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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 the number of variables in the dataset and , 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 , 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 are driven by the white noise , which is common to all the variables, the common factor, and by the idiosyncratic component . The common factor is orthogonal to the idiosyncratic components , the idiosyncratic components are mutually orthogonal (or weakly correlated). Lastly, the variations of the common factor affect the variable dynamically, that is through the lag polynomial . Asymptotic results for High-Dimensional Factor Models, particularly consistency of estimators of the common factors, are obtained for both and 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 . Dynamic principal components, based on the spectral density of the ’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 ’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 that are driven by a -dimensional white noise, with , 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.
Mónica Hernández Alava
The assessment of health-related quality of life is crucially important in the evaluation of healthcare technologies and services. In many countries, economic evaluation plays a prominent role in informing decision making often requiring preference-based measures (PBMs) to assess quality of life. These measures comprise two aspects: a descriptive system where patients can indicate the impact of ill health, and a value set based on the preferences of individuals for each of the health states that can be described. These values are required for the calculation of quality adjusted life years (QALYs), the measure for health benefit used in the vast majority of economic evaluations. The National Institute for Health and Care Excellence (NICE) has used cost per QALY as its preferred framework for economic evaluation of healthcare technologies since its inception in 1999.
However, there is often an evidence gap between the clinical measures that are available from clinical studies on the effect of a specific health technology and the PBMs needed to construct QALY measures. Instruments such as the EQ-5D have preference-based scoring systems and are favored by organizations such as NICE but are frequently absent from clinical studies of treatment effect. Even where a PBM is included this may still be insufficient for the needs of the economic evaluation. Trials may have insufficient follow-up, be underpowered to detect relevant events, or include the wrong PBM for the decision- making body.
Often this gap is bridged by “mapping”—estimating a relationship between observed clinical outcomes and PBMs, using data from a reference dataset containing both types of information. The estimated statistical model can then be used to predict what the PBM would have been in the clinical study given the available information.
There are two approaches to mapping linked to the structure of a PBM. The indirect approach (or response mapping) models the responses to the descriptive system using discrete data models. The expected health utility is calculated as a subsequent step using the estimated probability distribution of health states. The second approach (the direct approach) models the health state utility values directly.
Statistical models routinely used in the past for mapping are unable to consider the idiosyncrasies of health utility data. Often they do not work well in practice and can give seriously biased estimates of the value of treatments. Although the bias could, in principle, go in any direction, in practice it tends to result in underestimation of cost effectiveness and consequently distorted funding decisions. This has real effects on patients, clinicians, industry, and the general public.
These problems have led some analysts to mistakenly conclude that mapping always induces biases and should be avoided. However, the development and use of more appropriate models has refuted this claim. The need to improve the quality of mapping studies led to the formation of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Mapping to Estimate Health State Utility values from Non-Preference-Based Outcome Measures Task Force to develop good practice guidance in mapping.
Marcus Berliant and Ping Wang
General equilibrium theories of spatial agglomeration are closed models of agent location that explain the formation and growth of cities. There are several types of such theories: conventional Arrow-Debreu competitive equilibrium models and monopolistic competition models, as well as game theoretic models including search and matching setups. Three types of spatial agglomeration forces often come into play: trade, production, and knowledge transmission, under which cities are formed in equilibrium as marketplaces, factory towns, and idea laboratories, respectively. Agglomeration dynamics are linked to urban growth in the long run.
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.
The geography of economic activity refers to the distribution of population, production, and consumption of goods and services in geographic space. The geography of growth and development refers to the local growth and decline of economic activity and the overall distribution of these local changes within and across countries. The pattern of growth in space can vary substantially across regions, countries, and industries. Ultimately, these patterns can help explain the role that spatial frictions (like transport and migration costs) can play in the overall development of the world economy.
