The Link between Insurance and Banking Sectors: An ...

The Link between Insurance and Banking Sectors: An International Cross-Section Analysis of Life Insurance Demand B. Lorent Life insurance has become an increasingly important part of the financial sector. The past ten years have witnessed significant changes of the market conditions faced by the insurance industry. Two trends are especially crucial: the assimilation of banking-sector type activities by life insurers and the consolidation of financial services (e.g. bancassurance). This article identifies the factors determining consumption for life insurance products across 90 countries for the year 2005. We introduce new factors to account for the increased link between bank and insurance sectors. Using a larger dataset, our results confirm the existing literature by showing that countries with higher income, better developed financial system, better educated population and higher old ratio spend more money on life insurance products whereas life expectancy tends to decrease life insurance demand. Moreover, institutional, religious and legal factors are found to be important. The levels of inflation and interest rates, the young ratio and the size of the social security system appear to have no robust association with life insurance consumption. The set of new variables introduced: bancassurance and banking efficiency appear significant, with a negative impact on life insurance consumption. Restricting our sample to developed countries confirm previous results for banking efficiency and bancassurance. The results highlight that the increasing blurring of the boundaries between insurers and banks impact life insurance demand. Keywords: Life Insurance, Insurance Demand, Bancassurance, Cross-section Analysis JEL Classification: G22, C31

CEB Working Paper N° 10/040 August 2010

A revised version of this working paper may be available on the following webpage

http://www.solvay.edu/EN/Research/Bernheim/latestupdatesofCEBWorkingpapers.php Université Libre de Bruxelles - Solvay Brussels School of Economics and Management

Centre Emile Bernheim ULB CP145/01 50, avenue F.D. Roosevelt 1050 Brussels BELGIUM

e-mail: [email protected] Tel. : +32 (0)2/650.48.64 Fax : +32 (0)2/650.41.88

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The Link between Insurance and Banking Sectors: An International Cross-Section Analysis of Life Insurance

Demand*

Benjamin Lorent† This version, Augustus 2010

Abstract

Life insurance has become an increasingly important part of the financial sector. The past ten

years have witnessed significant changes of the market conditions faced by the insurance industry.

Two trends are especially crucial: the assimilation of banking-sector type activities by life insurers

and the consolidation of financial services (e.g. bancassurance). This article identifies the factors

determining consumption for life insurance products across 90 countries for the year 2005. We

introduce new factors to account for the increased link between bank and insurance sectors.

Using a larger dataset, our results confirm the existing literature by showing that countries with

higher income, better developed financial system, better educated population and higher old ratio

spend more money on life insurance products whereas life expectancy tends to decrease life

insurance demand. Moreover, institutional, religious and legal factors are found to be important.

The levels of inflation and interest rates, the young ratio and the size of the social security system

appear to have no robust association with life insurance consumption. The set of new variables

introduced: bancassurance and banking efficiency appear significant, with a negative impact on

life insurance consumption. Restricting our sample to developed countries confirm previous

results for banking efficiency and bancassurance. The results highlight that the increasing blurring

of the boundaries between insurers and banks impact life insurance demand.

JEL Classification: G22, C31

Keywords: Life Insurance, Insurance Demand, Bancassurance, Cross-section Analysis

* I am grateful to Professors Ariane Chapelle, my PhD supervisor, and André Farber for their fruitful comments and

help. I would like also to thank the Research Center E. Bernheim, the Marie-Christine Adam Foundation and the

Université Libre de Bruxelles for their financial support. † Research Center E. Bernheim, Solvay Brussels School of Economics and Management, Université Libre de

Bruxelles, avenue F.D. Roosevelt 21, CP 145/01, B-1050 Brussels, Belgium. Corresponding author’s email, phone

and fax: [email protected], +3226506551, +3226504188.

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1. Introduction The insurance sector has dramatically changed over the last twenty years. The time when

companies used to sell only classical insurance contracts is over. Life insurers have turned into

savings vehicles and play an increasingly role in the financial markets. The growing importance of

life insurance as a provider of financial services and of investment funds in capital markets is

especially pronounced in developed countries.

Life insurance policies offer two main services: income replacement for premature death and

savings instruments. They provide mortality coverage only, known as term policies, or they

combine mortality coverage with a savings component. The second category, known as whole life,

universal life, variable life…typically earn interest, which is returned to the policyholders trough

capital on maturation of the policy, policy dividends… Life insurers also sell annuity policies.

Initially, life insurance business was different from savings. Nowadays, with the increasing savings

type insurance, the situation has changed. Henceforth, savings type insurance products are

marketed on the basis of their investment characteristics: return and/or liquidity. The importance

of savings products is particularly noteworthy because this is the market where insurers face the

most vigorous competition from banks, mutual funds and investment advisory firms.

Due to demographic changes ahead the social security of most developed countries are under

increasing pressures. Many countries already face (or forecast very soon) the need for pension

reforms since the actual systems are insufficient to provide future generations the promised

benefits. Because many programs have been based on pay-as-you-go financing schemes, the

decrease in the ratio of working age to retired people has been forcing many countries to consider

scaling back their public pension systems, leading to their full or partial privatization. It provides

an opportunity for financial firms to exploit the growing demand for supplemental retirement

programs funded by life insurance and annuities and should provide growth opportunities for life

insurers in many markets.

The increasing blurring of the boundaries between insurers and banks is another trend faced by

the life insurance industry. Financial companies are now interconnected in a way without

historical precedent: bancassurance, assurfinance, cross-shareholding… Bancassurance is the

provision of insurance services by banks (assurfinance/allfinanz is the opposite trend, the

provision of banking services by insurers). In many countries, banking networks represent the

leading distribution channel for life insurance products as well as non-life products. The

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development of bancassurance is uneven around the world. It is highly developed in Europe,

especially in the South (France, Benelux and Spain). By contrast, the phenomenon is relatively

limited in Germany or in the United Kingdom. Bancassurance model is more recent in the US

because of prior legislative restrictions.

The consumption of life insurance differs greatly between industrial countries (from 1,408.6 $ per

capita in 19991 to 2,174.4 $ in 2008) and emerging countries (from 23.4 $ per capita in 1999 to

39.2 $ in 2008) leading to a huge unexploited market in developing countries. The large disparity

in the use of life insurance across countries raises questions about what causes this variation. This

has attracted considerable attention in investigating key factors influencing the demand for life

insurance. Main literature contributions are Dickinson and Khajuria (1986), Truett and Truett

(1990), Browne and Kim (1993), Outreville (1996), Ward and Zurbruegg (2002), Beck and Webb

(2003) and Li et al. (2007).

This study’s contribution resides in a new effort to understand what drives the life insurance

consumption within a sample of 90 countries2 for the period 2005. This study is then limited in

depth but not in breadth. The determinants of life insurance consumption can be expressed by an

equilibrium demand-and-supply model. Although many studies have identified determinants of

life insurance demand, little is known about factors affecting the supply of life insurance products,

especially the insurance price, the link between banking and insurance sectors and the regulation

of financial companies.

Most empirical studies include the financial development of a country as a potential determinant

of life insurance consumption but do not measure the real competition between banks and life

insurers. Then we improve on the existing literature in several ways. First, we introduce new

variables reflecting the increasing link and competition between banking and insurance sectors,

enhancing the savings characteristic of life insurance policies. These variables measure the

convergence between banks and insurers as bancassurance, but also the increased competition

between financial companies as banking profitability and efficiency or banking regulation.

Additionally, savings life insurance may induce economic factors to better explain life insurance

demand. In particular, inflation, interest rates might influence the consumption of life insurance

1 Swiss Re (various years) 2 The full set of countries is listed in appendix 1.

4

products. This paper is the first after Beck and Webb (2003) that study life insurance

determinants taking into account the savings role of life insurance products.

Besides the introduction of new variables, we use a new dataset that extends the coverage of

economies: 90 countries spread throughout the world. The choice of countries included within

the sample is mainly based on the availability of life insurance premium data. To our best

knowledge, Beck and Webb (2003) is the widest study including 68 economies. We split our

sample in two: developed and developing countries. The samples are mutually exclusive. Viewing

insurance as personal financial planning is more popular in developed countries than in the

developing world. We expect then different results regarding financial and economic variables

and factors linking banking and insurance sectors.

The results are expected to help policymakers identifies the supply and demand factors for life

insurance products. They might particularly help developing countries in their fostering of life

insurance markets. The rest of the paper is structured as follows. Section 2 provides a description

of the factors explaining life insurance consumption, including new variables introduced in this

study. Section 3 depicts the model and methodology used in our empirical model. Section 4

presents and analyzes the main results. A conclusion and further research are then presented in

the final section.

2. Determinants of Life Insurance Consumption Empirical tests have been conducted to measure the significance of factors determining life

insurance consumption. Principal work includes that of Browne and Kim (1993) and Outreville

(1996), whose findings show that the level of life insurance consumption within an economy can

be explained by the GPD, the price of insurance, the inflation, the dependency ratio and the

financial development. Browne and Kim’s study is based on 48 countries, throughout the world,

for two separate time periods (1980 and 1987). Based on a cross-sectional analysis of 48

developing countries, Outreville (1996) analyzed the demand for life insurance for the period

1986. Ward and Zurbruegg (2002) investigated the determinants of consumption in Asia (22

countries). They compared their panel with OECD countries. Analyzing the data from 1987

through 1998, they found evidence that improved civil rights and political stability leads to an

increase in the consumption of life insurance in the Asian countries as well as in OECD region.

