We study the impact of deportation fear on the incomplete take-up of federal safety net programs in the United States. We exploit changes in deportation fear due to the roll-out and intensity of Secure Communities (SC), an immigration enforcement program administered by the Immigration and Customs Enforcement Agency (ICE) from 2008 to 2014. The SC program empowers the federal government to check the immigration status of anyone arrested by local law enforcement agencies and has led to the issuance of over two million detainers and the forcible removal of approximately 380,000 immigrants. We estimate the spillover effects of SC on Hispanic citizens, finding significant declines in ACA sign-ups and food stamp take-up, particularly among mixed-status households and areas where deportation fear is highest. In contrast, we find little response to SC among Hispanic households residing in sanctuary cities. Our results are most consistent with network effects that perpetuate fear rather than lack of benefit information or stigma.
Innovation is a primary source of economic growth, and is accordingly the target of substantial academic and government attention. Grants are a key tool in the government’s arsenal of tools to promote innovation, but legal academic studies of that arsenal have given them short shrift. While patents, prizes, and regulator-enforced exclusivity are each the subject of a substantial literature, grants are typically addressed briefly, if at all. According to the conventional story, grants may be the only feasible tool to drive basic research, as opposed to applied research, but they are a blunt tool for that task.
Three critiques of grants underlie this narrative: grants are allocated by government bureaucrats who lack much of the relevant information for optimal decision-making; grants are purely ex ante funding mechanisms and therefore lack accountability; and grants misallocate risk by saddling the government all the downside risk and giving the innovator all the upside. These critiques are largely wrong. Focusing on grants awarded by the National Institutes of Health, the largest public funder of biomedical research, this Article delves deeply into how grants actually work. It shows that grants are awarded not by uninformed bureaucrats, but by panels of knowledgeable peer scientists with the benefit of extensive disclosures from applicants. It finds that grants provide accountability through repeated interactions over time. And it argues that the upside of grant-investments to the government is much greater than the lack of direct profits would suggest.
Grants also have two marked comparative strengths as innovation levers: they can support innovation where social value exceeds appropriable market value, and they can directly support innovation enablers — the people, institutions, processes, and infrastructure that shape and generate innovation. Where markets undervalue some socially important innovations, like cures for diseases of the poor, grants can help. Grants can also enable innovation by supporting its inputs: young or exceptional scientists, new institutions, research networks, and large datasets. Taken as a whole, grants do not form a monolithic, blunt innovation lever; instead, they provide a varied and nuanced set of policy options, and we should recognize and develop their usefulness in promoting major social goals.
Most scholars believe assisted reproductive technology is subject only to minimal regulation, especially by the federal government. This belief, I contend, is wrong. In this Article, I examine agency documents, statements by officials, and letters that the U.S. Food and Drug Administration (FDA) has sent to physicians and researchers over the past fifteen years to reveal an overlooked regulatory program. The FDA has been targeting new forms of assisted reproductive technology that involve small genetic modifications (“advanced assisted reproductive technologies” or “AARTs”) through regulatory actions that receive little public, media, or scholarly attention. I term this method of regulation “subterranean regulation.” Subterranean regulatory actions chill research as many physicians and researchers halt their research after receiving these letters or stop providing access to AARTs in the United States.
The existence of this unconventional and largely unnoticed regulatory practice raises a series of issues including whether the FDA should be regulating advanced assisted reproductive technologies at all. Moreover, a hidden, ad hoc regulatory practice is exactly the wrong kind of process to use when it comes to scientific innovations in fraught ethical areas, which includes not only assisted reproductive technology but also other DNA-modifying technologies such as gene editing (including CRISPR-Cas9). Ultimately, I recommend a regulatory approach that is as close to “minimal regulation” as possible.
Health care consolidation in the United States has been widespread at all levels and across all entities. This consolidation has extended beyond horizontal mergers of hospitals or other providers to include out-of-market mergers, or cross-market mergers. Cross-market mergers include the merger or acquisition of any health care entity that does not directly compete with the acquiring entity in the same product or geographic market. Antitrust enforcers have historically had little in the way of market theory, economic models, or empirical data to inform their analyses on the potential impacts of cross-market mergers on competition. However, recent developments in economic theory and empirical studies now offer evidence that cross-market mergers can, in some instances, harm competition and drive price increases in health care markets when a common insurer exists across those markets. This article aims to start a discussion among the health policy and antitrust communities about the potential for cross-market acquisitions to harm competition, whether existing antitrust laws could theoretically support a challenge to a cross-market acquisition, and the practical challenges to doing so. This article will argue that health policy analysts, antitrust enforcers, and academics should begin to consider the anti-competitive potential of cross-market acquisitions and develop a means to analyze them both legally and economically.
