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Economics can make immensely valuable contributions to our understanding of infectious disease transmission and the design of effective policy responses. The one unique characteristic of infectious diseases makes it also particularly complicated to analyze: the fact that it is transmitted from person to person. It explains why individuals’ behavior and externalities are a central topic for the economics of infectious diseases. Many public health interventions are built on the assumption that individuals are altruistic and consider the benefits and costs of their actions to others. This would imply that even infected individuals demand prevention, which stands in conflict with the economic theory of rational behavior. Empirical evidence is conflicting for infected individuals. For healthy individuals, evidence suggests that the demand for prevention is affected by real or perceived risk of infection. However, studies are plagued by underreporting of preventive behavior and non-random selection into testing. Some empirical studies have shown that the impact of prevention interventions could be far greater than one case prevented, resulting in significant externalities. Therefore, economic evaluations need to build on dynamic transmission models in order to correctly estimate these externalities. Future research needs are significant. Economic research needs to improve our understanding of the role of human behavior in disease transmission; support the better integration of economic and epidemiological modeling, evaluation of large-scale public health interventions with quasi-experimental methods, design of optimal subsidies for tackling the global threat of antimicrobial resistance, refocusing the research agenda toward underresearched diseases; and most importantly to assure that progress translates into saved lives on the ground by advising on effective health system strengthening.
Bénédicte Apouey, Gabriel Picone, and Joshua Wilde
Malaria is a potentially life-threatening disease transmitted through the bites of female anopheline mosquitos infected with protozoan parasites. Malaria remains one of the major causes of mortality by infectious disease: in 2015, there were an estimated 212 million cases and 429,000 deaths globally, according to the 2016 World Malaria Report. Children under 5 years in sub-Saharan Africa bear the greatest burden of the disease worldwide.
However, most of these cases could be prevented or treated. Several methods are highly effective in preventing malaria: in particular, sleeping under an insecticide-treated mosquito net (ITN), indoor residual spraying (IRS), and taking intermittent preventive treatment for pregnant women (IPTp). Regarding treatment, artesiminin-based combination therapy (ACT) is recommended as first-line treatment in many countries.
Compared with other actions, malaria prevention behaviors have some specific features. In particular, they produce public health externalities. For example, bed net usage creates positive externalities since bed nets not only directly protect the user, but also reduce transmission probabilities through reduction in the number of disease hosts, and in the case of ITNs, reduction of the vector itself. In contrast, ACT uptake creates both positive externalities when individuals with malaria are treated, and negative externalities in the case of overtreatment that speeds up the spread of long-run parasite resistance. Moreover, ITNs, IPTp, and ACTs are experience goods (meaning individuals only ascertain their benefits upon usage), which implies that current preventive actions are linked to past preventive behaviors.
Malaria prevention and eradication produce unambiguous benefits across various domains: economic conditions, educational outcomes, survival, fertility, and health. However, despite the high private returns to prevention, the adoption of antimalarial products and behaviors remains relatively low in malaria-affected areas.
A variety of explanations have been proposed for low adoption rates, including financial constraints, high prices, and absence of information. While recent studies highlight that all of these factors play a role, the main barrier to adoption is probably financial constraints. This finding has implications regarding the appropriate pricing policy for these health products. In addition, there is a shortage of causally identified research on the effect of cultural and psychological barriers to the adoption of preventive behaviors. The literature which does exist is from a few randomized control trials of few individuals in very specific geographic and cultural contexts, and may not be generalizable. As a result, there are still ample opportunities for research on applying the insights of behavioral economics to malaria-preventive behavior in particular. Moreover, little research has been done on the supply side, such as whether free or heavily subsidized distribution of prevention technologies is fiscally sustainable; finding effective methods to solve logistical problems which lead to shortages and ineffective alternative treatments to fill the gap; or training sufficient healthcare workers to ensure smooth and effective delivery. Given these gaps in the literature, there are still multiple fruitful avenues for research which may have a first-order effect on reducing the prevalence of malaria in the developing world.