Using a panel with data aggregated at different frequencies for 68 economies in 1961 - 2000, Beck

and Webb (2003) showed that economic indicators such as inflation, income per capita, banking

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sector development and religious and institutional indicators are the most robust predictors of the

use of life insurance. They drew their conclusions using the division of life insurance

consumption into the savings, mortality risk and annuity components. Li et al (2007) examined

the determinants of life insurance consumption in OECD countries. They found that life

insurance demand increases with the income, the number of dependents and level of education,

and decreases with life expectancy and social security expenditure.

In this paper we will focus on the link between banks and insurers. Therefore besides the basic

set of economic, social and institutional variables used in previous studies, are also incorporated

into the possible explanatory factors that affect life insurance density3: banking sector efficiency,

profitability and solvability, banking regulation and bancassurance indicators. For expository

purposes, each of the factors contained within the final estimation model are explained in depth

below.

2.1 Social Factor

Social security expenditures measure the average welfare as a percentage of GDP. Social security

systems generally provide a basic protection level for income termination in the event of the

death of a wage earner. Lewis (1989) postulated that social security displaces private insurance. As

government increases its social expenditures, the need for individuals to protect themselves

against longetivity and early death decreases. This is particularly accurate for life insurance

products with a mortality component or annuities. Moreover, as social security is financed by

taxes, high social expenditures can reduce individual income and consequently life insurance

purchase. Browne and Kim (1993), Ward and Zurbruegg (2002) and Beck and Webb (2003)

showed that the need for life insurance purchase is reduced when government spending on social

security is increased. We use the ratio health expenditure to GDP as a proxy for social security

expenditures. We expect a negative relationship between social security variable and life insurance

demand, especially in developed countries.

3 Life insurance density is defined as life premiums per capita. Using premium income as a proxy of life insurance

may become inappropriate because premiums represent total revenues that are equal to price times output (Cummins

et al. 1999). Indeed, differences in the price of products across countries and across insurance companies may give

rise to misleading inferences about life insurance expenditures across countries. Unfortunately due to the lack of data

regarding insurance price, we use this proxy.

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2.2 Economic Factors

All works related to life insurance consumption have shown a positive relation between income

level and life insurance consumption (see e.g. Fortune, 1973; Lewis, 1989; Browne and Kim, 1993;

Outreville, 1996). A person’s consumption and human capital increase along with income

creating a greater demand for insurance to safeguard the income potential of the insured and the

expected consumption of his or her dependents. In addition, as income increases, life insurance

becomes more affordable. Finally, increasing income may encourage individuals to diversify their

investments and direct a part of their income to savings products. It is therefore hypothesized

that the disposal personal income measured by GDP per capita is positively related to life

insurance expenditures.

We expect that a higher inflation rate will be negatively correlated with life insurance density.

Because life insurance products typically provide monetary benefits over a long-term, monetary

uncertainty has a substantial negative effect on the expected return of life insurance products. As

the present value of assets is calculating by discounting future cash-flows (the sums to be received

in the insurance contract), the benefits from purchasing life insurance decrease. Babbel (1981),

Browne and Kim (1993), Outreville (1996), Ward and Zurbruegg (2002) and Beck and Webb

(2003) have shown that inflationary expectations have a significant negative impact on life

insurance consumption.

A higher real interest rate increases life insurers’ investment returns and thus their profitability, in

turn offering greater profitability of financial relative to real investment for potential purchasers

of life insurance policies. This is particularly accurate for life savings instruments. Moreover

higher real interest rates increase the supply of capital and therefore the ability of life insurance

companies to answer to potential demand. On the other, higher interest rates may induce

consumers to reduce their life insurance purchases given the anticipation of higher returns.

Indeed, as life insurance products compete with other financial products, a higher real interest

rate may divert investment flows from insurance products to banking products. Therefore we do

not have any expectations regarding the sign of the relationship between life insurance premiums

and real interest rates. Outreville (1996) found an insignificant relationship between real interest

rates and life insurance demand. Browne and Kim (1993) and Ward and Zurbruegg (2002)

neglected this variable. Beck and Webb (2003) and Li et al (2007) found significant influence of

this variable, but with a contradictory sign.

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Insurance price is undoubtedly an important determinant of the consumption of life insurance.

Measuring the impact of price is, however, difficult due to the problem of actually determining

the price. The previous literature used life expectancy as a proxy for insurance prices (Outreville,

1996; Ward and Zurbruegg, 2002; Hwang and Greenford, 2005). Unfortunately using life

expectancy as a proxy for insurance price exhibits a number of problems (Ward and Zurbruegg,

2002). The price or the policy loading charge can be approximated by the ratio of total

expenditures on life insurance premium to the amount of life premium (Browne and Kim, 1993).

This ratio can be interpreted as the cost per dollar of life insurance coverage. Unfortunately, data

are relatively limited. Leaving out the price may subject the empirical testing to omitted variable

bias. Beck and Webb (2003) assumed that the price is a function of several supply-side factors

such as urbanization, monetary stability, institutional development, political stability and banking

sector development. All these factors affect the insurer’s ability to provide cost effective

insurance. In line with Beck and Webb (2003), we assume that insurance price is approximated by

supply factors, in this study by institutional development, political stability, financial development

and efficiency, profitability and regulation of banks. Indeed, banks and insurers are

complementary but they also compete each other as they offer similar products.

2.3 Financial Development and Banking Factors Outreville (1996), Ward and Zurbruegg (2002), Beck and Webb (2003) and Li et al (2007) showed

that life insurance development is significantly related to the country’s level of financial

development. The financial development can be an important source of growth in the insurance

industry. This might due to the fact that well functioning banks may increase the confidence of

consumers in other financial institutions. A more developed banking sector can also increase

competition with other financial sectors, especially insurance companies. This assertion is

particularly appropriate for banking-type products, i.e. savings instruments, offered by life

insurers. Ward and Zurbruegg (2002) used the variable private credit from banks and other

financial institutions over GDP as a proxy of financial development4. Private credit measures the

amount of savings that is channelled through debt-issuing financial intermediaries to private

borrowers. They showed a significantly positive link between financial development and life

insurance penetration in their two sub-samples (OECD countries and Asian countries). But the

effect is higher in developed countries. It would imply that the importance of the financial

4 The financial development can be measured in different ways. Outreville (1996) and Li et al (2007) used the (M1 –

M2)/M2 ratio in their empirical study. Beck and Webb (2003) introduced the banking development variable, defined

as total claims of deposit money banks on domestic non financial sector as a share of GDP.

8

intermediary sector is to complement insurance consumption in developed countries. In the

Asian countries where financial institution development is not as important the impact is not as

great. According to Levine et al. (2000), private credit is the best variable to measure financial

development. Private credit is more than the financial sector size as it includes an efficiency side

in its measure. Consequently we use this variable to measure financial development.

This study aims to go one step further and better gauge the banking sector efficiency. As

insurance and banking sectors are increasingly intricated, especially in the life business, the

banking efficiency and profitability may have an impact on life insurance supply, therefore on life

insurance consumption. A higher efficiency provides to banks new capital, fostering the offer of

savings instruments and the competition with insurers. Competition usually results in higher

quality products and lower prices for insurance policies, increasing life insurance consumption.

More efficient banks can also offer better returns to savers. But if the insurance sector is not able

to respond to the decreasing prices, the banking sector might gain market shares decreasing life

insurance demand. Therefore, we do not predict any sign for the relationship between banking

efficiency and life insurance consumption. The banking efficiency may be measured by the

following variables: net risk margin5, bank income cost ratio and overhead cost6. We also test the

efficiency hypothesis by using profitability variables: return on assets (ROA), return on equity

(ROE) and bank-z-score7. Finally we test our model for the variable bank concentration8 which is

defined as the ratio of the three largest banks assets to total banking sector assets. A highly

concentrated banking sector may result in lack of competitive pressure to attract savings which in

turn may decrease the competition with insurers and finally increase insurance prices. On the

other, a highly concentrated banking sector might lead to higher bank products’ price. We expect

then an ambiguous relationship between bank concentration and life insurance density.

There is no data or direct measure of insurance regulation. We therefore proxy some aspects of

insurance supervision using bank regulation variables. In many countries, the banking supervision

is linked to the insurance regulation. Sometimes, the same institution controls both activities. In

other countries, the control is organized jointly. In both cases, sound and vibrant practices in the 5 Defined as the accounting value of a bank’s net interest revenue as a share of its total assets 6 Defined as the accounting value of a bank’s overhead costs as a share of its total assets 7 Defined as a model to predict bankruptcies (see appendix 2 for further information). The higher the number the

lower the risk. 8 This indicator is based on bank-level data. Since the bank coverage is not 100%, variation across countries might be

driven by difference in coverage rather than difference in market structure.

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banking control may trigger same experiences in the insurance sector. As extensively studied in

the literature, the banking sector analysis allows us to make some assumptions regarding the

insurance sector. A regulated banking sector might foster higher confidence into the industry,

increasing confidence into the insurance sector as well. On the other, overregulated sector may

prevent or restrict companies to launch new activities, decreasing the competition with insurers.

We expect then an ambiguous relationship between bank regulation and life insurance demand.

As a measure of bank regulation, we identify three variables in the dataset provided by Barth et al.

(2004): bank activity regulatory, competition regulatory, and official supervisory power. Bank

activity regulatory’s variable measures the degree to which national regulatory authorities allow

banks to engage in other activities than traditional one: security, insurance and real estate activities.

The empirical evidence indicates that restricting bank activities has negative repercussions with

lower banking-sector efficiency (Barth et al., 2001; Barth et al., 2004). Considering the ambiguous

relationship between banking efficiency and life insurance demand, we do not expect any

relationship sign.

