Jean Lemaire
Sojung Park

JARIP
6 June 2013 A large body of insurance research looks forfactors that explain variation in demandacross countries Dependent variable:◦ Insurance Density ($US spent annually on life
insurance per capita)◦ Insurance Penetration (total life premium volume
divided by GDP)
◦ Easily obtained annually through Swiss Re’s Sigma

 GDP per capita
 Inflation (real, anticipated, feared)
 Development of banking sector
 Institutional indicators (investors’ protection,contract enforcement, political stability) Islam Education

 Dependency ratio Urbanization

 Size of social security system Life expectancy

 Market structure

These studies assume policyholders make rational
economic decisions – but is it reasonable toexpect rationality and competence with complex
and abstract policies?

 Chui and Kwok (2008, 2009) demonstrate thatthe inclusion of cultural variables in econometricanalysis greatly improves predictive ability ofregression models.
 Adjusted R2 increases from 0.70 to 0.83
Culture= collective mental programming= part of our conditioning that we share with othermembers of our nation, region, or group, but notwith members of other groups

“ Life insurance is sold, non-life
insurance is bought”
So culture may impact life and non-life insurance
sales in different ways – this justifies a similar study
in non-life insurance

Build a model to explain non-life insurance
variability, including cultural and politicalvariables
 Very little research, maybe because of a belief

that non-life insurance development is linked to
GDP and little else Beenstock et al (1988) prove relationshipbetween non-life density and GDP per capita,with elasticity > 1. Weak relationship once
countries become rich.

789Log (GDP)
1011-2-1
0Log (Penetration)12

2Log (Penetration)

10-1

10

10.2

10.4
10.6Log (GDP)10.811 Outreville (1990) adds level of financialdevelopment

 Browne et al (2000) add◦ foreign firms market share◦ legal system (Common v. Civil Law) Unbalanced panel data, 82 countries with total

population 5.67 billion, 1999-2008
 Dependent variable:◦ DEN: Insurance density ($US premium per capita)◦ PEN: Insurance penetration (premium as a % of GDP)
corrected for Purchasing Power Parity◦ DEN compares non-life purchases around the world
without adjusting for income◦ PEN measures insurance demand relative to the economy
size, with wealth effect removed◦ →PEN better here, as it measures allocation of income toinsurance GDP: GDP per capita, at Purchasing PowerParity
 URBAN: Urbanization. % of population living
in urban areas
 EDUC: Education: % of population enrolled inthird-level education
 HERFINDAHL: Market Concentration: Sum ofsquared market shares of 10 largest insurers
 COMMON, ISLAM: Legal system: Common,Civil, or Islamic Law

 Government stability

 Socioeconomic conditions
 Investment profile Internal conflict External conflict Corruption

 Military interference in politics
 Religious tensions

 Law and order Ethnic tensions

 Democratic accountability
 Bureaucratic quality Twelve highly correlated measures
 →Applied Principal Components Analysis tosummarize data
 Used first Principal Component in allregressions
 First PC has very high eigenvalue of 5.49,
explains 46% of total variance BUDD, CHRT, MUSLIM: Religious variables: % ofpopulation with Buddhist, Christian or Islamicbeliefs.
 HOFSTEDE Cultural Variables:◦ 1. PDI: Power Distance. Degree of inequality amongpeople with the population considers as normalHigh PDI: China, Mexico, India, Arab World
Low PDI: Israel, New Zealand, Ireland, Scandinavia◦ 2. IDV: Individualism. Degree to which people prefer toact as individuals rather than as a group
High IDV: US, UK, Australia, Netherlands
Low PDI: China, Korea, Thailand, Central America◦ 3. MAS: Masculinity. Evaluates whether gender differences
impact roles in social activities

High-feminity: Sweden, Korea, Uruguay, Portugal
High-masculinity: Japan, Switzerland, Austria, Venezuela◦ 4. UAI: Uncertainty Avoidance. Scores tolerance foruncertainty; measures preference for structure
Uncertainty avoiding: Japan, Russia, Belgium, Greece, Spain
Uncertainty seeking: Singapore, Sweden Hong Kong, UK Income per capita: Urbanization:
 Education:

 Market Concentration:

 Common Law:
 Political Risk:

 Buddhist, Christian, Islamic Beliefs:
 Power Distance: Individualism:
 Masculinity: Uncertainty Avoidance:

Positive
Positive
Positive
Negative
Positive
Positive
Negative
Negative
Positive
Ambiguous
Positive

