An international analysis of urbanizations and life insurance industry development: A new perspective

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1 An international analysis of urbanizations and life insurance industry development: A new perspective Xiaojun Shi and Zhu Yan May 23, 2015 Abstract Urbanization, as an integral process of economic development, has profound impacts on insurance industry development. how urbanization affects life-insurance industry. life-insurance industry development while some others disagree. However, the literature remains controversial on Some find that urbanization promotes This paper contributes to reexamine the urbanization and life-insurance industry development nexus from a new perspective. We argue that one shortfall of the previous literature is that it looks upon the in-fact heterogenous urbanizations across all countries as homogeneous in nature. To remedy this shortfall, we classify urbanizations into two types, namely, human-development enabling urbanization vs. disenabling urbanization by relating human-development index to urbanization at country level. Using an extended Compell s (1980) model, we derive our central hypothesis that only human-development enabling urbanization promotes life-insurance industry development. Using a large unbalanced panel dataset of 183 countries from 1990 to 2012, we find strong and robust evidence supporting this hypothesis. For the full sample, 1 percent increase in unclassified urbanization results in 2.3% increase in life-insurance depth globally. But human-development enabling urbanization causes a 31.3% increase, while disenabling urbanization has even a negative impact. Economically, we explain this result by the evidence that human capital accumulation due to human-development enabling urbanization is at the heart of life-insurance industry development. Moreover, our empirical modeling is novel by using a quadric-nominal regression method to account for the two-stage characteristic of modern service sector development including life-insurance industry. Our estimation yields an S-curve indicating life-insurance growth accelerates in the middle phase of human-development enabling urbanization. Keywords: Urbanization; Life-insurance; Human development; Human capital JEL Classification: O180 G220 O150 China Financial Policy Research Center, School of Finance, Renmin University of China, Beijing, , P.R.China. TEL: +86 (10) and [email protected]. Corresponding author. School of Finance, Renmin University of China, Beijing, , P.R.China. [email protected]. 1

2 1 Introduction This paper examines the impacts of urbanization, which is an integral process of economic development, on life-insurance industry development from a new perspective. Urbanization causes industry structure transformations, economic development and social-culture changes. It consequently has substantial impacts on service industry, including finance and insurance sectors, stimulating more demand of new financial service. But surprisingly, the literature is not consistent on how urbanization affects insurance industry development. On one hand, some find the evidence on the positive role of urbanization in promoting life-insurance industry development. Zelizer (1978) finds that urbanization is an important cause accounting for the booming life-insurance development in mid-19th century in America. Outreville (1996) finds that the decrease in agricultural population (i.e. increase in urban population) in developing countries contributes to life-insurance industry development. Huang and Gao (2003) find significant correlation between urbanization and life-insurance industry development in China which is the largest emerging economy. On the other hand, some others claim urbanization has no effect on life-insurance. Beck and Webb (2003) examine the determinants of life-insurance consumption in 68 nations and find urbanization is insignificant to life-insurance demand. Sen (2008) even finds that urbanization decreases life-insurance consumption when analyzing lifeinsurance market in 12 Asian economies as well as India. Section 2 summarizes details of the literature. We argue that the controversy in the literature is partly due to looking upon all urbanizations across countries as homogenous in nature. But, in fact, urbanizations across countries are different at least with respect to human-development enabling. A few countries undergo urbanizations labeled by mere urban population migration and building of steel-concrete jungles but contributing little to human-capital breeding and accumulation, possibly because limited resources strangle education, healthcare and services (IULA, 1986; Yusuf and Saich, 2008). Intuitionally, such steel-concrete urbanization has little effects on insurance industry development. In contrast, human-development enabling urbanizations accompanied with steady human-capital breeding and accumulation are promising in promoting life-insurance industry development. Obviously, the controversy in the literature could be solved given such a classification of urbanizations justified. Another issue in the literature regards the empirical method. Simple linear regression is employed in the literature to investigate how urbanization affects life-insurance industry development. However, recent empirical evidence on the two-stage characteristics of the modern service industry (e.g. Eichengreen and Gupta 2013) challenges the linear model. Eichengreen and Gupta (2013) find two waves of service sector growth, with the first wave rising at a decelerating rate in relatively low-income stage, and the second rising at an accelerating rate before leveling off in higher-income stage. A nonlinear model with quadric term is able to capture 2

3 the two-stage characteristic. Hence, we use a quadrinomial model, following Eichengreen and Gupta (2013), instead of simple linear model in our empirical analysis. As far as we know, we are the first to use the high-order-term model to relate life-insurance industry development to urbanization. More important, employing a high-order-term model is not only a methodological improvement but also enables us to describe the trajectory along which life-insurance industry grows with urbanization provided that the latter does cause the former. This trajectory is far from clear in the literature due to the use of simple linear model. In the empirical analysis, we employ an unbalanced panel dataset of 183 countries from 1990 to 2012 to test our central hypothesis. Our main models relate life-insurance industry development, represented by life-insurance penetration of a country, to classified urbanizations with controls under the quadrinomial model setup. Reverse-causality endogeneity is hardly a main concern in this specification because urbanization is a more generic and nation-wide decision which rarely depends on life-insurance development. But the heterogeneity across countries due to the variety in levels of economic and life-insurance industry development needs to be treated. We use a fixed-effect method to control the heterogeneity to some extent. Our first set of results classifies urbanizations across countries into two types, namely, human-development enabling vs. disenabling urbanizations. In this classification, we relate human-development index to urbanizations at country level, and group the countries having significant and positive coefficient on urbanization into the human-development enabling type, while the insignificant or negative ones into the disenabling type. Equipped with the classification of urbanizations across countries, we form a Differencein-Difference experiment to investigate how various urbanizations cause life-insurance industry development. This method has the advantage to identify causality instead of correlations. Specifically, we generate a dummy variable which equals one for the human-development enabling urbanization. The interaction term of the human-development enabling dummy and the urbanization variable is of the central interest, with urbanization and its type and the heterogeneity of countries controlled. We expect a significant and positive coefficient on the interaction term to present evidence that human-development enabling urbanization causes more growth in life-insurance industry development than disenabling urbanization. Estimation using the full sample suggests that 1% further urbanization increases life-insurance penetration by 2.3% globally. DID-estimation result indicates that human-development enabling urbanization causes a 31.3% increase, while disenabling urbanization has even negative impacts. Moreover, our main results stand robustness checks using alternative human development index, various model specifications, sub-sampling, subject to middle- and low-income countries only, and the impact of the 2008 financial crisis. Economically, we present further evidence that it is through human capital accumulation that human-development enabling ur- 3

