Can government subsidies and public mechanisms alleviate the physical and mental health vulnerability of China’s urban and rural residents?
Tóm tắt
Poverty vulnerability has been defined as the likelihood of a family falling into poverty in the upcoming months. Inequality is a major cause of poverty vulnerability in developing countries. There is evidence that establishing effective government subsidies and public service mechanisms significantly reduces health poverty vulnerability. One of the ways to study poverty vulnerability is by using empirical data such as income elasticity of demand to perform the analysis. Income elasticity refers to the extent to which changes in consumers’ income affect changes in demand for commodities or public goods. In this work, we assess health poverty vulnerability in rural and urban China. We provide two levels of evidence on the marginal effects of the design and implementation of government subsidies and public mechanisms in reducing health poverty vulnerability, before and after incorporating the income elasticity of demand for health. Multidimensional physical and mental health poverty indexes, according to the Oxford Poverty & Human Development Initiative and the Andersen model, were implemented to measure health poverty vulnerability by using the 2018 China Family Panel Survey database (CFPS) as the data source for empirical analysis. The income elasticity of demand for health care was used as the key mediating variable of impact. Our assessment was conducted by a two-level multidimensional logistic regression using STATA16 software. The first level regression indicates that the marginal utility of public mechanism (PM) in reducing urban and rural vulnerability as expected poverty on physical and mental health (VEP-PH&MH) was insignificant. On the other hand, government subsidies (GS) policies had a positive suppression effect on VEP-PH&MH to a relatively low degree. The second level regression found that given the diversity of health needs across individual households, i.e., the income elasticity of demand (HE) for health care products, PM and GS policies have a significant effect in reducing VEP-PH&MH in rural and urban areas. Our analysis has verified the significant positive impact of enacting accurate GS and PM policies on effectively reducing VEP-PH&MH in rural as well as urban areas. This study shows that implementing government subsidies and public mechanisms has a positive marginal effect on reducing VEP-PH&MH. Meanwhile, there are individual variations in health demands, urban-rural disparities, and regional disparities in the effects of GS and PM on inhibiting VEP-PH&MH. Therefore, special consideration needs to be given to the differences in the degree of health needs of individual residents among urban and rural areas and regions with varying economic development. Furthermore, considerations of this approach in the current worldwide scenario are analyzed.
Tài liệu tham khảo
WHO, “Primary health care on the road to universal health coverage: 2019 global monitoring report,” 2019.
Dickman SL, Himmelstein DU, Woolhandler S. Inequality and the health-care system in the USA. Lancet. 2017;389(10077):1431–41. https://doi.org/10.1016/S0140-6736(17)30398-7.
Zhang T, Xu Y, Ren J, Sun L, Liu C. Inequality in the distribution of health resources and health services in China: hospitals versus primary care institutions. Int J Equity Health. 2017;16(1):42. https://doi.org/10.1186/s12939-017-0543-9.
Pires LN, de Carvalho LB, Rawet EL. Multi-dimensional inequality and COVID-19 in Brazil. Investig Económica. 2021;80(315):33–58.
Alkire S, Roche JM, Ballon P, Foster J, Santos ME, Seth S. Multidimensional poverty measurement and analysis. New York: Oxford University Press; 2015.
Wang X, Feng H, Xia Q, Alkire S. On the relationship between income poverty and multidimensional poverty in China. In: Oxford poverty hum. dev. initiat., no. 712111005; 2016.
Alkire S, Roche JM, Vaz A. Changes over time in multidimensional poverty: methodology and results for 34 countries. World Dev. 2017;94:232–49. https://doi.org/10.1016/j.worlddev.2017.01.011.
Sachs JD. From millennium development goals to sustainable development goals. Lancet. 2012;379(9832):2206–11. https://doi.org/10.1016/S0140-6736(12)60685-0.
Liu Y, Guo Y, Zhou Y. Poverty alleviation in rural China: policy changes, future challenges and policy implications. China Agric Econ Rev. 2018;10(2):241–59. https://doi.org/10.1108/CAER-10-2017-0192.
