Min-Sun Horng
Department of Risk Management and Insurance,
Yung-Wang Chang
and Meiho Institute of Technology,
No. 2,
E-mail: u9127907@ccms.nkfust.edu.tw,
Tel: +886-7-351-7964.
Abstract
Several theories have been developed to explain the motives for purchasing non-life insurance (i.e., property-casualty insurance), but there have been few empirical tests conducted by emerging insurance markets. This study examines the determinants of non-life insurance consumption in
Keywords: Insurance demand; Non-life Insurance; Insurance Penetration;
JEL classification codes: G22; O16
1. Introduction
Despite the financial crisis in
Another insurance indicator, insurance density, is calculated by dividing direct gross premiums by the population. This represents the average insurance spending per capita in a given country. Table 1 also shows that, in 2005,
Table 1
2005 Ranks in Insurance Penetration and Insurance Density
| | Insurance Penetration (%) | Insurance Density (USD) | ||||||
| Country | Rank | Total | Life | Non-life | Rank | Total | Life | Non-life |
| | 1 | 14.11 | 11.17 | 2.93 | 20 | 2,146 | 1,699 | 446 |
| | 2 | 13.87 | 10.84 | 3.03 | 32 | 715 | 558 | 156 |
| | 3 | 12.45 | 8.90 | 3.55 | 2 | 4,599 | 3,287 | 1,312 |
| | 4 | 11.19 | 6.20 | 4.99 | 1 | 5,558 | 3,078 | 2,480 |
| | 5 | 11.15 | 8.36 | 2.79 | 4 | 3,986 | 2,989 | 997 |
| | 6 | 10.54 | 8.32 | 2.22 | 7 | 3,747 | 2,956 | 790 |
| | 7 | 10.25 | 7.27 | 2.98 | 22 | 1,706 | 1,211 | 496 |
| | 8 | 10.21 | 7.08 | 2.77 | 9 | 3,569 | 2,475 | 1,094 |
| | 9 | 9.52 | 6.38 | 3.13 | 15 | 2,545 | 2,213 | 332 |
| | 10 | 9.79 | 5.12 | 4.67 | 8 | 3,740 | 1,954 | 1,786 |
| | | 7.78 | 4.69 | 3.10 | | 1,514 | 912 | 602 |
| World | | 7.52 | 4.34 | 3.18 | | 519 | 300 | 219 |
Source: Swiss Reinsurance Company, Sigma Publications, No. 5/2006
Note: 1. Insurance Density: premiums per capita.
2. Insurance Penetration: premiums as share of GDP (%).
In Table 2, it can easily be seen that in recent years,
Table 2: 1996~2006 Total Gross Premium, Premium Density, and Insurance Penetration in
| Year | 1996 | 2006 |
| Gross Premium (USD million) | | |
| Non-life Insurance | 4,833 | 10,318 |
| Life Insurance | 10,994 | 41,245 |
| Total | 15,827 | 51,563 |
| Premium density (USD) | 740 | 2,250 |
| Insurance penetration (%) | 5.8 | 14.5 |
Source: Swiss Reinsurance Company, Sigma Publications, No. 4/1998 and No. 4/2007.
Note: 1. Insurance Density: premiums per capita.
2. Insurance Penetration: premiums as share of GDP (%).
Although the insurance industry has grown significantly in
The remainder of the study is divided into four sections. The following section presents the factors affecting non-life insurance. Next, this data utilized and the empirical framework of the study is described. Finally, the conclusions and implications of the study are discussed.
2. Determinants of non-life insurance demand
From an economic viewpoint, the demands for insurance are based on the expected utility paradigm (e.g. Mossin, 1968；Szpiro, 1985; Browne et al. 2000), and suggests that different factors influence insurance purchases of an individual. These factors include the individual‘s income and wealth, the price of insurance, the probability of loss, and the individual‘s degree of risk aversion. MacMinn and Han (1990) and Garven and MacMinn (1993) suggest that corporate insurance purchases could decrease the possibility of financial crisis. Basically, corporate demand for insurance is driven by the maximization of the current shareholder value (e.g. MacMinn, 1987; Regan and Hur, 2007). However, in a corporate insurance market with transaction costs, the same factors as individual purchase of insurance—income, the price of insurance, the probability of loss, risk aversion—are hypothesized to be important determinants of insurance demand by businesses.