4 banization promotes the life-insurance industry development of which the central role is to protect human capital risks. The estimation of the quadrinomial model plots an S-curve relating life-insurance industry development to urbanization. This picture indicates that the growth of the life-insurance industry accelerates in the middle of rapid urbanization phase before leveling off. For the developing countries undergoing human-development enabling urbanizations, the rapid urbanization phases also present golden eras to expand the life-insurance market. Our contribution to the literature is twofold. First, we find that not all urbanizations promote life-insurance industry development. Only human-development enabling urbanization causes life-insurance industry growth while disenabling urbanization does not. Our classification of urbanizations is economically important in exposing the central linkage of human capital breeding and accumulation via which urbanization causes life-insurance industry growth. As well, this classification is able to solve the controversy on the relationship between urbanization and life-insurance industry development in the literature. Second, our adoption of a quadrinomial regression model lends us advantage to represent the two-stage characteristics of service industry including life-insurance industry. The estimation also exhibits the trajectory of how life-insurance industry grows with urbanization. We are the first to find that life-insurance industry growth acceleration coincides with rapid urbanization, as far as we know. This sheds new light on the life-insurance industry development policies especially for developing countries. The remainder of our paper is organized as follows. Section 2 gives a specific literature review. Section 3 develops theoretical model and the central test hypothesis. Section 4 describes our empirical strategy, variable selection and sample construction. Section 5 presents the results and Section 6 concludes. 2 Literature review The literature remains inclusive on how urbanization impacts on life-insurance industry development. Some find positive evidence supporting that urbanization contributes to life-insurance industry development. Zelizer (1978) famously argues that urbanization as well as economic expansion accounts for the boom of life-insurance in mid-19th-century America. Zelizer (1978) observes that urbanization makes families atomized. Such a transformation in the structure of the family leads to the increasing dependence on salaries (i.e., return to human capital); more reliance on formal, impersonal system, such as life-insurance, to address economic and social uncertainty, rather than on informal arrangements such as aids from relatives and friends before urbanization. All these factors popularize life-insurance and this industry hence experiences rapid growth in densely populated cities. New York and Philadelphia, two cities with the greatest increase in population, also hosted the most developed life-insurance industry. Out- 4

5 reville (1996) argues that life-insurance is a substitution for deposit and hinges on disposable income and financial market development. The large share of agricultural population suffers from low income and even fluctuations due to climatic conditions and variations in agricultural commodity prices. In line with this argument, Outreville (1996) finds that the reduction in a- gricultural population leads to life-insurance industry growth in developing economies, and vice versa. It is found that a 1% increase in agricultural population is associated with 3% decrease in life-insurance premium. Similarly, Huang and Gao (2003) find significantly positive relationship between urbanization and life-insurance consumption in China, the largest developing economy. They argue that, in China, family size shrinks due to urbanization and fewer children to depend on when parents are old. As a result, the parents have to rely on themselves by accumulating financial assets including life-insurance to prepare for their retirements. Moreover, urbanization also reshapes social culture and individual values with an emphasis on the safety of household income, which increases the inclination for the urban households to purchase life-insurance. But some others find that urbanization has little effects on life-insurance industry growth. Beck and Webb (2003) test determinants of life-insurance consumption across 68 countries. They hypothesize that concentrated population helps to reduce operating costs of life-insurance companies; and less reliance on informal arrangements stimulates demand for life-insurance. But they fail to find supporting evidence from the data of 68 economies from 1961 to Sen (2008) employs a fixed effect estimation to investigate life-insurance markets in 12 Asian economies as well as India. He finds that urbanization is useful in explaining life-insurance density and penetration differences across these countries, but the correlation between urbanization and life-insurance industry development is negative. One possible reason, Sen (2008) argues, is that the migration of rural population to urban areas increases the share of the poor in cities. The inconsistent results in the literature call for a unifying method to conclude the debate. In this paper, we propose a new perspective to this end. At the heart of our method, we divide the urbanizations across countries into two types, namely, human-development enabling vs. disenabling. Our central hypothesis, as developed below by expanding Campell s (1980) model, is that enabling type urbanization promotes life-insurance industry development while the disenabling type does not. 3 Theoretical Model and Hypothesis We depart from the literature by assuming that urbanizations across countries are not at all homogenous regarding human-development enabling. The disenabling type of urbanization creates merely rural populations migration to urban areas, builds steel-concrete jungles but fails to improve the human-development enabling infrastructures such as education and training, 5

