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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.
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.
Health status measurement issues arise across a wide spectrum of applications in empirical health economics research as well as in public policy, clinical, and regulatory contexts. It is fitting that economists and other researchers working in these domains devote scientific attention to the measurement of those phenomena most central to their investigations. While often accepted and used uncritically, the particular measures of health status used in empirical investigations can have sometimes subtle but nonetheless important implications for research findings and policy action. How health is characterized and measured at the individual level and how such individual-level measures are summarized to characterize the health of groups and populations are entwined considerations. Such measurement issues have become increasingly salient given the wealth of health data available from population surveys, administrative sources, and clinical records in which researchers may be confronted with competing options for how they go about characterizing and measuring health. While recent work in health economics has seen significant advances in the econometric methods used to estimate and interpret quantities like treatment effects, the literature has seen less focus on some of the central measurement issues necessarily involved in such exercises. As such, increased attention ought to be devoted to measuring and understanding health status concepts that are relevant to decision makers’ objectives as opposed to those that are merely statistically convenient.
Economists have long regarded health care as a unique and challenging area of economic activity on account of the specialized knowledge of health care professionals (HCPs) and the relatively weak market mechanisms that operate. This places a consideration of how motivation and incentives might influence performance at the center of research. As in other domains economists have tended to focus on financial mechanisms and when considering HCPs have therefore examined how existing payment systems and potential alternatives might impact on behavior. There has long been a concern that simple arrangements such as fee-for-service, capitation, and salary payments might induce poor performance, and that has led to extensive investigation, both theoretical and empirical, on the linkage between payment and performance. An extensive and rapidly expanded field in economics, contract theory and mechanism design, had been applied to study these issues. The theory has highlighted both the potential benefits and the risks of incentive schemes to deal with the information asymmetries that abound in health care. There has been some expansion of such schemes in practice but these are often limited in application and the evidence for their effectiveness is mixed. Understanding why there is this relatively large gap between concept and application gives a guide to where future research can most productively be focused.
Hendrik Schmitz and Svenja Winkler
The terms information and risk aversion play central roles in healthcare economics. While risk aversion is among the main reasons for the existence of health insurance, information asymmetries between insured individual and insurance company potentially lead to moral hazard or adverse selection. This has implications for the optimal design of health insurance contracts, but whether there is indeed moral hazard or adverse selection are ultimately empirical questions. Recently, there was even a debate whether the opposite of adverse selection—advantageous selection—prevails. Private information on risk aversion might weigh out information asymmetries regarding risk type and lead to more insurance coverage of healthy individuals (instead of less insurance coverage in adverse selection).
Information and risk preferences are important not only in health insurance but more generally in health economics. For instance, they affect health behavior and, consequently, health outcomes. The degree of risk aversion, the ability to perceive risks, and the availability of information about risks partly explain why some individuals engage in unhealthy behavior while others refrain from smoking, drinking, or the like.
Information has several dimensions. Apart from information on one’s personal health status, risk preferences, or health risks, consumer information on provider quality or health insurance supply is central in the economics of healthcare. Even though healthcare systems are necessarily highly regulated throughout the world, all systems at least allow for some market elements. These typically include the possibility of consumer choice, for instance, regarding health insurance coverage or choice of medical provider. An important question is whether consumer choice elements work in the healthcare sector—that is, whether consumers actually make rational or optimal decisions—and whether more information can improve decision quality.
Law and economics is an important, growing field of specialization for both legal scholars and economists. It applies efficiency analysis to property, contracts, torts, procedure, and many other areas of the law. The use of economics as a methodology for understanding law is not immune to criticism. The rationality assumption and the efficiency principle have been intensively debated. Overall, the field has advanced in recent years by incorporating insights from psychology and other social sciences. In that respect, many questions concerning the efficiency of legal rules and norms are still open and respond to a multifaceted balance among diverse costs and benefits. The role of courts in explaining economic performance is a more specific area of analysis that emerged in the late 1990s. The relationship between law and economic growth is complex and debatable. An important literature has pointed to significant differences at the macro-level between the Anglo-American common law family and the civil law families. Although these initial results have been heavily scrutinized, other important subjects have surfaced such as convergence of legal systems, transplants, infrastructure of legal systems, rule of law and development, among others.
Life-cycle choices and outcomes over financial (e.g., savings, portfolio, work) and health-related variables (e.g., medical spending, habits, sickness, and mortality) are complex and intertwined. Indeed, labor/leisure choices can both affect and be conditioned by health outcomes, precautionary savings is determined by exposure to sickness and longevity risks, where the latter can both be altered through preventive medical and leisure decisions. Moreover, inevitable aging induces changes in the incentives and in the constraints for investing in one’s own health and saving resources for old age. Understanding these pathways poses numerous challenges for economic models.
The life-cycle data is indicative of continuous declines in health statuses and associated increases in exposure to morbidity, medical expenses, and mortality risks, with accelerating post-retirement dynamics. Theory suggests that risk-averse and forward-looking agents should rely on available instruments to insure against these risks. Indeed, market- and state-provided health insurance (e.g., Medicare) cover curative medical expenses. High end-of-life home and nursing-home expenses can be hedged through privately or publicly provided (e.g., Medicaid) long-term care insurance. The risk of outliving one’s financial resources can be hedged through annuities. The risk of not living long enough can be insured through life insurance.
