Does parental Medicaid coverage matter for children who are already covered by Medicaid? This pa- per examines the spillover effect from low-income parents’ Medicaid coverage to their children using cross-state variation in Medicaid eligibility between 1990 and 2004. I construct the index of Medicaid eligibility for parents measured by the fraction of eligible parent population based on the detailed Medicaid policy of each state. Using plausibly exogenous variation in constructed index, I provide new evidence that Medicaid expansion targeting low-income parents leads to a significant decline in the mortality rates of infants and children ages 1-4. The effect is larger and more significant among the black population than among the white population, and saves 3.1 black children’s lives during 15 years of analysis. The differential effect across races leads to a decline in the black-white mortality gap. I explain the difference in effectiveness of parental coverage as a result from the difference in the take-up of Medicaid. The Medicaid-eligible children with Medicaid-eligible parents are two times more likely to enroll, and eligible black children whose parents are also eligible are seven times more likely to enroll. Simulation results using the features of Medicaid expansion in the Affordable Care Act suggest that the different adoption of Medicaid expansion may enlarge existing mortality disparities across states. The spillover effect on children’s health from the Medicaid expansion for parents suggests that the benefit of the policy would reach beyond the direct benefi09ciaries and the evaluation of policy should take the additional benefit into consideration.
The labor market for nurses is unique in several ways. Nursing is a predominately female field. In 1977, 1.92% of nurses were male. This number has risen to 5.8% in 2004. 7 There are also strict requirements to become a RN. All RNs must complete one of three educational paths and obtain a license from a state agency in order to be employed. There exists variation in licensing schemes across states but nurses nationwide face the same educational choices. They may (1) earn a nursing diploma, (2) obtain an associate degree or (3) obtain a bachelor’s degree. All three options require education beyond high school. The nursing diploma typically takes three years to earn and is completed in a hospital. Associate degree programs typically take two years to complete and require course and clinical work
Two observational studies report on ED health care use in Tennessee after the Medicaid contraction. Using a census of all Tennessee ED visits between 2004 and 2006 (inclusive), Heavrin et al. (2011) describe a 22 percent decrease in adult Medicaid visits and a 39.5 percent increase in uninsured adult visits in Tennessee after the contraction. They also note a 2 percent increase in the fraction of uninsured ED visits that result in inpatient hospitalization. Emerson et al. (2012) use the census of ED discharges for one Tennessee county (Davidson, which includes the city of Nashville) for 2003-2007 and report increases in both the number of ED visits for ambulatory-sensitive conditions and hospital uncompensated care costs after the disenrollment. Although their data also contained inpatient hospitalizations, the paper focuses only on ED visits; they do, however, note that the number of uninsured inpatient admissions among non-elderly adults increased by 42 percent, but there was only a very minor decline of 0.6 percent in Medicaid hospital admissions. Because these studies only use Tennessee data, it is unclear that we can draw causal lessons from them because some changes may reflect national trends. Additionally, unlike scheduled direct inpatient hospitalizations that are price sensitive, ED visits are less responsive to insurance status. Therefore, while these studies based on data from ED visits are informative, it is also important to examine utilization of hospital-based care, both scheduled and otherwise, using a quasi-experimental study design.
Over the past several decades there has been an increased prevalence of both adult and childhood obesity in American society. This has received significant attention from researchers and governmental agencies, as there are both public health concerns and financial consequences associated with this condition. Obesity is associated with an increased risk for heart disease, stroke, type-2 diabetes, arthritis, and certain forms of cancer—all of which are serious (and costly) conditions. It has been estimated that medical expenditures associated with obesity account for 9% of all national medical expenditures in a given year. Nearly half of this bill is paid for by the government through Medicare and Medicaid expenditures, which translates to a higher tax bill for all tax-paying citizens. Moreover, it has been estimated that obese individuals who are Medicare (Medicaid) participants cost on average $1,486 ($864) more per year than their healthy weight counterparts (Finklestein et al. 2003).
