BY 4.0 license Open Access Published by De Gruyter January 22, 2021

The Intended and Unintended Effects of Opioid Policies on Prescription Opioids and Crime

Claudio Deiana and Ludovica Giua ORCID logo

Abstract

In response to the opioid crisis, US states have implemented policies to reduce the dispensing of opioids and curb drug mortality. Exploiting a long panel of county-level data, we analyse the combination of demand- and supply-side state opioid policies and evaluate their effect on opioids per capita dispensed and their unintended fallouts on drug-related crime. We demonstrate that only laws targeting the supply for opioids reduce the volume of prescribed drugs, while demand-side policies are less effective. We also emphasize that within supply-side state regulations, Pain Management Clinics Laws are the most successful in reducing the dispensation of prescription opioids. Remarkably, the drop in opioids distributed due to supply-side regulations is accompanied by negative externalities in the local market for illicit drugs.

JEL codes: I18; K32

1 Introduction

Since the late 1990s, the rapid escalation in the use of prescription and non-prescription opioid-based drugs in the US has originated the so-called opioid epidemic, the deadliest drug overdose crisis in American history. According to the Centres for Disease Control and Prevention (CDC), yearly deaths from a drug overdose in the US have increased five-fold since 1999, reaching 63,632 victims in 2016 only, i.e. more than those caused by car crashes and gun violence in the same year (Hedegaard, Warner, and Miniño 2017). In response to this dramatic crisis, many US states have progressively enacted several laws that restrict the prescribing and the dispensing of controlled substances and promote access to emergency services in case of opioid overdose.

In this paper, we assess the impact of a wide set of opioid state laws on the quantity of prescription opioids dispensed and on drug-related arrest rates. Understanding the relative importance of each type of law can provide useful insights to policy makers, the more comprehensive the analysis. Yet, research on their effectiveness has produced conflicting results.[1]

We draw on a number of opioid-related policies adopted in the US over the past decades, namely Prescription Drug Monitoring Programs (PDMP), Pain Management Clinics Laws (PMCL) and Doctor Shopping Laws (DSL). Some of these regulations aim at reducing the amount of prescription opioids dispensed either on the supply side (PMCL and PDMP) or the demand side (DSL) of the market for drugs, depending on whether they impose restrictions on the prescribers or the patients.[2]

In order to offer an extensive view of the dynamics occurring in this context, we first consider the combination of demand- and supply-side opioid state laws simultaneously and evaluate the effect of each type of regulation taking into account the impact of the other regulations. Using a difference-in-differences set-up and linking various sources of county-level panel data, we exploit the staggered timing in the implementation of these laws across US states to identify their causal effect on the number of opioids dispensed over the period 2001–2016. While the intention of the policy makers is aimed at reducing the abuse of prescription drugs, we examine the overall amount of opioid-based active ingredients distributed at the local level under the assumption that the higher their dispensation, the higher the potential rate of abuse.

We demonstrate that the implementation of supply-side state laws reduces the quantity of prescription opioids per capita dispensed at the county level. In terms of magnitude, PDMP and PMCL yield, on average, a 4 and 15% reduction in the per capita drug units dispensed, respectively. The former provide for the implementation of databases that monitor the prescription and dispensation of controlled substances. The latter set minimum requirements for pain management clinics to operate. On the contrary, interventions regulating the demand-side of the market, by obliging patients to disclose information on their prescription history to health care professionals (DSL), do not produce an overall appreciable statistical impact on prescription rates. Such weak response casts doubts on the real efficacy of this type of intervention.[3]

We also provide some first evidence on the unintended spillovers occurring between changes in opioid legal dispensation and criminal activities. Other studies point out that drug misuse correlates with adverse fallouts of various nature (Hansen et al. 2011), and opioid state laws have been shown to have indirect effects on suicides (Borgschulte, Corredor-Waldron, and Marshall 2018), neonatal abstinence syndrome births (Gihleb, Giuntella, and Zhang 2020a) and foster care admissions (Gihleb, Giuntella, and Zhang 2020b). Yet, although lawmakers have designed these regulations to limit the misuse of legally prescribed opioids, it is still unclear whether they might generate spillovers on the illicit market of drugs, given the links between drug misuse and crime (Dave, Deza, and Horn 2018; Dobkin, Nicosia, and Weinberg 2014; Doleac and Mukherjee 2018; Mallatt 2017; Meinhofer 2017).

Despite the legal deterrents against selling controlled substances without authorisation or possessing controlled substances without a prescription, the illegal market remains a relevant source of prescription opioids for many users. In fact, the inappropriate and unnecessary quantity of prescription drugs dispensed often translates in a large amount of pills that are diverted to family members or friends of patients or to the black market.[4] While restricting the availability of excess opioid drugs can reduce misuse and, in turn, lead to better health and a potential decrease in crime, it may also be that users who face obstacles in obtaining prescription opioids turn to the black market for substitutes.[5] Indeed, previous studies document an increase in consumption of similar or even more harmful opiates (e.g. heroin) following shocks to the supply of legally available opioid drugs (Alpert, Powell, and Pacula 2018; Evans, Lieber, and Power 2019).[6]

Along these lines, existing works find evidence compatible with substitution for other illegal drugs (Mallatt 2017; Meinhofer 2017) and, more generally, higher propensity to commit crime (Dave, Deza, and Horn 2018; Doleac and Mukherjee 2018), as opioid state laws lower the availability of legally prescribed drugs. Our results suggest that reducing the legal availability of these drugs may have unintentional negative externalities on the illegal market for drugs. In particular, we observe a significant response to the enforcement of Pain Management Clinics Laws on arrest rates for the sale of opium, cocaine, their derivatives and for synthetic narcotics.

With this paper, we contribute to the recently economic-oriented literature on the effects of opioid state laws on the quantity of legally dispensed prescription opioids and drug-related crime. Compared to the existing analyses, we examine the relative effectiveness of both supply- and demand-side opioid laws in reducing the amount of drugs dispensed. Our analysis includes the assessment of the effects of two largely under-studied sets of regulations (i.e. PMCL and DSL) and sheds light on the potential unintended effect of health policies on a broader domain, namely the market for illicit substances.[7] With respect to the existing works, we deliver results on drug arrests covering the entire US population for a 16-year long period, a larger set of supply and demand-side opioid state laws.

The remainder of the paper is organised as follows. Section 2 describes the policies implemented in the past decades to curb prescription rates and the effects of the opioid crisis. Data and empirical strategy are presented in Section 3. In Section 4 we present our results on the quantity of opioids dispensed and on the spillovers on criminal activities. Section 5 concludes.

2 Opioid State Laws and Their Potential Effects

The reaction of the policy makers to the opioid crisis has come mainly at the state level, with the implementation of several laws in different states at different times. The target of these policies varies in terms of the individuals involved (patients, prescribers, pharmacists, physicians) and of the types of limitations or incentives. Specifically, these regulations can be grouped into two main categories, supply and demand laws, which are described in Table 1.

Table 1:

State laws on prescription opioids.

LawNameDescription
Supply-side laws
PDMPPrescription drug monitoring programsImplementation of systems that collect information on prescriptions of controlled substances and that allow physicians and pharmacists to view a patient’s prescribing history.
PMCLPain management clinics lawsSets of regulations concerning the minimum requirements for a pain management clinic to be allowed to dispense prescription drugs.
Demand-side law
DSLDoctor shopping lawsObligation for patients to reveal to a health care practitioner about previous prescriptions received from other doctors and prohibition to obtain drugs through fraud, deceit, misrepresentation, etc.

Following this classification, we construct a dataset that summarises the date of adoption of the opioid state laws in the years 2001–2016 (Table A.1).[8] The timing of their implementation across US states is summarised in Figure A.1, while Figure A.2 shows the geographical distribution of the laws enacted by 2016. In what follows, we explain the details of each set of laws and we briefly outline their predicted impact on the outcomes.