In practice, however, the recourse to these hedging instruments remains less than predicted by theory. Slow-observed wealth drawdown after retirement is unexplained by bequest motives and suggests precautionary motives against health-related expenses. The excessive reliance on public pension (e.g., Social Security) and the post-retirement drop in consumption not related to work or health are both indicative of insufficient financial preparedness and run counter to consumption smoothing objectives. Moreover, the capacity to self-insure through preventive care and healthy habits is limited when aging is factored in. In conclusion, the observed health and financial life-cycle dynamics remain challenging for economic theory.
Long memory models are statistical models that describe strong correlation or dependence across time series data. This kind of phenomenon is often referred to as “long memory” or “long-range dependence.” It refers to persisting correlation between distant observations in a time series. For scalar time series observed at equal intervals of time that are covariance stationary, so that the mean, variance, and autocovariances (between observations separated by a lag j) do not vary over time, it typically implies that the autocovariances decay so slowly, as j increases, as not to be absolutely summable. However, it can also refer to certain nonstationary time series, including ones with an autoregressive unit root, that exhibit even stronger correlation at long lags. Evidence of long memory has often been been found in economic and financial time series, where the noted extension to possible nonstationarity can cover many macroeconomic time series, as well as in such fields as astronomy, agriculture, geophysics, and chemistry.
As long memory is now a technically well developed topic, formal definitions are needed. But by way of partial motivation, long memory models can be thought of as complementary to the very well known and widely applied stationary and invertible autoregressive and moving average (ARMA) models, whose autocovariances are not only summable but decay exponentially fast as a function of lag j. Such models are often referred to as “short memory” models, becuse there is negligible correlation across distant time intervals. These models are often combined with the most basic long memory ones, however, because together they offer the ability to describe both short and long memory feartures in many time series.
The majority of econometric models ignore the fact that many economic time series are sampled at different frequencies. A burgeoning literature pertains to econometric methods explicitly designed to handle data sampled at different frequencies. Broadly speaking these methods fall into two categories: (a) parameter driven, typically involving a state space representation, and (b) data driven, usually based on a mixed-data sampling (MIDAS)-type regression setting or related methods. The realm of applications of the class of mixed frequency models includes nowcasting—which is defined as the prediction of the present—as well as forecasting—typically the very near future—taking advantage of mixed frequency data structures. For multiple horizon forecasting, the topic of MIDAS regressions also relates to research regarding direct versus iterated forecasting.
Audrey Laporte and Brian S. Ferguson
One of the implications of the human capital literature of the 1960s was that a great many decisions individuals make that have consequences not just for the point in time when the decision is being made but also for the future can be thought of as involving investments in certain types of capital. In health economics, this led Michael Grossman to propose the concept of health capital, which refers not just to the individual’s illness status at any point in time, but to the more fundamental factors that affect the likelihood that she will be ill at any point in her life and also affect her life expectancy at each age. In Grossman’s model, an individual purchased health-related commodities that act through a health production function to improve her health. These commodities could be medical care, which could be seen as repair expenditures, or factors such as diet and exercise, which could be seen as ongoing additions to her health—the counterparts of adding savings to her financial capital on a regular basis. The individual was assumed to make decisions about her level of consumption of these commodities as part of an intertemporal utility-maximizing process that incorporated, through a budget constraint, the need to make tradeoffs between health-related goods and goods that had no health consequences. Pauline Ippolito showed that the same analytical techniques could be used to consider goods that were bad for health in the long run—bad diet and smoking, for example—still within the context of lifetime utility maximization. This raised the possibility that an individual might rationally take actions that were bad for her health in the long run. The logical extension of considering smoking as bad was adding recognition that smoking and other bad health habits were addictive. The notion of addictive commodities was already present in the literature on consumer behavior, but the consensus in that literature was that it was extremely difficult, if not impossible, to distinguish between a rational addict and a completely myopic consumer of addictive goods. Gary Becker and Kevin Murphy proposed an alternative approach to modeling a forward-looking, utility-maximizing consumer’s consumption of addictive commodities, based on the argument that an individual’s degree of addiction could be modeled as addiction capital, and which could be used to tackle the empirical problems that the consumer expenditure literature had experienced. That model has become the most widely used framework for empirical research by economists into the consumption of addictive goods, and, while the concept of rationality in addiction remains controversial, the Becker-Murphy framework also provides a basis for testing various alternative models of the consumption of addictive commodities, most notably those based on versions of time-inconsistent intertemporal decision making.
Many nonlinear time series models have been around for a long time and have originated outside of time series econometrics. The stochastic models popular univariate, dynamic single-equation, and vector autoregressive are presented and their properties considered. Deterministic nonlinear models are not reviewed. The use of nonlinear vector autoregressive models in macroeconometrics seems to be increasing, and because this may be viewed as a rather recent development, they receive somewhat more attention than their univariate counterparts. Vector threshold autoregressive, smooth transition autoregressive, Markov-switching, and random coefficient autoregressive models are covered along with nonlinear generalizations of vector autoregressive models with cointegrated variables. Two nonlinear panel models, although they cannot be argued to be typically macroeconometric models, have, however, been frequently applied to macroeconomic data as well. The use of all these models in macroeconomics is highlighted with applications in which model selection, an often difficult issue in nonlinear models, has received due attention. Given the large amount of nonlinear time series models, no unique best method of choosing between them seems to be available.