Growth of life insurance in India

Abstract

The paper examines the relationship between economic growth and life insurance. In this context, we study contributions made by some authors across international and Indian domains.

The literature review begins with examining the work done in the international context by Arena (2008) and Zheng (2008). Arena (2008) examines the causal effect of insurance on economic growth in a cross-country study. Zheng (2008) attempt to develop comprehensive paradigms for an international insurance comparison.

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In the Indian context, we examine the work done by Sadhak (2008) and Sinha (2005). Sadhak (2008) analyses the relationship between insurance and the macroeconomy. Sinha (2005) gives a crisp account of insurance in India since pre-independence times.

The paper wraps up with an examination of the Malhotra Committee report.

The effect of liberalization on the growth of life insurance in India

It is a commonly held belief that there is a strong interrelationship between insurance and the macroeconomy. Thus the objective of this review paper is to understand the factors that contribute to growth of life insurance.

Skipper (1997) highlights how insurance aids economic development in seven ways:

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First, it promotes financial stability.

Second, it substitutes for government security programs.

Third, it facilitates trade and commerce.

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Fourth, it mobilizes national savings.

Fifth, it enables risk to be managed more efficiently.

Sixth, insurers and reinsurers have economic incentives to help insureds reduce losses.

Seventh, it fosters a more efficient allocation of a country’s capital.

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Literature Review

This literature review consists of four sections:

I. Cross country study and a new paradigm.

II. Insurance and the Macroeconomy in India.

III. Progress of Insurance in India.

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IV. The Malhotra Committee report.

I. Cross country study and a new paradigm

Economic theory suggests that there is an interaction between insurance and the macroeconomy: growth in insurance promotes economic growth by giving support to savings that can be funnelled into the capital market. On the other hand, high economic growth will lead to demand for insurance.

• Arena (2008)

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Objective

The objective of Arena’s paper is to study the effect of insurance on economic growth.

Hypothesis

Considering the increased activity in insurance markets, in the recent decades, Arena hypothesizes that there is going to be an effect of insurance markets on economic growth. He expects to find a causal relationship between insurance market activity and economic growth; further there should be evidence of complementarity between insurance and banking as well as insurance and the stock market activity.

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Methodology

Arena uses the generalized method of moments (GMM) for dynamic models of panel data that were developed by Arellano and Bond (1991) and Arellano and Bover (1995).

The general regression equation to be estimated is:

Y_i,t = β’X_i,t + μ _t + η_i + ξ_i,t

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where subscripts i and t are country and time period; Y is the dependent variable representing economic growth; X is a set of time – and country-varying explanatory variables, proxies of banking, stock market and insurance market development and interaction terms; β is the vector of coefficients to be estimated; μ_t is an unobserved time-specific effect; η_i is an unobserved country specific effect, and ξ is the error term.

Control variables include average rate of secondary school enrolment for human capital investment; average inflation rate to account for monetary discipline; average growth of the terms of trade ratio and the average ratio of government consumption to GDP as a measure of government burden.

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Banking sector development is observed by using the ratio of bank claims on the private sector divided by the GDP.

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Stock market development is observed by taking the turnover ratio.

For explanatory variables of insurance market development, life and non-life insurance premiums are used as proxies. This was done given the absence of consistent time series data for the ratio of financial investments to GDP, that captures their role as institutional investors.

Data

He takes a pooled data set consisting of 56 countries grouped under the World Bank classification of High income, Middle income and Low income categories. There are 6 non – overlapping five year periods over 1976-2004. The data was taken from the Swiss Re database.

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Results

a) The Linear effects

For exposition, we take one of the equations for a linear effect. The equation is framed below:

Y = 0.162^*** – 0.015X₁^*** -0.003X₂ + 0.025X₃^*** + 0.138X₄ ^***+ 0.501X₅ ^* – 2.206X₆^*** – 0.003X₇^*** + 0.043X₈ ^***+ 0.055X₉^***

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^{*** significance at 1%}

^{** significance at 5%}

^{* significance at 10%}

Here, Y is the dependent variable representing average rate of real per capita GDP growth. The equation is dynamic as it includes the initial level of per capita GDP as an explanatory variable. The equation has various explanatory variables and various control variables. X₁ represents the log of initial GDP per capita; X₂ represents private credit to GDP; X₃ represents stock market turnover; X ₄ represents life and non life insurance to GDP; X ₅ represents the degree of openness; X ₆ represents government consumption; X ₇ represents inflation; X ₈ represents the terms of trade; X ₉ represents school enrolment.