2.1 Supply-Side Laws

Prescription Drug Monitoring Programs (PDMP) represent the most common and well-studied supply-side policy.[9] Since the early 2000s, PDMP have been increasingly implemented across US states. Full national coverage has been reached in 2017 with Missouri, the last state to adopt this type of regulation. It consists of state-level databases that monitor the prescription and the dispensing of controlled substances. The information contained in the system is available to all authorised health-care providers including physicians and pharmacists to prevent improper drug prescription or dispensation.[10] In some states, under certain circumstances, prescribers and dispensers are required to access PDMP by law (hence, called “must-access” or “mandate”), while in others the use of this system is “non mandated”.

Pain Management Clinics Laws (PMCL) embody all regulations aimed at preventing inappropriate prescribing and dispensing of controlled substances within clinics specialised in pain management. These clinics have been such a great source of prescription drugs that they are sometimes called “Pill Mills”. They have become such a serious issue in the context of the opioid crisis that PMCL have been implemented in one every five states since the mid-2000s. Although there is some heterogeneity across states, regulations associated with PMCL typically provide for requirements concerning the ownership, the licensing procedures, the operational standards and the personnel qualification of pain management clinics, facilities or practice locations. These interventions have resulted in a massive shutdown of pain management clinics that did not meet the new standards (Mallatt 2017).

Both PDMP and PMCL induce a shock on the supply side of the market for prescription opioids because they provide for restrictions to the agents supplying drugs (physicians and pharmacists).[11] As a consequence, we expect the two policies to have a negative effect on the volume of legally-dispensed drugs. Moreover, this drop might be accompanied by an increase in drug-related crimes due to users turning to the black market in search of drugs. However, the reduced availability of drugs from the legal channel may yield a shortage in the illegal market, hence contributing to a decline in arrests for drug sale or possession.

2.2 Demand-Side Laws

Doctor Shopping Laws (DSL) are also directed at limiting the amount of opioids dispensed but they involve the demand side of the market for drugs, as they impose restrictions on patients rather than on suppliers. They refer to any regulation that prohibits doctor shopping, i.e. the practice of obtaining controlled substances from multiple healthcare practitioners. The number of states that have adopted these laws has doubled since the year 2000 and is currently around a third of the total. DSL limit a patient’s ability to seek medications from multiple providers and prohibit withholding of any information that may be relevant to the physician or the pharmacist.

Theoretically, curtailing access to prescription drugs for non-medical use in this manner is potentially effective as health care providers are the most common source of opioids used non-medically (Substance Abuse and Mental Health Services Administration, 2014). Moreover, the previous medical literature has found a positive relationship between doctor shopping practices and overdose mortality risk (Peirce et al. 2012). Thus, this set of regulations is expected to negatively affect the amount of opioids available on the market from the demand side, especially when prescriptions are unnecessary or excessive. Nevertheless, in the absence of systematic surveillance and large heterogeneity in the legal implementation across US States, the regulation could turn out to be weakly effective, since heavy users especially might have a strong incentive not to disclose the relevant information to health care professionals to obtain more painkillers than necessary.

3 Data and Empirical Strategy

In this section we describe how we combine various sources to build our main dataset. Then, we illustrate the empirical strategy and provide some descriptive statistics.

3.1 Data Sources

The data on prescription opioids comes from the Automation of Reports and Consolidated Orders System (ARCOS), which is run by the Office of Diversion Control of the US Drug Enforcement Administration. Since the Controlled Substance Act of 1970, manufacturers of controlled substances are required to provide information on the amount of drugs produced and dispensed in the US. The yearly ARCOS reports provide a record of the quantities (in grams) of each controlled active ingredient dispensed in the US. This information is disaggregated at the three-digit zip code level across all US states.

We consider a set of opium-based active ingredients available in ARCOS, namely morphine, oxycodone, hydrocodone, hydromorphone, methadone, meperidine, and fentanyl classified as Schedule II or Schedule III drugs.[12] We build an overall indicator that accounts for the relative potency of these drugs so that each drug is converted into Morphine Gram Equivalent units (MGEs).[13] Since it considers the overall amount of opium-based active ingredients, this represents our main indicator to quantify the dispensation of prescription opioids.[14]

Then, we link our dataset to the Uniform Crime Reporting (UCR) Program Data provided by the Federal Bureau of Investigation, which contains the number of arrests disaggregated by county and by type of crime. We take into account drug-related crimes that involve the possession and selling of different substances such as opium, cocaine, marijuana and other synthetic drugs.

Finally, we match the information from ARCOS to the official population intercensal estimates at the county level, which include counts of the overall population and by sex, age band and race/ethnicity group.[15] The US Census is also the source of all the data used in the heterogeneity analysis about education, health care insurance coverage and employment in health services (County Business Patterns), while income comes from the Bureau of Economic Analysis. Drug and alcohol mortality data are drawn from the Global Health Data Exchange of the Institute for Health Metrics and Evaluation (University of Washington).

Our final sample comprises 3127 counties across the US that we follow during the period 2001–2016. To our knowledge, this is the first paper evaluating the effects of supply- and demand-side opioid state laws on the volume of prescribed opioids and on drug-related crime rates that exploit such an extensive dataset, both in terms of time span and of geographical coverage at the county level.

3.2 Empirical Model

We employ a typical regression difference-in-differences setting such that:

(1)Ycst=α+βLst+μMst+δt+γc+θrt+ϵcst,

where Ycst is the outcome of interest measured in county c, in state s and in year t. The set Lst includes dummy variables for each law as from Table 1, which take value 1 when the regulation is in force in a given state and 0 otherwise. Hence, the coefficient β corresponds to the treatment effect of interest, as it captures the effect of regulation on different outcomes while controlling for the other laws. We analyse such effect on the quantity of drugs distributed and on drug-related crime.[16]

We acknowledge the contemporaneous implementation of other state regulations, specifically aimed at reducing opioid overdose mortality (namely, Naloxone Access Laws and Good Samaritan Laws) by adding a set of two dummy variables (Mst). Their role in this context is discussed in Appendix B. Moreover, we include county (γc), year (δt) and region-year (θrt) fixed effects to control for fixed heterogeneity at local level, at time and region-by-year fluctuations, respectively. Errors are clustered at the state level.[17]

A critical assumption for our identification strategy is that states that enact opioid laws and those that do not adopt them behave similarly in the pre-implementation period, to ensure that the enactment of the laws is not endogenously related to trends in opioid prescriptions. We already control for time, county and region-year heterogeneity, but the event-study analysis approach helps to check for pre-existing trends, i.e. we verify the existence of parallel trends. This posits that the average change in the comparison group represents the counterfactual change in the treatment group if there were no treatment. If the leads in the event-study analysis are not statistically different from zero, this implies that the treated counties are trending similarly to the untreated counties prior to the policy, and this constant heterogeneity vanishes in difference. Thus, the identifying assumption of the differences-in-differences model would be supported. Hence, we also estimate the following equation:

(2)Ycst=α+π=51βl,πLl,st+π+τ=15βl,τLl,st+τ+βlLl,st+μMst+δt+γc+θrt+ϵcst,

which allows for five pre- and five post-treatment effects for each law l, while still controlling for the enforcement of all the other laws (−l). According to this specification, the baseline year is the one before the implementation of law l, while leads and lags are identified by the coefficients βl,π and βl,τ, respectively. Here, the β associated to π=5 and τ=5 include all periods prior to t − 5 and after t + 5, respectively. If the leads βl,π are not statistically different from zero we can assume that the parallel trends assumption holds. The βl,τ coefficients, instead, allow us to examine whether the treatment effect of law l fades, increases or stays constant over time. Additionally, a battery of robustness checks in support of our identification strategy is presented in Section 4.1 where we discuss potential confounding effects.

3.3 Descriptive Analysis

Figure 1 shows the raw average of MGE units per capita dispensed by year since the introduction of each policy. The portion to the left of the dashed vertical line corresponds to the years prior to the onset of each law. The graph depicts a constant increase in the average amount of drugs dispensed per capita, which is only slowed down after the introduction of the policies (i.e. to the right of the dashed line). The only exception to this inversion in trend seems to be associated with DSL, for which we do not observe any change in slope.

