Using panel data of 39 countries over the period 1979–2007, this paper is the first to empirically examine the influence of the KOF index of globalisation (overall and its three main sub-indices) on the development and convergence of international life insurance markets by a panel cointegration technique. We find that globalisation has a significant impact on the development of international life insurance markets and on reducing the deviation between individual countries’ life insurance penetration and the world average. Economic and social dimensions exert a similar effect as well, and the effect of economic globalisation is higher, while the effect of political dimension is not significant. In addition, social globalisation plays a dominant role on the interactive influence of different dimensions of globalisation, implying that socio-cultural factors are a latent factor behind economic or political influence. Finally, most countries’ structural breaks coincide with the fast growth wave of international life insurance markets. International life insurance.
Since globalisation is a trend in world development, a vast number of studies have sought to demonstrate whether globalisation is beneficial to economic or financial performances, though most previous ones only consider the sub-dimensions of globalisation such as openness to trade flows, capital mobility or the economic aspect. To our knowledge, no published literature has so far investigated empirical evidence on the relationship between a broad measure of globalisation and the activities of insurance markets. This paper herein is the first to investigate the influence of globalisation on the development and convergence of international life insurance markets using the panel data cointegration technique.
Using data of 39 countries over the period 1979–2007, we evaluate the effect of overall globalisation as well as of different dimensions of globalisation, including economic, social and political dimensions, on the development and the deviation of life insurance across countries. This paper emphasises that globalisation is not only an economic phenomenon, but that it is also important to take into consideration a variety of different aspects associated with global development.
This paper contributes to the literature in the following ways. First, we apply a new panel technique to examine the long-run relationship between life insurance markets and the indices of globalisation. Employing panel data econometrics allows us to take into consideration the presence of heterogeneity in the estimated parameters and dynamics across countries and increases the reliability of the findings resulting from cross-country analysis. Unlike the time-series or traditional static panel data approach, the panel cointegration model selectively pools the long-run information contained in the panel but also allows the short-run dynamics and fixed effects to be heterogeneous among different countries within the panel.
We further utilise the panel cointegration model with structural breaks extended by Westerlund
to examine if there are structural changes in life insurance markets worldwide.
Next, existing studies document the possible influence of globalisation on insurance development from points of theoretical description,
and we empirically verify the impact of globalisation. Finally, an analysis on alternative dimensions of globalisation enables us to identify which dimension of globalisation plays a more important role on life insurance development and how various aspects of globalisation influence each other. One of our findings—the interaction term of social globalisation has a significant impact on the specification of the economic or political dimension—implies that socio-cultural factors may play a latently moderate role behind the influence of economic and political aspects. This finding is somewhat consistent with Pepinsky's
notion that the route towards financial development cannot deviate from the broader social and political structures in which economic action takes place. In a robust analysis, we show that the effect of globalisation is unaltered even after controlling for the influence of economic development, that is, the globalisation does affect the development of life insurance markets but not merely proxy for economic development.
Understanding the influence of globalisation on the development and the convergence of international life insurance markets is quite beneficial. From the position of policymakers, globalisation is associated with liberalisation or openness related to economic, political or socio-cultural aspects within the economy. If globalisation impacts life insurance markets, then policymakers’ attitude towards globalisation may in turn indirectly affect the markets. For the insurance industry, if globalisation does promote life insurance markets’ development, then insurers can focus on under-developed life insurance markets, because it implies the existence of opportunities if globalisation can promote the convergence of international life insurance markets, that is, towards the world average.
To this end, we first estimate a world life insurance growth curve based on life insurance penetration across countries. As it is difficult to estimate life insurance penetration worldwide, we adopt Enz's
world insurance growth model to evaluate average penetrations around the world. The world life insurance growth curve, evaluated by a logistic function, is more appropriate than the simple international average method in that it takes into account the non-linear relationship between income and life insurance consumption. A smaller deviation value represents a lower difference of a country's life insurance penetration from the world's insurance penetration, indicating the convergence in international life insurance markets. We then attempt to investigate if globalisation could reduce the deviation level and which type of globalisation plays the most important role.
