The Effect of Digitalization and Human Capital on Life Insurance

Demand in Indonesia

Karin Amelia Safitri

, Safrin Arifin

Department of Insurance Administration and Actuary, Program Pendidikan Vokasi Universitas Indonesia

Department of Physiotherapy, Program Pendidikan Vokasi Universitas Indonesia

Keywords: Digitalization, Human Capital, Life Insurance.

Abstract: The insurance industry has a vital role in contributing to the rate of economic growth of a country which is

directly related to the human resources and the implementation of industry revolution 4.0 through digital

transformation. This study aims to provide the evidence on the contribution of digitalization based on

communication and information technology, and human capital consisted of age dependency ratio, labour

force, and life expectancy on life insurance demand in Indonesia. This study used the 16 years of annual

data for the period from 2002-2017 and analyzed by using principal component regression for the research

method. The result indicated that digitalization and human capital have a significant effect on the demand

for life insurance products at 5%. The findings show that age dependency ratio has a negative relationship

with life insurance demand as hypothesized. The labour force, life expectancy, individuals using the internet

and broadband subscription have a positive impact on life insurance demand. Insurance industries are

recommended to develop the human capital and their digital equipment to expand the business.

1 INTRODUCTION

Life Insurance in Indonesia has become a necessity

in the life of modern society today. In Indonesia,

demand for life insurance continues to grow in line

with increased income and public awareness of the

importance of risk anticipation. The number of life

insurance companies is continuously increasing, and

so is the variety of products offered in the market to

meet the demand. Thus, the life insurance industry

began to contribute to the Indonesian economy even

though it was still categorized as relatively low.

Digitalization is the result of technological

developments that are currently developing very

rapidly. Its primary purpose is to provide

convenience and efficiency both in all aspects, such

as labour, costs, procedures and others.

Digitalization is very synonymous with the use of

electronics and computers. The presence of

computer devices further simplifies and accelerates

the growth of the digital world. Computerization is

not only limited to computing devices. Now

computerization can easily affect other devices, such

as televisions and smartphones, have been

computerized with the addition of operating systems

like conventional computer devices. Salatin (2014)

states that the development of electronic sales make

the insurance company becomes more toward

product orientation compared with customer

orientation. Previous research conducted in Kenya

where Waita and Nairobi (2014) found a positive

impact of technological developments on the growth

of microinsurance period. Indonesia is a developing

country. The last few years, the development of

technology is used in daily real life. This

development makes the researchers stated that there

is a significant influence on the existing insurance,

Lin et al., (2012) states that technology affects the

cost efficiency in the insurance industry that is only

available in developed countries but not in

developing countries.

Human capital is a combination of knowledge,

skills, and individual ability to carry out their duties

so that they can create value to achieve goals. The

goals are related to the vision and target of the

company. According to Campbell (1980), he said to

optimal the purchase of insurance. It is based on

human capital uncertainty. Ostaszewski (2003)

further stated that life insurance is a business of

securing human capital that overcomes the

uncertainty and lack of individual human capital.

364

Amelia Saﬁtri, K. and Ariﬁn, S.

The Effect of Digitalization and Human Capital on Life Insurance Demand in Indonesia.

DOI: 10.5220/0010685200002967

In Proceedings of the 4th International Conference of Vocational Higher Education (ICVHE 2019) - Empowering Human Capital Towards Sustainable 4.0 Industry, pages 364-369

ISBN: 978-989-758-530-2; ISSN: 2184-9870

The objective of this study is to investigate the

impact of human capital and digitalization on the life

insurance demand. In this study, human capital is

measured by some variables, such as life

expectancy, labour force, age dependency as well as

the digitalization is also measured by the number of

individuals using internet and broadband

subscription.

2 METHODOLOGY

2.1 Data

The data used are secondary data that have

dependent variables and independent variables. The

dependent variable used is the number of life

insurance requests calculated based on the many

policies of life insurance (LI), that is, as the variable

y. Various measures of life insurance demand have

been used in empirical studies, such as premium

spending, insurance density and insurance

penetration (Beck and Webb, 2003). Dash (2018)

investigated the life insurance demand by using the

number of the policy holder to see the demographic

and socio-economic characteristics of the life

insurer. The independent variable used is human

capital measured by three indicators, namely life

expectancy (LE), labour force (LF), age dependency

ratio (AD), and digitalization which are measured by

two indicators namely the number of individuals

using the internet (ID), and the number of broadband

subscriptions (SB).

