Share versus per capita GDP

55

Figure 3.5: Plot of Liability Insurance Share versus per capita GDP

3.2.3. Non-life Total Premium Predictions

The non-life total premium and penetration is estimated by using the predicted premiums and

business line shares for motor, property and liability insurance. Specifically, the non-life total

penetration and premium are calculated by:

(7) ′,

′ , ′ , ′ ,

, , ,

(8) , ′,

where , is the predicted non-life total premium.

56

3.3. Model Estimation Results

We now describe our approach to selecting the best models for business line penetrations and

the share of each business line, both of which are used for our projections. To start, it is

necessary to explain that our estimations and projections rely on two currency conversion

rates: the market exchange rate (MER) and the purchasing power parity (PPP).

It is argued that market exchange rates tend to systematically understate the standard

of living in poor countries. For instance, Chinese GDP per capita is USD 4,428 in 2010 prices

if market exchange rates are used to convert local currency GDP per capita to USD. If PPP is

used as the conversion rate, China’s per capita GDP increases to USD 7,599 in 2010 prices.

Similarly, India’s per capita GDP is increased from USD 1,471 to USD 3,582 in 2010 prices.

The difference between these two conversion rates is not reconcilable. Therefore, we provide

projections under both assumptions. All of the following procedures are repeated under these

two conversion rates and the results are separately reported. Since parameter estimations and

model selections are consistent across the two assumptions, the following discussion focuses

primarily on results under the MER assumption to avoid significant repetition.20 Projection

results are reported under both assumptions.

3.3.1. Model Selection: Penetration Models

To identify the projection model, we carry out a backward elimination procedure by starting

with all candidate variables, testing them one by one for statistical significance, and deleting

any that are not significant at the 30% level. Table 3.2 reports the parameter estimates.

20 The estimation results under the PPP assumption are available upon request.

57

Table 3.2: Parameter Estimate for Penetration Models (MER)

Regardless of the number of variables retained after the backward elimination procedure, the

adjusted R2s for penetration models are approximately 0.9.

In contrast to the assumption made in the existing, the relationship between business

line penetrations and GDP per capita is not consistent across models. While GDP per capita is

statistically significant with positive signs for the motor penetration model, we observe

negative signs for property and liability penetration models. The negative sign implies that

premium volume tends to increase at a slower rate than GDP per capita as a country becomes

wealthier.

Investment and government consumption as a percent of GDP are positively

associated with insurance penetration while private consumption and exports variables

ParameterFull

ModelReduced

ModelFull

ModelReduced

ModelFull

ModelReduced

Model

Intercept 48.93 -2.86 -244.1 -245.3 -446.0 -442.3(0.715) (0.076) (<.0001) (<.0001) (<.0001) (<.0001)

Log(GDP per capita) 0.155 -0.607 -0.569 -0.237 -0.223(0.496) (0.011) (0.002) (0.307) (0.267)

Log(GDP per capita)*China 0.668 0.777 0.593 0.588 0.078(<.0001) (<.0001) (0.002) (<.0001) (0.742)

Log(GDP per capita)*India 0.392 0.466 -0.180 -1.725(<.0001) (<.0001) (0.265) (0.532)

Log (Investment; %) 0.094 0.156 0.107 0.604 0.599(0.313) (0.015) (0.348) (0.001) (<.0001)

Log(Private consumption; %) -0.392 -0.429 -0.071 -0.905 -0.925(0.307) (0.292) (0.865) (0.087) (0.059)

Log(Government consumption; %) -0.072 0.158 0.244 0.244(0.626) (0.444) (0.291) (0.295)

Log(Exports; %) -0.201 -0.219 -0.237 -0.267 -0.398 -0.399(0.008) (0.021) (0.004) (0.010) (0.014) (0.013)

Log(Insurance Price) 0.174 0.158 0.134 0.133 -0.106 -0.106(0.094) (0.164) (<.0001) (<.0001) (0.001) (0.001)

Log(Year) -6.969 32.135 32.334 58.324 57.845(0.543) (<.0001) (<.0001) (<.0001) (<.0001)

China -6.0174 -7.0695 -7.0695 -6.7936 -2.7613 -2.1322(<.0001) (<.0001) (<.0001) (<.0001) (0.074) (<.0001)

India -6.0174 -4.7609 -4.7609 -2.9185 -3.606 -2.7837(<.0001) (<.0001) (<.0001) (<.0001) (0.002) (<.0001)

Adjusted R2

0.907 0.905 0.885 0.887 0.894 0.894Observations 1742 1743 1786 1816 1237 1237

Motor Property Liability

58

indicate negative signs. It is counter-intuitive to observe that insurance price variables are

positively associated with motor and property penetration. The time variable is found to be

insignificant for the motor model but positive and significant for the property and liability

models. .

3.3.2. Business Line Share Models

The same backward elimination model selection procedure is used for the business line share

models as well. Both pooled cross-sectional model and fixed-effects model are reduced by

the backward elimination procedure using a 20% significance level, and the one with the best

fit statistic is selected as the prediction model. This model selection is carried out for motor

insurance, property insurance, and liability insurance, respectively. Table 3.3 shows the

selected models.

Table 3.3: Parameter Estimate of Selected Market Share Models (MER)

ParameterFull

ModelReduced

ModelFull

ModelReduced

ModelFull

ModelReduced

Model

Intercept -37.899 -21.709 -208.200 -212.980 -364.190 -309.140(0.417) (0.007) (0.065) (0.033) (0.005) (0.013)

Log(GDP per capita) 4.436 4.601 -3.110 -3.234 -3.119 -2.713(<.0001) (<.0001) (0.033) (0.024) (0.098) (0.077)

Log(GDP per capita) squared -0.237 -0.245 0.114 0.123 0.145 0.130(<.0001) (0.001) (0.110) (0.088) (0.135) (0.097)

Log (Investment; % of GDP) 0.027 0.062 0.609 0.612(0.865) (0.668) (<.0001) (<.0001)

Log(Private consumption; % of GDP) 0.012 0.279 -0.434(0.988) (0.566) (0.524)

Log(Government consumption; % of GDP) -0.240 0.341 0.318 0.293 0.234(0.540) (0.076) (0.127) (0.121) (0.064)

Log(Exports; % of GDP) 0.041 0.125 0.088 -0.012(0.864) (0.194) (0.294) (0.940)

Log(Insurance Price) -0.228 0.012 -0.109(0.143) (0.758) (0.097)

Log(Year) 2.307 29.335 30.282 49.514 41.730(0.921) (0.058) (0.012) (<.0001) (0.001)

China 1.411 1.475 -1.662 -1.767 -1.317 -1.034(0.055) (<.0001) (0.021) (0.002) (0.030) (0.010)

India 1.094 1.353 -2.790 -2.872 -2.100 -1.882(0.143) (0.010) (0.021) (0.010) (0.014) (0.014)

Adjusted R2

0.788 0.731 0.755 0.739 0.799 0.779Observations 1742 1743 1786 1816 1237 1237

Motor Property Liability

59

Business line shares have different relationships with GDP per capita. Motor

insurance share initially tends to increase as income increases, but the negative coefficient for

the quadratic term indicates that the association reverses at higher income levels. In contrast,

property insurance and liability insurance shares actually decline with income when income

is low but increase with income at higher income levels.

This result is consistent with what we observe in Figures 3.3-3.5. In Figure 3.5, both

China’s and India’s motor insurance share have historically grown with income. Since

current per capita GDP is still less than the threshold in both countries, it is expected that

motor insurance share will continue to increase in the future if other things being equal. In

contrast, property insurance share in both countries has tended to decline with income in

Figure 3.4. This is consistent with the estimated model, which predicts that the share of

property insurance will continue to decline in both countries. Liability insurance share does

not show large changes in Figure 3.5, and liability insurance share is not expected to start

increasing in the near future in either country.

In this section, we identified a set of regression models for the purpose of predicting

future insurance consumption in China and India. W selected logistic models with country

fixed-effects for each line of business to fit historical penetration and line share data. To

forecast future insurance consumption, however, we require assumptions for the explanatory

variables such as per capita GDP. In the next section, we provide detailed forecasts of our key

explanatory variables under two economic scenarios.

60

3.4. Projections for Explanatory Variables: Base Scenario

Projection of insurance consumption requires inputs for the determinants of insurance

consumption. These determinants will of course be affected by political, social and economic

developments in China and India. For each country, first we consider the base scenario for

purposes of projection and then change the parameter values to investigate the sensitivity of

the premium projection to different parameter values. In the following, we discuss our choice

of the base scenario.

3.4.1. GDP

A number of studies have been conducted to project the GDP growth rate of China, and there

are some studies forecasting both China’s and India’s economic development in the long run.

There are large discrepancies between studies that project the GDP growth rate for China and

India. 21 One well known forecast is the Asian Development Bank’s GDP projection (Lee and

Hong, 2010) which forecasts the Chinese GDP growth rate at 6.09% during 2011-2020 and

4.98% for the next ten years. The Indian GDP growth rate is forecast to be 4.67% during

2011-2020 and is 4.28% for the next ten years. To make sensitivity tests tractable, we set our

base scenario of GDP growth rate as 6% for China and 5% for India during 2011-2030.

While the GDP growth rate is an exogenous input in our forecast, we estimate the

lower bound of the confidence interval under financial crisis risk which would affect

economic outcomes. Our financial crisis data is taken from the IMF working paper entitled

Systemic Banking Crises: A New Database reported by Laeven and Valencia (2008). A

systemic banking crisis is broadly defined as a sharp increase of non-performing loans and

exhausted aggregate banking system capital, which may be accompanied by deteriorated

asset prices, sharp increases in real interest rates, and a slowdown or reversal in capital flows

(see Laeven and Valencia, 2008 for the detailed description of crisis types). They identify 124

banking crises over the period from 1970 to 2007.

To the probability of facing a financial crisis, we use a logistic model with an

indicator variable for a banking crisis for a dependent variable and a set of explanatory

21 Evaluating the discrepancy is, however, beyond the scope of our study. For instance, the Economist Intelligence Unit forecasts that the annual average growth rate in GDP will be 7.3% during 2011-2020 and 4.1% during 2021-2030 in China and 7.6% during 2011-2020 and 5.5% during 2021-2030 in India.

61

variables to identify statistically significant variables.22 As before, we start with a benchmark

model by assuming independence among observations with a pooled cross-sectional model

but augment the model by adding country fixed effects:

(8) Log ′

where q, and represents the probability of having a banking crisis, intercept, and a

normally distributed error term, respectively. Further, the x is a set of variables used for

penetration models. To identify the best model, we carry out a backward elimination

procedure with the same criteria as before. The selected model is the pooled cross-sectional

model because the fixed-effects model fails to obtain statistically significant country fixed-

effects. The estimation result of the reduced model is summarized as follows:

(9) Log 3.014 0.482Log Gov. Comsumption 0.562Log Exports

where the observation used in the regression is 2367 and the R2 is 0.04. The reduced model

depends only on population and financial freedom and both are positively associated with the

probability of financial crisis. To determine the lower bound of GDP with 5% confidence

level, we assume that the extent of economic loss after banking crisis is 20% of GDP which is

the average output losses of systemic banking crises during the first four years of the crisis

(See Laeven and Valencia, 2008, for the detail).23

3.4.2. Population

Population growth forecasts for China and India are obtained from separate studies. Chinese

population projections come from Wei and Jinju (2009), who forecast Chinese population

trends during 2005-2050. Among three scenarios of their projections, we choose the low

growth scenario because their forecasted 2010 population (1.344 billion) is the closest to the

actually 2010 population (1.338 billion). The difference, 6 million, is deducted from the

forecasted total population. Table 3.4 shows the forecasted population in China.

22 It is possible to have multiple banking crises in one year but we do not observe such a case in the database. For simplicity, we assume that banking crisis occurs at most once a year. 23 See page 24, Section IV, D for the average output losses. We approximate the 95% confidence level by the expected premium minus 1.65 times the standard deviation.

62

For India, our projection is based on population projections for 2001-2026 reported by the

National Commission on Population (2006). Two adjustments are made. First, there was a

gap between forecast and actual 2010 population since the report forecasts population from

2001. As above, the forecasts are adjusted by the difference in 2010. Second, the forecasts

end in 2026, so the last 4 years from 2027 to 2030 must be extrapolated. To do so, the growth

rate for 2026, 0.76%, is applied to the next 4 years.

Table 3.4: Projection of Population in China and India (million)

year China India year China India

2011 1,345 1,193 2021 1,388 1,339 2012 1,351 1,208 2022 1,389 1,352 2013 1,358 1,224 2023 1,390 1,365 2014 1,364 1,239 2024 1,390 1,377 2015 1,369 1,254 2025 1,390 1,388 2016 1,374 1,269 2026 1,389 1,398 2017 1,378 1,284 2027 1,388 1,409 2018 1,381 1,298 2028 1,386 1,420 2019 1,384 1,312 2029 1,384 1,431 2020 1,386 1,326 2030 1,381 1,442

Source: Wei and Jinju (2009) for China and the National Commission on Population (2006) for India. Their projections are modified by author.

3.4.3. Other Economic Development Measures

To forecast economic development measures as a percent of GDP, we apply the following

AR(2) model:

(9) Log Log Time Log Log

where and represents intercept and a normally distributed error term, respectively. The

subscripts i and t represent country and year, respectively. To identify the projection model,

we carry out a backward elimination procedure by starting with all candidate variables,

testing them one by one for statistical significance, and deleting any that are not significant at

the 30% level. Table 3.5 reports the parameter estimation of the EDM models and Table 3.6

report the predicted economic development measures (% of GDP).

63

Table 3.5: EDM AR(2) Model Estimation Results

Table 3.6: Projections of Economic Development Measures

3.4.4. Price of insurance

We use the 2010 price level as the base scenario. For China, the motor, property and liability

insurance price levels are 2.2, 2.0 and 2.6, respectively. For India, those price levels are 1.3,

1.3 and 2.2.

China InvestmentPrivate

consumptionGovernment consumption Exports

Intercept 8.896 10.536 4.394 -1.806(0.065) (0.075) (0.048) (0.240)

Log(Time) 1.588 -1.391 - 2.320(0.042) (0.053) - (0.096)

Lagged EDM (t-1) 0.960 1.049 0.875 1.116(<.0001) (<.0001) (<.0001) (<.0001)

Lagged EDM (t-2) -0.294 -0.208 -0.180 -0.311(0.149) (0.287) (0.271) (0.106)

Adjusted R2

0.75 0.92 0.53 0.92Obervation 31 31 31 31

India InvestmentPrivate

consumptionGovernment consumption Exports

Intercept - 11.379 2.129 -1.026- (0.249) (0.001) (0.127)

Log(Time) 0.591 -1.013 0.400 0.406(0.287) (0.268) (0.038) (0.138)

Lagged EDM (t-1) 0.641 0.586 0.994 0.539(<.0001) (<.0001) (<.0001) (<.0001)

Lagged EDM (t-2) 0.303 0.285 -0.306 0.497(0.004) (0.061) (0.036) (0.001)

Adjusted R2

0.99 0.92 0.86 0.97Obervation 49 49 49 49

YearGDP per

capita Invest.Priv. cons.

Gov. cons. Exports

GDP per capita Invest.

Priv. cons.

Gov. cons. Exports

2010 4,428 48.2 34.6 13.3 30.6 1,471 35.8 56.5 11.9 22.82015 5,793 43.7 34.8 14.3 33.6 1,753 37.2 56.7 11.9 27.12020 7,657 44.4 34.1 14.4 34.8 2,116 38.3 56.5 12.1 32.82025 10,217 44.9 33.1 14.4 36.1 2,580 39.4 56.1 12.2 39.32030 13,762 45.4 32.2 14.4 37.4 3,170 40.5 55.6 12.3 46.8

IndiaChina

64

3.5. Base Scenario Projections of Non-life Insurance Consumption

With projections for our key explanatory variables as described in the previous section, we

now proceed to forecast insurance consumption in China and India. The base scenario

projections are summarized in Table 3.7 (under the MER assumption) and Table 3.8 (under

the PPP assumption). The left side of those tables shows projections for China and the right

side shows projections for India.

3.5.1. Base Scenario Projection Results - Market Exchange Rate (MER) Assumption

A. China Projection Summary

Motor insurance penetration is expected to increase to 1.04 in 2020 and 1.65 in 2030.

Corresponding Chinese motor insurance premium is expected to be US$111 billion in 2020

and US$314 billion in 2030 under the baseline scenario. The annual growth rate of the motor

insurance market is approximately 11%. The predicted 2030 motor premium reaches 168% of

the 2010 US motor premium volume. The 5% lower bound premium is US$97 billion in 2020

and US$248 billion in 2030.

Property insurance penetration is expected to be 0.12% in 2020 and 0.14% in 2030.

Chinese property insurance premium is expected to be US$13 billion in 2020 and US$27

billion in 2030 under the base scenario. While property insurance premium is expected to

increase in the future due to economic growth, the growth rate of the premium is lower than

motor premium.

Liability insurance penetration shows little change during projection period. Chinese

liability insurance premium is expected to be US$3.3 billion in 2020 and US$7.2 billion in

2030. The premium volume is forecast to increase but remains small.

Non-life insurance penetration is forecast to be 1.3% in 2020 and 2.0% in 2030. The total

premium is expected to be US$137 billion in 2020 and US$386 billion in 2030 under the

baseline scenario. This analysis indicates that the key driver of nonlife insurance

consumption growth in China is expected to be motor insurance. The contribution of the

property and liability lines to the nonlife total premium volume is expected to remain

relatively low.

B. India Projection Summary

65

The motor insurance penetration is forecast to increase to 0.26% in 2020 and 0.3% in 2030.

Motor insurance premium is expected to be US$7.4 billion in 2020 and US$14 billion in

2030 under the base scenario. The annual growth rate will be about 6%. Comparing the

predicted premium volumes with those for China, the motor insurance market in India is

expected to remain small even in long-run.

Property insurance penetration is expected to decrease from 0.07% to 0.05% over the

projection period and the insurance premium is expected to be US$1.7 billion in 2020 and

US$2.4 billion in 2030 under the base scenario.

Liability insurance penetration is expected to slightly increase, with premium rising to

US$348 million in 2020 and US$629 million in 2030.

Total Indian non-life insurance premium is expected to be US$12 billion in 2020 and

US$20 billion in 2030 under the baseline scenario. Overall, India’s non-life insurance

consumption is similar to China’s in that the key driver of the nonlife insurance consumption

growth is expected to be motor insurance. In contrast to the prediction for Chinese market,

however, the growth of India’s nonlife insurance consumption is expected to be modest and

the size to remain small during the projection period.

The projection results under the PPP assumption are reported in Table 3.8, but the

discussion on the results is omitted to avoid significant repetition.

