1-20 of 52 Results

Article

George W. Evans and Bruce McGough

Adaptive learning is a boundedly rational alternative to rational expectations that is increasingly used in macroeconomics, monetary economics, and financial economics. The agent-level approach can be used to provide microfoundations for adaptive learning in macroeconomics. Two central issues of bounded rationality are simultaneously addressed at the agent level: replacing fully rational expectations of key variables with econometric forecasts and boundedly optimal decisions-making based on those forecasts. The real business cycle (RBC) model provides a useful laboratory for exhibiting alternative implementations of the agent-level approach. Specific implementations include shadow-price learning (and its anticipated-utility counterpart, iterated shadow-price learning), Euler-equation learning, and long-horizon learning. For each implementation the path of the economy is obtained by aggregating the boundedly rational agent-level decisions. A linearized RBC can be used to illustrate the effects of fiscal policy. For example, simulations can be used to illustrate the impact of a permanent increase in government spending and highlight the similarities and differences among the various implements of agent-level learning. These results also can be used to expose the differences among agent-level learning, reduced-form learning, and rational expectations. The different implementations of agent-level adaptive learning have differing advantages. A major advantage of shadow-price learning is its ease of implementation within the nonlinear RBC model. Compared to reduced-form learning, which is widely use because of its ease of application, agent-level learning both provides microfoundations, which ensure robustness to the Lucas critique, and provides the natural framework for applications of adaptive learning in heterogeneous-agent models.

Article

David E. Rapach and Guofu Zhou

Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive regression approach. The extensions are designed to improve out-of-sample performance in realistic environments characterized by large information sets and noisy data. Large information sets are relevant because there are a plethora of plausible stock return predictors. The information sets include variables typically associated with a rational time-varying market risk premium, as well as variables more likely to reflect market inefficiencies resulting from behavioral influences and information frictions. Noisy data stem from the intrinsically large unpredictable component in stock returns. When forecasting with large information sets and noisy data, it is vital to employ methods that incorporate the relevant information in the large set of predictors in a manner that guards against overfitting the data. Methods that improve out-of-sample market return prediction include forecast combination, principal component regression, partial least squares, the LASSO and elastic net from machine learning, and a newly developed C-ENet approach that relies on the elastic net to refine the simple combination forecast. Employing these methods, a number of studies provide statistically and economically significant evidence that the aggregate market return is predictable on an out-of-sample basis. Out-of-sample market return predictability based on a rich set of predictors thus appears to be a well-established empirical result in asset pricing.

Article

Henrik Cronqvist and Désirée-Jessica Pély

Corporate finance is about understanding the determinants and consequences of the investment and financing policies of corporations. In a standard neoclassical profit maximization framework, rational agents, that is, managers, make corporate finance decisions on behalf of rational principals, that is, shareholders. Over the past two decades, there has been a rapidly growing interest in augmenting standard finance frameworks with novel insights from cognitive psychology, and more recently, social psychology and sociology. This emerging subfield in finance research has been dubbed behavioral corporate finance, which differentiates between rational and behavioral agents and principals. The presence of behavioral shareholders, that is, principals, may lead to market timing and catering behavior by rational managers. Such managers will opportunistically time the market and exploit mispricing by investing capital, issuing securities, or borrowing debt when costs of capital are low and shunning equity, divesting assets, repurchasing securities, and paying back debt when costs of capital are high. Rational managers will also incite mispricing, for example, cater to non-standard preferences of shareholders through earnings management or by transitioning their firms into an in-fashion category to boost the stock’s price. The interaction of behavioral managers, that is, agents, with rational shareholders can also lead to distortions in corporate decision making. For example, managers may perceive fundamental values differently and systematically diverge from optimal decisions. Several personal traits, for example, overconfidence or narcissism, and environmental factors, for example, fatal natural disasters, shape behavioral managers’ preferences and beliefs, short or long term. These factors may bias the value perception by managers and thus lead to inferior decision making. An extension of behavioral corporate finance is social corporate finance, where agents and principals do not make decisions in a vacuum but rather are embedded in a dynamic social environment. Since managers and shareholders take a social position within and across markets, social psychology and sociology can be useful to understand how social traits, states, and activities shape corporate decision making if an individual’s psychology is not directly observable.