6 healthcare and employment service, and social security system. Such urbanization is incapable of raising the human capital of the country but instead may lead to worsened wealth of the migrated poor due to the loss of lands. In contrast, the human-development enabling urbanization is accompanied with steady improvements in these infrastructures, and hence steady accumulations of human capital. Accordingly, life-insurance industry grows with this accumulation to provide protections to the human capital risks. Following this reasoning, we assume human capital as a function of urbanizations and expand Campbell s (1980) classical life-insurance demand model to analyze how urbanization affects life-insurance demand. Campbell (1980) derives the demand for life-insurance under human capital uncertainty. For households without enough initial wealth, labor income uncertainty dominates capital income uncertainty. Life-insurance provides protections to human capital risk. Campbell (1980) assumes the household utility is the sum of consumption utility V and the bequest utility B, and derives the optimal demand for life-insurance to maximize households utility. Household utility is max [1 q x t]v [W x + R H x (INS)q x (1 + λ) t + [q x t]b[w x + (INS)(1 (q x )(1 + λ) t)], (1) INS where q represents the mortality probability of the wage earner; t represents time interval; λ is the loading charge in excess of fair premium; INS stands for life-insurance demand; H represents income on human capital. We assume that the accumulation of human capital is a function of urbanization: RH x = F (U r ), (2) where U r is urbanization; F (U r ) is an increasing function if urbanization benefits human development, otherwise, decreasing. Campbell (1980) expands the household utility around W x + R H x by a Taylor Series expansion, and finds the optimal insurance demand INS is: where b is short-handed coefficient. INS = R H x b[w x + R H x ], (3) By inserting (2) into (3), we have the optimal life-insurance demand with respect to urbanization as followed: INS = F (U r ) λ k k 1 A(F (U r )), (4) where A(F (U r )) = V (W x + F (U r )) V is the Pratt-Arrow absolute risk aversion coefficient with (W x + F (U r )) respect to W x + F (U r ); k = B V is the bequest intensity which is within the range of [0,1]. 6

7 The result of (4) indicates how urbanization affects life-insurance demand depends on the properties of A(F (U r )) and F (U r ) given other parameters. We assume that the representative consumer is CARA, namely, constant absolute risk aversion; or, DARA, namely, decreasing absolute risk aversion. In the human-development enabling urbanizations, we have F (U r ) > 0 and A (F (U r )) < 0 for DARA and A (F (U r )) = 0 for CARA. It is easy to find that INS (U r ) > 0 in the case of human-developing enabling urbanization. Thus, human-developing enabling urbanization promotes life-insurance demand for a CARA and DARA economy. Otherwise, in the human-development disenabling urbanizations, we have F (U r ) 0 and A (F (U r )) 0 for DARA and A (F (U r )) = 0 for CARA. In this case, INS (U r ) 0. Obviously, human-developing disenabling urbanization is incapable of increasing life-insurance demand for a CARA and DARA economy. Accordingly, we have our central hypothesis: Hypothesis: Human-development enabling urbanization has significantly positive effect on lifeinsurance industry development, while human-development disenabling urbanization has not such an effect. 4 Empirical models 4.1 Model specification We employ a two-stage empirical strategy to test the central hypothesis. In the first stage, we relate HDI (human-development index) of each country to its urbanization: HDI it = a i + b i urb it + Controls + ɛ i, (5) where HDI it is the human development index of country i at year t; urb it is the urbanization level of country i at year t. Countries with a significantly positive coefficient on urbanization are grouped into the human-development enabling urbanization type; otherwise are grouped into the disenabling type. Accordingly, a dummy (denoted as D) for the human-development enabling type is generated, which equals 1 for the enabling type countries. Our second-stage model has two features. Firstly, it is a quadrinomial model to account for the two-staged development characteristic of service industry, including life-insurance industry (Eichengreen and Gupta, 2013). The quadric terms are with respect to the economic development following Eichengreen and Gupta (2013). Secondly, we use a difference-in-difference method to find the causality from two types of urbanizations to life-insurance industry development. The central term of interest is the interaction of the urbanization type dummy with 7

8 the urbanization variable, urb it D i. Our second model is as followed: insu it = α 0 + α 1 Y it + α 2 Yit 2 + α 3 Yit 3 + α 4 Yit 4 + α 5 urb it + α 6 D i + α 7 urb it D i (6) k + α j Control it + ɛ it, where insu is life-insurance industry development in a country; Y represents log per capita income; urb is the urbanization; D is the urbanization type dummy which equals 1 given human-development enabling urbanization; Control includes controlling variables. We expect a significantly positive coefficient on the interaction term to support our central hypothesis. The significantly positive α 7 indicates that, with all other things equal, the human-development enabling urbanization causes life-insurance industry growth relative to the disenabling type. Moreover, an insignificant or negative α 5 indicates that human-development disenabling urbanization has little effects on life-insurance industry development. This DID specification has the advantage to infer the causality rather than present association when simple regression model is employed. Some more points regarding identification are worth mentioning. The potential endogeneity issue, if there were some, is largely mitigated by the causality experimental specification. Reverse causation issue is less likely in our case because urbanization process in a country is rarely affected by insurance industry. j=8 Basically, they are not at the same level of strategic importance for a country. Urbanization is more generic and overarching than insurance industry development for a country. Logically, urbanization has potential to affect the development of a specific service industry such as insurance but the reverse is not intuitional. Meanwhile, omitted variable problem is alleviated to some extent by the adoption of fixed-effect estimation. In addition, the estimation results of model (6) presented below suppress the dummy term of urbanization type because of the country fixed effect. 4.2 Variables Life-insurance industry development and urbanization The measurements of life-insurance industry development and urbanization in a country are the two central variables of interest. For the former, we use life-insurance penetration as the proxy, in consistence with previous literature such as Enz (2000), Beck and Web (2003), and Sen (2008). This is also in line with Eichengreen and Gupta (2013) who cion the term modern services industry to which life-insurance industry belongs. Urbanization is measured by the share of urban population in the total population. Such a measurement is also adopted by Beck and Web (2003); Huang and Gao (2003) and Sen (2008). 8