Pei-Ju Liao and Chong Kee Yip
In the past century, many developing countries have experienced rapid economic development, which is usually associated with a process of structural transformation and urbanization. Rural–urban migration, shifting the labor force from less productive agricultural sectors to more productive industrial sectors in cities, plays an important role in the growth process and thus has drawn economists’ attention. For instance, it is recognized that one of the important sources of China’s growth miracle is rural–urban migration.
At the early stage of economic development, an economy usually relies on labor-intensive industries for growth. Rural–urban migrants thus provide the necessary labor force to urban production. Since they are more productive in industrial sectors than in agricultural sectors, aggregate output increases and economic growth accelerates. In addition, abundant migrants affect the rates of return to capital by changing the capital–labor ratio. They also change the skill composition of the urban labor force and hence the relative wage of skilled to unskilled workers. Therefore, rural–urban migration has wide impacts on growth and income distribution of the macroeconomy.
What are the forces that drive rural–urban migration? It is well understood that cities attract rural migrants because of better job opportunities, better career prospects, and higher wages. Moreover, enjoying better social benefits such as better medical care in cities is another pull factor that initiates rural–urban migration. Finally, agricultural land scarcity in the countryside plays an important role on the push side for moving labor to cities.
The aforementioned driving forces of rural–urban migration are work-based. However, rural–urban migration could be education-based, which is rarely discussed in the literature. In the past decade, it has been proposed that cities are the places for accumulating human capital in work. It is also well established that most of the high-quality education institutions (including universities and specialized schools for art and music) are located in urban areas. A youth may first move to the city to attend college and then stay there for work after graduation. From this point of view, work-based migration does not paint the whole picture of rural–urban migration. In this article, we propose a balanced view that both the work-based and education-based channels are important to rural–urban migration. The migration story could be misleading if any of them is ignored.
Philip DeCicca, Donald S. Kenkel, Michael F. Lovenheim, and Erik Nesson
Smoking prevention has been a key component of health policy in developed nations for over half a century. Public policies to reduce the physical harm attributed to cigarette smoking, both externally and to the smoker, include cigarette taxation, smoking bans, and anti-smoking campaigns, among other publicly conceived strategies to reduce smoking initiation among the young and increase smoking cessation among current smokers. Despite the policy intensity of the past two decades, there remains debate regarding whether, and to what extent, the observed reductions in smoking are due to such policies. Indeed, while smoking rates in developed countries have fallen substantially over the past half century, it is difficult to separate secular trends toward greater investment in health from actual policy impacts. In other words, smoking rates might have declined in the absence of these anti-smoking policies, consistent with trends toward other healthy behaviors. These trends also may reflect longer-run responses to policies enacted many years ago, which also poses challenges for identification of causal policy effects. While smoking rates fell dramatically over this period, the gradient in smoking prevalence has become tilted toward lower socioeconomic status (SES) individuals. That is, cigarette smoking exhibited a relatively flat SES gradient 50 years ago, but today that gradient is much steeper: relatively less-educated and lower-income individuals are many times more likely to be cigarette smokers than their more highly educated and higher-income counterparts. Over time, consumers also have become less price-responsive, which has rendered cigarette taxation a less effective policy tool with which to reduce smoking. The emergence of tax avoidance strategies such as casual cigarette smuggling (e.g., cross-tax border purchasing) and purchasing from tax-free outlets (e.g., Native reservations in Canada and the United States) have likely contributed to reduced price sensitivity. Such behaviors have been of particular interest in the last decade as cigarette taxation has roughly doubled cigarette prices in many developed nations, creating often large incentives to avoid taxation for those who continue to smoke. Perhaps due to the perception that traditional policy has been ineffective, recent anti-smoking policy has focused more on the direct regulation of cigarettes and smoking behavior. The main non-price-based policy has been the rise of smoke-free air laws, which restrict smoking behavior in workplaces, restaurants, and bars. These regulations can reduce smoking prevalence and exposure to secondhand smoke among nonsmokers. However, they may also shift the location of smoking in ways that increase secondhand smoke exposure, particularly among children. Other non-tax regulations focus on the packaging (e.g., the movement towards plain packaging), advertising, and product attributes of cigarettes (e.g., nicotine content, cigarette flavor, etc.), and most are attempts to reduce smoking by making it less desirable to the actual or potential smoker. Perhaps not surprisingly, research in the economics of smoking prevention has followed these policy developments, though strong interest remains in both the evaluation of price- and non-price policies as well as any offsetting responses among smokers that may undermine the effectiveness of these regulations. While the past two decades have provided fertile ground for research in the economics of smoking, we expect this to continue, as governments search for more innovative and effective ways to reduce smoking.