Figure 1: Average MGEs dispensed by year since/to the introduction of the policies.Note: The labels in the x-axis are such that the zero, the negative and the positive values correspond to the year of, the years before and the years after the introduction of each policy, respectively. Treated states only (49 for PDMP, 10 for PMCL, 20 for DSL).

Figure 1:

Average MGEs dispensed by year since/to the introduction of the policies.

Note: The labels in the x-axis are such that the zero, the negative and the positive values correspond to the year of, the years before and the years after the introduction of each policy, respectively. Treated states only (49 for PDMP, 10 for PMCL, 20 for DSL).

Table A.2 reports the descriptive statistics of the main outcomes and control variables used in the analysis. Drug quantities are expressed in MGE units per capita to take into account the relative potency of each drug component. Overall, a total of 704 kg of prescription opioids (i.e. 30 g per capita) are dispensed in each county every year. Between 2001 and 2016 the total county-level average of MGE units has increased almost three-fold from 336 to 746 kg. The most commonly dispensed substances are morphine, methadone and hydrocodone, with around 13, 5 and 4 g per capita, respectively.

Figure A.3 in the Appendix describes the geographical distribution of the average MGE units in the years 2001 and 2016 (top and bottom panels, respectively). It is worth noting that had we considered the 2001 quartile distribution, we would have obtained an almost entirely red map for the year 2016. As a matter of fact, the median of the MGE units per capita distribution in 2016 is more than double the one in 2001 (14.51 and 7.08 MGEs per capita, respectively). For this reason, we construct the percentile thresholds based on the average distribution of MGE units per capita in the period 2001–2016. The colder (darker blue) areas, the lower the levels of POs per capita. Vice versa, the warmer (darker red) the area, the higher the dispensation of MGE units. Nevertheless, we observe a remarkable variation both across years and counties. The map for 2016 is much warmer compared to the one for 2001, which indicates that the dispensation of opioid analgesics per capita rises during the period analysed. Besides, the maps display a clear heterogeneity both within and across states.

4 Results

In this section we present our results. First, we assess whether, and to what extent, the policies under analysis yield a reduction in the amount of MGE units per capita dispensed in each county. Then, we estimate their unintended impact on drug-related crimes.

4.1 The Effect of State Laws on Prescription Opioids

Table 2 shows the main results on the amount of MGE prescription drugs dispensed in each county. In columns 1 to 4 we consider one set of state laws at a time: PDMP, PCML and DSL. Column 1 presents the effect of PDMP alone, while in column 2 we include an interaction term that accounts for the adoption of Mandate PDMP. In line with the existing literature, we find that the effect of Mandate PDMP is substantially higher than that of the non-compulsory PDMP. Coefficients suggest that ordinary PDMP reduces the quantity of MGEs by 4.5% while Mandate PDMP yields a further drop by 8.5%. Conversely, the overall impact of Mandate PDMP on MGE units per capita consists of a decrease by 13%. The coefficient associated to PMCL suggests that imposing more stringent operational restrictions to pain management clinics determines a decrease in the amount of MGE units dispensed by 15% (column 3). Conversely, in column 4, DSL, which compel patients to reveal to health care professionals whether they had already be prescribed or administered prescription drugs, does not appear to have any meaningful impact on the quantity of per capita MGE units.

Table 2:

Effect on drug quantities.

Dependent variable(1)(2)(3)(4)(5)(6)
MGEpcMGEpcMGEpcMGEpcMGEpcMGEpc
PDMP−0.040 (0.025)−0.045* (0.025)−0.038* (0.020)−0.041* (0.021)
Mandate PDMP−0.085** (0.037)−0.035 (0.024)
[0.130**][0.075**]
Pain management clinic law−0.150*** (0.028)−0.152*** (0.026)−0.141*** (0.026)
Doctor shopping law0.010 (0.043)0.037 (0.036)0.038 (0.035)
Observations50,03250,03250,03250,03250,03250,032
R-squared0.9870.9870.9870.9870.9870.987

  1. The dependent variable is the natural log of MGE per capita. Population-weighted OLS estimates, where the weight is computed as the share of the population in the county relative to the national population. All regressions include two dummy indicators capturing the enforcement of Naloxone Access Laws and Good Samaritan Laws, plus year, county and region-year fixed effects. Errors are clustered at the state level. Coefficient in square brackets is associated to βPDMP + βMandatePDMP. *p < 0.10, **p < 0.05, ***p < 0.01.

When we consider all sets of laws in the same model (columns 5 and 6), coefficients maintain the same sign and significance, with the exception of the one capturing the differential between PDMP and Mandate PDMP. Given that the different types of PDMP do not differ in their effect on the quantity of MGEs distributed, column 5 is our main specification.

Our estimates suggest a reduction in prescription opioids per capita following the enforcement of the state laws that aim at reducing abusive behaviour on the supply side, namely PDMP and PMCL. The negative impact in column 5 corresponds to an average reduction in the amount of MGE units per capita by almost 4% following the introduction of PDMP and by more than 15% after the enactment of PMCL.[18] Given that the average amount of MGEs per capita in the sample is 29.73, these effects translate in a drop by around 1.14 and 4.52 g per capita, respectively. Our results are in line with those of Mallatt (2017) and Meinhofer (2017), who estimate the negative effects of PDMP and PMCL on the total amount of oxycodone dispensed to be around 8 and 17%, respectively. The absence of effects predicted by DSL might be due to its weak implementation as well as to the fact that demand-side interventions generally receive less funding and attention compared to supply-side policies (Alpert, Powell, and Pacula 2018).

We provide evidence on the validity of our estimates with an event-study approach, which allows estimating lagged effects while testing for the absence of pre-existing trends. This is shown in Figure 2. The plots suggest the existence of parallel trends between treated and control units, as the coefficients in the pre-treatment period are never statistically different from zero. This points to the absence of a plausible systematic pattern in the distribution of MGE units per capita before the introduction of any opioid state law, which also allows us to exclude potential anticipation or announcement effects.

Figure 2: Event-study analysis: effect on drug quantities.Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t − 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure 2:

Event-study analysis: effect on drug quantities.

Note: Coefficients estimated as in Eq. (2). The coefficient associated to < 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >+ 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure 2 also highlights a small but persistent negative impact of PDMP on the outcome, while PMCL yield a sharp and increasingly large decrease in the quantity of MGE units per capita.[19] The introduction of DSL does not have any impact on dispensation rates. If anything, DSL might bring about a mild increase in the amount of MGE units per capita dispensed, although coefficients are never statistically significant.[20]

As we are in the presence of staggered time law implementation, we apply the decomposition of our estimates in the spirit of Goodman-Bacon (2018), based on comparisons across different treatment groups (namely, always treated, never treated and units that switch from being untreated to treated). Here, weights are based on the size of each treatment sub-group and on the variance of the treatment, which in turn depends on how distant is the onset of the treatment from the start and the end of the observational window. We apply the procedure to our three main estimates of the impact of opioids laws on the quantity of Morphine Gram Equivalent units. The decomposition exercise in Table 3 shows that the estimated effect for the PMCL coefficient is almost entirely driven by the comparison between never-treated units to those that enact this type of regulation starting from 2009. The comparison across treated units is much less relevant, although the sign of the coefficient is in line with the main estimate. For what concerns PDMP, the coefficient of −0.038 is derived by the comparison across treated units over time (timing groups and always-treated versus timing groups). While this is expected, given that 49 states out of 50 are eventually treated in our sample, it is reassuring that the estimated effect is not driven by the comparison with the sole state that is never treated (Missouri, which implemented PDMP in 2017). Finally, the results on Doctor Shopping Laws appear less precise, although the comparison between treated (timing groups) and never-treated suggests that, if anything, the effect should be slightly negative.

Table 3:

Effect on drug quantities: Goodman-Bacon decomposition.

