RAND researchers estimated the effects that four proposed out-of-network payment limits for hospital care—125 percent of Medicare payments (a strict limit), 200 percent of Medicare payments (a moderate limit), state average payment by private plans (a moderate limit), and 80 percent of average billed charges (a loose limit)—would have on negotiated in-network prices and total payments for hospital care. These four scenarios reflect the variation in base measure (traditional Medicare, market price, and charges) and payment generosity among existing policy proposals.
This paper evaluates the labor market effects of sick pay mandates in the United States. Using the National Compensation Survey and difference-in-differences models, we estimate their impact on coverage rates, sick leave use, labor costs, and non-mandated fringe benefits. Sick pay mandates increase coverage significantly by 13 percentage points from a baseline level of 66%. Newly covered employees take two additional sick days per year. We find little evidence that mandating sick pay crowds-out other non-mandated fringe benefits. We then develop a model of optimal sick pay provision along with a welfare analysis. Mandating sick pay likely increases welfare.
We provide the first investigation into whether and how much genes explain having health insurance coverage or not and possible mechanisms for genetic variation. Using a twin-design that compares identical and non-identical twins from a national sample of US twins from the National Survey of Midlife Development in the United States, we find that genetic effects explain over 40% of the variation in whether a person has any health coverage versus not, and nearly 50% of the variation in whether individuals younger than 65 have private coverage versus whether they have no coverage at all. Nearly one third of the genetic variation in being uninsured versus having private coverage is explained by employment industry, self-employment status, and income, and together with education, they explain over 40% of the genetic influence. Marital status, number of children, and available measures of health status, risk preferences, and prevention effort do not appear to be important channels for genetic effects. That genes have meaningful effects on the insurance status suggests an important source of heterogeneity in insurance take up.
A holistic assessment of the labor market effects of minimum wage regulation requires understanding employer compliance. The economics literature has paid little attention to this issue. We investigate how minimum wage increases and the strength of enforcement regimes affect the prevalence of subminimum wage payments. We find strong evidence that higher minimum wages lead to a greater prevalence of subminimum wage payments. We consistently estimate that increases in measured underpayment following minimum wage increases average between 10 and 25 percent of realized wage gains. We interpret this as evidence that minimum wage evasion and avoidance are an important reality in the low-wage labor market. Finally, we find that enforcement regimes play an important role in shaping both baseline compliance rates and the response of compliance to increases in minimum wages.
Too often, patients are surprised to find a bill from an out-of-network provider involved in their treatment who they had no control in choosing. Studies suggest that roughly 1 in 5 emergency room visits and 1 in 10 elective inpatient procedures result in the potential for a surprise out-of-network bill, most commonly when patient seek care at an in-network hospital but end up treated by certain emergency department or ancillary physicians (such as anesthesiologists, radiologists, pathologists, or assistant surgeons) who are outside their insurer’s provider network, and financial consequences can be devastating.In recent years, many states have moved to address surprise billing and a few federal proposals are floating around Congress. While there is broad bipartisan agreement that a problem exists, a solution can sometimes prove elusive. On Wednesday, February 20, 2019, the USC-Brookings Schaeffer Initiative for Health Policy will present new analysis detailing policy approaches to eliminate surprise out-of-network billing and bring together policymakers and stakeholders to discuss how to craft a solution.This event will be live webcast.
The impact of financial incentives on health and health care: Evidence from a large wellness program – Einav – 2019 – Health Economics – Wiley Online LibraryJanuary 10, 2019
Workplace wellness programs have become increasingly common in the United States, although there is not yet consensus regarding the ability of such programs to improve employees’ health and reduce health care costs. In this paper, we study a program offered by a large U.S. employer that provides substantial financial incentives directly tied to employees’ health. The program has a high participation rate among eligible employees, around 80%, and we analyze the data on the first 4 years of the program, linked to health care claims. We document robust improvements in employee health and a correlation between certain health improvements and reductions in health care cost. Despite the latter association, we cannot find direct evidence causally linking program participation to reduced health care costs, although it seems plausible that such a relationship will arise over longer horizons.
Exploring the Taxation of New York’s New Paid Family Medical Leave Benefit by Richard Barnes :: SSRNJanuary 2, 2019
This article examines the taxation of benefits received under New York’s new Paid Family Leave Act. The article argues that New York’s Paid Family Leave Act is unique when compared to similar provisions enacted in other states to date and that benefits paid under the tax are excluded from federal gross income by operation of Internal Revenue Code Sections 104 and 105. Additionally, the article contends that New York’s Department of Taxation and Finance’s Notice N-17-12 errs in concluding that amounts paid under the Act are includible in federal gross income. The article contends that insurance policies issued in compliance with the Act are “health insurance” and that income replacement benefits paid under the Act are paid for “sickness” as described in Code Sections 104 and 105.
Regulatory Pathways to Promote Treatment for Substance Use Disorder or Other Under-Treated Conditions Using Risk Adjustment by Matthew J. B. Lawrence :: SSRNOctober 19, 2018
This Commentary provides a legal analysis of the extent to which changes proposed by scholars to promote care for substance use disorder or other under-treated illnesses through risk adjustment could be implemented administratively, without legislation, in federal risk adjustment systems: Medicare’s privatized component, Medicare’s pharmaceutical component, and the individual and small group market. As the Commentary explains, federal laws governing risk adjustment provide broad discretion to regulators and can reasonably be interpreted to permit (or in the case of Part C even compel) full and final implementation through the administrative process of almost all of the changes that scholars have proposed.
Predictive analytics and “big data” are emerging as important new tools for diagnosing and treating patients. But as data collection becomes more pervasive, and as machine learning and analytical methods become more sophisticated, the companies that traffic in health-related big data will face competitive pressures to make more aggressive claims regarding what their programs can predict. Already, patients, practitioners, and payors are inundated with claims that software programs, “apps,” and other forms of predictive analytics can help solve some of the health care system’s most pressing problems. This article considers the evidence and substantiation that we should require of these claims, focusing on “health” claims, or claims to diagnose, treat, or manage diseases or other medical conditions. The problem is that three very different paradigms might apply, depending on whether we cast predictive analytics as akin to medical products, medical practice, or merely as medical information. Because big data methods are so opaque, its claims may be uniquely difficult to substantiate, requiring a new paradigm. This article offers a new framework that considers intended users and appropriate evidentiary baselines.
For recently released prisoners, the minimum wage and the availability of state Earned Income Tax Credits (EITCs) can influence both their ability to find employment and their potential legal wages relative to illegal sources of income, in turn affecting the probability they return to prison. Using administrative prison release records from nearly six million offenders released between 2000 and 2014, we use a difference-in-differences strategy to identify the effect of over two hundred state and federal minimum wage increases, as well as 21 state EITC programs, on recidivism. We find that the average minimum wage increase of $0.50 reduces the probability that men and women return to prison within 1 year by 2.8%. This implies that on average the effect of higher wages, drawing at least some released prisoners into the legal labor market, dominates any reduced employment in this population due to the minimum wage. These reductions in returns to incarcerations are observed for the potentially revenue generating crime categories of property and drug crimes; prison reentry for violent crimes are unchanged, supporting our framing that minimum wages affect crime that serves as a source of income. The availability of state EITCs also reduces recidivism, but only for women.