Dominic Hodgkin and Hilary S. Connery
Drug and alcohol use disorders, also called substance use disorders (SUD), are among the major health problems facing many countries, contributing a substantial burden in terms of mortality, morbidity, and economic impact. A considerable body of research is dedicated to reducing the social and individual burden of SUD.
One major focus of research has been the effectiveness of treatment for SUD, with studies examining both medication and behavioral treatments using randomized, controlled clinical trials. For opioid use disorder, there is a strong evidence base for medication treatment, particularly using agonist therapies (i.e., methadone and buprenorphine), but mixed evidence regarding the use of psychosocial interventions. For alcohol use disorder, there is evidence of modest effectiveness for two medications (acamprosate and naltrexone) and for various psychosocial treatments, especially for less severe alcohol use disorder syndromes. An important area for future research is how to make treatment more appealing to clients, given that client reluctance is an important contributor to the low utilization of effective treatments.
A second major focus of research has been the availability of medication treatments, building on existing theories of how innovations diffuse, and on the field of dissemination and implementation research. In the United States, this research identifies serious gaps in both the availability of SUD treatment programs and the availability of effective treatment within those programs. Key barriers include lack of on-site medical staff at many SUD treatment programs; restrictive policies of private insurers, states, and federal authorities; and widespread skepticism toward medication treatment among counseling staff and some administrators. Emerging research is promising for providing medication treatment in settings other than SUD treatment programs, such as community mental health centers, prisons, emergency departments, and homeless shelters.
There is still considerable room to make SUD treatment approaches more effective, more available, and—most importantly—more acceptable to clients.
The Effect of Education on Health and Mortality: A Review of Experimental and Quasi-Experimental Evidence
Titus Galama, Adriana Lleras-Muney, and Hans van Kippersluis
Education is strongly associated with better health and longer lives. However, the extent to which education causes health and longevity is widely debated. We develop a human capital framework to structure the interpretation of the empirical evidence and review evidence on the causal effects of education on mortality and its two most common preventable causes: smoking and obesity. We focus attention on evidence from randomized controlled trials, twin studies, and quasi-experiments. There is no convincing evidence of an effect of education on obesity, and the effects on smoking are only apparent when schooling reforms affect individuals’ track or their peer group, but not when they simply increase the duration of schooling. An effect of education on mortality exists in some contexts but not in others and seems to depend on (i) gender, (ii) the labor market returns to education, (iii) the quality of education, and (iv) whether education affects the quality of individuals’ peers.
The literature on the employment effects of minimum wages is about a century old, and includes hundreds of studies. Yet the debate among researchers about the employment effects of minimum wages remains intense and unsettled. Questions have arisen in the past research that, if answered, may prove most useful in making sense of the conflicting evidence. However, additional questions should be considered to better inform the policy debate, in particular in the context of the very high minimum wages coming on line in the United States, about which past research is quite uninformative.
Widely used modified least squares estimators for estimation and inference in cointegrating regressions are discussed. The standard case with cointegration in the I(1) setting is examined and some relevant extensions are sketched. These include cointegration analysis with panel data as well as nonlinear cointegrating relationships. Extensions to higher order (co)integration, seasonal (co)integration and fractional (co)integration are very briefly mentioned. Recent developments and some avenues for future research are discussed.