We extract from this dataset the variable bancassurance which measure the degree to which

national regulatory authorities allow banks to engage in insurance activities. The aim is to perceive

if bancassurance companies increase the visibility and accessibility of life insurance products,

fostering life insurance consumption. Bancassurance may allow banks to exploit economies of

scale and scope and thereby provide services more efficiently. On the other, companies selling

one mix of products (bank vs. insurance products) may be more specialized and efficient in their

role. Therefore mixed companies may have negative impacts on life insurance consumption

instead of fostering it. In addition, such complex companies cannot be effectively monitored due

to informational asymmetries and opacity. Finally, both the market and political power enjoyed

by such companies can impede competition and adversely influence policies. So we do not have

any expectation regarding the sign of the relationship between the bancassurance variable and life

insurance consumption. We stress that this variable proxies a possibility offered to companies

and not an obligation. If a country allows banks to engage in insurance activities, it is not clear

that companies will use that possibility.

Competition regulatory variable measures the specific legal requirements for obtaining a license to

operate as a bank. Some argue that effective screening of bank entry can promote stability.

Others stress the beneficial effects of competition and the harmful effect of restricting entry. We

expect an ambiguous relationship between this variable and the life insurance demand.

10

Official Supervisory power measures the extent to which official supervisory authorities have the

authority to take specific actions to prevent and correct problems. Some theoretical models stress

the advantages of granting broad powers to supervisors (due to informational asymmetries,

difficulty to monitor banks...). On the other hand, powerful supervisors may exert a negative

influence on banks’ performance. They may use their powers to benefit favoured-constituents,

attract campaign donations and extract bribes. Therefore we expect an ambiguous relationship

with life insurance consumption.

2.4 Demographic Factors

The degree of risk aversion in a country may be related to the predominant religion. Zelizer (1979)

noted that religion historically has provided a strong source of cultural opposition to life

insurance. Religion may also shape national view regarding property rights, competition, taxation

and the role of the state. Browne and Kim (1993), Outreville (1996), Ward and Zurbruegg (2002)

and Beck and Webb (2003) included in their set of variables a religious proxy. Browne and Kim

(1993) showed that predominantly Islamic countries consume less life insurance than non-Islamic

countries. In some Muslim-dominated countries, there are possible legal or political conditions

that may hamper life insurance growth. Followers of Islam have traditionally disapproved life

insurance consumption because it is considered a hedge against Allah9. Beck and Webb (2003)

went further and incorporated a broader measure of religious inclination by including

Protestantism, Catholicism and a composite of other religions, defined as the ratio of the

adherents of a religion to the entire population. We use the same approach in this paper. While

we expect a negative relationship between Muslim population and life insurance consumption, we

do not have any prior expectations regarding the sign of relationship with other religions.

One of the main purposes of life insurance is to protect dependents against financial troubles,

following the premature death of the household earner. In line with previous literature, the

average number of dependents per potential life insurance consumer in a country is approximated

by the young dependency ratio10. As this ratio grows, it is more likely that income-earners will

purchase life insurance in the event of a premature death. Beenstock et al. (1986), Truett and

Truett (1990) and Browne and Kim (1993) found a significant and positive relationship.

The old dependency ratio, defined as the ratio of old dependents to the working age population

has been introduced by Beck and Webb (2003). It is expected to decrease the demand for the 9 Takaful is an exception, but not widely developed. 10 Defined as the number of young dependents over the total working population.

11

mortality risk component and increase the demand for the savings and annuity components of

life insurance products. We expect then an ambiguous overall effect of the old dependency ratio.

Life insurance consumption is hypothesized to increase with the wage earner’s probability of

death (Lewis, 1989). Considering that life expectancy is inversely related to the probability of

death, we predict a negative relationship between life expectancy and life insurance consumption.

On the other, as argued by Outreville (1996), longer life expectancy may decrease the price of life

insurance and therefore tends to stimulate its consumption. Moreover, countries with a longer life

expectancy should increase demand for products with an annuity component. Earlier studies have

found contradictory conclusions: Beenstock et al. (1986), Outreville (1996) and Ward and

Zurbruegg (2002) found a positive significant relationship whereas Beck and Webb (2003) and Li

et al. (2007) showed negative impact of life expectancy on life insurance demand.

Life insurance consumption should rise with education for several reasons. First of all, as showed

by Browne and Kim (1993), education is a good proxy to measure the risk aversion. An

individual’s education level is positively related to greater risk aversion. Secondly, a higher level of

education may increase the ability of people to understand the benefits and complexity of risk

management and long-term savings considering the social security financing issue. Finally, a

higher educated population is generally associated with higher salaries and then the need to

protect higher income. Browne and Kim (1993), Outreville (1996), Ward and Zurbruegg (2002),

Beck and Webb (2003) and Li et al. (2007) found a positive relationship between life insurance

consumption and the level of education. The average years of schooling in the population over

age 25 is used as a proxy for the level of education11.

2.5 Institutional and Legal Factors

A stable political and legal system is important for a vibrant and growing life insurance market.

Lack of political stability may prevent the construction of a long term relationship between the

customer and the life insurance company. The same argument is true for a weak legal system.

Levine (1997, 1998) showed that a good investor protection will induce a higher economic

11 We mainly use two different databases: Cohen and Soto (2007) and Barro and Lee (2001). The former is our main

data source. The average years of schooling is built by multiplying the population’s shares of educational attainment

by the appropriate length (in years) of each educational category. Cohen and Soto (2007) computed data until 2000

and made a projection for 2010. We used both values to extrapolate the data level in 2005. As Barro and Lee (2001)

computed data until 2000, we made projections for the period 2005. For Estonia, Latvia and Lithuania we use data

provided by Monga (2004).

12

growth. This situation is particularly applicable to life insurance products where relationship with

companies tends to be long term. Moreover, with the increasing complexity of life insurance

products, policyholder can suffer from informational asymmetry. The absence of sound legal

system may also hamper the efficiency of insurers’ investment, decreasing the profitability and

finally increasing the insurance price. Clearly then, the functioning of a working legal system and

the protection it may afford to policyholders is a major determinant of insurance market

penetration. Ward and Zurbruegg (2002) examined the impact of legal and political determinants

on life insurance consumption within Asian and OECD countries. They highlight that in Asia an

improvement in the legal system has a significant and positive impact on life insurance demand.

In order to capture the potential instability of political system, we consider the variable checks12.

It measures checks and balances within a country’s political system which is the number of

individuals with a veto within the political apparatus. This variable has been introduced by Ward

and Zurbruegg (2002). Number of checks should enhance the political efficiency; therefore we

expect a positive relationship between this variable and life insurance demand. Another set of

variables used for the measure of sound and stable political institutions is the indicators built by

Kaufman et al. (2008). They developed indices that measure various aspects of governance. They

constructed aggregating indicators of six dimensions13 for a sample of 212 countries. We expect

also a positive relationship between this set of variables and life insurance consumption. Beck and

Webb (2003) found a positive significant relationship between these six indicators and life

insurance demand.

Finally, we introduce the index of property right, provided by the Heritage Foundation (2008),

which measures the property right protection. This index is defined as an assessment of the

ability of individuals to accumulate private property, secured by clear laws that are fully enforced

by the state. With improvements of the index of property rights in a given country we would

12 As a substitute for checks, we introduce the variable legislative competition which is an index of the degree of

competitiveness of the last legislative election, ranging from 1 to 7. A greater competition will limit the ability of the

elite to dictate policy and institutional development. As the variable checks, we predict a positive relationship with

life insurance demand. 13 Voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of

corruption, see appendix 2 for definition of all variables. The data reflects the views on governance of public sector,

private sector and NGO experts, as well as thousand of citizens. The indicators are based on several hundred

individual variables measuring perceptions of governance, drawn from 35 separate data sources constructed by 32

different organizations around the world.

13

expect the protection and enforcement of property rights to facilitate the transaction of insurance

policies. An ability to appeal the breach of life insurance contracts by insurers reduces the value

of such contracts to consumers and may deter them from committing large sums of money to

these products. Therefore, it is hypothesized a positive relationship with life insurance

consumption.

Table 1: Summary of the Expected Effect on Life Insurance Demand

Variable Proxy

Expected effect on life

insurance demand

Variable Proxy

Expected effect on life

insurance demand

Life Premium per Capita PREM Demographic

Social and Economic Muslim Muslim - Catholic Cath +/-

Health Expenditure HEALTH - Protestant Prot +/- GDP per Capita GDP + Other Other +/-

Inflation INFL - Young Ratio YOUNG +/- Real Interest Rate INT +/- Old Ratio OLD +/-

Private Credit PRIVATECREDIT + Life Expectancy LIFEEXP +/- Average Years of Schooling EDUC +

Banking Sector Efficiency

Institutional and

Legal

Bank Overhead Bank_overhead +/- Bank Net Risk Margin Net_risk_margin +/- Voice and Accountability VA +

Bank Cost COST +/- Political Stability Pol_stab + Bank ROA ROA +/- Government Effectiveness Gov_eff + Bank ROE ROE +/- Regulatory Quality Reg_qual +

Bank-z-score Bank_z_score +/- Rule of Law ROL + Bank Concentration CONC +/- Control of Corruption Corr +

Index of Property Rights PropFree + Banking Regulation

Bank Activities

Regulatory REGACT +/-

Bancassurance BANC +/- Competition Regulatory REGCOM +/-

Official Supervisory Power REGOFF +/-

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3. Methodology and Data The baseline model examines the relationship between main hypothesized factors and life

insurance consumption. In our model, we use log-linear form introduced by Outreville (1996)

and Browne and Kim (1993) for some variables. The log linear specification is frequently used

for estimating demand models. This creates linearity in the model and also provides for the

estimation of elasticities. To determine which variable should be specified in log form, the Box-

Cox procedure is employed.

