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The Economics of Cognitive Aging  

Fabrizio Mazzonna and Franco Peracchi

Population aging, the combined effect of declining fertility and rising life expectancy, is one of the fundamental trends observed in developed counties and, increasingly, in developing countries as well. A key aspect of the aging process is the decline of cognitive ability. Cognitive aging is an important and complex phenomenon, and its risk factors and economic consequences are still not well understood. For instance, the relationship between cognitive aging and productivity matters for long-term economic growth. Cognitive functioning is also crucial for decision-making because it influences individuals’ ability to process information and to make the right choices, and older individuals are increasingly required to make complex financial, health, and long-term-care decisions that might affect their health, resources, and welfare. This article presents evidence from economics and other fields that have investigated this phenomenon from different perspectives. A common empirical finding is the hump-shaped profile of cognitive performance over the life cycle. Another is the large variability of observed age profiles, not only at the individual level but also across sociodemographic groups and countries. The age profiles of cognitive performance also vary depending on the cognitive task considered, reflecting the different combinations of cognitive skills that they require. The literature usually distinguishes between two main types of cognitive skills: fluid intelligence and crystallized intelligence. The first consists of the basic mechanisms of processing new information, while the second reflects acquired knowledge. Unlike fluid intelligence, which declines rapidly as people get older, crystallized intelligence tends to be maintained at older ages. Differences in the age profiles of cognitive performance across tasks partly reflect differences in the importance of these two types of intelligence. For instance, tasks where learning, problem-solving, and processing speed are essential tend to be associated with a faster decline, while tasks where experience matters more tend to be associated with a slower decline. Various life events and behaviors over the life cycle also contribute to the large heterogeneity in the observed age profiles of cognitive performance. This source of variation includes not only early-life events and investments (e.g., formal education), but also midlife and later-life events (e.g., health shocks) and individual choices (e.g., health behaviors or retirement). From an economic viewpoint, cognitive abilities may be regarded as one dimension of human capital, along with education, health, and noncognitive abilities. Economists have mainly focused their attention on human capital accumulation, and much less so on human capital deterioration. One explanation is that early-life investments appears to be more profitable than investments later in life. However, recent evidence from neuropsychology suggests that the human brain is malleable and open to enhancement even later in adulthood. Therefore, more economic research is needed to study how human capital depreciates over the life cycle and whether cognitive decline can be controlled.


Modeling Chronic Diseases in Relation to Risk Factors  

Pieter van Baal and Hendriek Boshuizen

In most countries, non-communicable diseases have taken over infectious diseases as the most important causes of death. Many non-communicable diseases that were previously lethal diseases have become chronic, and this has changed the healthcare landscape in terms of treatment and prevention options. Currently, a large part of healthcare spending is targeted at curing and caring for the elderly, who have multiple chronic diseases. In this context prevention plays an important role, as there are many risk factors amenable to prevention policies that are related to multiple chronic diseases. This article discusses the use of simulation modeling to better understand the relations between chronic diseases and their risk factors with the aim to inform health policy. Simulation modeling sheds light on important policy questions related to population aging and priority setting. The focus is on the modeling of multiple chronic diseases in the general population and how to consistently model the relations between chronic diseases and their risk factors by combining various data sources. Methodological issues in chronic disease modeling and how these relate to the availability of data are discussed. Here, a distinction is made between (a) issues related to the construction of the epidemiological simulation model and (b) issues related to linking outcomes of the epidemiological simulation model to economic relevant outcomes such as quality of life, healthcare spending and labor market participation. Based on this distinction, several simulation models are discussed that link risk factors to multiple chronic diseases in order to explore how these issues are handled in practice. Recommendations for future research are provided.


The Economic and Political Effects of Immigration: Evidence from the Age of Mass Migration  

Marco Tabellini

Between 1850 and 1920, during the Age of Mass Migration, more than 30 million Europeans moved to the United States. European immigrants provided an ample supply of cheap labor as well as specific skills and know-how, contributing to American economic growth. These positive effects were not short-lived, but are still evident in the 21st century: areas of the United States that received more European immigrants during the Age of Mass Migration have higher income per capita, a more educated population, and lower poverty rates. Despite its economic benefits, immigration triggered hostile political reactions, which were driven by cultural differences between immigrants and natives, and culminated in the introduction of country-specific quotas. In contrast to the concerns prevailing at the time, European immigrants eventually assimilated. The process was facilitated by the inflow of 1.5 million African Americans who left the rural South to move to northern and western cities between 1915 and 1930. Black in-migration increased the salience of skin color, as opposed to that of religion and nativity, as a defining feature of in- and out-groups of the society. This reduced the perceived distance between native whites and European immigrants, thereby facilitating the integration of the latter. European immigrants also had long-lasting effects on American ideology. Parts of the country that hosted more immigrants during the Age of Mass Migration have a more liberal ideology and stronger preferences for redistribution well into the 21st century. This resulted from the transmission of political ideology from (more left-leaning) immigrants to natives.