As factors that influence the development of life insurance markets come from economic and socio-cultural aspects,
we examine the effect of overall globalisation as well as of sub-dimensions, including economic, social and political dimensions. Using the KOF index of globalisation developed by Dreher,
we find that globalisation exerts a significant effect on life insurance markets’ development and on the reduction of the deviation in international life insurance markets. Among the sub-indices, economic and social dimensions individually have a similar influence, and economic globalisation has a higher effect. The political factor, however, neither influences life insurance development nor does it lead to a reduction in the deviation of life insurance markets across countries. Furthermore, in an interactive analysis the interaction term of social aspect exerts a significant influence on the economic or political specification, whose interaction term does not, however, have a significant effect in social specification. Finally, in a panel cointegration test with multiple structural breaks, we find that most countries have structural break points coincident with the fast growth period of the world life insurance market—a phenomenon that might be derived from globalisation.
The paper proceeds as follows. The next section discusses the relation between globalisation and the development of international life insurance markets. The subsequent section describes the econometric methods and the data. The penultimate section presents the empirical findings and implications, while the final section presents the conclusions.
Globalisation and international life insurance markets
document that global similarities such as deregulation, intensification of competition and, generally, rapid growth in insurance sales in insurance markets worldwide are reshaping insurance markets. Several homogeneous trends bring about a closer association of international insurance markets, including the similarity of insurance products, the emergence of innovative products such as universal life insurance and variable life insurance and annuity, the frequent occurrence of global risks, the revolution in information technology, the growing importance of supranational agencies such as the World Bank and the World Trade Organization (WTO), and so on.
Based on Cummins and Venard's
argument, we infer that globalisation could gradually reduce the difference in the development of insurance markets across countries. Enz
indicates that increased globalisation in the insurance sector might bring about an integration towards the world average. Di Vaio and Enflo
show that globalisation has been accompanied by convergence of per capita income. As income is the most direct factor and exerts a positive impact on the purchase decision of insurance, it could be conjectured that the difference of insurance consumption across countries should diminish with the convergence of income level.
In addition to economic development level, factors in economic, socio-cultural and legal and political aspects affect the development of insurance markets.
Some similar development trends among these factors may diminish the difference of life insurance development across countries. An expected trend that could bring life insurance markets worldwide to a similar avenue in the future comes from the demographic factor—ageing populations, which in turn might cause the change of social security systems, an economic determinant of life insurance demand. A well-developed social security system leads to less demand for life insurance and annuities. The ageing populations, however, disturb standard social security systems because fewer working age people have to shoulder the pension benefits of more and more retirees, pushing the country to introduce capital-backed pension systems that usually increase the demand for life insurance and annuities. According to the report by the United Nations, the population of older persons is growing globally at a rate of 2.6 per cent per year, considerably faster than the population as a whole with a growth rate of 1.2 per cent annually.
The higher growth of the older population is expected to persist at least until 2050. Such rapid growth will necessitate extensive economic and social adjustments in most countries. Moreover, the potential support ratio (the number of persons aged 15–64 years for each older person aged 65 years or over) declined from 12 to nine potential workers per older person between 1950 and 2009, and will drop further to four by 2050. The reduction of the ratio has important implications for social security schemes, particularly for pay-as-you-go systems. The similarity in the population development across countries would reduce differences between countries’ development of life insurance markets. As these trends are related to social or economic aspects within a country, this paper provides consistent findings that globalisation in social and economic dimensions are more important in reducing the difference of life insurance markets across countries.
Another important trend is the increased importance of insurance markets in developing and emerging countries due to the high growth potential of their insurance markets. Zheng et al.
indicate that the relative level of insurance growth in developing markets has increased and that the insurance industry in emerging markets and Brazil, Russia, India and China is even undergoing rapid development and has achieved a relatively high level. Berry-Stölzle et al.
note that insurance market growth rates in emerging markets exceed those available in most developed countries, and high growth rates have attracted new and existing firms to these markets. Liberalisation and openness have raised the attraction of these markets, as demonstrated by Arena,
Aetna life insurance company
who argues that the process of financial liberalisation has accelerated growth in insurance market activity during the last decade, particularly in emerging markets, and by Ma and Pope,
who document that trade liberalisation is one important factor for the host country to attract international life insurers’ participation. We exemplify two important growth markets, China and India, to illustrate more clearly the possible effects of globalisation or openness on the development of life insurance markets.
indicate that the development of insurance markets in China can be divided into three stages: resumption and restoration (1980–1985), market-oriented reform (1986–1991) and opening up and rapid growth (1992–present). China began to open its insurance market in 1992. The average annual growth rate of life insurance premiums over the period 1992–2009 is 28.94 per cent compared to 21.74 per cent over the period 1986–1991.
