Family Life Cycle, Asset Portfolio, and Commercial Health Insurance Demand in China
Abstract
:1. Introduction
2. Literature Review and Hypothesis Proposal
3. Study Design
3.1. Variable Specifications
3.1.1. Explained Variables
3.1.2. Core Explanatory Variables
3.1.3. Control Variables
3.2. Data Introduction
3.3. Model Setting
4. Empirical Analysis
4.1. Multicollinearity Test and Heteroscedasticity Test
4.2. Analysis of Empirical Results
4.3. Robustness Test
4.4. Explanation of Endogeneity
5. Further Discussion
5.1. Analysis of Heterogeneity of Household Registration Types
5.2. Analysis of Heterogeneity of Regional Type
6. Conclusions, Implications, and Prospects
6.1. Conclusions
6.2. Policy Implications
- (1)
- Improve the age structure of the population and promote the rationalization of the family life cycle. The specific measures for this are as follows: Firstly, the government actively formulates relevant laws and regulations, such as providing maternity allowances and improving maternity insurance benefits, in order to guide young people who are of appropriate age to enter the marriage palace as early as possible, to actively publicize and implement the “open three children” policy, and to encourage newlywed families to have more children, so as to achieve a long-term balance in the population age structure. Secondly, we should actively promote the construction of a harmonious family featuring “a loving husband and wife, respecting the old, and caring for the young”, in order to promote the rationalization of the family life cycle and the accumulation of wealth, so as to create a stable micro investment unit for the development of commercial health insurance;
- (2)
- Accelerate the modernization of economic construction and improve the family financial condition. The specific measures for this are as follows: Firstly, we will appropriately raise the minimum wage and the poverty alleviation threshold, increase the living allowances for low-income families, and reduce their tax burden. Secondly, we should improve the wage increase mechanism and the payment guarantee mechanism for enterprise employees in order to raise their wage level. We will strengthen job skills training and provide more job opportunities for enterprise employees in order to further expand the scope of social employment. Thirdly, we should speed up the urbanization process of the registered population, continue to implement the strategy of developing the midwestern regions, and speed up the construction of infrastructure and supporting facilities in rural areas and the midwestern regions. Fourthly, we should take the market as a barometer, transform the traditional industries with advanced technologies in order to optimize them, and upgrade the industrial structure;
- (3)
- Optimize family asset portfolio allocation. The specific measures for this are as follows: Firstly, we will actively implement regional housing purchase restrictions and differentiated housing credit policies, do our best to stabilize the real estate market by controlling the housing prices, preventing bubbles and risks, and comprehensively standardize the real estate market. Secondly, we will strengthen the vehicle construction system, improve the vehicle management files, and improve the safe driving mechanism, in order to create a harmonious and civilized traffic atmosphere. Thirdly, we will enhance the popularization of family financial education and financial knowledge, appropriately guide more families to participate in the financial asset market, and diversify the allocation of family assets, so as to promote the steady growth of the demand for commercial health insurance.
6.3. Research Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Marè, M.; Motroni, A.; Porcelli, F. How Family Ties Affect Trust, Tax Morale and Underground Economy. J. Econ. Behav. Organ. 2020, 174, 235–252. [Google Scholar] [CrossRef]
- Liu, H. The Investigation about the Heritage over Generations for the Family Enterprise and Firms Financialization Process. Adv. Multimed. 2022, 2022, 2807116. [Google Scholar] [CrossRef]
- Sherraden, M.S.; Huang, J.; Jones, J.L.; Callahan, C. Building Financial Capability and Assets in America’s Families. Fam. Soc. J. Contemp. Soc. Serv. 2022, 103, 3–6. [Google Scholar] [CrossRef]
- Hamilton, L. Asset Limits in Public Assistance and Savings Behavior among Low-Income Families. Soc. Sci. Q. 2021, 102, 454–467. [Google Scholar] [CrossRef]
- Xu, B.C.; Xu, X.N.; Zhao, J.C.; Zhang, M. Influence of Internet Use on Commercial Health Insurance of Chinese Residents. Front. Public Health 2022, 10, 907124. [Google Scholar] [CrossRef]
- China Economic Trends Research Institute. China Household Wealth Survey Report (2018) released—The Growth of Real Estate Net Worth is the Core Factor of Household Wealth Growth. Economic Daily 2018. (In Chinese) [Google Scholar]
- Survey Data—China Household Finance Survey (CHFS). Available online: https://chfser.swufe.edu.cn/datas/ (accessed on 2 February 2022).