Article

Matteo M. Galizzi and Daniel Wiesen

The state-of-the-art literature at the interface between experimental and behavioral economics and health economics is reviewed by identifying and discussing 10 areas of potential debate about behavioral experiments in health. By doing so, the different streams and areas of application of the growing field of behavioral experiments in health are reviewed, by discussing which significant questions remain to be discussed, and by highlighting the rationale and the scope for the further development of behavioral experiments in health in the years to come.

Article

Nikolaus Robalino and Arthur Robson

Modern economic theory rests on the basic assumption that agents’ choices are guided by preferences. The question of where such preferences might have come from has traditionally been ignored or viewed agnostically. The biological approach to economic behavior addresses the issue of the origins of economic preferences explicitly. This approach assumes that economic preferences are shaped by the forces of natural selection. For example, an important theoretical insight delivered thus far by this approach is that individuals ought to be more risk averse to aggregate than to idiosyncratic risk. Additionally the approach has delivered an evolutionary basis for hedonic and adaptive utility and an evolutionary rationale for “theory of mind.” Related empirical work has studied the evolution of time preferences, loss aversion, and explored the deep evolutionary determinants of long-run economic development.

Article

Cristina Bellés-Obrero and Judit Vall Castelló

The impact of macroeconomic fluctuations on health and mortality rates has been a highly studied topic in the field of economics. Many studies, using fixed-effects models, find that mortality is procyclical in many countries, such as the United States, Germany, Spain, France, Pacific-Asian nations, Mexico, and Canada. On the other hand, a small number of studies find that mortality decreases during economic expansion. Differences in the social insurance systems and labor market institutions across countries may explain some of the disparities found in the literature. Studies examining the effects of more recent recessions are less conclusive, finding mortality to be less procyclical, or even countercyclical. This new finding could be explained by changes over time in the mechanisms behind the association between business cycle conditions and mortality. A related strand of the literature has focused on understanding the effect of economic fluctuations on infant health at birth and/or child mortality. While infant mortality is found to be procyclical in countries like the United States and Spain, the opposite is found in developing countries. Even though the association between business cycle conditions and mortality has been extensively documented, a much stronger effort is needed to understand the mechanisms behind the relationship between business cycle conditions and health. Many studies have examined the association between macroeconomic fluctuations and smoking, drinking, weight disorders, eating habits, and physical activity, although results are rather mixed. The only well-established finding is that mental health deteriorates during economic slowdowns. An important challenge is the fact that the comparison of the main results across studies proves to be complicated due to the variety of empirical methods and time spans used. Furthermore, estimates have been found to be sensitive to the use of different levels of geographic aggregation, model specifications, and proxies of macroeconomic fluctuations.

Article

Samuel Muehlemann and Stefan Wolter

The economic reasons why firms engage in apprenticeship training are twofold. First, apprenticeship training is a potentially cost-effective strategy for filling a firm’s future vacancies, particularly if skilled labor on the external labor market is scarce. Second, apprentices can be cost-effective substitutes for other types of labor in the current production process. As current and expected business and labor market conditions determine a firm’s expected work volume and thus its future demand for skilled labor, they are potentially important drivers of a firm’s training decisions. Empirical studies have found that the business cycle affects apprenticeship markets. However, while the economic magnitude of these effects is moderate on average, there is substantial heterogeneity across countries, even among those that at first sight seem very similar in terms of their apprenticeship systems. Moreover, identification of business cycle effects is a difficult task. First, statistics on apprenticeship markets are often less developed than labor market statistics, making empirical analyses of demand and supply impossible in many cases. In particular, data about unfilled apprenticeship vacancies and unsuccessful applicants are paramount for assessing potential market failures and analyzing the extent to which business cycle fluctuations may amplify imbalances in apprenticeship markets. Second, the intensity of business cycle effects on apprenticeship markets is not completely exogenous, as governments typically undertake a variety of measures, which differ across countries and may change over time, to reduce the adverse effects of economic downturns on apprenticeship markets. During the economic crisis related to the COVID-19 global pandemic, many countries took unprecedented actions to support their economies in general and reacted swiftly to introduce measures such as the provision of financial subsidies for training firms or the establishment of apprenticeship task forces. As statistics on apprenticeship markets improve over time, such heterogeneity in policy measures should be exploited to improve our understanding of the business cycle and its relationship with apprenticeships.