After China's entry into the WTO in 2001, many restrictions to foreign insurers were relaxed after the effectiveness of The Regulations on Administration of Foreign-Funded Insurance Companies in 2002. The annual growth rate over the period 2002–2009 is 23.19 per cent. The figures show that openness benefits the growth of life insurance markets.
Many international insurance companies started to enter China's insurance market in a faster pace after the opening of the market. In 2001, approximately only ten subsidiaries of foreign insurers or joint ventures in the life insurance sector operated in China, with a market share of about 1.94 per cent in premium.
In 2009, the number of foreign life insurers was 28 (59 life insurers in total), most of which are from developed economies such as Canada, the Netherlands, the United States, the United Kingdom, etc., with a market share of 5.23 per cent.
International insurers will introduce more innovative products into the markets, which may further stimulate the demand for insurance.
The effect of liberalisation on insurance development could also be observed in another fast growth market, India. Before 1999, the life insurance market in India is dominated by the Life Insurance Corporation (LIC), a nationalised monopoly created by merging all life insurers in 1956.
In 1999, the passing of the Insurance Regulatory and Development Authority Act repealed the monopoly position of LIC. New licences were granted and the private sector was allowed into the insurance business in 2000. The average annual growth rate of life insurance premiums increased substantially from 8.67 per cent over the period 1979–1999 to 20.78 per cent over the period 2000–2009. Although joint venture is the major access to the market because of the limitation of ownership up to 26 per cent, foreign companies from developed countries, for example, AIG, Allianz, ING, MetLife, etc., have entered the market since the opening. The number of private life insurance companies, the majority of which have foreign partners, increased from 12 in 2003
to 23 in 2011. The market share of private companies in the new underwritten business was only 2 per cent in the fiscal year 2001–2002 because LIC possessed the share of 98 per cent,
but the share increased to 36 per cent as of January 2008.
The openness promotes growth and brings about changes for the life insurance market in India. The government is examining the possibility of increasing the share of foreign ownership to 49 per cent.
It should be expected that the market will attract more foreign insurers in the future if this happens due to the high growth economy and the huge population.
Methodology and data
The sample used consists of time-series data on 39 countries over the 1979–2007 period.
The measure of globalisation takes the index recently developed by the KOF database of the Swiss Economic Institute (“Konjunkturforschungsstelle”), proposed by Dreher
and updated in Dreher et al.'s study
(see Table A1 of Appendix ). The 2009 KOF index of globalisation calculates an overall index (GLOB) as well as the three main dimensions of globalisation, including economic (ECO), social (SOC) and political (POL), as detailed in Dreher et al.'s study
Higher values represent great globalisation, and all globalisation indices range between 0 (not globalised) and 100 (globalised). Data for real GDP per capita (in 2000 U.S. dollars) are obtained from World Development Indicators. Life insurance penetration (INS) data are obtained from Financial Structure Data set established by Beck and Demirgüç-Kunt
who took insurance premiums data from Sigma reported by the Swiss Reinsurance Company.
The KOF index of globalisation has been used to investigate the relationship between globalisation and economic growth.
It has also been widely used to examine the impact of globalisation on social and economic dimensions of the country, including human welfare and quality of life, socio-cultural development, economic policy and so on.
Several globalisation indices have been proposed but are not adopted due to their limitations. The World Markets Research Centre G-Index presented by Randolph
is excluded because it focuses only on the economic aspects of the globalisation process.
