This paper investigates the relationship between insurance development and economic growth by employing GMM models on a dynamic panel data set of 77 economies for the period 1994–2005. Insurance density is used to measure the development of insurance. Controlled by a simple conditioning information set and a policy information set, we can draw a conclusion that insurance development is positively correlated with economic growth. The sample is then divided into developed and developing economies. For the developing economies, the overall insurance development, life insurance and non-life insurance development play a much more important role than they do for the developed economies.
There has been a great interest in the role of financial institutions in economic growth. Economists refer to some work of researchers in the late 19th and early 20th centuries who discussed the significance of finance for economic growth. In recent times, a number of studies have analysed various issues with respect to the role of the banking sector in economic growth. The most prominent studies have been conducted by Levine and his colleagues. For instance, King and Levine demonstrated the connection between bank development and economic growth, which was confirmed by later studies such as Levine, Beck et al., Levine et al., Rousseau and Wachtel, and Beck and Levine.Footnote 1 The studies of the relationship between financial development and economic growth have been shown to be robust using different econometric methods. For instance, Levine and ZervosFootnote 2 used cross-country regressions, whereas LevineFootnote 3 used cross-country instrumental variables regressions. The recent studies by Beck et al., Levine et al., and Beck and LevineFootnote 4 used dynamic panel GMM estimations, whereas Rousseau and WachtelFootnote 5 used panel Generalised Method of Moments (GMM) estimation for a Vector Autoregression (VAR) model.
Although comparing with studies on banks’ role in economic growth, the role of insurance is relatively less examined, there has been increasing literature on this issue recently. Insurance is of great importance to a modern society by making many economic activities possible in addition to its contributions to the economies in terms of its size, employment, managed assets, and so on.Footnote 6 In fact, economic growth is characterised by the soundness of a national insurance market.Footnote 7,Footnote 8,Footnote 9 Outreville9 emphasised on the importance of property-liability insurance and life insurance, respectively, in developing economies and their growth. Skipper10 stated that insurance contributed to the economy from the following aspects: “ … (1). Promotes financial stability and reduces anxiety; (2). Can substitute for government security programs; (3). Facilitates trade and commerce; (4). Mobilizes savings; (5). Enables risk to be managed more efficiently; (6). Encourages loss mitigation; (7). Fosters a more efficient capital allocation … ”.10 SigmaFootnote 10, EnzFootnote 11, and Ward and ZurbrueggFootnote 12 described the relationship between insurance market development and economic development as an “S Curve”, which stated the starting sharp and then smooth increase of insurance development corresponding to the lower and higher stages of economic development, respectively. Ward and ZurbrueggFootnote 13 argued the insurance contributions to economic growth from the following aspects: risk transfer and indemnification services and financial intermediary services. They further analysed the above two economic contributions in terms of the following factors: productivity improvement and innovation facilitation for the former services and production efficiency enhancement, investment opportunity increases, reduction in the waste of early monetary realisation, and insurance institutional monitoring benefits for the latter services. Webb et al.Footnote 14 argued that life and property/liability insurers can contribute to economic growth from the following aspects: (1) Life insurance can increase productivity by reducing the demand for liquidity and by shifting from unproductive use to more productive use of resources. This is similar to the role of banks on investment quality documented by Pagano.Footnote 15 (2) Property/liability insurers provide an extra risk-financing choice, which potentially reduces the probability of firm financial distress and firm bankruptcy costs. This influences investment decisions in a particular economy. (3) Insurers may potentially increase expected investment returns by reducing the costs of risk financing, because insurers can: “(a) Excel in offering risk-pooling services through the identification of standardised risks and simplification of contracts, (b) Provide optimal investments and asset-liability matching, (c) Provide valuable and cost-effective administrative services related to risk management and claims payments, and (d) Offer products that are tax-deductible business expenses in many markets” (p. 6).15 Regarding the theoretical relationship between insurance and economic growth, Webb et al.16 has a detailed argument. According to Webb et al.,15 based on a Solow-Swan neoclassical growth model, assuming a Cobb-Douglas type of production model, which states that production growth is due to labour, capital, and technology, the following factors should be added in the augmented growth model: financial activities of property/liability insurers and life insurers, which with banks may measure the differences in productivity and investments based on institutional factors and savings rate. As can be seen from the above analysis, it is expected in this paper that insurance activities should have a positive impact on economic growth. However, this impact may vary across different countries and across different lines of insurance business.