Zhou Y, Guo Y, Liu Y, Wu W, Li Y. Targeted poverty alleviation and land policy innovation: some practice and policy implications from China. Land Use Policy. 2018;74:53–65. https://doi.org/10.1016/j.landusepol.2017.04.037.
Ravallion M, Jalan J. Transient poverty in rural China. World Bank Policy Res Work Pap. 1996;357(1616):338–57.
Sen A. Poverty: an ordinal approach to measurement. Econometrica. 1976;44(2):219. https://doi.org/10.2307/1912718.
R. Warshaw, “Health disparities affect millions in rural U.S. communities,” 2017. https://www.aamc.org/news-insights/health-disparities-affect-millions-rural-us-communities (accessed Mar. 30, 2021).
Elliott S, Satterfield SJ, Solorzano G, Bowen S, Hardison-Moody A, Williams L. Disenfranchised: how lower income mothers navigated the social safety net during the COVID-19 pandemic. Socius. 2021;7:23780231211031690. https://doi.org/10.1177/23780231211031690.
Gonzalez D, Zuckerman S, Kenney GM, Karpman M. Almost half of adults in families losing work during the pandemic avoided health care because of costs or COVID-19 concerns. Washington: Urban Institute; 2020.
Gill I, Schellekens P. COVID-19 is a developing country pandemic: Brookings; 2021. Available: https://www.brookings.edu/.../2021/05/27/covid-19-is-a-developing-country-pandemic.
Wang X, He G. Digital financial inclusion and farmers’ vulnerability to poverty: evidence from rural China. Sustainability. 2020;12(4):1668. https://doi.org/10.3390/su12041668.
Liu W, Xu J, Li J. The influence of poverty alleviation resettlement on rural household livelihood vulnerability in the Western mountainous areas, China. Sustainability. 2018;10(8):2793. https://doi.org/10.3390/su10082793.
Gloede O, Menkhoff L, Waibel H. Shocks, individual risk attitude, and vulnerability to poverty among rural households in Thailand and Vietnam. World Dev. 71(C):54–78. https://doi.org/10.1016/j.worlddev.2013.11.005.
Dutta I, Foster J, Mishra A. On measuring vulnerability to poverty. Soc Choice Welfare. 2011;37(4):743–61 Available: http://www.jstor.org/stable/23026335.
Ward PS. Transient poverty, poverty dynamics, and vulnerability to poverty: an empirical analysis using a balanced panel from rural China. World Dev. 2016;78:541–53. https://doi.org/10.1016/j.worlddev.2015.10.022.
World Bank. World development report 2000/2001: attacking poverty: The World Bank; 2000. Available: http://documents.worldbank.org/curated/en/673161468161371338/World-development-report-2000-2001-attacking-poverty-overview.
Chen J, Rong S, Song M. Poverty vulnerability and poverty causes in rural China. Soc Indic Res. 2021;153(1):65–91. https://doi.org/10.1007/s11205-020-02481-x.
Imai KS, Wang X, Kang W. Poverty and vulnerability in rural China: effects of taxation. J Chinese Econ Bus Stud. 2010;8(4):399–425. https://doi.org/10.1080/14765284.2010.513177.
Glewwe P, Hall G. Are some groups more vulnerable to macroeconomic shocks than others? Hypothesis tests based on panel data from Peru. J Dev Econ. 1998;56(1):181–206. https://doi.org/10.1016/S0304-3878(98)00058-3.
Chaudhuri S. Assessing vulnerability to poverty : concepts , empirical methods and illustrative examples. In: Dep. Econ.; 2003. p. 56. Available: http://info.worldbank.org/etools/docs/library/97185/keny_0304/ke_0304/vulnerability-assessment.pdf.
Dercon S, Krishnan P. Vulnerability, seasonality and poverty in Ethiopia. J Dev Stud. 2000;36(6):25–53. https://doi.org/10.1080/00220380008422653.