PDMPPain management clinic lawDoctor shopping law
βWeightβWeightβWeight
Timing groups−0.0260.254−0.0510.080−0.0070.085
Always versus timing−0.0800.6820.0070.249
Never versus timing0.0620.028−0.1770.866−0.0470.623
Always versus never−0.3740.0001.8070.001

  1. Decomposition of the estimated effects performed as proposed by Goodman-Bacon (2018) and using the Stata command bacondecomp (Goodman-Bacon, Goldring, and Nichols 2019). Groups are defined as follows: Timing are units that become treated during our period of observation; Always are always treated; Never are never treated counties.

4.2 Robustness Checks

Table A.3 shows a set of robustness checks. In column 1 we account for additional changes in the institutional framework which might potentially interfere with the dispensation of prescription opioids and bias our estimated effects: the introduction of Medicare Part D in 2006 and the reformulation of OxyContin in 2010. Medicare Part D is a federal program that subsidizes the costs of prescription drugs and of prescription drug insurance premiums for Medicare beneficiaries. Thus, it might disproportionately compensate for the incidence of the policies on the amount of prescription opioids dispensed in areas with a larger number of beneficiaries. We proxy the exposure to the program with the share of people aged 65 or over at county level, interacted with a dummy variable that takes value 1 in the years after 2006 and 0 otherwise as in Powell, Pacula, and Taylor (2015).

OxyContin was reformulated in 2010 with the intent of making it more difficult to abuse this drug.[21]Alpert, Powell, and Pacula (2018) and Evans, Lieber, and Power (2019) show that its reformulation has been followed by a significant drop in the prescribing rates of this drug. At the same time, however, they find evidence of substitution towards other opioid-based substances, especially fentanyl and heroin. Hence, we include an interaction between the amount of oxycodone dispensed in each county in 2000 and a dummy that takes value 1 in the years after 2010 (Alpert, Powell, and Pacula 2018; Evans, Lieber, and Power 2019; Mallatt 2017). We obtain comparable results with the inclusion of such indicators, which suggests that these changes to the institutional framework do not influence the estimated impact of the laws on the amount of dispensed prescription opioids per capita.[22]

In column 2 of Table A.3 we include a set of time-varying demographic indicators to rule out, or at least alleviate, the possibility that trends in the composition of the population may be correlated with the propensity of a state to institute specific opioid regulations. The absence of confounding effects is confirmed by the fact that the coefficients of interest are not statistically different from the main specification. In column 3 we estimate a model in which state-specific linear time trends are estimated only based on the pre-treatment periods and groups. We partial out pre-treatment trends after de-meaning all the dependent and independent variables following the procedure of Goodman-Bacon (2018), and, reassuringly, we obtain estimates that are almost identical to our main effects.[23] In the next columns, we include linear and quadratic trends based on the initial level of MGE units per capita and state-specific linear and quadratic trends.[24]

In column 8 we exclude methadone from the dependent variable. Methadone is considered clinically different from other prescription opioids and often used in the treatment of opioid and heroin addiction in replacement therapies (Paulozzi 2012). Yet, in some states methadone is also one of the most widely diverted and abused drugs (Cicero and Inciardi 2005; Jones 2016). Coherently, the magnitude and significance of the coefficients imply that the enactment of state laws limiting the dispensing of opioids on the supply-side (PDMP especially) disproportionately impacts on the amount of methadone dispensed. Unfortunately, whether this is due to abuse rates falling or to a change in prescribing practices by suppliers cannot be tested here. DSL, if anything, are associated with an increase in the volume of opioids other than methadone, possibly because the detection of “ordinary” patients performing doctor shopping is more difficult for health care professionals in good faith, while methadone users are subject to stricter control.

We also investigate the existence of potential spillovers from the local labour market performance. In the spirit of Pei, Pischke, and Schwandt (2019), we test whether employment and unemployment rates are correlated with the implementation of opioid state laws, finding that the coefficients associated with opioid regulations are not different from zero (Table D.2). Moreover, if we add employment and unemployment rates as controls to the main specification, we obtain identical results.[25]

Finally, the recent paper by Horwitz et al. (2018) highlights issues deriving from recurring differences in measuring the correct starting dates of PDMP in the literature. Although, as stated by the authors, the definition of the implementation dates is not always clear-cut, a similar point of estimate would support the reliability of our choice of dates. In Table D.3 we compare our measure of PDMP with the list provided by Horwitz et al. (2018). Columns 1–4 display our estimates using four different definitions of PDMP assembled by Horwitz et al. (2018, Table 2, pp. 31–32), namely enactment, contingent on funding, electronic and user access. The last six columns refer to dates taken from publicly available databases, as selected by Horwitz et al. (2018, Table 3, pp. 33–34). The estimated coefficients are similar to our baseline.

4.3 The Effect of State Laws on Drug-Related Crime

Next, because of the predicted disruption to the legal market for opioid painkillers in combination with their high potential for addiction, we estimate whether the opioid state laws have any indirect fallout on the illegal market for drugs. While a few recent studies look at the effects of some opioid state laws on crime (Dave, Deza, and Horn 2018; Doleac and Mukherjee 2018; Mallatt 2017; Meinhofer 2017), the empirical evidence on the unintended impact of these laws on the market for illicit drugs is still scarce.

Table 4 shows the estimated effects of opioid state laws on crime related to the possession and sale of opium and derivatives and of synthetic opioids (column 1). We consider this as our main indicator for drug-related crime as it contains the two categories of drugs that are attributable to the diversion of opioids. We then investigate the effect of the laws on each category of drug-related crimes separately as grouped by UCR (2000): cocaine, opium and their derivatives such as morphine, codeine and heroin (column 2), synthetic narcotics including semi-synthetic and synthetic opioids like oxycodone, methadone and fentanyl (column 3), marijuana and hashish (column 4) and other non-narcotic drugs such as benzedrine (column 5).[26]

Table 4:

Effect on drug-related crime.

Dependent variable(1)(2)(3)(4)(5)
Possession & salePossession & sale
Opium & syntheticsOpiumSyntheticsMarijuanaNon-narcotics
PDMP−0.038 (0.108)0.011 (0.102)−0.068 (0.131)0.022 (0.102)0.011 (0.098)
Pain management clinic law0.214** (0.106)0.221* (0.111)−0.002 (0.116)0.066 (0.074)0.068 (0.138)
Doctor shopping law−0.034 (0.167)−0.085 (0.134)0.007 (0.185)0.006 (0.193)−0.185 (0.122)
Observations50,03250,03250,03250,03250,032
R-squared0.9400.9380.9390.9500.927

  1. Dependent variables expressed in natural logs and in per capita terms. Population-weighted OLS estimates. All regressions include two dummy indicators capturing the enforcement of Naloxone Access Laws and Good Samaritan Laws, plus year, county and region-year fixed effects. Errors are clustered at the state level. *p < 0.10, **p < 0.05, ***p < 0.01.

PDMP do not seem to be associated with any changes in drug-related crime, while PMCL have a positive impact on the possession and the sale of drugs, for which arrest rates rise by 21%, i.e. 20 people every 100,000 inhabitants. Conversely, we do not find any statistical significance for DSL. The event-study analyses corresponding to the coefficients from column 1 are shown in Figure A.4, where point estimates indicate an increase in arrests for the possession or sale of opium and synthetic drugs, although the significance is weak. The plots also suggest that the common trend assumption holds in all cases, as the coefficients in the pre-implementation period are never statistically different from zero.