One oft-adopted multidimensional globalisation index is the A.T. Kearny/ Foreign Policy measure. This measure is, however, criticised for its ad hoc procedure of determining the weights of its components
and the omission of important dimensions.
notes that variables in economic dimension of the A.T. Kearny/Foreign Policy measure determine 50 per cent of the value of the overall index, which may impair its multidimensionality. Another multidimensional index is the CSGR globalisation index prepared by Lockwood and Redoano,
which is not utilised because currently its data are updated only up to 2004.
Deviation of life insurance markets
The growth in insurance premiums in an economy correlates intimately with GDP growth. The literature has indicated that the insurance premium grows non-linearly with the growth of GDP rather than in a linear pattern. Carter and Dickinson
developed a logistic model to portray the relationship between insurance penetration and GDP per capita. The logistic model is also called “the S-curve model” since it is characterised by the shape of the letter “S”. The S-curve model depicts that insurance penetration grows slowly at a low income level, but expands quickly after the income attains a certain level, and then maintains a plateau as an even higher income level is realised. This pattern is more consistent with the reality than the assumption embedded in the linear model. We thus estimate the world insurance development curve based on the logistic model.
Two alternative models can also be applied to measure insurance growth: the simple linear model and the logarithmic linear model. Zheng et al.
indicate that the two methods have some obvious limitations due to unrealistic assumptions and thus cannot be applied in a general model. The simple linear model assumes an identical growth rate for insurance premium and GDP, and the logarithmic linear model assumes a constant income elasticity of premium as well as a constant income elasticity of penetration. In contrast, the logistic model or the S-curve model considers the non-linear effect of income on the demand for insurance. Although the logistic model ignores other factors such as social, political, cultural and demographic aspects, it is still preferable because studies have shown that the growth and development of the overall economy is the key factor for insurance growth in the long term.
We define the deviation of life insurance markets across countries as the difference, in absolute terms, between actual life insurance penetration and the estimated world average penetration. We interpret the reduction of this deviation as the convergence of international life insurance markets. In economics or financial literature, one usual used measure of income convergence is sigma convergence, meaning that the dispersion of real per capita GDP levels of a group of economies tends to decrease over time.
Our definition here is different, but similar in spirit.
Panel unit-root tests
relax the assumption of the Levin, Lin and Chu (LLC) test by allowing β
to vary across units under the alternative hypothesis, which implies that some or all of the individual series are stationary. The IPS test is more general as it allows for heterogeneity in the autoregressive coefficients across panel members. Heterogeneity results from the differences in country-specific characteristics.
test has the advantage over the IPS test, because it does not depend on different lag lengths in the individual ADF regression.
Panel cointegration tests
proposes two types of panel cointegration tests. One is based on the within-dimension approach (panel test), including four test statistics: the panel v-statistic, the panel r-statistic, the panel PP-statistic, and the panel ADF-statistic; the other is based on the between-dimension approach (group test), including three statistics: the group r-statistic, the group PP-statistic and the group ADF-statistic. These statistics are calculated by simply averaging the separately estimated coefficients for each member. All test statistics are distributed as standard normal asymptotically. The panel v-statistic is related to a one-sided test in which large positive values reject the null of no cointegration. The remaining statistics diverge to negative infinitely, indicating that large negative values reject the null. The critical values are tabulated in Pedroni's study.
Fully modified OLS approach
Panel cointegration test with structural breaks
Table 1 reports the average as well as the rank of globalisation indices and life insurance penetration. The results indicate that countries with higher globalisation have higher life insurance penetration. As can be seen, most countries with an overall globalisation index (GLOB) above the median country (Greece) also have life insurance penetration above the median country (Malaysia), except for Austria, Spain, New Zealand and Italy. Hence, globalisation should have some positive association with life insurance development.
Table 2 shows the estimated parameters of Eq. (1) for the S-curve model, all of which are significant at the significance level of 1 per cent, based on the logistic function of Enz.
All parameters are significantly different from zero; C
is less than 1, which implies penetration increases with real GDP per capita. According to these estimates, we calculate the world life insurance growth curve and then subtract it from actual penetrations of the country to obtain the deviation, in absolute terms (DEV).
Some conjectures emerge for the plots above. For life insurance consumption, the economic factors, for example income level, should have the most direct effect. Hence, a reduction in the difference of income level may have some impact on decreasing the divergence of life insurance development across countries. However, socio-cultural or political factors may have some interference. For instance, Muslim-dominated countries with high-income levels such as the United Arab Emirates, Qatar and Kuwait have life insurance penetration below the global average since religious opposition to life insurance still remains in these countries.