Insit = α + β1 Xit,Econ + β2 Yi, Inst+ β3 PRISK+ β4 Zi, Cult + γ DYear + εit

Advantages of panel data analysis:* Uses cross-sectional and time series* Increases number of observations* Make inference about a country basedon other countries* More accurate predictions

Variable(1)

Log GDP0.1157***

URBAN

0.0020

EDUC0.0050

Log HERF-.1389***COMMON0.1785***

PRISK

0.1212***

Adj. R20.534

(2)

(3)

(4)

(5)

Variable

(1)

(2)

Log GDP0.1157***0.1301***

URBAN

0.00200.0027**EDUC

0.00500.0001

Log HERF-.1389***-.1244***

COMMON

0.1785***0.1679***PRISK

0.1212***

0.0917***MUSLIM

Adj. R2-.0049***

0.534

0.583

(3)

(4)

(5)

Variable

(1)

(2)

(3)

Log GDP0.1157***0.0218URBAN

0.00200.0037***EDUC

0.0050-.0031

Log HERF-.1389***-.1577***COMMON

0.1785***0.2105***PRISK

0.1212***

0.1130***

PDI-.0069***

IDV

0.0023***

MAS0.0020***UAI

0.0056***

Adj. R2

0.534

0.594

(4)

(5)

Variable

(1)

(2)

(3)

(4)

Log GDP0.1157***

0.0536URBAN

0.00200.0038***EDUC

0.0050-.0070**Log HERF-.1389***-.1405***COMMON

0.1785***0.1816***

PRISK

0.1212***0.0743***MUSLIM-.0046***PDI-.0054***IDV

0.0042***

MAS0.0013*

UAI

0.0048***Adj. R20.534

0.583

0.594

0.635

(5)

Variable

(1)

(2)

(3)

(4)

(5)

Log GDP0.1157***

0.0536URBAN

0.00200.0038***EDUC

0.0050-.0070**Log HERF-.1389***-.1405***-.1372***COMMON

0.1785***0.1816***0.1202***

PRISK

0.1212***0.0743***0.2063***MUSLIM-.0046***-.0051***PDI-.0054***-.0046***IDV

0.0042***0.0037***MAS

0.0013*0.0003UAI

0.0048***0.0037***

0.635

0.612

Adj. R2

0.534

0.583

0.594

Variable

(1)

(2)

(3)

(4)

Log GDP0.3167***0.2754***0.2642***0.2422***

URBAN

0.0058***0.0063***0.0062***0.0065***EDUC-0.0023-0.0078*-0.0022-0.0074

Log HERF-0.1404***-0.1403***-0.1738***-0.1688***

COMMON

0.3963***0.3522***0.3730***0.3189***PRISK

0.1082***0.0990***0.1208***0.1032***MUSLIM-0.0027***-0.0026***

PDI-0.0056***-0.0047***

IDV-0.0024*-0.0007

MAS0.0019

0.0006

UAI0.0004-0.0000

0.559

0.575

Adjusted R2

0.548

0.567

Variable

(1)

(2)

(3)

(4)

Log GDP-0.3726***-0.1516***-0.2967***-0.1648**

URBAN-0.0033-0.00230.00070.0011EDUC

0.0265***0.0224***0.00430.0037

Log HERF-0.1515***-0.0940***-0.1699***-0.1248***

COMMON-0.0289-0.0120-0.0366-0.0286

PRISK

0.1543***0.0884***0.0612***0.0275*MUSLIM-0.0070***-0.0048***

PDI-0.0083***-0.0066***

IDV

0.0080***0.0081***

MAS0.0010

0.0013*

UAI00037***

0.0033***

0.638

0.661

Adj R2

0.456

0.512

 Fama-MacBeath Cluster option
 Robust regression
 Random effect panel regression
 Density as dependent variable Non-parametric bootstrap
 Blocking Very strong results, variables significant at 1%

level, despite biases against finding
meaningful relations
 For rich countries, regression coefficients for
cultural variables average 0.8%. For every
10-point change in a cultural variable,
Hofstede, log (penetration) increases by 8%
 National and regional cultures do matter formanagement. Culture only evolves veryslowly. National differences are here to stay.

 Culture should be incorporated in the decision

process of multinational insurers wishing toenter a new market.
 Increasing income, low political risk, a favorable
business environment matter.
 But so does culture. Countries scoring low on
Power Distance, and high on Individualism andUncertainty Avoidance, have a higher growthpotential.
 Within heterogeneous countries (China), market
segmentation strategies should aim at subgroups that exhibit the best cultural scores

Culture matters
especially –and maybe
surprisinglyin richer countries