Given the hypotheses specified above, the following baseline model is proposed:

Log(PREM)i = αi + β1log(GDP)i + β2log(EDUC)i + β3log(PRIVATECREDIT)i +

β4log(YOUNG)i + β5(INFL)i + β6log(OLD)i + β7log(LIFEEXP)i + β8(INT)i + β9log(HEALTH)i

+ εi

Here, the subscript i represents the country, αi are cross-sectional intercept terms, β1,…, β9 are the

slope parameters, and εi are random error terms.

PREM = demand for life insurance (per capita in constant US dollars)

GDP = income (GDP per capita in constant US dollars)

EDUC = education level (average years of schooling)

PRIVATECREDIT = measure of the financial development (average of previous 5 years16)

YOUNG = young dependency ratio

INFL = anticipated inflation (average of previous 7 years)

OLD = old dependency ratio

LIFEEXP = life expectancy at birth

HEALTH = health expenditure (as a percentage of GDP)

INT = real interest rates

Subsequent regressions include a larger set of potential determinants of life insurance

consumption, including economic factors, banking efficiency and regulation variables and

institutional and legal factors. 16 We averaged some data over five years because several of our explanatory variables are subject to short-term

fluctuations related to the business cycle and/or political events: PRIVATECREDIT, banking efficiency variables

and institutional and legal factors.

15

We use cross-section data for 90 countries17 in 2005. A cross-country comparison allow us

studying socioeconomic variables that change slowly over time, such as life expectancy, old or

young ratios, which go undetected in time series analyses. The year 2005 was chosen mainly for

two reasons. First of all some data are not available after 2005. Secondly data for banking

regulation including bancassurance have been collected in 200318. To take the effects of potential

regulation system reforms into account, we estimate that a two-year period is reasonable.

Data for life insurance premiums are obtained from Swiss Reinsurance Company, the African

Insurance Organization19 and the Association of Latin America Insurance Regulators20 and are

expressed in constant US dollars per capita. Life insurance consumption is represented by

premium income (PREM), which is equal to the price of insurance times the amount of insurance

sold.

The choice of the countries included in the sample was based on the availability of data,

especially life insurance premiums. The sample is split in two: developing and developed

countries 21 . Viewing insurance as personal financial planning is more popular in developed

countries than developing countries. We expect then different results regarding factors linking

banking and insurance sectors or some financial and economic factors. This distinction avoids

mixing different country characteristics and heterogeneous consumer demand. Moreover, it

allows to compare variables elasticities of demand for life insurance in the different samples.

Tables 2 and 3 present the summary statistics for the basic regression variables22. Demand for life

insurance exhibits a large dispersion across full sample countries. One quarter of the sample

presents a demand less than 7 $, whereas the higher quartile presents a demand higher than 589 $.

As we can see, there is a large variation in the economic and financial variables. On the other

hand, sociodemographic and social characteristics are seen to be more similar across the full

sample.

It is noticeable that the level of insurance life premium is higher in the developed countries. This

is not surprising given the higher GDP per capita and the higher financial development. In 17 See appendix 1, for the list of countries included in the sample. 18 Banking regulation database (Bart et al., 2004) is only available for the periods 2003 and 2007. 19 Burkina Faso, Côte d’Ivoire, Cameroon, Ethiopia, Gabon, Malawi, Senegal, Tanzania, Togo, Uganda and Zambia. 20 Guatemala and Honduras. 21 Distinction is based on World Bank classification (see appendix 1). 22 See appendix 3 for the summary statistics of the other variables.

16

addition, education, political and institutional factors, all thought to promote insurance

consumption, are more favourable in developed countries. Life insurance consumption in the full

sample represents a substantial portion of per capita income, with an average of 4.27 percent

corresponding to purchase per capita of 595.45 $ over an income of 13,928 $. Life insurance

density in developed countries represents 4.44 percent of income per capita, while this percentage

appears to be very low in developing countries with an average of 0.62 percent.

Table 2: Main Explanatory Variables by Life Insurance Sales Quartiles (Full Sample)

Variable Quartile 1 Quartile 2 Quartile 3 Quartile 4

PREM 6.21 53.13 589.57 7,229.52

GDP 2,275 5,585 25,560 65,011

EDUC 5.68 8.06 10.27 13

PRIVATECREDIT 0.21 0.42 0.96 1.78

YOUNG 0.26 0.36 0.53 1.03

INF 0.02 0.03 0.07 1.44

OLD 0.07 0.11 0.22 0.3

LIFEEXP 70 73.58 79 82.08

HEALTH 0.05 0.07 0.08 0.16

INT 0.03 0.04 0.07 0.43

Many of the potential determinants of life insurance consumption are highly correlated with one

another (see table 4). Richer countries have older populations, longer life expectancies, higher

levels of schooling and better developed financial systems. The simple correlation coefficients of

all variables with life insurance density exhibit the expected sign, with the exception of HEALTH.

Life insurance consumption is higher for countries in which governments spend more money on

health expenditure. Life insurance expenditures appear to be negatively correlated with the young

dependency ratio. One problem associated with the variable YOUNG is that it rather proxies the

young adult population than the number of dependents.

The high correlations between the explanatory variables underscore the importance of

performing multivariate regression analysis. Including correlated variables will increase the risk of

generating estimates with high standard errors due the high level of collinearity. As the number of

observations is higher in this analysis compared to previous studies, this multicollinearity issue

would be partly alleviated.

Part II – An International Cross-Section Analysis of Life Insurance Demand 17

Table 3: Variable Descriptive Statistics

Full Sample (N=90) PREM GDP EDUC PRIVATECREDIT YOUNG INF OLD LIFEEXP HEALTH INT

Mean 595.45 13,928.53 7.95 0.61 0.43 0.07 0.14 70.25 0.07 0.06 Median 53.13 5,585.34 8.06 0.42 0.36 0.03 0.11 73.58 0.07 0.04 Std Dev 1,144 15,749 3.1 0.48 0.21 0.16 0.08 11.88 0.03 0.07 Minimum 0.06 155.78 0.69 0.04 0.2 -0.02 0.02 35 0.02 -0.05 Maximum 7,230 65,011 13 1.78 1.03 1.44 0.3 82.08 0.16 0.43 Developed Countries (N=57) PREM GDP EDUC PRIVATECREDIT YOUNG INF OLD LIFEEXP HEALTH INT Mean 934.5 21,069 9.62 0.79 0.31 0.05 0.18 75.21 0.08 0.05 Median 216.04 17,566.18 9.58 0.83 0.27 0.03 0.19 77.71 0.08 0.05 Std Dev 1,327 15,875 2.19 0.49 0.1 0.07 0.07 8.11 0.02 0.04 Minimum 3.93 3,511 4.64 0.1 0.2 -0.02 0.02 34.97 0.02 -0.03 Maximum 7,230 65,011 13 1.78 0.6 0.38 0.3 82.08 0.16 0.18 Developing Countries (N=33) PREM GDP EDUC PRIVATECREDIT YOUNG INF OLD LIFEEXP HEALTH INT Mean 9.81 1,595 5.07 0.31 0.63 0.12 0.07 61.68 0.05 0.07 Median 5.05 1,276.4 5.14 0.24 0.57 0.05 0.07 68.13 0.05 0.04 Std Dev 14.87 1,052 2.18 0.25 0.2 0.25 0.02 12.55 0.02 0.09 Minimum 0.06 155.78 0.69 0.04 0.31 0.008 0.05 38.41 0.018 -0.04 Maximum 65.61 3,622 10.27 1.11 1.03 1.44 0.12 74.67 0.12 0.43

Table 4: Correlations

Full Sample (N=90) PREM GDP EDUC PRIVATECREDIT YOUNG INF OLD LIFEEXP HEALTH INT

PREM 1 0.90445 0.75147 0.80343 -0.74106 -0.27288 0.7039 0.62762 0.47552 -0.16434 GDP 0.90445 1 0.78291 0.72230 -0.79274 -0.20802 0.71397 0.70951 0.46762 -0.12781

EDUC 0.75147 0.78291 1 0.60257 -0.76069 -0.27976 0.6879 0.68920 0.42542 -0.17188 PRIVATECREDIT 0.80343 0.72230 0.60257 1 -0.63051 -0.45236 0.55015 0.67424 0.43794 -0.32807

YOUNG -0.74106 -0.79274 -0.76069 -0.63051 1 0.27978 -0.83591 -0.74303 -0.36416 0.30326 INF -0.27288 -0.20802 -0.27976 -0.45236 0.27978 1 -0.23471 -0.34746 -0.31005 0.62128 OLD 0.7039 0.71397 0.6879 0.55015 -0.83591 -0.23471 1 0.63338 0.60015 -0.22886

LIFEEXP 0.62762 0.70951 0.68920 0.67424 -0.74303 -0.34746 0.63338 1 0.29838 -0.25909 HEALTH 0.47552 0.46762 0.42542 0.43794 -0.36416 0.31005 0.60015 0.29838 1 -0.1585

INT -0.16434 -0.12781 -0.17188 -0.32807 0.30326 0.62128 -0.22886 -0.25909 -0.1585 1


4. Empirical Results

The first set of regression includes the basic subset of social and economic variables which can

lead to a comparison of the results from previous research by, for example, Browne and Kim

(1993) or Beck and Webb (2003).

The results show that the variation of life insurance density across countries varies with the GDP

per capita, the average years of schooling, the private credit ratio, the old dependency ratio and

the life expectancy. These five variables show significant coefficients in our baseline regression.