This implies that the influence in socio-cultural aspects may neutralise the effect of economic aspects. Therefore, the interactive influence between alternative dimensions within the country complicates the impact of globalisation. As the KOF overall index of globalisation is derived from the three sub-indices, a non-linear pattern may result from the impact of the social index.
Socio-cultural aspects should be the most complicated dimension, which could be justified by Dreher
who argues that the flow of information and ideas encompasses the hardest dimensions to pin down when evaluating globalisation and by Keohane and Nye
who contend that these aspects consist of the most pervasive form of globalism. Put another way, the non-linear pattern above may reflect the sophisticated interactive relation among sub-dimensions of globalisation. Steger
notes that globalisation is “a multidimensional set of social processes that create, multiply, stretch and intensify worldwide social interdependencies and exchanges while at the same time fostering in people a growing awareness of deepening connections between the local and the distant”. We investigate the interactive effect between sub-indices of globalisation in a later section.
Panel unit-root test results
To avoid spurious regressions, the first necessary step is to test whether the data is stationary or not. Table 3 presents the results from the panel unit-root tests at the 1 per cent significance level based on the studies of Levin et al.
the ADF-Fisher Chi-square, and the PP-Fisher Chi-square. All statistics strongly show that the six variables—INS, DEV, GLOB, ECO, SOC and POL—have a unit root, implying that all variables follow the I(1) process at 1 per cent significance levels. To determine whether there exists a long-run relationship between the convergence of international life insurance penetrations and globalisation, the panel cointegration test is performed.
Globalisation and the development of life insurance markets
Table 4 reports the panel cointegration test results between globalisation and life insurance penetration (INS). At first glance, the overall test statistics show a weak relationship between globalisation and life insurance development. GLOB and ECO have significant coefficients on two ADF-type tests and SOC has one above the 10 per cent significance level, but no statistic is significant on POL. The relationship is also weak as the three sub-indices are incorporated together as independent variables (denoted as EPS), possibly due to the offsetting effect among sub-indices. The results indicate that globalisation in the long run is correlated with life insurance development and that among the three sub-indices, economic globalisation has the most significant influence on life insurance development, while the impact of political globalisation is the weakest. We analyse how the long-run relationship evolves through FMOLS estimation based on the significance of two ADF-type tests (Models 1 and 2 of Table 4).
As Table 5 shows, both GLOB and ECO exert a significant positive effect on the development of life insurance markets. Hence, in the long run the development of life insurance rises with the increase of globalisation, and the economic factor has a more direct effect than social and political dimensions.
Globalisation and the convergence of international life insurance markets
Table 6 reports the panel cointegration test results for the deviation of international life insurance markets (DEV) and globalisation. All test statistics for the overall globalisation index reject the null hypothesis of no cointegration at the 10 per cent significance level at least. The evidence is slightly weak when we consider the sub-indices of globalisation. Six out of the seven statistics, except for the group ρ statistic, significantly reject the null hypothesis of no cointegration relationship between the deviation of life insurance markets and economic and social globalisation above the 5 per cent significance level, respectively. The cointegration relationship is even weaker when political globalisation is considered or when the three sub-indices are incorporated together in the cointegrating structure, with five and four statistics being statistically significant above the 10 per cent level, respectively.
Small business insurance
Table 7 presents the FMOLS estimation results. The panel parameter for GLOB is significantly negative at the significance level of 1 per cent, with the coefficient estimator −0.64 indicating that a 1 per cent increase in the degree of globalisation leads to a 0.64 per cent decrease in the difference between one country's life insurance penetration and the world average. This suggests that the deviation among international life insurance markets diminishes with the evolution of globalisation or, put differently, that globalisation is helpful for the convergence of international life insurance markets in the long run.