9 4.2.2 Measuring human development We use Human Development Index (HDI) from United Nations Development Program (UNDP) to measure human development in a country. Methodologically, UNDP s HDI is an average of three sub-indices: income index, education index and health index. Each subindex is also a composite measure constructed from related economic or demographic variables. UNDP used to employ arithmetic average. In 2010, UNDP revised the components of the index and changed arithmetic average to geometric average. The data employed in this paper are all following the revised and geometric average methodology to ensure consistency. Human-development measurement is at the heart of our classification of urbanizations. To ensure the reliability of our results, we need to address some criticisms on the UNDP s HDI despite its increasing popularity and methodological improvements already made. Pinar et al. (2013) rigorously criticize that UNDP s HDI has two shortcomings. Firstly, it does not take the dependence among different components into account. And, secondly, equal weights assigned to three sub-indexes are at odd with the reality. Consequently, Pinar et al. (2013) rebuild the HDI using a stochastic dominance approach. They assign , and to the education, GDP and life expectancy sub-index respectively. Following Pinar et al. (2013), we use their HDI results to check the robustness of our main results Control variables Control variables are chosen according to the literature. Lewis (1989) suggests that household s demographic structure is a determinant of the demand for life-insurance. Truett and Truett (1990) and Browne and Kim (1993) find that dependency ratio affects life-insurance demand. Accordingly, we use population growth, young dependency ratio, old dependency ratio and life expectancy as the demographic control variables. Following Beck and Web (2003), we employ secondary enrollment rate to control further education effects. Moreover, financial development is also controlled following Outreville (1996). Similar to Outreville (1996), we use the ratio of M2 to GDP as the proxy of financial deepening. Furthermore, external influence of the government is controlled to account for the heterogeneity of the quality of governments across the sampled countries. Following Nandha and Smyth (2013), we use the geometric mean of six Worldwide Governance Indicators, namely, control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, rule of law and voice and accountability, to control governmental heterogeneity. 4.3 The data Our sample covers 183 countries from 1990 to The data are collected from various sources including World Bank database and UNDP database. Basically, we use the list of 9

10 countries/areas of the UNDP database to match the data from World Development Indicators (WDI) database so as to keep as many observations with data on HDI as possible. Eventually, we have a 183 country-level dataset. Life-insurance penetration data are from Global Financial Development (GFD) database of World Bank. Table 1 defines the variables used in this paper and lists the data sources as well. Table 2 presents the summary statistics of the variables. 5 Results 5.1 Model specification test Similar to Eichengreen and Gupta (2013), we use the lowess plot to validate the nonlinear modeling relating life-insurance penetration to the log GDP per capita. Figure 1 exhibits non-linear pattern and high-order correlation. This necessitates a high-order nonlinear model to capture the regularity of life-insurance industry development with economic development. Moreover, the result of the Hausman Test (with p-value of ) indicates a fixed-effect estimation is appropriate. ================Insert Figure 1 around here================= 5.2 Empirical Results Human-development enabling urbanization causes life-insurance industry growth The estimation of Model (5) finds that 147 among the sampled 183 countries urbanize with significant bettering in human-development, while 16 countries witnessing significantly negative effects and the remaining 25 countries having insignificant effects. This result indicates that urbanizations are different across countries in the nature with respect to human-development enabling. Table 2 presents the estimation results of Model (6). The parsimonious model estimation in column [1] suggests that urbanization has positive effects on life-insurance industry development globally. But the effect is not sizable with a small coefficient of 2.3% on the urbanization. This result is consistent with the strand of the literature supporting the promotion effect of urbanization on life-insurance industry development. The DID estimation result in column [2] strongly supports our central hypothesis. The coefficient on the interaction term of urbanization type and the urbanization variable is sizable and significantly positive. This suggests that the human-development enabling type of urbanization causes life-insurance industry growth relative to the disenabling type, ceteris paribus. Quantitatively, a 1% increase in human-development enabling urbanization causes 31.3% more 10

11 increase in life-insurance penetration than the disenabling urbanization. Moreover, the urbanization variable has a significantly negative coefficient in column [2]. This suggests that human-development disenabling urbanization has no effects on promoting life-insurance industry development. Even worse, the disenabling urbanization may hinder life-insurance industry development. We further subject our hypothesis to a stricter test using only middle- and low-income countries. This test also helps address the potential concern that both high levels of urbanization and life-insurance development in high-income countries may render the evidence using the full sample unreliable. In other words, limiting the sample to middle- and low-income countries makes the DID estimation more convincing, because these sub-sample countries resemble more except the aspect of urbanization than the full sample countries. A significant positive coefficient on the cross-term here presents cleaner evidence on the causality from enabling type urbanization to life insurance industry development. Columns [3] and [4] of Table 2 present the estimation results. Again, urbanization has significantly positive effects on life-insurance industry in the middle and low income countries. But the effect is smaller than the global effect. It reduces to 1.3%. However, the DID estimation indicates that the life-insurance industry promotion effect of human-development enabling urbanization is more pronounced in the middle and low income countries. Column [4] presents a larger coefficient on the interaction term. It increases to 48.8%. This result not only presents compelling evidence supporting our central hypothesis, but also has important policy implication. It suggests that, in middle and low income countries, human-development enabling urbanization causes life-insurance industry to grow at a more rapid pace than in high-income countries. ================Insert Table 2 around here================= ================Insert Table 3 around here================= Equipped with these estimation results, we are able to plot the trajectory of life-insurance industry development with respect to urbanization. Figures 2 and 3 plot the trajectories in human-development enabling vs. disenabling countries respectively. Overall, life-insurance penetration in human-development enabling countries is generally larger than the disenabling countries. Life-insurance industry development exhibits strikingly different patterns for humandevelopment enabling vs. disenabling countries. In the former, life-insurance industry grows with urbanization in a slight S-curve shape. This pattern suggests that in the middle of the urbanization process in these countries, the life-insurance industry has a golden opportunity of accelerating its growth. Moreover, this result adds to the literature on the insurance industry related to economic growth, such as Enz (2000) who presents also an S-curve of insurance penetration relative to GDP per capita. However, ours is life-insurance penetration relative to urbanization in a sort of countries. 11