This chapter examines the impact of local house prices on health and health behaviors of the individuals using the Behavioral Risk Factor Surveillance System (BRFSS) from 2001 to 2012. The conceptual approach is based on the pure wealth mechanism. Under this mechanism, a decrease in local level house prices increases the likelihood that home- owners will face a negative wealth shock. A negative wealth shock could result in the poor health of the homeowners through the reduction in expenditures on preventive health- care and physical activities which are considered as normal goods (Xu, 2013; Yilmazer, Babiarz, and Liu, 2015; Lusardi, Schneider, and Tufano, 2015). However, the shock could also result in the reduction of the risky healthy behaviors such as smoking and alcohol consumption (normal goods) which could positively impact homeowners’ health (Fichera and Gathergood, 2013; Van Kippersluis and Galama, 2013). The directional impact on health is thus ambiguous for homeowners. On the one hand, if house prices and rents are positively correlated in a given area, renters face a positive wealth shock as house prices fall, because it reflects that renting or potential buying become inexpensive. On the other hand, the health effect for renters depends on whether the improved health resulting from the increase in expenditures on preventive healthcare and physical activity dominates the deterioration in health resulting from the rise in risky health behaviors or vice-versa.
The second category of preventable risk factors consists of those factors that relate to characteristics of an individual’s living environment, such as air pollution, noise pollution or crime. In contrast to behav- ioral risk factors, environmental risk factors are not directly influenceable by individuals themselves. Some of these environmental characteristics and their potential spillover effects on individual health, most notably air and noise pollution, are already subject to an intense scientific as well as public debate (e.g. Janke, 2014; Bilger and Carrieri, 2013; Brink, 2011; Boes et al., 2013). The increased awareness of the effects of environmental characteristics is also reflected by several recent policy interventions that aim at reducing individuals’ exposure to these risk factors, such as bans on night flights or the introduction of low-emission zones in larger cities that became effective in Germany over the last years. While these prominent factors often lead to measurable impact on individuals’ physical health and, therefore, are in the focus of public interest, other potential threats that mainly affect individuals’ mental well-being have gained less attention. Understanding the role of environmental risk factors with respect to mental well-being is also emphasized by increasing prevalence rates of mental health problems, which have become a major concern in many developed societies. In Germany, for example, the number of diag- noses related to mental and behavioral disorders have increased by around 33 percent from 2000 to 2012 (Federal Statistical Office Germany, 2014b). In the same period of time, deaths caused by mental health problems have even increased by about 260 percent (Federal Statistical Office Germany, 2014a). Against this background, chapter 5 considers potential spillover effects of local crime rates on men- tal well-being of inhabitants. Crime and its potential spillover effects on mental well-being are one example
To study the population characteristics of the abusers, I aggregated the data annually, indicating if each individual had cases of abuse/dependence for alcohol, opioids, cocaine, amphetamines, or cannabis. Figure 2 shows the number of people who visited medical providers for any substance misuse during 2001-2012. The trends are similar to those reported by SAMHSA (2014a), which comes from the National Survey on Drug Use and Health. In 2001, the number of people with cannabis abuse problems was about 20% higher than those with opioid abuse/addiction. But opioid cases have grown much faster, and by 2012, there were twice as many cases of opioid abuse. Fortunately, the total number of individuals with cocaine abuse problems declined, and the number has stayed almost constant since 2005 for those with cannabis and amphetamine abuse problems. Table 5 shows that there is a high correlation between the abuse of different types of substances, with the highest being 0.35 for the correlation between opioid and other medications abuse. The correlation between abuse of opioids and other substances, including cocaine, cannabis, and amphetamines is 0.18, 0.14, and 0.10, respectively. For the rest of the data summary,
may be important. On the supply side of the labor market, fixed costs of being employed may make part time employment unattractive to many individuals (Hamermesh and Donald, 2007). On the demand side of the labor market, some features of technology could induce firms to impose constraints on hours of work. If there are fixed costs of hiring, training, and employing a worker, then firms may impose a minimum hours constraint on their workers (Hamermesh, 1993). If production takes place in teams, then the absence of a team member could reduce team productivity. In this case firms might require the presence of workers at specific times, reducing the flexibility of workers in scheduling their hours of work. Other factors could result in reluctance of firms to hire older workers under the same terms as younger workers, but would not result in hours restrictions placed on older workers who age in place. For example, workers could face statistical discrimination in the labor market as a result of the application of group characteristics to all members of the group (Hellerstein, Neumark, and Troske, 1999). The short expected duration of future employment of an older worker reduces the incentive of a firm to train and promote older workers, despite the fact that some older workers may plan to remain employed for a long time (Hutchens, 1988).