The comparison between coefficients in columns 2 and 3 of Table 4 might suggest that the effect associated to the introduction of minimum requirements to pain management clinics (PMCL) might be driven by the diversion of illicit drugs such as heroin (or cocaine), rather than synthetic drugs (including opioids). However, a more in-depth observation of the phenomenon suggests that the enactment of PCML has significantly increased crimes related to the sale of both types of drugs. This is clearly evident from the plots reported in Figure 3, which demonstrate that Pain Management Clinics Laws produce a large unintended increase in arrests for the sale of these drugs.[27] As shown in the previous subsection, PMCL constitute the set of policies under analysis that substantially curb the dispensation of prescription opioids. The differential increase in arrest rates following their enactment is coherent with prior works uncovering the existence of a substitution across different opioid drugs when the legal alternative becomes less viable (Alpert, Powell, and Pacula 2018; Evans, Lieber, and Power 2019; Mallatt 2017). Moreover, columns 4 and 5 in Table 4 show that there is no effect on arrests for possession or sale of marijuana or other non-narcotic drugs. This is an expected result, given that the state laws under study are specifically aimed at tackling the over-dispensation of opioid-based drugs.[28]

Figure 3: Event-study analysis: Effect on drug-related crime of PMCL.Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t − 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure 3:

Event-study analysis: Effect on drug-related crime of PMCL.

Note: Coefficients estimated as in Eq. (2). The coefficient associated to < 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >+ 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

As additional evidence, we investigate whether the introduction of opioid state laws is associated with changes in police forces. We do so to test whether arrest rates related to the dispensation of illegal substitutes of opium are affected by unobserved differences in law enforcement and policing public expenditure across counties. We proxy this by using the number of police officers per 1000 inhabitants as dependent variable and find that this is not correlated to any of the laws under consideration (column 9, Table A.3).[29]

5 Conclusion

The United States are currently struck by an unprecedented epidemic of drug overdoses that has begun at the end of the 1990s with the rise in prescribing rates of opioid medications and is still causing tens of thousands of deaths across the country every year. In total, over 350,000 US Americans have died of opioid-related overdose since 1999. According to the Centers for Disease Control and Prevention (2017), in 2006 doctors wrote 72.4 opioid prescriptions per 100 persons. The prescription rate has been increasing annually by 4.1% until 2008 and by 1.1% in 2008–2012 and has finally started to decrease since 2012, reaching a rate of 66.5 per 100 persons in 2016. That year, 19.1 per 100 persons received one or more opioid prescriptions, with 3.5 prescriptions per patient on average.

Our analysis suggests that the recent declining trends in the dispensation of prescription opioids might have been supported by the sets of opioid state laws implemented in recent years. These laws aim at limiting the quantity of opioids prescribed by physicians or dispensed by pharmacists, tackling the supply, Prescription Drug Monitoring Programs and Pain Management Clinics Laws, or the demand, Doctor Shopping Laws, for opioids. We assess the effects of these policies on per capita grams of opioids dispensed and on drug-related crime rates.

We find that state laws targeting the supply for opioids yield an overall reduction in the quantity of MGE units per capita, particularly in the case of PCMLs, which have brought to the closure of a considerable number of the so-called “pill mills”. Per contra, regulating the demand for opioids through DSL appears to be less adequate, as they do not yield significant effects on any outcome. Our results also reveal that the effectiveness of PMCL in reducing the quantity of legally dispensed opioids is somewhat counterbalanced by an increase in arrest rates for the possession and the sale of opium-based drugs.

Developing effective tools to regulate and alleviate the costs of opioid crisis and its unintended effects should be a high priority on the agenda of policy makers and researchers, not only with reference to the US context but also to other countries which have recently seen an upward trend in prescription rates and in drug-related deaths, namely the UK, Germany, France, Spain and the Netherlands (Helmerhorst et al. 2017).

Our results suggest important policy implications. First, state laws targeting the supply and the demand for legal prescription opioids do not have the same effectiveness in reducing the overall volume of drugs dispensed. Second, policies that restrict the availability of legally-dispensed prescription opioids have important indirect effects on drug-related crime rates, which are driven by the sale and the possession of opium and synthetic drugs. This unveils the existence of a close relationship between the legal and the illegal markets for drugs, which should not be neglected.

Figure A.1: Onset of opioid-related policies by year.Note: Each marker corresponds to the total number of states in which a given policy is in effect in a given year.

Figure A.1:

Onset of opioid-related policies by year.

Note: Each marker corresponds to the total number of states in which a given policy is in effect in a given year.

Figure A.2: State laws (2001–2016).Note: Blue states are those where a given law is in place by the end of the period.

Figure A.2:

State laws (2001–2016).

Note: Blue states are those where a given law is in place by the end of the period.

Figure A.3: Geographical distribution of MGE in 2001 and 2016.Note: Geographical distribution of the MGE units per capita in 2001 and 2016. Thresholds are set at the 1st, 5th, 10th, 25th, 50th, 75th, 90th, 99th percentiles of the 2001–2016 average distribution.

Figure A.3:

Geographical distribution of MGE in 2001 and 2016.

Note: Geographical distribution of the MGE units per capita in 2001 and 2016. Thresholds are set at the 1st, 5th, 10th, 25th, 50th, 75th, 90th, 99th percentiles of the 2001–2016 average distribution.

Figure A.4: Event-study analysis: effect on drug-related crime.Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t − 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure A.4:

Event-study analysis: effect on drug-related crime.

Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t  5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure B.1: Event-study analysis: effect on drug quantities.Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t − 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure B.1:

Event-study analysis: effect on drug quantities.

Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t  5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure D.1: Event-study analysis: effect of mandate PDMP.Note: Coefficients are the sum of PDMP and mandate PDMP (as in column 6, Table 2). The coefficient associated to <t − 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure D.1:

Event-study analysis: effect of mandate PDMP.

Note: Coefficients are the sum of PDMP and mandate PDMP (as in column 6, Table 2). The coefficient associated to < 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >+ 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure D.2: Event-study analysis: effect on drug quantities, quarterly data.Note: Based on quarterly data. Coefficients estimated as in Eq. (2), where year fixed effects are replaced with quarter fixed effects. The coefficient associated to <t − 15 pertains to all periods prior to the 15th quarter before the implementation of the law. The coefficient associated to >t + 15 refers to all quarters from the 15th after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure D.2:

Event-study analysis: effect on drug quantities, quarterly data.

Note: Based on quarterly data. Coefficients estimated as in Eq. (2), where year fixed effects are replaced with quarter fixed effects. The coefficient associated to <t  15 pertains to all periods prior to the 15th quarter before the implementation of the law. The coefficient associated to >t + 15 refers to all quarters from the 15th after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure D.3: Effect on drug quantities: excluding states one-by-one note: Coefficients estimated as in Eq. (1). Standard errors clustered at state level, 95% confidence intervals are shown.

Figure D.3:

Effect on drug quantities: excluding states one-by-one note: Coefficients estimated as in Eq. (1). Standard errors clustered at state level, 95% confidence intervals are shown.

Figure D.4: Event-study analysis: Effect on drug-related crime by type.Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t − 5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Figure D.4:

Event-study analysis: Effect on drug-related crime by type.

Note: Coefficients estimated as in Eq. (2). The coefficient associated to <t  5 pertains to all periods prior to the fifth year before the implementation of the law. The coefficient associated to >t + 5 refers to all years from the fifth after the implementation of the law. Standard errors are clustered at the state level and 95% confidence intervals are shown.

Table A.1:

Dates of opioid state laws.