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EXPLANATORY VARIABLE	PROXY
Insurance market development	Life insurance premium to GDP and the ratio of non-life premium to GDP
CONTROL VARIABLES Can You Tackle Complex Essay Topics? Definitely! From astrophysics to literary theory, our advanced-degree writers thrive on tough topics. They’ll research deeply and deliver a clear, sharp paper that meets your level—high school to Ph.D.	PROXY
Human capital investment	Average secondary school enrolment
Inflation	Average inflation rate How Do You Meet My Professor’s Standards? We tailor your paper to your rubric—structure, tone, everything. Our writers decode academic expectations, and editors polish it to perfection, ensuring it’s grade-ready.
Terms of trade	Relative price of exports to Imports
Government	Government consumption What’s Your Editing Process? Upload your draft, tell us your goals, and our editors will refine it—boosting arguments, fixing errors, and keeping your voice. You’ll get a polished paper that’s ready to shine.
Banking sector development	Private credit to GDP
Stock market development	Turnover ratio

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Coefficient for initial level of per capita GDP is negative as expected – growth rates are inversely related to initial levels of GDP per capita.

Coefficient of private credit to GDP is negative. However, the result is not significant.

The coefficient of stock market activity is positive. This is because liquid equity markets make investment less risky and more attractive, by allowing savers to acquire an asset (equity) and to sell it quickly and cheaply if they need access to their savings.

The coefficient of government spending is negative. This gives support to studies that show that beyond a certain level, government spending does not have a positive effect on the economy.

The coefficient of inflation is negative. This is expected, since inflation leads to uncertainty about future profitability of investment projects, reduces international competitiveness and distorts borrowing and lending.

The coefficient of degree of openness is positive. This is because trade promotes a competitive environment which leads to efficient resource allocation; this promotes growth.

The coefficient of degree of terms of trade is positive. This is because a high terms of trade increases returns to producers. This in turn raises investment, promoting economic growth.

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The coefficient for human capital is positive. This is because economic development depends on advances in technological and scientific knowledge.

Further, the author analyses in terms of income group of the countries. He finds that in case of life insurance, the conclusions for the linear effect of insurance on economic growth would hold good only for high income countries. This is because he finds the coefficient on life insurance for developing countries as not significant.

In case of non life insurance, the author finds that his conclusion for linear effect of insurance on economic growth hold good for both high income and developing countries.

b) Non – Linear effects.

For life insurance, the coefficients of the linear and quadratic term are positive but not significant; for non-life, the coefficient for the linear term is negative but not significant while the coefficient for the quadratic term is positive but not significant.

c) Complementarities

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In case of interaction between insurance variables and private credit the coefficient of interaction term is negative and significant. This suggests that banking sector and insurance (life and non-life premiums to GDP) are substitutes than complements.

In case of interaction between stock market turnover and insurance variables, the coefficient of interaction term is negative. This suggests that stock market and insurance ( life and non-life premiums to GDP) are substitutes than complements.

However, the author notes that the results are contradictory and exist due to collinearity issues.

Findings

The important finding of the paper is that both life and non-life insurance have a positive and significant causal effect on economic growth. Further, high – income countries drive the results in case of life insurance. On the other hand, both high income and developing countries drive the results in case of non-life insurance.

• Zheng (2008)

The objective of this paper is to build a new paradigm for international insurance comparison.

The paper has two parts :

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a) Constructing the Benchmark Ratio of insurance penetration.

b) Decomposing growth rates by a ‘Trichotomy’.

a) The Benchmark Ratio of Insurance Penetration (B.R.I.P)

Zheng (2008) consider the insurance industry as one of economic segments whose growth is related to the level of economic development.