To further examine the divergent effect of various dimensions of globalisation, we replicate the analysis with the sub-indices. As shown in Models 2–4 of Table 7, economic and social aspects have a significantly negative coefficient on the deviation of life insurance development at the 1 per cent level, but the coefficient of political globalisation is not significant. This implies that globalisation through economic and social dimensions should exert an impact on the convergence of life insurance markets across countries. The coefficient estimator in economic globalisation is −0.52, indicating that a 1 per cent increase in economic globalisation leads to a 0.52 per cent decrease in the divergence of the world life insurance market as compared to 0.49 per cent in social globalisation. The largest coefficient in absolute terms suggests that economic globalisation has a more significant effect on the convergence of the world life insurance market when alternative globalisation is considered individually. This may be because economic conditions, for example, income level or the degree of development, exert a more direct effect on life insurance consumption compared to other factors.
As economic, social and political dimensions within the system may interact with each other, we examine the joint effect by analysing the model with the three sub-indices as independent variables. As Model 5 in Table 7 shows, social globalisation still has a significantly negative coefficient on the deviation of international life insurance markets at the significance level of 1 per cent. However, the effect of economic factor is no longer significant and political globalisation now has a significant effect. This implies that the effect in alternative dimensions may offset one another. We further perform an interactive analysis to reveal the interrelationship between them more clearly.
The interactive effect of different dimensions of globalisation
note that economic, cultural and political dimensions of globalisation potentially reinforce each other. To assess the interdependence, we perform FMOLS technique on the sub-indices of globalisation and various interaction terms.
Doing so allows us to evaluate the direct effect as well as the indirect effect from alternative indices, both of which consist of the total effect.
As shown in Table 8, three findings are observed. First, the direct effect of social globalisation is significantly negative, but the indirect effect from the other two sub-indices is not significant, and the total effect is negative. As Models 3 and 4 presented, an increase in social globalisation results in a decrease in the deviation of international life insurance markets at the significance level of 5 per cent, but the interaction terms (ECO × SOC and SOC × POL) enter insignificantly. This means social globalisation uniformly exerts a promotion effect on the convergence of the world life insurance markets, and the remainder two dimensions have no significance influence on such an important role played by social globalisation.
Second, when incorporating the influence of social globalisation, the effects of economic and political globalisations are positive, but the effect is tempered by social globalisation, as shown in Models 1 and 6. This implies that social globalisation plays a moderating role on the influence of economic and political globalisations on the convergence of the world life insurance markets.
Third, the effects of economic and political globalisations are not clearly identified when the indirect effects from the alternative dimension are considered, as shown in Models 2 and 5. The direct effect of economic globalisation is negative and the indirect effect from the political dimension is negative as well, with a negative total effect. The direct effect of political globalisation is positive and the indirect effect from the economic dimension is negative, with a positive total effect. Nevertheless, none of their coefficients is statistically significant.
Does the globalisation proxy for economic development?
Some suspicion may emerge that it is economic development but not globalisation that promotes the development of life insurance markets because of the close association between globalisation and economic development, that is, globalisation index is only a proxy of economic development. To address this issue, we replicate the analyses above by controlling for economic development, proxied by GDP per capita (ED), to see if the effect of the globalisation index alters. As Table 9 shows, the findings on globalisation indices are qualitatively similar.
The overall index as well as sub-dimensions of globalisation is significantly and positively correlated with life insurance development at the 1 per cent significance level even after the inclusion of the measure of economic development, as shown in Panel A of Table 9. Panel B shows that the overall index and social dimension exert a positive impact in reducing the deviation of life insurance markets worldwide, although the effect of economic and political dimensions is insignificant. This finding somewhat corresponds to the results in Table 8 that social globalisation plays a more important role than do other two dimensions on reducing the deviation of international life insurance markets when we consider variables jointly.
Finally, the analysis of interaction effect between globalisation and economic development in Panel C shows that globalisation does matter in reducing the deviation of life insurance markets, but the impact is tempered by economic development, which is reflected in the positive coefficients on interaction terms. This suggests that globalisation still exerts a positive impact in reducing the difference of life insurance development across countries, even though some of the effect may be picked up by the progress of economic development.
The concept of cointegration is used to capture the notion that non-stationary variables may nonetheless possess long-run equilibrium relationships and may thus exhibit a tendency to move together in the long run.