Our strategy would be to use our calibrated model to simulate what would happen if firms had less access to foreign high-skilled labor in the 1990s boom and compare these simulations to the earlier boom. Comparisons between simulation results with different values of γ and what actually happened earlier would help narrow plausible values for γ. Intuitively, if demand is relatively elastic, the loss of access to foreigners would have relatively little impact on wages, but a large impact on total CS employment. Whereas a less elastic demand curve would have a large effect on wages and less of an effect on total CS employment. This kind of exercise is valid only under the strong assumptions that our economic model accurately reflects that labor market for IT workers, and that the demand and supply elasticities were the same during the two periods and that the two shocks generated shifts in the labor demand of roughly the same magnitude. However heroic such assumptions might be, the strategy fails for a simpler reason. The strategy requires comparing wage and employment changes for a small segment of the workforce across periods. Our estimates were simply not reliable enough for such exercises to be meaningful.
The following are the four most relevant studies for the present paper, al- though only the first three use mother fixed effects. Black et al. (2016) analyse birth order effects on adult health behaviours and health, with administrative data from Norway. They find that late born display higher smoking rates and lower self-assessed physical and mental health, although they score better than first born on body mass index and other biomarkers. Black et al. (2018) find that birth order negatively affects adult non-cognitive skills in a sample of Swedish men examined as part of the military enlistment. Later-born men are also less likely to be in jobs that require leadership and they score lower in all Big 5 per- sonality traits. Both cited studies find lower parental investments for higher birth order children, including behaviours such as smoking during pregnancy, breast- feeding and help with homework. A similar conclusion is reached by Lehmann et al. (2018), who use a cohort of children from the US (NLSY-C) to assess birth order effects on children cognitive and non-cognitive skills, as well as educational outcomes in adulthood. They find that lower parental investments, measured as antenatal care, behaviour in pregnancy, breastfeeding and cognitive stimula- tion, can account for the negative effect of higher birth order on cognitive skills in childhood, but find no effect on average childhood non-cognitive outcomes. Finally, Argys et al. (2006) are the only other economic study looking at adoles- cent risky behaviours. Using NLSY79, a longitudinal study of adolescents from the US, they find a positive association between higher birth order and smoking, use of marijuana, alcohol consumption and earlier initiation of sexual behaviour in adolescence. They estimate OLS regressions with birth order expressed by a dummy variable for having any older siblings. In the present paper, I use a fixed-effect strategy, netting out the effect of family size and other family-specific factors that are constant across groups of siblings. The strategy thus allows for a more robust estimation of the association between birth order, risky behaviours and non-cognitive skills for adolescents, a population that has not been examined before in studies using fixed-effect strategies. Institutionally, the ages considered correspond to the period going from the end of primary school to the end of compulsory schooling, covering a key stage of individual development.
Community health centers (CHCs) are an important type of the health care safety net provider, along with public hospitals and physicians that provide free or discounted care to the poor. CHCs specialize in the provision of outpatient primary and preventive health care services, and serve patients regardless of their insurance status and ability to pay. The majority of CHCs are non-profit organizations, but some are public. The state of California started issuing permits to this type of the provider in the 1930’s (Saviano, 2009). Today, these clinics depend on patient revenue – which mainly comes from Medicaid 1 and Medicare 2 reimbursements – as well as government grants and contracts, foundation grants, and charitable contributions. 3 In 2002, the CHC sector began a period of expansion due to increased levels of federal funding under Section 330 of the Public Health Service (PHS) Act, a federal law that governs many aspects of health care in the United States. This initiative was authorized by the George W. Bush Administration with the intent of decreasing health disparities in the U.S. 4 More than half of California’s CHCs today receive Section 330 grants, which confer them the status of federally qualified health centers (FQHCs).
On March 23 rd , 2010, President Barack Obama signed into law the Patient Protection and Affordable Care Act (ACA), the first successful attempt at major health reform in nearly half a century. At the time of its passage, the need for health reform was particularly urgent. The rate of employer-sponsored insurance coverage had steadily declined since it was first measured in 1987, covering only 55 percent of Americans in an employer-based health insurance system (DeNavas-Walt, Proctor, and Smith 2011). At the same time, the private alternative to employer-sponsored coverage—insurance purchased directly from insurers in the non-employer market—was often difficult to obtain. Because these markets are characterized by adverse selection, insurance premiums were high relative to comparable employer-sponsored plans, and many individuals with high medical costs were excluded from the market altogether. As a result, nearly 55 million Americans, or 16 percent of the population, lacked health insurance coverage, putting them at risk of bad health outcomes and financial insecurity (DeNavas-Walt, Proctor, and Smith 2011; Council of Economic Advisers 2011).