Sherry Glied and Richard Frank
Mental health economics addresses problems that are common to all of health economics, but that occur with greater severity in this context. Several characteristics of mental health conditions—age of onset, chronicity, observability, and external effects—make them particularly economically challenging, and a range of policies have evolved to address these problems. The need for insurance—and for social insurance—to address mental health problems has grown. There is an expanding number of effective treatments available for mental health conditions, and these treatments can be relatively costly. The particular characteristics of mental health conditions exacerbate the usual problems of moral hazard, adverse selection, and agency. There is increased recognition, in both the policy and economics literatures, of the array of services and supports required to enable people with severe mental illnesses to function in society’s mainstream. The need for such non-medical services, generates economic problems of cross-system coordination and opportunism. Moreover, the impairments imposed by mental disorders have become more disruptive to the labor market because the nature of work is changing in a manner that creates special disadvantages to people with these conditions. New directions for mental health economics would address these effects.
Gerard J. van den Berg and Maarten Lindeboom
Modern-day famines are caused by unusual impediments or interventions in society, effectively imposing severe market restrictions and preventing the free movement of people and goods. Long-run health effects of exposure to famine are commonly studied to obtain insights into the long-run effects of malnutrition at early ages. This line of research has faced major methodological and data challenges. Recent research in various disciplines, such as economics, epidemiology, and demography, has made great progress in dealing with these issues. Malnutrition around birth affects a range of later-life individual outcomes, including health, educational, and economic outcomes.
Alfred Duncan and Charles Nolan
In recent decades, macroeconomic researchers have looked to incorporate financial intermediaries explicitly into business-cycle models. These modeling developments have helped us to understand the role of the financial sector in the transmission of policy and external shocks into macroeconomic dynamics. They also have helped us to understand better the consequences of financial instability for the macroeconomy. Large gaps remain in our knowledge of the interactions between the financial sector and macroeconomic outcomes. Specifically, the effects of financial stability and macroprudential policies are not well understood.
One of the most fundamental results in health economics is that a greater socio-economic status is associated with better health outcomes. However, the experience of financial pressure and lack of resources transcends the notion of low income and poverty. Families of all income categories can experience financial pressure and lack of resources. This article reviews the literature examining the relationship between financial strain and various health outcomes. There are three main approaches to the measurement of financial strain found in the research literature, each one capturing a slightly different aspect: the family’s debt position, the availability of emergency funds, and inability to meet current financial obligations.
There are two main hypotheses explaining how financial strain may affect health. First, financial strain indicates a lower amount of financial resources available to individuals and families. This may have a dual impact on health. On the one hand, lower financial resources may lead to a decrease in consumption of substances such as tobacco that are harmful to health. On the other hand, lower financial resources may also negatively affect healthcare access, healthcare utilization, and adherence to treatment, with each contributing to a decline in health. Second, financial strain may produce greater uncertainty with regard to the availability of financial resources at present as well as in the future, thereby resulting in elevated stress, which may, in turn, result in poorer health outcomes. Examining the relationship between financial strain and health is complicated because it appears to be bidirectional. It is not only the case that financial strain may impact health but that health may impact financial strain.
The research literature consistently finds that financial strain has a detrimental impact on a variety of mental health outcomes. This relationship has been documented for a variety of financial strain indicators, including non-collateralized (unsecure) debt, mortgage debt, and the inability to meet current financial obligations. The research on the association between financial strain and health behavior outcomes is more ambiguous. As one example, there are mixed results concerning whether financial strain results in a higher likelihood of obesity. This research has considered various indicators of financial strain, including credit card debt and the inability to meet current financial obligations. It appears that both among adults and children there is no consistent evidence on the impact of financial strain on body weight. Similarly, the results on the impact of financial strain on alcohol use and substance abuse are mixed.
A number of significant questions regarding the relationship between financial strain and health remain unresolved. The majority of the existing studies focus on health outcomes among adults. There is a lack of understanding regarding how family exposure to financial strain can affect children. Additionally, very little is known about the implications of long-term exposure to financial strain. There are also some very important methodological challenges in this area of research related to establishing causality. Establishing causality and learning more about the implications of the exposure to financial strain could have important policy implications for a variety of safety net programs.