66

Table 3.7: Base Scenario Projection Summary for China and India (MER)

Base Scenario China India

Assumptions 2010 2015 2020 2025 2030 2010 2015 2020 2025 2030

GDP (2010 USD billion) 5,926 7,930 10,612 14,202 19,005 1,722 2,198 2,806 3,581 4,570

Population (million) 1,338 1,369 1,386 1,390 1,381 1,177 1,254 1,326 1,388 1,442

GDP per capita (2010 USD) 4,428 5,793 7,657 10,217 13,762 1,471 1,753 2,116 2,580 3,170

Investment (% of GDP) 48 44 44 45 45 36 37 38 39 40

Private consumption (% of GDP) 35 35 34 33 32 56 57 56 56 56

Gov. consumption (% of GDP) 13 14 14 14 14 12 12 12 12 12

Export (% of GDP) 31 34 35 36 37 23 27 33 39 47

Motor Insurance

Penetration (%) 0.71 0.84 1.04 1.31 1.65 0.24 0.25 0.26 0.28 0.30

Premium (2010 USD m) 44,361 66,405 110,587 185,683 314,033 3,368 5,502 7,400 10,026 13,660

Average annual growth rate 0.11 0.11 0.11 0.11 0.06 0.06 0.06 0.06

5% Lower Bound Premium 61,120 97,010 154,596 247,636 5,053 6,528 8,524 11,248

Property Insurance

Penetration (%) 0.11 0.11 0.12 0.13 0.14 0.07 0.07 0.06 0.06 0.05

Premium (2010 USD m) 6,780 9,062 13,083 18,876 27,265 916 1,469 1,734 2,041 2,395



Liability Insurance

Penetration (%) 0.03 0.03 0.03 0.03 0.04 0.01 0.012 0.012 0.013 0.014

Premium (2010 USD m) 1,719 2,240 3,306 4,873 7,171 165 259 348 467 629


5% Lower Bound Premium 2,062 2,900 4,057 5,655 238 307 397 518

Nonlife Total

Penetration (%) 0.97 1.050 1.293 1.611 2.032 0.38 0.425 0.426 0.432 0.443

Premium (2010 USD m) 57,539 83,255 137,222 228,831 386,110 6,537 9,349 11,964 15,485 20,251



6% GDP Growth 5% GDP Growth

67

Table 3.8: Base Scenario Projection Summary for China and India (PPP)

Base Scenario (PPP) China India

Assumptions 2010 2015 2020 2025 2030 2010 2015 2020 2025 2030

GDP (2010 USD billion) 10,170 13,609 18,212 24,372 32,615 4,195 5,354 6,833 8,721 11,130

Population (million) 1,338 1,369 1,386 1,390 1,381 1,177 1,254 1,326 1,388 1,442

GDP per capita (2010 USD) 7,599 9,941 13,140 17,534 23,617 3,582 4,269 5,154 6,284 7,721

Investment (% of GDP) 48 44 44 45 45 36 37 38 39 40

Private consumption (% of GDP) 35 35 34 33 32 56 57 56 56 56

Gov. consumption (% of GDP) 13 14 14 14 14 12 12 12 12 12

Exports (% of GDP) 31 34 35 36 37 23 27 33 39 47

Motor Insurance

Penetration (%) 0.66 0.78 0.97 1.20 1.51 0.20 0.22 0.24 0.25 0.27

Premium (2010 USD m) 71,087 106,743 176,011 293,229 492,944 7,256 12,025 16,199 22,049 30,250



Property Insurance

Penetration (%) 0.10 0.10 0.11 0.12 0.13 0.06 0.05 0.05 0.04 0.04

Premium (2010 USD m) 10,864 14,202 20,226 28,792 41,014 1,973 2,900 3,324 3,789 4,298



Liability Insurance

Penetration (%) 0.03 0.03 0.03 0.03 0.04 0.01 0.010 0.011 0.012 0.012

Premium (2010 USD m) 2,754 3,708 5,547 8,317 12,454 355 552 744 1,007 1,364


5% Lower Bound Premium 3,413 4,866 6,924 9,821 507 656 856 1123

Nonlife Total

Penetration (%) 0.97 0.971 1.195 1.499 1.916 0.38 0.351 0.352 0.359 0.373

Premium (2010 USD m) 92,205 132,129 217,670 365,258 624,744 6,537 18,808 24,033 31,324 41,564



6% GDP Growth 5% GDP Growth

68

3.5.2. Sensitivity Tests - Market Exchange Rate (MER) Assumption

Tables 3.9-3.14 show the results of sensitivity tests by changing input values.24 Note that the

changes are cumulative for GDP but not for other inputs. For instance, GDPs under different

scenarios are recalculated by a new GDP growth rate. In contrast, for instance, predictions

under -10% investment scenario is calculated simply by reducing the base scenario

investment in a particular year by 10%.

A. GDP

Table 3.9 shows how forecasts change when changing the GDP growth rate assumption. The

upper and lower panels present predictions for China and India, respectively. For China,

predictions under 4%, 5%, 6%, and 7% annual growth rates are reported. For India,

predictions under 3%, 4%, 5%, and 6% annual growth rates are reported.

For China, a one percent increase in the GDP growth rate increases GDP per capita by

10% in 2020 and 21% in 2030. Motor insurance premium volume increases by 18% in 2020

and by 39% in 2030. The percent change of property insurance volume is similar to the

percent change of GDP, meaning that the income elasticity of property insurance

consumption is approximately one. Similarly for the downside, a 1% decline of GDP growth

rate would be expected to reduce motor premium by 15% in 2020 and 28% in 2030. Liability

premium volume is less sensitive than the other two lines. Overall, non-life insurance

consumption is highly sensitive to changes in the GDP growth rate due to the sensitivity of

motor insurance premium. Changes in theGDP growth rate relative to forecast should be

expected to have a profound influence on the course of nonlife insurance consumption in

China.

The lower panel reports the same sensitivity check for India. The results are similar to

the results observed for China. The premium volumes in India are also sensitive to the GDP

growth rate but less so than China.

B. Other Economic Development Measures

Table 3.10 reports premium volume sensitivity to investment as a percentage of GDP.

Motor insurance and liability insurance premium volumes are positively associated with

investment, but the impact on motor insurance is not large.

24 The same set of sensitivity tests is done under the PPP assumption but not reported to avoid repetition since the results are directionally similar. The results are available upon request.

69

Table 3.11 indicates that the sensitivity of nonlife insurance comsumption to private

consumption is significant and negative. Thus, a larger proportion of private consumption in

GDP implies less nonlife insurance consumption. This raises the possibility that our base

scenario is too optimistic for China. The reason is that the China’s proportion of investment

may not be sustainable (48% for China and 22% for the world average in 2010), and the

current proportion of private consumption in China is exceptionally low (35% for China and

67% for the world average in 2010). Therefore, it is plausible that investmentwill decline in

importance in China while private consumption will rise. Both trends re adversely associated

with nonlife insurance consumption, and the impact of such a change in GDP composition

could be significant (See Table 3.15 for additional discussion).

Table 3.12 shows that changes in forecasted government consumption would be

expected to have a relatively minor effect on overall nonlife insurance consumption, as the

only line that is sensitive to changes in government assumption is the relatively unimportant

liability line. In any case, government consumption is a relatively stable figure and both

countries are close to the world average (15%), so the impact is expected to be small.

Table 3.13 demonstrates that nonlife premiums are not highly sensitive to changes in

the assumption about exports, although the association is negative. That said, if China

continues as one of the largest exporters in the world, and if India increases the proportion of

exports in GDP, the consequences for nonlife insurance consumption would be negative.

The sensitivity of nonlife insurance premiums to changes in assumptions about the

insurance price are shown in Table 3.14. Premium volumes appear to be relatively insensitive

to the changes in the price assumption.

Finally, we provide projections under the base GDP growth rate scenario (6% for

China and 5% for India) with the world average GDP composition in Table 3.15. Investment,

private consumption, government consumption, and exports under this scenario are 22%,

67%, 15%, and 37%, respectively. The projection results are striking for China because the

predicted nonlife total premium is approximately 30% less than the predicted 2030 premium

volume under the base scenario. The predicted premiums are slightly less than the 5% GDP

growth rate scenario (See Table 3.9). This unfavorable result can be explained by the current

high proportion of investment (48%) and the low proportion of private consumption (35%) as

mentioned above. If China’s national accounts converge to the world average by slowing

down investment and increasing private consumption, the impact would be similar to a

negative 1% GDP growth rate if other things being equal. In contrast, India’s current GDP

70

composition is relatively close to the world average. Therefore, the impact of this scenario is

not substantially large.

Table 3.9: Sensitivity Test – GDP Growth Rate (MER)

Growth Rate Scenario: China

Variables 2010 2020 2030 2020 2030 2020 2030 2020 2030

GDP (2010 USD b) 5,926 8,772 12,984 9,653 15,723 10,612 19,005 11,657 22,932Percent change - -17.3% -31.7% -9.0% -17.3% - - 9.8% 20.7%

Motor InsurancePremium (2010 USD m) 44,361 78,947 160,261 93,515 224,732 110,587 314,033 130,564 437,290Percent change - -28.6% -49.0% -15.4% -28.4% - - 18.1% 39.2%

Property InsurancePremium (2010 USD m) 6,780 10774 18491 11878 22474 13083 27265 14397 33017Percent change - -17.6% -32.2% -9.2% -17.6% - - 10.0% 21.1%

Liability InsurancePremium (2010 USD m) 1,719 2,851 5,333 3,071 6,189 3,306 7,171 3,557 8,299Percent change - -13.8% -25.6% -7.1% -13.7% - - 7.6% 15.7%

Nonlife TotalPremium (2010 USD m) 57,539 98,585 197,687 115,509 272,117 137,222 386,110 161,611 538,868Percent change - -28.2% -48.8% -15.8% -29.5% - - 17.8% 39.6%

Growth Rate Scenario: India

Variables 2010 2020 2030 2020 2030 2020 2030 2020 2030

GDP (2010 USD b) 1,722 2,315 3,111 2,550 3,774 2,806 4,570 3,084 5,524Percent change - -17.5% -31.9% -9.1% -17.4% - - 9.9% 20.9%

Motor InsurancePremium (2010 USD m) 3,368 5,583 7,778 6,432 10,322 7,400 13,660 8,501 18,030Percent change - -24.6% -43.1% -13.1% -24.4% - - 14.9% 32.0%

Property InsurancePremium (2010 USD m) 916 1,596 2,029 1,664 2,205 1,734 2,395 1,806 2,599Percent change - -8.0% -15.3% -4.0% -7.9% - - 4.2% 8.5%

Liability InsurancePremium (2010 USD m) 165 299 466 323 542 348 629 374 729Percent change - -14.1% -25.9% -7.2% -13.8% - - 7.5% 15.9%

Nonlife TotalPremium (2010 USD m) 6,537 9,472 12,490 10,647 15,888 11,964 20,251 13,443 25,869Percent change - -20.8% -38.3% -11.0% -21.5% - - 12.4% 27.7%

4% GDP Growth 5% GDP Growth6% GDP Growth (Base Scenario) 7% GDP Growth

3% GDP Growth 4% GDP Growth5% GDP Growth (Base Scenario) 6% GDP Growth

71

Table 3.10: Sensitivity Test - Investment (MER)

Investment Scenario: China

Variables 2010 2020 2030 2020 2030 2020 2030

Investment (% of GDP) 48 34 35 44 45 54 55

Motor InsurancePremium (2010 USD m) 44,361 106,307 302,254 110,587 314,033 114,115 323,779Percent change - -3.9% -3.8% - - 3.2% 3.1%

Property InsurancePremium (2010 USD m) 6,780 13083 27,265 13,083 27,265 13,083 27,265Percent change - 0.0% 0.0% - - 0.0% 0.0%

Liability InsurancePremium (2010 USD m) 1,719 2,837 6,178 3,306 7,171 3,734 8,079Percent change - -14.2% -13.8% - - 12.9% 12.7%

Nonlife TotalPremium (2010 USD m) 57,539 132,693 373,723 137,222 386,110 140,915 396,214Percent change - -3.3% -3.2% - - 2.7% 2.6%

Investment Scenario: India

Variables 2010 2020 2030 2020 2030 2020 2030

Investment (% of GDP) 36 28 30 38 40 48 50


Property InsurancePremium (2010 USD m) 916 1,734 2,395 1,734 2,395 1,734 2,395Percent change - 0.0% 0.0% - - 0.0% 0.0%

Liability InsurancePremium (2010 USD m) 165 290 531 348 629 399 718Percent change - -16.7% -15.6% - - 14.7% 14.1%


Investment -10% points

Investment +10% pointsBase Scenario

Investment -10% points Base Scenario

Investment +10% points

72

Table 3.11: Sensitivity Test – Private Consumption (MER)

Private Consumption Scenario: China

Variables 2010 2020 2030 2020 2030 2020 2030

Private consumption (% of GDP) 35 24 22 34 32 44 42

Motor InsurancePremium (2010 USD m) 44,361 128,149 367,242 110,587 314,033 99,135 280,160Percent change - 15.9% 16.9% - - -10.4% -10.8%

Property InsurancePremium (2010 USD m) 6,780 13,083 27,265 13,083 27,265 13,083 27,265Percent change - 0.0% 0.0% - - 0.0% 0.0%

Liability InsurancePremium (2010 USD m) 1,719 4,558 10,108 3,306 7,171 2,606 5,587Percent change - 37.9% 41.0% - - -21.2% -22.1%

Nonlife TotalPremium (2010 USD m) 57,539 157,553 448,320 137,222 386,110 124,089 346,822Percent change - 14.8% 16.1% - - -9.6% -10.2%

Private Consumption Scenario: India

Variables 2010 2020 2030 2020 2030 2020 2030

Private consumption (% of GDP) 56 46 46 56 56 66 66



Liability InsurancePremium (2010 USD m) 165 416 755 348 629 299 540Percent change - 19.5% 20.0% - - -14.1% -14.1%


Private Consumption -10% points Base Scenario

Private Consumption +10% points

Private Consumption -10% points Base Scenario

Private Consumption +10% points

73

Table 3.12: Sensitivity Test – Government Consumption (MER)

Government Consumption Scenario: China

Variables 2010 2020 2030 2020 2030 2020 2030

Government consumption (% of GDP) 13 4 4 14 14 24 24

Motor InsurancePremium (2010 USD m) 44,361 110,587 314,033 110,587 314,033 110,587 314,033Percent change - 0.0% 0.0% - - 0.0% 0.0%

Property InsurancePremium (2010 USD m) 6,780 13,083 27,265 13,083 27,265 13,083 27,265Percent change - 0.0% 0.0% - - 0.0% 0.0%

Liability InsurancePremium (2010 USD m) 1,719 2,477 5,373 3,306 7,171 3,760 8,154Percent change - -25.1% -25.1% - - 13.7% 13.7%


Government Consumption Scenario: India

Variables 2010 2020 2030 2020 2030 2020 2030

Government consumption (% of GDP) 12 2 2 12 12 22 22

Motor InsurancePremium (2010 USD m) 3,368 7,400 13,660 7,400 13,660 7,400 13,660Percent change - 0.0% 0.0% - - 0.0% 0.0%


Liability InsurancePremium (2010 USD m) 165 226 417 348 629 403 727Percent change - -35.1% -33.7% - - 15.8% 15.6%


Government Consumption -10% points Base Scenario

Government Consumption +10% points

Government Consumption -10% points Base Scenario

Government Consumption +10% points

74

Table 3.13: Sensitivity Test - Exports (MER)

Exports Scenario: China

Variables 2010 2020 2030 2020 2030 2020 2030

Exports (% of GDP) 31 25 27 35 37 45 47


Property InsurancePremium (2010 USD m) 6,780 14,321 29,627 13083 27265 12,230 25,594Percent change - 9.5% 8.7% - - -6.5% -6.1%

Liability InsurancePremium (2010 USD m) 1,719 3,785 8,119 3,306 7,171 2,989 6,524Percent change - 14.5% 13.2% - - -9.6% -9.0%


Exports Scenario: India

Variables 2010 2020 2030 2020 2030 2020 2030

Exports (% of GDP) 23 23 37 33 47 43 57


Property InsurancePremium (2010 USD m) 916 1,911 2,554 1,734 2,395 1,615 2,274Percent change - 10.2% 6.6% - - -6.9% -5.1%



Exports -10% points Base Scenario

Exports +10% points

Exports -10% points Base Scenario

Exports +10% points

75

Table 3.14: Sensitivity Test – Insurance Price (MER)

Price Scenario: China

Variables 2010 2020 2030 2020 2030 2020 2030

Motor Insurance Price (=1/loss ratio) 2.2 2.0 2.0 2.2 2.2 2.4 2.4Property Insurance Price 2.0 1.8 1.8 2.0 2.0 2.2 2.2Liability Insurance Price 2.6 2.3 2.3 2.6 2.6 2.9 2.9

Motor InsurancePremium (2010 USD m) 44,361 108826 309062 110,587 314,033 111,965 317,921Percent change - -1.6% -1.6% - - 1.2% 1.2%

Property InsurancePremium (2010 USD m) 6,780 12901 26887 13083 27265 13,249 27,612Percent change - -1.4% -1.4% - - 1.3% 1.3%

Liability InsurancePremium (2010 USD m) 1,719 3349 7265 3,306 7,171 3,268 7,089Percent change - 1.3% 1.3% - - -1.1% -1.1%


Price Scenario: India

Variables 2010 2020 2030 2020 2030 2020 2030

Motor Insurance Price (=1/loss ratio) 1.3 1.2 1.2 1.3 1.3 1.5 1.5Property Insurance Price 1.3 1.2 1.2 1.3 1.3 1.5 1.5Liability Insurance Price 2.2 2.0 2.0 2.2 2.2 2.4 2.4


Property InsurancePremium (2010 USD m) 916 1,710 2,361 1,734 2,395 1,761 2,432Percent change - -1.4% -1.4% - - 1.6% 1.5%



Insurance Price -10% Base Scenario

Insurance Price +10%

Insurance Price -10% Base Scenario

Insurance Price +10%

76

Table 3.15: Base Scenario with Average Economic Development Measures (MER)

Base Scenario with Average Economic Development Measures

Assumptions 2010 2020 2030 2010 2020 2030

GDP (2010 USD billion) 5,926 10,612 19,005 1,722 2,806 4,570

Population (million) 1,338 1,386 1,381 1,177 1,326 1,442

GDP per capita (2010 USD) 4,428 7,657 13,762 1,471 2,116 3,170

Investment (% of GDP) 48 22 22 36 22 22

Private consumption (% of GDP) 35 67 67 56 67 67

Gov. consumption (% of GDP) 13 15 15 12 15 15

Exports (% of GDP) 31 37 37 23 37 37

Motor Insurance

Penetration (%) 0.71 0.69 1.08 0.24 0.22 0.26

Premium (2010 USD m) 44,361 73,306 206,200 3,368 6,135 12,057

Property Insurance

Penetration (%) 0.11 0.12 0.14 0.07 0.06 0.06

Premium (2010 USD m) 6,780 12,842 27,280 916 1,676 2,545

Liability Insurance

Penetration (%) 0.03 0.01 0.01 0.01 0.01 0.01

Premium (2010 USD m) 1,719 1,151 2,407 165 215 426

Nonlife Total

Penetration (%) 0.97 0.90 1.39 0.38 0.36 0.40

Premium (2010 USD m) 57,539 95,110 264,064 6,537 10,060 18,266

China India

77

3.6. Conclusion

In this section, we explored a new approach to forecasting the long-term growth of non-life

insurance consumption by line of business and presented our projections for China and India.

Our approach is unique in its reliance on a rich set of macroeconomic development

indicators. In addition, this approach forecasts the insurance volume of each business line

first and then adds them up with the market share of each line to determine the total non-life

premium volume.

We consider a variety of possible future scenarios, including a relatively pessimistic

scenario where a financial crisis retards growth in the insurance industry. This scenario is

particularly useful as marking a possible worst case for insurance consumption growth. This

is particularly relevant to both China and India. Should China further liberalize its capital

controls and banking system, it will become more vulnerable to financial crises. India has

already reached a very high level of banking crisis risk. However, caution should be

exercised when interpreting the predicted probability of banking crises, as the prediction

model has a poor fit.

Findings for China’s and India’s premium volume forecasts can be summarized as

follows. First, motor insurance is expected to the major driver of nonlife insurance

consumption growth in both countries over the next 20 years. While property insurance and

liability insurance both are also expected to grow, their penetrations are expected not to

increase significantly.

Our sensitivity tests highlight three assumptions that are require special attention.

