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.
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.
Might American deaths of despair spread to other developed countries? On the one hand, perhaps not. Parsing the data shows just how uniquely bleak the situation is in the United States. When it comes to deaths of despair, the United States is hopefully less a bellwether than a warning, an example for the rest of the world of what to avoid. On the other hand, there are genuine reasons for concern. Already, deaths from drug overdose, alcohol, and suicide are on the rise in Australia, Canada, Ireland, and the United Kingdom. Although those countries have better health-care systems, stronger safety nets, and better control of opioids than the United States, their less educated citizens also face the relentless threats of globalization, outsourcing, and automation that erode working-class ways of life throughout the West and have helped fuel the crisis of deaths of despair in the United States.
In the past, gains in longevity went hand in hand with broader improvements in health-care systems, governance, and infrastructure. That meant the byproducts of better health—a growing young work force, less deadly cities, and a shift in countries’ health-care needs to the problems of older people—were sources of wider prosperity and inclusion. Today, improvements in health are driven more by targeted medical interventions and international aid than by general development. Without that development, the changes that now accompany declines in infectious diseases are potential sources of instability: rising youth unemployment, overcrowded and underbuilt cities, surging rates of premature chronic diseases, and more migration.
Every year, smoking costs the U.S. more than $300 billion, which includes both medical care and lost productivity. Unfortunately, some people will have to pay more depending on the state in which they live.To encourage the estimated 34.2 million tobacco users in the U.S. to kick the dangerous habit, WalletHub looked into the true per-person cost of smoking in each of the 50 states and the District of Columbia. We calculated the potential monetary losses — including both the lifetime and annual cost of a cigarette pack per day, health care expenditures, income losses and other costs — brought on by smoking and exposure to secondhand smoke. Read on for the complete ranking and analysis, insight from a panel of experts and a full description of our methodology.
Using decades of variation in the federal and state Earned Income Tax Credit (EITC) and the Panel Study of Income Dynamics (PSID) dataset, I examine the impact of exposure to EITC expansions in utero and during childhood on health outcomes in adulthood. In order to overcome the confounding relationship between family income and health outcomes, this study uses the maximum EITC benefit as the key variable. Reduced-form estimates show that EITC expansions had a positive impact on self-reported health status and other health measurements. Specifically, a $1000 increase in the maximum EITC exposure from ages 13 to 18 corresponds with a 0.125 point increase in the reported health status during adulthood. In addition, being exposed to EITC expansions in utero increases reported health status by 0.043 point. Relative to the range of reported health of 1 to 5 and the standard deviation of 0.97, these are very small effects. Nonetheless, these health effects are consequential, associating with increases in both family income and maternal labor supply. Labor supply induced time substitution effect competes with the income enhancement, with different relative impacts at different age intervals, creating a “U-shaped” overall health consequence.
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.
Importance The United States spends more on health care than any other country, with costs approaching 18% of the gross domestic product (GDP). Prior studies estimated that approximately 30% of health care spending may be considered waste. Despite efforts to reduce overtreatment, improve care, and address overpayment, it is likely that substantial waste in US health care spending remains. Objectives To estimate current levels of waste in the US health care system in 6 previously developed domains and to report estimates of potential savings for each domain. Evidence A search of peer-reviewed and “gray” literature from January 2012 to May 2019 focused on the 6 waste domains previously identified by the Institute of Medicine and Berwick and Hackbarth: failure of care delivery, failure of care coordination, overtreatment or low-value care, pricing failure, fraud and abuse, and administrative complexity. For each domain, available estimates of waste-related costs and data from interventions shown to reduce waste-related costs were recorded, converted to annual estimates in 2019 dollars for national populations when necessary, and combined into ranges or summed as appropriate. Findings The review yielded 71 estimates from 54 unique peer-reviewed publications, government-based reports, and reports from the gray literature. Computations yielded the following estimated ranges of total annual cost of waste: failure of care delivery, $102.4 billion to $165.7 billion; failure of care coordination, $27.2 billion to $78.2 billion; overtreatment or low-value care, $75.7 billion to $101.2 billion; pricing failure, $230.7 billion to $240.5 billion; fraud and abuse, $58.5 billion to $83.9 billion; and administrative complexity, $265.6 billion. The estimated annual savings from measures to eliminate waste were as follows: failure of care delivery, $44.4 billion to $93.3 billion; failure of care coordination, $29.6 billion to $38.2 billion; overtreatment or low-value care, $12.8 billion to $28.6 billion; pricing failure, $81.4 billion to $91.2 billion; and fraud and abuse, $22.8 billion to $30.8 billion. No studies were identified that focused on interventions targeting administrative complexity. The estimated total annual costs of waste were $760 billion to $935 billion and savings from interventions that address waste were $191 billion to $282 billion. Conclusions and Relevance In this review based on 6 previously identified domains of health care waste, the estimated cost of waste in the US health care system ranged from $760 billion to $935 billion, accounting for approximately 25% of total health care spending, and the projected potential savings from interventions that reduce waste, excluding savings from administrative complexity, ranged from $191 billion to $282 billion, representing a potential 25% reduction in the total cost of waste. Implementation of effective measures to eliminate waste represents an opportunity reduce the continued increases in US health care expenditures.Do you want to read t
In this paper, we analyze each of Warren’s proposals in detail. While some of Warren’s proposals would in fact succeed in raising taxes or reducing spending, others would not. All in all, while Warren and her advisors estimate that her proposals would fully fund “Medicare for All,” we estimate that, altogether, the Warren plan would increase the primary federal deficit by $15 trillion over ten years. (We did not calculate the cost of interest payments to service $15 trillion of additional borrowing.)