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Latent Causal Socioeconomic Health Index

Author

Listed:
  • Swen Kuh
  • Grace S. Chiu
  • Anton H. Westveld

Abstract

This research develops a model-based LAtent Causal Socioeconomic Health (LACSH) index at the national level. Motivated by the need for a holistic national well-being index, we build upon the latent health factor index (LHFI) approach that has been used to assess the unobservable ecological/ecosystem health. LHFI integratively models the relationship between metrics, latent health, and covariates that drive the notion of health. In this paper, the LHFI structure is integrated with spatial modeling and statistical causal modeling. Our efforts are focused on developing the integrated framework to facilitate the understanding of how an observational continuous variable might have causally affected a latent trait that exhibits spatial correlation. A novel visualization technique to evaluate covariate balance is also introduced for the case of a continuous policy (treatment) variable. Our resulting LACSH framework and visualization tool are illustrated through two global case studies on national socioeconomic health (latent trait), each with various metrics and covariates pertaining to different aspects of societal health, and the treatment variable being mandatory maternity leave days and government expenditure on healthcare, respectively. We validate our model by two simulation studies. All approaches are structured in a Bayesian hierarchical framework and results are obtained by Markov chain Monte Carlo techniques.

Suggested Citation

  • Swen Kuh & Grace S. Chiu & Anton H. Westveld, 2020. "Latent Causal Socioeconomic Health Index," Papers 2009.12217, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2009.12217
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    1. Keele, Luke J. & Titiunik, Rocío, 2015. "Geographic Boundaries as Regression Discontinuities," Political Analysis, Cambridge University Press, vol. 23(1), pages 127-155, January.
    2. Jeni Klugman & Francisco Rodríguez & Hyung-Jin Choi, 2011. "The HDI 2010: new controversies, old critiques," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(2), pages 249-288, June.
    3. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    4. Corwin M. Zigler & Krista Watts & Robert W. Yeh & Yun Wang & Brent A. Coull & Francesca Dominici, 2013. "Model Feedback in Bayesian Propensity Score Estimation," Biometrics, The International Biometric Society, vol. 69(1), pages 263-273, March.
    5. Grace S Chiu & Margaret A Wu & Lin Lu, 2013. "Model-Based Assessment of Estuary Ecosystem Health Using the Latent Health Factor Index, with Application to the Richibucto Estuary," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    6. Corwin Matthew Zigler, 2016. "The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 47-54, February.
    7. David Kaplan & Jianshen Chen, 2012. "A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 581-609, July.
    8. Auke Rijpma, 2016. "What can’t money buy? Wellbeing and GDP since 1820," Working Papers 0078, Utrecht University, Centre for Global Economic History.
    9. John F. Helliwell & Haifang Huang, 2014. "New Measures Of The Costs Of Unemployment: Evidence From The Subjective Well-Being Of 3.3 Million Americans," Economic Inquiry, Western Economic Association International, vol. 52(4), pages 1485-1502, October.
    10. Shawn Treier & Simon Jackman, 2008. "Democracy as a Latent Variable," American Journal of Political Science, John Wiley & Sons, vol. 52(1), pages 201-217, January.
    11. McCandless Lawrence C & Douglas Ian J. & Evans Stephen J. & Smeeth Liam, 2010. "Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-24, March.
    12. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    13. Yang, Lin, 2018. "Measuring well-being: a multidimensional index integrating subjective well-being and preferences," LSE Research Online Documents on Economics 87789, London School of Economics and Political Science, LSE Library.
    14. Hanson, Timothy E., 2006. "Inference for Mixtures of Finite Polya Tree Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1548-1565, December.
    15. Lin Yang, 2018. "Measuring Well-being: A Multidimensional Index Integrating Subjective Well-being and Preferences," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 19(4), pages 456-476, October.
    16. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    17. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    18. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    19. Jackman, Simon, 2001. "Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking," Political Analysis, Cambridge University Press, vol. 9(3), pages 227-241, January.
    20. Sudipto Banerjee, 2005. "On Geodetic Distance Computations in Spatial Modeling," Biometrics, The International Biometric Society, vol. 61(2), pages 617-625, June.
    21. Jeffrey D. Sachs & Richard Layard & John F. Helliwell, 2018. "World Happiness Report 2018," Working Papers id:12761, eSocialSciences.
    22. Corwin Matthew Zigler & Francesca Dominici, 2014. "Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model-Averaged Causal Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 95-107, March.
    23. Horrace, William C., 2005. "Some results on the multivariate truncated normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 94(1), pages 209-221, May.
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