2.1 Income
Income level is hypothesized to positively influence insurance demand. Beenstock et al. (1988) point out a positive relationship in industrialized countries between national income and non-life insurance spending. Browne et al. (2000) analyzes general liability and motor vehicle insurance in OECD countries, and finds a significant positive relationship between premium density and GNP per capita. Additionally, Esho et al. (2004) examines developed and developing countries between 1984 and 1998, and finds a strong positive relationship between national income and the non-life insurance premium. Outreville (1990) and Ward and Zurbruegg (2002) strongly emphasize that the insurance industry, through risk transfer, financial intermediation, and employment can generate externalities and economic growth.
2.2 Price of Insurance
The demand for any product or service is affected by its price. In this study, the inverse of the loss ratio, i.e. defined as premiums divided by claims, is taken to measure the price of insurance and is expected to be negatively correlated with the insurance demand. Cummin and Danzon (1997) and Esho et al. (2004) use a similar measure in their study of price determination. Browne et al. (2000), based on the economic theory, considered that if the market of a country excludes the competition of foreign business, it will lead to merchandise of low quality and high prices. And the ratio of the premium volume of foreign companies in the market will be taken as the measure of price, i.e. the negative correlation between the shares of the market of foreign insurance companies and the price of insurance. In fact, considering the price with the probability of loss (i.e., claims ¸ premiums) and profit (i.e., premiums – claims) would be more tightly related.
2.3 Risk Aversion
The primary motive for purchasing insurance is risk aversion to avoid loss. Therefore, the level of risk aversion is hypothesized to be positively correlated with insurance demand. Schlesinger (1981) demonstrates that an individual with a higher loss probability, a higher degree of risk aversion, or a lower level of initial wealth, will purchase more insurance. Mayers and Smith (1990) believe that closely held firms are more likely to purchase insurance than firms with less-concentrated ownership for the same reason that an individual purchases insurance—risk aversion. Mayers and Smith (1990) further indicate a supposition that a company does not exhibit proper risk aversion, because risk aversion is not so obvious to the corporate purchasers of insurance. As stated previously, even though risk aversion could not perfectly explain why consumers would buy insurance, it is still an important indicator.
Although risk aversion is a “rational” motive for an individual’s purchase of insurance, unfortunately, it is difficult to measure. According to the discussion of Browne and Kim (1993), in general, a higher level of education may lead to a greater degree of risk aversion and greater awareness of the necessity of insurance. Nonetheless, Szpiro and Outreville (1988) proved the negative correlation between the level of education and risk aversion. They deemed that higher education leads to lower risk aversion, and that, in turn, leads to more risk-taking by skilled and well-educated people. When Browne et al. (2000) and Esho et al. (2004) were discussing non-life insurance; they also took the level of education as a proxy for risk aversion.
2.4 Others
The factors that influence the demand for insurance also can be compartmentalized by the different law systems in various countries and areas. For instance, Esho et al. (2004) pointed out that, other thing equal, automobile insurance consumption is greater in common-law countries than in statutory-law countries. Other factors, like the degree of economic development and market structure, would also influence the demand of non-life insurance. However, the purchase of insurance could help with tax saving and clients’ demands for insurance (Ma and Pope, 2003). These factors would influence the demand of non-life insurance, more or less, but would not do much harm.
3. Empirical Analysis
3.1 Data
This study follows the recommendations of previously studies and uses real GDP per capita to measure the value of income (Ward and Zurbruegg, 2002). Annual GDP data was obtained from the AREMOS database of the Taiwan Ministry of Education for the period of 1970 through 2005. Insurance density (i.e., premiums per capita) was utilized as a proxy of insurance demand (Browne et al., 2000; Li et al., 2007). Annual premium data was acquired from the Taiwan Statistical Yearbook for the period of 1970 through 2005. The level of education refers to the Statistics Yearbook published by the Ministry of the Interior (
3.2 Unit root tests
When the variables are non-stationary or exhibit a unit root, the procedures of conventional regressions may not be appropriate (Engle and Granger, 1987; Enders, 2003). The augmented Dickey–Fuller (1981, ADF) method was applied to assess the existence of unit roots and identify the order of integration for each variable. ADF tests were represented by the following regression equations:[3]
(1)
(2)
(3)
Where 
is the level of the variable, 
is a drift term, and 
is the time trend. 
is a normally distributed random error term with mean zero and constant variance. Δ denotes the series in first difference. 
is the number of lags necessary to obtain white noise.
Results of the ADF test for stationarity are reported in Table 3. The ADF statistics for all series levels except LPRICE were not significant at the 5% significance level, implying that the null hypothesis of a unit root test cannot be rejected. In addition, the corresponding statistics for their first-order differences are significant at the 5% significance level and thus suggest rejecting the null hypothesis. The results concluded that ΔLFIRE, ΔLCAR, ΔLINCOME, ΔLPRICE and ΔEDU variables were stationary.