Alexandrina Stoyanova and David Cantarero-Prieto
Long-term care (LTC) systems entitle frail and disabled people, who experience declines in physical and mental capacities, to quality care and support from an appropriately trained workforce and aim to preserve individual health and promote personal well-being for people of all ages. Myriad social factors pose significant challenges to LTC services and systems worldwide. Leading among these factors is the aging population—that is, the growing proportion of older people, the main recipients of LTC, in the population—and the implications not only for the health and social protection sectors, but almost all other segments of society. The number of elderly citizens has increased significantly in recent years in most countries and regions, and the pace of that growth is expected to accelerate in the forthcoming decades. The rapid demographic evolution has been accompanied by substantial social changes that have modified the traditional pattern of delivery LTC. Although families (and friends) still provide most of the help and care to relatives with functional limitations, changes in the population structure, such as weakened family ties, increased participation of women in the labor market, and withdrawal of early retirement policies, have resulted in a decrease in the provision of informal care. Thus, the growing demands for care, together with a lower potential supply of informal care, is likely to put pressure on the provision of formal care services in terms of both quantity and quality. Other related concerns include the sustainable financing of LTC services, which has declined significantly in recent years, and the pursuit of equity.
The current institutional background regarding LTC differs substantially across countries, but they all face similar challenges. Addressing these challenges requires a comprehensive approach that allows for the adoption of the “right” mix of policies between those aiming at informal care and those focusing on the provision and financing of formal LTC services.
Marissa Collins, Neil McHugh, Rachel Baker, Alec Morton, Lucy Frith, Keith Syrett, and Cam Donaldson
Health and social care organizations work within the context of limited resources. Different techniques to aid resource allocation and decision-making exist and are important as scarcity of resources in health and social care is inescapable. Healthcare systems, regardless of how they are organized, must decide what services to provide given the resources available. This is particularly clear in systems funded by taxation, which have limited budgets and other limited resources (staff, skills, facilities, etc.) and in which the claims on these resources outstrip supply.
Healthcare spending in many countries is not expected to increase over the short or medium term. Therefore, frameworks to set priorities are increasingly required. Four disciplines provide perspectives on priority setting: economics, decision analysis, ethics, and law. Although there is overlap amongst these perspectives, they are underpinned by different principles and processes for priority setting. As the values and viewpoints of those involved in priority setting in health and social care will differ, it is important to consider how these could be included to inform a priority setting process. It is proposed that these perspectives and the consideration of values and viewpoints could be brought together in a combined priority setting framework for use within local health and social care organizations.
High-Dimensional Dynamic Factor Models have their origin in macroeconomics, precisely in empirical research on Business Cycles. The central idea, going back to the work of Burns and Mitchell in the years 1940, is that the fluctuations of all the macro and sectoral variables in the economy are driven by a “reference cycle,” that is, a one-dimensional latent cause of variation. After a fairly long process of generalization and formalization, the literature settled at the beginning of the year 2000 on a model in which (1) both n the number of variables in the dataset and T, the number of observations for each variable, may be large, and (2) all the variables in the dataset depend dynamically on a fixed independent of n, a number of “common factors,” plus variable-specific, usually called “idiosyncratic,” components. The structure of the model can be exemplified as follows:
where the observable variables xit are driven by the white noise ut, which is common to all the variables, the common factor, and by the idiosyncratic component ξit. The common factor ut is orthogonal to the idiosyncratic components ξit, the idiosyncratic components are mutually orthogonal (or weakly correlated). Lastly, the variations of the common factor ut affect the variable xit dynamically, that is through the lag polynomial αi+βiL. Asymptotic results for High-Dimensional Factor Models, particularly consistency of estimators of the common factors, are obtained for both n and T tending to infinity.
Model (∗), generalized to allow for more than one common factor and a rich dynamic loading of the factors, has been studied in a fairly vast literature, with many applications based on macroeconomic datasets: (a) forecasting of inflation, industrial production, and unemployment; (b) structural macroeconomic analysis; and (c) construction of indicators of the Business Cycle. This literature can be broadly classified as belonging to the time- or the frequency-domain approach. The works based on the second are the subject of the present chapter.