The estimated coefficients have the expected sign and are statistically significant to the 1, 5 or 10

per cent level. Since for several of our variables logarithmic values have been taken, the estimated

coefficients are measures of elasticity. Any value greater than 1 indicates that a change in that

variable will drive an even bigger change in the level of life insurance consumption. More

specifically, an increase in income of 10 per cent will increase the consumption of life insurance

by 11.5 per cent. The results are consistent with the models of Browne and Kim (1993),

Outreville (1996), Beck Webb (2003) or Li et al (2007).

Table 5: Socioeconomic and Financial Determinants of Life Insurance Consumption

Variable Parameter (t-Stat)

Constant 11.7 (3.24)***

GDP 1.15 (7.78)***

EDUC 0.62 (1.78)*

PRIVATECREDIT 1.16 (6.21)***

YOUNG 0.39 (0.7)

INFL -0.44 (-0.49)

OLD 0.89 (2.31)**

LIFEEXP -2.93 (-3.41)***

HEALTH -0.46 (-1.28)

INT 1.59 (0.76)

Adjusted R² 0.8785 Observations 90 F-test 72.51***

* significant at the 10 percent level

** significant at the 5 percent level

*** significant at the 1 percent level

Source: Author’s calculations


The negative sign for the life expectancy supports the negative effect associated with the lower

probability of the income earner’s premature death. The county’s level of financial development

appears to be a strong determinant of its life insurance consumption. It implies that the

importance of the financial sector is to complement insurance consumption. The insurance sector

will benefit from a strong financial industry. Contrary to most previous models, inflation rate, real

interest rate, health expenditures and young dependency ratio cannot explain the variation in life

insurance density across countries. Replacing the inflation rate with the expected inflation rate

which is the average of the inflation rate in the current and the following year confirms the results.

The aim of this study was to better measure the link between banks and life insurers through their

increased competition and convergence. Table 6 presents results with banking variables as

dependent variables. In the first columns (1 to 4) we use banking efficiency and profitability

variables as potential determinants of life insurance consumption.

The bank concentration (CONC) is negatively related to life insurance consumption consistent

with the view that a higher concentrated banking sector decrease competition with the insurance

sector. Profitability (ROA) and efficiency variables (COST 23 ) are positively related to life

insurance density. Higher level of cost indicates lower efficiency. Therefore the overall effect of

banking efficiency and profitability is ambiguous. However from variables measuring the bank

efficiency, only bank-z-score factor enters significantly in our model. Bank-z-score is an indicator

of banking stability including a profitability component24. If profits are assumed to follow a

normal distribution, it can be shown that the bank-z-score is the inverse of the probability of

insolvency (see Boyd, Graham and Hewitt, 1993 or De Nicolo, 2000). A higher bank-z-score

indicates that the bank is more stable. The negative relationship with life insurance demand could

reflect either a measure of customers’ confidence in the banking system or a mixing effect of

profitability and customers’ confidence. A higher confidence might encourage savers to invest in

banking products instead of life insurance products. Secondly, as bank-z-score includes a

profitability measure, a higher score may allow banks to offer better returns than life insurance

products and at a lower price increasing banking products attraction and decreasing life insurance

consumption. This negative relationship shows that banking industry has an influence on life

23 As an alternative measure, we also tested net risk margin, bank overhead and ROE. With the exception of a

negative relationship between ROE and life insurance demand, the results were the same. 24 Bank-z-score = (ROA + equity/assets)/σ(ROA)


insurance consumption, especially via savings insurance. Moreover, including bank-z-score

increases the impact of economic factors, GDP and PRIVATECREDIT.

Table 6: Banking Determinants of Life Insurance Consumption

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Constant 12.62 (3.34)***

1.35 (3.07)***

11.59 (3.19)***

11.74 (3.33)***

13.59 (3.21)***

12.26 (2.93)***

13.76 (3.33)***

13.59 (3.21)***

GDP 1.19 (7.61)***

1.14 (7.62)***

1.16 (7.75)***

1.19 (8.17)***

1.1 (6.72)***

1.13 (6.75)***

1.11 (6.85)**

1.1 (6.72)***

EDUC 0.58 (1.65)

0.64 (1.8)*

0.59 (1.66)

0.51 (1.45)

0.35 (0.9)

0.42 ²(1.08)

0.56 (1.44)

0.35 (0.9)


1.16 (6.19)***

1.18 (6.13)***

1.21 (6.58)***

1.08 (4.95)***

1.07 (4.76)***

1.15 (5.33)***

1.08 (4.95)***

YOUNG 0.4 (0.72)

0.47 (0.8)

0.33 (0.57)

0.35 (0.63)

0.02 (0.04)

0.04 (0.07)

0.12 (0.21)

0.02 (0.04)

INFL -0.59 (-0.63)

-0.38 (-0.41)

-0.46 (-0.51)

-0.7 (-0.78)

-2.09 (-1.1)

-2.32 (-1.19)

-1.69 (-0.9)

-2.09 (-1.1)

OLD 0.91 (2.35)**

0.95 (2.35)**

0.83 (2.02)**

0.84 (2.22)**

0.63 (1.5)

0.64 (1.51)

0.68 (1.65)

0.63 (1.5)

LIFEEXP -3.15 (-3.5)***

-2.8 (-3.1)***

-3.02 (-3.42)***

-2.97 (-3.52)***

-2.86 (-3)***

-2.96 (-3.05)***

-3.36 (-3.43)***

-2.86 (-3)***

HEALTH -0.48 (-1.35)

-0.47 (-1.3)

-0.47 (-1.32)

-0.51 (-1.46)

-0.02 (-0.05)

-0.1 (-0.24)

-0.14 (-0.32)

-0.02 (-0.04)

INT 1.85 (0.87)

1.49 (0.7)

1.72 (0.81)

2.15 (1.04)

-0.32 (-1.18)

0.81 (0.34

0.82 (0.35)

-0.32 (-1.18)

CONC -0.51 (-0.83)

ROA 2.15 (0.49)

COST 0.36 (0.51)

Bank_z_score -0.04 (-2.14)**

REGCOM -0.11 (-1.18)

REGACT 0.04 (0.68)

BANC 0.6 (1.83)**

REGOFF -0.07 (-2.34)**

Adjusted R² 0.8780 0.8773 0.8774 0.8837 0.8559 0.8539 0.8599 0.8635 Observations 90 90 90 90 78 78 78 78 F-test 65.07*** 64.66*** 64.68*** 68.63*** 46.72*** 45.99*** 48.25*** 49.7*** * significant at the 10 percent level



Note: The numbers in parentheses are t-statistics.


As a measure of banking regulation, we identify four variables (columns 5 to 8): competition

regulatory (REGCOM), bank activities regulatory (REGACT), bancassurance (BANC), and

official supervisory power (REGOFF). Our results are based on a panel of 78 countries25 as data

for other countries are missing.

Bancassurance variable is significant and has a positive sign. It means that countries where the

banking regulator restricts the ability of banks to engage in insurance underwriting and selling

25 See appendix 1 for details regarding countries included in this sample


display a higher life insurance demand. This result shows that life insurance consumption

increases as banks are restricted to sell insurance policies. The reason may arise from the

specialization effect of life insurance business. An insurance company, as opposed to a

bancassurance company or a bank selling directly insurance policies may have a deeper

knowledge of the life insurance market and can better offer and sell life insurance products.

REGOFF 26 is negatively and significantly related to life insurance density. The explanation might

be threefold. Firstly, as some theoretical models stress the advantages of granting broad powers

to supervisors, the banking efficiency may be increased. On the other hand, powerful supervisors

may exert a negative influence on banks’ performances. As we find a negative relationship

between banking efficiency and life insurance demand, the first effect may be preferred. Finally,

as banking and insurance regulation are increasingly related, banking regulation practices may

influence the insurance sector as well. Therefore, a powerful supervisor in the insurance sector

may exert a negative influence on insurers’ performances.

In order to understand the role of the political stability and the legal system in promoting life

insurance consumption, the third group of results augments the earlier specification with political,

institutional and legal variables (see table 7). We use checks (CHECKS)27 as a measure of the

political environment and Kaufman’s indicators (voice and accountability (VA), political stability

(Pol_stab), government efficiency (Gov_eff), regulatory quality (Reg_qual), rule of law (ROL) and

control of corruption28 (Corr)) as a measure of institutional environment (columns 1 to 7). Both

political and institutional variables improve the quality of the model. CHECKS and VA enter

significantly at the 5 percent level in the regression. The overall results do show evidence to

support the hypothesis that political and institutional factors are determinants of life insurance

consumption in economies. This is clearly important to policymakers and new entrants, especially

in developing countries, as it highlights that these variables are key structural determinants of

future expansion. The index of property right has a significant and positive effect on the

consumption of life insurance. A sound legal environment is essential, considering the long-term

and complex aspect of life insurance products.

26 As an alternative measure, we use the variable supervisory forbearance discretion which measures the ability of

supervisory authorities to engage in forbearance when confronted with violation of laws and regulations or other

imprudent behaviours. This variable did not enter significantly in the regression. 27 As an alternative, we use the legislative competition. It did not enter significantly into our model. 28 As an alternative, we use the index of Free of Corruption. The results were identical.