Article

Diane McIntyre, Amarech G. Obse, Edwine W. Barasa, and John E. Ataguba

Within the context of the Sustainable Development Goals, it is important to critically review research on healthcare financing in sub-Saharan Africa (SSA) from the perspective of the universal health coverage (UHC) goals of financial protection and access to quality health services for all. There is a concerning reliance on direct out-of-pocket payments in many SSA countries, accounting for an average of 36% of current health expenditure compared to only 22% in the rest of the world. Contributions to health insurance schemes, whether voluntary or mandatory, contribute a small share of current health expenditure. While domestic mandatory prepayment mechanisms (tax and mandatory insurance) is the next largest category of healthcare financing in SSA (35%), a relatively large share of funding in SSA (14% compared to <1% in the rest of the world) is attributable to, sometimes unstable, external funding sources. There is a growing recognition of the need to reduce out-of-pocket payments and increase domestic mandatory prepayment financing to move towards UHC. Many SSA countries have declared a preference for achieving this through contributory health insurance schemes, particularly for formal sector workers, with service entitlements tied to contributions. Policy debates about whether a contributory approach is the most efficient, equitable and sustainable means of financing progress to UHC are emotive and infused with “conventional wisdom.” A range of research questions must be addressed to provide a more comprehensive empirical evidence base for these debates and to support progress to UHC.

Article

Since the 1980s policymakers have identified a wide range of policy interventions to improve hospital performance. Some of these have been initiated at the level of government, whereas others have taken the form of decisions made by individual hospitals but have been guided by regulatory or financial incentives. Studies investigating the impact that some of the most important of these interventions have had on hospital performance can be grouped into four different research streams. Among the research streams, the strongest evidence exists for the effects of privatization. Studies on this topic use longitudinal designs with control groups and have found robust increases in efficiency and financial performance. Evidence on the entry of hospitals into health systems and the effects of this on efficiency is similarly strong. Although the other three streams of research also contain well-conducted studies with valuable findings, they are predominantly cross-sectional in design and therefore cannot establish causation. While the effects of introducing DRG-based hospital payments and of specialization are largely unclear, vertical and horizontal cooperation probably have a positive effect on efficiency and financial performance. Lastly, the drivers of improved efficiency or financial performance are very different depending on the reform or intervention being investigated; however, reductions in the number of staff and improved bargaining power in purchasing stand out as being of particular importance. Several promising avenues for future investigation are identified. One of these is situated within a new area of research examining the link between changes in the prices of treatments and hospitals’ responses. As there is evidence of unintended effects, future studies should attempt to distinguish between changes in hospitals’ responses at the intensive margin (e.g., upcoding) versus the extensive margin (e.g., increase in admissions). When looking at the effects of entering into a health system and of privatizations, there is still considerable need for research. With privatizations, in particular, the underlying processes are not yet fully understood, and the potential trade-offs between increases in performance and changes in the quality of care have not been sufficiently examined. Lastly, there is substantial need for further papers in the areas of multi-institutional arrangements and cooperation, as well as specialization. In both research streams, natural experiments carried out using program evaluation design are lacking. One of the main challenges here, however, is that cooperation and specialization cannot be directly observed but rather must be constructed based on survey or administrative data.

Article

Katarina Juselius

The cointegrated VAR approach combines differences of variables with cointegration among them and by doing so allows the user to study both long-run and short-run effects in the same model. The CVAR describes an economic system where variables have been pushed away from long-run equilibria by exogenous shocks (the pushing forces) and where short-run adjustments forces pull them back toward long-run equilibria (the pulling forces). In this model framework, basic assumptions underlying a theory model can be translated into testable hypotheses on the order of integration and cointegration of key variables and their relationships. The set of hypotheses describes the empirical regularities we would expect to see in the data if the long-run properties of a theory model are empirically relevant.

Article

Giovanni Federico

The literature on market integration explores the development of the commodity market with data on prices, which is a useful complement to analysis of trade and the only feasible approach when data on trade are not available. Data on prices and quantity can help in understanding when markets developed, why, and the degree to which their development increased welfare and economic growth. Integration progressed slowly throughout the early modern period, with significant acceleration in the first half of the 19th century. Causes of integration include development of transportation infrastructure, changes in barriers to trade, and short-term shocks, such as wars. Literature on the effects of market integration is limited and strategies for estimating the effects of market integration are must be developed.