We start with a brief description of early work on Dynamic Factor Models. Formal definitions and the main Representation Theorem follow. The latter determines the number of common factors in the model by means of the spectral density matrix of the vector (x1tx2t⋯xnt). Dynamic principal components, based on the spectral density of the x’s, are then used to construct estimators of the common factors.
These results, obtained in early 2000, are compared to the literature based on the time-domain approach, in which the covariance matrix of the x’s and its (static) principal components are used instead of the spectral density and dynamic principal components. Dynamic principal components produce two-sided estimators, which are good within the sample but unfit for forecasting. The estimators based on the time-domain approach are simple and one-sided. However, they require the restriction of finite dimension for the space spanned by the factors.
Recent papers have constructed one-sided estimators based on the frequency-domain method for the unrestricted model. These results exploit results on stochastic processes of dimension n that are driven by a q-dimensional white noise, with q<n, that is, singular vector stochastic processes. The main features of this literature are described with some detail.
Lastly, we report and comment the results of an empirical paper, the last in a long list, comparing predictions obtained with time- and frequency-domain methods. The paper uses a large monthly U.S. dataset including the Great Moderation and the Great Recession.
Albert N. Link and John T. Scott
Science parks, also called research parks, technology parks, or technopolis infrastructures, have increased rapidly in number as many countries have adopted the approach of bringing research-based organizations together in a park. A science park’s cluster of research and technology-based organizations is often located on or near a university campus. The juxtaposition of ongoing research of both the university and the park tenants creates a two-way flow of knowledge; knowledge is transferred between the university and firms, and all parties develop knowledge more effectively because of their symbiotic relationship.
Theory and evidence support the belief that the geographic proximity provided to the participating organizations by a science park creates a dynamic cluster that accelerates economic growth and international competitiveness through the innovation-enabling exchanges of knowledge and the transfer of technologies. The process of creating innovations is more efficient because of the agglomeration of research and technology-based firms on or near a university campus. The proximity of a park to multiple sources of knowledge provides greater opportunities for the creation and acquisition of knowledge, especially tacit knowledge, and the geographic proximity therefore reduces the search and acquisition costs for that knowledge.
The clustering of multiple research and technology-based organizations within a park enables knowledge spillovers, and with greater productivity from research resources and lower costs, prices for new technologies can be lower, stimulating their use and regional development and growth. In addition to the clustering of the organizations within a park, the geographic proximity of universities affiliated with a park matters too. Evidence shows that a park’s employment growth is greater, other things being the same, when its affiliated university is geographically closer, although evidence suggests that effect has lessened in the 21st century because of the information and communications technology revolution. Further stimulating regional growth, university spin-off companies are more prevalent in a park when it is geographically closer to the affiliated university. The two-way flow of knowledge enabled by clusters of research and technology-based firms in science parks benefits firms located on the park and the affiliated universities.
Understanding the mechanisms by which the innovative performance of research and technology-based organizations is increased by their geographic proximity in a science park is important for formulating public and private sector policies toward park formations because successful national innovation systems require the two-way knowledge flow, among firms in a park and between firms and universities, that is fostered by the science park infrastructure.
Richard Smith and Johanna Hanefeld
Global trade—the movement of goods, services, people, and capital between countries—is at the center of modern globalization. Since the late 20th century trade has also become established as a critical determinant of public health. As the raison d’être of trade is to increase both wealth and the availability of goods and services, changing trade patterns will inevitably impact many of the known determinants of health, including employment, nutrition, environmental factors, social capital, and education. Trade will also impact the health sector itself, most clearly through direct trade in health-related goods and services (such as pharmaceuticals, health workers, foreign direct investment in health services, and mobile patients), but also more broadly in determining tax receipts and thus overall public expenditures. It is also the case that trade—especially rapid and widespread movement of people, animals, and goods—may facilitate the rapid and widespread spread of disease. Trade, and associated policies governing and responding to that trade, has thus become increasingly recognized as a critical driver of health issues.