First, as expected, insurance consumption is highly sensitive to changes in the assumptions

about the growth rate of per capita GDP. In China, a one percent increase in GDP per capita

results in 18% increase of nonlife total premium in 2020 and 40% increase in 2030. Predicted

premium volumes are also highly sensitive and negatively related to assumptions about the

future roles of private consumption and exports.

78

4. Discussion on nonlife insurance consumption growth in China and

India

Both China and India currently feature nonlife insurance consumption that is somewhat low

relative to what has been seen in other countries at similar levels of economic development.

A key unanswered question is whether or not these shortfalls reflect characteristics endemic

to the societies in question (e.g., a fatalistic outlook at the individual level; or aversion to

purchasing insurance). If the low level of consumption reflects persistent cultural or

institutional characteristics, it may well be that both countries will continue to feature

relatively low levels of insurance consumption at later stages of development.

On the other hand, it is possible that we will witness convergence. China, for example,

still features a level of government involvement in the marketplace that is relatively high

among its peers. It is possible that if China chooses to emphasize private institutions and

markets, rates of nonlife insurance consumption will “catch up” to what has been seen in

other countries at similar stages of economic development. Such convergence could appear

through a variety of channels.

In this section, we discuss other potential factors that would affect nonlife insurance

consumption in China and India. The discussion help us to evaluate the extent of uncertainty

about market growth in the long-run.

79

4.1. Potential Sources of Growth

Someone forecasting the future of the US insurance market in 1900 would have been

extraordinarily prescient to predict the staggering growth of insurance related to third party

claims. Most observers of China are sceptical that third-party lines will play a large role in

the future, and this seems a reasonable assumption since few countries have followed the U.S.

example in this regard (outside of motor insurance). Still, the possibility exists. If China

continues along a path of market-oriented reforms, it is possible that more and more disputes

will be resolved in the courts and that financing of risks like workplace injuries will be placed

more and more in private hands. Such developments could potentially vault liability and

casualty insurance into a more prominent position.

In a similar manner, market-oriented reforms may encourage China’s tiny property

insurance market. In other countries, the requirements of financiers often dictate the purchase

of collateral protection in the form of property and liability insurance. Transition to greater

private financing in China might be associated with the propagation of similar requirements,

which, given China’s investment-led economy, could potentially translate into spectacular

growth in property insurance.

The experience in the U.S. suggests that the level and nature of government

intervention can have a profound influence on the extent of private insurance market

exposure to catastrophic risks. As explained in Appendix 4A, this differential can largely be

explained by policy choices respecting intervention in the insurance market (e.g., flood

insurance was “federalized” while windstorm insurance was not) and banking regulation

(where the mortgage finance system was designed with the feature of requiring windstorm

coverage which stimulated the private industry to provide such coverage).

The U.S. is not unusual in this regard. Many countries intervene in catastrophe

markets, with considerable variation in the nature of the intervention. Both China and India

will make choices here, and their choices will go a long way toward determining the level of

private underwriter involvement in catastrophe risk in their respective countries.

Someone forecasting the future of the US insurance market in 1900 probably would

have missed the profound impact of the automobile usage on premium volume. A forecaster

today is not going to make the same mistake in China and India. Vehicle usage in these

countries lags that of developed countries by more than an order of magnitude, so it seems

evident that motor insurance premium volume will rise significantly in both countries as

80

development proceeds---and our forecasting models of course attempt to take this into

account.

However, the deeper point is that technological and societal changes can have

profound effects on the property-casualty insurance markets. Moreover, what these changes

are may be difficult to discern at any given point in time as they are happening. Is there any

ongoing process of technological change that will transform the nature of property and

liability risks? And, if so, how will such changes manifest in the particular contexts of China

and India?

81

4.2. Discussions on Non-life Insurance Consumption in China

4.2.1. General Determinants

A. Property Rights and Legal Rights

The People’s Republic of China has been a socialist state since its establishment in 1949.

According to the Constitution (see NPC, 2004), the basis of the socialist economic system of

the People’s Republic of China is socialist public ownership of the means of production,

namely, ownership by the whole people and collective ownership by the working people.

Land in the cities is owned by the State. Land in the rural and suburban areas is owned by

collectives except for those portions which belong to the State as prescribed by law; housing

sites and privately farmed plots of cropland and hilly land are also owned by collectives.

Since China’s economic reform in 1978, China has been developing as a “socialist

market economy.” The transformation of China’s housing policy in 1988 from a planned

public housing system to a market-oriented housing industry (Implementation Plan for a

Gradual Housing System Reform in Cities and Towns) marked the beginning of private

ownership of real estate property. Along with rapidly growing private enterprises, ownership

of private property is growing, especially in cities. Chinese people have become more and

more concerned about private property rights. However, due to the socialist nature of China,

the ownership of real estate property does not include the ownership of the land. Property

owners are allowed to use the land to which their property is attached for 70 years.

Passed on 16 March 2007 and becoming effective 1 October 2007, the Property

Rights Law is the first legislation in China to formally establish the legal foundation for

property rights, especially land-use rights and ownership of immovables. It creates the

framework to regulate all property rights and states that “state property, collective property

and private property” are all under protection. Though some opponents argued that this

Property Rights Law diverges from the fundamental principles relating to socialism, the

enactment of the Property Rights Law clarified the fundamental infrastructure of China’s

“socialist market economy.”

A comprehensive civil law system is critical to the demand for liability insurance line

of business. Without a well-defined civil liability structure, tort liability is hard to establish,

and it is also difficult to determine the remedy amount. Therefore, liability insurance is

currently not well-established. The predominant ethical and philosophical system in China is

Confucianism. Confucianism emphasizes “rule of man” instead of “rule of law.” It was not

until China started to implement the “open door” policy in the 1980s that China’s civil law

82

system became important, as part of the basis for the “socialist market economy.” Though

several civil laws such as Contract Law (1999), Property Rights Law (2007) and Tort Law

(2009) have come into force, the completion of China’s civil code still has long way to go.

Some legal scholars predict that, with the development of “socialist market economy,”

“[f]urther books on tort law, private international law, rights of the person as well as general

part and possibly family law and law of succession shall follow.” (See Rehm and Julius,

2009)

B. Loan/Mortgage requirement of property insurance

China started to transform from a centralized public housing system to a market housing

system supplemented by government sponsored housing programs in the late 1980s. Since

the mid 1990s, individuals have been allowed to take out mortgage loans when purchasing

regular market housing.

Before 2006, mortgage insurance was compulsory for a mortgage loan application.

The standard mortgage insurance available in the Chinese insurance market includes two

parts of coverage, the normal property insurance part, and the accident insurance for the

mortgage loan borrowers. For the latter part, it indemnifies the beneficiary (the mortgage loan

lender, usually the bank) only when the borrower is dead or disabled due to accidents. It

contains no protection against mortgage default risk due to other reasons such as the decrease

of property value or the credit default of the borrower. Since its inception, compulsory

mortgage insurance has been criticized for the low claim rate and extensive exclusions. The

mortgage insurance premium is paid in an up-front one-time payment. As it is not uncommon

in China to repay the mortgage loan early, the prepayment rate is significant.

In 2007, commercial banks relaxed the compulsory requirement, so mortgage

insurance is no longer mandatory when applying for a mortgage loan. Subsequently, the

demand for mortgage insurance decreased dramatically. Recently, the government in China

has been tightening mortgage rules for fear of a housing meltdown. Thus, it is possible that

mortgage insurance will become more important in the mortgage market.

C. Government Intervention for Catastrophic events

As a constitutionally socialist state ruled solely by the Communist Party of China, China has

historically had extensive government involvement in disaster relief. The general public often

assumes that the local and even the central government will provide assistance in the event of

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natural disasters such as earthquakes or floods, as well as man-made catastrophes like food

safety incidents and fires due to negligence. Due to the fact that governments in China indeed

spend significant resources in the aftermath of catastrophic events, insurance consumption in

certain lines of business (e.g. catastrophe insurance) may be highly discouraged.

Take the 2008 Wen Chuan earthquake as an example. Ninety minutes after the 8.0

magnitude earthquake, China’s Premier Wen Jiabao flew to the earthquake area to oversee

the rescue work. On the same day, 50,000 troops were sent to help the disaster relief.

According to the Ministry of Civil Affairs, as of September 25, 2008, Wen Chuan earthquake

caused 69,227 known deaths, 374,643 injuries, and 17,923 people were listed as missing.

The total direct loss amount due to the Wen Chuan Earthquake (according to the report of the

State Council Information Office on September 4, 2008) is 845.1 billion yuan (about 123.7

billion USD), of which 27.4% is due to personal property losses, 20.4% is due to school,

hospital and other non-personal property losses, and 21.9% is due to infrastructure losses.

Yet, the closed claim amounts related to the Wen Chuan earthquake in all lines of

insurance business, according to CIRC’s news release of May 10, 2009, is only 1.66 billion

RMB (0.24 billion USD), which amounts to 0.2% of the direct loss. The closed claims

represent 96.7% of the total number of claims at the first anniversary of the earthquake.

The estimated total cost to reconstruct the impacted area is 1 trillion yuan (about

146.4 billion USD). The central government provided funding of 220.3 billion yuan to

finance reconstruction projects. The Sichuan provincial government invested 40 billion yuan

from their provincial budget. Eighteen other provinces in China who were assigned by the

Ministry of Civil Affairs to aid each impacted county spent 78 billion yuan on reconstruction-

related projects. The governments of Hong Kong SAR and Macau SAR in total contributed

13 billion yuan. China’s Ministry of Finance provided a loan of about 8 billion yuan, and the

loans provided by financial institutions for reconstruction projects amounted to about 390

billion yuan. Donation from all other sources including individuals, businesses, charities,

foreign governments, and international organizations counted for about 59.4 billion yuan. In

addition, all the members of the Communist Party of China---about 80 million individuals---

paid a special party membership fee to help the earthquake victims: Their total contribution

was approximately 8 billion yuan.

While China’s level of intervention may be regarded as relatively extreme, significant

government assistance in the aftermath of natural disasters is fairly common in other

countries. On the other hand, government intervention is less common for many types of

man-made disasters. In a society of strict regulation and civil code, liability is well defined

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and victims can seek compensation from the negligent party. But this is not necessarily the

case in China. On November 15 of 2010, a large-scale fire destroyed a 28-story high-rise

apartment building in the city of Shanghai. Of the 440 inhabitants in the building, at least 58

were killed and more than 70 were injured. An investigation suggested that the fire may have

been caused by the accidental ignition of polyurethane foam insulation used on the building's

outer walls while the whole building was under renovation. The Shanghai government

announced that negligence by unlicensed welders led to this tragedy. Several welders, along

with the responsible people from the contractor and three government employees (for

permitting illicit construction practices to occur), were detained after the fire.

However, Han Zheng, the mayor of Shanghai, claimed that the city was largely

responsible for the disaster. He said, “Poor supervision of the city's construction industry

was one of the causes behind the high-rise apartment building fire. And we are responsible

for that.” The Shanghai government announced that the families of each victim of the fire

received 960,000 yuan (about 144,000 USD) in compensation. The compensation included

650,000 yuan for every death (according to the Tort Law of People’s Republic of China) and

310,000 yuan in financial assistance (it is claimed explicitly that the amount is from the

government and charities). Survivors were also fully compensated for the loss of possessions

and property. The investigation and compensation information about the fire is not fully

transparent and available to the public. Who is responsible for the 650,000 yuan death

compensation and the more expensive real estate property losses (the average apartment price

per square meter of around 43,000 yuan makes the area the most expensive in Shanghai) is

unclear. According to Southern Weekly (a well-known Chinese newspaper) (Southern

Weekly, 2010), it is very likely that the Shanghai government paid for the death and property

compensation as the contractor was a subsidiary of a state-owned enterprise and the

government also assumed the liability from poor supervision. On top of that, the Shanghai

government waived all medical expenses related to this incident for patients who obtained the

treatment in public hospitals. The public Housing Funding Authority also waived the unpaid

housing funding for all the apartments in the building.

Compared with the total loss of 500 million yuan, insurer involvement in this incident

was limited. Only 2% of the loss amount (5 million yuan) was covered by community

comprehensive liability insurance, and 1.4 million yuan were paid by home property

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insurance and mortgage insurance. Life and pension insurers were responsible for 2 million

yuan in claims.25

This on one hand suggests inadequate insurance awareness among the general public.

On the other hand, the case also illustrates why individuals assume that the government will

take the responsibility for relief efforts as well as financial assistance after a catastrophe event

occurs.

The Chinese government is exploring alternatives to catastrophe loss financing

besides ex post government funding, especially through the catastrophe insurance mechanism.

In CIRC’s annual meeting in January of 2012, legislation to establish catastrophe insurance

and the inclusion of catastrophe insurance in China’s national disaster relief system were

among the most important objectives identified for the year.

D. Collectivist Culture

In individualist cultures such as the US, individuals are, in theory, personally responsible for

their decisions and have to bear the adverse consequences by themselves. Commercially

available insurance is therefore a major tool to protect individuals from fortuitous losses.

Chinese culture, on the other hand, is often regarded as socially collectivist in the sense that

family and community members have a greater level of responsibility to help each other in

cases of misfortune.

Fortuitous or catastrophic losses experienced by an individual have usually been

financed ex-post by personal savings, family or friends’ financial aid, donations from the

community and charities, and government assistance. This diversification in loss financing

reduces the severity of risks perceived by members of collectivist cultures like China. It can

be considered as an implicit form of mutual insurance and serves as an alternative to

insurance that is commercially available (Triandis, 1989).

However, as the Chinese economy develops, the culture may be turning toward more

individualism. Family and community structures are not as enclosed as before. It is also

harder for the “only child” generation to seek financial aid from their limited family members.

This trend may force individuals to prepare for the worst with alternative recourses such as

indemnification from insurance. This may gradually boost non-life insurance demand in

China.

25 Another well-known incident with large government intervention is the 2008 China milk scandal.

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E. Risk Preference

Risk preference is a determinant for insurance demand, especially for in personal lines of

business. In theory, risk averse individuals will demand more insurance than risk neutral or

risk loving individuals. Numerous empirical studies have found that Chinese in general are

less risk averse than some other nationalities such as Americans. If this evidence is taken at

face value, Chinese people are more willing to take risk in general, and low risk aversion

would have a negative impact on insurance demand (Hsee and Weber, 1998).

F. Awareness of Insurance and Insurance Education

Not until the economic reforms of the late 1970’s did China restart the operation of the

insurance industry. In the past three decades, insurance penetration in China has increased

rapidly along with the economic development. However, awareness of insurance is still

relatively low in China compared with many developed countries. The lack of insurance

awareness exhibits two layers. The first layer is a lack of awareness of the value of insurance.

The second layer is a lack of awareness of the variety of insurance products.

Life insurance was the first type of private insurance to become popular in China. Yet,

Chinese people purchase life insurance mostly as a saving or investment tool rather than as

protection against the death of the insured. The reasons for reluctance to buy mortality

protection are difficult to pin down. Cultural differences may play a role. Some may be

unwilling to pay for something that does not produce benefits while they are alive. And

superstitious people believe that purchasing a benefit contingent on death may bring bad luck.

Similar issues may exist in private property insurance.

For commercial non-life insurance, state-owned enterprises historically internalized

losses. The transition of the economy features more private enterprises using insurance as a

loss financing mechanism. The insurance consumption of such enterprises is mainly based on

the traditional types of insurance for direct loss, such as commercial property, liability, and

workers compensation. Lack of awareness of the variety of insurance products, especially for

indirect loss, may currently be inhibiting enterprises in China from more extensive use of

insurance as a risk management tool.

Consider the following illustration. After the 2008 Wen Chuan earthquake, the total

paid claims were 1.66 billion RMB (0.24 billion USD), with the single largest claim being

0.72 billion RMB (0.10 billion USD), or 43% of the total claim amount. The policyholder

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was Lafarge Shui On Cement Ltd., a joint venture of the French-based Lafarge Group and

Hong Kong-based SOCAM. The claim was mainly from their business interruption insurance

policy for the loss of income due to the earthquake. Their commercial property insurance

policy also had special riders to cover all perils including earthquake. When the huge claim

amount was announced in the news, many insurance brokers immediately received inquiries

from corporations asking about the type of insurance policy Lafarge Shui On Cement Ltd.

had. Corporations never exposed to this type of indirect loss insurance coverage indeed

learned a lesson from this experience.

Compared with local firms, international firms may have expertise in risk

management from their global operations and tend to be heavier users of insurance as a risk

financing tool. With economic development in China, more lessons about insurance and risk

management will be learned by Chinese corporations from their international peers and rivals.

This seems likely to increase non-life insurance demand in China in the future.

Some argue that, unlike the banking industry, the insurance industry in general does

not have a good reputation in China, with the bad reputation largely coming from a

misunderstanding of the insurance mechanism and miscommunication between insurance

industry and the general public. Insurance products are thought to be expensive, though this

may be because consumers’ subjective discount rates are very high for unlikely events.

Insurance agents are considered by some to be dishonest and only interested in earning

commissions. The insurance industry is perceived as being highly profitable and able to avoid

claims payments due to contractual exclusions. Altogether, these views may be at least partly

attributable to a lack of insurance awareness as well as insurance education.

Being aware of the inadequacy of insurance education, the Ministry of Education

together with the CIRC announced a Guidance of Enhancing Insurance Education in

December of 2006 (Ministry of Education, 2006). The following points are covered in the

guidance:

To include insurance education to the national education system in order to increase

the insurance awareness for all students in primary, secondary and tertiary education

systems.

To enhance insurance professional education in order to produce qualified insurance

professionals.

To encourage insurance innovation for education institutions.

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To strengthen the cooperation between the insurance and education industry for

insurance awareness campaigns, insurance education, insurance career development,

etc.

Provinces in China have taken a variety of actions to implement the guidance. For

instance, in Shanghai, the Shanghai Insurance Institute (an association of insurance

companies and educational institutions) collaborated with Fudan University to host an

“Insurance Forum” as a part of the “Campaign for Insurance Entering Universities.”

Speakers from the insurance industry were invited to talk with university students about

insurance and insurance professions. The institute also published insurance comics for the

general public. With these insurance awareness campaigns, the idea of insurance as a risk

management tool is expected to be fostered. This may contribute to increased insurance

demand by individuals.

4.2.2. Insurance Market Determinants

A. Compulsory insurance

Motor Third Party Liability Insurance

Motor Third Party Liability (MTPL) insurance became compulsory for all motor vehicles

(including cars, motorcycles, and tractors) in China (Regulation on Motor Third Party

Liability Insurance released by the State Council) in July 2006. As motor insurance is

already the most important non-life insurance line in China, and motor ownership in China is

increasing dramatically with economic development, compulsory MTPL insurance creates

great market potential for non-life insurance demand in China.

Unlike other commercial insurance, MTPL insurance is centrally regulated by the

CIRC. Though the MTPL insurance regulation does not specify a minimum required policy

limit, the CIRC is empowered to standardize MTPL insurance policy clauses as well as

premiums.

The first MTPL insurance premium table was announced by the CIRC in 2006 and

was revised in 2008. According to the CIRC’s premium table, premium varies only by the

vehicle size (number of seats), vehicle usage (commercial or non-commercial) and vehicle

type (automobile, bus, motorcycles and etc.). There is no premium differentiation for vehicles

other than tractors among different geographic regions in China. The coverage limit is fixed

at 110,000 RMB (about 17,000 USD) for a death benefit, 10,000 RMB (about 1,500 USD)

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for medical expenses and 2,000 RMB (about 300 USD) for at-fault property damage and 100

RMB (about 15 USD) for no-fault property damage. A bonus-malus system is in place.

