Notes - The plotted curve reports the predicted low birth weight differences between 3rd generation Mexicans and white natives for each level of selection on health at migration, assuming that the intergenerational correlation in health ρ is equal to 0.35 and the effect of maternal health on birth weight, γ, is equal to 0.75 (baseline). The scenario considered assumes that Mexicans fully assimilate in behaviors but incorporates the estimated effect on birth weight of the observed socioeconomic differences between second-generation Mexicans and white natives (less than full socioeconomic assimilation, µ M X 2 = −0.1). The lower dashed line (y = −0.001) describes the observed
The analysis uses a sample of 12,385 observations from 3,786 households from the 1999, 2001, 2003, and 2005 waves of the PSID. From an initial sample of 9,148 households with non- missing data, I restrict the sample to households that are observed at least twice during the study period (N= 7,438) with a prime working-age head aged 25 to 54 (N= 5,226). I chose this age range rather than what is used in the literature, 25-64 (Gumus and Regan 2009; Heim and Lurie 2010, 2013b, 2013a; Gurley-Calvez 2011) because I want to exclude households that are eligible for early-retiree health insurance. Because people who can retire from their current work might chose to become self-employed because of early retiree health insurance rather than tax deductions, I exclude them from the sample. I further restrict the sample to exclude the nine group-of-one states where the self-employed do not purchase health insurance in the non-group market (CO, CT, DE, FL, HI, MI, MS, NC, RI), the two states that changed regulations in the middle of the study period (KY, NH), and Oregon, which has community rating but no guaranteed issue (N= 3,786).
According to the official website (http://www.cpc.unc.edu/projects/china), the China Health and Nutrition Survey (CHNS) is a collaborative project between the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. Its main goal is to study the effects of the health, nutrition, and family planning policies and programs implemented by national and local governments and examine the dynamic impact of the economic transformation of Chinese society on the health and nutritional status of its population. The survey is collected over a 3-day period with a sample of about 4,400 households, including 26,000 individuals in nine provinces. In addition, detailed community data were collected.
Next, I take into account mother’s and child’s health. First, I restrict the sample to children with mothers who are in very good or excellent health. It may be the case that mothers with serious health issues stay out of the labour force even when their youngest child is enrolled into childcare. In this case the effect of maternal employment is different for mothers with different health status and they differently respond to the instrument. Row (3) shows that restricting the sample in this way does not change the qualitative results, but the estimated effects are larger. Second, I restrict the sample to children without any chronic diseases. One might be concerned that if a child has any chronic disease their mother can either stop working or reduce working hours even if the youngest child has attends childcare. Also, the presence of chronic diseases itself can affect the child’s weight. Excluding children with chronic disease should give us an idea whether the main findings might be driven by child health problems. Based on the data I consider chronic diseases related to heart, lung, liver, kidney, stomach and spinal diseases. Row (4) shows that the results are again very similar to the main results except that the probability to become overweight becomes not statistically significant. To a certain extent, these findings show that the main results are not driven by reverse causality.
In 2018, over 67 thousand Americans died from a drug overdose (Hedegaard et al., 2020). Although the substantial rise in drug-related mortalities over the past decade has been largely driven by illicit opioid use, the origins of this epidemic are rooted in opioid prescribing patterns that began in the 1990s and persisted throughout the 2000s. While pharmaceutical companies, physicians, and patients have received increased scrutiny in recent years over their respective roles in the ongoing crisis, the actions and behaviors of private health insurers have gone largely overlooked. These firms play an integral role in coordinating care between large segments of the U.S. population and health care providers; as a result, health insurers are uniquely positioned to monitor and observe patterns of opioid prescribing and use. Furthermore, despite recent efforts at the federal level to address the opioid epidemic, many public health advocates contend that the most impactful change will occur through communal ventures. Because of their influence in local health care markets, developing a better understanding of how private health insurers’ incentives interact with enrollee opioid use is of first order concern.