12 In contrast, the pattern in the disenabling type countries exhibits an unattractive U-shape. In this pattern, life-insurance industry may even shrink in the first stage of urbanization and then recovers to the previously developed level in the second stage. In the long run, lifeinsurance industry remains in the same level without further development, despite urbanization with concrete jungles built. ================Insert Figure 2 around here================= ================Insert Figure 3 around here================= The economic significance So far, we have statistical evidence supporting our central hypothesis. Economically, we need to know the linkage between urbanization and life-insurance industry development. According to our theoretical model, human capital is at the heart of the linkage. This section tests this human capital accumulation linkage of urbanization causing life-insurance industry development. We use the average monthly salary of a country as the proxy of human capital. We collect the salary data from the database of International Labour Organization (ILO). The countries with only one-year data are dropped. All the salary data are equalized to the month base and deflated to the baseline year We test the human capital accumulation linkage in two steps. Firstly, we relate the logarithm of the average monthly salary to urbanization and the interaction term of urbanization and the human-development enabling dummy with other controls. Table 4 presents the estimation results and gives a large-sized positive and significant coefficient on the interaction term. Moreover, the coefficient on the urbanization is negative. These results suggest that, indeed, only the human-development enabling urbanization benefits the accumulation of human capital. In the second step, we use a quadrinomial specification, very similar to Model (6), relating life-insurance penetration to our human capital variable. In both parsimonious and full models, we have consistent results of sizable and significantly positive coefficient on the human capital variable. Table 5 confirms that our choice of the salary proxy is appropriate as well. In combination, results in Table 4 and 5 suggest that human-development enabling urbanization promotes life-insurance industry development due to its role in improving the accumulations of human capital. While the disenabling urbanization fails in human capital bettering and accumulation and hence lose the opportunity to expand the life-insurance industry. ================Insert Table 4 around here================= ================Insert Table 5 around here================= 12

13 5.2.3 Robustness tests An array of robustness checks are in order. Firstly, we check the robustness of our main results to the alternative HDI by Pinar et al. (2013). The advantage of the alternative HDI lies in the stochastic dominance method which is promising in addressing, to some extent, the issues of equal weights and not considering correlations in the UNDP s HDI. The alternative HDI assigns , and to the weights on education, income and health indexes, respectively. Estimation of model (5) using the alternative HDI finds urbanization in 140 countries having significantly positive effect on human development, while 14 countries having negative effect and 25 countries having no effect. In using the alternative HDI, 170 countries in total remain in the estimation due to data availability. Estimation of Model (6) using the alternative HDI is presented in Table 6. Without surprise, the results in Table 6 are similar to Table 3. The significantly positive coefficient on the interaction term of urbanization and the human-development enabling dummy as well as the significantly negative coefficient on the urbanization presents robustly supporting evidence for our central hypothesis. The alternative HDI only results in slight changes in the sizes of the coefficients. As in the main results, we also subject the test using the alternative HDI to the middle and low income countries only. Columns [3] and [4] in Table 6 present the results. Again, the coefficient on the interaction term is significantly positive and the one on urbanization is significantly negative. Moreover, the coefficient on the interaction term becomes larger than using the full sample, as is the case in Table 3. In sum, Table 6 presents strong evidence that our main results are robust to the alternative HDI measurement claimed to be more rigorous. ================Insert Table 6 around here================= Secondly, we check the robustness of our main results to the alternative regression specification. We degenerate the high-order model to the simple linear regression. Table 7 presents the results. Columns [1], [3] and [5] estimate the parsimonious specification and columns [1], [4] and [6] estimate the full model including the interaction term. Alternative HDI is also used again in this set of checks. Obviously, our central hypothesis stands this simplified model specification. ================Insert Table 7 around here================= Thirdly, we remove the outliers in the sample to check the robustness of our main results to a cleaned sample. We drop 8 countries with highest level of urbanization and lowest level of urbanization respectively. Using the data of the remaining countries, we estimate Model (6) employing UNPD s and the alternative HDI respectively. Table 8 presents the results. In all cases, our main results remain robustly. ================Insert Table 8 around here================= 13

14 Finally, we use the 2008 Global Financial Crisis (GFC) as a natural experiment to check the robustness of our main results. The GFC causes global economic recession and hence has substantially impeding impacts on the undergoing urbanization processes. As our sample period is as of 2012 when the prolonged effects of the GFC is still witnessed, we form a Crisis Dummy which equals 1 for years before 2008 and 0 otherwise. Given the robustness of our main results, we expect that human-development enabling urbanizing countries experience a slowdown in life insurance industry development due to the GFC. To check this, a triple-difference, namely, urb it HDIDummy i CrisisDummy it, is added to Model 6 with the crisis dummy controlled as well. The estimation of this full-fledged model is presented in Table 9. As before, a sub-sample of middle- and low-income countries is also used to present further results. The coefficients on the main explanatory variables exhibit similar results to our main results in Table 3. We have a significantly negative coefficient on the triple-difference term using the full sample. This indicates that, globally, the GFC has a significantly negative effect on the development of life insurance industry in the human-development enabling countries, as expected. More interestingly, our sub-sample estimation suggests that, in middle- and low-income countries, the GFC has insignificant impacts on urbanization and life insurance development nexus. This result is intuitional because the GFC are largely a game of rich countries mainly involving US and Europe but the middle- and low-income countries are kept a distance from the epicenter of this turmoil. Hence, the direct impacts of the GFC on the middle- and low-income countries are less pronounced and this reflects in the insignificance on life insurance industry development. ================Insert Table 9 around here================= 6 Conclusion How urbanization is related to life-insurance industry development is inclusive in the literature. We argue that this inclusiveness is due to the literature taking urbanizations across countries as homogenous in human-development nature. But our empirical evidence indicates that urbanizations in some countries are human-development enabling while others are not. Consequently, we propose a new perspective to understand the relationship between urbanizations and life-insurance industry development and present international evidence. Our maim point is to divide urbanizations across countries into two types: the human-development enabling vs. disenabling type urbanization. The former is a process of urbanization accompanying with substantial improvement and accumulation of human capital, while the latter is merely urbanization with concrete jungles and population migration but little bettering of human capital. Our central hypothesis, derived from an extended Compell s (1980) model, is that human-development enabling urbanization promotes life-insurance industry development but the disenabling one does not. Using a large dataset of 183 countries covering 23 years, we 14