2.2 Methodology

The steps in data analysis are as follows:

1. Arrange the hypothesis in the form as follows.

a. H1: Life expectancy has a positive effect

on demand for life insurance

b. H2: The number of workers has a positive

influence on demand for life insurance

c. H3: Age dependency ratio has a negative

influence on life insurance demand

d. H4: The number of individuals who use

the internet has a positive influence on

demand for life insurance

e. H5: The amount of broadband

subscriptions has a positive influence on

the demand for life insurance

2. Explore data with descriptive statistics.

3. See the relationship of each variable X with the

variable Y using a scatter plot and see the value

of the correlation between independent

variables.

4. Perform a regression analysis to determine the

regression model with the least-squares method

5. Check the non-multicollinearity assumption by

looking at the VIF value, looking at the

coefficient of determination (R2)

6. Handling multicollinearity problems if the

assumptions of non-multicollinearity are the

regression of the main components that are

looking for eigenvalues and eigenvectors,

7. Calculating the score of the main components,

determine the number of principal components

to be used

8. Regressing between component scores

obtained with the dependent variable

9. Returns the regression equation to the standard

variable form

10. Calculate the standard error for each regression

coefficient and test using the t-test

11. Returns the regression equation to the original

variable form

12. Interpret the primary component regression

model.

2.3 Principal Component Regression

The standard form of multiple linear regression

model with independent variables is in the following

equation (Montgomery dan Peck, 1992).

𝑌



𝛽



𝛽



𝑋



⋯𝛽



𝑋

,

𝜀







With : 𝑌



is the independent variable for the 𝑖-th

observation, for

𝑖  1,2, … , 𝑛; 𝛽



,𝛽



,… 𝛽



These are the parameters;

𝑋



, 𝑋



,…,𝑋

,

Above is the dependent variables ; 𝜀



is the

residual (error) for the observed i-th which is

assumed to be normally distributed independently

and identical with the average 0 (zero) and variance

𝜎



The method used to estimate the model

parameters

Linear multiple regression is the least squares

method or often also called the ordinary least square

method (OLS). This OLS method aims to minimize

the sum of squares error, OLS estimators for 𝛽 are

as follows (Montgomery dan Peck, 1992).

𝛽







𝑋



𝑋





𝑋



𝑌 2

This study examines the effect of human capital

and digitalization on demand for life insurance. The

variable of human capital consists of life

expectancy, many workers, age dependency ratio.

While the digitalization variable consists of many

The Effect of Digitalization and Human Capital on Life Insurance Demand in Indonesia

365

individuals who use the internet and broadband

subscriptions. To test the hypothesis using multiple

linear regression with the analysis model used in this

study is:

𝐿𝐼



𝛼𝛽



𝐿𝐸  𝛽



𝐹𝐿  𝛽



𝐴𝐷  𝛽



𝐼𝐷

𝛽



𝐵𝑆 3

Multicollinearity is the linear relationship

between independent variables X in multiple

regression models. High multicollinearity causes the

probability of accepting the wrong hypothesis to

increase, and the value of R squared is high, but

none of the few coefficients is estimated to be

statistically significant. The correlation coefficient

between the X variable and the large VIF (Variance

Inflation Factors) value is a characteristic of

multicollinearity problems.

The principal component regression forms the

relationship between the dependent variable and the

principal component selected from the independent

variable (Ul-Saufie et al. 2011). The principal

component regression can solve the multicollinearity

problem (Montgomery dan Peck, 1992). The model

for principal component regression is as follows.

𝑌𝑤



𝑤



𝐾



𝑤



𝐾



⋯𝑤



𝐾



 𝑣





With 𝐾



,𝐾



,…,𝐾



is principal component

explanatory variables, 𝑤



is intercept or intersection

point of the Y, 𝑤



,𝑤



,…,𝑤



is the principal

component regression coefficient, 𝑣 is an error

factor.

3 RESULT AND DISCUSSION

The time tren of age dependency ration and life

expectancy from 2002 till 2017 can be seen from the

figure below

Figure 1. The Age Dependency Ratio and Life Expectancy

2002-2017 in Indonesia

From Figure 1. It can be seen that age

dependency consistently decreases and life

expectancy continues to increase every year. Life

Expectancy (AHH) is an estimate of the average

additional age of a person expected to continue to

live. AHH can also be defined as the average

number of years a person has lived after the person

reached his x-th birthday. A commonly used

measure is the life expectancy at birth that reflects

the health condition at the time. Generally, regarding

AHH, the average number of years means that

someone has lived since the person was born.

Dependency Ratio is the ratio between the

population aged 0-14 years, plus the total population

65 years and over (both referred to as not the labour

force) compared to the number of population aged

15-64 years (labour force).