Table 7: Institutional, Political and Legal Determinants of Life Insurance Consumption Variable (1) (2) (3) (4) (5) (6) (7) (8)

Constant 9.77 (2.67)***

9.61 (2.66)***

11.99 (3.19)***

10.61 (2.9)***

11 (2.96)***

11.49 (3.12)***

11.76 (3.21)***

6.79 (1.62)

GDP 1.17 (8.06)***

1.01 (6.55)***

1.17 (7.3)***

1.01 (5.83)***

1.05 (5.53)***

1.12 (6.39)***

1.16 (5.86)***

1.01 (6.37)***

EDUC 0.42 (1.15)

0.44 (1.24)

0.62 (1.74)*

0.54 (1.52)

0.61 (1.74)*

0.63 (1.79)*

0.62 (1.73)*

0.46 (1.3)


1.08 (5.92)***

1.17 (6.11)***

1.04 (5.23)***

1.05 (4.69)***

1.12 (5.16)***

1.17 (5.44)***

1.01 (5.19)***

YOUNG 0.26 (0.46)

0.18 (0.33)

0.36 (0.62)

0.37 (0.67)

0.33 (0.58)

0.4 (0.71)

0.4 (0.7)

0.21 (0.39)

INFL -0.27 (-0.31)

-0.16 (-0.18)

-0.51 (-0.54)

0.08 (0.08)

-0.33 (-0.36)

-0.39 (-0.42)

-0.46 (-0.49)

-0.19 (-0.2)

OLD 0.66 (1.68)*

0.45 (1.06)

0.91 (2.32)**

0.75 (1.9)*

0.79 (1.95)*

0.88 (2.26)**

0.9 (2.28)**

0.78 (2.03)**

LIFEEXP -2.71 (-3.19)***

-2.48 (-2.89)***

-3.02 (-3.3)***

-2.49 (-2.77)***

-2.68 (-2.94)***

-2.85 (-3.18)***

-2.96 (-3.28)***

-2.29 (-2.56)**

HEALTH -0.44 (-1.26)

-0.57 (-1.63)

-0.44 (-1.21)

-0.46 (-1.29)

-0.47 (-1.31)

-0.48 (-1.32)

-0.45 (-1.26)

-0.41 (-1.18)

INT 1.37 (0.66)

1.22 (0.59)

1.76 (0.8)

1.0 (0.51)

1.65 (0.78)

1.53 (0.72)

1.59 (0.75)

1.44 (0.7)

CHECKS 0.19 (2.07)**

VA 0.58 (2.36)**

Pol_stab -0.06 (0.7685)

Reg_qual 0.44 (1.51)

Gov_eff 0.25 (0.81)

ROL 0.09 (0.34)

Corr -0.03 (-0.11)

PropFree 0.83 (2.15)**

Adjusted R² 0.8816 0.8851 0.8771 0.8804 0.878 0.8772 0.877 0.8838 Observations 89 90 90 90 90 90 90 90 F-test 66.52*** 69.55*** 64.52*** 66.53*** 65.04*** 64.55*** 64.45*** 68.67***




Note: The numbers in parentheses are t-statistics


Consistent with previous studies, the percentage of Muslim population explains the variation in

life insurance consumption across countries at a 5 per cent level. The results suggest that life

insurance consumption is less in predominantly Islamic countries. Other religions do not have

any effect on life insurance density.


Table 8: Religion Determinants of Life Insurance Consumption

Variable (1) (2) (3)

Constant 8.85 (2.38)***

2.98 (3.54)***

11.91 (3.18)***

GDP 1.14 (7.89)***

1.15 (7.82)***

1.16 (7.44)***

EDUC 0.38 (1.05)

0.62 (1.78)*

0.65 (1.77)*


1.23 (6.45)***

1.16 (6.18)***

YOUNG 0.14 (0.25)

0.24 (0.42)

0.45 (0.73)

INFL -0.38 (-0.43)

-0.36 (-0.4)

-0.43 (-0.47)

OLD 0.47 (1.12)

0.83 (2.15)**

0.92 (2.28)**

LIFEEXP -2.33 (-2.65)***

-3.83 (-3.75)***

-2.98 (-3.34)***

HEALTH -0.45 (-1.3)

-0.57 (-1.58)

-0.46 (-1.28)

INT 0.72 (0.35)

0.93 (0.44)

1.51 (0.71)

Muslim -0.99 (-2.32)**

Cath 0.49 (1.54)

Prot -0.12 (-0.23)

Adjusted R² 0.8848 0.8806 0.8771 Observations 90 90 90 F-test 69.38*** 66.62*** 64.49***






Table 9 presents results from cross-country regressions in the sub-samples. For each variable it

gives results for two baselines, one for the developed countries sample and one for the

developing countries. We remark that the developed model explains better life insurance

consumption than the developing model.

As expected, life insurance density in developed countries increases with income per capita,

private credit and decreases with life expectation. The old ratio and the education level do not

appear as significant variables anymore. This result is not surprising as both variables appear

more stable in developed countries. With the exception of the health expenditure in developed

countries and the inflation rate in developing countries, the estimated coefficients have the

expected signs. Restricting our sample to developing countries confirms the results for education

level, development of the financial sector and old ratio but not for GDP per capita and life

expectation.


Table 9: Socioeconomic, Demographic and Financial Determinants of Life Insurance Consumption in Developed and Developing Countries

Variable Developed Countries Developing

Countries

Constant 13 (2.32)** 15.65

(2.05)*

GDP 1.14 (4.68)*** 0.56

(1.47)

EDUC 0.28 (0.32) 0.81

(1.86)*

PRIVATECREDIT 1.035 (3.7)*** 0.94

(2.4)**

YOUNG 0.22 (0.28) 0.35

(0.31)

INFL -2.51 (-1.01) 0.51

(0.41)

OLD 0.48 (0.83) 2.64

(2.27)**

LIFEEXP -2.82 (-2.27)** -1.99

(-1.15)

HEALTH 0.21 (0.33) -0.54

(-1.12)

INT -0.26 (-0.07) 1.21

(0.38)

Adjusted R² 0.7593 0.6425

Observations 57 33

F-test 20.63*** 7.39*** * significant at the 10 percent level





With higher estimated coefficients, the impact of the income and the financial sector

development are of greater importance for insurance consumption in developed countries, as

opposed to developing countries. The former result might be surprising as previous studies (e.g.

Ward and Zurbruegg, 2002) pointed the “S curve” relationship between income and life

insurance consumption. The theory contends that the consumption of life insurance rapidly

accelerates as an economy begins to develop. At higher levels of income, insurance consumption

becomes less sensitive to income difference. However, in developed countries, the increase of

income creates a greater demand for insurance to safeguard the income potential of the insured

and the expected consumption of his or her dependents. Moreover as life insurance is a luxury

good, income per capita in developing countries may not be sufficient to allow customers to

afford life insurance products. Finally, prices may play an important role in the life insurance

demand. Higher prices relative to the income per capita in developing countries could explain the

lower impact of income per capita.


In developed countries, a 10 per cent improvement in financial development

(PRIVATECREDIT) will lead to approximately a 10 per cent increase in life insurance

consumption. In developing countries, the improvement in consumption would be around 9 per

cent. This is an interesting result as it would imply that the importance of the financial sector to

complement life insurance consumption is more important in developed countries. However, in

developing countries, PRIVATECREDIT turns out to be one of the key determinants of life

insurance consumption. In particular, the impact is more important than GDP per capita. A well-

functioning financial system increases the visibility and the competition between financial

companies, leading to a higher confidence of consumers in life insurers. In developing countries,

the level of infrastructure, especially the financial system, is far less developed and stable than

wealthier countries. This result shows that demand factor, i.e. income per capita, is less decisive

than supply factor, i.e. financial sector development. The conclusion is inverted in developed

countries; the income is more elastic than the financial development. The results are

contradictory to Ward and Zurbruegg (2002) conclusions.

Life insurance density decreases with a higher life expectancy only in developed countries.

Regarding developing sample, Outreville (1996) found a positive and significant sign between life

expectancy and life insurance demand in a sample of 48 developing countries. The author used

the actuarially fair price of life insurance as a proxy for life expectancy at birth. He postulates that

a long life span decreases the price for insurance. Our results contradict this theory and support

the Lewis’ hypothesis (1989).

Turning to our additional explanatory variables (see table 10), the level of institutional

development, measured by voice and accountability (VA) and regulatory quality (Reg_qual),

explain life insurance demand only in developing countries. This is not surprising considering the

relatively high level of institutional factors in developed countries. This is clearly important to

policymakers and new entrants to the developing markets, as it highlights the institutional

environment as a key structural determinant of future expansion in these markets. Factors

affecting life insurance supply are again more significant in developing countries compared to

wealthier economies.

In the developed sample, economic variables seem to play a higher significant role in life

insurance sales than legal or institutional factors. The results confirm our previous conclusions

for bank_z_score and BANC variables. The convergence between insurers and banks, e.g.

bancassurance is particularly important in developed countries. Together with a significant


bank_z_score, the results show that the increased competition with banks impacts life insurance

consumption in developing countries. This is consistent with the view of insurance policies being

savings instruments only in wealthier countries.

Table 10: Additional Determinants of Life Insurance Consumption in Developed and Developing Countries

Developed Countries Developing Countries

Variable (1) (2) Variable (1) (2)

Constant 17.04 (2.98)***

12.2 (2.27)** Constant 13.9

(1.95)* 14.49

(1.97)*

GDP 1.15 (4.9)***

1.3 (5.31)*** GDP 0.71

(1.97)* 0.56

(1.52)

EDUC 0.76 (0.86)

0.16 (0.19) EDUC 0.42

(0.96) 0.64 (1.5)


1.01 (3.78)*** PRIVATECREDIT 0.99

(2.71)** 0.87

(2.3)**

YOUNG 0.3 (0.4)

0.31 (0.41) YOUNG -0.68

(-0.59) -0.08

(-0.07)

INFL -1.37 (-0.56)

-3.01 (-1.26) INFL 1.86

(0.63) 1.11

(0.88)

OLD 0.55 (0.99)

0.57 (1.03) OLD 1.12

(0.88) 1.93

(1.64)

LIFEEXP -4.21 (-3.1)***

-2.96 (-2.48)** LIFEEXP -2.63

(-1.61) -2.19

(-1.31)

HEALTH 0.13 (0.2)

-0.06 (-0.09) HEALTH -0.51

(-1.12) -0.66

(-1.41)

INT -0.43 (-0.12)

2.13 (0.58) INT 1.86

(0.63) 0.73

(0.24)

BANC 0.89 (2.16)** VA 0.92

(2.21)**

Bank_z_score -0.05 (-2.23)** Reg_qual 0,93

(1,77)*

Adjusted R² 0.7768 0.778 Adjusted R² 0.694 0.6729

Observations 57 57 Observations 33 33

F-test 20.49*** 20.63*** F-test 8.26*** 7.58*** *significant at the 10 percent level

** significant at the 5 percent level Source:




5. Conclusion

The past ten years have witnessed significant changes of the market conditions faced by the

insurance sector. Two trends are especially crucial: the assimilation of banking-sector type

activities by life insurers and the consolidation of financial services (bancassurance or

assurfinance/allfinance). These trends might have an impact on the key determinants of life


insurance consumption. This article examines the determinants of life insurance consumption by

using cross-section analysis. We estimate the determinants in a panel of 90 countries in 2005.

