Insurance penetration and insurance density are the two important indicators, which provide the level of development of insurance sector in an economy. Insurance penetration is defined as the ratio of total premium collected to the total Gross Domestic Product (GDP) of an economy and is usually expressed in the percentage form.
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The insurance density is defined as the ratio of total premium to the total population in the country and is expressed in currency units. Further, the ratio of insurance density to the insurance penetration is simply the per capita GDP, an indicator of per person economic activity of the economy.
The per capita GDP is often regarded as a good proxy for the standard of living of the people in the economy and is often used to compare the relative standard of living between the economies.
Relationship between insurance penetration and per capita GDP
There are several studies [Carter and Dickinson (1992), Enz (2000), Kamiya (2012), Sastry (2011), Sinha et al (2012), Zheng et al (2008), etc.], which have attempted to examine the nature of inter-relationship between the insurance penetration and the per capita GDP. These studies have revealed that a positive relationship holds between insurance penetration and per capita GDP.
Insurance penetration normally increases with the increase in the per capita GDP. The relationship between the two could be linear or non-linear (curvilinear). A simple linear relationship will mean that the income elasticity of demand for insurance is a constant. In case, the relation is curvilinear, the elasticity would no more be a constant and would change with the level of per capita GDP and would be dictated by the mathematical form of the non-linearity (such as, exponential, logistic etc.).
The studies of Carter and Dickinson (1992) and Enz (2000) indicated that the relationship between the insurance penetration and per capita GDP can be explained with an S-curve (a non-linear form). They demonstrated that the insurance penetration cannot go on increasing with the same pace forever with income per capita. The study of Enz (2000) proposed a logistic curve, which tracks an S-curve appropriately. Enz (2000) analyzed the insurance penetration by plotting it with the per capita GDP for select countries both for the life and non-life segments, separately.
It revealed that there exists a level of per capita GDP at which the income elasticity of demand for insurance reaches to the maximum level for both segments (life and non-life) of insurance. Subsequent to this point of maxima, the insurance penetration starts decelerating (increasing at a slower rate) with the increase of per capita GDP.
The study also attempted to identify the countries, which are consistently above or below the S-curve, and indicated that these deviations are on account of other factors (for example, socio-demographic and cultural characteristics), which are largely country-specific, which affect the insurance business of these specific counties.
Indian Scenario
The scatter plot of S-curve in the study of Enz (2000) reveals that insurance penetration in India lies well above the point of the estimated S-curve. Accordingly, given the assumptions of model in Enz (2000), it is indicated that the insurance penetration in India is higher than what the S-curve suggests. It is interesting to note that there exist other prominent factors (other than the per capita GDP), which influence the growth of Indian insurance business positively.
These factors could be demand driven (such as, socio-demographic characteristics of prospect/policyholders, risk appetite, etc.) or, supply driven (such as, quality of distribution channel, product innovation, etc.) or combination of both. While the presence of other factors (other than per capita GDP) is evident in case of India, it is expected that these would vary across various states and union territories of India.
Treating these states and union territories as independent economies, the insurance penetration as well as insurance density of individual states and union territories can be computed using their respective Premium underwritten, GDP and Population.
Data descriptions and limitations
The state-wise computed data of insurance penetration and insurance density is provided in Statement No. 9. It has been computed annually for the period 2006-07 to 2011-12. The premium figures pertain only to the individual new business premium, that is, the first year premium (both single premium and regular premium). Accordingly, it does not cover business of renewal premium of individual business of life insurance and any group business of life insurance.
Further, it does not cover any business of non-life insurance. This is in accordance with state-wise individual new business premium data filed with the IRDA.
It may be noted that this sub-set of data represented close to 40 per cent of total life insurance in the years 2006-07 (38.67 percent) and 2007-08 (39.45 per cent), but declined subsequently, especially in the recent years.
The share of individual new business premium stood at 22.56 per cent of the total life insurance premium in 2011-12 (against 28.52 per cent in the year 2010-11). The changing share of this subset of data reflects changes in insurance penetration as well as in insurance density in exactly the same fashion, as these are directly co-related.