There is a fast growing body of recent literature on interconnection of commodity markets or the role of financialization in markets co-movements. Saghaian (2010) presents empirical results using vector autoregression (VAR) and Granger causality supplemented by a directed graph theory modeling approach to identify the links and plausible contemporaneous causal structures between energy and commodities in the grain sector (wheat, soybean and corn). Although Saghaian (2010) finds strong correlation among oil and food prices with monthly data from 1996 to 2008, there is mixed evidence of a causal link from oil to the other three commodities. B¨ uy¨ uk¸sahin and Robe (2017) model dynamic correlations between equity market and commodity in grains and livestock sector, and find that world business cycle shocks have a substantial and long-lasting impact on the food markets co-movements with equity market, while changes in the intensity of financial speculation have a short-lived and not significant impact on cross-market return linkages using various specifications of structural vector autoregression (SVAR). Tang and Xiong (2012) find increasing correlation since 2004, but they model dynamics of correlations by rolling-window for all pairwise combinations of commodities one after another, which is inefficient as they do not explicitly take all information into account and not necessarily robust to the structural change in correlations. Adams and Gl¨ uck (2015) consider structural breaks in correlations but their sample only include eight commodities and also do not provide a joint estimation of dependence structure in futures returns. Most of these studies, however, only focus on specific commodities or just use low frequency data (monthly or weekly), and one may want to know if these empirical results are still robust if relative high frequency information of more futures markets is used.
cigarettes, adjusted for inflation to 2000 dollars. There are also several other state/county/year- varying policies that might affect infant birth outcomes; these controls are in the vector P. We include a simulated variable to account for changes in public health insurance eligibility. It is constructed the same way as Currie and Gruber (1996). Using a 1990 national sample, we calculate for each state and each year the percent of infant and children who would be eligible for Medicaid or State Child Health Insurance Programs (SCHIP). This variable varies only by legislative generosity within each state and over time, which does not capture the demographic characteristics of an actual state population that might affect infant health outcomes. We also control for the maximum Aid to Families with Dependent Children (AFDC) or Temporary Aid to Needy Families (TANF) benefit for a 3-person family in each state and year. County-level control variables in the model are: annual unemployment rate, per capita personal income and population. 10 We merge the data on bans and policy controls to the infant birth data by state/county of infant birth and quarter/year of conception, which is imputed using birth month, birth year and weeks of gestation. 11 The summary statistics of smoking bans, cigarette taxes and other state level controls are presented in Table 1.
adult height is essentially determined by events in the first three years of life, and height can have long-lasting negative impacts on health status (Strauss and Thomas, 2008). Using a 1958 British birth cohort who were followed prospectively into their adult years. Case et al., (2005) explore the long-lasting associations between childhood health on adult health, exam performance, employment, and measures of socioeconomic status (SES). They show that people who experienced low birth weight or chronic disease when they were children had worse health, poor exam performance, and lower working statuses, even after controlling for parental background, such as education and income. Almond (2006) use 1918 Influenza Pandemic, which was unexpected and short, to identify the in-uterus conditions effects on late health. Using roughly one-third of those born in early 1919 whose mothers contracted influenza while pregnant as treatment group, and those born in early 1918, who had essentially zero prenatal exposure to the 1918 pandemic as control group, he find that children of infected mothers were more likely to be disabled and experienced lower, as well as less educational attainment. Currie and Walker (2011) investigate the introduction of E-ZPass (electronic toll collection) greatly decrease traffic congestion and vehicle emissions near highway toll plazas. Their results suggest that traffic congestion contributes significantly to adverse health conditions among infants.
The characteristics of an individual, including their age, health, and wealth, are other factors that play an important role, in the retirement decision. These factors are expected to either enhance or detract from the effect of RHI. While RHI is an attractive benefit offered to an employee, a person who is in good health, with a long expected life span, may behave differently than a person in poor health who has a shorter expected life span. Since those who are likely to live-longer need to spread their consumption over a longer period of time, it is expected that, regardless of access to RHI, they will delay retirement longer than a similar person who is in poor-health. Thus, it is expected that RHI will have a greater influence on the behavior of the less healthy. Linsenmeier (2002), however, finds that the effect of RHI is not significantly different for a person in poor health than for a person in good health.
paternal samples. We also provide statistics for a subset sample, conditional on at least one sister (or brother) of the mother (or father) being observed in the samples (denoted as sibling sample). The statistics are similar between the whole and sibling samples, suggesting that our source of variation comes from a subsample that is not selective. We note that the fraction of LBW increased drastically from G2 to G3. There is also a slight increase in the fraction of IUGR as measured by all three criteria, but not as much. Two policy changes are responsible for these trends. First, the birth reporting requirement becomes more stringent after 1994. Before that year, it was common to not report a birth if the newborn was dead. Second, the National Health Insurance (NHI) program implemented in 1995 provides the entire Taiwan population with access to health care at a very low cost. Better medical care allows more preterm births and a weak fetus to survive. In our samples, both policy changes affect the entire third generation, but not the second generation, which explains the observed differences. In section 1.4.4, we account for the potential bias that the probability of observation in samples for the second generation may be correlated with birth weight.
