1
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).
3
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
5
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
8
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.
10
(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.
16
Figure 1.8: Plot of Nonlife Density versus GDP per capita
Figure 1.9: Plot of Motor Density versus GDP per capita
17
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
20
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
25
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
26
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.
27
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
* for AIC indicates the best fit model.
ParameterGDP
ModelInvest. Model
Private Cons. Model
Gov. Cons. Model
Exports Model
Imports Model
Private Credit Model
1.624(<.0001)
0.563(<.0001)
1.020(<.0001)
0.558(<.0001)
0.403(<.0001)
0.453(<.0001)
0.409(<.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 1995 1949 1946 1945 1976 1976 1969
Number of Countries 126 125 125 125 126 126 126
AIC 778* 1056 1117 1175 1225 1229 874
LOG(private credit)
LOG(GDP)
LOG(Investment)
LOG(Private consumption)
LOG(Government consumption)
LOG(Exports)
LOG(Imports)
35
Table 2.6: Estimation Results - Property 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
0.935(<.0001)
0.353(<.0001)
0.664(<.0001)
0.290(0.002)
0.402(<.0001)
0.350(<.0001)
0.347(<.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 2051 2005 2002 2001 2032 2032 2021
Number of Countries 126 125 125 125 126 126 126
AIC 1385 1281 1291 1342 1414 1472 1057*
LOG(private credit)
LOG(GDP)
LOG(Investment)
LOG(Private consumption)
LOG(Government consumption)
LOG(Exports)
LOG(Imports)
36
Table 2.7: Estimation Results - Liability 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
1.361(<.0001)
0.371(0.016)
0.674(0.042)
0.610(0.003)
0.574(<.0001)
0.392(0.020)
0.306(0.009)
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 1398 1380 1377 1376 1380 1380 1374
Number of Countries 126 125 125 125 126 126 126
AIC 1882 1917 1923 1894 1887 1931 1821*
LOG(private credit)
LOG(GDP)
LOG(Investment)
LOG(Private consumption)
LOG(Government consumption)
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
Private Credit 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
Private Credit 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
Private Credit 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
Private Credit 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)
Log (EDM per capita)
Log (EDM per capita)
Log (EDM per capita)
38
* for AIC indicates the best fit model.
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
* for AIC indicates the best fit model.
Nonlife Penetration ModelGDP
ModelInvest. Model
Private Cons. Model
Gov. Cons. Model
Exports Model
Imports Model
Private Credit 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
Private Credit 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
Private Credit 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
Private Credit 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
* for AIC indicates the best fit model.
ParameterGDP
ModelInvest. Model
Private Cons. Model
Gov. Cons. Model
Exports Model
Imports Model
Private Credit 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)
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 1864 1824 1821 1820 1845 1845 1837
Number of Countries 119 118 118 118 119 119 118
AIC 239* 478 629 674 579 674 278
LOG(private credit)
LOG(GDP)
LOG(Investment)
LOG(Private consumption)
LOG(Government consumption)
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
2.6.2. Penetration Models
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
Parameter Nonlife Motor Property Liability
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.
3.1.1. Penetration Models
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
Parameter Nonlife Motor Property Liability
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
Average annual growth rate 0.08 0.08 0.08 0.08 0.03 0.03 0.03 0.03
5% Lower Bound Premium 8,341 11,477 15,716 21,500 1,349 1,530 1,735 1,972
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
Average annual growth rate 0.08 0.08 0.08 0.08 0.06 0.06 0.06 0.06
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
Average annual growth rate 0.10 0.11 0.11 0.11 0.05 0.05 0.05 0.06
5% Lower Bound Premium 76,233 119,381 188,211 299,228 6,344 7,945 10,094 13,019
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
Average annual growth rate 0.10 0.11 0.11 0.11 0.06 0.06 0.06 0.07
5% Lower Bound Premium 98,248 154,401 244,137 388,720 11,043 14,290 18,745 24,909
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
Average annual growth rate 0.07 0.07 0.07 0.07 0.03 0.03 0.03 0.03
5% Lower Bound Premium 13,071 17,742 23,971 32,342 2,663 2,933 3,221 3,539
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
Average annual growth rate 0.08 0.08 0.08 0.08 0.06 0.06 0.06 0.06
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
Average annual growth rate 0.10 0.11 0.11 0.12 0.05 0.05 0.06 0.06
5% Lower Bound Premium 120,425 187,633 295,783 472,536 13,090 16,192 20,401 26,184
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
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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
Motor InsurancePremium (2010 USD m) 3,368 7,059 13,070 7,400 13,660 7,671 14,138Percent change - -4.6% -4.3% - - 3.7% 3.5%
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%
Nonlife TotalPremium (2010 USD m) 6,537 11,517 19,498 11,964 20,251 12,319 20,859Percent change - -3.7% -3.7% - - 3.0% 3.0%
Investment -10% points
Investment +10% pointsBase Scenario
Investment -10% points Base Scenario
Investment +10% points
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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
Motor InsurancePremium (2010 USD m) 3,368 8,043 14,869 7,400 13,660 6,901 12,727Percent change - 8.7% 8.9% - - -6.7% -6.8%
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) 165 416 755 348 629 299 540Percent change - 19.5% 20.0% - - -14.1% -14.1%
Nonlife TotalPremium (2010 USD m) 6,537 12,862 21,872 11,964 20,251 11,273 19,010Percent change - 7.5% 8.0% - - -5.8% -6.1%
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%
Nonlife TotalPremium (2010 USD m) 57,539 141,993 396,629 137,222 386,110 134,758 380,538Percent change - 3.5% 2.7% - - -1.8% -1.4%
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%
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) 165 226 417 348 629 403 727Percent change - -35.1% -33.7% - - 15.8% 15.6%
Nonlife TotalPremium (2010 USD m) 6,537 12,828 21,226 11,964 20,251 11,620 19,832Percent change - 7.2% 4.8% - - -2.9% -2.1%
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
Motor InsurancePremium (2010 USD m) 44,361 119,020 335,820 110,587 314,033 104,688 298,380Percent change - 7.6% 6.9% - - -5.3% -5.0%
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%
Nonlife TotalPremium (2010 USD m) 57,539 148,624 414,734 137,222 386,110 129,295 365,639Percent change - 8.3% 7.4% - - -5.8% -5.3%
Exports Scenario: India
Variables 2010 2020 2030 2020 2030 2020 2030
Exports (% of GDP) 23 23 37 33 47 43 57
Motor InsurancePremium (2010 USD m) 3,368 8,011 14,397 7,400 13,660 6,981 13,094Percent change - 8.3% 5.4% - - -5.7% -4.1%