Accordingly, the insurance penetration for this subset of data stands at 0.78 per cent in 2011-12, while the insurance penetration of total life insurance is 3.47 per cent. The insurance penetration of total life insurance penetration is 3.40 per cent in 2011-12 as per the Swiss Re estimates.
The state-wise data on Gross Domestic Product and per capita Net Domestic Product have been taken from the Central Statistical Organization (CSO), Ministry of Statistics & Programme Implementation (MoSPI), Government of India. While, the Ministry publishes both the Gross Domestic Product (GDP) and Net Domestic Product (NDP) for various states/UTs (at current prices and constant prices), it publishes only Per Capita Net Domestic Product and not the Per Capita Gross Domestic Product. In accordance with the definition of the insurance penetration, the GDP is used for the computation of insurance penetration of various states and union territories. Further, it is taken at the current prices in order to be compatible with the premium figures.
The state-wise data on population is available through Census 2001 and Census 2011. These are used to estimate the statewise population data for various years (2006-07 to 2011-12) using the Compound Average Growth Rate (CAGR) of the respective states and union territories with application of simple interpolations. India is a large country with 35 states/union territories with varying levels of per capita GDP, insurance penetration and insurance density.
The per capita NDP of India stood at `60,972 in 2011-12. The same, however, varied significantly across the states and union territories ranging from a low of Rs.24,681 (for Bihar) to a high of Rs.1,92,652 (for Goa) and Rs.1,75,812 (for Delhi). In the present context, 3 union territories viz. Dadra & Nagra Haveli, Daman & Diu and Lakshadeep have not been considered because of their meager figures.
Accordingly, 32 states/union territories have only been considered. It may be noted that these three union territories have insignificant contribution in the total life insurance premium.
The study of Sinha et al (2012) identified the per capita number of agents and the per capita number of insurance offices (both are supply driven), as two other influencing factors, apart from per capita GDP, which explained together large section of data appropriately.
The above study also carried out a multiple linear regression analysis to
(i) identify statistically significant factors influencing the insurance penetration and density,
(ii) to identify the outlier states/union territories, which are deviating from the estimated straight line statistically significantly and, (iii) to classify the states/union territories, which have under-insurance, over insurance and adequate insurance given the assumptions of the model.
In case of life insurance, given the high importance of per capita number of agents in the country, it is imperative to identify the states and union territories, which are at the extreme ends of under-insurance and over-insurance. That is, there are few states, which have a low premium figure, despite the fact that relatively higher agents have been deployed by the various insurance companies therein.
In contrast, few states are capable of underwriting good insurance business despite relatively low agents deployed therein. This poses questions on the allocation of insurance agents by the insurers in various parts of the country.
There could be possibility of inappropriate (inadequate or more than adequate) deployment of agents in particular region(s). Thus, the insurers need to analyze the mis-allocation, if any, in the distribution of agents, offices etc. and optimize it, subject to constraints. This may facilitate in increasing the business volumes, and thus, pushing up both insurance penetration and insurance density of the country.
References
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- Carter, R L and Dickinson, G M (1992): Obstacles to the liberalization of trade in insurance, Thames Essay No.58, Hemel Hempstead: Harverster Wheatsheaf, See Appendix IV, 175-188.
- Enz, R (2000): The S-curve relation between per-capita income and insurance penetration, Geneva Papers on Risk and Insurance,
- 25 (3): 396-406.
- Handbook on Indian Insurance Statistics 2010-11, IRDA.
- Sastry, D V S (2011): Life insurance penetration in India, Journal of Social and Economic Policy, Vol. 8, No. 2, 207-215.
- Sinha, R K, Nizamuddin M M and Alam, I (2012): An Investigation of insurance penetration and density of India by geography,
- 16th Annual Conference of Asia-Pacific Risk and Insurance Association (APRIA), July 2012, Seoul, South Korea.
- Swiss Re: Various Sigma Reports.
- Zheng W, Liu, Y and Yiting, D: “New paradigm for international insurance comparison: with an application to comparison of seven insurance markets.
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Courtesy IRDA Annual Report 2011-12