The interaction of agglomeration and congestion forces determines the density of economic activity in particular locations. Agglomeration forces refer to forces that bring together agents and firms by conveying benefits from locating close to each other, or for locating in a particular area. Examples include local technology and institutions, natural resources and local amenities, infrastructure, as well as knowledge spillovers. Congestion forces refer to the disadvantages of locating close to each other. They include traffic, high land prices, as well as crime and other urban dis-amenities. The balance of these forces is mediated by the ability of individuals, firms, good and services, as well as ideas and technology, to move across space: namely, migration, relocation, transport, commuting and communication costs. These spatial frictions together with the varying strength of congestion and agglomeration forces determines the distribution of economic activity. Changes in these forces and frictions—some purposefully made by agents given the economic environment they face and some exogenous—determine the geography of growth and development.
The main evolution of the forces that influence the geography of growth and development have been changes in transport technology, the diffusion of general-purpose technologies, and the structural transformation of economies from agriculture, to manufacturing, to service-oriented economies. There are many challenges in modeling and quantifying these forces and their effects. Nevertheless, doing so is essential to evaluate the impact of a variety of phenomena, from climate change to the effects of globalization and advances in information technology.
Pao-Li Chang and Wen-Tai Hsu
This article reviews interrelated power-law phenomena in geography and trade. Given the empirical evidence on the gravity equation in trade flows across countries and regions, its theoretical underpinnings are reviewed. The gravity equation amounts to saying that trade flows follow a power law in distance (or geographic barriers). It is concluded that in the environment with firm heterogeneity, the power law in firm size is the key condition for the gravity equation to arise. A distribution is said to follow a power law if its tail probability follows a power function in the distribution’s right tail. The second part of this article reviews the literature that provides the microfoundation for the power law in firm size and reviews how this power law (in firm size) may be related to the power laws in other distributions (in incomes, firm productivity and city size).
Hites Ahir and Prakash Loungani
On average across countries, house prices have been on an upward trend over the past 50 years, following a 100-year period over which there was no long-term increase. The rising trend in prices reflects a demand boost due to greater availability of housing finance running up against supply constraints, as land has increasingly become a fixed factor for many reasons. The entire 150-year period has been marked by boom and bust cycles around the trend. These also reflect episodes of demand momentum—due to cheap finance or reasonable or unreasonable expectations of higher incomes—meeting a sluggish supply response. Policy options to manage boom–bust cycles, given the significant costs to the economy from house price busts, are discussed.
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.
Sushant Acharya and Paolo Pesenti
Global policy spillovers can be defined as the effect of policy changes in one country on economic outcomes in other countries. The literature has mainly focused on monetary policy interdependencies and has identified three channels through which policy spillovers can materialize. The first is the expenditure-shifting channel—a monetary expansion in one country depreciates its currency, making its goods cheaper relative to those in other countries and shifting global demand toward domestic tradable goods. The second is the expenditure-changing channel—expansionary monetary policy in one country raises both domestic and foreign expenditure. The third is the financial spillovers channel—expansionary monetary policy in one country eases financial conditions in other economies. The literature generally finds that the net transmission effect is positive but small. However, estimated spillovers vary widely across countries and over time. In the aftermath of the Great Recession, the policy debate has devoted special attention to the possibility that the magnitude and sign of international spillovers might have changed in an environment of low interest rates worldwide, as the expenditure-shifting channel becomes more relevant when the effective lower bound reduces the effectiveness of conventional monetary policies.
Scott Baier and Samuel Standaert
The gravity model of international trade states that the volume of trade between two countries is proportional to their economic mass and a measure of their relative trade frictions. Perhaps because of its intuitive appeal, the gravity model has been the workhorse model of international trade for more than 50 years. While the initial empirical work using the gravity model lacked sound theoretical underpinnings, the theoretical developments have highlighted how a gravity-like specification can be derived from many models with varying assumptions about preferences, technology, and market structure. Along the strengthening of the theoretical roots of the gravity model, the way in which it is estimated has also evolved significantly since the start of the new millennium. Depending on the exact characteristics of regression, different estimation methods should be used to estimate the gravity model.