Not until recently (May of 2012, when the Regulation on Motor Third Party Liability

Insurance was amended) did China open up the MTPL insurance market to foreign insurers.

In the past, only domestic insurers were entitled to sell compulsory MTPL insurance.

Currently, three large domestic insurance companies (PICC, CPIC, and Pingan) hold about

70% of the market of motor insurance. Foreign insurers only have about 1% of the market.

As the amended MTPL regulation allows foreign insurers to sell MTPL insurance, foreign

insurers foresee business growth in motor insurance.

Work-Related Injury Insurance

Work-Related Injury Insurance regulation came into effect via the State Council in 2004. The

early version of the regulation covered only enterprises and small businesses. In 2010, the

regulation was amended, and the coverage was extended to public institutions, social groups,

non-profit organizations, foundations, law firms and accounting firms. Currently, work-

related injury insurance is offered through commercial channels and also as voluntary social

insurance.

New legislation has been proposed to change work-related injury insurance

completely to social insurance. In the draft of Regulation on Social Security Report and

Payment (see Legislative Affairs Office, 2011), the proposed social security contribution by

individuals and employers would also include a work-related injury insurance component. If

the amendment is passed, commercial workers compensation insurers would exit the market.

Besides these two types of compulsory insurance, there is compulsory insurance for

certain industries in China. Such compulsory insurance includes travel agent liability

insurance and travel accident insurance for travel agents, construction worker insurance for

the construction industry, coal miner accident insurance for the coal mining industry, railway

train passenger accident insurance for the railway industry, liability insurance for cargo

shippers and others. For a complete list, see Yu (2009).

B. Insurance Products

The insurance market in China is only about three decades old, and the availability of

insurance products is limited compared with many well-developed insurance markets.

Though the number of insurance products filed with the CIRC exceeds 11,000, prevailing

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insurance products with high market acceptance are limited to certain types. For non-life

insurance, the market is highly concentrated on motor insurance, with the market share of

more than 70%. Other non-life insurance, such as liability insurance, construction insurance,

homeowners insurance and credit insurance are not well developed and marketed.

According to the 2010 China Insurance Yearbook, one of the priorities of the CIRC is

to encourage the development of the liability insurance market in China, especially in the

sectors of medical care, transportation, education and environmental protection. Traditionally,

in socialist China, the government has used tax revenues as well as the profits from state-

owned enterprises to finance all of the potential risks in the public sectors. The CIRC’s

initiative shows the Chinese government’s determination to implement a market-based

approach for public sector risk management. As a result, in year 2009, pilot programs

featuring pollution liability insurance were launched in 8 provinces, and medical malpractice

insurance was introduced as an experiment in 16 provinces.

C. Insurance Company Operations

Insurance companies in China, like the insurance market itself, do not have very long history.

The non-life insurance market is dominated by the three largest players: PICC P&C, CPIC

P&C, and Pingan P&C together hold more than 60% market share. Under “Socialism with

Chinese Characteristics,” the two largest players---along with many smaller players---are

majority-owned by the state. Some believe that this means they have inherited the

characteristics of many state-owned enterprises, such as low efficiency and high operational

costs. In the 2010 China Insurance Yearbook, the CIRC pointed out four problems with

insurance companies in China. Firstly, high operational costs were observed for insurance

companies in China due to poor management. In particular, strategic management, core

business development and human resource investment have not been effectively planned and

implemented. Secondly, the variety of available insurance products is limited. Thirdly,

unhealthy competition among insurance companies has reduced market efficiency. It is

common for insurance companies seeking market share to provide excessive commissions to

agents and brokers: This not only increases costs but also violates some regulations. Lastly,

bad faith is a major problem with insurance company operations. Misleading sales practices

and dissatisfaction with claims handling make up 65% of the complaints filed with the CIRC.

All of these problems contribute to the suboptimal development of the insurance

market and discourage insurance demand. As the CIRC starts to address the problems and the

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market becomes more competitive, it is possible that the situation will improve in the near

future. Such improvement could have a positive impact on non-life insurance demand in

China.

4.2.3. Determinants of Individual Line of Business

A. Motor Insurance

Motor insurance currently is the most important non-life insurance line in China with a

market share of more than 70%. As a result of economic reform, private ownership of

automobiles started to rise in the past decade, especially in regions with adequate wealth

accumulation. However, current vehicle ownership in China is very low by international

standards, with only 37 vehicles per 1,000 people in 2008. To illustrate, Table 4.1 shows that

vehicle ownership is rising rapidly in China but still falls far short of the levels in more

developed countries.

Demand for motor insurance is highly dependent on vehicle ownership. Economic

growth is the main contributor to vehicle ownership growth. Other factors such as

urbanization, fuel cost and culture also impact the vehicle ownership in a country. In

KPMG’s Global Automotive Executive Survey 2012 (KPMG, 2012), a majority of the

automotive executives participating in the survey believed that China’s annual vehicle sales

in 2016 will be between 20-24 million. Forecasts of 2020 vehicle ownership per 1000 people

in China vary from a conservative estimate of 70 to an optimistic estimate of 410 in cities like

Beijing (Gu et al., 2010).

As discussed above, growing vehicle ownership adds to insurable interest and

substantially increases the demand for motor insurance. The compulsory motor third party

liability insurance regulation also supports the demand for motor insurance. Given these

factors, expansion of the motor insurance market is expected.

Table 4.1: Vehicle Ownership per 1000 People

Year China Hong Kong Japan Korea Singapore United States

2003 15 72 581 303 133 796

2004 21 72 586 311 134 808

2005 24 - - 319 137 675

2006 28 70 - 328 141 -

2007 32 72 595 - 149 820

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2008 37 73 593 346 150 809

B. Property Insurance

China’s impressive economic development has been associated with construction of

infrastructure, residential structures and commercial structures. This has established a

tremendous amount of insurable assets and boosted property insurance demand. In addition,

the opening of the property market allows foreign investors to be involved in property

investment and management in China. Foreign investors tend to be heavier users of insurance

when managing risks associated with their property exposures. Both of these factors will

positively impact the demand for property insurance in China.

C. Liability Insurance

As discussed earlier, the legal and regulatory environment is a key for liability insurance

demand. The legal environment in China is moving toward a comprehensive civil law system

which will provide the legal basis for tort liability. The clarification of tort liability raises the

awareness of liability insurance and encourages consumption of the same.

Another factor that may boost liability insurance demand is the government’s

initiative to manage public sector liability risks with a market oriented approach. This

initiative may significantly increase liability insurance demand.

Uncertainty about regulatory interventions brings uncertainty to forecasts of liability

insurance demand. Laws making certain types of liability insurance compulsory will of

course increase demand.

4.3. Discussions on Non-life Insurance Consumption in India

4.3.1. General Determinants

A. Education

A recent pre-launch survey for the awareness campaign carried out by the National Council

for Applied Economic Research and sponsored by the IRDA (NCAER, 2011) indicates: (i) a

higher proportion of insured households lie in the higher education category; (ii) the

proportion of illiterates and those educated only up to primary school is higher among

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uninsured households; and (iii) education could be an important prerequisite for household

awareness and understanding of the benefits of insurance.

Education plays an important role in enhancing the knowledge of people and in

improving their confidence level in dealing with insurance. There is an increasing level of

education at the higher secondary and tertiary level. This may contribute to growth in

insurance purchasing in the long term.

B. Risk Aversion (Risk Awareness and Attitude)

“Nothing will happen to me. If something happens it is fate.” This foregoing logic is often

summed up in the word “karma.” The concept has permeated Indian culture for centuries.

Some believe that belief in karma may lead households to absorb risk rather than mitigate it,

which makes it difficult to sell insurance.

Events such as the earthquake in Gujarat in 2001, the Tsunami in South India in 2004,

and the floods in Mumbai in 2005 have raised public awareness of insurance. The insured

losses in each of these events were estimated to be less than 5% of the economic losses. The

Head of the Gujarat Disaster Management Agency noted that the public were informed of the

need to have earthquake resistant structures for their homes and property insurance as a

financial back up. The homes as restored were made of RCC frame structure and are

earthquake-resistant.

Insurance in India has been a significant priority neither in household management

nor in business management. Ten years ago, excluding automobile insurance that constituted

40% of market premium, 90 per cent of the remaining gross market premium was from

business and manufacturing. Little was seen from personal lines (other than auto), and the

corporate purchase was likely driven more by compulsion than as a consequence of a serious

study of risk exposures.

C. Religion

In India, there is the potential to introduce Islamic insurance or takaful. The Muslim

population in India was 177 million in 2010 and is projected to grow to 236 million in 2030.

D. Legal System

The legal environment features relatively little litigation and little judicial activism. The

following may be observed in the Indian legal context:

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Lack of litigiousness

Fear of protracted judicial processes

High cost of stamp duty to register a complaint and pursue litigation

Fear of being socially maligned as a litigant

The Indian judiciary is founded upon the basis of the British legal system. The British set up

court buildings and established a set of laws. After independence, India built upon these laws

with the enactment of the Constitution in 1950 and other laws integral to it. It is of interest

that the Indian legal system has not witnessed disputes relating to racial discrimination in a

country where caste and religion are diverse. Also, unlike the West, the system does not

feature punitive damages.

It is widely felt that India has a reliable judiciary and legal system. This is a source of

encouragement for businesses to invest and transact in India. However, from an insurer’s

perspective, the relative infrequency of litigation and other factors have limited liability

insurance premium growth.

There is legislation requiring compulsory purchase of public liability insurance by

units handling hazardous substances. This was enacted in 1991. However, some believe that

its enforcement is weak, and compliance is poor.

E. Loan Requirement

Lenders requirements are a key driver of the purchase of comprehensive coverage in motor

insurance. One of the main reasons behind the unprecedented growth in Indian vehicles has

been the availability of easy financing options for a growing middle-class (ICRA Rating

Feature, 2011 March and September).

With loans and financial assistance, many consumers have been able to become car

owners. The nearly 10% annual growth of the auto industry has been possible due to the

direct and indirect help of financiers in India. Realizing the immense potential, some vehicle

manufacturers have even tied up with financiers who provide guaranteed auto finance loans

to customers.

A recent pre-launch survey for awareness campaign (NCAER, 2011) noted that motor

insurance is the most sought after by the public whether they have life insurance or not.

4.3.2. Supply Side Determinants: Insurance Market Regulation

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A. Product Design

Products are required to be filed and approved by the regulator prior to their use. Going

forward it is expected that, after gaining confidence in the propriety of market conduct, the

regulator will withdraw to a position of oversight. Less aggressive regulation may lead to

greater customization of products and faster market development. This will increase potential

for penetration.

B. Obligation

When insurers are registered, regulations require insurers to sell insurance in rural areas and

to underserved sectors of the population. The IRDA’s regulations on rural and social sectors

states that every general insurer carrying on the business of insurance in 2009-2010 and

thereafter has to write 7 percent of its gross written premium in rural areas. (Rural areas are

determined by a published list of rural areas set by the Census.)

The regulations also require a minimum of 665,500 lives to be insured in the social

sector in 2009-2010 and thereafter. Insurers who do not comply will pay a penalty. The

“social sector” includes the unorganised sector, informal sector, economically vulnerable

classes and other categories of persons, both in rural and urban areas.26

C. Motor Own Damage

With effect from 1st January 2008, IRDA confirmed removal of controls on pricing of risks as

notified from 1st January 2007.27 During this period IRDA guided and closely monitored the

26 “Unorganised sector” includes self-employed workers such as agricultural labourers, bidi workers, brick kiln workers, carpenters, cobblers, construction workers, fishermen, hamals, handicraft artisans, handloom and khadi workers, lady tailors, leather and tannery workers, papad makers, powerloom workers, physically handicapped self-employed persons, primary milk producers, rickshaw pullers, safai karmacharis, salt growers, seri culture workers, sugarcane cutters, tendu leaf collectors, toddy tappers, vegetable vendors, washerwomen, working women in hills, or such other categories of persons. “economically vulnerable classes” means persons who live below the poverty line. “other categories of persons” includes persons with disability as defined in the Persons with Disabilities (Equal Opportunities, Protection of Rights, and Full Participation) Act, 1995 and who may not be gainfully employed; and also includes guardians who need insurance to protect spastic persons or persons with disability; 27 Insurance Regulatory & Development Authority, http://www.irda.gov.in/Defaulthome.aspx?page=H1, with links to Tariff Advisory Committee and Insurance Information Bureau for archival data on regulations, circulars and statistics

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transition of the market from a tariff system of regulated rates to a free market mechanism

subject to file-and-use regulations.

The market continued to use the erstwhile tariff as a source to determine rates. At the

direct insured’s level, quotes often use the tariff as a reference point. Private insurers have

added new riders for additional premium and introduced new services such as a cashless

service at select garages. Over the 5 years since deregulation, the average motor premium for

comprehensive insurance continues to range from 2 to 3 per cent of the value of the vehicle.

Apparently, the Indian insurers have sought to preserve the level of premium received in the

post liberalization phase even if this means providing wider coverage and value-added

services.

Very recently, the IRDA announced an increase in the deductible for motor own

damage insurance. This would improve incentives for insureds to be careful. The industry

expects a marginal improvement in the loss ratio because of this change.

A market-wide database is being made available through the Insurance Information

Bureau. Insurance companies are working to establish their own centralized databases that

will be accessible at the field office level. One private insurer has incorporated actuarial

ratings into their IT system with rate calculators that are accessible at the field office level.

This will be done by other insurers as well over the next five years. In twenty years the IT

infrastructure of Indian insurers will be well established and will allow for more widespread

application of scientific rating practices.

D. Motor Third Party Liability

Since 2006, many issues relating to the adequacy of the premium charged for the coverage

and various alternatives to address the issue were brought to the attention of IRDA. On 4th

January 2011, IRDA released an Exposure Draft setting out the revised schedule of premiums.

Based on responses received on the Exposure Draft and concerns expressed by various

stakeholders during discussions, IRDA released the revised schedule of motor third party

liability premium at about 15% less than what had been proposed in the Exposure Draft for

goods-carrying and passenger-carrying vehicles. As noted earlier, this tariff is now replaced

with a fresh tariff with effect from 1st April 2012.

Independently, IRDA appointed KP Sarma, an actuary, to review the reserves of the

commercial motor third party market pool and its accounts. Following his report, IRDA

announced on 12th March 2011 stringent requirements for additional reserving, solvency

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relief, compliance and a peer review by the Government Actuaries Department (GAD) from

UK. A key finding of the KP Sarma report was that the ultimate loss ratios are 172.3%,

181.81,% and 194.15% for the years 2007-08, 2008-09, and 2009-10, respectively. Despite

this estimate, the pool has maintained reserves at 126% for all the years the pool has

underwritten third party motor liability. The insurers’ response was to raise loss provisions to

153% for each of the years including 2010-11. They sought a lower solvency margin of 1.0

(as opposed to 1.5) and to value their assets at fair market value. However, IRDA laid down

requirements of a 1.3 solvency ratio reaching to 1.5 in the year 2014. No insurer was to

declare a dividend without prior approval of IRDA in cases where the solvency ratio was less

than 1.5.

Following analysis of the situation by IRDA and based on the GAD report on under

reserving, data inadequacies and pool administration, IRDA directed: (1) Dismantling the

market pool with effect from 31st March 2012; and (2) Setting up of an Indian Motor Third

Party Declined Risk pool. IRDA’s new motor third party liability tariff across all classes of

motor vehicles is to come into effect from 1st April 2012. Each insurer now transacts this

class of business without any pooling. The legacy of the market pool in terms of outstanding

claims, reserves and management is assigned to each insurer according to what was ceded by

the insurer to the market pool. This is harsh for those insurers who used in the pool

substantially in the past.

IRDA has put through this major reform in motor third party liability insurance

having conceived of it as early as year 2002. The Appointed Actuaries of each insurer are

charged with ensuring correct reserving for this line of business, as the management is now

shifted from GIC to the respective insurers.

Price increases in the future will depend on claims experience. There can be lobbying

for reduction both politically and through business chambers. These may influence rate

increases in future. It now appears that the system is in place for the long term. This can be

factored as built-in increases for, say, the next 20 years.

E. Property

The Tariff Advisory Committee via its circular ref. TAC/7/06 dated 4th December 2006

decided that the rates, terms, conditions and regulations applicable to Fire, Engineering,

Motor, Workmen’s Compensation and other classes of business currently under tariffs shall

be withdrawn effective from 1st January 2007.

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Insurers filed their products and rates with IRDA. While they awaited approval the

insurers needed to handle the issue of renewal notices. IRDA allowed the insurers to quote

20% lower than the dismantled fire and engineering tariffs until the approvals were received.

On 14th December 2007 IRDA convened a meeting of non-life insurers to enlist their

cooperation on high standards of underwriting and business conduct. IRDA accepted the rate

schedules and rating guides as filed by the insurers on the stipulation that they are in

compliance with the underwriting policy as approved by the respective Boards of Directors

and on the condition that they are designed so as to produce an operating ratio (incurred

claims plus commission and expenses of management) not exceeding 100% on a gross

underwriting basis. IRDA retains the right to inquire about or require changes to any such

rate schedules and rating guides at its sole discretion.

With effect from 1st January 2008, IRDA confirmed removal of pricing controls as

notified from 1st January 2007. During this period IRDA guided and closely monitored the

transition of the market from tariff to free market mechanism subject to file-and-use

regulations.

The General Insurance Council has undertaken the responsibility of developing

Standard Market Wordings for Fire, Engineering and Motor policies to be followed by

insurers in the tariff free regime.

4.3.3. Forecasting the future

A. Uncertainty in Economic Growth: Middle Income Trap28

Steps are being taken to reduce the likelihood of India falling into the “middle income trap”

when economic growth tapers off in the next five or ten years. India has laid emphasis upon

growth through inclusion with due emphasis upon the bottom of the income pyramid. The

productivity of the middle class, domestic consumption and continued competitiveness

overseas are expected to insulate India from a shock of falling into the middle income trap.

For its own economic and resource reasons India stands out differently from other countries

in regard to past experiences of falling into the middle income trap. Uncertainty as to India’s

economic future is more attributable to political uncertainty.

28 The middle-income trap is defined by a situation where a country which attains a certain income gets stuck at that level. For example, South Africa and Brazil have stayed for decades within the “middle income” range (about $1,000 to $12,000 gross national income per person measured in 2010 money).

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B. Uncertainty in Insurance Market Growth

India is weak in its non-life insurance penetration. Consumption of insurance is not led by

demand. This may change in health insurance, as corporations are purchasing group health

insurance and now the retail sales of health insurance are gradually increasing. Regulatory

tariffs for motor third party liability insurance are helping to increase motor insurance

premium. A growing vehicle population is contributing to tmotor insurance premium growth.

Regulatory emphasis upon micro-insurance, mono-line agents, and the need to spread

distribution to rural areas are expected to assist penetration in the line of property insurance.

Otherwise the insurance market struggles to raise awareness of itself and insurance services.

Penetration for the Indian insurance industry is one of the biggest challenges and a market

uncertainty.

Recent reserving requirements in Motor Third Party liability insurance have set back

results of insurance companies. The Insurance Regulator has provided regulatory relief for

complying with solvency norms in entirety until the year 2016. This situation constrains

insurance companies from expanding and demands additional capital for any such efforts.

With inflation and interest rates remaining high, the potential for corporations to declare

dividends and for the shares to appreciate is uncertain. This affects the growth plans of

insurance companies.

The consequences of a catastrophe upon the population are a concern of the

government. There is no active partnership with the insurance industry in managing

catastrophe risk. Over the last two years a dialogue has taken place between the government,

insurance industry and other stakeholders on how to take this forward.