The …rst speci…cation summarized in Panel A indicates that urban ‡oor counties have signi…cantly higher advertising for Medicare products. The estimate of $6.35 is sub- stantial, as it slightly exceeds the mean of our dependent variable, though its precision is limited with a standard error of $2.23. This is not surprising given that we have just 210 DMAs and the dependent variable is highly skewed. The corresponding estimate in Panel B, which uses the broader health insurance measure as the dependent variable, is also large in magnitude and statistically signi…cant. Both estimates are robust to the inclusion of ad prices (speci…cation 2) and per-capita FFS expenditures (speci…cation 3) in the DMA-year. One concern with this …rst set of estimates is that urban ‡oor counties may attract more advertising for reasons unrelated to MA reimbursement generosity. To address this concern, in the fourth speci…cation we add a control for per-capita credit card advertising in the DMA-year. This variable should not be a¤ected by the generosity of MA reimbursement though should control for unobserved factors that in‡uence the intensity of advertising in an area. While this variable is signi…cantly positively related with both of our dependent variables, it has little impact on our coe¢ cient estimates of interest.
Outside of Whitehall, research has examined major shocks to status from receiving awards, such as winning the Nobel Prize (Rablen and Oswald, 2008), election to the Major League Baseball (MLB) Hall of Fame (Becker, Chay, and Swaminathan, 2007), or receiving an Oscar (Sylvestre, Huszti, and Hanley, 2006). The assumption behind these studies has tended to be that status should improve health, and there is ev- idence of this for the Nobel Prize and MLB Hall of Fame but inconclusive results for Oscar winners. However, unobserved heterogeneity and the process of choosing winners may limit what can be drawn from the findings. For example, the physical attributes of Oscar nominees differ in ways that affect their health, and bias may stem from correlation with the likelihood of winning an Oscar. People may also undertake different lifestyle decisions, follow different diets, and value their health in unobserved ways. The same might be said for Nobel laureates and their peers. Moreover, actors, baseball players, and academics are all professionals who can be financially compensated for their work. Higher income associated with status may thus confound comparisons of longevity within these populations. Since Track and Field athletes in the early 1900s were all amateurs, the Olympic setting does not face this problem. Another issue is that these other studies judge performance over a longer time frame. For example, baseball players nominated for the Hall of Fame are assessed over their entire career. The long duration of such assessment increases the chance that the factors that lead people to succeed may be correlated with their mortality prospects. It is reasonable to believe that there is less unobserved hetero- geneity among Olympic athletes within any given event than among Nobel laureates, Oscar nominees, or MLB players. 49
Substantial evidence now suggests that an exposure to Chinese import competition may have adverse effects on several dimensions of manufacturing firms in developed countries, including the survival rate of manufacturing plants (Bernard et al., 2006), large contraction in manufacturing employment (Acemoglu et al., 2016; Pierce and Schott, 2016), depressing wages and the employment prospects for occupations and skills which can be substituted from Chinese goods (David et al., 2013; Ebenstein et al., 2014; Utar, 2014; Autor et al., 2016). Similarly, there is an other stream of literature that studies the effect of Chinese import competition on the performance of manufacturing firms in developing economics. However, this growing literature, have mainly focused on the effect of increasing Chinese import competition for Mexican maquiladoras competing in the U.S market. For instance, Utar and Ruiz (2013) argued that Competition from China has negative and significant impact on employment and plant growth, both through the intensive and the extensive margin, on the most unskilled labor intensive sectors, leading to sectoral reallocation. Furthermore, Iacovone et al. (2013), Chinese import penetration reduces sales of smaller Mexican plants and more marginal products and they are more likely to cease. Moreover, in the case of Colombian firms, the only reference on the implications of international competition on manufacturing firm performance, has been presented by Zapata (2018) finding that competition from China has negative impact on employment, sales and value added. Additionally, encourages plant exit and discourages entry, whereas, skill upgrading only occurs in more productive and more capital-intensive plants.