StateSupplyDemandMortality
PDMPPDMPPain managementDoctorNaloxoneGood
Non mandatoryMandatoryClinic lawShopping lawAccess lawSamaritan law
AL04/01/200601/01/201410/05/201608/10/2014
AK01/08/201115/03/201680/09/2008
AZ10/01/200806/08/2016
AR01/03/201322/07/201522/07/2015
CA193901/01/200801/01/2013
CO01/07/200710/05/201329/05/2012
CT01/07/2008196701/10/200301/10/2011
DE01/03/201201/03/201204/08/201431/08/2013
DC15/08/201619/03/201319/03/2013
FL01/09/201101/07/2011200322/01/201601/10/2012
GA01/07/201301/07/201301/07/199024/04/201424/04/2014
HI1943199116/06/201607/07/2015
ID196701/07/2015
IL196109/09/201501/01/201001/06/2012
IN199801/07/201601/07/2016
IA01/01/200906/04/2016
KS01/02/201107/04/2017
KY199901/07/201212/07/201225/06/201325/03/2013
LA01/11/200801/08/201411/07/2005200706/06/201601/08/2014
ME01/07/200431/01/200329/04/2014
MD20/08/201301/10/201301/10/2014
MA199401/06/201302/08/201202/08/2012
MI198914/10/201404/01/2017
MN04/01/201010/05/201401/07/2014
MS04/05/200624/03/201115/03/201701/07/2016
MO07/01/201728/08/2016
MT12/03/2012200704/05/201704/05/2017
NE14/04/201128/05/201502/05/2017
NV199701/10/2007197101/10/201501/10/2015
NH02/09/2014199002/06/201506/08/2015
NJ01/09/201101/07/201302/05/2013
NM01/01/200501/09/201203/04/201115/06/2007
NY197301/08/2013197324/06/201418/09/2011
NC01/07/2007200509/04/201309/04/2013
ND09/01/200701/08/201521/04/2017
OH01/06/200601/11/201120/05/201111/03/201414/09/2016
OK199101/11/2013
OR01/06/201106/06/201303/03/2016
PA197329/11/201401/12/2014
RI197918/06/201218/06/2012
SC01/02/2008197805/06/2016
SD05/12/2011199001/07/201627/03/2017
TN01/12/200601/01/201301/04/2013200701/07/201401/07/2015
TX198202/05/201001/09/198901/09/2015
UT1996199013/05/201320/05/2014
VT01/01/200901/11/201323/03/196801/07/201305/06/2013
VA09/01/200301/07/201301/07/2015
WA07/10/201110/06/201010/06/2010
WV01/07/199501/06/201210/03/2012200210/06/201612/06/2016
WI01/04/201317/03/201609/04/201409/04/2014
WY01/07/2004200801/07/2017
Table A.2:

Descriptive statistics.

Variable nameMeanSt. dev.MinMax
MGE per capita29.731798.16250.17135729.6470
MGE per capita (no methadone)24.278974.71910.15394004.4160
Possession and sale of opium/synthetics0.00100.00280.00000.5534
Possession and sale of opium0.00060.00270.00000.5533
Possession and sale of synthetics0.00040.00090.00000.0519
Possession and sale of marijuana0.00210.01600.00002.9826
Possession and sale of other non-narcotics0.00080.00670.00001.3830
Population97,049312,4705510,200,000
Share of females0.50080.02180.27850.5737
Share of people aged 65+0.16010.04380.01680.5583
Share of blacks0.09010.14520.00000.8626
Share of hispanics0.08000.13040.00000.9729
Table A.3:

Effect on drug quantities: robustness checks I.

Dependent variable(1)(2)(3)(4)(5)(6)(7)(8)(9)
MGEpcMGEpcMGEpcMGEpcMGEpcMGEpcMGEpcMGEpcPolice
AllAllAllAllAllAllAll(No methadone)
PDMP−0.038* (0.019)−0.037* (0.021)−0.038* (0.020)−0.039* (0.020)−0.039* (0.020)−0.035 (0.027)−0.035 (0.027)−0.006 (0.023)−0.019 (0.013)
Pain management clinic law−0.158*** (0.025)−0.140*** (0.029)−0.152*** (0.026)−0.150*** (0.026)−0.150*** (0.026)−0.072* (0.038)−0.071* (0.038)−0.109*** (0.025)−0.017 (0.022)
Doctor shopping law0.021 (0.033)0.035 (0.039)0.037 (0.036)0.038 (0.035)0.038 (0.035)0.056 (0.034)0.057 (0.034)0.064** (0.027)0.007 (0.013)
Observations50,03250,03250,03250,03250,03250,03250,03250,03249,874
R-squared0.9880.9880.8410.9870.9870.9890.9890.9900.950
Other laws
Controls
Bacon-Goodman procedure
Initial POs exposureLinearSquared
State-linear trend
State-quadratic trend

  1. The dependent variable is expressed as natural log. Population-weighted OLS estimates. All regressions include two indicators for the enforcement of Naloxone Access Laws and Good Samaritan Laws, plus year, county and region-year fixed effects. Institutional changes: indicators for the introduction of Medicare (2006) and the reformulation of OxyContin (2010). Other controls: population, share of females, share of over65, share of blacks, share of hispanics. Initial POs Exposure indicate trends based on the initial level of MGEs in the county. Police rate is the rate of officials. Errors are clustered at state level. *p < 0.10, **p < 0.05, ***p < 0.01.

Table B.1:

Effect on drug quantities.

Dependent variables(1)(2)(3)
MGEpcMGEpcPossession & sale
AllNo methadoneOpium & synthetics
Naloxone access law0.003 (0.019)−0.027* (0.016)0.062 (0.060)
Good Samaritan law0.004 (0.021)−0.018 (0.020)−0.013 (0.116)
Observations50,03250,03250,032
R-squared0.9870.9900.940

  1. See note to Table 2.

Table C.1:

Effect on drug quantities: heterogeneity.

Dependent variable(1)(2)(3)(4)(5)(6)(7)
MGEpc
PDMP * low exposure0.031 (0.027)0.027 (0.025)0.023 (0.025)−0.053** (0.026)−0.062*** (0.022)−0.067** (0.033)−0.077*** (0.026)
PDMP * medium exposure−0.030 (0.025)−0.033 (0.026)−0.006 (0.026)−0.041* (0.022)−0.009 (0.021)−0.042** (0.020)−0.035* (0.020)
PDMP * high exposure−0.053** (0.021)−0.052** (0.020)−0.053** (0.021)−0.007 (0.029)0.016 (0.025)−0.025 (0.025)−0.015 (0.028)
PMCL * low exposure−0.127*** (0.028)−0.098** (0.037)−0.090** (0.042)−0.183*** (0.050)−0.158*** (0.024)−0.155*** (0.057)−0.151*** (0.050)
PMCL * medium exposure−0.134*** (0.032)−0.137*** (0.027)−0.110*** (0.035)−0.136*** (0.031)−0.139*** (0.033)−0.129*** (0.026)−0.109*** (0.029)
PMCL * high exposure−0.164*** (0.031)−0.163*** (0.029)−0.166*** (0.028)−0.159*** (0.037)−0.099*** (0.035)−0.174*** (0.025)−0.196*** (0.030)
DSL * low exposure0.081** (0.035)0.098* (0.051)0.015 (0.058)0.047 (0.038)0.031 (0.035)0.016 (0.054)0.066 (0.044)
DSL * medium exposure0.058 (0.041)0.067* (0.038)0.059 (0.038)0.039 (0.039)0.049 (0.040)0.045 (0.035)0.032 (0.041)
DSL * high exposure0.019 (0.036)0.012 (0.036)0.029 (0.037)0.018 (0.041)0.029 (0.044)0.032 (0.038)0.020 (0.035)
Observation50,03250,03250,03250,03250,03250,03250,032
R-squared0.9870.9870.9870.9870.9870.9870.987
HeterogeneityIncomeEducationHealthHealthMGEpcDrugAlcohol
InsuranceSectorMortalityMortality

  1. Dependent variables are expressed in natural logs and in per capita terms. Population-weighted OLS estimates. All regressions include year, county and region-year fixed effects. Errors are clustered at the state level. The interaction terms are defined on the basis of three dummy variables that take value 1 if the initial county level is below the 25th percentile (Low), between the 25th and the 75th percentile (Medium) and above the 75th percentile (High) of the state distribution and 0 otherwise, respectively. *p < 0.10, **p < 0.05, ***p < 0.01.

Table C.2:

Effect on crime: heterogeneity.