- Yaari, M.E. Uncertain Lifetime, Life Insurance, and the Theory of the Consumer. Rev. Econ. Stud. 1965, 32, 137–150. [Google Scholar] [CrossRef]
- Showers, V.E.; Shotick, J.A. The Effects of Household Characteristics on Demand for Insurance: A Tobit Analysis. J. Risk Insur. 1994, 61, 492–502. [Google Scholar] [CrossRef]
- Bernheim, B.D.; Forni, L.; Gokhale, J.; Kotlikoff, L.G. The Mismatch between Life Insurance Holdings and Financial Vulnerabilities: Evidence from the Health and Retirement Study. Am. Econ. Rev. 2003, 93, 354–365. [Google Scholar] [CrossRef]
- Wang, X.; Sun, Q.; Wang, X. A study on the Selection of family Life Insurance Assets and Other Assets in China: Based on Life cycle risk and asset allocation. Contemp. Econ. Sci. (In Chinese). 2013, 35, 1–10+124. [Google Scholar]
- Hammond, J.D.; Houston, D.B.; Melander, E.R. Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation. J. Risk Insur. 1967, 34, 397–408. [Google Scholar] [CrossRef]
- Mantis, G.; Farmer, R.N. Demand for Life Insurance. J. Risk Insur. 1968, 35, 247–256. [Google Scholar] [CrossRef]
- Anderson, D.R.; Nevin, J.R. Determinants of Young Marrieds’ Life Insurance Purchasing Behavior: An Empirical Investigation. J. Risk Insur. 1975, 42, 375–387. [Google Scholar] [CrossRef]
- Guiso, L.; Jappelli, T. Background Uncertainty and the Demand for Insurance against Insurable Risks. Geneva Pap. Risk Insur. Theory 1998, 23, 7–27. [Google Scholar] [CrossRef]
- Hau, A. Liquidity, Estate Liquidation, Charitable Motives, and Life Insurance Demand by Retired Singles. J. Risk Insur. 2000, 67, 123–141. [Google Scholar] [CrossRef] [Green Version]
- Albouy, F.X.; Blagoutine, D. Insurance and Transition Economics: The Insurance Market in Russia. Geneva Pap. Risk Insur. Issues Pract. 2001, 26, 467–479. [Google Scholar] [CrossRef]
- Liu, G.G.; Wu, X.D.; Peng, C.Y.; Fu, A.Z. Urbanization and Health Care in Rural China. Contemp. Econ. Policy 2003, 21, 11–24. [Google Scholar] [CrossRef] [Green Version]
- Chang, F.R. Life Insurance, Precautionary Saving and Contingent Bequest. Math. Soc. Sci. 2004, 48, 55–67. [Google Scholar] [CrossRef] [Green Version]
- Mocan, H.N.; Tekin, E.; Zax, J.S. The Demand for Medical Care in Urban China. World Dev. 2004, 32, 289–304. [Google Scholar] [CrossRef] [Green Version]
- Lin, Y.J.; Grace, M.F. Household Life Cycle Protection: Life Insurance Holdings, Financial Vulnerability and Portfolio Implications. J. Risk Insur. 2007, 74, 141–173. [Google Scholar] [CrossRef]
- Calvet, L.E.; Sodini, P. Twin Picks: Disentangling the Determinants of Risk-Taking in Household Portfolios. J. Financ. 2014, 69, 867–906. [Google Scholar] [CrossRef] [Green Version]
- Christiansen, C.; Joensen, J.S.; Rangvid, J. Understanding the Effects of Marriage and Divorce on Financial Investments: The Role of Background Risk Sharing. Econ. Inq. 2015, 53, 431–447. [Google Scholar] [CrossRef]
- Shi, X.J.; Wang, H.J.; Xing, C.B. The Role of Life Insurance in an Emerging Economy: Human Capital Protection, Assets Allocation and Social Interaction. J. Bank. Financ. 2015, 50, 19–33. [Google Scholar] [CrossRef]