15 present statistically significant and economically substantial evidence supporting our central hypothesis. Our results are also policy relevant for urbanization and life-insurance industry development in the developing countries. The quadrinomial model employed in this paper lends us advantage to plot the trajectory of life-insurance industry development related to urbanizations across countries. We find the presence of a golden period of life-insurance industry development in the middle of human-development enabling urbanization. During this period, life-insurance industry grows at an accelerating pace with urbanization. This suggests, firstly, urbanization with the focus on human capital improvement and accumulation is in order for developing countries; and secondly, seize the middle phase of urbanization to grow life-insurance industry and hence provide effective protections on human capital risks. References [1] Beck, T., Webb, I., 2003, Economic, Demographic, and Institutional Determinants of life-insurance Consumption across Countries, The World Bank Economic Review, Vol. 17, No.1, pp: [2] Browne, M. J., K. Kim, 1993, An International Analysis of life-insurance Demand, The Journal of Risk and Insurance, 60(4): [3] Chen et al., 2006, Human capital, assets allocation, and life-insurance.financial Analysts Journal, 62: [4] Combell, RA., 1980, The demand for life-insurance: An application of the economics of uncertainty, Journal of Finance, 35: [5] Dercon et al., 2014, Offering rainfall insurance to informal insurance groups: Evidence from a field experiment in Ethiopia, Journal of Development Economics, 106, 132C143. [6] Eichengreen, B., Gupta, P., 2013, The two waves of service-sector growth, Oxford Economic Papers, 65: 96C123. [7] Enz, R., The S-Curve Relation Between Per-Capita Income and Insurance Penetration, The Geneva Papers on Risk and Insurance, Vol. 25, No.3, 2000, [8] Huang, T., Gao, S., 2003, The Determinants of the Demand for life-insurance in an Emerging Economy-The Case of China, Managerial Finance, Vol. 29, No. 5/6. [9] IULA (International Union of Local Authorities), Urbanization in Developing Countries. Netheriands: Springer-Science+Business Media, B.V [10] Janvry et al., 2014, The demand for insurance against common shocks, Journal of Development Economics, 106, 227C238. [11] Lewis, F. D., 1989, Dependants and the Demand for life-insurance, American Economic Review, 79(3): [12] Nandha, M., Smyth, R., 2013, Quality of governance and human development, Working Paper. [13] Mayers, D., Smith, Jr. C. W., 1983, The Interdependence of Individual Portfolio Decisions and the Demand for Insurance, Journal of Political Economy, 91: [14] Outreville, J. F., 1996, life-insurance Markets in Developing Countries, The Journal of Risk and Insurance, Vol. 63, No. 2, pp: [15] Pinar et al., 2013, Measuring human development: a stochastic dominance approach, Journal of Economic Growth, 18:

16 [16] Pliska, S. R. and Ye, J., (2007), Optimal life-insurance purchase and consumption/investment under uncertain lifetime, Journal of Banking and Finance, 31: [17] Sen, S., 2008, An Analysis of life-insurance Demand Determinants for Selected Asian Economies and India, Working Paper. [18] Shi X Wang H-J and Xing C., 2015, The role of life-insurance in an emerging economy: Human capital protection, assets allocation and social interaction, Journal of Banking & Finance, (50): 19C33. [19] Truett, D. B., Truett, L. J., 1990, The Demand for life-insurance in Mexico and the United States: A Comparative Study, Journal of Risk and Insurance, 57(2): [20] Yaari, ME., (1965), Uncertain lifetime, life-insurance, and the theory of the consumer, Review of Economic Studies, 32: [21] Yusuf, S. and T. Saich, China Urbanizes: Consequences, Strategies, and Policies. Washington DC: The International Bank for Reconstruction and Development / The World Bank. [22] Zelizer, V. A., 1978, Human Values and the Market: The Case of life-insurance and Death in 19th-Century America, The American Journal of Sociology, Vol.84, No.3. 16

17 Figure 1: Lowess plot of the relationship between log GDP per capita income and lifeinsurance/gdp. 17

18 Table 1: Definitions and sources of variables Variables Definitions Sources Life-insurance penetration Share of life-insurance premium in GDP (%) GFD GDP Per Capita GDP per capita (constant 2005 US$) WDI Urbanization Share of urban population in total population WDI (%) Human Development Index Average of a country s achievements in life expectancy, UNDP education and GNI per capita, nor- malized between 0 and 1. Population growth Annual population growth rate (%) WDI Old dependency ratio Ratio of the population over age 65 to the population WDI ages Young dependency ratio Ratio of the population under age 15 to the population WDI ages Life expectancy Years of life expectancy at birth. WDI Secondary enrollment rate Gross secondary enrollment ratio. WDI Financial development Ratio of M2 to GDP. GFD Government governance Geometric mean of six Worldwide Governance WGI Indicators, including control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, rule of law and voice and accountability. Average salary Average monthly wage by converting into US dollars using 2005 PPP conversion factor ILO GFD stands for Global Financial Development database of World Bank; WDI stands for World Development Indicators database; UNDP stands for United Nations Development Program database; ILO stands for International Labour Organization database. 18

19 Figure 2: Lowess plot of the relationship between human-development enabling urbanization and life-insurance/gdp. 19

20 Figure 3: Lowess plot of the relationship between human-development disenabling urbanization and life-insurance/gdp. 20

21 Table 2: Summary Statistics Full sample Subsample of middle- and low-income countries Variable Observation Mean Std. Dev. Min Max Observation Mean Std. Dev. Min Max Life-insurance penetration Log GDP per capita Log GDP per capita, squared Log GDP per capita, cube Log GDP per capita, quartic Urbanization D Urbanization*D Population growth Old dependency ratio Young dependency ratio Life expectancy Secondary enrollment rate Financial development Government governance