Table 3
Unit Root Test
| |
| |
| |
| ||||||
| Variables | ADF statistic | 95% Critical values | p value | | ADF statistic | 95% Critical values | p value | | ADF statistic | 95% Critical values | p value |
| LFIRE | 3.90 | -1.95 | 1.00 | | -2.17 | -2.95 | 0.22 | | -2.36 | -3.55 | 0.39 |
| LCAR | 1.35 | -1.95 | 0.95 | | -2.46 | -2.95 | 0.13 | | -0.07 | -3.55 | 0.99 |
| LINCOME | 2.07 | -1.95 | 0.99 | | -1.50 | -2.95 | 0.52 | | -1.25 | -3.55 | 0.88 |
| LPRICE | -1.46 | -1.95 | 0.13 | | -3.98 | -2.95 | 0.00 | | -3.84 | -3.54 | 0.03 |
| LEDU | 2.86 | -1.95 | 1.00 | | -0.13 | -2.95 | 0.94 | | -2.22 | -3.54 | 0.47 |
| ΔLFIRE | -3.36 | -1.95 | 0.00 | | -4.34 | -2.95 | 0.00 | | -4.64 | -3.55 | 0.00 |
| ΔLCAR | -1.95 | -1.95 | 0.05 | | -2.80 | -2.95 | 0.07 | | -4.48 | -3.55 | 0.01 |
| ΔLINCOME | -3.20 | -1.95 | 0.00 | | -4.05 | -2.95 | 0.00 | | -4.25 | -3.55 | 0.01 |
| ΔLPRICE | -4.33 | -1.95 | 0.00 | | -4.25 | -2.96 | 0.00 | | -4.34 | -3.56 | 0.01 |
| ΔLEDU | -5.57 | -1.95 | 0.00 | | -6.94 | -2.95 | 0.00 | | -6.91 | -3.55 | 0.00 |
Note: Δ denotes series in first difference. The lag parameters are selected based on the SIC. The critical values of the ADF test and one sided p-values are based on MacKinnon (1991, 1996). EViews 5.0 was used as the statistical software package for all tests.
3.3 Model Estimation
This study utilizes the disaggregated data and specifications of the regression model based on theory, in order to examine the relationship between the premium density and the independent variables. In this way, we might understand more about the decision of insurance demand. The regression specification is:
(4)
where 
is the dependent variable, 
is the parameter estimator of the intercept, 
stand for slope parameters, and 
is the random deviation. The model was estimated separately for fire and automobile premium densities as dependent variables.
If the time series of the variable is nonstationary, the regression equation can be written as:
(5)
where 
denotes the series in first difference. 
is the parameter estimator of the intercept. 
stand for slope parameters and is the random deviation.
Table 4 reports the results from the ordinary least square regression in levels. In the presence of nonstationary variables, this regression output (as Eqs. 4) looks good, but the results lack any economic meaning.
Table 4
Empirical Model Estimation in levels
| | LFIREt | | LCARt | ||||
| Variables | Coefficients | t-statistic | p-value | | Coefficients | t-statistic | p-value |
| Intercept | -0.48 | -2.81 | 0.01 | | -3.74 | -10.05 | 0.00 |
| LINCOMEt | 0.70 | 11.26 | 0.00 | | 1.61 | 11.84 | 0.00 |
| LPRICEt | -0.05 | -0.53 | 0.60 | | -0.08 | -0.37 | 0.72 |
| LEDUt | 0.40 | 4.60 | 0.00 | | 0.37 | 1.96 | 0.06 |
| R2 | 0.98 | | 0.97 | ||||
| DWa | 0.90 | | 0.24 | ||||
| LB – Qb | χ2 (6) = 24.25[0.00] | | χ2 (6) = 61.66[0.00] | ||||
| ARCHc | χ2 (6) = 5.75[0.45] | | χ2 (6) = 22.15[0.00] | ||||
| Normald | χ2 (2) = 1.86[0.40] | | χ2 (2) = 1.12[0.57] | ||||
| FFe | χ2 (1) = 2.84[0.09] | | χ2 (1) = 28.13[0.00] | ||||
Notes: The number of lags is chosen by making the SIC statistic as small as possible; it should also be large enough to remove any serial correlation in the residuals. a Durbin–Watson test. b Lagrange multiplier test for residual serial correlation. c Based on the regression of squared residuals on squared fitted values. d Based on the test of the skewness and kurtosis of residuals. e Ramsey’s RESET test using the square of fitted values.