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%
Liability InsurancePremium (2010 USD m) 165 402 692 348 629 313 582Percent change - 15.5% 10.0% - - -10.1% -7.5%
Nonlife TotalPremium (2010 USD m) 6,537 13,090 21,466 11,964 20,251 11,202 19,323Percent change - 9.4% 6.0% - - -6.4% -4.6%
Exports -10% points Base Scenario
Exports +10% points
Exports -10% points Base Scenario
Exports +10% points
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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%
Nonlife TotalPremium (2010 USD m) 57,539 135,170 380,287 137,222 386,110 138,850 390,711Percent change - -1.5% -1.5% - - 1.2% 1.2%
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
Motor InsurancePremium (2010 USD m) 3,368 7,276 13,432 7,400 13,660 7,536 13,912Percent change - -1.7% -1.7% - - 1.8% 1.8%
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%
Liability InsurancePremium (2010 USD m) 165 351 635 348 629 344 623Percent change - 0.9% 1.0% - - -1.1% -1.0%
Nonlife TotalPremium (2010 USD m) 6,537 11,782 19,942 11,964 20,251 12,166 20,595Percent change - -1.5% -1.5% - - 1.7% 1.7%
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
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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.
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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
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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?
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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
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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
96
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
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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).
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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.
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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$45 billion in insured loss (loss estimate under one-year loss development). However, the
total economic damages amounted to just 1.14% of US GDP.36 The insured loss/premium
ratio defined by the insured loss incurred by the event divided by property insurance written
gross premium is not significantly large due to the size of the US property insurance market.
Yet, the insured loss/premium ratio based only on the flood insurance premium reached
2,669%.
34 A flood occurred in Maharashtra, India in August 2006. The flood created USD 0.56 billion in insured losses which is larger than the insured loss from the 2008 China Sichuan EQ. To avoid repetition, the flood event is omitted. 35 Preliminary data taken from Axco Global Statistics. 36 GDPs are taken from one year prior to the event year to avoid the impact of the economic damage on GDP.
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The New Zealand Christchurch earthquake in February 2011 was an extreme case in
that 80% of the US$ 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
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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)
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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
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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)
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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
Log GDP per Capita (Left Axis)
Log Nonlife Density (Right Axis)
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
Log GDP per Capita (Left Axis)
Log Nonlife Density (Right Axis)
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
Log GDP per Capita (Left Axis)
Log Nonlife Density (Right Axis)
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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.
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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.
Independent VariablesBase
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
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
137
Table 6.4: Empirical Model Estimation: Motor 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 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
5 Years after Crisis * Income -0.00038-1.41
10 Years after Crisis * Income -0.00088 ***-4.68
15 Years after Crisis * Income -0.00075 ***-3.64
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
Year Dummy Variables Yes Yes Yes Yes YesCountry Fixed-effects Yes Yes Yes Yes Yes
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
*, ** 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 -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
5 Years after Crisis * Income 0.00036 *1.76
10 Years after Crisis * Income -0.00047-1.34
15 Years after Crisis * Income -0.00053 ***-6.35
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
Year Dummy Variables Yes Yes Yes Yes YesCountry Fixed-effects Yes Yes Yes Yes Yes
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
*, ** 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 -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
2 Years after Crisis * Income 0.00018 *1.83
5 Years after Crisis * Income 0.00023 **2.45
10 Years after Crisis * Income -0.00012-0.76
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
Year Dummy Variables Yes Yes Yes Yes YesCountry Fixed-effects Yes Yes Yes Yes Yes
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
*, ** 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 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
Dependent Variable: Property Premium Density
Dependent Variable: Liability Premium Density
Dependent Variable: Nonlife Premium Density
Dependent Variable: Motor Premium Density
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.
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 0.00176 *** 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
Dependent Variable: Nonlife Premium Density
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
*, ** 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.
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 0.000809 *** 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
Dependent Variable: Motor Premium Density
Currency Crisis Models
Debt Crisis Models
Banking Crisis Models
147
Table 6.10: Summary of the Effect of Crisis by Country Income Group: Property 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.
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 0.00025 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
Dependent Variable: Property Premium Density
Currency Crisis Models
Debt Crisis Models
Banking Crisis Models
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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
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 0.00029 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
Dependent Variable: Liability Premium Density
Currency Crisis Models
Debt Crisis Models
Banking 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.
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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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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.