22 Table 3: Test the central hypothesis: human-development enabling urbanization causes lifeinsurance industry development Full sample Subsample of middle- and low-income countries Variable [1] [2] [3] [4] Log GDP per capita ** (-0.83) (-1.52) (-2.12) (-1.15) Log GDP per capita, squared * 9.922** (1.11) (1.67) (2.23) (1.16) Log GDP per capita, cube * ** (-1.42) (-1.77) (-2.32) (-1.12) Log GDP per capita, quartic 0.012* 0.042* 0.034** (1.82) (1.84) (2.42) (1.06) Urbanization 0.023*** ** 0.013** *** (3.32) (-2.41) (2.02) (-3.42) Urbanization*D 0.313*** 0.488*** (2.98) (3.78) Population growth * (-1.64) (-1.10) Old dependency ratio 0.175*** 0.392*** (3.75) (3.55) Young dependency ratio 0.062*** 0.082*** (3.89) (4.49) Life expectancy ** ** (-2.03) (-2.08) Secondary enrollment rate (-0.09) (-0.12) Financial development (-0.74) (0.44) Government governance 0.746*** 0.069** (2.86) (2.25) Constant ** (0.52) (1.27) (1.97) (1.05) Country Fixed Effect Yes Yes Yes Yes Observations Number of countries R-square This table reports estimation results of Model (6) using full sample and subsample middle- and low-income countries. The dependent variable for Panel [1] through [4] is life insurance penetration, expressed by life-insurance premiums by GDP. Regressors include the four powers of log per capita income, as well as Urbanization,one of the demographic determinants, and other control variables. Urbanization D, the interaction term of urbanization and dummy variable for HDI, indicates effects of human-development enabling urbanization on life-insurance, whereas dummy term, D, eliminated by Stata in avoidance of collinearity, is not included in the results. Panel [1] and [3]report the estimation results of Models (6) without control variables using full sample and subsample respectively. Panel [2] and [4] report the estimation results of Models (6) including control variables using full sample and subsample respectively. *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. 22

23 Table 4: Relationship between urbanization and average wage Variable [1] [2] Urbanization *** *** (-12.69) (-8.75) Urbanization*D 1.894*** 1.351*** (13.20) (8.34) Log GDP per capita 1.271*** (6.26) Constant *** *** (11.92) (4.13) Country Fixed Effect Yes Yes Observations Number of countries R-square This table reports estimation results of regression of human capital, proxied by average wage, on urbanization. The dependent variable for Panel [1] and [2] is average wage. Regressors include Urbanization, the main explanatory variable we focus, and other control variables. Urbanization D, the interaction term of urbanization and dummy variable for HDI, indicates effects of human-development enabling urbanization on human capital, whereas dummy term, D, eliminated by Stata in avoidance of collinearity, is not included in the results. *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. 23

24 Table 5: Relationship between average wage and life-insurance Variable [1] [2] [3] Log average wage 1.049*** 0.369** 0.335** (19.43) (2.33) (2.07) Log GDP per capita 2.271*** (6.55) (-0.33) Log GDP per capita, squared (0.41) Log GDP per capita, cube (-0.48) Log GDP per capita, quartic (0.48) Population growth 0.208* 0.201* (1.79) (1.69) Old dependency ratio (-0.45) (-0.20) Young dependency ratio (-0.07) (0.26) Life expectancy * * (-1.90) (-1.81) Secondary enrollment rate (-0.56) (-0.35) Financial development (1.23) (1.10) Government governance (-0.60) (-0.26) Constant *** *** (36.27) (3.21) (0.35) Country Fixed Effect NO NO NO Observations Number of countries R-square This table reports estimation results of regression of life insurance penetration on human capital, proxied by average wage. The dependent variable for Panel [1] through [3] is life insurance penetration. Regressors include average wage, indicating the level of human capital, and other control variables. In Panel [1], average wage is the only explanatory variable. Panel [2] adds the first power of log per capita income as a control for economic variable.panel [3]report the estimation results of effects of human capital on life-insurance, after adding all the other control variables. *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. 24

25 Table 6: Robustness check using alternative HDI following Pinar et al. (2013) Full sample Subsample of middle- and low-income countries Variable [1] [2] [3] [4] Log GDP per capita ** ** (-1.20) (-2.10) (-2.12) (-1.25) Log GDP per capita, squared ** 9.922** (0.132) (2.29) (2.23) (1.25) Log GDP per capita, cube * ** ** (-1.85) (-2.42) (-2.32) (-1.21) Log GDP per capita, quartic 0.014** 0.058** 0.034** (2.28) (2.53) (2.42) (1.14) Urbanization 0.023*** ** 0.013** ** (3.27) (-2.53) (2.02) (-2.28) Urbanization*D 0.286*** 0.299*** (3.06) (2.63) Population growth (-1.47) (-0.99) Old dependency ratio 0.197*** 0.422*** (4.26) (3.70) Young dependency ratio 0.055*** 0.069*** (3.47) (3.88) Life expectancy ** * (-1.96) (-1.94) Secondary enrollment rate (0.13) (0.35) Financial development (-1.27) (0.50) Government governance 0.739*** 0.702** (2.86) (2.27) Constant * ** (0.86) (1.83) (1.97) (1.17) Country Fixed Effect Yes Yes Yes Yes Observations Number of countries R-square This table reports estimation results of Model (6) using adjusted HDI data. The construction of adjusted HDI resets the weights assigned to three sub-indexes, i.e. income, education and health index, and respectively, according to Pinar et al. (2013). The dependent variable for Panel [1] through [4] is life insurance penetration, expressed by life-insurance premiums by GDP. Regressors include the four powers of log per capita income, as well as Urbanization,one of the demographic determinants, and other control variables. Urbanization D, the interaction term of urbanization and dummy variable for HDI, indicates effects of human-development enabling urbanization on life-insurance, whereas dummy term, D, eliminated by Stata in avoidance of collinearity, is not included in the results. Panel [1] and [3]report the estimation results of Models (6) without control variables using full sample and subsample respectively. Panel [2] and [4] report the estimation results of Models (6) including control variables using full sample and subsample respectively. *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. 25