This paper explores the use of bundling to reduce adverse selection in insurance mar-kets and its application to social health insurance programs. When the choice to buy health insurance is made at the household level, bundling the insurance policies of household mem-bers eliminates the eﬀect of adverse selection within a household since the household can no longer select only sick members to enroll. However, this can exacerbate adverse selection across households, as healthier households might choose to drop out of the insurance market. The net eﬀect of this trade-oﬀ depends on the characteristics of the household demand for medical care and risk preferences. I explore this issue using individual survey data on insur-ance enrollment and medical spending in Vietnam that contain detailed information about the structure of the household. The reduced-form evidence suggests that income, own-price and cross-member substitution eﬀects play important roles in the demand for medical care, which aﬀects a household’s selection of members into insurance. I then develop and estimate a model of household insurance bundle choice and medical utilization that accounts for these features. The results suggest that much of the adverse selection is concentrated within the household. Counterfactual analysis reveals that under optimal pricing, household bundling yields signiﬁcantly higher consumer surplus and insurance enrollment than individual pur-chase. Furthermore, the insurance market is less susceptible to complete unraveling under household bundling.
I estimate the labor supply response after the implementation of a full population disability insurance (DI) reform in Norway. The reform implemented an economic incentive program to continually encourage DI beneficiaries to increase their labor supply, and the causal analysis finds positive intensive and extensive labor supply responses after the reform. The average working hours increased by an average of 4.5 percentage points, while the probability of working increased by an average of 0.6 percentage points. However, there is significant heterogeneity in the estimated effects, which are consistently higher for full than they are for partial beneficiaries. For 100 % beneficiaries, the working hours increased by an average of 10.6 percentage points, and the labor force participation of young men that claim full DI benefits increased by 3.9 percentage points. The results from the analysis indicate that economic incentives can mobilize the unused potential labor supply in groups of poor health; this makes a sound economic incentive structure essential to the long-term viability of the DI program.
THC is the intoxicant most commonly detected in US drivers, with approximately 13% of drivers testing positive for marijuana use, compared to the 8% that show a measurable amount of alcohol (NHTSA, 2015). (The two figures are not strictly comparable because cannabis remains detectable for much longer than alcohol, and also for long after the driver is no longer impaired; therefore, the difference in rates does not show that stoned driving is more common than drunk driving.) Cannabis intoxication has been shown to impair reaction time and visual-spatial judgment.
Many states, including those where cannabis sales are now permitted by state law, have laws against cannabis-impaired driving based on the drunk-driving model, defining criminally intoxicated driving as driving with more than a threshold amount of intoxicant in one’s bloodstream—a per se standard—as opposed to actual impairment. That approach neglects crucial differences between alcohol and cannabis in their detectability, their pharmacokinetics, and their impact on highway safety.
Cannabis intoxication is more difficult to reliably detect chemically than alcohol intoxication. A breath alcohol test is (1) cheap and reliable; (2) sufficiently simple and non-invasive to administer at the roadside; and (3) a good proxy for alcohol in the brain, which in turn is (4) a good proxy for subjective intoxication and for measurable driving impairment. In addition, (5) the dose-effect curve linking blood alcohol to fatality risk is well-established and steep.
None of those things is true for cannabis. A breath test remains to be developed. Oral-fluid testing can demonstrate recent use but not the level of impairment. A blood test requires a trained phlebotomist and therefore a trip to a medical facility, and blood THC levels drop very sharply over time-periods measured in minutes. Blood THC is not a good proxy either for recency of use or for impairment, and the dose-effect curve for fatality risk remains a matter of sharp controversy. The maximum risk for cannabis intoxication alone, unmixed with alcohol or other drugs, appears to be more comparable to risks such as talking on a hands-free cellphone (legal in all states) than to driving with a BAC above 0.08, let alone the rapidly-rising risks at higher BACs. Moreover, the lipid-solubility of THC means that a frequent cannabis user will always have measurable THC in his or her blood, even when that person has not used recently and is neither subjectively intoxicated nor objectively impaired. That suggests criminalizing only combination use, while treating driving under the influence of cannabis (however this is to be proven) as a traffic offense, like speeding.