IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i13p2314-d244310.html
   My bibliography  Save this article

Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community

Author

Listed:
  • Hai-Yan Yu

    (School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Department of Statistics, Eberly College of Science, The Pennsylvania State University, University Park, PA 16802, USA)

  • Jing-Jing Chen

    (School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

  • Jying-Nan Wang

    (College of International Finance and Trade, Zhejiang Yuexiu University of Foreign Languages, Shaoxing 312000, China)

  • Ya-Ling Chiu

    (College of International Business, Zhejiang Yuexiu University of Foreign Languages, Shaoxing 312000, China)

  • Hang Qiu

    (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Li-Ya Wang

    (Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Inequality of health services for different specialty categories not only occurs in different areas in the world, but also happens in the online service platform. In the online health community (OHC), health services often display inequality for different specialty categories, including both online views and medical consultations for offline registered services. Moreover, how the city-level factors impact the inequality of health services in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities with rich healthcare resources with the potential control cities. For each of the specialty categories, we first estimated the average treatment effect of the specialty category’s Gini coefficient (SCGini) with the balanced covariates. For the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. Similarly, for the Gini coefficient of serviced patients, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. The results support the argument that the total Gini coefficient of the doctors in OHCs shows that the inequality in health services is still very serious. This study contributes to the development of a theoretically grounded understanding of the causal effect of city-level factors on the inequality of health services in an online to offline health service setting. In the future, heterogeneous results should be considered for distinct groups of doctors who provide different combinations of online contributions and online attendance.

Suggested Citation

  • Hai-Yan Yu & Jing-Jing Chen & Jying-Nan Wang & Ya-Ling Chiu & Hang Qiu & Li-Ya Wang, 2019. "Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community," IJERPH, MDPI, vol. 16(13), pages 1-18, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:13:p:2314-:d:244310
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/13/2314/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/13/2314/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. repec:cup:cbooks:9780511761942 is not listed on IDEAS
    3. Francesca Greselin & Ričardas Zitikis, 2018. "From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective," Econometrics, MDPI, vol. 6(1), pages 1-20, January.
    4. Alberto Cavallo, 2017. "Are Online and Offline Prices Similar? Evidence from Large Multi-channel Retailers," American Economic Review, American Economic Association, vol. 107(1), pages 283-303, January.
    5. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    6. Pucher, J. & Buehler, R. & Bassett, D.R. & Dannenberg, A.L., 2010. "Walking and cycling to health: A comparative analysis of city, state, and international data," American Journal of Public Health, American Public Health Association, vol. 100(10), pages 1986-1992.
    7. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    8. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
    9. Erik Brynjolfsson & Yu (Jeffrey) Hu & Duncan Simester, 2011. "Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales," Management Science, INFORMS, vol. 57(8), pages 1373-1386, August.
    10. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    11. Hang Qiu & Kun Tan & Feiyu Long & Liya Wang & Haiyan Yu & Ren Deng & Hu Long & Yanlong Zhang & Jingping Pan, 2018. "The Burden of COPD Morbidity Attributable to the Interaction between Ambient Air Pollution and Temperature in Chengdu, China," IJERPH, MDPI, vol. 15(3), pages 1-15, March.
    12. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    13. David Neumark & William Wascher, 1992. "Employment Effects of Minimum and Subminimum Wages: Panel Data on State Minimum Wage Laws," ILR Review, Cornell University, ILR School, vol. 46(1), pages 55-81, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    2. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    3. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    4. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    5. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    6. Elaine M. Wolf & Douglas A. Wolf, 2008. "Mixed Results in a Transitional Planning Program for Alternative School Students," Evaluation Review, , vol. 32(2), pages 187-215, April.
    7. Li Liang & Greene Tom, 2013. "A Weighting Analogue to Pair Matching in Propensity Score Analysis," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 215-234, July.
    8. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    9. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute of Labor Economics (IZA).
    10. Burger, Anže & Hogan, Teresa & Kotnik, Patricia & Rao, Sandeep & Sakinç, Mustafa Erdem, 2023. "Does acquisition lead to the growth of high-tech scale-ups? Evidence from Europe," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Esaka, Taro, 2013. "Evaluating the effect of de facto pegs on currency crises," Journal of Policy Modeling, Elsevier, vol. 35(6), pages 943-963.
    12. Miguel Minutti‐Meza, 2013. "Does Auditor Industry Specialization Improve Audit Quality?," Journal of Accounting Research, Wiley Blackwell, vol. 51(4), pages 779-817, September.
    13. Kim, Ja Young & Bartholomew, Keith & Ewing, Reid, 2020. "Another one rides the bus? The connections between bus stop amenities, bus ridership, and ADA paratransit demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 280-288.
    14. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.
    15. Matthew A. COLE & Robert R.J. ELLIOTT & OKUBO Toshihiro & Liyun ZHANG, 2017. "The Pollution Outsourcing Hypothesis: An empirical test for Japan," Discussion papers 17096, Research Institute of Economy, Trade and Industry (RIETI).
    16. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    17. Lenis, David & Ackerman, Benjamin & Stuart, Elizabeth A., 2018. "Measuring model misspecification: Application to propensity score methods with complex survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 48-57.
    18. Michael Lechner & Anthony Strittmatter, 2019. "Practical procedures to deal with common support problems in matching estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
    19. Wendimu, Mengistu Assefa & Henningsen, Arne & Gibbon, Peter, 2016. "Sugarcane Outgrowers in Ethiopia: “Forced” to Remain Poor?," World Development, Elsevier, vol. 83(C), pages 84-97.
    20. Patrick Christian Feihle & Jochen Lawrenz, 2017. "The Issuance of German SME Bonds and its Impact on Operating Performance," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(3), pages 227-259, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:13:p:2314-:d:244310. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.