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4.4. Concluding Remark

In this section, we discuss several relevant topics in those two prominent insurance markets

and highlight that forecasting long-term growth of non-life insurance consumption is

challenging in that there are many uncertain factors surrounding the market. It is particularly

true for developing countries such as China and India. We believe that it is not realistic to

quantify and model all the factors, so we provide qualitative discussion on unmodeled factors

affecting non-life insurance consumption in both countries.

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Appendix 4A The Historical Development of the U.S. Insurance Industry: The U.S. Market in 1900 and in 2000

Property-casualty insurance consumption in 1900 amounted to about 1% of GDP and $6300

per capita in 2010 dollars. About 90% of the consumption was dedicated to fire insurance.

Indeed, the business was so heavily weighted toward Fire coverage that it was typically

referred to as the “Fire and Marine” industry in those days, as opposed to the “Property-

Liability” industry or “Property-Casualty” industry. This is not to say that liability coverage

did not exist in 1900. It did, as did coverage against property perils other than Fire.

Nevertheless, the volume of premiums in coverages other than Fire insurance was relatively

small.

The 20th century turned out to be a golden age for the property-casualty industry in the

United States. The sector grew much faster than the overall economy, increasing its share of

GDP to around 3%-4% by the turn of the millennium. Part of this growth is shown in Figure

4A.1.

Figure 4A.1 US Nonlife Penetration (NPW/GDP)

What occurred over the course of the 20th century would have been quite hard to fathom for a

prognosticator in 1900. At the time it would surely have been tempting to focus on property

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

1930 1940 1950 1960 1970 1980 1990 2000

NPW/GDP

102

insurance---Fire insurance in particular---as it comprised the vast majority of property-

casualty insurance premiums in 1900. However, Fire insurance, despite its dominant position

in 1900, did not turn out to be the growth engine of the industry during the 20th century.

Indeed, by the year 2000, property insurance of the type that predominated in 190029 (that is,

on structures, cargo and personal effects) amounted to $97 billion (slightly less than 1% of

GDP)---despite a great expansion in the penetration of coverage against perils other than Fire.

Growth came instead from two main forces at the societal level---1) technological change,

and 2) a stunning change in the assignment of financial responsibility for injuries.

A. Autos and Lawsuits

Technological change came in the form of the automobile. The automobile had been invented

before 1900, but it was not until first decade of the 20th century that mass production started

the process of putting vehicles within the reach of the general population. Mayhem on the

roads soon followed, and by 2000 vehicle owners were shelling out nearly $58 billion per

year to protect themselves from the financial consequences of damage to their vehicles.

Injuries to others caused by their vehicles, however, were the bigger boon to insurance

industry: In 2000, the industry collected more than $85 billion to protect auto owners and

drivers from the financial consequences of damages caused to others and their property.

Thus, the automobile alone generated $143 billion of insurance premium volume in the

United States in 2000, or about 1.4% of GDP.

Automobile liability insurance was one example of a proliferation of insurance

coverages relating to potential third party claims. Workers Compensation insurance,

purchased pursuant to state laws enacted in the early 1900’s which assigned strict liability to

employers for workplace injuries, produced nearly $30 billion in premium. Other Liability,

Medical Malpractice, and Products Liability accounted for nearly $34 billion in premium.

All in all, third party coverages accounted for more than half of the $319 billion in direct

premium written in the United States in 2000.

Thus, over the course of the 20th century, insurance premiums in relation to GDP rose from

around 1% to over 3%. However, virtually all of this growth came from insurance coverages

that seemed relatively unimportant at the beginning of the century. Perhaps there were some

hints of the change afoot at the start of the 20th century, but it would have taken a very keen

29 For purposes of comparison, I have tallied the lines of Homeowners, Farmowners, Fire, Allied Lines, Earthquake, CMP, Ocean Marine, Inland Marine, Fidelity, Surety, Burglary and Theft, Boiler and Machinery, and Other Lines. This categorization wraps in a bit of liability insurance (most notably through the CMP line).

103

observer to anticipate the dramatic structural transformations that occurred during the 20th

century in the United States.

In 1900, civil litigation in the United States was a much less rewarding enterprise than

it is today. Legal doctrines of the era generally provided strong defenses for defendants. For

examples, the Assumption of Risk doctrine was commonly applied, where an injured plaintiff

was denied redress if he or she was judged to have been in a position to appreciate the risks

involved with an activity before the ensuing injury; the Fellow Servant Rule was applied by

employers to deny redress to injured workers in cases where the actions of other employees

had contributed to the accident; the doctrine of Contributory Negligence denied redress in

cases where the actions of the plaintiff had contributed in any way to the injury.

Moreover, even when plaintiffs had a strong case, it was not uncommon for them to

recover sums that seem paltry in comparison with the amounts today: Expectations were far

different, perhaps due in part to the long odds associated with litigation; and even successful

litigants could well find themselves confronting an effectively judgment-proofed defendant.

An oft-cited example from the early 1900’s is the Triangle Shirtwaist Company Fire which,

despite claiming the lives of over 100 garment industry workers in New York in 1911,

produced puny settlements. Also poorly compensated were the victims in the General

Slocum disaster of 1904, where a poorly maintained and incompetently operated excursion

boat caught fire and sank, killing about 1000 passengers.

Plaintiffs in both of these tragedies faced at least two barriers to compensation. First,

societal norms do not appear to have favored significant recoveries through litigation; second,

defendants often possessed limited resources even when compensation was awarded, making

actual recoveries difficult or impossible. However, though the environment in 1900 was

decidedly hostile to plaintiffs by today’s standards, both barriers appear to have been in the

process of lowering. A change in the status quo was afoot and gathered momentum over the

course of the 20th century, so much so that by the end of the century the weight of advantage

had moved significantly in the direction of plaintiffs.

With respect to workplace accidents, a number of states adopted limitations to

employer defenses in the first decade of the 20th century, and, by 1920, 42 states had adopted

Workers Compensation laws---which assigned a form of strict liability on employers for

workplace accidents and imposed financial responsibility on employers, who were required

to purchase insurance to provide for claims by injured workers.

The notion of what constituted an accident changed dramatically over the course of

the 20th century, and this change is vividly illustrated in Workers Compensation cases. In

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Young v. Melrose Granite Company (1922), a Minnesota court struck down Young’s claim.

Though his body had clearly atrophied after years of work as a stonecutter, the court ruled

that this was not an “accident” because nothing “happened violently or suddenly…” and,

further, that injuries occurring due to “ordinary overwork” were not compensable. A

harbinger of the change in societal attitudes toward long-term occupational diseases and

injuries could be found in Beaver v. Morris-Knudsen Co. (1934), where an Idaho court found

in favor of the widow of a deceased rock crusher by ruling that silica dust [grinding] on a

workman’s lungs for many months” was indeed an “accident.”30

Societal standards for compensable damages also changed dramatically. An

illustration is provided by Christy Brothers Circus v. Turnage (1928), where a Georgia court

considered the case a woman who had suffered the indignity of having a horse “[evacuate]

his bowels into her lap.” The court awarded $500 to the woman, but, importantly, rejected her

reasoning that she was entitled to an award because of “mental pain and suffering”---instead

arguing that the physical contact with the horse dung constituted a compensable physical

injury.31 No such logical contortion was necessary by the end of the century, by which time

the awarding of noneconomic damages was commonplace.

The notion of who could be sued also changed substantially during the 20th century.

In MacPherson vs. Buick Motor Company (1916), the New York Court of Appeals set a new

precedent by allowing the driver of a defective automobile to sue the manufacturer directly

(rather than the dealership), thereby opening the door for product liability cases.32 Notions of

governmental immunity also eroded during the 20th century, as did the doctrine of charitable

immunity. In Bing vs. Thunig (1957), a New York court fired a volley against the doctrine of

charitable immunity by holding a hospital liable for the actions of one of its acting

physicians.33 Notions of governmental immunity also eroded during the 20th century.

Along with the shift in societal norms regarding the assignment of legal responsibility

and the nature of damages came an increase in efforts to ensure that defendants would be able

to meet potential judgments. By the end of the 20th century, compulsory insurance in both

Workers Compensation and in Automobile Liability was the norm in most states.

B. Property Insurance

30 Case references and analysis taken from Page 362 of: Friedman, Lawrence (2002), American Law in the Twentieth Century, Yale University Press: New Haven. 31 Friedman, p. 358. 32 Friedman, p. 356. 33 Friedman, p. 359.

105

As noted above, property insurance premiums have grown little if at all in relation to the

economy, but this characterization masks some important compositional changes.

Firefighting technology advanced significantly, as did the effectiveness of building codes and

other initiatives at fire prevention, so the cost of protecting property against the specific peril

of fire presumably declined significantly over the past century. However, this was offset by

coverage expansion in a number of areas.

Fire coverage itself expanded dramatically in scope during the 20th century. The New

York Standard Fire Insurance Policy of 1886 was still the regulatory benchmark in 1900, and

it was characterized by restrictive exclusions (e.g., collapse; neglect by the insured to

preserve property), lengthy limitations on unscheduled personal property, no coverage for

additions and appurtenances, and tough voidance provisions (e.g., vacancy for a period of

more than 10 days; presence of a chattel mortgage). Exclusions and voidance provisions in

modern contracts are of course much less burdensome, and coverage for most unscheduled

personal property is now standard, as is coverage for additions and appurtenances.

Another dramatic shift came in the perils covered in standard policies. In 1900,

insurance against other perils was obtained in a hodge-podge of separate policies, and, though

widely available, was relatively unimportant in terms of premium volume. The NJSRO

introduced a “supplemental contract” in 1930 which bundled coverage from a number of

these additional perils into a single contract which could be sold along with the Fire contract.

This was eventually converted into the Extended Coverage Endorsement, which simplified

matters by referencing the underlying Fire contract. These innovations made it easier for

consumers to access multiple peril coverage and ultimately led to the innovation of multi-

peril policies in the 1950’s.

By the end of the 20th century, typical property coverage was much broader and

deeper than in 1900, and this helped offset the decline in the importance of the fire premium.

In addition, institutional infrastructure had been developed to further boost demand for

property insurance. Bank regulation and the mortgage finance system both featured

requirements of property insurance: For example, government-sponsored enterprises Fannie

Mae and Freddie Mac require purchased residential loans to carry hazard insurance, and this

requirement reflects typical standards of prudential banking regulation.

C. Catastrophe Coverage

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Limits of progress are found in various catastrophe markets in the United States. One might

have guessed that flood coverage and earthquake coverage would have eventually been

wrapped into the multi-peril policies developed in the 1950’s, but this has not been the case.

The United States experience mirrors the pattern observed in other developed countries,

where catastrophe insurance markets often feature significant levels of government

intervention.

Partly motivated by the lack of a well-developed private market for residential flood

insurance, the National Flood Insurance Act was passed in 1968 and created the National

Flood Insurance Program, which is underwritten by the federal government offers basic flood

insurance to households. Most residential flood insurance in the United States is written

through this federal program, with penetration reaching up to 50% in high risk zones---

largely on the strength of subsidized pricing and federal requirements for flood insurance on

any federally related mortgage in high risk areas. But private market coverage for flood is

extremely limited, with the main exposures found in Difference-in-Conditions (DICs)

policies for businesses.

Earthquake coverage is typically not required by mortgage lenders and, as a

consequence, is not usually purchased by households. California’s market was once an

exception to this rule. It developed gradually and was even thriving by the early 1990’s, when

perhaps a third of households had coverage. However, the Northridge Earthquake of 1994

caused a major retreat of private underwriters and ultimately led to the creation of a state

facility for earthquake insurance called the California Earthquake Authority. Today, only

around 10% of California’s homes are insured, and these purchase rates are high relative to

other parts of the country with earthquake risk. About 2/3 of the California exposure comes

through the California Earthquake Authority---which, unlike the National Flood Insurance

Program, does purchase private reinsurance. Additional private market exposure is

attributable to commercial DIC risks.

Windstorm coverage is typically required in the mortgage finance system, and thus,

not surprisingly, windstorm insurance is a much hotter political issue than earthquake

coverage---especially in coastal of the southeastern United States. Various state-level

facilities have been created to deal with residual market problems in high-risk areas, but

private market engagement in the risk is much higher than with earthquake.

Following the terrorist attacks of September 11, 2001, a federal reinsurance program

dubbed the Terrorism Risk Insurance Program was created to ease pressures in the market for

terrorism coverage.

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5. Catastrophe Exposure Growth in China and India

Growth of nonlife insurance consumption suggests potential growth of insurer exposure to

natural and man-made catastrophes in China and India. In this section, we investigate the

potential size of economic damages and insured losses. We are constrained here by data

limitations in that the level of aggregation in our data permits only limited analysis of trends

in local and regional exposures. Moreover, without access to catastrophe models, we are not

in a good position to speculate on events that could happen but have not happened. Thus, we

confine our attention to considering the impact associated with recent actual large loss events,

undergoing the thought experiment of what would happen if these specific events were to

recur in the future.

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5.1. Recent Catastrophes

Panel A in Table 5.1 lists significant recent catastrophes from non-China/India countries, and

Panel B lists recent four catastrophes that triggered the largest insured losses in China and

India since 2003.34 We list each of the loss events in Panel A.

Table 5.1: List of Recent Major Catastrophes

Date

Total damage (2011 USD b)

Total damage (% of GDP)

Insured loss (2011 USD b)

Property penetration (t-1; %)

Insured loss/ damage (%)

Insured loss/ premium (%)

Panel A

Hurricane Katrina Aug-05 155 1.14 51.8 0.98 33.3 38.9

NZ Christchurch EQ Feb-11 15 10.51 12.0 0.83 80.0 1012.9

Japan northeast EQ Mar-11 210 3.85 35.0 0.24 16.7 267.1

Thailand flood Jul-11 30 9.40 12.0 0.18 40.0 2089.9 Panel B

India Maharashtra flood Jul-05 5 0.47 1.3 0.1 25.3 118.0

China snow storms Jan-08 21 0.57 1.4 0.09 6.5 41.3

China Sichuan EQ May-08 132 3.58 0.4 0.09 0.3 12.1

China floods May-10 54 1.05 0.8 0.12 1.5 12.7

Source: Swiss Re Sigma, World Bank Indicators, US Bureau of Labor Statistics

In the US, hurricanes cause large insured losses due to the size of the economic

damages and the penetration of the National Flood Insurance Program (about US$2.8 billion

in premium, 0.02% penetration in 2011).35 Five of the ten most costly insured catastrophe

losses since 1970 were caused by US hurricanes, the largest being Hurricane Katrina with

US$45 billion in insured loss (loss estimate under one-year loss development). However, the

total economic damages amounted to just 1.14% of US GDP.36 The insured loss/premium

ratio defined by the insured loss incurred by the event divided by property insurance written

gross premium is not significantly large due to the size of the US property insurance market.

Yet, the insured loss/premium ratio based only on the flood insurance premium reached

2,669%.

34 A flood occurred in Maharashtra, India in August 2006. The flood created USD 0.56 billion in insured losses which is larger than the insured loss from the 2008 China Sichuan EQ. To avoid repetition, the flood event is omitted. 35 Preliminary data taken from Axco Global Statistics. 36 GDPs are taken from one year prior to the event year to avoid the impact of the economic damage on GDP.

109

The New Zealand Christchurch earthquake in February 2011 was an extreme case in

that 80% of the US$ 15 billion economic damage was covered by insurance. The insured

loss/premium ratio reached 1,013%. This event shows that relatively small economic damage

can cause relatively large insured losses in the presence of a government catastrophe

insurance program. In New Zealand, the government catastrophe insurance program called

EQCover is compulsory for private fire insurance policy purchasers (The premium for

earthquake coverage is estimated to be US$ 277 million, which represents a 0.17%

penetration rate in 2011).37 The insured loss/premium ratio based only on the earthquake

premium is 4,332%.

The Japan northeast earthquake that triggered a devastating tsunami in March 2011

illustrates another extreme case. The economic loss reached US$ 210 billion, which is among

the largest for the listed catastrophes, but only 16.7% of the damage was covered by

insurance due to the low penetration of earthquake coverage in severely affected regions. In

Japan, earthquake coverage is not compulsory: Fire insurance policyholders voluntarily add

coverage against EQ. EQ insurance premium is US$ 1.65 billion, with a penetration rate of

0.03% in 2010.38 The insured loss/premium ratio of this event calculated solely by the

earthquake premium reached 2,121%.

The Thailand flood in July 2011 was also unprecedented in that it produced the largest

insured loss (US$12 billion) caused by fresh water flood. The insured loss/premium ratio

(with respect to property premium) was a stunning 2,090%. This loss event is different from

the three events above in that the huge insured loss was not associated with a government

catastrophe insurance program: Instead, the exposures were generated by private all peril

policies. This case highlights the potential for significant industry exposure to catastrophic

loss in emerging markets.

37 Preliminary data taken from Axco Global Statistics 38 The US earthquake insurance premium is approximately USD 2 billion and the penetration is 0.01%.

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5.2. Growth of Economic Damages and Insured Losses

5.2.1. Growth of Economic Damages

Panel B in Table 5.1 shows the size of the largest insured catastrophe losses in China and

India. In general, the economic damages were far greater than the insured damages. For

instance, the 2008 Sichuan earthquake caused US$132 billion of damages (in 2011 prices)

which is 3.58% of China’s GDP. This economic damage is the largest of the four events

listed. The amount of economic damage will of course increase according to the country’s

economic growth. Therefore, as the economy grows fast in China and India, exposure should

grow fast in both countries.

Figure 5.1 illustrates the hypothetical economic damages of these loss events under

the assumption that economic damages track with GDP growth as outlined in our baseline

scenario. If an earthquake similar to the 2008 Sichuan EQ hits in 2020, the economic loss

would reach US$293 billion. The potential economic damage increases to US$ 526 billion in

2030. Thus, the economic damage in 2020 (2030) could be approximately 2.2 times (4 times)

greater than the damage seen in 2008.

Figure 5.1: Predicted Economic Damage of Hypothetical Cat Events

Damage (USD b)

0

50

100

150

200

250

300

350

400

450

500

550

600

Year

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

PLOT China_2008_Sichuan_EQ China_2010_FloodsChina_2008_Snow_Storms India_2005_Maharashtra_Flood

111

5.2.2. Growth of Insured Losses

As indicated in Panel B in Table 5.1, the largest insured catastrophic loss in China was the

US$ 1.4 billion associated with snow storms in 2008; the corresponding figure for India was

the US$ 1.3 billion caused by flooding in 2005. The insured losses are considerably smaller

than those listed in Panel A.

Many factors will of course influence the growth of insured catastrophic exposure in

these countries, including the course of development, government policy regarding

catastrophe insurance, and so forth. We do not contemplate these complications here, but

instead simply illustrate how catastrophe losses associated with historic events might increase

with economic development and insurance market growth. Here we investigate potential

growth of the insured losses. The future insured losses are estimated by considering two

separate factors:

Predicted Insured Loss Predicted Property Premium Insured Loss/Premium Ratio

A simple approach uses property insurance premium predicted in the previous section and

fixes the event-specific insured loss/premium ratio given in Table 5.1 Panel B. Note that the

exposure growth of the loss event illustrated in this calculation is solely attributed to the size

of the predicted national property premium.

Figure 5.2 illustrates the growth of the insured loss for the historic catastrophic loss

events of Table 5.1 Panel B. The growth pattern of the predicted insured losses is slightly

different between China and India because the level of the property insurance consumption

growth is slower in India. The projected insured loss associated with the catastrophic events

is the largest in China’s snow storms in 2008 and reaches US$11 billion. All other predicted

insured losses fall below US$4 billion even in 2030.