The regression discontinuity research design is compelling because it overcomes the omitted variable bias problem given a modest set of assumptions. Moreover, some of the assumptions are partially testable. Interpretation of the effects of Medicare eligibility on outcomes as causal requires two assumptions (Lee and Lemieux, 2009). First, the conditional expectation functions of the potential outcomes must be continuous with respect to age across the Medicare eligibil- ity threshold, which is analogous to saying that in the absence of treatment at age 65 outcomes would trend smoothly. There are two standard test for violation of the continuity assumption. First, there should be no changes in other variables across the threshold if this assumption holds. I test for discontinuities in educa- tion, race, labor market participation, health status, and Social Security receipt and present the results in Figure A3 and Table A2. These estimates are all statis- tically insignificant except for a decline in the fraction of the sample that is white, which is likely due to sampling error given that none of the other background characteristics change at the threshold. Second, I plot the density function of the running variable (spousal age) and test for a discontinuity at the age-65 threshold
This study focuses on report cards and financial performance in the nursing home industry for several reasons. First, in December 2008, the Centers for Medicare and Medicaid Services (CMS) adopted a summarized five-star rating design to its existing public reporting website ― Nursing Home Compare, which publishes quality information for each nursing home that participates in Medicare or Medicaid. This new star design is expected to be much easier for consumers to understand. Compared to previous versions of nursing home report cards that presented detailed but complex quality metrics, the straightforward graphical presentation of five stars has a potential to generate a stronger effect for high-performing nursing homes. In the management and economics literature, existing empirical evidence generally supports the argument that consumers’ perceived quality of firms, which is often based on some types of rankings, is positively associated with financial performance (McGuire et al., 1990; Deephouse, 2000; Roberts and Dowling, 2002; Raithel and Schwaiger, 2015). However, to date, few studies have addressed this issue focusing on the new public reporting system in the nursing home industry. The ease of interpretation and access to the five-star rating system has provided
Bünnings and Tauchmann (2015) and others and consider risk preferences as fixed over time, using the average value of an individual’s responses. Table 1.3 shows that risk aversion gives rise to advantageous selection in favour of the public sector: after controlling for observables, risk-averse individuals are less willing to opt out of public insurance and less likely to be hospitalised. Switching to private insurance implies uncertainty about future premiums, as changes in family status translate into premium changes in private insurance. For example, a privately insured couple who become parents has to pay for their child in private health insurance, whereas the child is insured free of charge in public insurance. This may explain why risk-averse individuals prefer public insurance. The finding that risk-averse individuals tend to be less risky has been observed also in other contexts (Finkelstein and McGarry 2006). Possible explanations for this result are that the risk averse use more preventive care, or that
Thailand’s Universal Coverage Scheme (UCS) is a primary-care-focus program providing a comprehensive package of health services including disease prevention and health promotion universally to the population who do not have access to health coverage via their employment. The program was launched during 2001 and 2002 and was initially called the 30-baht policy due to the price per visit at 30 baht (≈ $1). The fee was eliminated by the subsequent government in 2006. The program was first piloted in six provinces in 2001 and was rapidly rolled out to all 76 provinces by early 2002. According to Manachotpon and Wagstaff (2012b), the program was gradually rolled out in four waves within the period of nine months total. The UCSs objectives include creating the equitable access among the entire Thai population and reducing the financial risk from health expenses of major diseases and injuries, such as the impoverishment from medical bills. The take-up process was simple. Individuals were required to register at designated hospitals, chosen from the list of approved care providers in the district health system network. The approved list of hospitals consists mainly of public providers and a small number of private providers. UCS users are required to get primary care from their designated hospitals, with the possibilities to be referred to other hospitals if needed. The program increases the population coverage from 68% in 2001 to 95% by 2004 as shown in Figure 1, covering around 65% of the population. Prior to this implementation, the government provide public health insurance via Medical Welfare Scheme (MWS), targeting children, elderly, and low-income earners, and Voluntary Health Card Scheme, premium-based insurance with government subsidies. After the UCS, these public schemes were eliminated and the enrollees were moved into the UCS.
to college health clinics at deep discounts in order to attract brand loyalty among young consumers and receive tax deductions. As a result, students could obtain inexpensive birth control and colleges earned a bit of revenue by adding a small markup to help support other health initiatives around campus (Wasley 2007). This arrangement ended on January 1, 2007, when the Deficit Reduction Act of 2005 (DRA) went into effect, eliminating the incentives for pharmaceutical companies to sell drugs below retail price to all but a limited list of organizations, not including college health clinics. Consequently, the typical price of prescription birth control jumped from between $5 and $10 a month to between $30 and $50 a month on college campuses around the country. College health professionals worried that the change would lead to fewer women using the Pill, fewer well-woman visits (that could have lasting impacts on health), more use of emergency contraception, and more unintended pregnancies (Chaker 2007, Wasley 2007).