Article

Jennifer L. Castle and David F. Hendry

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

Article

Leandro Prados de la Escosura and Blanca Sánchez-Alonso

In assessments of modern-day Spain’s economic progress and living standards, inadequate natural resources, inefficient institutions, lack of education and entrepreneurship, and foreign dependency are frequently blamed on poor performance up to the mid-20th century, but no persuasive arguments were provided to explain why such adverse circumstances reversed, giving way to the fast transformation that started in the 1950s. Hence, it is necessary to first inquire how much economic progress has been achieved in Spain and what impact it had on living standards and income distribution since the end of the Peninsular War to the present day, and second to provide an interpretation. Research published in the 2010s supports the view that income per person has improved remarkably, driven by increases in labor productivity, which derived, in turn, from a more intense and efficient use of physical and human capital per worker. Exposure to international competition represented a decisive element behind growth performance. From an European perspective, Spain underperformed until 1950. Thereafter, Spain’s economy managed to catch up with more advanced countries until 2007. Although the distribution of the fruits of growth did not follow a linear trend, but a Kuznetsian inverted U pattern, higher levels of income per capita are matched by lower inequality, suggesting that Spaniards’ material wellbeing improved substantially during the modern era.

Article

Michael Drummond, Rosanna Tarricone, and Aleksandra Torbica

There are a number of challenges in the economic evaluation of medical devices (MDs). They are typically less regulated than pharmaceuticals, and the clinical evidence requirements for market authorization are generally lower. There are also specific characteristics of MDs, such as the device–user interaction (learning curve), the incremental nature of innovation, the dynamic nature of pricing, and the broader organizational impact. Therefore, a number of initiatives need to be taken in order to facilitate the economic evaluation of MDs. First, the regulatory processes for MDs need to be strengthened and more closely aligned to the needs of economic evaluation. Second, the methods of economic evaluation need to be enhanced by improving the analysis of the available clinical data, establishing high-quality clinical registries, and better recognizing MDs’ specific characteristics. Third, the market entry and diffusion of MDs need to be better managed by understanding the key influences on MD diffusion and linking diffusion with cost-effectiveness evidence through the use of performance-based risk-sharing arrangements.

Article

Anthony J. Venables

Economic activity is unevenly distributed across space, both internationally and within countries. What determines this spatial distribution, and how is it shaped by trade? Classical trade theory gives the insights of comparative advantage and gains from trade but is firmly aspatial, modeling countries as points and trade (in goods and factors of production) as either perfectly frictionless or impossible. Modern theory places this in a spatial context in which geographical considerations influence the volume of trade between places. Gravity models tell us that distance is important, with each doubling of distance between places halving the volume of trade. Modeling the location decisions of firms gives a theory of location of activity based on factor costs (as in classical theory) and also on proximity to markets, proximity to suppliers, and the extent of competition in each market. It follows from this that—if there is a high degree of mobility—firms and economic activity as a whole may tend to cluster, providing an explanation of observed spatial unevenness. In some circumstances falling trade barriers may trigger the deindustrialization of some areas as activity clusters in fewer places. In other circumstances falling barriers may enable activity to spread out, reducing inequalities within and between countries. Research over the past several decades has established the mechanisms that cause these changes and placed them in full general equilibrium models of the economy. Empirical work has quantified many of the important relationships. However, geography and trade remains an area where progress is needed to develop robust tools that can be used to inform place-based policies (concerning trade, transport, infrastructure, and local economic development), particularly in view of the huge expenditures that such policies incur.

Article

The economy of territory that became the United States evolved dramatically from ca. 1000 ce to 1776. Before Europeans arrived, the spread of maize agriculture shifted economic practices in Indigenous communities. The arrival of Europeans, starting with the Spanish in the West Indies in 1492, brought wide-ranging change, including the spread of Old World infectious disease and the arrival of land- and resource-hungry migrants. Europeans, eager to extract material wealth, came to rely on the trade in enslaved Africans to produce profitable crops such as tobacco, rice, and sugar, and they maintained connections with Indigenous communities to sustain the fur trade. The declining number of Indigenous peoples, combined with growing numbers of those of European or African origin, altered the demographic profile of North America, particularly in the territory east of the Mississippi River. Over time, Europeans’ consumer choices expanded, though the wealth gap between white colonists grew, as did the economic gap between free colonists, on the one hand, and unfree Black and Native peoples on the other.

