This essay examines topics in healtheconomics. The first study uses data obtained from the Health and Retirement Study (HRS) and the Rand HRS files, to examine the relationship between access to retiree health insurance (RHI) and the decision to leave one’s career job. This paper does not restrict attention to individual’s who choose to take a full retirement, as recent data indicates that only 51.4% of individuals leave a career job and fully retire, while nearly 25% leave their career job, and pursue a partial retirement. In this paper a Cox Proportional Hazard Model with time varying covariates is utilized to estimate the probability that an individual disengages from their career job, given they have not yet done so. Results indicate that those with access to RHI are significantly more likely to leave their career employer in all time periods than identical individuals without RHI.
Summary statistics regarding our final sample are detailed in Table 3.3. Research in- dicates that physicians may interact with new technology differently than administrative staff. Hu, Chau, Sheng, and Tam (1999) consistently find that physicians differ from other types of users when accepting technology, specifically in the area of telemedicine. These differences can be attributed to their specialized training, autonomous practices, and pro- fessional work environments [Walter and Lopez, 2008]. Because of our limited physician sample size, we conduct our analysis at the clinical vs. non-clinical level. Clinical em- ployees are those actively providing medical treatment to patients, while non-clinical staff would include receptionists, billing specialists, and administrators who do not directly provide medical care. Non-clinical staff often have more experience using IT systems in the office, as they normally conduct billing and scheduling through specialized software systems; Barker et al. (2003) find prior levels of IT usage to influence adoption behavior, so this difference could influence non-clinical providers’ acceptance of a new EHR in a different manner than clinical staff members. Additionally, Yi et al. (2006) and Math- ieson (1991) posit that medical providers may behave differently in the presence of EHRs because of the availability of support staff to deal with the system on their behalf.
Tanzania is a large country with many different agro-ecological zones and hence a great range of climatic characteristics. The d iverse climatic conditions, corresponding to the country’s varied topology, (other factors held constant) put different farming households at different risk of storage losses. For example, farmers in humid and relatively less warm tropical temperatures are at larger risk since such conditions favor the reproduction and growth of pests, insects, fungus, and other cereal destructive organisms. In effect, households in different climatic zones are affected differently by climate change. There is already strong evidence that climate change is an issue in the country, as indicated by the drastic change in the annual mean rainfall from 1,067mm in 1960-1990 to 767 mm in 2001-2009. Rowhani et al. (2011) predict that a temperature increase of 2 0 C by 2050 will reduce the average maize, sorghum and rice yields in the country by 13%, 9%, and 8%, respectively. Although nothing is known about the impact on storage losses, they are likely to increase and therefore worsen the situation. Understanding how farmers adopt storage technologies and preservation methods in response to these climatic factors is important. In the current situation of climate change, where less humid and relatively cold areas become wet and warm, adoption of relevant storage technologies could be a useful adaptation strategy.
The German health insurance system is characterized by the coexistence of SHI and PHI. The fact that the majority of the German population is insured under the SHI – most of them mandatory – while certain subgroups of the population, such as civil servants, the self-employed and high earners, may opt out for substitutive PHI has been the subject of intense discussions in terms of both efficiency and fairness. Potential positive selection into the PHI, mainly due to risk-adjusted health premiums under the PHI, may lead to undesirable market outcomes in the German health care market. In addition, privately insured are often considered as privileged because of different and possibly better medical treatments. Although the regulatory framework and related incentives clearly suggest who will prefer which type of insurance, the empirical evidence is still inconclusive. In chapter 2 we investigate deter- minants that led individuals to switch the type of insurance using data from the SOEP for the years of 1997-2010. Besides socioeconomic characteristics and health risks, which are suspected to be the main drivers in this decision, we also analyze the role of previously unconsidered personality traits, such as risk aversion or altruistic attitudes, that could affect the decision to opt out of the SHI. Applying a haz- ard model in discrete time and accounting for potential endogeneity of self-reported health through an IV approach, the estimation results yield robust evidence on the choice of health insurance type that is consistent with pragmatic decision making, with both incentives set by regulation and personality traits as relevant determinants. For instance, the SHI is preferred by individuals who benefit from free insurance coverage of dependents or those who would have to pay high risk-adjusted premiums un- der the PHI, i.e. bad health risks. This advantageous selection in favor of the PHI may further increase pressure on the SHI which is already severely affected by demographic change. In addition, we also find convincing empirical evidence for the notion that risk-loving individuals have a higher proba- bility of buying PHI, however, we observe no significant effect of the measure of altruism. Overall the results suggest that the choice between both systems seems to be a less emotional issue and, hence, policy debates should focus more on how to design a framework that foster more competition between both systems.