112

Figure 5.2: Predicted Insured Loss of Hypothetical Cat Events

The projections of the preceding subsection are of course crude and are predicated on

events that were relatively insignificant in terms of insured losses. But future events might

follow different patterns. While recent events are certainly worth contemplating, catastrophic

events that have NOT happened in recent memory could have far greater impacts in China or

India.

As indicated in Panel B Table 5.1, the 2005 India Maharashtra Flood is a case where

insured loss/damage ratio (25%) and insured loss/premium ratio (118%) are both relatively

high among events in China and India. This large loss was caused by exposure to flood risk in

Mumbai, the largest city in India. Even with a relatively small premium base, future

catastrophes in certain areas could produce significant losses if certain conditions are met.

The 2011 Thailand flood illustrates this possibility. Property insurance penetration in

Thailand is not high, and there is no flood insurance program to promote flood coverage in

the Chao Phraya River basin. Yet, the insured loss/total property premium ratio was 2090%,

as the flood happened to strike Bangkok and commercial policies tended to cover flood risk.

Insured Loss (USD b)

0

1

2

3

4

5

6

7

8

9

10

11

12

Year

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

PLOT China_2008_Sichuan_EQ China_2010_FloodsChina_2008_Snow_Storms India_2005_Maharashtra_Flood

113

It is possible that similar outcomes could be realized in China and India. In China,

flooding has historically been a major problem due to the relatively dense population and rich

cities along rivers. Recent development of commercial properties and residential areas could

be unknown risk factors. Completion of the Three Gorges Dam also poses risk for

unprecedented flood, though it may also reduce the frequency of floods along the Yangtze

River. In India, flooding caused by storms has been a major type of large loss event as well,

though the lack of flood insurance has to date kept losses relatively low. Devastating floods

brought Mumbai to a standstill in July 2005, resulting in insured losses of US$ 844 in current

prices.

If we were to use the Thailand flooding as a benchmark for predicting potential losses

in China and India, we would of course come up with a far more alarming answer than in the

previous subsection. Assuming 2,090% of insured loss/premium ratio recorded by the 2011

Thailand flood for the 2010 China floods and the 2005 India Maharashtra flood, simple

calculation produces the future insured loss. The severity of the 2010 Chinese floods

increases to US$202 billion in 2020 and the severity of the 2005 India Maharashtra flood

increases to US$36 billion in 2020. However, this scenario is unlikely to be realized because

such a huge insured loss/premium ratio event would not be likely to occur in the large

countries such as China and India, as their urban production centers and population are more

diversified geographically than in Thailand---where much is concentrated around Bangkok.

114

5.3. Regional Penetration Rate

Country level projections can provide some crude guidance on catastrophe exposure growth,

but it should be noted that there is a large discrepancy between provinces and cities in China.

Uneven penetration of property insurance affects the association between insured loss and the

type of catastrophe.39 Figure 5.3 plots province-city level commercial property insurance

penetration in 2010.40 The country can be divided into four rating zones for commercial

property windstorm/flood rating purposes according to the Insurance Association of China

(IAC) and the China Insurance Regulatory Commission (CIRC). The rates are lowest in Zone

1 and highest in Zone 4. The highest penetration is recorded in Beijing at 0.17% for

commercial property, and the lowest is Henan province at 0.028%. Clearly, Beijing and

Shanghai are separated from other regions in commercial property insurance penetration.

In contrast, property insurance penetration in Figure 5.4 includes both commercial

property and agricultural insurance. In China, commercial property and agricultural insurance

altogether make up 90% of total property insurance premiums (US$ 6.8 billion at 0.12%

penetration).41 Provinces such as Heilongjiang and Inner Mongolia are among the provinces

with the highest penetration rates due to large agricultural insurance consumption.

39 There is no publicly available state-level insurance data in India. 40 Dalian, Ningbo, Qingdao, Shenzhen, Xiamen, Ningxia, and Xinjiang are excluded in Figure 3. 41 Commercial property premium is USD 4.0 billion at 0.07% penetration and agriculture insurance premium is USD 2.0 billion at 0.04% in 2010. Personal property insurance premium is not recorded in China Insurance Yearbook.

115

Figure 5.3: Chinese Province-City Level Commercial Property Penetration in 2010

116

Figure 5.4: Chinese Province-city level Property Penetration in 2010

Table 5.2 lists, at the insurer-province level, the 10 largest claim payments recorded in

the China Insurance Yearbook since 2005.42 It indicates that 2 large losses were associated

with agricultural insurance in Heilongjiang where agricultural insurance penetration is high.

However, 6 out of the 10 largest claims were caused by the 2008 China snow storm and

reported in provinces where the property penetration is not high.

42 The large claim payment recorded in China Insurance Yearbook may not contain all large claims paid by insurers.

117

Table 5.2: Insurer-Province Level Large Claim Payments in China

Province Year Insurer Business line Loss description Claim Paid (2010 USD

m) Heilongjiang 2009 Yangguang

Agri. Agriculture Various natural disasters 128

Guizhou 2008 Various insures Commercial Snow storm: Southern Electrical Grid Co. 127

Zhejiang 2005 PICC All lines Typhoon 113

Sichuan 2009 Ping An Commercial 5.21 Sichuan EQ 110

Hunan 2008 PICC Commercial Ice disaster 106

Zhejiang 2008 PICC Commercial Snow storm caused 20624 claims 105

Heilongjiang 2010 Yangguang Agri.

Agriculture Various natural disasters 103

Guizhou 2008 PICC Commercial Snow storm: Guizhou Electrical Grid Co. 93

Jiangxi 2008 PICC Commercial Snow storm 70

Jiangxi 2008 various Commercial Snow storm: damages to electricity 64

118

5.4. Catastrophe Insurance Programs

There are other developments that would increase catastrophe exposure through the increase

of penetration. Coverage against specific types of catastrophes could be substantially

increased by the introduction of government insurance programs, for instance. As mentioned

above, the US, New Zealand, and Japan have government catastrophe insurance programs, or

policies in lending markets, that facilitate catastrophe insurance penetration. EQC insurance

in New Zealand is compulsory for fire insurance policy purchasers and the coverage is

comprehensive, while the US National Flood Insurance Program and the California

Earthquake Authority (CEA) earthquake insurance program are literally catastrophe type

specific and not mandatory for most purchasers. However, US banking and finance regulation

provides strong incentives for properties to carry windstorm coverage.

119

5.5. Reaction in India: Natural Catastrophic Perils Minimum Rate

Following major catastrophic losses in India and other parts of the world, Indian insurers are

being forced to charge minimum rates for catastrophic AOG (Act of God) perils under fire

and engineering policies with effect from 1 March 2012. In view of recent natural

catastrophes such as the Thailand flood and Japanese EQ in 2011, insurers in India have

decided to charge separate rates for STFI (Storm, Tempest, Flood and Inundation) and

Earthquake. The policyholder has an option to opt out of STFI cover. EQ is add-on coverage.

The detail of the rates is provided below. They may charge over and above these rates

depending upon the past claim history and other features of the risk.

Rates charged for Fire and IAR Policies in India

A) STFI Rates: Occupancy Rate (per mille) Dwellings 0.01 Non Industrial - Hotels, Shops as per Section III of Erstwhile Fire Tariff 0.05 Industrial including utilities located outside the industrial units 0.10 Standalone storage outside manufacturing plant - rated under section VI. 0.15

B) Earthquake Rate to be charged -Zone wise for both Industrial and Non Industrial Zone* IV III II I Non Industrial - Dwellings, Shops, Hotels 0.05 0.05 0.05 0.05 Industrial, Storage Outside Manufacturing, Utilities outside manufacturing

0.05 0.1 0.25 0.5

* The EQ Zone follows the erstwhile tariff.

Rates to be charged for Engineering Policies

A) STFI Rates for EEI/ CPM/ CAR/ EAR: 0.15 per mille per annum

B) Earthquake Rate to be charged – Zone wise Zone IV III II I EAR/ CAR/ EEI/ CPM (Annual Rates) 0.05 0.1 0.25 0.5

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6. Impact of Financial Crisis on Non-life Insurance Consumption43

We study the connection between financial crises and non-life insurance consumption in a

panel of 129 countries covering the period 1988-2008. After controlling for income and other

determinants of insurance consumption, we find a significant negative and persistent

association between the occurrence of a crisis and subsequent level of per capita non-life

insurance consumption of about 15% over a period 15 years. We interpret this finding in the

context of the macroeconomic literature on the real effects of financial crises, noting its

consistency with a reduction in risk-taking at the societal level.

43 Working paper (Kamiya, Zanjani and Lee, 2013)

121

6.1. Introduction

An interesting and unappreciated consequence of the Great Depression in the United States

was a significant retardation in the development of the property-casualty insurance market.

As can be seen in Figure 1, the premium volume in relation to GDP stagnated for more than

20 years; indeed, at the nadir during the early 1940’s, the penetration rate (i.e., total

premium/GDP) was not far off the levels of 1900 and was significantly below the levels seen

on the eve of the financial crisis in the late 1920’s.

Figure 1 Non-life Insurance Premium Penetration (NPW/GDP) in the US

Similar outcomes were observed in Scandinavia following regional banking crises of

the early 1990’s (see Figure 2). And the same characterization applies to a number of

countries after the Southeast Asian crisis of the late 1990’s (see Figure 3). In all of these

cases, the country’s economy recovered lost ground relatively quickly. Yet, the non-life

insurance market recovery lagged significantly, and the industry failed to recover its pre-

crisis standing in these economies even after a decade or more.

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

1930 1940 1950 1960 1970 1980 1990 2000

NPW/GDP

122

Figure 2: Effect of Scandinavian Banking Crisis on Non-life Insurance Consumption

6

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

10

10.1

10.2

10.3

10.4

10.5

10.6

10.7

10.8

10.9

1990 1995 2000 2005 2010

Sweden

Log GDP per Capita (Left Axis)Log Nonlife Density (Right Axis)

6

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

9.6

9.8

10

10.2

10.4

10.6

10.8

11

1990 1995 2000 2005 2010

Finland

Log GDP per Capita (Left Axis)Log Nonlife Density (Right Axis)

123

Figure 3: Effect of Southeast Asian Crisis on Non-life Insurance Consumption

2.5

2.7

2.9

3.1

3.3

3.5

3.7

3.9

4.1

4.3

4.5

7.1

7.3

7.5

7.7

7.9

8.1

8.3

8.5

1990 1995 2000 2005 2010

Thailand

Log GDP per Capita (Left Axis)

Log Nonlife Density (Right Axis)

4

4.2

4.4

4.6

4.8

5

5.2

5.4

7.7

7.9

8.1

8.3

8.5

8.7

8.9

9.1

1990 1995 2000 2005 2010

Malaysia



4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

9

9.4

9.6

9.8

10

10.2

10.4

10.6

1993 1998 2003 2008

Brunei



5

5.5

6

6.5

7

7.5

8

9.6

9.8

10

10.2

10.4

10.6

1992 1997 2002 2007

Singapore



124

In this section, we show that the foregoing are not isolated anecdotes but instead

examples of a consistent pattern. Unlike banking, where evidence suggests that aggregate

deposits experience little change even in the short to intermediate term after a financial crisis

(Demirguc-Kunt, Detragiache, and Gupta (2006)), we find evidence that per capita non-life

insurance premiums experience a long-term reduction after financial crises---on the order of

about 15% over a period of 15 years.

This finding may help to shed light on a recent puzzle in the macroeconomic literature

concerning the real effects of financial crises. A growing body of evidence suggests that

crises are associated with significant and persistent effects on real output (Cerra and Saxena,

2008; Furceri and Zdzienicka, 2011; Furceri and Mourougane, 2012), but the reasons for

these effects are unclear. A variety of mechanisms by which crises might lower potential

output have been suggested in the literature (see Furceri and Mourougane, 2012). In

particular, one important line of reasoning focuses on the investment channel, arguing that

crises raise uncertainty and risk premia associated with investment (Pindyck, 1991; Pindyck

and Solimano, 1993), perhaps due to the impact of crises on investor confidence (Rioja, Rios-

Avila, and Valev, 2011).

Broadly speaking, non-life insurance is used to protect investments by businesses and

households, and the decline in non-life insurance premiums relative to GDP may suggest a

shift away from higher risk and higher return investments toward safer activities, a shift

mirrored in the banking system’s shift away from private credit toward safer investments

after a financial crisis (Demirguc-Kunt, Detragiache, and Gupta, 2006).

This study also contributes to the literature on insurance consumption. A number of

studies analyze the determinants of insurance demand (e.g., Truett and Truett, 1990; Browne

and Kim, 1993; Outreville, 1996; Browne et al., 2000; Ward and Zurbruegg, 2002). Others

have studied the nexus between insurance and economic growth (e.g., Ward and Zurbruegg,

2000; Zeits, 2003; Hussels et al., 2005; Outreville, 2012). Still others have studied the

determinants of insurance market growth (see, for instance, Enz, 2000, Zheng et al., 2009;

Zheng et al., 2008) by modeling the relationship between GDP and insurance consumption

measures.

However, to our knowledge, ours is the first study to quantitatively evaluate the

impact of financial crises on non-life insurance markets. The effects could be significant.

Financial crises may affect the supply side of the market by impairing assets on insurer

balance sheets. They may also affect demand for insurance if, as suggested above, activities

125

which had previously boosted insurance demand are curtailed due to a loss of confidence or

an unfavorable investment climate.

The remainder of this article is organized as follows. In Section 6.2, determinants of

insurance consumption are identified and briefly explained. In Section 6.3, the methodology

of our empirical estimation is described. Estimation results are discussed in Section 6.4. A

summary of our findings and a discussion of limitations in our work are contained in Section

6.5.

126

6.2. Determinants of Insurance Consumption

Income

A positive relationship between the size of economy and insurance consumption is a robust

finding in the literature. To our knowledge, all existing studies on forecasting insurance

consumption use a country’s GDP per capita as a proxy for the country’s level of income (see,

for instance, Zheng et al., 2009; Zheng et al., 2008).44 This is of course an intuitive finding:

A country’s economic growth increases the volume of insurable assets and provides the

means for consumers to buy insurance.

Notably, Browne, Chung, and Frees (2000) identify factors associated with insurance

density of two lines of business---motor and general liability---in OECD countries. They fit

models to identify factors that explain insurance consumption. They find that income as

measured by per capita GNP has a stronger association with changes in motor insurance

consumption than with general liability insurance consumption.

We introduce per capita GDP as a proxy for a country’s level of income. See Table 1 for

summary statistics of all variables.

Human Capital

The level of education is considered as one of the important determinants of non-life

insurance consumption because education adds to an individual’s human capital and could

increase risk awareness. Although education is often used as a proxy for risk aversion in

existing studies, the empirical results are mixed.

In theory, accumulation of human capital could be a driver of economic growth, which will,

in turn, generate insurable assets in non-life insurance. For example, individuals with higher

human capital earn higher wages and are thus more likely to purchase motor vehicles and

houses.

Accumulation of human capital can also serve to increase liability exposures.

Damage awards in tort cases involving personal injuries or deaths often reference the future

discounted earnings associated with a human life. When human capital in a country is less

developed, the extent of liability risk in the country tends to be limited because injured

party’s imputed loss of earnings is likely to be smaller. Thus, insurance consumption in 44 There are studies that discuss an endogenous relationship between insurance market growth and overall economy or financial market growth. Ward and Zurbruegg (2000) examine the dynamic relationship between economic growth and growth in the insurance industry and find the causal relationships between economic growth and insurance market development well vary across countries. In this report, we assume away the effect of insurance market growth on economic growth.

127

liability-related lines of business is expected to be tightly positively associated with the level

of education.

We use education defined by the ratio of tertiary education school enrollment to total

population aged 20-24 as a proxy for a country’s education level.

Risk Aversion

The demand for insurance in theory can be justified by individual risk aversion, and the

degree of risk aversion is expected to be positively associated with the level of insurance

consumption (see, for instance, Pratt, 1964; Mossin, 1968). It has been shown that the

positive relationship between the degree of risk aversion and insurance consumption is robust

under various assumptions (Schlesinger, 1981).

In contrast to the individual’s demand for insurance, classic expected utility theory

cannot be directly applied to the corporate demand for insurance because corporations’

insurable risks are theoretically diversifiable by investors. However, Mayers and Smith

(1982) provide a list of theoretical reasons for the purchase of insurance by corporations

without assuming risk-averse corporations. Their list includes the comparative advantage of

risk shifting, reducing bankruptcy costs, real services provided by insurance purchase, and

reducing costs of shareholder monitoring. Within this framework, corporate purchase of

insurance can thus be explained in the context of shareholder value maximization.

In empirical studies of insurance demand, the level of education in a country is often

used as a proxy for risk aversion (Truett and Truett, 1990; Browne and Kim, 1993; Outreville,

1996; Browne et al., 2000; Ward and Zurbruegg, 2002). For instance, Browne et al. (2000)

measure the level of education by the ratio of total enrollment in third-level educational

institutions to the total population aged 20-24. The general hypothesis is that a higher level of

education may lead to a greater degree of risk aversion and better knowledge of insurance

products, though their findings are inconclusive.

In this study, we follow previous studies by using the level of education as a measure

of a country’s level of risk aversion.

Urban Population

We measure urban population as the percentage of the total population living in urban areas.

There are several ways in which urbanization could affect insurance consumption. For

example, the urban population measure might be negatively associated with motor insurance

because owning motor vehicles costs more in cities. Governments may implement a policy to

128

restrict the number of motor vehicles on road to mitigate heavy traffic. An extreme case can

be observed in city countries such as Singapore. In Singapore, new car purchasers must

obtain a COE (Certificate of Entitlement) which is very costly.45 Ceteris paribus, urbanization

would then be associated with decreased demand for motor insurance. For instance, Browne

et al. (2000) find that urban population is inversely associated with both motor insurance

consumption and liability insurance consumption.

Insurance Regulation

Insurance regulators may impose trade barriers to protect their local insurance industry, and

the exclusion of foreign insurers in a country may reduce competition and thus raise prices.

This protection policy could thus result in lower insurance consumption.

Countries may also differ in terms of financial infrastructure, which lead to large

differences in insurance production costs. Whatever the reason, it is expected that insurance

price will be negatively related to non-life insurance consumption. Browne et al. (2000) use

foreign firm market share as a proxy for the price of insurance and find a negative

relationship with motor insurance but the opposite relationship with liability insurance. We

use a direct proxy of insurance price defined by the inverse of loss ratio.

There are other determinants identified in the literature such as religion, legal system

and geographic region. These time-invariant determinants are captured by including country-

specific intercepts in estimation models. The country-specific intercepts also reduce omitted

variable concerns.

45 Category B (Cars 1,601cc and above) COE price in April 2012 Second Open Bidding is SG$ 91,000 (=US$ 73,000).

129

6.3. Methodology

6.3.1. Data Sources and Characteristics

Taking advantage of established theory of insurance demand from individual households, we

use premium density defined by per capita values of non-life insurance premiums to represent

a country’s insurance consumption. Non-life insurance consumption is measured by premium

density of each of non-life insurance total and three lines of non-life insurance: motor,

property and liability.

As used in the previous sections, country-level insurance market related data, gross

written premiums and loss ratios are taken from Axco Global Statistics. The premium data

covers 129 countries and our sample period is 21 years from 1988 to 2008. The country-year

premium data are matched with economic variables such as GDP retrieved from the World

Bank. See Table 6.1 for summary statistics of all variables.