The design of trade policies that reduce the potential health risks associated with freer trade while maximizing the positive impact of trade liberalization on the social determinants of health is still in its infancy. There remains a lack of sound empirical evidence demonstrating how trade liberalization links directly and indirectly to health. Even though the positive link between increased trade, poverty reduction, and economic growth is widely accepted, evidence regarding the impact of trade liberalization on the social determinants of health varies from one national context to another. Hence, adapting trade liberalization to national conditions is important in ensuring desired outcomes. Yet although evidence is necessary, it is not sufficient to ensure that health is more integrated in trade negotiations and decision-making. There is a substantive requirement for those with a health remit to engage in negotiation with those from other sectors and from other geographic locations.
David E. Bloom, Michael Kuhn, and Klaus Prettner
The strong observable correlation between health and economic growth is crucial for economic development and sustained well-being, but the underlying causality and mechanisms are difficult to conceptualize. Three issues are of central concern. First, assessing and disentangling causality between health and economic growth are empirically challenging. Second, the relation between health and economic growth changes over the process of economic development. In less developed countries, poor health often reduces labor force participation, particularly among women, and deters investments in education such that fertility stays high and the economy remains trapped in a stagnation equilibrium. By contrast, in more developed countries, health investments primarily lead to rising longevity, which may not significantly affect labor force participation and workforce productivity. Third, different dimensions of health (mortality vs. morbidity, children’s and women’s health, and health at older ages) relate to different economic effects. By changing the duration and riskiness of the life course, mortality affects individual investment choices, whereas morbidity relates more directly to work productivity and education. Children’s health affects their education and has long-lasting implications for labor force participation and productivity later in life. Women’s health is associated with substantial intergenerational spillover effects and influences women’s empowerment and fertility decisions. Finally, health at older ages has implications for retirement and care.
Jordan Everson and Melinda Beeuwkes Buntin
The potential for health information technology (HIT) to reshape the information-intensive healthcare industry has been recognized for decades. Nevertheless, the adoption and use of IT in healthcare has lagged behind other industries, motivating governments to take a role in supporting its use to achieve envisioned benefits. This dynamic has led to three major strands of research. Firstly, the relatively slow and uneven adoption of HIT, coupled with government programs intended to speed adoption, has raised the issue of who is adopting HIT, and the impact of public programs on rates of adoption and diffusion. Secondly, the realization of benefits from HIT appears to be occurring more slowly than its proponents had hoped, leading to an ongoing need to empirically measure the effect of its use on the quality and efficiency of healthcare as well as the contexts under which benefits are best realized. Thirdly, increases in the adoption and use of HIT have led to the potential for interoperable exchange of patient information and the dynamic use of that information to drive improvements in the healthcare delivery system; however, these applications require developing new approaches to overcoming barriers to collaboration between healthcare organizations and the HIT industry itself. Intertwined through each of these issues is the interaction between HIT as a tool for standardization and systemic change in the practice of healthcare, and healthcare professionals’ desire to preserve autonomy within the increasingly structured healthcare delivery system. Innovative approaches to improve the interactions between professionals, technology, and market forces are therefore necessary to capitalize on the promise of HIT and develop a continually learning health system.
Health insurance increases the demand for healthcare. Since the RAND Health Insurance Experiment in the 1970s this has been demonstrated in many contexts and many countries. From an economic point of view this fact raises the concern that individuals demand too much healthcare if insured, which generates a welfare loss to society. This so-called moral hazard effect arises because individuals demand healthcare that has less value to them than it costs to provide it. For that reason, modern health insurance plans include demand side cost-sharing instruments like deductibles and copayments. There is a large and growing literature analyzing the effects of these cost-sharing instruments on healthcare demand.
Three issues have recently received increasing attention. First, cost-sharing instruments such as yearly deductibles combined with stop losses create nonlinear price schedules and dynamic incentives. This generates the question of whether patients understand the incentives and what price individuals use to determine their healthcare demand. Second, it appears implausible that patients know the benefits of healthcare (which is crucial for the moral hazard argument). If patients systematically underestimated these benefits they would demand too little healthcare without health insurance. Providing health insurance and increasing healthcare demand in this case may increase social welfare. Finally, what is the role of healthcare providers? They have been completely absent in the majority of the literature analyzing the demand for healthcare, but there is striking evidence that the physicians often determine large parts of healthcare spending.