We present summary statistics of our sample in Table 4.1. Sample averages and standard deviation of non-indicator variables, as well as percentages of indicator variables are reported separately in the upper and lower panel of Table 4.1. The first two columns of Table 4.1 report information of married couples during the sample period. On average, wives have slightly higher BMI and less education than husbands. Roughly 25% of couples are obese or overweight in our sample. Two thirds of couples in our sample have at least one child and about half of them have health insurance. In the couple sample, 67% of men are smokers while only 3% of women smoke frequently; men have higher employment rate than women (82.3 % v.s. 70.7%). Around 30% of the couples come from urban areas. China uses a residence registration system called ”hukou”, which classifies people as rural or urban residents, to restrict free migration and determine eligibility to local resources such as public education, medical care and pension plan. For example, school-age children from rural areas do not have access to public schools in urban areas, even if they have been living in the urban areas. We are interested to learn whether China’s economic transformation had affected overall health condition of people with urban or rural hukou differently, given that generally urban areas have benefited more from the transformation during the sample period. For this purpose, we conduct our analysis for urban and rural areas separably.
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
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.
Information on smoking behavior is from the Current Population Survey To- bacco Use Supplements (CPS-TUS) for years 1998 to 2003 and 2006 to 2007. 8 The Current Population Survey is a nationally representative, monthly house- hold survey of labor force participation. The monthly survey often includes sets of supplemental questions on particular topic such as health, schooling, fertility, and immigration. Periodically, respondents are asked a series of questions about smoking and other tobacco-related behaviors as part of the Tobacco Use Supple- ment, which is sponsored by the National Cancer Institute and the Centers for Disease Control. The CPS-TUS is a repeated cross section of individuals useful for describing smoking behavior of Americans over time. Individuals who have smoked at least 100 cigarettes in their entire life are identified as current or for- mer smokers and are asked a series of follow-up questions about smoking behavior. The smoking measures used in this paper are self and proxy reports of smoking at least some days and smoking every day.
To explore the impact of reimbursements on …rm advertising, we utilize data from Kantar Media. Kantar Ad$pender contains advertising data at the media-product-year- designated market area (DMA) level. Because DMAs are bigger than counties, we need to aggregate our reimbursement data. We create variables that represent the percentage of Medicare bene…ciaries in a DMA that live in an urban, urban ‡oor, and ‡oor county. We examine the impact of these variables on TV spot advertising spending per Medicare bene…ciary in a DMA. We de…ne this measure in two ways. In the …rst, we restrict our analysis to products with "Medicare" in their name. This includes Medicare Advantage plans, but also Part D and Medicare supplement plans as well. Furthermore, not all carriers report a speci…c Medicare line. The Kantar data does not allow us to distinguish between these products and data may be an overestimate or an underestimate of the amount of advertising for MA plans. However, we have no reason to believe that advertising for Medicare supplement or Part D plans would vary with ‡oor status. Average spending per Medicare enrollee is $5.90 per year. In the second de…nition and panel of Table 13, we take Kantar de…nition of "health insurance" as given, noting that not all Medicare products are denoted by name. While we would prefer to restrict to only Medicare Advantage products within health insurance, the products are not coded …nely enough in the data. However, Medicare products comprise the bulk of individual insurance plans sold (and, presumably, targeted advertising) within all DMAs.The dependent variable is skewed, with only about half of DMAs having advertising, but total spending in the 90th percentile of DMAs is $2.2 million per year.
