Essays on Information Disclosure, Healthcare Marketing & Consumption
dc.contributor.author | Guo, Tong | |
dc.date.accessioned | 2018-10-25T17:41:30Z | |
dc.date.available | NO_RESTRICTION | |
dc.date.available | 2018-10-25T17:41:30Z | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/146018 | |
dc.description.abstract | Recent regulatory changes have introduced more transparency to healthcare practice and marketing. The intention of these regulatory changes is to help consumers make more informed decisions, to reduce healthcare costs, and to resolve conflict-of-interest issues. My work in this area aims to understand if and to what extent such regulations achieve the desired goals, and in what ways firms and physicians are impacted. In addition, my work also investigates whether there are unintended consequences of such regulation. My dissertation studies the disclosure of a specific form of information: marketing payments to physicians from pharmaceutical firms and their rivals. In two essays, I investigate how making this information public changes physician prescriptions and firm payments, as well as whether there are unintended consequences of such regulation. In the first essay of my dissertation, "Let the Sun Shine In: The Impact of Industry Payment Disclosure on Physician Prescription Behavior", I provide evidence on the effectiveness of increased transparency of physicians' industry financial ties in reducing physician prescriptions. Specifically, I use individual-level claims data from a major provider of health insurance in the U.S. and employ a difference-in-difference research design to study the effect of the payment disclosure law introduced in Massachusetts in June 2009. The research design exploits the fact that while physicians operating in Massachusetts were impacted by the legislation, their counterparts in the neighboring states of Connecticut and New York were not. In order to keep the groups of physicians comparable, I restrict my analysis to the physicians in the counties that are on the border of these states. I find that the Massachusetts disclosure law resulted in a decline in prescriptions in all three drug classes studied: statins, antidepressants, and antipsychotics. My findings are robust under alternative controls, time periods, and variable transformations. I show that the effect is highly heterogeneous across brands and physician groups, and that the decrease in prescription is unlikely due to changes in financial incentives. In the second essay, "The Effect of Information Disclosure on Industry Payments to Physicians", I seek answer to the following question: does disclosing industry payment information influence subsequent payments to physicians? I quantify the impact of information disclosure during 2014-2015 (after ACA Physician Open Payment Act) on direct-to-physician payments. In essence, I use a quasi-experimental difference-in-difference research design to find control "clones" for every physician-product pair in the states with and without prior disclosure laws, facilitated by recent advances in machine learning methods. The novel algorithm (Wager and Athey, 2017) is computationally efficient and robust to model mis-specifications, while preserving consistency and asymptotic normality. Using a 29-month national panel covering $100 million in payments between 16 anti-diabetics brands and 50,000 physicians, I find that the monthly payments declined by 2% on average due to disclosure. However, there is considerable heterogeneity in the treatment effects with 14% of the drug-physician pairs showing a significant increase in their monthly payment. Moreover, the decline in payment is smaller among drugs with larger marketing expenditure, and among physicians who were paid more heavily pre-disclosure and prescribed more heavily. Thus, while information disclosure did lead to reduction in payments on average (as intended by policy makers), the effect is limited on big drugs and popular physicians. I further explore potential mechanisms that are consistent with the data pattern. | |
dc.language.iso | en_US | |
dc.subject | Pharmaceutical Marketing | |
dc.subject | Information Disclosure | |
dc.subject | Causal Inference | |
dc.subject | Heterogeneous Treatment Effect | |
dc.subject | Public Policy | |
dc.subject | Machine Learning | |
dc.title | Essays on Information Disclosure, Healthcare Marketing & Consumption | |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | PhD | en_US |
dc.description.thesisdegreediscipline | Business Administration | |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | |
dc.contributor.committeemember | Manchanda, Puneet | |
dc.contributor.committeemember | Sriram, Srinivasaraghavan | |
dc.contributor.committeemember | Zhu, Ji | |
dc.contributor.committeemember | Miller, Sarah Marie | |
dc.subject.hlbsecondlevel | Marketing | |
dc.subject.hlbtoplevel | Business and Economics | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146018/1/tongguo_1.pdf | |
dc.identifier.orcid | 0000-0002-3171-2890 | |
dc.identifier.name-orcid | Guo, Tong; 0000-0002-3171-2890 | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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