Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 131-136). / This thesis consists of three chapters on the economics of pain management, focusing on the effects of public policies that reduce availability of opioid pain relievers. In each of the three chapters, I exploit state-level variation in the introduction of Prescription Monitoring Program (PMP) laws as a source of plausibly exogenous variation in availability of prescription opioids. I employ several rich data sources, including individual-level administrative medical claims data linked to work absences and disability experience, and National Vital Statistics System mortality data, to investigate a series of questions regarding the optimal regulation of opioid pain relievers. In Chapter 1, I document important welfare tradeoffs in the regulation of prescription opioids. I find that prescribing restrictions achieve a key policy goal in reducing opioid overdose deaths by about 12%, but also find substantial costs, including increased pain in the hospital setting, more missed work days for injured and disabled workers, and increased total medical spending. A back-of-the-envelope welfare calculation suggests welfare losses and gains from regulation are on the same order of magnitude - approximately $12.1 billion per year in increased costs from inpatient and outpatient medical spending plus lost wages, compared to $7.3 billion per year in benefits from lives saved from opioid overdose. In Chapter 2, I consider illicit opioid and heroin abuse. I investigate the determinants of the nearly 40% year-on-year increase in heroin mortality since 2010, focusing on the relationship between prescription opioids and heroin, by decomposing demand- and supply-side effects of recent crackdowns on prescription opioids. I utilize a county-border strategy that tests for and then exploits cross-state spillovers from neighboring-state crackdowns. I find evidence that heroin is a short-run substitute and long-run complement for prescription opioids. Underlying this relationship are two opposing forces: a reduction in illicit opioid supply drives up use of heroin, but a reduction in medical provision of opioids reduces that demand in the long run. Finally, in Chapter 3, I apply machine learning techniques to study heterogeneous responses to prescribing restrictions, identifying, characterizing, and studying more closely the marginal patients who lose access to prescription opioids after PMP introduction. / by Angela E. Kilby. / Ph. D.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/107321 |
| Date | January 2016 |
| Creators | Kilby, Angela E |
| Contributors | Jonathan Gruber and Heidi Williams., Massachusetts Institute of Technology. Department of Economics., Massachusetts Institute of Technology. Department of Economics. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 162 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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