Specifically, some reminders on the analysis above should come to mind. The cointegration analysis is an atheoretical approach, and the existence of the cointegration relationship cannot explain causal chains between variables. To be specific, if two variables are cointegrated, then one variable, say globalisation, may not impact another, say insurance development, directly but indirectly via its influence on other variables, for example, the demand for insurance or the price. Hence, we could interpret that globalisation alters the effect of some determinants on insurance development, which in turn influences the insurance market. Considering the examples of China and India noted above, globalisation or liberalisation brings about changes on factors impacting insurance development, for example, the introduction of innovative products stimulates the demand or the increased competition lowers the price, in turn affecting the insurance market. Whether a causal relationship exists between globalisation and life insurance development needs to be confirmed by more formal econometric approaches, which are beyond the focus of this paper.
The effects of multiple structural breaks
The analyses above indicate that globalisation does exert an influence in reducing the difference in the development of life insurance markets across countries. Since globalisation to some extent implies communication around the world, one may ask if the development of life insurance markets in one country corresponds to some trends in the world life insurance market. In this sub-section we further employ a panel cointegration technique with multiple structural breaks proposed by Westerlund
to examine whether structural breaks exist and whether estimated break points in one country coincide with some specific periods in the world life insurance market.
The reasons why we allow for structural breaks include the following. First, structural breaks are a common phenomenon that is usually observed in association with specific events such as political regime shifts, international conflicts, financial liberalisation and regulations. Second, considering structural breaks in empirical models allows us to obtain more detailed and meaningful information on the characteristics of globalisation. Third, an economic or financial system’s instability may unfortunately be reflected in the estimated models, such that when the models are used for inference, they can induce misleading results. Due to the interdependent influence between various dimensions of globalisation as noted above, we include the three sub-indices in the analysis of panel cointegration with structural breaks.
If we take 10 per cent as the criterion of high growth rate, then, as noted in the introduction, the international life insurance market experiences high growth in the following periods: more than 30 per cent in 1985–1987 and 1993, and more than 10 per cent in 1988, 1990, 1994, 1995, 1999, 2004 and 2006. As Table 10 shows, life insurance markets in 24 out of the 39 countries experience structural changes. Most countries (18 out of 24) have a structural break during the two periods with special high growth rates above 30 per cent. The results show that overall the estimated structural break point in most countries has happened along with the rapid growth period in the world life insurance market, implying that the life insurance development in one country may not be isolated from the world market. Globalisation, perhaps, is the propeller behind the coincidence. The results also suggest that multiple structural changes in panel cointegration relationships are important and need to be taken into account under the specifications for the relationship between globalisation and life insurance markets. Hence, the specifications, comprising changing economic and financial events, do raise some important questions concerning the long-run relationships in these series.
Two implications arise from our findings above. First, the potential interdependence between globalisation in different dimensions should not be ignored. As Model 5 in Table 7 shows, economic globalisation becomes insignificant when we include political globalisation. This could be expected since politics and economy usually influence each other. As Alesina et al.
argue, the pattern of trade openness and economic integration affects country formation and separation and vice versa.
Second, for the prominent role of social globalisation when variables are considered jointly, we provide some possible explanations. Polanyi
observes that economic orders are not segregated from broader political and social orders and further argues the “embeddedness” of economic systems in broader social orders and that the understanding of market systems cannot be isolated from the realisation of the broader social orders in which they are located. Pepinsky
indicates that the concept of embeddedness helps emphasise that the route towards financial development cannot deviate from the broader social and political structures in which economic action takes place. As life insurance is a sub-sector of the financial system, Pepinsky's argument should be applicable to life insurance markets. Our results reflect this perspective.
arguments, this paper infers that socio-cultural factors are latent elements for the development of the life insurance market. Economic factors, on the other hand, are explicit elements since higher income or improvement in economic conditions has a more direct effect on the purchase of life insurance. This can be reflected in Table 7 where globalisation in economic dimension has the largest effect on the convergence of life insurance markets in the world when the sub-dimension of globalisation is considered individually. However, when socio-cultural factors are considered together, the effect of economic factors is tempered. This can be observed from some countries’ life insurance development, for example, predominantly Muslim countries have high-income levels but smaller life insurance markets due to religious beliefs.