Besides variables used by previous research, we introduce new factors reflecting the increasing

link between bank and insurance sectors. Using a larger dataset that has been previously

examined and consistent with previous research, the cross-country estimations show that

countries with higher income, better developed financial system, better educated population and

higher old ratio spent more money on life insurance products. A higher life expectancy decreases

life insurance density, supporting the model developed by Lewis (1989). The young dependency

ratio, expected inflation rate, real interest rate and health expenditures have no strong association

with life insurance density. In addition, religious and institutional differences can explain some of

the variation in life insurance consumption across countries.

From new variables introduced in this study, bancassurance and banking efficiency measured by

the bank-z-score turn out to be significant. The result is particularly interesting for the

bancassurance variable as the relationship sign is negative. It means that the more a banking

regulator restricts banks to engage in insurance underwriting and selling the more customers buy

life insurance policies. Similarly, bank efficiency and solvability measured by the bank-z-score is

negatively related to life insurance demand. The increased competition and convergence between

financial industries has a negative impact on life insurance consumption.

Splitting our sample into developed and developing countries shows that well-developed financial

sector continue to predict higher life insurance consumption across countries. Moreover, the

results suggest that the older the population and higher the education level, the more people in

developing countries will consume life insurance products. In developed countries, the life

expectancy decreases the life insurance density confirming the Lewis’s hypothesis. More telling is

the difference in the income and financial development elasticities between developed economies

and the developing countries. The impact of the income per capita and the financial sector

development are of greater importance in developed economies.

The results confirm our previous conclusions for banking efficiency and solvability and

bancassurance in developed countries. This is not surprising as the link between bank and

insurers is far more developed in the wealthiest countries. In developing countries, there is

evidence that high level of governance stimulates life insurance consumption. This result,


together with a stronger effect of financial development compared to the income per capita,

show that supply factors play a beneficial role in determining life insurance density.

The results matter for policymakers to understand determinants of life insurance consumption,

especially in developing countries. Taking into account the impact of insurance development on

economic growth (Ward and Zurbruegg, 2000), these results provide strong insights for fostering

life insurance business.

Most of the studied determinants of life insurance consumption regard demand factors. More

research needs to be done on the supply side, especially the insurance price and insurance

regulation factors. Investigation of the effect of the life insurance concentration could also lead to

further understanding of life insurance consumption. A highly concentrated insurance sector may

result in lack of competitive pressure which in turn may increase insurance prices. On the other

hand, bigger insurance groups may be able to spread the fixed costs of operating over a broader

customer base thus is likely to offer lower prices.


References

Babbel, David F. 1981. “Inflation, Indexation and Life Insurance Sales in Brazil.” Journal of Risk and Insurance 48: 115 - 35. Barro, Robert. J and Jong Wha Lee. 2001. “International Data on Educational Attainment: Updates and Implications.” Oxford Economic Papers 53: 541 - 563. Barth, James R., Gerard Caprio Jr. and Ross Levine. 2001. “Banking System around the Globe: Do Regulations and Ownership Affect Performance and Stability?” in: Mishkin, F.S. eds., Prudential Supervision: What Works and What Doesn’t. University of Chicago Press, Chicago, Illinois. Barth, James R., Gerard Caprio Jr. and Ross Levine. 2004. “Bank Regulation and Supervision: What Works Best?” Journal of Financial Intermediation 13: 205 - 248. Beck, Thorsten, Asli Demirgüç-Kunt and Ross Levine. 2003. “Law, Endowments, and Finance.” Journal of Financial Economics 70: 137 - 181. Beck, Thorsten and Ian Webb. 2003. “Economic, Demographic, and Institutional Determinants of Life Insurance Consumption across Countries.” World Bank Economic Review 17: 51 - 88. Beenstock, Michael, Gerry Dickinson, and Sajay Khajuria. 1986. “The Determination of Life Premiums: an International Cross-Section Analysis 1970-1981.” Insurance: Mathematics and Economics 5: 261 - 270. Boyd, John H., Stanley L. Graham and R. Shawn Hewitt. 1993. “Bank Holding Company Merges with Non Bank Financial Firms: Effects on the Risk of Failure.” Journal of Banking and Finance 17: 43 - 63. Browne, Mark J. and Kihong Kim. 1993. “An International Analysis of Life Insurance Demand.” Journal of Risk and Insurance 60: 616 - 634. Cohen, Daniel and Marcelo Soto. 2006. “Growth and Human Capital: Good Data, Good Results.” Journal of Economic Growth 12: 51 - 76. Cummins, David J., Sharon Tennyson and Mary A. Weiss. 1999. “Consolidation and Efficiency in the US Life Insurance Industry.” Journal of Banking and Finance 23: 325 - 357. De Nicolo, Gianni. 2000. “Size, Charter Value and Risk in Banking: An international Perspective.” International Finance Discussion Paper N° 689. Fortune, Peter. 1973. “A Theory of Optimal Life Insurance: Development and Tests.” Journal of Finance 27: 587 - 600. Heritage Foundation. 2008. “The Index of Economic Freedom.” http://www.heritage.org/Index/ Hwang, Tienyu and Brian Greenford. 2005. “A Cross-Section Analysis of the Determinants of Life Insurance Consumption in Mainland China, Hong Kong, and Taiwan.” Risk management and insurance review 8: 103 - 125.


Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi. 2008. “Governance Matters VII: Aggregate and Individual Governance Indicators, 1996 - 2007.” World Bank Research Working Paper 4654. Levine, Ross. 1997. “Financial Development and Economic Growth: Views and Agenda.” Journal of Economic Literature 35: 688 - 726. Levine, Ross. 1998. “The Legal Environment, Banks, and Long-run Economic Growth.” Journal of Money, Credit, and Banking 30: 596 - 620. Levine, Ross, Norman Loayza and Thorsten Beck. 2000. “Financial Intermediation and Growth: Causality and Causes.” Journal of Monetary Economics 46: 31 - 77. Lewis, Frank, D. 1989. “Dependents and the Demand for Life Insurance.” American Economic Review 79: 452 - 466. Li, Donghui, Moshirian, Fariborz, Nguyen, Pascal and Timothy Wee. 2007. “The Demand for Life Insurance in OECD.” The Journal of Risk and Insurance 74: 637 - 652. Monga, Célestin. 2004. “Latvia’s Macroeconomic Options in the Medium Term: Fiscal and Monetary Challenges of EU Membership.” World Bank Policy Research Working Paper 3307. Outreville, Francois J. 1996. “Life Insurance Markets in Developing Countries.” Journal of Risk and Insurance 63: 263 - 278. Swiss Reinsurance Company. Various years. “World Insurance” Sigma Swiss Reinsurance Company, Zurich. Truett, Dale B. and Lila J. Truett. 1990. “The Demand for Life Insurance in Mexico and the United States: A Comparative Study.” The Journal of Risk and Insurance 57: 321 - 328. Ward, Damian and Ralf Zurbruegg. 2000. “Does Insurance Promote Economic Growth? Evidence from OECD Countries.” The Journal of Risk and Insurance 67: 489 - 506. Ward, Damian and Ralf Zurbruegg. 2002. “Law, Politics and Life Insurance Consumption in Asia.” The Geneva Papers on Risk and Insurance 27: 395 - 412. Zelizer, Vivian R. 1979. “Morals and Markets: The Development of Life Insurance in the United States.” Columbia University Press, New York, NY.


Appendix 1: Economies Included in the Sample (in brackets, the World Bank income ranking) Algeria (2)*

Angola (2)

Argentina (3)*

Australia (4)*

Austria (4)*

Bahrain (4)*

Bangladesh (1)

Belgium (4)*

Botswana (3)*

Brazil (3)*

Bulgaria (3)*

Burkina Faso (1)*

Cameroon (2)*

Canada (4)*

Chile (3)*

China (2)

Colombia (2)*

Costa Rica (3)*

Côte d'Ivoire (1)*

Croatia (3)*

Cyprus (4)*

Czech Republic (4)*

Denmark (4)*

Dominican Republic (2)

Ecuador (2)*

Egypt (2)*

El Salvador (2)*

Estonia (3)*

Ethiopia (1)

Finland (4)*

France (4)*

Gabon (3)*

Germany (4)*

Greece (4)*

Guatemala (2)*

Honduras (2)*

Hong-Kong (4)*

Hungary (3)*

Iceland (4)*

India (1)*

Indonesia (2)

Iran (2)

Ireland (4)*

Israel (4)*

Italy (4)*

Jamaica (2)

Japan (4)*

Jordan (2)*

Kenya (1)*

Kuwait (4)*

Latvia (3)*

Lithuania (3)*

Malawi (1)

Malaysia (3)*

Malta (4)*

Mauritius (3)*

Mexico (3)*

Morocco (2)*

Netherlands (4)*

New Zealand (4)*

Nigeria (1)*

Norway (4)*

Pakistan (1)*

Panama (3)*

Peru (2)*

Philippines (2)*

Poland (3)*

Portugal (4)*

Romania (3)*

Russia (3)*

Singapore (4)*

Slovakia (3)*

Slovenia (4)*

South Africa (3)*

South Korea (4)*

Spain (4)*

Sri Lanka (2)*

Sweden (4)*

Switzerland (4)*

Tanzania (1)

Thailand (2)*

Trinidad and Tobago (4)*

Tunisia (2)*

Turkey (3)*

Uganda (1)

United Kingdom (4)*

United States (4)*

Uruguay (3)*

Venezuela (3)*

Zambia (1)

* Included in the banking regulation

(1) and (2) = Developing Countries

(3) and (4) = Developed Countries

Part II - An International Cross-Section Analysis of Life Insurance Demand 32

Appendix 2: Definitions and Sources of Variables

Variable Source Definition/ Calculation

Average Years of Schooling Cohen Santo - Barro Lee

The average years of schooling in the population over age 25.