Bailey MJ, Danziger S, editors. Legacies of the war on poverty: Russell Sage Foundation; 2013. Available: https://www.jstor.org/stable/10.7758/9781610448147.
Ligon E, Schechter L. Measuring vulnerability. Econ J. 2003;113(486):C95–C102. https://doi.org/10.1111/1468-0297.00117.
Chaudhuri S, Jalan J, Suryahadi A. Assessing household vulnerability to poverty from cross-sectional data: a methodology and estimates from Indonesia. In: World, vol. 0102–52; 2002. p. 36. Available: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=6086807498683160254related:vpYxtyuveFQJ.
Islam TMT, Minier J, Ziliak JP. On persistent poverty in a rich country. South Econ J. 2015;81(3):653–78. https://doi.org/10.4284/0038-4038-2012.243.
Feeny S, McDonald L. Vulnerability to multidimensional poverty: findings from households in Melanesia. J Dev Stud. 2016;52(3):447–64. https://doi.org/10.1080/00220388.2015.1075974.
Bollinger CR, Hirsch BT. Is earnings nonresponse ignorable? Rev Econ Stat. 2013;95(2):407–16. https://doi.org/10.1162/REST_a_00264.
Novignon J, Nonvignon J, Mussa R, Chiwaula LS. Health and vulnerability to poverty in Ghana: evidence from the Ghana living standards survey round 5. Health Econ Rev. 2012;2(1):1–9. https://doi.org/10.1186/2191-1991-2-11.
Lu J, Zhang M, Zhang J, Xu C, Cheng B. Can health poverty alleviation project reduce the economic vulnerability of poor households? Evidence from Chifeng City, China. Comput Ind Eng. 2021;162:107762. https://doi.org/10.1016/j.cie.2021.107762.
Ziliak JP, Gundersen C, Smeeding T, Bartfeld J, Matters SNAP. How food stamps affect health and well-being. Stanford: Stanford University Press; 2015.
Satterthwaite D, Tacoli C. Health poverty vulnerability. In: Rakodi C, Lloyd-Jones T, editors. Urban Livelihoods. London: Routledge; 2002.
Azeem MM, Mugera AW, Schilizzi S. Poverty and vulnerability in the Punjab, Pakistan: a multilevel analysis. J Asian Econ. 2016;44:57–72. https://doi.org/10.1016/j.asieco.2016.04.001.
Klasen S, Waibel H. Vulnerability to poverty in South-East Asia: drivers, measurement, responses, and policy issues. World Dev. 2014;71:1–3. https://doi.org/10.1016/j.worlddev.2014.01.007.
He Y, Zhou L, Li J, Wu J. An empirical analysis of the impact of income inequality and social capital on physical and mental health - take China’s micro-database analysis as an example. Int J Equity Health. 2021;20(1):1–14. https://doi.org/10.1186/s12939-021-01560-w.
Maqsood A, Abbas J, Rehman G, Mubeen R. The paradigm shift for educational system continuance in the advent of COVID-19 pandemic: mental health challenges and reflections. Curr Res Behav Sci. 2021;2:100011. https://doi.org/10.1016/j.crbeha.2020.100011.
Darviri C, Fouka G, Gnardellis C, Artemiadis AK, Tigani X, Alexopoulos EC. Determinants of self-rated health in a representative sample of a rural population: a cross-sectional study in Greece. Int J Environ Res Public Health. 2012;9(3):943–54. https://doi.org/10.3390/ijerph9030943.
Jivraj S. Are self-reported health inequalities widening by income? An analysis of British pseudo birth cohorts born, 1920-1970. J Epidemiol Community Health. 2020;74(3):255–9. https://doi.org/10.1136/jech-2019-213186.
Joutsenniemi KE, et al. Official marital status, cohabiting, and self-rated health-time trends in Finland, 1978-2001. Eur J Pub Health. 2006;16(5):476–83. https://doi.org/10.1093/eurpub/cki221.