The empirical results of this paper, by employing GMM models on a dynamic panel data set of 77 economies for the period 1994–2005 and controlled by a simple conditioning information set and a policy information set, have shown that insurance development is positively correlated with economic growth. This paper reports the analysis of insurance development and economic growth by breaking them into life and non-life insurance as well as developed and developing economies. It has been shown that for the developing economies, the overall insurance development, life insurance and non-life insurance development play a much more important role than they do for the developed economies. This finding has significant policy implications in that “it could give empirical ground to the micro-insurance policy strategy of the World Bank and the UN-ISDR and nicely complement the theory of the wealth effects of insurance”.Footnote 16
The remaining parts of this paper are structured as follows: the following section discusses the data used in the empirical analysis and the econometric methodology. The penultimate section discusses the empirical results. The final section concludes.
Data in the insurance industry and empirical methodology
This paper evaluates the long-run relationship between insurance development and economic growth. In doing so, it will differentiate between developed and developing economies and the role that life and non-life insurance development could play for economic growth. This paper uses a panel data set of 77 economies over the period 1994–2005. Table A1 in the Appendix lists the names of the economies used in this study. Table 1 reports summary statistics for 77 economies used in this study by referring to the information on economic growth, insurance density, life and non-life insurance density. As can be seen from Table 1, the development of insurance is largely different in life and non-life insurance lines and across different economies.
Table 2 reports global insurance income from 1998 to 2005. As can be seen from Table 2, in 2005, there were a total amount of US$3426 billion in worldwide insurance premiums, with life insurance US$1974 billion and non-life insurance US$1452 billion.
From Figure 1, it can be seen that the growth patterns between 1994 and 2005 for life and non-life insurance differ from each other. For instance, there was a large fluctuation for life insurance, while the fluctuation in non-life insurance is relatively small. As can also be seen from Figure 1, there have been apparently unbalanced growth patterns between life and non-life insurance over this period. For instance, in 2005, the growth rates for total, life and non-life premiums were 2.5 per cent, 3.9 per cent, 0.6 per cent, respectively, after deducting inflation.Footnote 17
To measure insurance development, we use the insurance density, measured by annual premium payments divided by population and converted into U.S. currency. As a way of assessing the independent connection between insurance development and economic growth, we control for other potential factors influencing economic growth in this paper. In order to control for convergence, in the simple conditioning information set, we include the initial real gross domestic product (GDP) per capita. As a way of controlling for human capital accumulation, we use gross enrolment ratio of tertiary students. In the other model that deals with the policy conditioning information set, we use one of the following factors: inflation rate, trade balance, and gross fixed assets investment. All of the above factors are supposed to have an impact on the conventional growth model by influencing one or more of the growth factors, such as labour, capital, and technology.
In order to empirically test the relationship between insurance development and economic growth and also to avoid the statistical problems associated with the use of ordinary least squares, the panel data technique is used in this paper. We use the same methodology used in Beck and LevineFootnote 18 for banks, stock markets, and economic growth for the purpose of testing the role of insurance development in economic growth. In this paper, we follow Beck and Levine19 to employ the following regression equation, Eq. (1):
where y is GDP after taking into account inflation and by the logarithm transformation, X is the independent variables other than lagged y, η, and ɛ is unobservable country-specific effect and error term, respectively, i means country, and t means time period. We use two-step GMM estimators of dynamic panel data with fixed effects to estimate the model, which is promoted by Holtz-Eakin et al.,Footnote 19 and Arellano and Bond.Footnote 20 In the model, we also include period dummy variables to control for time-specific effects.