Dependent variable(1)(2)(3)(4)(5)(6)(7)
Possession & sale: Opium & synthetics
PDMP * low exposure0.106 (0.131)0.061 (0.168)0.212 (0.141)0.052 (0.115)−0.110 (0.099)0.058 (0.130)0.028 (0.115)
PDMP * medium exposure−0.006 (0.130)0.023 (0.123)0.088 (0.134)−0.044 (0.106)0.047 (0.127)−0.017 (0.125)0.004 (0.120)
PDMP * high exposure−0.079 (0.101)−0.089 (0.101)−0.096 (0.100)−0.169 (0.180)0.150 (0.149)−0.094 (0.103)−0.151 (0.118)
PMCL * low exposure0.216* (0.119)0.430*** (0.118)0.286*** (0.096)0.173 (0.121)0.202* (0.117)0.293** (0.127)0.245** (0.098)
PMCL * medium exposure0.264** (0.115)0.209* (0.104)0.304*** (0.107)0.249** (0.114)0.259** (0.105)0.289** (0.123)0.244** (0.120)
PMCL * high exposure0.189* (0.108)0.197* (0.113)0.185 (0.118)0.187 (0.125)0.265*** (0.096)0.121 (0.115)0.189 (0.116)
DSL * low exposure0.177 (0.221)0.101 (0.225)0.102 (0.184)−0.177 (0.151)−0.038 (0.167)−0.150 (0.190)−0.037 (0.178)
DSL * medium exposure−0.050 (0.166)−0.032 (0.163)−0.009 (0.170)−0.028 (0.167)−0.050 (0.175)−0.079 (0.173)−0.103 (0.177)
DSL * high exposure−0.051 (0.170)−0.052 (0.174)−0.052 (0.171)0.108 (0.205)0.095 (0.209)0.054 (0.155)0.060 (0.163)
Observations50,03250,03250,03250,03250,03250,03250,032
R-squared0.9400.9400.9400.9400.9400.9400.940
HeterogeneityIncomeEducationHealthHealthMGEpcDrugAlcohol
InsuranceSectorMortalityMortality

  1. Dependent variables are expressed in natural logs and in per capita terms. Population-weighted OLS estimates. All regressions include year, county and region-year fixed effects. Errors are clustered at the state level. The interaction terms are defined on the basis of three dummy variables that take value 1 if the initial county level is below the 25th percentile (Low), between the 25th and the 75th percentile (Medium) and above the 75th percentile (High) of the state distribution and 0 otherwise, respectively. *p < 0.10, **p < 0.05, ***p < 0.01.

Table D.1:

Effect on drug quantities: quarterly data.

Dependent variable(1)(2)
MGEpcMGEpc
PDMP−0.036* (0.018)−0.039** (0.019)
Mandate PDMP−0.035 (0.022)
Pain management clinic law−0.164*** (0.026)−0.153*** (0.026)
Doctor shopping law0.011 (0.041)0.012 (0.041)
Observations200,376200,376
R-squared0.9870.987

  1. The dependent variable is the natural log of MGE per capita. Population-weighted OLS estimates, where the weight is computed as the share of the population in the county relative to the national population. All regressions include two dummy indicators capturing the enforcement of Naloxone Access Laws and Good Samaritan Laws, plus quarter, county and region-year fixed effects. Errors are clustered at the state level. *p < 0.10, **p < 0.05, ***p < 0.01.

Table D.2:

Effect on drug quantities: robustness checks II.

Dependent variable(1)(2)(3)(4)
MGEpcMGEpcEmployment rateUnemployment rate
PDMP−0.038* (0.020)−0.038* (0.020)−0.005 (0.004)−0.017 (0.028)
Pain management clinic law−0.152*** (0.026)−0.153*** (0.026)0.006 (0.007)−0.032 (0.039)
Doctor shopping law0.037 (0.036)0.036 (0.036)0.004 (0.010)−0.059* (0.034)
Employment rate0.023 (0.188)0.012 (0.174)
Unemployment rate−0.016 (0.050)
Observations50,03250,03250,03250,032
R-squared0.9870.9870.9200.899

  1. The dependent variable is expressed in terms of natural log. Population-weighted OLS estimates. All regressions include two dummy indicators capturing the enforcement of Naloxone Access Laws and Good Samaritan Laws, plus year, county and region-year fixed effects. Errors are clustered at the state level. *p < 0.10, **p < 0.05, ***p < 0.01.

Table D.3:

Effect on drug quantities using Horwitz et al. (2018)

(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)
PDMPPDAPSNAMSDL
EnactmentContingent on fundingElectronicUser accessEnabling legislationOperationalUser accessEnactmentCollectionUser access
Dep. var.: MGEpc−0.037* (0.020)−0.037* (0.019)−0.034* (0.019)−0.025 (0.024)−0.022 (0.021)−0.045** (0.021)−0.039* (0.023)−0.016 (0.020)−0.039* (0.021)−0.007 (0.022)
Observations50,03250,03250,03250,03250,03250,03250,03250,03250,03250,032
R-squared0.9870.9870.9870.9870.9870.9870.9870.9870.9870.987

  1. We replicate column 5 of Table 2 using the dates for PDMP provided by (Horwitz et al. 2018). Errors are clustered at the state level. *p < 0.10, **p < 0.05, ***p < 0.01.

Table D.4:

Effect on drug-related crime: by age.

Dependent variable(1)(2)(3)(4)(5)(6)(7)(8)
Age 16–24: Poss/SaleAge 25–44: Poss/SaleAge 45–64: Poss/SaleAge 65+: Poss/Sale
OpiumSyntheticsOpiumSyntheticsOpiumSyntheticsOpiumSynthetics
PDMP0.006 (0.083)−0.077 (0.114)0.017 (0.095)−0.084 (0.129)0.014 (0.078)−0.019 (0.088)−0.008 (0.041)0.025 (0.031)
Pain management clinic law0.148 (0.132)−0.011 (0.090)0.216** (0.100)0.051 (0.124)0.151** (0.069)−0.004 (0.086)0.049 (0.037)−0.037 (0.046)
Doctor shopping law−0.086 (0.134)0.000 (0.143)−0.098 (0.118)0.009 (0.177)−0.139 (0.099)0.000 (0.142)−0.055 (0.042)−0.017 (0.040)
Observations50,03250,03250,03250,03250,03250,03250,03250,032
R-squared0.9300.9310.9330.9310.9230.9260.9060.973

  1. Dependent variables are the natural log of crime rates. Population-weighted OLS estimates. All regressions include year, county and region-year fixed effects. Errors are clustered at the state level. *p < 0.10, **p < 0.05, ***p < 0.01.


Corresponding author: Ludovica Giua, European Commission, Joint Research Centre (JRC), Via Fermi, Ispra, VA, 21027, Italy, E-mail:

This paper has been presented at the Essex Crime Workshop (University of Essex, 2017), the ESPE Conference (Antwerp, 2018) the EALE Conference (Lyon, 2018) and the RES Conference (Coventry, 2019). A previous version of the paper circulated with the title: “The US Opioid Epidemic: Prescription Opioids, Labour Market Conditions and Crime”, available at https://mpra.ub.uni-muenchen.de/85712/. We would like to thank Erich Battistin, Michele Belot, Jordi Blanes-I- Vidal, Rui Costa, Marco Francesconi, Eva Gavrilova, Giovanni Mastrobuoni, Roberto Nisticó, Erik Plug for their valuable comments. Opinions expressed herein are those of the authors only and do not react the views of, or involve any responsibility for, the institution to which they are affiliated. Any errors are fault of the authors only.


Appendix A: Tables and Figures

We retrieve the dates of adoption from the inventory reports published by the Centers for Disease Control and Prevention (CDC) under the Public Health Law Program (https://www.cdc.gov/phlp/publications/topic/prescription.html) and Brandeis University’s Prescription Drug Monitoring Program Training and Technical Assistance Center (https://www.pdmpassist.org/content/pdmp-legislation-operational-dates), state legislative laws and bills, government newsletters. When we cannot find information from official sources we rely on the previous literature (Mallatt 2017; NAL and GSL; PMCL; Popovici et al. 2017; Rees et al. 2019). Whenever we cannot recover the exact month of adoption, we assign July 1st as starting date.