Thomas E. Getzen
During the 18th and 19th centuries, medical spending in the United States rose slowly, on average about .25% faster than gross domestic product (GDP), and varied widely between rural and urban regions. Accumulating scientific advances caused spending to accelerate by 1910. From 1930 to 1955, rapid per-capita income growth accommodated major medical expansion while keeping the health share of GDP almost constant. During the 1950s and 1960s, prosperity and investment in research, the workforce, and hospitals caused a rapid surge in spending and consolidated a truly national health system. Excess growth rates (above GDP growth) were above +5% per year from 1966 to 1970, which would have doubled the health-sector share in fifteen years had it not moderated, falling under +3% in the 1980s, +2% in 1990s, and +1.5% since 2005. The question of when national health expenditure growth can be brought into line with GDP and made sustainable for the long run is still open. A review of historical data over three centuries forces confrontation with issues regarding what to include and how long events continue to effect national health accounting and policy. Empirical analysis at a national scale over multiple decades fails to support a position that many of the commonly discussed variables (obesity, aging, mortality rates, coinsurance) do cause significant shifts in expenditure trends. What does become clear is that there are long and variable lags before macroeconomic and technological events affect spending: three to six years for business cycles and multiple decades for major recessions, scientific discoveries, and organizational change. Health-financing mechanisms, such as employer-based health insurance, Medicare, and the Affordable Care Act (Obamacare) are seen to be both cause and effect, taking years to develop and affecting spending for decades to come.
Guilds ruled many European crafts and trades from the Middle Ages to the Industrial Revolution. Each guild regulated entry to its occupation, requiring any practitioner to become a guild member and then limiting admission to the guild. Guilds intervened in the markets for their members’ products, striving to keep prices high, limit output, suppress competition, and block innovations that might disrupt the status quo. Guilds also acted in input markets, seeking to control access to raw materials, keep wages low, hinder employers from competing for workers, and prevent workers from agitating for better conditions. Guilds treated women particularly severely, usually excluding them from apprenticeship and forbidding any female other than a guild member’s widow from running a workshop. Guilds invested large sums in lobbying governments and political elites to grant, maintain, and extend these privileges.
Guilds had the potential to compensate for their cartelistic activities by creating countervailing benefits. Guild quality certification was one possible solution to information asymmetries between producers and consumers, which could have made markets work better. Guild apprenticeship had the potential to solve imperfections in markets for skilled training, and thus to encourage human capital investment. The cartel profits generated by guilds could in theory have encouraged technological innovation by enabling guild masters to appropriate more of the social benefits of their innovations, while guild journeymanship and spatial clustering could diffuse new technical knowledge. A rich scholarship on European guilds makes it possible to assess the degree to which guilds created such benefits, outweighing the harm they caused.
After about 1500, guild strength diverged across Europe, declining gradually in Flanders, the Netherlands, and England, surviving in France and Italy, and intensifying across large tracts of Iberia, Scandinavia, and the German-speaking lands. The activities of guilds contributed to variations across Europe in economic performance, urban growth, and inequality. Guilds interacted significantly with both markets and states, which helps explain why European economies diverged in the crucial centuries before industrialization.
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.
Important health system challenges in the east and southeast Asian countries/territories of Japan, South Korea, Taiwan, Hong Kong, Malaysia, China, Thailand, Vietnam, Indonesia, the Philippines, Laos, Myanmar, and Cambodia exist. The most commonly adopted health system among these areas is social health insurance. The high-income, aging societies of Japan, South Korea, and Taiwan have adopted single-payer/single-pipe systems with a single uniform benefit package and a single fee schedule for paying providers for services included in the benefit package. All three have achieved universal coverage with relatively equitable access to affordable care. All grapple with overutilization, aging populations, and hospital-centric and curative-focused care that is ill-suited for addressing an increasing chronic disease burden. Rising patient expectations and demand for expensive technologies contribute to rising costs. Korea also faces comparatively poorer financial risk protection.