Fu XZ, et al. Inequity in inpatient services utilization: a longitudinal comparative analysis of middle-aged and elderly patients with the chronic non-communicable diseases in China. Int J Equity Health. 2020;19(1):1–17. https://doi.org/10.1186/s12939-019-1117-9.
Zhou Y, Yao X, Jian W. Improving health equity: changes in self-assessed health across income groups in China. Int J Equity Health. 2018;17(1):1–11. https://doi.org/10.1186/s12939-018-0808-y.
Zhong Y, Wang J, Nicholas S. Gender, childhood and adult socioeconomic inequalities in functional disability among Chinese older adults. Int J Equity Health. 2017;16(1):1–11. https://doi.org/10.1186/s12939-017-0662-3.
Gu H, et al. Measurement and decomposition of income-related inequality in self-rated health among the elderly in China 14 economics 1402 applied economics 11 medical and health sciences 1117 public health and health services 14 economics 1403 econometrics. Int J Equity Health. 2019;18(1):1–11. https://doi.org/10.1186/s12939-019-0909-2.
Berkman LF. Assessing the physical health effects of social networks and social support. Annu Rev Public Health. 1984;5(1):413–32. https://doi.org/10.1146/annurev.pu.05.050184.002213.
Nieminen T, Härkänen T, Martelin T, Borodulin K, Koskinen S. Social capital and all-cause mortality among Finnish men and women aged 30-79. Eur J Pub Health. 2015;25(6):972–8. https://doi.org/10.1093/eurpub/ckv058.
Noguchi M, Kobayashi T, Iwase T, Suzuki E, Kawachi I, Takao S. Social capital and suicidal ideation in community-dwelling older residents: a multilevel analysis of 10,094 subjects in Japan. Am J Geriatr Psychiatry. 2017;25(1):37–47. https://doi.org/10.1016/j.jagp.2016.10.014.
Choi KW, et al. An exposure-wide and Mendelian randomization approach to identifying modifiable factors for the prevention of depression. Am J Psychiatry. 2020;177(10):944–54. https://doi.org/10.1176/appi.ajp.2020.19111158.
Hossain MK, Hassanzadeganroudsari M, Apostolopoulos V. The emergence of new strains of SARS-CoV-2. What does it mean for COVID-19 vaccines? Expert Rev Vaccines. 2021;20(6):635–8. https://doi.org/10.1080/14760584.2021.1915140.
van Oosterhout C, Hall N, Ly H, Tyler KM. COVID-19 evolution during the pandemic – implications of new SARS-CoV-2 variants on disease control and public health policies. Virulence. 2021;12(1):507–8. https://doi.org/10.1080/21505594.2021.1877066.
Andersen R. A behavioral model of families’ use of health services. In: Research Ser., no. 25. Chicago: Center for Health Administration Studies, University of Chicago; 1968. p. 111.
Li Y, Huang L. Assessing the impact of public transfer payments on the vulnerability of rural households to health care poverty in China. BMC Health Serv Res. 2022;22(1):242. https://doi.org/10.1186/s12913-022-07604-3.
Alkire S, Foster J. Understandings and misunderstandings of multidimensional poverty measurement. J Econ Inequal. 2011;9(2):289–314. https://doi.org/10.1007/s10888-011-9181-4.
Oxford Poverty & Human Development Initiative, “Global multidimensional poverty index 2020,” 2020.
Christiaensen LJ, Subbarao K. Towards an understanding of household vulnerability in rural Kenya. J Afr Econ. 2005;14(4):520–58. https://doi.org/10.1093/jae/eji008.
Amemiya T. The maximum likelihood and the nonlinear three-stage least squares estimator in the general nonlinear simultaneous equation model. Econometrica. 1977;45(4):955–68. https://doi.org/10.2307/1912684.