Appendix B: State Laws Contrasting Opioid Mortality

In addition to PDMP, PMCL and DSL, several states have enacted Naloxone Access Laws (NAL) and Good Samaritan Laws (GSL). These laws have been designed and implemented with the intention to reduce the number of fatal overdoses due to the abuse of opioids by providing incentives and support to those seeking medical assistance in the case of an overdose emergency. NAL allow administering naloxone, a lifesaving medication that blocks or reverses the effects of an opioid overdose, to individuals experiencing an overdose due to opioids without incurring in any civil, criminal or disciplinary prosecution (Davis and Carr 2015). GSL grant some form of immunity or mitigation in the prosecution or at sentencing for people who call emergency medical assistance in the case of an overdose. The aim of GSL is specifically to encourage people who otherwise would not reach for help for the fear of being charged for possession of drugs. These laws have been enforced fairly recently (since 2010) in most of the states that currently have such regulations.

Their predicted effects are potentially ambiguous. On the one hand, these laws reduce the opportunity costs associated with drug abuse. In fact, they might reduce the risk of death per use, thereby making riskier opioid use more appealing, and they might save the lives of active drug users, who survive to continue abusing opioids (Doleac and Mukherjee 2018).[30] Hence, we might observe an increase in the quantity of opioids dispensed. On the other hand, increased access to medical assistance and counseling, both in the case of NAL and of GSL, might improve the health and psychological conditions of users and persuade them to quit drugs. In this case, we would expect a reduction in drug-related crimes. In particular, GSL are expected to determine a reduction in the arrest rate for drug possession because of the immunity and mitigation in court granted to the person that seeks medical assistance.

We employ two dummy indicators to control for the implementation of NAL and GSL in all our specification, given their relatedness to the analysis herein presented. In the main specification we find no statistical impact of the two laws on the amount of MGE per capita dispensed. The coefficients are reported in Table B.1, column 1. The event study analysis is presented in Figure B.1, where no tangible patterns arise. The weak impact of NAL and GSL might be a consequence of the lower opportunity cost of doing drugs that these laws generate (Doleac and Mukherjee 2018).

The exclusion of methadone results in negative coefficients (and significant in the case of NAL). These are displayed in column 2. On the one hand, medical assistance and counseling can bring about a reduction in the overall amount of opioids dispensed, but not in the quantity of methadone distributed, which is attributable to higher take-up of rehabilitation programs.[31] On the other hand, the lower opportunity costs of drug abuse generated by these laws determine an increase in the amount of drugs that are typically consumed by heavy users (namely, methadone). This is coherent with the findings by Rees et al. (2019), who claim that the relationship between NAL and opioid-related deaths that do not involve heroin is stronger than the relationship between NAL and heroin-related deaths.[32]

Finally, the two laws addressing drug-related mortality do not appear to have a significant impact on the arrest rates for the possession of opioid-based drugs.

Appendix C: Heterogeneity on Initial Local Features

Case and Deaton (2015) trace out the origin of the recent surge in drug, but also alcohol and suicide, mortality, i.e. deaths of despair, to the prolonged deterioration of socio-economic conditions in the US. Other studies highlight the importance of drug supply factors and of medical practices and norms, which have contributed to the rise in the amount of drugs prescribed and mortality (Harris et al. 2020; Krueger 2017; Ruhm 2019).[33]

Here, we investigate whether the enforcement of opioid state laws has different effects depending on the initial levels of relevant socio-economic and drug environment indicators at the county level. The former category includes income per capita in 1990, the share of people with a degree in 1990 and the share of people with medical insurance coverage in 1998. These are all proxies for socio-economic status and living conditions, which have recently been associated with the increase in mortality due to drugs, alcohol and suicides and the abuse of prescription drugs (Case and Deaton 2015, 2017).

The second group of indicators encompasses the share of workers in the health sector, the number of opioids dispensed in 2000 and mortality rates due to alcohol and drug abuse disorders in 1990. While, in a broad sense, the share of workers in the health sector might identify access to health services (similarly to health insurance coverage), it is intended here as a proxy for the supply of health services. The assumption is that a larger relative ratio of physicians, pharmacists, and health care professionals per inhabitant is likely to translate into higher availability of suppliers. As a matter of fact, highly exposed counties consume almost three more grams per capita than less exposed areas. This variable and the quantity of MGE per capita dispensed in 2000 pick up cross-county heterogeneities in prescribing practices.[34] Mortality rates for drugs and alcohol refer to 1990, a period that is antecedent to the outbreak of prescription opioids that was characterised by the abuse of other substances such as crack cocaine (Fryer et al. 2013). As such, they are meant to capture structural differences in risky behavior across local communities.

For each proxy, we consider the levels at the beginning of the period, where baseline years vary depending on data availability, in order to limit potential issues of reverse causality. We exploit the within-state distribution of each indicator to determine whether a county is subject to high, medium or low “exposure”. That is, each area is ranked with respect to the counties within the same state, to ensure that the exposure does not simply capture geographical differences. Then, we interact each law with three dummy variables that take value 1 if the county’s initial level of exposure is below the 33rd percentile (low), between the 33rd and the 66th percentiles (medium) and above the 66th percentile (high) of its state distribution and 0 otherwise. This specification allows understanding whether the enactment of the opioid state laws yields differential effects on the outcomes of interest depending on the initial conditions of a given county relative to other areas within the same state.

First, we run this exercise on the amount of MGE per capita dispensed. Table C.1 reports the estimated coefficients for each of the specified exposures. Column 1, 2 and 3 refer to income per capita, the share of graduates and the proportion of people with health insurance coverage at the beginning of the period, respectively. As discussed above, they all serve as proxies for the social and economic composition of the population in the county and, as expected, they yield similar results. We find that in wealthier and highly-educated counties the enactment of supply-side laws has an overall negative impact on the quantity of MGE units per capita dispensed. Specifically, PDMP bring about a decrease in the outcome in highly exposed areas only, while the enforcement of PMCL always yields a drop in prescription rates at all levels of exposure, though the magnitude of the coefficients is larger in relatively better-off areas. Conversely, DSL have a mildly positive impact on the outcome in poorer and less educated counties, suggesting that their enforcement is possibly detrimental among communities that are relatively more deprived at the beginning of the period.[35]

In columns 4 and 5 we consider the share of workers in the health sector as a measure of the supply of health services and the amount of MGEs per capita dispensed in 2000, respectively, while columns 6 and 7 refer to mortality rates due to drugs and alcohol in 1990. All these measures are meant to capture different dimensions of what Ruhm (2019) refers to as “drug environment”. Our estimates suggest that supply-side laws are indeed more effective in areas that are relatively less familiar with substance and alcohol abuse and where drug suppliers are less densely localized. As far as DSL, coefficients do not display a clear pattern and are hardly statistically different from zero.

Results on arrest rates for possession or sale of opium-based and synthetic drugs are reported in Table C.2. We find that the enforcement of supply- and demand-side laws yield higher levels of crime in relatively more deprived areas compared to wealthier counties (columns 1–3). This result, in combination with the one discussed above, suggests that when opioid state laws are introduced, although they bite less in poorer areas, they induce a positive shock to the illicit market for drugs. Conversely, the response in counties that are better-off in relative terms possibly translates into lower drug-related crime rates.

When it comes to differences in exposure to the drug environment, coefficients do not differ substantially across groups (columns 4–7). On the one hand, state laws are more effective in reducing the amount of legally-dispensed opioids in less exposed areas; here, more people might turn to the black market to compensate for the absence of medical prescription drugs, thus increasing crime rates. This suggests the existence of a substitution effect across the legal and illegal markets for drugs. On the other hand, counties with a relatively higher initial supply of drugs and mortality rates, already characterized by high crime rates, do not display significant increases in the outcome variable.[36] Possibly, such unfavorable conditions make it harder for opioid regulations to have a significant role in reducing drug-related crime rates. This is especially evident in the case of PMCL.

Appendix D: Additional Tables and Figures

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Received: 2020-08-04
Accepted: 2021-01-06
Published Online: 2021-01-22

© 2021 Claudio Deiana and Ludovica Giua, published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.