- Saavedra, M. Children’s Health Insurance, Family Income, and Welfare Enrollment. Child. Youth Serv. Rev. 2017, 73, 182–186. [Google Scholar] [CrossRef]
- Xiao, W. Effects of Marital Status on Household Commercial Health Insurance Participation Behavior. J. Interdiscip. Math. 2018, 21, 397–407. [Google Scholar] [CrossRef]
- Wang, Q.; Zhou, Y.; Ding, X.R.; Ying, X.H. Demand for Long-Term Care Insurance in China. Int. J. Environ. Res. Public Health 2018, 15, 6. [Google Scholar] [CrossRef] [Green Version]
- Segodi, M.P.; Sibindi, A.B. Determinants of Life Insurance Demand: Empirical Evidence from BRICS Countries. Risks 2022, 10, 73. [Google Scholar] [CrossRef]
- Headen, R.S.; Lee, J.F. Life Insurance Demand and Household Portfolio Behavior. J. Risk Insur. 1974, 41, 685–698. [Google Scholar] [CrossRef]
- Meyer, J.; Ormiston, M.B. Demand for Insurance in a Portfolio Setting. Geneva Pap. Risk Insur. Theory 1995, 20, 203–211. [Google Scholar] [CrossRef]
- Giesbert, L.; Steiner, S.; Bendig, M. Participation in Micro Life Insurance and the Use of Other Financial Services in Ghana. J. Risk Insur. 2011, 78, 7–35. [Google Scholar] [CrossRef]
- Poterba, J.M.; Samwick, A.A. Household Portfolio Allocation over the Life Cycle; Ogura, S., Tachibanaki, T., Wise, D.A., Eds.; Aging Issues in the United States and Japan; University of Chicago Press: Chicago, IL, USA, 2001; pp. 65–103. [Google Scholar]
- Shum, P.; Faig, M. What Explains Household Stock Holdings? J. Bank. Financ. 2005, 30, 2579–2597. [Google Scholar] [CrossRef] [Green Version]
- Yogo, M. Portfolio Choice in Retirement: Health Risk and the Demand for Annuities, Housing, and Risky Assets. J. Monet. Econ. 2016, 80, 17–34. [Google Scholar] [CrossRef]
- Cragg, J.G. Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods. Economica 1971, 39, 829–844. [Google Scholar] [CrossRef]
- Duan, N.H.; Manning, W.G.; Morris, C.N.; Newhouse, J.P. A Comparison of Alternative Models for the Demand for Medical Care. J. Bus. Econ. Stat. 2012, 1, 115–126. [Google Scholar]
- Pohlmeier, W.; Ulrich, V. An Econometric Model of the 2-Part Decision Making Process in the Demand for Health Care. J. Hum. Resour. 1995, 30, 339–361. [Google Scholar] [CrossRef]
- Cardak, B.A.; Wilkins, R. The Determinants of Household Risky Asset Holdings: Australian Evidence on Background Risk and Other Factors. J. Bank. Financ. 2009, 33, 850–860. [Google Scholar] [CrossRef] [Green Version]
- Wang, C.; Yao, L.; Chai, S. The Influence of Age Structure on Household Asset Allocation and its Regional Differences. Int. Financ. Res. 2017, 2, 76–86. (In Chinese) [Google Scholar]
- Li, S.L.; Yang, Y.F. An Empirical Study on the Influence of the Basic Medical Insurance for Urban and Rural Residents on Family Financial Asset Allocation. Front. Public Health 2021, 9, 725608. [Google Scholar] [CrossRef]
- Chen, Q. Advanced Econometrics and stata Applications. Second Edition. Higher Education Press: Beijing, China, 2014. (In Chinese) [Google Scholar]
- Stock, J.H.; Yogo, M. Testing for Weak Instruments in Linear IV Regression. NBER Tech. Work. Pap. 2005, 14, 80–108. [Google Scholar]