Bancassurance (Restriction Activities)

Banking Regulation Survey - World

Bank

Measure that indicates whether bank activities in insurance is restricted or not.

Unrestricted = 1: full range of the activities can be conducted directly in the

banks; Permitted = 2: full range of the activities van be conducted directly in

the banks, but some or all must be conducted in subsidiaries; Restricted = 3:

less than full range of activities can be conducted in the bank or subsidiaries; and prohibited = 4: the activity cannot

be conducted in either the bank or subsidiaries.

Bank Activities Restrictions Banking Regulation

Survey - World Bank

Sum of four measures that indicate whether bank activities in the securities,

insurance and real estate markets are restricted or not.

Bank Concentration Financial Structure

Dataset - World Bank

Degree of concentration in the banking system, calculated as the fraction of assets held by three largest banks

Bank Income Cost Ratio Financial Structure


Bank's Income/ Bank's Costs

Bank-z-score Financial Structure


Bank-z-score = (ROA+equity/assets)/σ(ROA)

Catholic World Factbook - CIA Catholic population/ Total population

Checks Database of

Political Institutions - World Bank

Checks are the number of influential veto players in legislative and executive

initiatives.

Competition Regulatory Banking Regulation

Survey - World Bank

Measure the specific legal requirements for obtaining a licence to operate as a

bank.

Control of Corruption

Worldwide Governance

Indicators – World Bank

Measures perceptions of the extent to which public power is exercised for

private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and

private interests.

Government Effectiveness



Measures perceptions of the quality of public services, the quality of the civil

service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such

policies.


Income World Bank GDP per capita (constant US$)

Index of Property Rights Heritage Foundation

Index of the degree to which the government enforces laws that protect

private property.

Inflation Rate IMF Difference of the consumer price index

Legislative Competition Database of

Political Institutions - World Bank

Index of the degree of competitiveness of the last legislative election, ranging from 1 (non-competitive) to 7 (most

competitive)

Life Expectancy World Bank Number of years the average individual in a country is expected to live

Life Insurance Premiums

African Insurance Organization - Association of Latin America

Insurance Regulators - Swiss

Re

Total life insurance premiums

Muslim World Factbook -CIA Muslim population/ Total population

Net Risk Margin Financial Structure


Accounting value of a bank's net interest revenue as a share of its total assets

Official Supervisory Power Banking Regulation

Survey - World Bank

Measures whether the supervisory authorities have the authority to take

specific actions to prevent and correct problems

Old dependency Ratio UNESCO Ratio of old dependents to the working-age population.

Other World Factbook - CIA Other population/ Total population

Overhead Cost Financial Structure


Accounting value of a bank's overhead costs as a share of its total assets

Political Stability



Measures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism.

Private Credit IMF Private credit by deposit money banks

and other financials institutions to GDP, calculated using the deflation method

Protestant World Factbook - CIA Protestant population/ Total population

Real Interest Rate IMF Nominal Interest rate minus the inflation rate. The nominal interest rate is the


lending rate, if not available the discount rate.

Regulatory Quality



Measures perceptions of the ability of the government to formulate and

implement sound policies and regulations that permit and promote

private sector development.

Return on Assets Financial Structure


Net Income/Total Assets

Return on Equity Financial Structure


Net Income/Shareholders' equity

Rule of Law



Measures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in

particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood

of crime and violence.

Social Security World Bank Health expenditure (as a % of GDP)

Voice and Accountability



Measures perceptions of the extent to which a country's citizens are able to

participate in selecting their government, as well as freedom of expression,

freedom of association, and a free media.

Young Dependency Ratio UNESCO Ratio of young dependents to the working-age population


Appendix 3: Descriptive Statistics Full Sample

Bank Overhead

Bank Net Risk

Margin

Bank Cost

Bank ROA

Bank ROE

Bank-z-score

Bank Concentration

Bank Activities

Regulatory Bancassurance

Competition Regulatory

Official Supervisory

Power Mean 4.37% 4.78% 67.91% 0.95% 12.27% 8.24 65.74% 9.56 2.56 7.31 12.83 Median 3.92% 3.45% 68.55% 0.99% 10.77% 6.74 64.59% 10.00 3.00 8.00 12.50 Std Dev 2.43% 3.00% 17.33% 2.58% 10.27% 5.79 18.57% 2.29 0.78 1.14 3.81 Minimum 0.97% 1.13% 27.32% -19.05% -13.03% 0.44 25.15% 4.00 1.00 3.00 4.00 Maximum 11.27% 13.44% 122.43% 5.45% 56.60% 41.35 100.00% 15.00 4.00 8.00 20.00 Developed Countries

Bank Overhead

Bank Net Risk

Margin

Bank Cost

Bank ROA

Bank ROE

Bank-z-score

Bank Concentration

Bank Activities




Power Mean 4.08% 3.97% 69.14% 1.06% 11.03% 8.59 67.13% 9.23 2.54 7.21 12.67 Median 3.75% 2.98% 69.49% 0.90% 9.65% 6.79 64.74% 10.00 3.00 8.00 12.00 Std Dev 2.15% 2.59% 18.26% 1.40% 10.72% 6.34 19.75% 2.20 0.80 1.26 3.56 Minimum 1.01% 1.13% 27.32% -3.48% -13.03% 2.40 25.15% 5.00 1.00 3.00 5.00 Maximum 11.27% 13.44% 122.43% 5.00% 56.60% 41.35 100.00% 15.00 4.00 8.00 19.00 Developing Countries

Bank Overhead

Bank Net Risk

Margin

Bank Cost

Bank ROA

Bank ROE

Bank-z-score

Bank Concentration

Bank Activities




Power Mean 4.88% 6.16% 65.77% 0.76% 14.40% 7.63 63.32% 10.48 2.62 7.57 13.29 Median 4.36% 5.63% 64.02% 1.04% 12.94% 6.15 64.56% 11.00 3.00 8.00 15.00 Std Dev 2.82% 3.16% 15.64% 3.88% 9.21% 4.73 16.33% 2.40 0.75 0.69 4.48 Minimum 0.97% 1.92% 33.75% -19.05% -4.92% 0.44 34.27% 4.00 1.00 6.00 4.00 Maximum 11.15% 12.88% 98.54% 5.45% 35.52% 22.79 90.32% 14.00 4.00 8.00 20.00


Appendix 3: Descriptive Statistics (cont’d)

Full Sample Muslim Catholic Protestant Voice and

Accountability Political Stability


Regulatory Quality

Rule of LawControl of Corruption

Index of Property Rights

Mean 17.78% 35.20% 14.26% 0.35 0.08 0.46 0.46 0.32 0.36 57.62 Median 1.50% 25.00% 2.00% 0.44 0.16 0.41 0.47 0.26 0.22 50.00 Std Dev 31.82% 36.89% 23.12% 0.90 0.93 0.97 0.87 0.99 1.06 22.66 Minimum 0.00% 0.00% 0.00% - 1.47 - 1.98 -1.17 -1.43 -1.45 -1.28 10.00 Maximum 99.80% 98.00% 95.00% 1.66 1.59 2.18 1.86 1.97 2.42 90.00

Developed Countries

Muslim Catholic Protestant Voice and



Regulatory Quality



Mean 8.09% 38.45% 16.67% 0.85 0.60 1.00 0.95 0.85 0.92 68.84 Median 0.00% 26.40% 2.00% 1.00 0.77 0.92 0.97 0.80 0.82 70.00 Std Dev 21.21% 37.55% 26.05% 0.65 0.64 0.80 0.64 0.81 0.92 19.86 Minimum 0.00% 0.00% 0.00% -0.72 -1.19 -0.87 -0.90 -1.13 - 0.96 30.00 Maximum 99.80% 98.00% 95.00% 1.66 1.59 2.18 1.86 1.97 2.42 90.00

Developing Countries

Muslim Catholic Protestant Voice and



Regulatory Quality



Mean 37.04% 33.82% 5.46% -0.47 -0.86 -0.40 -0.27 -0.55 -0.50 40.29 Median 13.40% 12.10% 0.00% -0.43 -0.88 -0.42 -0.25 -0.65 -0.41 34.00 Std Dev 42.39% 39.71% 11.27% 0.49 0.63 0.41 0.38 0.55 0.40 10.55 Minimum 0.00% 0.00% 0.00% -1.32 -1.98 -1.08 - 1.04 -1.45 -1.27 30.00 Maximum 99.00% 97.00% 45.00% 0.35 0.14 0.54 0.30 0.35 0.26 62.00

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