Table 4 also presents the empirical model in first differences. Thus, the discussion focuses on results obtained from first-differenced model (as Eqs. 5).
Table 4
Empirical Model Estimation in first differences
| | ∆LFIREt | | ∆LCARt | ||||
| Variables | Coefficients | t-statistic | p-value | | Coefficients | t-statistic | p-value |
| Intercept | -0.44 | -2.59 | 0.01 | | -3.67 | -9.74 | 0.00 |
| ∆LINCOMEt-1 | 0.68 | 10.62 | 0.00 | | 1.57 | 11.17 | 0.00 |
| ∆LPRICE t-1 | -0.06 | -0.61 | 0.55 | | -0.10 | -0.44 | 0.66 |
| ∆LEDU t-1 | 0.44 | 4.69 | 0.00 | | 0.46 | 2.24 | 0.03 |
| R2 | 0.98 | | 0.97 | ||||
| DWa | 0.95 | | 0.24 | ||||
| LB – Qb | χ2 (6) = 23.28[0.00] | | χ2 (6) = 57.05[0.00] | ||||
| ARCHc | χ2 (6) = 5.04[0.54] | | χ2 (6) = 20.44[0.00] | ||||
| Normald | χ2 (2) = 1.47[0.48] | | χ2 (2) = 1.14[0.56] | ||||
| FFe | χ2 (1) = 7.74[0.01] | | χ2 (1) = 12.87[0.00] | ||||
Notes: The number of lags is chosen by making the SIC statistic as small as possible; it should also be large enough to remove any serial correlation in the residuals. a Durbin–Watson test. b Lagrange multiplier test for residual serial correlation. c Based on the regression of squared residuals on squared fitted values. d Based on the test of the skewness and kurtosis of residuals. e Ramsey’s RESET test using the square of fitted values.
3.3.1 Income
As reported in Table 5, the relationship between the dependent variable premium density and the independent variable LINCOME (i.e., real GDP per capita) is positive and statistically significant in both the fire and automobile insurance model. Our data is consistent with the findings from prior studies in that income is positively correlated with insurance demand. Therefore, the results suggest that the higher the income is, the more they would purchase insurance. Although income is statistically significant in both models, the regression coefficient of 0.68 and 1.57 (Table 5), the coefficient estimates suggest that changes in income have a more pronounced effect on automobile insurance demand than on fire insurance demand. As a result, the proof shows that income has a far greater effect on automobile insurance demand than on fire insurance demand.
3.3.2 Price of Insurance
As reported in Table 5, the regression coefficients of the premium density of automobile insurance and the independent variable of insurance price is -0.10. This is consistent with the negative relationship in economical implications, though the p-value is statistically insignificant. At the same time, the regression coefficients of the premium density for fire insurance and the independent variable of insurance price is -0.06. This is also consistent with the negative relationship in economical implication, though the p-value is also statistically insignificant.
The domestic insurance industry, in its early ages, was strictly limited by government agencies. This enabled it to become stabilized. However, in later years, even with the openness for foreign insurance companies or domestic companies, the insurance industry has turned into an oligopoly market. As a result, the price variation of insurance products is not obvious; nevertheless, the relationship between insurance price and premium density is statistically insignificant.
3.3.3 Risk Aversion
The risk-aversion measure used in this study, for people over the age of 15, is statistically significant in all models. This result means that the higher the level of education, the greater the demand for insurance. For fire insurance, the regression coefficient was strong statistically significant, and that of automobile insurance is barely statistically significant. This corresponds to the prior research of Browne and Kim (1993), which showed that a high level of education leads to high risk aversion and these people would buy more insurance.
4. Conclusions
Since the 1970s, the global insurance market has developed quickly, and
This study examines the determinants of non-life insurance consumption in
Managerial implications will now be suggested. The income elasticity of demand for insurance can assist insurance companies in precisely determining the consumption of insurance products. Furthermore, the insurance industry should consider different marketing strategies for the marketing mix between fire insurance and automobile insurance. In future studies, we recommend insurance researchers compare multiple emerging markets using the same variables we have used in this study. Based on the results, the insurer can carry out this model to predict future insurance demand and decision-making to enter the market or promote insurance products.
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[1] For more detailed information on the economic growth rate, refer to the following website: http://www.dgbas.gov.tw
[2]
[3] There are three cases for the ADF unit root test discussed in Hamilton (1994) and Enders (2003). To select the optimal lag-length for each model, we select a model with the lowest value of the SIC.