26 Table 7: Robustness tests using simple linear estimation Full sample Subsample of middle- and low-income countries Original HDI Adjusted HDI Original HDI Variable [1] [2] [3] [4] [5] [6] Log GDP per capita 0.885*** 1.108*** 0.901*** 1.137*** 0.579*** 1.338*** (8.79) (2.79) (9.00) (2.87) (5.73) (2.60) Urbanization 0.014** -2.15** 0.014** ** 0.011* *** (2.05) (-2.29) (1.99) (-2.29) (1.74) (-3.66) Urbanization*D 0.322*** 0.289*** 0.539*** (3.07) (3.08) (4.24) Population growth (-1.53) (-1.16) (-1.03) Old dependency ratio 0.141*** 0.162*** 0.294*** (3.20) (3.70) (2.79) Young dependency ratio 0.063*** 0.056*** 0.089*** (3.92) (3.56) (4.93) Life expectancy * (-1.67) (-1.54) (-1.60) Secondary enrollment rate (-0.20) (-0.19) (-0.68) Financial development (-0.60) (-0.91) (0.50) Government governance 0.627** 0.642** 0.564* (2.48) (2.55) (1.85 Constant *** ** *** ** *** *** (-9.74) (-2.39) (-9.99) (-2.41) (-6.77) (-3.14) Country Fixed Effect Yes Yes Yes Yes Yes Yes Observations Number of countries R-square This table reports estimation results of Model (6) using linear regression. The dependent variable for Panel [1] through [4] is life insurance penetration, expressed by life-insurance premiums by GDP. Regressors include only first power of log per capita income, as well as Urbanization,one of the demographic determinants, and other control variables. Urbanization D, the interaction term of urbanization and dummy variable for HDI, indicates effects of human-development enabling urbanization on life-insurance, whereas dummy term, D, eliminated by Stata in avoidance of collinearity, is not included in the results. Panel [1] through [4] report the estimation results using full sample, of which Panel [3] and [4] using adjusted HDI data for further robustness test.panel [5] and [6]report the estimation results using subsample of middle- and low-income countries. *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. 26

27 Table 8: Robustness tests using the sample truncated at the 4st and 96th percentile of urbanization Full sample Subsample of middle- and low-income countries Variable [1] [2] [3] [4] Log GDP per capita ** ** (-1.20) (-2.10) (-2.12) (-1.25) Log GDP per capita, squared ** 9.922** (0.132) (2.29) (2.23) (1.25) Log GDP per capita, cube * ** ** (-1.85) (-2.42) (-2.32) (-1.21) Log GDP per capita, quartic 0.014** 0.058** 0.034** (2.28) (2.53) (2.42) (1.14) Urbanization 0.023*** ** 0.013** ** (3.27) (-2.53) (2.02) (-2.28) Urbanization*D 0.286*** 0.299*** (3.06) (2.63) Population growth (-1.47) (-0.99) Old dependency ratio 0.197*** 0.422*** (4.26) (3.70) Young dependency ratio 0.055*** 0.069*** (3.47) (3.88) Life expectancy ** * (-1.96) (-1.94) Secondary enrollment rate (0.13) (0.35) Financial development (-1.27) (0.50) Government governance 0.739*** 0.702** (2.86) (2.27) Constant * ** (0.86) (1.83) (1.97) (1.17) Country Fixed Effect Yes Yes Yes Yes Observations Number of countries R-square This table reports estimation results of Model (6) using sample truncated at 4th and 96th percentile of urbanization. The dependent variable for Panel [1] through [4] is life insurance penetration, expressed by life-insurance premiums by GDP. Regressors include only first power of log per capita income, as well as Urbanization,one of the demographic determinants, and other control variables. Urbanization D, the interaction term of urbanization and dummy variable for HDI, indicates effects of human-development enabling urbanization on life-insurance, whereas dummy term, D, eliminated by Stata in avoidance of collinearity, is not included in the results. Panel [1] and [2] report the estimation results using original HDI data, while Panel [3] and [4] use adjusted HDI data. *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. 27

28 Table 9: Robustness test examining the impact of the 2008 Global Financial Crisis Full sample Subsample of middle- and low-income countries Variable [1] [2] [3] [4] Log GDP per capita ** (-0.83) (-1.49) (-2.12) (-1.12) Log GDP per capita, squared ** (1.11) (1.60) (2.23) (1.12) Log GDP per capita, cube * ** (-1.42) (-1.66) (-2.32) (-1.08) Log GDP per capita, quartic 0.012* 0.039* 0.034** (1.82) (1.69) (2.42) (1.02) Urbanization 0.023*** ** 0.013** *** (3.32) (-2.15) (2.02) (-3.37) Urbanization*HDI Dummy 0.315*** 0.489*** (3.02) (3.78) Urbanization*HDI dummy*crisis Dummy 0.004*** (3.01) (0.22) Population growth (-1.37) (-1.11) Old dependency ratio 0.214*** 0.395** (4.45) (3.55) Young dependency ratio 0.057*** 0.081*** (3.51) (4.45) Life expectancy ** (-1.48) (-2.07) Secondary enrollment rate (-0.38) (-0.12) Financial development (-0.09) (0.46) Government governance 0.773*** 0.692** (2.99) (2.26) Constant ** (0.52) (1.24) (1.97) (1.02) Country Fixed Effect Yes Yes Yes Yes Observations Number of countries R-square This table reports estimation results of Model (6) adding another interaction, Urbanization HDIDummy CrisisDummy to examine the impact of the 2008 Global Financial Crisis. The dependent variable for Panel [1] through [4] is life insurance penetration, expressed by life-insurance premiums by GDP. Regressors include the four powers of log per capita income, as well as Urbanization,one of the demographic determinants, and other control variables. Urbanization HDIDummy, the interaction term of urbanization and dummy variable for HDI, indicates effects of human-development enabling urbanization on life-insurance, whereas dummy term, HDIDummy, eliminated by Stata in avoidance of collinearity, is not included in the results. Urbanization HDIDummy CrisisDummy, the interaction term of urbanization, HDIDummy and CrisisDummy, reveals the effects of human-development enabling urbanization on life-insurance before the GFC. Panel [1] and [2] report the estimation results using full sample data, while Panel [3] and [4] use data from subsample of middle- and low-income countries. 28 *, **, and *** denote significance at the 0.1, 0.05, and 0.01 level, respectively.