- Andrews, I.; Stock, J.H.; Sun, L.Y. Weak Instruments in Instrumental Variables Regression: Theory and Practice. Annu. Rev. Econ. 2019, 11, 727–753. [Google Scholar] [CrossRef]
- Murendo, C.; Mutsonziwa, K. Financial Literacy and Savings Decisions by Adult Financial Consumers in Zimbabwe. Int. J. Consum. Stud. 2017, 41, 95–103. [Google Scholar] [CrossRef]
Name of Variable | Symbol of Variable | Explanation of Variables |
---|---|---|
Breadth of health insurance | H | Dummy variable, health insurance or not (yes = 1, no = 0) |
Depth of health insurance (CNY) | lnH_expense | Natural logarithm of health insurance premium expenditure; ln (family health insurance premium expenditure + 1) |
Head of household’s age (years old) | head_age | Head of household’s actual age |
Square of the head of household’s age (years old) | head_age2 | The square of the head of household’s actual age |
Total household income (CNY) | lntotal_inc | ln (total household income + 1) |
Total household consumption (CNY) | lntotal_consump | ln (total household consumption + 1) |
Total household assets (CNY) | lntotal_asset | ln (total household assets + 1) |
Total household debt (CNY) | lntotal_debt | ln (total household debt + 1) |
Share of real estate assets (%) | house_asset | Household real estate assets/total household assets × 100 |
Share of vehicle assets (%) | vehicle_asset | Household vehicle assets/total household assets × 100 |
Share of savings assets (%) | saving_asset | Household savings assets/total household assets × 100; Household savings assets = cash + demand deposits + time deposits + gold value + bond market value |
Share of investment assets (%) | invest_asset | Household investment assets/total household assets × 100; Household investment assets = stock market value + fund market value + financial derivatives market value + wealth management product value |
Urban household registration or not | town | Dummy variable, urban household registration or not (yes = 1, no = 0) |
Eastern region or not | east | Dummy variable, eastern region or not (yes = 1, no = 0) |
Health index | health | Values from “very good” to “very poor” are 1–5; among them, “Very good” = 1, “good” = 2, “fair” = 3, “poor” = 4, and “very poor” = 5 |
Variable | Families without Health Insurance | Families with Health Insurance | |||||
---|---|---|---|---|---|---|---|
Obs | Mean | Std. Dev. | Obs | Mean | Std. Dev. | ||
Family life cycle | head_age | 15,385 | 54.44 | 9.851 | 1534 | 49.18 | 9.561 |
head_age2 | 15,385 | 3060.66 | 1037.598 | 1534 | 2509.76 | 958.789 | |
Family financial status | lntotal_inc | 15,385 | 15.41 | 0.034 | 1534 | 15.43 | 0.047 |
lntotal_consump | 15,385 | 10.76 | 0.768 | 1534 | 11.29 | 0.638 | |
lntotal_asset | 15,385 | 13.58 | 0.917 | 1534 | 14.22 | 0.911 | |
lntotal_debt | 15,385 | 4.89 | 5.437 | 1534 | 6.04 | 5.814 | |
Family asset portfolio | house_asset | 15,385 | 42.86 | 26.981 | 1534 | 53.26 | 24.049 |
vehicle_asset | 15,385 | 15.11 | 18.592 | 1534 | 26.77 | 13.086 | |
saving_asset | 15,385 | 3.28 | 4.585 | 1534 | 4.34 | 4.782 | |
invest_asset | 15,385 | 0.41 | 1.270 | 1534 | 1.16 | 1.914 |