To assess the influence of the new Ghanaian health insurance scheme (NHIS), we analyse cross-sectional data from the fifth Ghanaian Living Standard Survey, which is representative for the 2005 Ghanaian population. As the NHIS was implemented at the district level, we can use the variation in when individuals were interviewed at the various sub-districts. Since most districts introduced the NHIS during the survey period in 2005, we compare individuals who were interviewed before and after the introduction of the insurance scheme, conditional on fixed effects of district and interview month. Using the quasi-exogenous variation in the availability of formal health insurance, we apply ordinary least squares estimates to estimate the impact of the new insurance scheme on making and receiving transfers at the extensive and intensive margin. We also test whether the treatment effect depends on the relationship between the recipient and donor of a transfer. Our empirical findings indicate that introducing a formal health insurance scheme reduces the probability of making transfers. In addition, the number of remittances decreases significantly. We also find that a relatively close relationship between the recipient and donor of a transfer, holding everything else constant, reduces the crowding out. One potential explanation for this heterogeneity is sharing obligations, which are known to be strong in developing countries and are recognised by the contemporary literature as being a barrier to economic growth (e.g. Grimm et al., 2013). The decrease in informal transfer networks due to formal insurance is found to be lowest in kinship networks; therefore, our findings raise the question of whether formal insurance can overcome this issue, at least in the short run. Our analysis of health-related outcomes suggests that the NHIS reduces respondents’ OOP expenditures, which is in line with our expectations. Overall, our findings indicate that public health insurance schemes strongly affect how healthcare services are paid for and may also support economic development in the long run. However, the results also emphasise the effects of new policies on existing institutions, which can be an important issue in many health policy contexts.
I use Prescription Drug Plan Formulary, Pharmacy Network, and Pricing Information Files from the Centers for Medicare and Medicaid Services to test for differences in benefit design for opioids across MA-PDPs and SA-PDPs. These data contain detailed informa- tion on the universe of Part D formularies and plans, including the list of covered drugs on each formulary, utilization management rules corresponding to each formulary-drug combination, and the level of cost-sharing associated with each plan-drug combination. For formulary-drug combinations that have a quantity limit restriction, the data contain both the quantity limit amount and the days limit; for example, 90 tablets within 30 days. I use data from 2008, 2009, 2011, 2013, and 2015. These data years represent a period of significant change in the medical community’s perception of safe opioid prescribing levels; between 2009 and 2011, researchers documented a heightened risk of adverse health events linked to high daily dosages of opioids, particularly among the elderly (Dunn et al., 2010; Saunders et al., 2010; Bohnert et al., 2011).
This chapter examines the impacts of housing and financial wealth on healthcare spending using the matched household data constructed from the Consumer Expenditure Survey (CES) and the Survey of Consumer Finances (SCF). The human capital model of demand for health (Grossman, 1972, 2000) suggests that wealth has a positive effect on healthcare spending as higher wealth will relax the budget constraint of individuals and allows individuals to consumer more healthcare, ceteris paribus (Kim and Ruhm, 2012). 1 This implies that prior studies which use only income as a proxy for economic resources do not provide a complete picture of how resources affect healthcare spending (Goodman, 1989). Additionally, the exclusion of wealth raises the concern of endogeneity 2 (Feinstein, 1993). Furthermore, literature in macroeconomics and finance indicates that wealth effect might differ depending upon the forms of wealth, for example, housing wealth and financial wealth, because of difference in features such as uncertainty of shocks, liquidity, tax treatment, and mental accounting by households with respect to the forms of wealth they hold (Case, Quigley, and Shiller, 2005; Bostic, Gabriel, and Painter, 2009; Belsky, 2010). However, in what direction the housing wealth effect differs from the financial wealth effect is an empirical issue.
Stickrath, 2015). Furthermore, marijuana may affect fetal brain growth and neurodevelopment through its interaction with the endocannabinoid system (Volkow et al., 2017). Despite this, the evidence of the effects of marijuana use in pregnancy on various measures of infant health is mixed. While many studies find associations between maternal marijuana use and fetal growth restriction, preterm birth, increased placement in NICU/ICU, and other adverse neonatal outcomes, many others report no such associations, and some even find beneficial effects of prenatal marijuana use. Overall, the effects of cannabis on early-life outcomes remain largely unknown due to confounding factors such as tobacco, alcohol, and other drug exposure, as well as socio-demographic characteristics not considered by existing studies (Gunn et al., 2016).
