There have been more than 700,000 opioid overdose deaths since 2000. To analyze the opioid epidemic, a model is constructed where individuals choose whether to use opioids recreationally, knowing the probabilities of addiction and dying. These odds are functions of recreational opioid usage. The model is fit to estimated Markov chains from the US data that summarize the transitions into and out of opioid addiction as well as to a deadly overdose. The epidemic is broken down into two subperiods: 2000-2010 and 2010–2019. The opioid epidemic's drivers, their impact on employment, and the impact of medical interventions are examined. Lax prescribing practices and misinformation about the risk of addiction are important drivers of the first half of the epidemic. Falling prices for black-market opioids combined with an increase in their lethality are found to be important for the second half.
The role of friends in the US opioid epidemic is examined. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), adults aged 25-34 and their high school best friends are focused on. An instrumental variable technique is employed to estimate peer effects in opioid misuse. Severe injuries in the previous year are used as an instrument for opioid misuse in order to estimate the causal impact of someone misusing opioids on the probability that their best friends also misuse. The estimated peer effects are significant: Having a best friend with a reported serious injury in the previous year increases the probability of own opioid misuse by around 7 percentage points in a population where 17 percent ever misuses opioids. The effect is driven by individuals without a college degree and those who live in the same county as their best friends.
According to Pareto, the distribution of income depends on "the nature of the people comprising a society, on the organization of the latter, and, also, in part, on chance."