Variable | Obs | Mean | Std. Dev. | Max | Min | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|
H | 16,919 | 0.091 | 0.287 | 1 | 0 | 2.851 | 9.129 |
lnH_expense | 1534 | 8.251 | 1.216 | 10.12 | 5.3 | −0.645 | 3.033 |
head_age | 16,919 | 53.96 | 9.940 | 70 | 25 | −0.365 | 2.421 |
head_age2 | 16,919 | 3010.72 | 1042.743 | 4900 | 625 | −0.014 | 2.107 |
lntotal_inc | 16,919 | 15.415 | 0.036 | 15.5 | 15.4 | 1.962 | 4.848 |
lntotal_consump | 16,919 | 10.805 | 0.773 | 12.2 | 9.28 | −0.098 | 2.339 |
lntotal_asset | 16,919 | 13.638 | 0.935 | 15.6 | 12.4 | 0.565 | 2.332 |
lntotal_debt | 16,919 | 4.991 | 5.483 | 12.9 | 0 | 0.262 | 1.194 |
house_asset | 16,919 | 43.802 | 26.894 | 88.01 | 1.8 | 0.009 | 1.819 |
vehicle_asset | 16,919 | 25.718 | 18.468 | 63.24 | 2.77 | 0.610 | 2.206 |
saving_asset | 16,919 | 3.376 | 4.613 | 16.63 | 0.001 | 1.690 | 4.857 |
invest_asset | 16,919 | 0.481 | 1.359 | 5.33 | 0.001 | 2.901 | 10.002 |
Core Independent Variable | Model 1 | Model 2 | ||||
---|---|---|---|---|---|---|
The Value of VIF | The Value of 1/VIF | Result | The Value of VIF | The Value of 1/VIF | Result | |
head_age | 2.34 | 0.428 | Non-collinearity | 2.06 | 0.486 | Non-collinearity |
lntotal_inc | 1.47 | 0.682 | Non-collinearity | 1.52 | 0.657 | Non-collinearity |
lntotal_consump | 1.74 | 0.574 | Non-collinearity | 1.69 | 0.593 | Non-collinearity |
lntotal_asset | 8.29 | 0.121 | Non-collinearity | 7.35 | 0.136 | Non-collinearity |
lntotal_debt | 1.15 | 0.871 | Non-collinearity | 1.16 | 0.863 | Non-collinearity |
house_asset | 3.47 | 0.289 | Non-collinearity | 2.16 | 0.462 | Non-collinearity |
vehicle_asset | 8.89 | 0.112 | Non-collinearity | 6.99 | 0.143 | Non-collinearity |
saving_asset | 1.36 | 0.736 | Non-collinearity | 1.33 | 0.751 | Non-collinearity |
invest_asset | 1.28 | 0.782 | Non-collinearity | 1.24 | 0.805 | Non-collinearity |
Model Name | White Test | Result | |
---|---|---|---|
The Value of Chi-Square | The Value of p | ||
Model 1 | 1658.45 | 0.0000 *** | Heteroscedasticity |
Model 2 | 159.97 | 0.0001 *** | Heteroscedasticity |
Variable | (1) | (2) |
---|---|---|
Model 1: The Breadth of Health Insurance | Model 2: The Depth of Health Insurance | |
Probit_1 | Tobit_1 | |
head_age | 0.0069 *** (3.40) | 0.1164 *** (4.32) |
head_age2 | −0.0001 *** (−4.89) | −0.0012 *** (−4.67) |
lntotal_inc | 0.1312 ** (2.18) | 0.7616 (1.03) |
lntotal_consump | 0.0283 *** (7.71) | 0.1273 ** (2.36) |
lntotal_asset | −0.0071 (−1.17) | 0.2013 ** (2.41) |
lntotal_debt | 0.0015 *** (3.88) | 0.0003 (0.06) |
house_asset | −0.0007 *** (−5.70) | −0.0041 ** (−2.40) |
vehicle_asset | −0.0002 *** (−5.72) | −0.0003 (−0.49) |
saving_asset | 0.0002 (0.30) | −0.0028 (−0.42) |
invest_asset | 0.0079 *** (5.80) | 0.0022 (0.14) |
_cons | −17.5136 *** (−2.80) | −10.7074 (−0.97) |
Control variables | Yes | Yes |
Obs | 16,919 | 16,919 |
Pseudo R2 | 0.1257 | 0.0613 |
Variable | Breadth of Health Insurance: Whether to Purchase Health Insurance | Depth of Health Insurance: Health Insurance Premium Expenditure | ||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Probit_2 | Logit | Tobit_2 | Heckman | |
head_age | 0.0082 *** (3.98) | 0.1175 *** (3.55) | ||