The Economics of Long-Term Care  

Norman Bannenberg, Martin Karlsson, and Hendrik Schmitz

Long-term care (LTC) is arguably the sector of the economy that is most sensitive to population aging: its recipients are typically older than 80 years whereas most care providers are of working age. Thus, a number of ongoing societal trends interact in the determination of market outcomes in the LTC sector: trends in longevity and healthy life expectancy interact with changing family structures and norms in shaping the need for services. The supply side is additionally affected by changes in employment patterns, in particular regarding the transition into retirement, as well as by cross-regional imbalances in demographic and economic conditions. The economic literature on long-term care considers many of these issues, aims at understanding this steadily growing sector, and at guiding policy. Key economic studies on long-term care address determinants of the demand for long-term care, like disability and socio-economic status; the two most important providers: informal family caregivers and nursing homes; and the financing and funding of LTC.


Aging and Healthcare Costs  

Martin Karlsson, Tor Iversen, and Henning Øien

An open issue in the economics literature is whether healthcare expenditure (HCE) is so concentrated in the last years before death that the age profiles in spending will change when longevity increases. The seminal article “aging of Population and HealthCare Expenditure: A Red Herring?” by Zweifel and colleagues argued that that age is a distraction in explaining growth in HCE. The argument was based on the observation that age did not predict HCE after controlling for time to death (TTD). The authors were soon criticized for the use of a Heckman selection model in this context. Most of the recent literature makes use of variants of a two-part model and seems to give some role to age as well in the explanation. Age seems to matter more for long-term care expenditures (LTCE) than for acute hospital care. When disability is accounted for, the effects of age and TTD diminish. Not many articles validate their approach by comparing properties of different estimation models. In order to evaluate popular models used in the literature and to gain an understanding of the divergent results of previous studies, an empirical analysis based on a claims data set from Germany is conducted. This analysis generates a number of useful insights. There is a significant age gradient in HCE, most for LTCE, and costs of dying are substantial. These “costs of dying” have, however, a limited impact on the age gradient in HCE. These findings are interpreted as evidence against the red herring hypothesis as initially stated. The results indicate that the choice of estimation method makes little difference and if they differ, ordinary least squares regression tends to perform better than the alternatives. When validating the methods out of sample and out of period, there is no evidence that including TTD leads to better predictions of aggregate future HCE. It appears that the literature might benefit from focusing on the predictive power of the estimators instead of their actual fit to the data within the sample.


Age-Period-Cohort Models  

Zoë Fannon and Bent Nielsen

Outcomes of interest often depend on the age, period, or cohort of the individual observed, where cohort and age add up to period. An example is consumption: consumption patterns change over the lifecycle (age) but are also affected by the availability of products at different times (period) and by birth-cohort-specific habits and preferences (cohort). Age-period-cohort (APC) models are additive models where the predictor is a sum of three time effects, which are functions of age, period, and cohort, respectively. Variations of these models are available for data aggregated over age, period, and cohort, and for data drawn from repeated cross-sections, where the time effects can be combined with individual covariates. The age, period, and cohort time effects are intertwined. Inclusion of an indicator variable for each level of age, period, and cohort results in perfect collinearity, which is referred to as “the age-period-cohort identification problem.” Estimation can be done by dropping some indicator variables. However, dropping indicators has adverse consequences such as the time effects are not individually interpretable and inference becomes complicated. These consequences are avoided by instead decomposing the time effects into linear and non-linear components and noting that the identification problem relates to the linear components, whereas the non-linear components are identifiable. Thus, confusion is avoided by keeping the identifiable non-linear components of the time effects and the unidentifiable linear components apart. A variety of hypotheses of practical interest can be expressed in terms of the non-linear components.


