Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group


First Advisor

Petra Todd


Opioid abuse is currently the most significant public health problem in the US. Many US

states have implemented prescription drug monitoring programs (PDMPs) in response. In

the rst paper, I use a new micro-level medical claims database to exploit state-level and

time-series variations in PDMP implementation and shed light on the impacts of these

programs. My results show that PDMPs have led to an overall 14% reduction in the odds

ratio of abuse/addiction. Also, there is evidence of substantial heterogeneity in impacts,

with larger impacts for females and minorities. Another nding is that at least 23% of

opioid abuse is a result of drug diversion to nonmedical opioid users. PDMPs were not

successful in decreasing the rate of abuse for this group, and, in fact, there is some

evidence that they increased the diversion to heroin. Finally, I show that PDMPs'

eectiveness varies by type of insurance and that they are more eective in reducing abuse

rates in the general population as compared with Medicare Part D recipients. I use my

estimates to analyze the potential eects of modifying PDMPs to include giving insurance

providers access to electronic databases, providing educational programs for less-educated

people, and expanding their \must access" requirement. In the second chapter, I estimate

dierent models for opioid demand and compare their performance. My results suggest

that the NB2 and Poisson FE models best match the data. Using these models for

calculating the marginal effect of insurance characteristics provides suggestive evidence of

the best insurance design to reduce the demand for opioids.