head_age2 | −0.0001 *** (−5.45) | −0.0012 *** (−3.30) | ||
couple_age | 0.0078 *** (3.48) | 0.1312 *** (4.36) | ||
couple _age2 | −0.0001 *** (−4.89) | −0.0014 *** (−4.70) | ||
lntotal_inc | 0.1312 ** (2.19) | 0.1157 ** (2.00) | 0.7821 (1.06) | 0.7811 (0.96) |
lntotal_consump | 0.0284 *** (7.74) | 0.0285 *** (7.51) | 0.1271 ** (2.22) | 0.1335 ** (1.11) |
lntotal_asset | −0.0071 (−1.18) | −0.0111 (−1.83) | 0.1993 ** (2.38) | 0.2007 ** (2.38) |
lntotal_debt | 0.0015 *** (3.89) | 0.0015 *** (3.75) | 0.0003 (0.05) | 0.0005 (0.08) |
house_asset | −0.0007 *** (−5.71) | −0.0007 *** (−5.83) | −0.0041 ** (−2.41) | −0.0042 ** (−2.11) |
vehicle_asset | −0.0002 *** (−5.73) | −0.0002 *** (−6.22) | −0.0003 (−0.51) | −0.0003 (−0.36) |
saving_asset | 0.0001 (0.30) | 0.0001 (0.17) | −0.0029 (−0.42) | −0.0027 (−0.39) |
invest_asset | 0.0079 *** (5.79) | 0.0073 *** (5.76) | 0.0022 (0.14) | 0.0039 (0.12) |
_cons | −17.6671 *** (−2.82) | −29.7619 *** (−2.59) | −11.3410 (−1.03) | −11.1283 (−0.84) |
Control variables | Yes | Yes | Yes | Yes |
Obs | 16,919 | 16,919 | 16,919 | 16,919 |
Variable | Breadth of Health Insurance: Whether to Purchase Health Insurance | Depth of Health Insurance: Health Insurance Premium Expenditure | |||
---|---|---|---|---|---|
(1) | (2) | (3) | (4) | ||
Probit_1 | IV-Probit | Tobit_1 | IV-Tobit | ||
head_age | 0.0069 *** (3.40) | 0.0479 *** (3.49) | 0.1164 *** (4.32) | 0.1154 *** (4.28) | |
head_age2 | −0.0001 *** (−4.89) | −0.0007 *** (−5.03) | −0.0012 *** (−4.67) | −0.0012 *** (−4.64) | |
lntotal_inc | 0.1312 ** (2.18) | 0.9866 ** (2.05) | 0.7616 (1.03) | 0.4183 (0.42) | |
lntotal_consump | 0.0283 *** (7.71) | 0.1891 *** (6.90) | 0.1273 ** (2.36) | 0.1118 ** (1.84) | |
lntotal_asset | −0.0071 (−1.17) | −0.0376 (−0.86) | 0.2013 ** (2.41) | 0.3079 ** (3.39) | |
lntotal_debt | 0.0015 *** (3.88) | 0.0105 *** (3.79) | 0.0003 (0.06) | 0.0007 (0.13) | |
house_asset | −0.0007 *** (−5.70) | −0.0051 *** (−5.48) | −0.0041 ** (−2.40) | −0.0042 ** (−2.46) | |
vehicle_asset | −0.0002 *** (−5.72) | −0.0014 *** (−5.38) | −0.0003 (−0.49) | 0.0003 (0.46) | |
saving_asset | 0.0002 (0.30) | 0.0015 (0.43) | −0.0028 (−0.42) | −0.0013 (−0.20) | |
invest_asset | 0.0079 *** (5.80) | 0.0531 *** (5.56) | 0.0022 (0.14) | 0.0002 (0.01) | |
_cons | −17.5136 *** (−2.8) | −18.7569 *** (−2.6) | −10.7074 (−0.97) | −6.7968 (−0.46) | |
Control variables | Yes | Yes | Yes | Yes | |
Obs | 16,919 | 16,919 | 16,919 | 16,919 | |
Wald test | Chi square value | 12.58 | 12.18 | ||
p value | 0.0135 ** | 0.0161 ** | |||
F value of the first stage | lntotal_inc1 | 4424.04 | 259.32 | ||
lntotal_consump1 | 41,284.97 | 1770.83 | |||
lntotal_asset1 | >99,999.00 | 5817.84 | |||
lntotal_debt1 | >99,999.00 | 42,157.00 |
Variable | Breadth of Health Insurance: Whether to Purchase Health Insurance | Depth of Health Insurance: Health Insurance Premium Expenditure | ||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Town-Probit_1 | Rural-Probit_2 | Town-Tobit_1 | Rural-Tobit_2 | |
head_age | 0.0117 *** (3.50) | 0.0015 (0.70) | 0.1023 *** (3.63) | 0.2090 ** (2.57) |