Below is figure 2, which shows the labour force

rate from 2002 to 2017.

Figure 2. Labor Force 2002-2017 in Indonesia

The labour force participation rates are the

number of persons who are employed and

unemployed but looking for a job divided by the

total working-age population. Labor Force

Participation Rate in Indonesia averaged 84,3

percent from 2002 until 2017, reaching an all-time

high of 87.9 percent in 2012 and a record low of

78.8 percent in 2003.

Figure 3. Individuals using Internet and Fixed Broadband

Subscription 2002-2017 in Indonesia

2002

2006

2010

2014

Life Expectancy

Age Depedency

age

depe

…

2002 2004 2006 2008 2010 2012 2014 2016

0,5

1,5

2,5

2002

2006

2010

2014

broadbandsubscription

IndividuuseInternet

Ind…

ICVHE 2019 - The International Conference of Vocational Higher Education (ICVHE) “Empowering Human Capital Towards Sustainable

4.0 Industry”

366

Broadband refers to an internet bandwidth

connection. The term bandwidth is generally used to

refer to data transfer speeds, in terms of computer

networks and internet connections. Data transfers are

usually measured in bits per second (bps). In

broadband internet connections, transfer speeds are

very high compared to dial-up internet connections.

There are various types of broadband internet

connections, depending on speed, cost and

availability (Figure 3).

Fixed broadband subscription refers to a fixed

subscription for high-speed access to the public

Internet (TCP / IP connection), at downstream

speeds equal to, or higher than, 256 kbit / s. This

subscription includes cable modems, DSL, fibre-to-

the-home / building, other fixed bandwidth (cable)

subscriptions, satellite broadband and terrestrial

fixed wireless broadband. This total is measured

regardless of payment method. This calculation

includes residential subscriptions and subscriptions

to organizations (Figure 4).

Figure 4. The Number of Life Insurance Policy Holder

2002-2017 in Indonesia

In this study, the number of life insurance

demand is calculated based on the number of

policies. The demand for life insurance is fluctuating

every year. The average increase in the number of

life insurance policies annually is 7.7%. In 2008, the

number of policies increased to 39.64% and a

decrease in the number of life insurance policies by

39.25%. Likewise, in 2015 there was an increase in

the number of life insurance policies by 35%. Below

is the table of descriptive statistics (Table 1).

Table 1. Descriptive Statistics

Variable Mean Dev S

. Min Max Med

Age depedency 51,36 1,88 48,53 54,13 51,26

Labor Force 84,28 2,92 78,84 87,92 85,33

Life

Expectancy

69,26 1,45 66,60 71,06 69,51

Internet 11,60 9,00 2,13 32,29 9,42

Broadban

0,85 0,78 0,02 2,36 0,86

Life Ins.

Demand

14,10 4,04 7,86 21,04 14,81

From the results of multiple linear regression

analysis obtained an R squared value of 80,1%,

indicating that the relationship between life

insurance demand with the independent variable was

80,1% while other factors caused the remaining

18,9%. Henceforth it is necessary to do simultaneous

tests and individual tests to see the effects

simultaneously and individually between the

independent variable and the dependent variable.

From the analysis results obtained the calculated F

value 8,07 with P-value 0,003, it can be said that the

independent variables simultaneously affect the

dependent variable. Table 3 shows the variance

inflation factors (VIF), which indicated the

multicollinearity problem.

Table 2. Variance Inflation Factors among Variables

o. Variable VIF

e Dependenc

101,45

2 Labor Force 1,39

3 Life Expectancy 41,5

4 Individuals use

interne

26,1

5 Broadband

Subscription

55,4

According to Table 2., time series regression

model produced a tremendous value of VIF, which

is more than 1. Multicollinearity also can be detected

by calculating the correlation coefficient as Table 3

shown below.

Table 3. The Correlation Coefficient among Variables

Variables Labor

Force

Life

Indv.

Internet

Broadba

nd Subs.

Life

Ins.

e De

. -0,45 -0,9 -0,94* -0,97* -0,80*

Lab. Force 0,48 0,43 0,41 0,31

Life Ex

. 0,88* 0,92* 0,77*

Indv.

Intent

0,98* 0,71*

Broadband

Subs.

0,70*

From Table 3, almost all correlation coefficients

between the two variables are more significant than

0.5. This result also proves that there are

multicollinearity problems.

The next step is to perform a principal

component regression analysis. In this analysis, the

initial step taken is to transform the independent

variable 𝑋 into a variable Z by using the correlation

matrix because it is assumed that the units used in

the independent variable are not the same in order to

obtain new data with variable Z. After getting the

eigenvalue and the score of the principal component

2002

2005

2008

2011

2014

2017

life

insuranc

The Effect of Digitalization and Human Capital on Life Insurance Demand in Indonesia

367

then determine which principal component meets the

criteria of having eigenvalues greater than 1 (𝜆1).