China, Thailand, Vietnam, Indonesia, and the Philippines have also adopted social health insurance, though not single-payer systems. China and Thailand have established noncontributory schemes, whereby the government heavily subsidizes poor and non-poor populations. General tax revenue is used to extend coverage to those outside formal-sector employment. Both countries use multiple, unintegrated schemes to cover their populations. Thailand has improved access to care and financial risk protection. While China has improved insurance coverage, financial risk protection gains have been limited due to low levels of service coverage, fee-for-service payment systems, poor gatekeeping, and the fee schedule that incentivizes overprescription of tests and medicine. Indonesia, Vietnam, and the Philippines use contributory schemes. Government revenue provides insurance coverage for the poor, near-poor, and selected vulnerable populations; the rest of the population must contribute to enroll. Therefore, expanding insurance coverage to the informal sector has been a significant challenge.
Instead of social health insurance, Hong Kong and Malaysia have two-tiered health systems where the public sector is financed by general tax revenue and the private sector is financed primarily by out-of-pocket payments and limited private insurance. There is universal access to care; free or subsidized, good-quality public-sector services provide financial risk protection. However, Hong Kong and Malaysia have fragmented delivery systems, weak primary care, budgetary strains, and inequitable access to private care (which may offer shorter wait times and better perceived quality).
Laos, Cambodia, and Myanmar’s health systems feature high out-of-pocket spending, low government investment in health, and reliance on external aid. User fees, low insurance coverage, unequal distribution of health services, and fragmented financing pose pressing challenges to achieving equitable access and adequate financial risk protection.
These countries/territories are diverse in terms of demographics, epidemiological profiles, and stages of economic development, and thus they face different health system challenges and opportunities. This diversity also suggests that these nations/territories will utilize different types of health systems to achieve universal health coverage, whereby all people have equitable access to affordable, good-quality care with adequate financial risk protection.
Jan C. van Ours
There are three main topics in research on the effects of work on health.
The first topic is workplace accidents where the main issues are reporting behavior and workplace safety policies. A worker seems to be less inclined to report a workplace accident for fear of job loss when unemployment is high or when the worker has a temporary contract that may not be renewed. Workplace safety legislation has intended to reduce the incidence and severity of workplace accidents but empirical evidence on this result is unclear.
The second topic is employment and health where the focus is on how job characteristics and job loss affect health, in particular mental health. Physically demanding jobs have negative health effects. The effects of working hours vary and the effects of job loss on physical and mental health are not uniform. Job loss seems to increase mortality.
The third topic concerns retirement and health. Retirement seems to have a negative effect on cognitive skills and short-term positive effects on overall health. Other than that, the effects are very inconsistent, that is, even with as clear a measure as mortality, it is not clear whether life expectancy goes up, goes down, or remains constant due to retirement.
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.
Gregory Colman, Dhaval Dave, and Otto Lenhart
Health insurance depends on labor market activity more in the U.S. than in any other high-income country. A majority of the population are insured through an employer (known as employer-sponsored insurance or ESI), benefiting from the risk pooling and economies of scale available to group insurance plans. Some workers may therefore be reluctant to leave a job for fear of losing such low-cost insurance, a tendency known as “job lock,” or may switch jobs or work more hours merely to obtain it, known as “job push.” Others obtain insurance through government programs for which eligibility depends on income. They too may adapt their work effort to remain eligible for insurance. Those without access to ESI or who are too young or earn too much to qualify for public coverage (Medicare and Medicaid) can buy insurance only in the individual or non-group market, where prices are high and variable. Most studies using data from before the passage of the Patient Protection and Affordable Care Act (ACA) in 2010 support the prediction that ESI reduced job mobility, labor-force participation, retirement, and self-employment prior to the ACA, but find little effect on the labor supply of public insurance. The ACA profoundly changed the health insurance market in the U.S., removing restrictions on obtaining insurance from new employers or on the individual market and expanding Medicaid eligibility to previously ineligible adults. Research on the ACA, however, has not found substantial labor supply effects. These results may reflect that the reforms to the individual market mainly affected those who were previously uninsured rather than workers with ESI, that the theoretical labor market effects of expansions in public coverage are ambiguous, and that the effect would be found only among the relatively small number on the fringes of eligibility.