Long-Distance Trade in Medieval Europe  

Mika Kallioinen

Traditional historiography has overestimated the significance of long-distance trade in the medieval economy. However, it could be argued that, because of its dynamic nature, long-distance trade played a more important role in economic development than its relative size would suggest. The term commercial revolution was introduced in the 1950s to refer to the rapid growth of European trade from about the 10th century. Long-distance trade then expanded, with the commercial integration of the two economic poles in the Mediterranean and in Flanders and the contiguous areas. It has been quantitatively shown that the integration of European markets began in the late medieval period, with rapid advancement beginning in the 16th century. The expansion of medieval trade has been attributed to advanced business techniques, such as the appearance of new forms of partnerships and novel financial and insurance systems. Many economic historians have also emphasized merchants’ relations, especially the establishment of networks to organize trade. More recently, major contributions to institutional economic history have focused on various economic institutions that reduced the uncertainties inherent in premodern economies. The early reputation-based institutions identified in the literature, such as the systems of the Maghribis in the Mediterranean, Champagne fairs in France, and the Italian city-states, were not optimal for changing conditions that accompanied expansion of trade, as the number of merchants increased and the relations among them became more anonymous, as generally happened during the Middle Ages. An intercommunal conciliation mechanism evolved in medieval northern Europe that supported trade among a large number of distant communities. This institution encouraged merchants to travel to distant towns and establish relations, even with persons they did not already know.


Financing and Policy for Long-Term Care  

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.


The Lifetime Dynamics of Health and Wealth  

Pascal St-Amour

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.


Early and Medieval Periods in German Economic History  

Thilo R. Huning and Fabian Wahl

The study of the Holy Roman Empire, a medieval state on the territory of modern-day Germany and Central Europe, has attracted generations of qualitative economic historians and quantitative scholars from various fields. Its bordering position between Roman and Germanic legacies, its Carolingian inheritance, and the numerous small states emerging from 1150 onward, on the one hand, are suspected to have hindered market integration, and on the other, allowed states to compete. This has inspired many research questions around differences and communalities in culture, the origin of the state, the integration of good and financial markets, and technology inventions, such the printing press. While little is still known about the economy of the rural population, cities and their economic conditions have been extensively studied from the angles of economic geography, institutionalism, and for their influence on early human capital accumulation. The literature has stressed that Germany at this time cannot be seen as a closed economy, but only in the context of Europe and the wider world. Global events, such as the Black Death, and European particularities, such as the Catholic Church, never stopped at countries’ borders. As such, the literature provides an understanding for the prelude to radical changes, such as the Lutheran Reformation, religious wars, and the coming of the modern age with its economic innovations.


Economic Growth in the United States, 1790 to 1860  

Thomas Weiss

In the early 21st century, the U.S. economy stood at or very near the top of any ranking of the world’s economies, more obviously so in terms of gross domestic product (GDP), but also when measured by GDP per capita. The current standing of any country reflects three things: how well off it was when it began modern economic growth, how long it has been growing, and how rapidly productivity increased each year. Americans are inclined to think that it was the last of these items that accounted for their country’s success. And there is some truth to the notion that America’s lofty status was due to the continual increases in the efficiency of its factors of production—but that is not the whole story. The rate at which the U.S. economy has grown over its long history—roughly 1.5% per year measured by output per capita—has been modest in comparison with most other advanced nations. The high value of GDP per capita in the United States is due in no small part to the fact that it was already among the world’s highest back in the early 19th century, when the new nation was poised to begin modern economic growth. The United States was also an early starter, so has experienced growth for a very long time—longer than almost every other nation in the world. The sustained growth in real GDP per capita began sometime in the period 1790 to 1860, although the exact timing of the transition, and even its nature, are still uncertain. Continual efforts to improve the statistical record have narrowed down the time frame in which the transition took place and improved our understanding of the forces that facilitated the transition, but questions remain. In order to understand how the United States made the transition from a slow-growing British colony to a more rapidly advancing, free-standing economy, it is necessary to know more precisely when it made that transition.


The Economics of End-of-Life Spending  

Hans Olav Melberg

End-of-life spending is commonly defined as all health costs in the 12 months before death. Typically, the costs represent about 10% of all health expenses in many countries, and there is a large debate about the effectiveness of the spending and whether it should be increased or decreased. Assuming that health spending is effective in improving health, and using a wide definition of benefits from end-of-life spending, several economists have argued for increased spending in the last years of life. Others remain skeptical about the effectiveness of such spending based on both experimental evidence and the observation that geographic within-country variations in spending are not correlated with variations in mortality.


The Growth of Health Spending in the United States From 1776 to 2026  

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.