The global impact of the COVID-19 pandemic has been profound, leaving a significant imprint on physical health, the economy, and mental well-being. Researchers have undertaken empirical investigations across different countries, with a primary focus on understanding the association between lockdown measures—an essential public health intervention—and mental health. These studies aim to discern the causal effect of lockdowns on mental well-being. Three notable studies have adopted natural experiments to explore the causal effect of lockdowns on mental health in diverse countries. Despite variations in their research methodologies, these studies collectively support the conclusion that lockdowns have had detrimental consequences on mental health. Furthermore, they reveal that the intensity of these negative effects varies among distinct population groups. Certain segments of the population, such as women, have borne a more profound burden of the mental health costs associated with lockdown measures. In light of these findings, it becomes imperative to consider the implications for mental health when implementing public health interventions, especially during crises like the COVID-19 pandemic. While rigorous measures like lockdowns are essential for safeguarding public health, striking a balance with robust mental health support policies becomes crucial to mitigating the adverse impacts on mental well-being.
61-80 of 376 Results
Article
COVID-19 and Mental Health: Natural Experiments of the Costs of Lockdowns
Climent Quintana-Domeque and Jingya Zeng
Article
Creative Destruction, Technology Disruption, and Growth
Thomas Clarke
The origins of modern technological change provide the context necessary to understand present-day technological transformation, to investigate the impact of the new digital technologies, and to examine the phenomenon of digital disruption of established industries and occupations. How these contemporary technologies will transform industries and institutions, or serve to create new industries and institutions, will unfold in time. The implications of the relationships between these pervasive new forms of digital transformation and the accompanying new business models, business strategies, innovation, and capabilities are being worked through at global, national, corporate, and local levels. Whatever the technological future holds it will be defined by continual adaptation, perpetual innovation, and the search for new potential.
Presently, the world is experiencing the impact of waves of innovation created by the rapid advance of digital networks, software, and information and communication technology systems that have transformed workplaces, cities, and whole economies. These digital technologies are converging and coalescing into intelligent technology systems that facilitate and structure our lives. Through creative destruction, digital technologies fundamentally challenge existing routines, capabilities, and structures by which organizations presently operate, adapt, and innovate. In turn, digital technologies stimulate a higher rate of both technological and business model innovation, moving from producer innovation toward more user-collaborative and open-collaborative innovation. However, as dominant global platform technologies emerge, some impending dilemmas associated with the concentration and monopolization of digital markets become salient. The extent of the contribution made by digital transformation to economic growth and environmental sustainability requires a critical appraisal.
Article
Credit Ratings and Rating Agencies
Miles Livingston and Lei Zhou
Credit rating agencies have developed as an information intermediary in the credit market because there are very large numbers of bonds outstanding with many different features. The Securities Industry and Financial Markets Association reports over $20 trillion of corporate bonds, mortgaged-backed securities, and asset-backed securities in the United States. The vast size of the bond markets, the number of different bond issues, and the complexity of these securities result in a massive amount of information for potential investors to evaluate. The magnitude of the information creates the need for independent companies to provide objective evaluations of the ability of bond issuers to pay their contractually binding obligations. The result is credit rating agencies (CRAs), private companies that monitor debt securities/issuers and provide information to investors about the potential default risk of individual bond issues and issuing firms.
Rating agencies provide ratings for many types of debt instruments including corporate bonds, debt instruments backed by assets such as mortgages (mortgage-backed securities), short-term debt of corporations, municipal government debt, and debt issued by central governments (sovereign bonds).
The three largest rating agencies are Moody’s, Standard & Poor’s, and Fitch. These agencies provide ratings that are indicators of the relative probability of default. Bonds with the highest rating of AAA have very low probabilities of default and consequently the yields on these bonds are relatively low. As the ratings decline, the probability of default increases and the bond yields increase.
Ratings are important to institutional investors such as insurance companies, pension funds, and mutual funds. These large investors are often restricted to purchasing exclusively or primarily bonds in the highest rating categories. Consequently, the highest ratings are usually called investment grade. The lower ratings are usually designated as high-yield or “junk bonds.”
There is a controversy about the possibility of inflated ratings. Since issuers pay rating agencies for providing ratings, there may be an incentive for the rating agencies to provide inflated ratings in exchange for fees. In the U.S. corporate bond market, at least two and often three agencies provide ratings. Multiple ratings make it difficult for one rating agency to provide inflated ratings. Rating agencies are regulated by the Securities and Exchange Commission to ensure that agencies follow reasonable procedures.