Regression without insurance density and endogeneity test of insurance density
For testing the impact of insurance development on the growth of an economy, we conduct a series of regressions excluding the insurance density from the explanatory variables. The statistics of those regressions are given in Table 3. If the regressors include all economic factors, which are gross enrolment ratio of tertiary students lagged 2, inflation rate, trade balance, and gross fixed assets investment, the sign of the coefficient of gross enrollment ratio of tertiary students lagged 2 is positive, which is reasonable. If regressing inflation rate and gross fixed assets investment separately with gross enrollment ratio of tertiary students lagged 2 one by one into the regressors, we can have reasonable regressions. If we put trade balance and gross enrollment ratio of tertiary students lagged 2 together into regressors, the coefficient of trade balance is negative, which is not reasonable. However, the R2 of all those regressions are lower than the corresponding regressions including insurance density. It has demonstrated that insurance development really improves the economic growth. In fact, in the history of human economic society, investment and demand take the first position to promote the economic growth, and then for the long run come the education and technique innovation. These factors do not exclude the positive impact of the development of insurance on economic growth, at least it improves the economic growth, in term of promoting the society stability and security. This paper argues that insurance density with other important variables really improve the economic growth, in another words, it has a positive impact on economic growth. For testing the endogeneity of insurance density, we implement the Hausman test. The result shows the insurance density is very significantly endogenous. Therefore in the next regression we use GMM method in the dynamic panel modeling.
Insurance density and economic growth
Using the econometric methods outlined above, this section presents regression results concerning the relationship between economic growth and insurance density. The regressions simply include the logarithm of initial real per capita GDP, the logarithm of gross enrolment ratio of tertiary students, the logarithm of gross fixed assets investment, the average inflation rate over the period, and trade balance of the economies. We present the panel estimator regressions in Table 4.
The results in Table 4 show a statistically and economically significant relationship between the insurance development and economic growth. The first column reports the result of the pure regression without the use of the variables forming the policy conditioning information set (trade balance, inflation rate, gross fixed assets investment). Insurance density is positively correlated with economic growth at the 5 per cent significance level in the columns 1–5, where the following potential econometric problems are absent: simultaneity bias, omitted variables, serial correlations, and over-fitting problems. Inflation rate and trade balance have negative signs and enter the regression significantly. Gross fixed assets investment is positively correlated with the economic growth. In the last column, we include all the policy conditioning information set and conclude that economic growth is also positively influenced by insurance density. Regarding the magnitude of the measured effects, it has been shown in the last column that there is about 4.781 per cent increase in economic growth given 1 per cent increase in total insurance density. For the purpose of comparison, the enhancement impact of banking activities on economic growth is no more than 1.8 per cent given 1 per cent increase in bank credit (Table 4, Beck and Levine19).Footnote 21
Life insurance density and economic growth
The results for the panel regressions in Table 5 show that life insurance density has a significantly positive impact on economic growth. The results do not reject the close connection between economic growth and life insurance density with the coefficient ranging from 1.657 to 2.640. Serial correlations in the error term and over-fitting problems are absent. Total fixed assets investment plays a significant role in economic growth, and inflation rate and trade balance do not pass the test in the last column. Regarding the magnitude of the measured effects, it has been shown in the last column that there is about 1.728 per cent increase in economic growth given 1 per cent increase in life insurance density, which is very close to the enhancement impact of banking activities on economic growth (no more than 1.8 per cent given 1 per cent increase in bank credit documented in Table 4 by Beck and Levine19).
Non-life insurance density and economic growth
In Table 6, the panel results are more robust than life insurance as described in Table 5. The coefficient value ranges from 4.180 to 4.962. Non-life insurance density is positively and significantly correlated with economic growth when using the same conditioning information sets and policy information sets. Inflation rate and trade balance have negative signs and enter the regression significantly. The gross fixed assets investment is positively correlated with economic growth. Once again, serial correlations in the error term and over-fitting problems are absent. Regarding the magnitude of the measured effects, it has been shown in the last column that there is about 4.180 per cent increase in economic growth given 1 per cent increase in non-life insurance density. For the purpose of comparison, the enhancement impact of banking activities on economic growth is no more than 1.8 per cent given 1 per cent increase in bank credit (Table 4, Beck and Levine19).
