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part of our ongoing series, below is a blog written by a team of actuaries and
students who presented about the use of cultural variable analyses in insurance
at Pinnacle University (Pinnacle U) in March 2022.
For insurance companies who wish
to understand existing markets and enter new and emerging markets, being able
to accurately predict insurance demand is critical.
Currently, demand for insurance is
predicted predominately by analyzing economic factors (e.g., gross domestic
product [GDP], inflation rate). However, we often see gaps in insurance demand
between two different countries, despite the similarity of those countries’ economic
conditions. This difference indicates that insurance market dynamics cannot
always be captured through economic conditions, or through an economic analysis.
Clearly, some additional new and
improved rating variables would be useful to more effectively predict
insurance demand in different markets.
Culture helps us
better understand risk perception, which is how people view and
understand risk. Two people with the same risk tolerance viewing an identical
risk may develop drastically different perceptions of
its impact. The cultural backgrounds of the two individuals
are foundational in developing their respective senses of risk
perception. Understanding that disparity
is fundamental in understanding insurance demand
– so, could cultural variables act as new and improved independent variables to
determine risk perception?
To understand the
potential helpfulness of cultural variables in assessing insurance demand, our
Pinnacle University team looked at three different cultural summaries – Hofstede’s Cultural Dimensions,
the Happiness Index and the Human Development Index. Our team quantitatively analyzed
the cultural factors of each summary and their relationships with insurance
demand, as measured by insurance
penetration, which is defined as the ratio of insurance premium written to
the overall GDP.
Hofstede Cultural Dimensions
In the 1960s, internationally renowned social
psychologist Geert Hofstede conducted a worldwide study of employees in
multiple industries in an attempt to define a set of national values.
He identified systematic differences in national
cultures in four primary dimensions:
In 2012, a University of
Pennsylvania team utilized Hofstede’s dimensions as predictor variables in a
linear regression with insurance penetration as the target variable1. The
results of the analysis are shown below in Figure 1:
Ultimately, the results show that insurance companies looking to
enter new markets may be able to achieve more significant penetration by
identifying countries that score “low” in Power Distance and “high” in Individualism
and Uncertainty Avoidance.
However, there is an important caveat to consider. For the
University of Pennsylvania study, countries were categorized as “high-income”
or “low-income” using an income per capita of $20,000 as a cut-off point. The study
results indicated that high-income countries found all cultural variables
significant (except for Masculinity/Femininity), whereas low-income countries
found only Power Distance significant.
In other words, Individualism and Uncertainty Avoidance’s relationship
to insurance penetration was too weak to consider for low-income
countries. This finding supports the indication that cultural analysis can
be supplemental to economic analysis. While culture, of course, permeates
all aspects of life in all layers of societies, its influence on insurance seems
to be only felt after basic needs, such as food, clothing and shelter, are
Human Development Index
The Human Development Index (HDI) was created by the United
Nations Development Program in the 1990s to emphasize that certain characteristics
of a citizenry are better criteria than economic growth for assessing a
The HDI uses three key dimensions of human development: education,
health and financial stability.
In our analysis, education and health are considered cultural
variables, while financial stability is an economic variable. Health is
measured by life expectancy. Education is measured by average years of
schooling for adults and by expected years of schooling for new students.
Financial stability is measured by gross national income per capita, or the
total amount of money earned by a nation's people and businesses.
Our analysis involved two linear
regression models. The first was a univariate regression, with the HDI as the
explanatory variable and insurance penetration as the response. Our calculations
found that the HDI had a significant, positive relationship to insurance
The second was a multivariate
linear regression that used the HDI’s components as explanatory variables and
insurance penetration as the response. We found that gross national income and
life expectancy had significant, positive relationships with insurance
penetration. Education variables, however, had an ambiguous or
insignificant relationship with insurance penetration. Life expectancy’s significant
positive relationship directly supports our claim that cultural variables are
productive in helping to predict insurance penetration.
To study happiness and how it may
impact insurance demand, we utilized the World Happiness Report, created by the
United Nations Sustainable Development Solutions Network.
This report assigns a happiness
score to an individual nation, determined by a variety of metrics, such as
measures of people’s trust in their governments or what level of social support
Our original hypothesis stated
that happier people may be more willing to purchase insurance to preserve their
happiness via an increased feeling of stability. After testing our hypothesis
by running different models through the Happiness Index data, our results were
inconclusive. However, a graphical analysis showed some linear relationships
between happiness variables and insurance penetration. For example, two
happiness variables – “Social Support” and “Human Expectancy” – can be seen in Figures
2 and 3:
Similar studies offer
competing theories about happiness and its relationship with risk; our findings
mirror these conflicting results. For example, the Affect Infusion Model (AIM) (Forgas, 1995) says that
positive moods lead to risk-seeking behavior, whereas the Mood Maintenance
Hypothesis (MMH) suggests positive moods cause risk-averse behavior. Such model
disagreements are evidence that the insurance industry may consider further
research into understanding cultural factors as they relate to risk,
risk-related behaviors and insurance penetration.
Our Pinnacle U team believes that
analyzing cultural shifts alongside economic variables can more accurately explain
and predict insurance penetration, because economically identical countries do
not have the same demand for insurance. By quantitatively analyzing variables
found in cultural summaries, insurers can better understand the risk perception
of customers to grow existing markets and explore new target markets.
more information on the type of linear regression used in the preceding
analyses, see guide located here: https://www.scribbr.com/statistics/linear-regression-in-r/
S., & Lemaire, J. (2012). The Impact of Culture on the Demand for Non-life
Alec Panayotov is an actuarial analyst I with Pinnacle Actuarial
Resources in the Chicago office. He holds a Bachelor of Science degree in
actuarial science with a minor in business administration from the University
of Illinois at Urbana/Champaign, and has experience in assignments involving
predictive analytics, microcaptive loss reserving and loss funding. He is
actively pursuing membership in the Casualty Actuarial Society (CAS) through
the examination process.
Jaidev Goel is currently enrolled in the Master of
Science actuarial program at the University of Texas at Dallas. He is actively
pursuing his membership in the Society of Actuaries (SOA) through the
Joe Alberts is an actuarial analyst II with Pinnacle Actuarial
Resources in the Chicago office. He holds a Bachelor of Science degree in
mathematics with a minor in physics from Illinois Wesleyan University, and has
experience in assignments involving loss reserving, loss cost projections and
group captives. He is actively pursuing membership in the CAS through the
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