The paper also adds to this literature by considering in detail how job mobility contributes to catching up. Other papers also consider the mechanisms of catching up. Motivated by a model of task-specific human capital (as in Gibbons and Waldman, 2004) most papers consider the role of the first firm in explaining the initial and persistent losses (Oreopoulos et al., 2012; Brunner and Kuhn, 2014; Liu et al., 2016). They generally find that the first employer plays an important role in explaining the losses. Recent papers also highlight the role of mismatch. They find that workers who start in a recesison are more likely to work in sectors that are not typical for their field of study (Liu et al., 2016; Altonji et al., 2016). Oreopoulos et al. (2012) also consider the role of job mobility for Canadian college graduates. They find that job mobility increases in the first five years on the labor market. Primarily graduates at the top end of the skill distribution are more likely to switch firms, while those at the bottom remain stuck at lower quality firms. After the first five years, the remaining catching up is within the firm. In addition to considering job mobility and firm quality, this paper is the first to consider how young workers recover through climbing the job ladder. There is recent evidence that during recessions the quality of vacancies is lower and that the probability of moving up the job ladder declines (Moscarini and Postel-Vinay, 2016; Haltiwanger et al., 2017). This paper confirms for the Netherlands that workers are indeed more likely to start at lower rungs of the job ladder in a recession. While most workers recover through job mobility, I do find some evidence that up to 8 years after graduation workers are more likely to remain lower on the job ladder.
We start by examining how support for the policy varies with age. This is illustrated in figure 7, which plots the percentage of consumers in different age brackets that would vote in favor of eliminating Medicare. Our result shows that the majority of young consumers are better off without Medicare. In particular, we find that the percentage of votes by age in favor of eliminating the program exceeds 50 percent for all ages up to the age of 48. This result is due to the wage and tax effect discussed earlier. Although it takes time to transition to the new steady state, these consumers are still young enough to reap most of the benefits associated with higher labor earnings. The majority of older consumers, on the other hand, are better off with Medicare. Note, however, that the percentage of votes by age in favor of eliminating the program does not decline monotonically with age. We find that the percentage of consumers that are better off without Medicare increases late in life. This is driven by the interplay between Medicare and Medicaid highlighted earlier. That is, more old consumers qualify for Medicaid in the economy without Medicare, which in turn lowers their cost of going through the transition. After summing across votes, we find that 56.8 percent of the population alive in the period of the reform is better off without Medicare. That is, the majority of the population would vote in favor of eliminating the program.
First, minimum wages may only affect the labor market with a delay. Even in indus- tries where adjustment costs are considered to be minimal (e.g. because of significant turnover), adjusting non-labor inputs may be costly (Hamermesh, 1993). Similarly, firms may not be able to freely respond to a changed policy environment because of sunk investment costs (Aaronson et al., 2017). It is thus important for empirical studies to allow for minimum wage effects with delay (Baker et al., 1999; Burkhauser et al., 2000; Keil et al., 2001). In addition, not only do firms respond slowly to new policies, it may also take a considerable amount of time for the labor market to tran- sition from one equilibrium to another, as theoretically argued by Diamond (1981). Meer and West (2016) investigate this hypothesis. They find that since adjustments take time, employment effects are more visible in net job creation than in employment levels.
Theory has long emphasized the importance of private information in explaining credit-market failures. Information asymmetries and the resulting credit constraints have been used to explain anomalous behavior in consumption, borrowing, and labor supply. Motivated in part by this re- search, policymakers and lenders have experimented with various interventions to circumvent such problems. Yet, the success of these strategies depends on which information asymmetries are em- pirically relevant. Credit scoring and information coordination can help mitigate selection prob- lems, while incentive problems are better addressed by improved collection or repayment schemes. This paper provides new evidence on the empirical relevance of asymmetric information using administrative data from the payday-lending market. Payday loans are short-term loans of $100 to $500. Loan fees average $15 to $20 per $100 of principal, implying an annual percentage rate (APR) of over 400 percent. Despite these high interest rates, payday lenders have more storefronts in the United States than McDonald’s and Starbucks combined, with nearly 19 million households receiving a payday loan in 2010 (Skiba and Tobacman 2011). The payday-loan market is also extremely high risk, with more than 19 percent of initial loans in our sample ending in default.