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Estimating Heterogeneity in the Benefits of Medical Treatment Intensity

  • William N. Evans
  • Craig L. Garthwaite

Federal and state laws passed in the late 1990 increased considerably postpartum stays for newborns. Using all births in California over the 1995-2001 period, 2SLS estimates suggest that for the average newborn impacted by the law, increased treatment intensity had modest and statistically insignificant (p-value>0.05) impacts on readmission probabilities. Allowing the treatment effect to vary by pre-existing conditions or the pre-law propensity score of being discharged early, two objective measures of medical need, demonstrates that the law had large and statistically significant impacts for those with the greatest likelihood of a readmission. These results demonstrate heterogeneity in the returns to greater treatment intensity, and the returns to the average and marginal patient vary considerably.

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File URL: http://www.nber.org/papers/w15309.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15309.

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Date of creation: Sep 2009
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Publication status: published as William N. Evans & Craig Garthwaite, 2012. "Estimating Heterogeneity in the Benefits of Medical Treatment Intensity," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 635-649, August.
Handle: RePEc:nbr:nberwo:15309
Note: CH HC HE
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  1. Amy Finkelstein & Robin McKnight, 2005. "What Did Medicare Do (And Was It Worth It)?," NBER Working Papers 11609, National Bureau of Economic Research, Inc.
  2. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York.
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  6. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 487-535.
  7. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects and Econometric Policy Evaluation," NBER Working Papers 11259, National Bureau of Economic Research, Inc.
  8. Evans, William N. & Garthwaite, Craig & Wei, Heng, 2008. "The impact of early discharge laws on the health of newborns," Journal of Health Economics, Elsevier, vol. 27(4), pages 843-870, July.
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  10. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
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  12. Amitabh Chandra & Douglas O. Staiger, 2007. "Productivity Spillovers in Health Care: Evidence from the Treatment of Heart Attacks," Journal of Political Economy, University of Chicago Press, vol. 115, pages 103-140.
  13. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
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