130

Table 6.1: Descriptive Statistics

6.3.2. Financial Crisis

Our financial crisis data is taken from the IMF working paper entitled Systemic Banking

Crises: A New Database reported by Laeven and Valencia (2008), which is an updated

version of prior studies, Caprio and Klingebiel (1996) and Caprio et al. (2005). The dataset

covers all systemically important financial crises for the period 1970 to 2007, with detailed

Variable MeanStandard Deviation Minimum Maximum

Dependent Variables

Nonlife Premium Density (USD per capita) 232.37 380.37 0.04 1956.63

Motor Premium Density (USD per capita) 108.52 165.76 0.03 752.01

Property Premium Density (USD per capita) 64.77 117.60 0.04 718.61

Liability Premium Density (USD per capita) 37.41 67.68 0.00 428.49

Financial Crisis Variables

Crisis (Any type of crisis) 0.07 0.25 0 1

Banking Crisis 0.02 0.15 0 1

Currency Crisis 0.03 0.17 0 1

Debt Crisis 0.02 0.14 0 1

Other Independent Variables

Income (USD per capita) 10822.48 15409.52 148.39 88163.39

Investment Ratio (% of GDP) 22.50 8.40 1.72 61.71

Private Credit (% of GDP) 46.07 45.39 0.82 283.61

Population (million people) 47.25 152.07 1.00 1338.30

Education (third-level education ratio) 28.61 23.46 0.20 103.87

Urban Population (urbanization ratio) 53.04 23.15 5.64 100.00

Muslim (as a major population) 0.28 0.45 0 1

Common Law (as a primary law) 0.26 0.44 0 1

Islamic Law (as a primary law) 0.15 0.35 0 1

Nonlife Insurance Price 2.77 10.76 -61.73 322.58

Motor Insurance Price 1.99 7.01 0.44 294.12

Property Insurance Price 3.44 4.81 -49.02 90.09

Liability Insurance Price 10.31 70.33 -555.56 1428.57

High Income Country 0.26 0.44 0 1

Middle Income Country 0.46 0.50 0 1

Low Income Country 0.29 0.45 0 1

131

information about the type of crisis and policy responses employed to resolve crises. The

study identifies three types of crisis: banking crisis, currency crisis and sovereign debt crisis.

A systemic banking crisis is broadly defined as a sharp increase of non-performing

loans and exhausted aggregate banking system capital, which may be accompanied by

deteriorated asset prices, sharp increases in real interest rates, and a slowdown or reversal in

capital flows (see Laeven and Valencia, 2008 for the detail description of types of crisis).

They identify 124 banking crises over the period of 1970 to 2007. A currency crisis is defined

in the paper as “a nominal depreciation of the currency of at least 30 percent that is also at

least a 10 percent increase in the rate of depreciation compared to the year before.” They

identify 208 currency crises during the period 1970-2007. Sixty-three sovereign debt default

and restructuring are identified during the period 1970-2007 by several prior studies.

Although our sample period is from 1988 to 2008, crises happening before the sample

period are considered as long as the post-crisis window (see below) is included in the sample

period.

Using the database, we prepare four country-year dummy variables for financial crisis:

Crisis: 1 if any of the three types of crisis is reported in the year; 0 otherwise.

Banking Crisis: 1 if a banking crisis is reported in the year; 0 otherwise.

Currency Crisis: 1 if a currency crisis is reported in the year; 0 otherwise.

Debt Crisis: 1 if a debt crisis is reported in the year; 0 otherwise.

A financial crisis may have a long-term effect on insurance consumption, and we are

interested in identifying this effect in subsequent years after a crisis. For each of the crisis

measures above, we prepare 4 variables with different post-crisis windows: 2 Years (5 Years,

10 Years and 15 Years) after Crisis variable, which is 1 during the post-crisis window [0, 1]

( [0, 4] , [0, 9], and [0, 14]) where a crisis is reported at year 0; and 0 otherwise. For each type

of crisis we have the following crisis variables:

2 Years (5 Years, 10 Years and 15 Years) after Banking Crisis

2 Years (5 Years, 10 Years and 15 Years) after Currency Crisis

2 Years (5 Years, 10 Years and 15 Years) after Debt Crisis

132

6.3.3. Model Estimation

To estimate the relation between financial crises on insurance consumption, we investigate

the difference in insurance consumption between countries which experienced financial crises

and countries which did not while controlling for determinants of insurance consumption

such as GDP and social factors.

A. Effect of Financial Crisis

To identify the impact of financial crisis on insurance consumption, we first focus on the

Crisis measure, which is an indicator variable for any of the three crisis types. The

specification is as follows:

Density Income n Years after Crisis Income Year X (1)

where represents a country fixed-effect, the Xit represents a vector of other explanatory

variables, and represents a normally distributed error term. Our primary interest is the

interaction term, which captures the additional impact (as a percentage of income) of the

financial crisis on insurance consumption in a post-crisis period. We run the model with four

different post-crisis windows (2 years, 5 years, 10 years and 15 years) for each premium

density: total non-life, motor, property and liability.

As a robustness check, we also investigate the marginal effect of a financial crisis by

introducing a crisis dummy variable instead of the interaction term.

Density Income n Years after Crisis Year X (2)

The coefficient for the crisis dummy variable can be interpreted as the marginal effect in U.S.

dollars instead of the percentage of income.

B. Types of Financial Crisis: Banking Crisis, Currency Crisis and Debt Crisis

The impact may depend on the type of financial crisis. To evaluate the heterogeneity, we

estimate the model with the following crisis variables: n Years after Banking Crisis, n Years

after Currency Crisis and n Years after Debt Crisis, separately. The specification used in this

analysis is still Equation (1) but the crisis variable therein is replaced by these three crisis

variables. As a robustness check, we also prepare pre-crisis windows: 2 years with [-1, 0]

133

window, 5 years with [-4, 0] window, 10 years with [-9, 0] window, and 15 years with [-14,

0] window. These pre-crisis windows allow us to confirm that an observed adverse impact is

unique in a post-crisis period.

C. Difference between Country Income Levels: High Income, Middle Income and Low

Income

The extent of an adverse impact on insurance consumption may depend not only on the type

of a crisis but also on a country’s income level. To evaluate the difference, we introduce other

interaction terms in the model:

Density Income n Year after Crisis High Income Income

n Year after Crisis Middle Income Income n Year after Crisis Low Income

Income Year X (3)

This model contains three interaction terms according to country income group.

Countries are categorized into three types: high-income, middle-income and low-income. The

categorization criteria follow the World Bank country income group classification. Note that

this model is estimated with 24 different crisis variables: 3 different types of crisis (banking

crisis, currency crisis and debt crisis) and 8 different period windows (4 post-crisis windows

and 4 pre-crisis windows) for each line of business.

134

6.4. Estimation Results

We conduct several tests to validate the estimation models. First, we investigate potential

heteroscedasticity by checking the relationship between standardized residuals and predicted

value. The plots indicate no serious heteroscedasticity problems. To reduce the concern,

standard errors are calculated by empirical estimates. Second, normality tests support that

normality assumption. Third, we also fit a random effects model to check the robustness of

our estimation results and find little difference in the parameter estimates between the fixed-

effects model and the random-effects model. We also run pooled cross-sectional models

(without αi term). However, F-tests indicate that the fixed-effects models fit better than the

pooled cross-sectional models. Thus, the discussion focuses on results obtained from the

fixed-effects models.46

A. Effect of Financial Crisis

Table 6.2 reports the parameter estimates for non-life insurance aggregate premium density.

The table contains five estimation results: one without crisis variable and four with different

crisis variables for the comparison purpose. Year dummy variables and country-specific

dummy variables are included in all models but the estimation results are omitted.

As indicated in the table, the relationship between premium density and income (GDP

per capita in 2010 US dollars) is positive and statistically significant in all models. The

estimated parameter implies that non-life premium density is about 1.6%-1.8% of income if

other things being equal. Insurance price variable indicates the relationship between

insurance price and non-life premium density is positive and statistically significant, though

the price variables are statistically insignificant in each line of business (See Tables 6.4-6.6).

A possible explanation of the positive relationship between the price variable and premium

density is that greater price and quality competition induces higher insurance consumption.

46 The estimation results of the random-effects model and the pooled cross-sectional models are available upon request.

135

Table 6.2: Empirical Model Estimation: Non-life Premium Density

*, ** and *** represent statistical significance with 10%, 5% and 1%, respectively. For each variable, the upper row shows parameter estimate and the lower row indicates the t-statistics.

Independent VariablesBase

Model[0, 1]

Model[0, 4]

Model[0, 9]

Model[0, 14] Model

Intercept 1928.52 1250.29 1209.54 1530.77 1950.611.59 1.01 0.98 1.43 1.90

Income (USD) 0.01803 *** 0.01841 *** 0.01842 *** 0.01595 *** 0.01722 ***7.51 7.01 6.77 7.41 7.79

2 Years after Crisis * Income 0.00097 *1.84

5 Years after Crisis * Income 0.000431.20

10 Years after Crisis * Income -0.00200 **-2.31

15 Years after Crisis * Income -0.00256 ***-6.54

LOG (Population) -107.670 -81.7971 -79.7545 -91.3393 -124.340 *-1.36 -1.00 -0.97 -1.28 -1.79

LOG (Education) 11.1296 12.9626 12.1290 10.2734 18.16560.77 0.95 0.9 0.75 1.35

LOG (Urban Population) -42.2785 2.75670 4.95270 -6.83660 16.1359-0.51 0.03 0.06 -0.10 0.23

LOG (Nonlife Insurance Price) 12.2471 *** 10.9312 ** 11.2426 ** 9.51770 *** 6.84260 **2.64 2.53 2.53 3.12 2.22

Year Dummy Variables Yes Yes Yes Yes YesCountry Fixed-effects Yes Yes Yes Yes Yes

R2

0.985 0.990 0.990 0.990 0.990Observation 1198 1129 1129 1129 1129

Dependent Variable: Nonlife Premium Density

136

Table 6.3: Empirical Model Estimation: Non-life Premium Density with Simple Crisis Dummy Variables

*, *, ** and *** represent statistical significance with 10%, 5% and 1%, respectively. For each variable, the upper row shows parameter estimate and the lower row indicates the t-statistics.


Model[0, 1]

Model[0, 4]

Model[0, 9]

Model[0, 14] Model

Intercept 1928.5 1242.2 1194.9 1439.7 1713.51.59 1.03 0.98 1.19 1.5

Income (USD) 0.0180 *** 0.0180 *** 0.0180 *** 0.0180 *** 0.0177 ***7.51 6.99 6.96 7.26 7.27

2 years after crisis 10.666 **2.1

5 years after crisis 9.0586 *1.89

10 years after crisis -10.875-1.39

15 years after crisis -34.492 ***-3.02

LOG (Population) -107.67 -79.700 -78.519 -91.769 -110.18-1.36 -0.99 -0.97 -1.13 -1.46

LOG (Education) 11.130 12.128 12.455 9.7647 12.5020.77 0.89 0.93 0.72 0.9

LOG (Urban Population) -42.279 -0.9865 4.5231 4.834 15.669-0.51 -0.01 0.06 0.06 0.21

LOG (Nonlife Insurance Price) 12.247 *** 10.855 ** 10.962 ** 11.935 *** 10.018 **2.64 2.52 2.57 2.7 2.54


R2

0.985 0.990 0.990 0.990 0.990Observation 1198 1129 1129 1129 1129


137

Table 6.4: Empirical Model Estimation: Motor Premium Density



Model[0, 1]

Model[0, 4]

Model[0, 9]

Model[0, 14] Model

Intercept 1572.47 * 1723.18 * 1737.54 * 1840.59 ** 1918.85 *2.13 2.23 2.30 2.59 2.57

Income (USD) 0.00733 *** 0.00720 *** 0.00691 *** 0.00634 *** 0.00703 ***9.03 7.46 7.44 7.82 7.93

2 Years after Crisis * Income -0.00014-0.42




LOG (Population) -78.5709 * -90.1909 * -89.6523 * -95.2333 ** -103.200 **-1.74 -1.88 -1.91 -2.16 -2.21

LOG (Education) 16.0494 ** 18.5843 *** 17.6164 *** 18.6438 *** 21.1292 ***2.42 2.93 2.82 3.01 3.24

LOG (Urban Population) -63.3308 -54.7224 -57.6386 -56.9926 -48.7493-1.48 -1.27 -1.36 -1.4 -1.18

LOG (Motor Insurance Price) 5.62550 5.03140 5.25380 5.06150 4.699301.20 1.02 1.06 1.04 0.97


R2

0.987 0.987 0.987 0.988 0.988Observation 1145 1078 1078 1078 1078

Dependent Variable: Motor Premium Density

138

Table 6.5: Empirical Model Estimation: Property Premium Density



Model[0, 1]

Model[0, 4]

Model[0, 9]

Model[0, 14] Model

Intercept -423.240 -464.810 -493.770 -409.470 -332.430-1.44 -1.58 -1.65 -1.27 -1.10

Income (USD) 0.00573 *** 0.00608 *** 0.00616 *** 0.00533 *** 0.00565 ***7.19 7.02 6.97 5.63 7.12

2 Years after Crisis * Income 0.00066 ***3.09




LOG (Population) 23.6179 24.2438 25.4014 23.1052 16.67231.09 1.10 1.14 1.01 0.75

LOG (Education) -5.28770 -4.82710 -5.18850 -6.53010 -4.89930-1.47 -1.28 -1.36 -1.59 -1.26

LOG (Urban Population) -1.17760 2.72770 4.90100 0.52940 5.5543-0.04 0.08 0.15 0.02 0.17

LOG (Property Insurance Price) 1.19610 1.46310 * 1.4413 * 1.51220 1.496401.50 1.86 1.75 1.92 1.86


R2

0.988 0.989 0.989 0.989 0.989Observation 1183 1112 1112 1112 1112

Dependent Variable: Property Premium Density

139

Table 6.6: Empirical Model Estimation: Liability Premium Density



Model[0, 1]

Model[0, 4]

Model[0, 9]

Model[0, 14] Model

Intercept -380.400 -405.900 -413.590 -395.480 -283.150-0.99 -1.02 -1.04 -1.01 -0.75

Income (USD) 0.00518 *** 0.00529 *** 0.00541 *** 0.00519 *** 0.00515 ***18.3 18.7 16.7 16.6 18.8


5 Years after Crisis * Income 0.00023 **2.45


15 Years after Crisis * Income -0.00037 *-1.91

LOG (Population) 5.03190 2.66230 1.70540 2.90070 -4.474800.13 0.07 0.04 0.07 -0.12

LOG (Education) -6.42810 -6.44230 -5.97040 -6.86980 -5.86660-1.18 -1.08 -1.03 -1.13 -1.00

LOG (Urban Population) 56.8055 70.9255 75.2238 68.8858 71.80260.73 0.87 0.92 0.85 0.96

LOG (Liability Insurance Price) -0.11830 -0.18240 -0.24840 -0.09296 -0.01767-0.18 -0.27 -0.37 -0.14 -0.03


R2

0.968 0.969 0.970 0.969 0.969Observation 825 789 789 789 789

Dependent Variable: Liability Premium Density

140

Our primary interest lies in the four interaction terms with crisis variables. Each of those

crisis variables represents a different post-crisis window. We find that the estimated

parameters are positive and statistically significant for the 2-year crisis variable, but are

negative and significant for 10- and 15-year variables. These results confirm that there are

additional post financial crisis changes in non-life insurance consumption that cannot be

captured by changes in the level of income. The positive coefficients in the short run (2-5

years) imply that there is excess consumption of non-life insurance during the initial period

after a crisis. In contrast, the large and negative coefficients in longer windows (10-15 years)

mean that the long-term effect is negative. The 15-year crisis variable indicates that the

adverse effect reaches 15% of the coefficient of income (=0.00256/0.01722) on average in

the 15 years after the crisis.

Table 6.3 show similar results obtained by simple crisis dummy variables instead of

the interaction terms. We find that the crisis variables are positive and statistically significant

in 2-year and 5-year windows, and negative and significant in 15-year windows. These

results also confirm that there are marginal post-crisis effects of non-life insurance

consumption. The positive coefficient in 2-year window means that there is excess

consumption of non-life insurance by $10.7 during a crisis period. In contrast, the negative

coefficients in 15-year window mean that non-life insurance consumption is reduced by

$34.5 (about 15% of the average density of non-life insurance $232.4) every year after a

crisis. The aggregate loss of non-life premium density is $517.5 (=$34.5*15) for 15 years

after a crisis.

A negative sign in the long run is a robust result regardless of line of business (see

Tables 6.4-6.6). Thus, the effect appears consistent across lines. On the other hand, the short-

run positive effect is observed in both property insurance and liability insurance (Tables 6.5-

6.6) but not in motor insurance (Table 6.4). In a word, insurance consumption response after

a financial crisis varies between lines of business in the short run but the long run adverse

effect appears in all 3 lines.

However, we should note that these results may not be interpreted as the change in the

sensitivity of insurance consumption to the level of income. Estimating the non-life insurance

density model with which premium density and income are both in a log scale, we find no

statistically significant relationship between premium density and financial crisis occurrence.

Several statistically significant negative coefficients are found in lines of business, the results

141

are not as strong as aforementioned results. Thus, there is no robust evidence of change in the

sensitivity of non-life insurance consumption to income after financial crisis period.

B. Types of Financial Crisis: Banking Crisis, Currency Crisis and Debt Crisis

The effect of a financial crisis on insurance consumption could depend on the type of crisis:

banking crisis, currency crisis, or debt crisis. Table 6.7 shows a summary of crisis parameter

estimates by types of financial crisis. The upper panel shows the results for non-life premium

density. The right hand side of the upper panel contains results for post-crisis windows, and

the left hand side of the panel contains results for pre-crisis windows. Results for these pre-

crisis windows are reported to investigate whether there is systematic pattern during pre-crisis

periods. The upper (middle and lower) line of each panel shows estimated coefficients for the

banking (currency and debt) crisis term with different crisis period windows.

The estimated models include other variables but the parameter estimates reported in

the table are limited to crisis variable interaction terms. The non-life insurance panel indicates

that a positive effect appears long before a crisis and that a long-run adverse effect is

associated with both banking crises and currency crises. Thus, we confirm that negative

impact of a financial crisis is limited to a post-crisis period with years of lag and that the

positive effect is not initiated during a financial crisis. There is no evidence of an additional

impact of a debt crisis on non-life insurance consumption.

The motor insurance panel in Table 6.7 shows a similar pattern of positive signs in

pre-crisis windows and negative signs in long-run post-crisis windows. A notable result for

motor insurance is that a debt crisis induces a large negative effect only during a crisis period.

The property insurance panel also shows a positive effect in a pre-crisis period and a negative

effect in a long-run from both banking and currency crises. In contrast, a debt crisis induces a

positive effect in the pre-crisis windows. A distinctive feature of the liability insurance

models is that there is no negative sign while we find several positive and statistically

significant coefficients. Thus, liability insurance consumption is not suffered by additional

adverse impact if each type of financial crisis is investigated separately.

Thus, the effects of banking crises and currency crises on insurance consumption are

qualitatively similar: positive effects in a pre-crisis period and negative effects in a post-crisis

period, but the change of the sign does not appear immediately after a crisis. The impact of a

debt crisis depends on the line of business, and deviation from expected insurance

consumption level is limited to during a crisis period.