Prescription drugs represent one of the fastest-growing areas of healthcare spending (Martin et al., 2016) and remain a mainstay of effective treatment for costly chronic conditions. Numerous studies have examined the effects of health insurance coverage on drug utilization among the elderly, particularly around the creation of the Medicare Part D program (Kaestner and Khan, 2012; Ketcham and Simon, 2008; Lichtenberg and Sun, 2007; Yin et al., 2008). However, much less is known about the effects of health insurance on prescription drug use among low-income adults, who are primarily insured through Medicaid or lack health insurance entirely. The passage of the Affordable Care Act (ACA) in 2010 and the subsequent Supreme Court ruling making Medicaid expansion optional provided a valuable opportunity for evaluating the responsiveness of low-income individuals to insurance expansions in terms of prescription drug usage. Thus far, 31 states plus the District of Columbia have adopted the expansions. We leverage this natural experiment to examine changes in utilization across drug classes, as well as brand and generic forms of prescriptions; in addition, we use state-level variation in Medicaid drug copayments to estimate the price elasticity of demand in this population.
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,
Therefore, in chapter two, it has been shown how unemployment impacts risky behaviors. Since unemployment can be seen as a strong economic change, the results of this part of the thesis reveal evidence about the general health impact of negative economic alterations. In contrast to previous literature, the results yield no evidence for a negative impact of unemployment on risky behaviors. In particular, by using data from the GSOEP four different measures for risky behaviors has been analyzed (diet, alcohol consumption, physical activity, and smoking). While the analysis shows no evidence for an adjustment of the consumption of addictive goods after becoming unemployed, individuals alter their food consumption as well as their level of physical activity in a positive way. This finding can probably be traced back on a shift in the opportunity costs of time. Since the preparation of healthy food and a high level of sports consumption is typically time consuming, but generally not expensive, additional time can be used to foster such activities.
Our ability to identify the welfare effects of the timing of pineapple adoption and disadoption rests on the challenging task of finding an instrument, z, that is correlated with the decision and duration of cultivating pineapple, but not with changes in farmers’ welfare status. We use two instruments, the lowest 1998 soil organic matter (SOM) and acidity (pH) levels observed among a farmer’s cultivated plots. Pineapple thrives in low pH and high SOM soils, so the likelihood that a farmer adopts and the duration s/he cultivates pineapple should be related to one or both of these variables. Meanwhile, high acidity (i.e., low pH) lowers the productivity of virtually all other crops grown in Akwapim South, while high SOM has the opposite effect. We would therefore not expect these variables to jointly affect change in farmer asset holdings independently of their effect on pineapple cultivation. But we also test the exclusionary restriction necessary for these to serve as suitable instruments for pineapple cultivation and confirm the statistical defensibility of this instrumental variables estimation strategy. The most acidic soil recorded had a pH of 4.3 with a mean pH of 6. The mean soil organic matter was 2.46 percent.
Agricultural commercialization is a critical pathway to stimulate a structural transformation in developing countries, as it allows poor smallholders to generate more income for better welfare. Analyses of determinants of market participation have focused on identifying factors influencing participation in the output market. However, transaction costs--i.e information and search costs, may exist in the input market -fertilizers/seed-and may be highly different from those existing in the output market; this can result in jeopardizing the production of marketable surplus. In addition, technologyadoption (input side) has a great impact on farm productivity and thus on the propensity to market products (Teklewold 2016). A few studies (Alene et al. 2008; Asfaw et al. 2012) have included determinants of input market participation when analyzing output market participation. However, these studies suffer from some limitations: i) they analyze output market participation distinctly from input market participation; and therefore, they might hide important heterogeneities among participants either in the output market or the input market; ii) they are located in specific regions in Kenya and pay attention to specific crops markets (maize, or pigeonpea) or analyze specific input market (fertilizer or seed), while the determinants of output market participation depend on the context and the nature of crop, and farmers’ demands for inputs may include both fertilizers and seeds. To the best of our knowledge, the only paper that jointly analyzed technologyadoption (input side) and output market participation was Teklewold (2016). Nevertheless, he only focused on that joint modeling and did not analyze the impact of such strategic choices on farm household’s welfare.