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.
Joachim Winter and Amelie Wuppermann
Choice of health insurance plans has become a key element of many healthcare systems around the world along with a general expansion of patient choice under the label of “Consumer-Directed Healthcare.” Allowing consumers to choose their insurance plan was commonly associated with the aim of enhancing competition between insurers and thus to contribute to the efficient delivery of healthcare. However, the evidence is accruing that consumers have difficulties in making health insurance decisions in their best interest. For example, many consumers choose plans with which they spend more in terms of premiums and out-of-pocket costs than in other available options. This has consequences for the individual consumer’s budget as well as for the functioning of the insurance market.
The literature puts forward several possible reasons for consumers’ difficulties in making health insurance choices in their best interest. First, consumers may not have a sufficient level of knowledge of insurance products; for example, they might not understand insurance terminology. Second, the environment or architecture in which consumers make their decision may be too complicated. Health insurance products vary in a large number of features that consumers have to evaluate when comparing options, introducing search or hassle costs. Third, consumers may be prone to psychological biases and employ decision-making heuristics that impede good choices. For example, they might choose the plan with the cheapest premium, ignoring other important plan features that determine total cost, such as copayments. There is also evidence that consumer education programs, simplification of the choice environment, or introducing nudges such as setting smart defaults facilitate consumer decision making.
Despite recent progress in our understanding of consumer choices in health insurance markets, important challenges remain. Evidence-based healthcare policy should be based on an evaluation of whether different interventions aimed at facilitating consumer choices result in welfare improvements. Ultimately, this requires measuring consumer utility, an issue that is vividly debated in the literature. Furthermore, welfare calculations necessitate an understanding of how interventions will affect the supply of health insurance, including supply reactions to changes in demand. This depends on the specific regulatory setting and characteristics of the specific market.
André Medici and Maureen Lewis
Latin American and Caribbean (LAC) countries have experienced a long-term process of improvement in populational health conditions, shifting their health priorities from child–mother care and transmissible diseases to non-communicable diseases (NCDs). However, persistent socioeconomic inequalities create barriers to achieve universal health coverage (UHC). Despite a high level of governmental commitment to UHC, and rising coverage, approximately 25% of the population does not have access to healthcare, particularly in rural and outlying areas.
Health system quality issues have been largely ignored, and inefficiency, from health financing to health delivery, is not on the policy agenda. The use of incentives to improve performance are rare in LAC health systems and there are political barriers to introduce reforms in payment systems in the public sector, though the private sector has opportunity to adapt change.
Fragmentation in the financing of healthcare is a common theme in the region. Most systems retain social health insurance (SHI) schemes, mostly for the formal sector, and in some cases have more than one; and parallel National Health System (NHS)-type arrangements for the poor and those in the informal labor market. The cost and inefficiency in delivery and financing is considerable.
Regional health economics literature stresses inadequate funding—despite the fact that the region has the highest inequality in access and spends the most on healthcare across the regions—and analyzes multiple aspects of health equity. The agenda needs to move from these debates to designing and leveraging delivery and payment systems that target performance and efficiency.
The absence of research on payment arrangements and performance is a symptom of a health management culture based on processes rather than results. Indeed, health services in the region remain rooted in a culture of fee-for-service and supply-driven models, where expenditures are independent of outcomes.
Health policy reforms in LAC need to address efficiency rather than equity, integrate healthcare delivery, and tackle provider payment reforms. The integration of medical records, adherence to protocols and clinical pathways, establishment of health networks built around primary healthcare, along with harmonized incentives and payment systems, offer a direction for reforms that allow adapting to existing circumstances and institutions. This offers the best path for sustainable UHC in the region.