head_age2 | −0.0002 *** (−5.05) | −0.00022 (−0.98) | −0.0011 *** (−3.92) | −0.0022 *** (−2.79) |
lntotal_inc | 0.1950 ** (2.06) | 0.0262 (0.34) | 0.8594 (1.14) | −1.3935 (−0.56) |
lntotal_consump | 0.0315 *** (4.60) | 0.0199 *** (6.61) | 0.1871 *** (3.03) | −0.0305 (−0.22) |
lntotal_asset | −0.0024 (−0.23) | 0.0171 ** (2.16) | 0.1701 ** (1.76) | −0.0510 (−0.19) |
lntotal_debt | 0.0027 *** (4.18) | −0.00003 (−0.09) | 0.0014 (0.26) | −0.0023 (−0.15) |
house_asset | −0.0014 *** (−6.37) | 0.0001 (0.90) | −0.0023 (−1.29) | −0.0137 *** (−2.77) |
vehicle_asset | −0.0003 *** (−4.28) | 0.00002 (0.55) | −0.0004 (−0.57) | −0.0026 ** (−1.85) |
saving_asset | −0.0015 (−1.72) | 0.0014 *** (3.07) | −0.0023 (−0.32) | −0.0069 (−0.38) |
invest_asset | 0.0115 *** (5.67) | 0.0085 *** (2.87) | 0.0107 (0.67) | −0.1296 (−1.51) |
_cons | −17.5558 *** (−2.56) | −13.7889 (−0.86) | −11.1839 (−1.00) | 26.1598 (0.69) |
Control variables | Yes | Yes | Yes | Yes |
Obs | 8813 | 8106 | 8813 | 8106 |
Pseudo R2 | 0.0749 | 0.0742 | 0.0288 | 0.0223 |
Variable | Breadth of Health Insurance: Whether to Purchase Health Insurance | Depth of Health Insurance: Health Insurance Premium Expenditure | ||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
East-Probit_1 | Midwest-Probit_2 | East-Tobit_1 | Midwest-Tobit_2 | |
head_age | 0.0056 ** (1.75) | 0.0051 ** (2.02) | 0.1091 *** (3.08) | 0.0976 ** (2.17) |
head_age2 | −0.0001 *** (−3.07) | −0.00007 *** (−3.03) | −0.0013 *** (−3.65) | −0.0010 ** (−2.32) |
lntotal_inc | 0.0842 (0.95) | 0.1468 * (1.75) | 2.0327 ** (2.11) | −1.9001 (−1.51) |
lntotal_consump | 0.2452 *** (7.74) | 0.0251 *** (5.77) | 0.1448 ** (1.82) | 0.1593 * (1.82) |
lntotal_asset | 0.0032 (0.35) | −0.0152 (−1.58) | 0.1845 ** (1.72) | 0.3128 * (1.90) |
lntotal_debt | 0.0014 *** (2.17) | 0.0011 ** (2.17) | 0.0027 (0.40) | −0.0050 (−0.58) |
house_asset | −0.0007 *** (−3.43) | −0.0002 (−1.17) | −0.0029 (−1.20) | 0.0010 ** (0.36) |
vehicle_asset | −0.0002 *** (−3.57) | −0.0002 *** (−4.91) | −0.0005 (−0.57) | −0.0010 * (−1.03) |
saving_asset | 0.0007 (0.87) | 0.0003 (0.48) | −0.0036 (−0.39) | −0.0001 (−0.01) |
invest_asset | 0.0125 *** (6.21) | 0.0078 *** (4.12) | 0.0258 (1.25) | 0.0227 (0.80) |
_cons | −12.2220 (−1.54) | −20.3087 ** (−2.02) | −29.4327 (−2.06) | 29.5654 (1.56) |
Control variables | Yes | Yes | Yes | Yes |
Obs | 8044 | 8875 | 8044 | 8875 |
Pseudo R2 | 0.1159 | 0.0999 | 0.0401 | 0.0459 |
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Tian, L.; Dong, H. Family Life Cycle, Asset Portfolio, and Commercial Health Insurance Demand in China. Int. J. Environ. Res. Public Health 2022, 19, 16795. https://doi.org/10.3390/ijerph192416795
Tian L, Dong H. Family Life Cycle, Asset Portfolio, and Commercial Health Insurance Demand in China. International Journal of Environmental Research and Public Health. 2022; 19(24):16795. https://doi.org/10.3390/ijerph192416795
Chicago/Turabian StyleTian, Ling, and Haisong Dong. 2022. "Family Life Cycle, Asset Portfolio, and Commercial Health Insurance Demand in China" International Journal of Environmental Research and Public Health 19, no. 24: 16795. https://doi.org/10.3390/ijerph192416795