PC1 is the principal components selected. Below is

the scree plot related to determining the eigenvalue

(Figure 5).

Figure 5. The Scree Plot of Eigen Value and the

Component Number

The next step is to regress the dependent variable

Y with the PC1, obtained a regression equation as

follows.

𝑌  14,10  1,50𝑃𝐶1 … 5

The regression equation obtained from the

standard variable is returned to the original variable

form (with the X variable) so that the primary

component regression model is obtained as follows.

𝑌  46,11  19,95𝑋



 11,89𝑋



 34,28𝑋



5,73𝑋



 0,07𝑋



…6

The estimated regression coefficients of the

variables in Model 6 are reported in Table 4 below.

Table 4. The Coefficient and Calculated t-test of Principal

Components

Var. Z Coeff.

Dev. S

d 𝑠



𝑦





Calculated t

Z1 -0,73 0,043 -17,1721*

Z2 0,41 0,021 17,1721*

Z3 0,76 0,042 17,1721*

Z4 0,71 0,042 17,1721*

Z5 0,72 0,042 17,1721*

For the life insurance demand function, the test

statistics indicate that most of the variables are

statistically significant with the expected sign. It

suggests that in model 6, an increase of one percent

of age dependency is associated with a decrease of

about 19 percent in life insurance consumption. An

increase of 1 percent of forced labour is associated

with an increase of about 11,9 percent in life

insurance consumption. The coefficients of the

number of a broadband subscription, labour force,

life expectancy and individual using the internet

have positive signs and in each case are highly

statistically significant as expected. The coefficient

of age dependency have a negative impact and

statistically significant on life insurance demands as

hypothesized.

4 CONCLUSIONS

The growing demand for life insurance is

inseparable from the influence of the increasing

development on digital technology and human

capital. The age dependency ratio, labour force, life

expectancy, broadband subscriptions and individual

using internet services are the variables which

statistically affect the life insurance demand. It could

be a consideration for the policymakers of the

insurance industry to start developing the online

premium policy or the online claim system and their

human capital index.

REFERENCES

Apampa, O. R. 2010. Evaluation of ICT Penetration in

Selected Insurance Companies : The Lagos

Experience. J. Emerg. Trends Comput. Inf. Sci., vol. 1,

no. 1, pp. 24–30.

Beck, T.; Webb, I. 2018. Economic, Demographic and

Institutional Determinants of Life Insurance

Consumption Across Countries, World Bank

Economic Review. 17(1):51-88.

Campbell, R.A. 1980. The demand for life insurance: an

application of the economics of uncertainty. The

Journal of Finance. Vol. 35, No. 5, pp. 1155-1172.

Dash G. 2018. Determinants of life insurance demand:

evidences from India. Asia Pasific Journal of

Advanced Bussiness and Social Studies.

Lin, H. J., Wen, M. M., & Lin, W. T. 2012. The

relationships between information technology, e-

commerce, and e-finance in the financial institutions:

Evidence from the insurance industry. Lect. Notes

Comput. Sci. (including Subser. Lect. Notes Artif.

Intell. Lect. Notes Bioinformatics), vol. 7197 LNAI,

no. PART 2, pp. 194–206.

Montgomery D., Peck E. 1992. Introduction to Linear

Regression Analysis. John Wiley and Sons, New

York, NY.

Ostaszewski, K. 2003. Is life Insurance a human capital

derivatives business?. Journal of Insurance Issues, 26,

1, pp. 1-14.

Salatin, P., Yadollahi, F. and Eslamboichi, S. 2014. The

effect of ICT on the insurance industry in selected

countries,” Res. J. Econ. Bussiness ICT, vol. 9, no. 1.

Ul-Sulfie, A., Yahya, A. S., Ramli, N. A. 2011. Improving

multiple linear regression model using principal

component analysis for predicting pm10 concentration

in Seberang Prai, Pulau Pinang. International Journal

of Environmental Sciences. Vol. 2 No. 2.

ICVHE 2019 - The International Conference of Vocational Higher Education (ICVHE) “Empowering Human Capital Towards Sustainable

4.0 Industry”

368

Waita, M.C. & Nairobi, U. 2014. “the Relationship

Between Advancement in Technology, Competition,

Subsidies and the Growth of Microinsurance in

Kenya,” no. October.

The Effect of Digitalization and Human Capital on Life Insurance Demand in Indonesia

369