Article

Jason M. Fletcher

Two interrelated advances in genetics have occurred which have ushered in the growing field of genoeconomics. The first is a rapid expansion of so-called big data featuring genetic information collected from large population–based samples. The second is enhancements to computational and predictive power to aggregate small genetic effects across the genome into single summary measures called polygenic scores (PGSs). Together, these advances will be incorporated broadly with economic research, with strong possibilities for new insights and methodological techniques.

Article

Samuel Berlinski and Marcos Vera-Hernández

A set of policies is at the center of the agenda on early childhood development: parenting programs, childcare regulation and subsidies, cash and in-kind transfers, and parental leave policies. Incentives are embedded in these policies, and households react to them differently. They also have varying effects on child development, both in developed and developing countries. We have learned much about the impact of these policies in the past 20 years. We know that parenting programs can enhance child development, that centre based care might increase female labor force participation and child development, that parental leave policies beyond three months don’t cause improvement in children outcomes, and that the effects of transfers depend much on their design. In this review, we focus on the incentives embedded in these policies, and how they interact with the context and decision makers to understand the heterogeneity of effects and the mechanisms through which these policies work. We conclude by identifying areas of future research.

Article

Economics can make immensely valuable contributions to our understanding of infectious disease transmission and the design of effective policy responses. The one unique characteristic of infectious diseases makes it also particularly complicated to analyze: the fact that it is transmitted from person to person. It explains why individuals’ behavior and externalities are a central topic for the economics of infectious diseases. Many public health interventions are built on the assumption that individuals are altruistic and consider the benefits and costs of their actions to others. This would imply that even infected individuals demand prevention, which stands in conflict with the economic theory of rational behavior. Empirical evidence is conflicting for infected individuals. For healthy individuals, evidence suggests that the demand for prevention is affected by real or perceived risk of infection. However, studies are plagued by underreporting of preventive behavior and non-random selection into testing. Some empirical studies have shown that the impact of prevention interventions could be far greater than one case prevented, resulting in significant externalities. Therefore, economic evaluations need to build on dynamic transmission models in order to correctly estimate these externalities. Future research needs are significant. Economic research needs to improve our understanding of the role of human behavior in disease transmission; support the better integration of economic and epidemiological modeling, evaluation of large-scale public health interventions with quasi-experimental methods, design of optimal subsidies for tackling the global threat of antimicrobial resistance, refocusing the research agenda toward underresearched diseases; and most importantly to assure that progress translates into saved lives on the ground by advising on effective health system strengthening.

Article

Pei-Ju Liao and Chong Kee Yip

In the past century, many developing countries have experienced rapid economic development, which is usually associated with a process of structural transformation and urbanization. Rural–urban migration, shifting the labor force from less productive agricultural sectors to more productive industrial sectors in cities, plays an important role in the growth process and thus has drawn economists’ attention. For instance, it is recognized that one of the important sources of China’s growth miracle is rural–urban migration. At the early stage of economic development, an economy usually relies on labor-intensive industries for growth. Rural–urban migrants thus provide the necessary labor force to urban production. Since they are more productive in industrial sectors than in agricultural sectors, aggregate output increases and economic growth accelerates. In addition, abundant migrants affect the rates of return to capital by changing the capital–labor ratio. They also change the skill composition of the urban labor force and hence the relative wage of skilled to unskilled workers. Therefore, rural–urban migration has wide impacts on growth and income distribution of the macroeconomy. What are the forces that drive rural–urban migration? It is well understood that cities attract rural migrants because of better job opportunities, better career prospects, and higher wages. Moreover, enjoying better social benefits such as better medical care in cities is another pull factor that initiates rural–urban migration. Finally, agricultural land scarcity in the countryside plays an important role on the push side for moving labor to cities. The aforementioned driving forces of rural–urban migration are work-based. However, rural–urban migration could be education-based, which is rarely discussed in the literature. In the past decade, it has been proposed that cities are the places for accumulating human capital in work. It is also well established that most of the high-quality education institutions (including universities and specialized schools for art and music) are located in urban areas. A youth may first move to the city to attend college and then stay there for work after graduation. From this point of view, work-based migration does not paint the whole picture of rural–urban migration. In this article, we propose a balanced view that both the work-based and education-based channels are important to rural–urban migration. The migration story could be misleading if any of them is ignored.