In terms of the magnitude of the impact, it is apparent from the above analysis that non-life insurance has a much more significant impact on economic growth than life insurance. In addition, in Tables 4, 5, 6, initial income per capita has a negative effect on economic growth, which indicates that the higher the historical economic growth is, the slower the economy boosts. This supports the “S Curve” argument by Sigma11 and Enz.12 Gross enrolment ratio of tertiary students lagged 2 periods has positively influenced economic growth in most equations. So, the education factor is quite important for a country's long-term development.
The comparison between developed economies and developing economies
This section of the paper attempts to separately investigate the relationship between insurance development and economic growth for industrial and developing economies over the period 1994–2003. Classification of economies is based on 2002 Gross National Income (GNI) per capita calculated using the World Bank Atlas method. The income groups are: low income, $735 or less; lower middle income, $736–$2,935; upper middle income, $2,936–$9,075; and high income, $9,076 or more. According to our empirical analysis, we divide 77 economies discussed in the paper into two kinds: developed economies and developing economies. The developed economies are selected from high-income economies publicised by the World Bank. Low-income and middle-income economies are sometimes referred to as developing economies. We also put the upper middle-income economies into the developing economies group. Finally, we get 32 industrialised economies and 45 undeveloped economies. The means of these economies’ indicators can be seen in Tables A2 and A3 in the Appendix.
According to Tables 7, 8, 9, the results in developing markets provide strong support for the insurance services: overall insurance density is strongly associated with economic growth. This close relationship holds after controlling for potential simultaneity bias and omitted variable bias. More specifically, as shown in Table 7, overall insurance density is closely associated with economic growth, with a coefficient of 9.172 significant at 1 per cent level for developing economies, much larger than that for developed economies (a coefficient of 1.873 at 5 per cent significance level). This means there is about 9.172 per cent increase in economic growth given 1 per cent increase in overall insurance density for the developing economies, compared to 1.873 per cent increase in economic growth given 1 per cent increase in non-life insurance density for the developed economies. It has been shown in Table 8 that life insurance only has a significant impact on economic growth for the developing economies, not for the developed economies. There is about 2.495 per cent increase in economic growth given 1 per cent increase in life insurance density for the developing economies. As shown in Table 9, non-life insurance density is closely associated with economic growth, with a coefficient of 8.76 significant at 1 per cent level for the developing economies, much larger and significant than that for the developed economies (a coefficient of 1.309 at 5 per cent significance level). This means that there is about 8.76 per cent increase in economic growth given 1 per cent increase in non-life insurance density for the developing economies, compared to 1.309 per cent increase in economic growth given 1 per cent increase in non-life insurance density for the developed economies. As can be seen from the above analysis, for developing economies, life insurance, non-life insurance, and total insurance play a much more import role than they do for the developed economies.
In this paper, we have combined cross-sectional and time series data to examine the relationship between insurance development and economic growth in 27 economies over the period of 1994–2005. We used GMM models on dynamic panel data to conclude that there is fairly strong evidence in favour of the hypothesis that insurance development contributes to economic growth. This relationship is more significant for non-life insurance than for life insurance. We then divide the economies into two groups and compare the different roles of insurance in the developed and developing economies. The result indicates that insurance, including life insurance and non-life insurance business, play a much more important function in developing economies than they do in developed countries. Through the combination of the results of Beck and Levine19 and this paper, we can argue that overall financial development containing stock markets, banks and insurance is significantly correlated with economic growth. The empirical results of this paper suggest that non-life insurance is of great importance for economic growth in developing countries and should be strengthened in these countries.
Thanks to the anonymous referee to point this out.
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We use logarithm of insurance density and Beck and Levine (2004) use logarithm of banking credit. So it should be interpreted with precautions when comparing two different measures.
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Han, L., Li, D., Moshirian, F. et al. Insurance Development and Economic Growth. Geneva Pap Risk Insur Issues Pract 35, 183–199 (2010). https://doi.org/10.1057/gpp.2010.4
- insurance development
- economic growth
- dynamic panel data