Just as insurance ‘density’ is an adjustment to premium income by considering the population factor, and just as insurance ‘penetration’ is adjustment of insurance density by the GDP per capita, the BRIP is an adjustment of penetration by a ‘benchmark’ level of world average penetration at that country’s economic development stage. Thus, the Benchmark Ratio of Insurance Penetration (B.R.I.P) gives the penetration level of the country, in relation to the world average insurance penetration at a country’s economic level :

The numerator is the penetration level of the country. The denominator comprises of the logistic function. The logistic model for insurance penetration was given by Enz (2000), who described that insurance penetration and GDP per capita are related by an S – shaped curve. Zheng (2008) term it as the ‘ordinary growth model’.

Note that the S – curve is a logistic function represented by Y= 1/(C₁+C₂.C₃^x) , where, C₁ C₂ and C₃ are the three parameters and X is growth rate.

Zheng (2008) describes the benchmark penetration as premiums divided by GDP:

Y = premium / G.D.P.= 1 / (C₁+C₂.C₃^x),

where, Y is insurance penetration, X is the independent variable real GDP per capita. C₁ ,C₂ and C₃ are the three parameters of the logistic function. The normal case of penetration increasing as real GDP per capita increases, is when C₃<1. The steepness of the S-curve increases upto the level of income given by the inflection point - and decreases thereafter.

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A pooled dataset comprising of 95 countries and regions over the last 27 years (1980-2008) was taken from the Sigma database of Swiss Re.

On this basis, the estimates of the BRIP for world life insurance, non-life insurance and the insurance industry aggregate are got by plotting the regression curves for life, non-life and insurance industry aggregate.

As seen in the diagram above, the regression curves resemble the shape of the letter ‘S’, S-curve model. The insurance penetration rises with the GDP per capita.

Further, various levels of GDP per capita have different growth rates of insurance penetration: at low levels of GDP per capita, the growth rate of insurance penetration is relatively slow. However, as the GDP per capita rises, the growth rate of insurance penetration also increases. However, after a certain level, the insurance penetration tends to plateau.

Thus, if BRIP =1, it means that country’s actual penetration is equal to the world average penetration at that economic development stage. If BRIP <1, the actual penetration is less than world average; if BRIP>1, the actual penetration is greater than world average level. The world average level of penetration is given by the relevant S – curve.

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Zheng (2008) find that rankings of the insurance industries of developed countries under B.R.I.P descend compared to the ranks got by using traditional indicators; similarly, the rankings of emerging countries under B.R.I.P rise compared to the ranks got by using traditional indicators.

b). Decomposing growth rates by ‘Trichotomy’

The authors now modify the ‘ordinary growth model’ by a ‘Trichotomy’ of decomposing growth.

For attempting the Trichotomy, the ordinary growth model has to be modified to bring out the effects of the economic and institutional factors. This is done by modifying the ordinary model by including country specific dummies which include like the legal system, culture, religion, social security on the insurance growth.

Growth is decomposed into ‘Regular growth’, ‘Deepening growth’ and ‘Institutional growth’.

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‘Regular growth’ measures the insurance growth that happens while keeping the insurance penetration unchanged, i.e., premiums/GDP are increasing at the same pace. This arises out of economic factors.

‘Deepening growth’ caused by the increase of insurance penetration induced by economic growth. This also arises out of economic factors.

‘Institutional Growth’ is the residual that remains after the economic factors of growth, (represented by the Regular and Deepening growth) are deducted from deducted from the overall aggregate growth. It is caused by institutional factors that are country specific such as legal system, culture, religion etc.

After performing the decomposition by using the ‘adjusted growth model’, the authors show that insurance growth in developed countries is mainly driven by economic factors (i.e., regular and deepening), while institutional factors act as the major driving power for the insurance growth in emerging countries.

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The authors remark that institutional aspects facilitate growth of the private insurance industry especially in case of developing countries.

However, as the economy develops, the contribution of the institutional factors to the insurance growth gradually decreases; the economic factors begin to play a more active role in driving the insurance growth.

Finally, in case of developed countries, the social security system is well developed. This acts as a substitute for insurance. As such, insurance growth is hindered.

The authors conclude the following:

Firstly, there should be recognition of insurance growth level of each country or region, relative to their own stage of development, as given by BRIP;

Secondly, insurance growth in developed countries is driven by economic factors while in emerging countries is driven by institutional factors.

Thirdly, as an economy develops, the contribution of institutional factors would gradually decrease and economic factors play a greater role. Consequently the emerging countries should upgrade its growth strategy to attain sustainable development.

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