Babitsch B, Gohl D, von Lengerke T. Re-revisiting Andersen’s Behavioral Model of Health Services Use: a systematic review of studies from 1998–2011. Psychosoc Med. 2012;9:Doc11. https://doi.org/10.3205/psm000089.
Andersen R. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1–10.
Alkire S, Kanagaratnam U, Suppa N. The global multidimensional poverty index (MPI): 2020 revision. In: OPHI MPI Methodol. Notes 49. Oxford: Oxford Poverty Hum. Dev. Initiat. Univ.; 2020.
Institute of Social Science Survey. China family panel studies: Peking University; 2021. http://www.isss.pku.edu.cn/cfps/ (accessed Mar. 30, 2021)
Diakoulaki D, Mavrotas G, Papayannakis L. Determining objective weights in multiple criteria problems: the CRITIC method. Comput OR. 1995;22:763–70. https://doi.org/10.1016/0305-0548(94)00059-H.
Villani C. Chapter 2C - H theorem and trend to equilibrium, vol. 1. North-Holland: S. Friedlander and D. B. T.-H. of M. F. D. Serre; 2002. p. 189–244.
Lozano S, Iribarren D, Moreira MT, Feijoo G. Environmental impact efficiency in mussel cultivation. Resour Conserv Recycl. 2010;54(12):1269–77. https://doi.org/10.1016/j.resconrec.2010.04.004.
Glauben T, Prehn S, Brümmer B, Loy J-P. Options trading in agricultural futures markets: a reasonable instrument of risk hedging, or a driver of agricultural price volatility? World Dev. 2012;40(4):784–95.
Dimova R, Wolff FC. Are private transfers poverty and inequality reducing? Household level evidence from Bulgaria. J Comp Econ. 2008;36(4):584–98. https://doi.org/10.1016/j.jce.2008.05.002.
Brady D. The welfare state and relative poverty in rich Western democracies, 1967-1997. Soc Forces. 2005;83(4):1329–64 Available: http://www.jstor.org/stable/3598396.
Dillman DA, House CC. Measuring what we spend: toward a new consumer expenditure survey. Washington: National Academies Press; 2012.
Kakwani N, Subbarao K. Poverty among the elderly in sub-Saharan Africa and the role of social pensions. J Dev Stud. 2007;43(6):987–1008. https://doi.org/10.1080/00220380701466476.
Yuan Y, Wang M, Zhu Y, Huang X, Xiong X. Urbanization’s effects on the urban-rural income gap in China: a meta-regression analysis. Land Use Policy. 2020;99:104995. https://doi.org/10.1016/j.landusepol.2020.104995.
Yin L, Guangming L. Research on the sources and channels of inequality of opportunity in China. China Ind Econ. 2019;9:60–78. https://doi.org/10.19581/j.cnki.ciejournal.2019.09.004.
European Union, “Climate Action. 2050 long-term strategy,” 2020.
Barman A, Das R, De PK. Impact of COVID-19 in food supply chain: disruptions and recovery strategy. Curr. Res. Behav. Sci. 2021;2:100017. https://doi.org/10.1016/j.crbeha.2021.100017.
Pieper D, Kotte N, Ober P. The effect of a voucher incentive on a survey response rate in the clinical setting: a quasi-randomized controlled trial. BMC Med Res Methodol. 2018;18(1):86. https://doi.org/10.1186/s12874-018-0544-4.
Jung J, Tran C. Health care financing over the life cycle, Universal Medical Vouchers and Welfare; 2010.
Dennis E, et al. Falling living standards during the COVID-19 crisis: quantitative evidence from nine developing countries. Sci Adv. 2022;7(6):eabe0997. https://doi.org/10.1126/sciadv.abe0997.
Liu S, Coyte PC, Fu M, Zhang Q. Measurement and determinants of catastrophic health expenditure among elderly households in China using longitudinal data from the CHARLS. Int J Equity Health. 2021;20(1):1–9. https://doi.org/10.1186/s12939-020-01336-8.