This table presents summary statistics for annual health expenditure for different cohorts of the Health and Retirement Survey from 1992 to 2010. AHEAD were born 1923 or earlier; CODA (Children of Depression) were born from 1924 from 1930; HRS were born 1931-1941; WBs (War Babies) were born from 1942 to 1947; EBBs (Early Boomers) were born from 1948 to 1953; MBB (Mid Boomers) were born from 1954 to 1959. All dollar amounts are in 2010 dollars using CPI data from the Federal Reserve of Minneapolis.
Does the accessibility of wealth near one’s end of life increase longevity? This paper presents new evidence by studying a unique corner of the market that is well suited to answer this question: the secondary market for life insurance policies, also known as the life settlement market. Using the shocks to the financial strength rating of insurance companies to instrument for settling a policy, the paper is able to show that the accessibility of wealth near the end of life is important for longevity. Testing mechanisms for this eﬀect, the paper provides evidence that individuals with more fragile health, those with severe diagnoses, and limited access to healthcare benefit most from accessing their wealth. The paper test alternative stories to show that the results are not driven by the regional supply of primary care doctors, social- economical factors, biases in life expectancy estimates, or spurious correlations.
Other papers have exploited the Swiss context to infer the impact of immigration on native wages but none of them investigate the role of language in determining labor market outcomes. For instance, Gerfin and Kaiser (2010) replicate the OP model in Switzerland without controlling for the linguistic background. 3 It is also worth mentioning Beerli and Peri (2015), who exploit the labor market liberalization of cross-border workers between 1999 and 2007 to infer the impact of a large inflow of foreign workers on wages and employment opportunities. Differently from us, they find a positive effect on the wages of highly educated workers and on the working hours of less educated workers. The different results should be due to the large differences in the identification strategies adopted. Indeed, while they exploit a policy instrument that only involved cross-border workers at local labor market level, we evaluate the impact of overall migration flows at national level. Thus, on the one hand, the wage effects computed in this paper should be better able to capture the overall effects of immigration, considering all types of migrants (recent and non-recent) with respect to the counterfactual of no immigration. On the other hand, their identification strategy is more targeted to a specific policy change and is better able to capture the different facets of a labor market liberalization.
Data used in this study are from the China Health and Retirement Longitudinal Study (CHARLS). It is a nationally panel survey targeting the middle-aged and senior populations carried out from 2011. The following three waves of data were collected in 2013, 2014 and 2015 respectively. In this paper, we combined all waves of the survey to create a comprehensive dataset with rich information on the three generations. In the survey, a household with at least one member 45 years old or above is randomly selected, and this member becomes the main respondent. Information is collected on main respondents and their spouses, together with the parents on both sides and all children of the couple regardless of where they live. Information on other family members, such as the grandchil- dren of the main respondents, are available if they live together with the main respondents. In the first wave of data collected in 2011 and 2012, 17708 individuals who are from 10257 households and 150 counties successfully responded to the survey. This random sample is large enough to represent the whole aged population. This dataset contains detailed educational attainment information for three generations regardless of whether they live together and the fourth generation if they live in the same household. Using the information on the first three generations, nationally representative three-generation mobility can be measured for the first time for China.
I would like to thank my advisors, Dimitri Vayanos and Georgy Chabakauri for all the invaluable guidance and support I received. Their comments and suggestions improved this thesis a lot. Their personal example helped me to develop as a scholar. I also benefited from numerous discussions with permanent and visiting fac- ulty at the London School of Economics. I would like to thank Amil Dasgupta, Daniel Ferreira, Dong Lou, Peter Kondor, Albert (Pete) Kyle, Semyon Malamud, Igor Makarov, Ian Martin, Rohit Rahi, Anna Obizhaeva, Martin Oehmke, Marzena Rostek, Gyuri Venter, Kathy Yuan and Kostas Zachariadis. Peter Kondor and Igor Makarov provided vital help during my job market year. Marzena Rostek provided invaluable enthusiasm, support and feedback for the first chapter of the thesis.