142

Table 6.7: Summary of the Effect of Crisis by Type of Crisis


Independent Variables[-14, 0] Model

[-9, 0] Model

[-4, 0] Model

[-1, 0] Model

[0, 1] Model

[0, 4] Model

[0, 9] Model

[0, 14] Model

n Years before/after Banking Crisis 0.00164 *** 0.00151 ** 0.00258 *** 0.00129 ** 0.00078 * 0.00050 -0.00170 * -0.00222 *** * Income 2.71 2.53 3.53 2.33 1.65 1.24 -1.68 -4.28n Years before/after Currency Crisis 0.00314 *** 0.00310 *** 0.00309 *** 0.00309 *** 0.00191 *** 0.00068 -0.00141 ** -0.00228 *** * Income 4.89 4.77 4.86 3.74 2.82 1.22 -2.21 -4.45n Years before/after Debt Crisis 0.00049 0.00049 0.00032 0.00000 -0.00057 -0.00061 0.00010 -0.00117

* Income 0.43 0.43 0.42 0.00 -0.9 -0.94 0.16 -1.26

n Years before/after Banking Crisis 0.00073 ** 0.00059 ** 0.00051 ** 0.00019 -0.00006 -0.00025 -0.00075 *** -0.00069 ***

* Income 2.39 2.53 2.34 0.48 -0.17 -0.9 -4.13 -4.28n Years before/after Currency Crisis 0.00114 * 0.00110 * 0.00105 * 0.00063 ** -0.00021 -0.00084 *** -0.00144 *** -0.00114 *** * Income 1.91 1.85 1.76 2.14 -0.73 -3.37 -5.67 -3.13n Years before/after Debt Crisis -0.00018 -0.00018 -0.00041 -0.00108 *** -0.00120 ** -0.00088 0.00010 -0.00094 * Income -0.22 -0.22 -0.8 -2.84 -2.38 -1.53 0.16 -0.82

n Years before/after Banking Crisis 0.00027 0.00020 0.00085 *** 0.00075 *** 0.00068 *** 0.00039 * -0.00040 -0.00049 *** * Income 1.14 0.92 5.47 3.60 3.14 1.87 -1.03 -4.93n Years before/after Currency Crisis 0.00074 *** 0.000738 *** 0.00079 *** 0.00071 *** 0.00049 * 0.00028 -0.00041 -0.00047 *** * Income 2.8 2.81 3.23 3.3 1.95 0.74 -0.98 -2.85n Years before/after Debt Crisis 0.00071 0.00071 0.00072 ** 0.00090 ** 0.00052 0.00030 0.00018 -0.00013

* Income 1.61 1.61 2.12 2.24 1.18 0.86 0.46 -0.31

n Years before/after Banking Crisis 0.00030 0.00034 ** 0.00095 *** 0.00055 ** 0.00021 *** 0.00021 ** -0.00008 -0.00031 * Income 1.5 2.04 2.71 2.45 3.10 2.11 -0.45 -1.41n Years before/after Currency Crisis 0.00011 0.00010 0.00013 0.00001 0.00007 0.00022 0.00027 * -0.00016

* Income 0.53 0.51 0.60 0.03 0.22 0.86 1.88 -0.69n Years before/after Debt Crisis 0.00066 0.00066 0.00065 0.00079 * 0.00054 0.00036 * 0.00028 -0.00002 * Income 1.00 1.00 1.31 1.74 1.49 1.89 0.96 -0.06





143

C. Differences Among Country Income Groups: High Income, Middle Income and Low

Income

Non-life Insurance Consumption

Table 6.8 reports a summary of estimation model (3). Only estimated coefficients for

interaction terms between crisis variable, income group and income for non-life insurance

consumption are reported. The upper (middle and lower) panel reports the results for banking

crisis (currency crisis and debt crisis), and the upper (middle and lower) line of each panel

shows the results for high-income (middle-income and low-income) countries. Table 8 shows

that excess consumption in a pre-crisis period is observed only in high-income countries. A

negative long-run impact in the case of banking crisis and currency crisis is observed in both

high-income and middle-income countries.47 The debt crisis panel provides an evidence that

only low-income countries suffer additional adverse effect in the long-run after debt crisis.

Motor Insurance Consumption

Table 6.9 reports a summary of estimation results for motor insurance. Overall, the impact on

motor insurance is similar to non-life total in that (1) excess consumption in a pre-crisis

period is observed only in high-income countries; (2) a negative long-run impact in the case

of banking crisis and currency crisis is observed in both high-income and middle-income

countries; and (3) only low-income countries suffer additional adverse effect in a long run

after debt crisis.

Property Insurance Consumption

Table 6.10 indicates a post-crisis negative effect is observed only in high-income countries.

Thus, middle-income countries and low-income countries are not associated with additional

negative impact on property insurance consumption. One interesting observation is that low-

income countries show persistent negative signs in pre-banking crisis windows. This means

that low-income country’s property insurance consumption is below the expected level in

pre-crisis period but recovers the expected level of consumption after a banking crisis.

Liability Insurance Consumption

47 We also run models with an interaction term between crisis variable and country income group, which allows estimating the effect of financial crisis in a dollar amount. For instance, the adverse effect in 15-year model for high-income countries becomes $63 (corresponding to -0.00225 in [0,14] window model in Table 7), which represents approximately 9 percent of the average non-life density for high-income countries ($708).

144

A summary of estimated coefficients for liability insurance is reported in Table 6.11. There

are several findings. First, a long-run negative effect in post-crisis windows is limited to high-

income countries after a debt crisis. Second, only middle-income countries are distinguished

from other income groups by a positive effect in post-crisis periods in all types of crisis.

Overall, we find that a combination of a positive effect in a pre-crisis period and a

negative effect in the long-run after the crisis is pervasive in high-income countries. Middle-

income countries also show a negative long-run effect after the crisis but tend not to have

excess consumption in pre-crisis periods. Impact of crises on low-income countries is limited,

and the statistically significant effect is negative in the pre-crisis period. It is also indicated

that insurance consumption in middle-income and low-income countries is positively affected

by a crisis in some cases. These findings imply that high-income country insurance markets

tend to experience the largest adverse impact after financial crises.

145

Table 6.8: Summary of the Effect of Crisis by Country Income Group: Non-life Premium Density

*, ** and *** represent statistical significance with 10%, 5% and 1%, respectively. For each variable, the upper row shows parameter estimate and the lower row indicates the t-statistics. Note: No debt crisis is reported for high-income countries after 1988, the first year of the sample period. Therefore, parameters for a pre-crisis period are not available in this table.


[-9, 0] Model

[-4, 0] Model

[-1, 0] Model

[0, 1] Model

[0, 4] Model

[0, 9] Model

[0, 14] Model

n Years before/after Banking Crisis 0.00176 *** 0.00161 *** 0.002759 *** 0.00127 ** 0.00069 0.00043 -0.00183 * -0.00225 *** * High Income * Income 2.82 2.58 3.71 2.16 1.41 0.98 -1.76 -4.26

n Years before/after Banking Crisis 0.00017 0.00010 0.00013 0.00154 0.00246 *** 0.00142 * 0.00023 -0.00162 ** * Middle Income * Income 0.14 0.09 0.12 1.48 2.60 1.88 0.32 -2.19

n Years before/after Banking Crisis -0.00974 -0.01006 -0.00502 -0.01367 -0.00956 0.01240 0.00203 0.00246 * Low Income * Income -0.53 -0.54 -0.28 -0.89 -0.57 1.21 0.22 0.28

n Years before/after Currency Crisis 0.00356 *** 0.00356 *** 0.00356 *** 0.00355 *** 0.00222 *** 0.00076 -0.00152 ** -0.00230 *** * High Income * Income 7.2 7.19 7.23 4.10 3.01 1.35 -2.24 -4.39

n Years before/after Currency Crisis 0.00099 0.00081 0.00079 0.00112 0.00087 0.00033 -0.00042 -0.00187 * * Middle Income * Income 0.99 0.83 0.89 1.21 1.01 0.42 -0.43 -1.77

n Years before/after Currency Crisis 0.00698 0.00667 0.00977 0.00292 0.00520 0.00391 -0.00450 -0.00271 * Low Income * Income 0.79 0.76 1.10 0.47 0.65 0.43 -0.51 -0.31

n Years before/after Debt Crisis - - - - -0.00100 -0.00192 *** -0.00069 -0.00117 * High Income * Income - - - - -1.17 -2.95 -0.79 -1.01

n Years before/after Debt Crisis 0.00049 0.00049 0.00033 0.00001 -0.00051 -0.00020 0.00049 -0.00120 * Middle Income * Income 0.43 0.43 0.42 0.00 -0.74 -0.29 0.69 -1.29n Years before/after Debt Crisis -0.00006 -0.00006 -0.00021 0.00033 0.00028 0.01010 -0.00074 -0.02072 ** * Low Income * Income 0.00 0.00 -0.02 0.03 0.02 0.74 -0.06 -2.00


Currency Crisis Models

Debt Crisis Models

Banking Crisis Models

146

Table 6.9: Summary of the Effect of Crisis by Country Income Group: Motor Premium Density



[-9, 0] Model

[-4, 0] Model

[-1, 0] Model

[0, 1] Model

[0, 4] Model

[0, 9] Model

[0, 14] Model

n Years before/after Banking Crisis 0.000809 *** 0.00065 *** 0.00058 *** 0.00014 -0.00012 -0.00029 -0.00075 *** -0.00066 *** * High Income * Income 2.71 2.89 2.72 0.35 -0.3 -0.98 -4.04 -4.26

n Years before/after Banking Crisis -0.00023 -0.00027 -0.0004 0.00085 0.00098 0.00025 -0.00066 -0.00132 ** * Middle Income * Income -0.26 -0.30 -0.54 1.37 1.45 0.47 -1.21 -2.07

n Years before/after Banking Crisis 0.01294 0.01294 0.01274 0.00004 0.00170 0.00817 -0.00209 -0.00509 * Low Income * Income 1.23 1.24 1.28 0.00 0.23 2.31 -0.39 -0.83

n Years before/after Currency Crisis 0.001229 ** 0.00122 ** 0.00122 ** 0.00072 ** -0.00013 -0.00084 *** -0.00139 *** -0.00108 *** * High Income * Income 1.99 1.98 1.96 2.21 -0.52 -4.06 -5.29 -2.92

n Years before/after Currency Crisis 0.00069 0.00051 0.000252 0.00023 -0.00053 -0.00084 -0.00197 *** -0.00263 *** * Middle Income * Income 0.80 0.62 0.34 0.41 -0.70 -1.52 -2.98 -3.05

n Years before/after Currency Crisis -0.00304 -0.00330 -0.00237 -0.0024 0.00193 0.00285 -0.00226 -0.00318 * Low Income * Income -0.25 -0.27 -0.24 -0.33 0.30 0.55 -0.37 -0.50

n Years before/after Debt Crisis - - - - -0.00236 *** -0.00162 * 0.00001 -0.00043 * High Income * Income - - - - -3.47 -1.84 -0.01 -0.29

n Years before/after Debt Crisis -0.00018 -0.00018 -0.00041 -0.00108 *** -0.00100 ** -0.00062 0.00017 -0.00128 * Middle Income * Income -0.22 -0.22 -0.81 -2.85 -2.26 -1.09 0.31 -1.32n Years before/after Debt Crisis 0.00764 0.00764 0.00755 * 0.00516 -0.00447 -0.00703 -0.00958 -0.01730 ** * Low Income * Income 1.65 1.65 1.67 0.36 -0.61 -0.92 -1.45 -2.40



Debt Crisis Models


147

Table 6.10: Summary of the Effect of Crisis by Country Income Group: Property Premium Density



[-9, 0] Model

[-4, 0] Model

[-1, 0] Model

[0, 1] Model

[0, 4] Model

[0, 9] Model

[0, 14] Model

n Years before/after Banking Crisis 0.00025 0.00017 0.00086 *** 0.00076 *** 0.00067 *** 0.00036 * -0.00046 -0.00051 *** * High Income * Income 1.03 0.81 5.36 3.53 3.00 1.68 -1.15 -4.95

n Years before/after Banking Crisis 0.00049 0.00048 0.00066 * 0.00053 0.00097 *** 0.00073 ** 0.00041 -0.00007 * Middle Income * Income 1.08 1.05 1.87 1.35 2.64 2.24 1.43 -0.39

n Years before/after Banking Crisis -0.01043 ** -0.01048 ** -0.00953 ** -0.00983 *** -0.00533 -0.00086 0.00191 0.00311 * Low Income * Income -2.38 -2.39 -2.32 -2.77 -0.79 -0.15 0.66 1.24

n Years before/after Currency Crisis 0.00089 *** 0.00089 *** 0.00089 *** 0.00076 *** 0.00036 0.00016 -0.00052 -0.00051 *** * High Income * Income 3.32 3.33 3.36 2.97 1.30 0.38 -1.18 -3.02

n Years before/after Currency Crisis 0.00005 0.00005 0.00032 0.00050 0.00090 *** 0.00078 ** 0.00059 ** 0.00019 * Middle Income * Income 0.11 0.13 0.85 1.64 2.79 2.46 2.23 0.82

n Years before/after Currency Crisis -0.00193 -0.00193 -0.00066 -0.00253 -0.00007 0.00112 0.00042 0.00036 * Low Income * Income -0.39 -0.39 -0.18 -0.57 -0.01 0.29 0.13 0.13

n Years before/after Debt Crisis - - - - 0.00078 *** -0.00017 -0.00041 -0.00054 * High Income * Income - - - - 2.62 -0.4 -0.87 -0.99

n Years before/after Debt Crisis 0.00071 0.00071 0.00072 ** 0.00091 ** 0.00049 0.00044 0.00040 0.00001 * Middle Income * Income 1.62 1.62 2.14 2.26 1.02 1.24 1.17 0.03n Years before/after Debt Crisis -0.00055 -0.00055 -0.00070 -0.00782 ** -0.00178 0.00423 0.00444 -0.00056 * Low Income * Income -0.08 -0.08 -0.11 -2.22 -0.26 0.87 1.26 -0.18



Debt Crisis Models


148

Table 6.11: Summary of the Effect of Crisis by Country Income Group: Liability Premium Density

*, ** and *** represent statistical significance with 10%, 5% and 1%, respectively. For each variable, the upper row shows parameter estimate and the lower row indicates the t-statistics. Note: No debt crisis is reported for high-income countries after 1988, the first year of the sample period. Therefore, parameters for a pre-crisis period are not available in this table


[-9, 0] Model

[-4, 0] Model

[-1, 0] Model

[0, 1] Model

[0, 4] Model

[0, 9] Model

[0, 14] Model

n Years before/after Banking Crisis 0.00029 0.00033 * 0.00098 *** 0.00053 ** 0.00013 ** 0.00012 -0.00015 -0.00033 * High Income * Income 1.41 1.96 2.72 2.21 2.41 1.22 -0.90 -1.50

n Years before/after Banking Crisis 0.00047 0.00047 0.00042 0.00073 * 0.00130 *** 0.00128 *** 0.00100 *** 0.00046 * Middle Income * Income 0.98 1.00 1.06 1.91 3.12 3.11 2.67 1.47

n Years before/after Banking Crisis - - - - - - -0.00022 0.00191 * Low Income * Income - - - - - - -0.04 0.44

n Years before/after Currency Crisis -0.00005 -0.00005 -0.00005 -0.00036 *** -0.00041 *** -0.00005 0.00013 -0.00021 * High Income * Income -0.42 -0.45 -0.42 -3.67 -4.8 -0.41 0.65 -0.81

n Years before/after Currency Crisis 0.00057 0.00054 0.00064 * 0.00078 ** 0.00086 *** 0.00089 *** 0.00112 ** 0.00049 * Middle Income * Income 1.35 1.28 1.65 2.44 2.93 2.71 2.43 0.92

n Years before/after Currency Crisis 0.00851 0.00839 -0.00836 * -0.00715 * 0.00166 0.00550 0.00078 -0.00546 * Low Income * Income 1.04 1.03 -1.75 -1.72 0.51 1.62 0.21 -1.31

n Years before/after Debt Crisis - - - - 0.00006 0.00013 -0.00016 -0.00038 * * High Income * Income - - - - 0.74 1.43 -1.12 -1.95

n Years before/after Debt Crisis 0.00066 0.00066 0.00065 0.00079 * 0.00092 *** 0.00130 *** 0.00098 *** 0.00029 * Middle Income * Income 1.00 1.00 1.31 1.74 3.05 3.72 2.62 0.79n Years before/after Debt Crisis 0.00424 0.00424 0.00444 0.00325 0.00193 0.00565 * -0.00082 -0.00543 * Low Income * Income 0.93 0.93 0.95 0.95 0.59 1.71 -0.22 -1.54



Debt Crisis Models


6.5. Conclusion

Insurance consumption can be primarily explained by the level of income on an individual

level or national income in aggregate. A financial crisis adversely affects individual income

and national output, which is accompanied by insurance consumption. We hypothesize that

there are additional impacts which cannot be captured by the level of income and investigate

the additional effect of a financial crisis on non-life insurance consumption.

We confirm that there are additional post financial crisis effects on non-life insurance

consumption which cannot be captured by change in the level of income. The impact after

financial crisis varies between lines of business in the short run but adverse effect in a long

run is consistent between lines.

Extending our crisis period window to pre-crisis periods and looking into the impact

by types of a crisis, we find that motor insurance and property insurance show a distinct

pattern in banking crisis and currency crisis: positive effects in a pre-crisis period and a

negative effect in a post-crisis period with years of lag. Further we confirm that high-income

countries tend to have the largest adverse impact of a financial crisis on insurance

consumption among country income groups. We identify that there is even a positive effect

of a crisis on property insurance and liability consumption in both middle-income and low-

income countries.

There are several unanswered questions. First, why do high-income countries show

excess insurance consumption in a pre-crisis period? Second, why does a positive effect

continues even after a crisis? And, probably most importantly, why is there a long-run

adverse effect of banking crisis and currency crisis on non-life insurance consumption? These

questions are left for future work.

150

7. Concluding Remark

In this study, we forecast China’s and India’s non-life insurance consumption by line of

business in long-run and discuss several relevant topics in those two prominent insurance

markets.

We propose a new projection methodology which requires predicting both non-life

insurance total premium and the share of each line of business. This approach forecasts the

non-life insurance volume first and then determines the portion of the total premium taken by

lines of business. In fact, predicted premiums under this approach are quite different from

premiums independently modeled by line of business. After studying both sets of results and

consulting with insurance professionals, we decide to take current approach---as it yielded the

most plausible results.

In addition, our regression model includes a lagged dependent variable term, and,

hence, prediction results do not generate a large gap between the most recent observed

premium and predicted premium. Existing studies using static models have this problem and

use arbitrary methods to capture the dynamics of residual. Our approach avoids this problem.

Findings are summarized as follows. First, motor insurance is the major driver of non-

life insurance consumption growth in both countries in the next 20 years. Second, China and

India will face different changes in non-life market composition. Third, the impact of non-

life insurance consumption growth on cat risk growth is likely to be limited, as property

insurance premiums are expected to experience relatively slow growth. Fourth, the impact of

a major economic downturn on non-life insurance consumption is expected to be strong.

Should a financial crisis occur in either country, growth could be set back by 15 years or

more.

Forecasting long-term growth of non-life insurance consumption is challenging in that

there are many uncertain factors surrounding the market. It is particularly true for developing

countries such as China and India. We believe that it is not realistic and impossible to

quantify and model all the factors, so we provide qualitative discussion on unmodeled factors

affecting non-life insurance consumption in both countries.

We strongly believe that estimating long-term growth of insurance consumption in

those countries is important and should be updated because of their potential importance in

the global insurance market.

151

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Beenstock, M., G. Dickinson, and S. Khajuria, 1986, The Determination of Life Premiums: An International Cross-Section Analysis, 1970–1981, Insurance: Mathematics and Economics, 5: 261-270.

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9. Acknowledgements

The authors thank Insurance Risk and Finance Research Centre (IRFRC) at Nanyang

Business School for financial support and Michel Dacorogna, Sie Liang Lau, and Janice

Cowley for their precious comments and suggestions. We also thank Michael Powers for his

comments and suggestions on this project. We would also like to thank Sun Ying for her

assistance on the project.

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