Article
Crises in the Housing Market: Causes, Consequences, and Policy Lessons
Carlos Garriga and Aaron Hedlund
The global financial crisis of 2007–2009 helped usher in a stronger consensus about the central role that housing plays in shaping economic activity, particularly during large boom and bust episodes. The latest research regards the causes, consequences, and policy implications of housing crises with a broad focus that includes empirical and structural analysis, insights from the 2000s experience in the United States, and perspectives from around the globe. Even with the significant degree of heterogeneity in legal environments, institutions, and economic fundamentals over time and across countries, several common themes emerge. Research indicates that fundamentals such as productivity, income, and demographics play an important role in generating sustained movements in house prices. While these forces can also contribute to boom-bust episodes, periods of large house price swings often reflect an evolving housing premium caused by financial innovation and shifts in expectations, which are in turn amplified by changes to the liquidity of homes. Regarding credit, the latest evidence indicates that expansions in lending to marginal borrowers via the subprime market may not be entirely to blame for the run-up in mortgage debt and prices that preceded the 2007–2009 financial crisis. Instead, the expansion in credit manifested by lower mortgage rates was broad-based and caused borrowers across a wide range of incomes and credit scores to dramatically increase their mortgage debt. To whatever extent changing beliefs about future housing appreciation may have contributed to higher realized house price growth in the 2000s, it appears that neither borrowers nor lenders anticipated the subsequent collapse in house prices. However, expectations about future credit conditions—including the prospect of rising interest rates—may have contributed to the downturn. For macroeconomists and those otherwise interested in the broader economic implications of the housing market, a growing body of evidence combining micro data and structural modeling finds that large swings in house prices can produce large disruptions to consumption, the labor market, and output. Central to this transmission is the composition of household balance sheets—not just the amount of net worth, but also how that net worth is allocated between short term liquid assets, illiquid housing wealth, and long-term defaultable mortgage debt. By shaping the incentive to default, foreclosure laws have a profound ex-ante effect on the supply of credit as well as on the ex-post economic response to large shocks that affect households’ degree of financial distress. On the policy front, research finds mixed results for some of the crisis-related interventions implemented in the U.S. while providing guidance for future measures should another housing bust of similar or greater magnitude reoccur. Lessons are also provided for the development of macroprudential policy aimed at preventing such a future crisis without unduly constraining economic performance in good times.
Article
Data Revisions and Real-Time Forecasting
Michael P. Clements and Ana Beatriz Galvão
At a given point in time, a forecaster will have access to data on macroeconomic variables that have been subject to different numbers of rounds of revisions, leading to varying degrees of data maturity. Observations referring to the very recent past will be first-release data, or data which has as yet been revised only a few times. Observations referring to a decade ago will typically have been subject to many rounds of revisions. How should the forecaster use the data to generate forecasts of the future? The conventional approach would be to estimate the forecasting model using the latest vintage of data available at that time, implicitly ignoring the differences in data maturity across observations.
The conventional approach for real-time forecasting treats the data as given, that is, it ignores the fact that it will be revised. In some cases, the costs of this approach are point predictions and assessments of forecasting uncertainty that are less accurate than approaches to forecasting that explicitly allow for data revisions. There are several ways to “allow for data revisions,” including modeling the data revisions explicitly, an agnostic or reduced-form approach, and using only largely unrevised data. The choice of method partly depends on whether the aim is to forecast an earlier release or the fully revised values.
Article
Demystifying Electronic Payment Systems and Digital Currencies
Lin William Cong, David Easley, and Eswar Prasad
Digital payments have transformed how consumers and businesses transact. New technologies such as mobile wallets and buy-now-pay-later financing offer additional convenience and expand access. However, the proliferation of private platforms raises concerns about market power, privacy, and stability. Since 2008, fast payment systems and decentralized cryptocurrencies have enabled direct transactions with fewer or no intermediaries, but they face challenges such as the lack of value stability and scalability. Central banks are responding by designing digital versions of fiat currencies. Retail central bank digital currencies (CBDCs) could promote financial inclusion and provide a central bank–backed alternative to private platforms. CBDCs raise complex policy trade-offs around disintermediating banks, enabling negative interest rates, preserving privacy, and coexistence with cash. International CBDCs may facilitate cheaper cross-border payments. While digital payments offer efficiency, key design choices around transaction fees and governance determine accessibility. High or unpredictable fees on blockchain platforms disproportionately affect smaller users. Initiatives that broaden token ownership help democratize networks. Tensions persist between decentralization, security, scalability, and privacy. Further research can improve digital payment technologies and clarify policy implications.
Article
Design of Discrete Choice Experiments
Deborah J. Street and Rosalie Viney
Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.
Article
Developmental Origins of Health Inequality
Gabriella Conti, Giacomo Mason, and Stavros Poupakis
Building on early animal studies, 20th-century researchers increasingly explored the fact that early events—ranging from conception to childhood—affect a child’s health trajectory in the long-term. By the 21st century, a wide body of research had emerged, incorporating the original fetal origins hypothesis into the developmental origins of health and disease. Evidence from Organization for Economic Cooperation and Development (OECD) countries suggests that health inequalities are strongly correlated with many dimensions of socioeconomic status, such as educational attainment, and that they tend to increase with age and carry stark intergenerational implications. Different economic theories have been developed to rationalize this evidence, with an overarching comprehensive framework still lacking. Existing models widely rely on human capital theory, which has given rise to separate dynamic models of adult and child health capital within a production function framework. A large body of empirical evidence has also found support for the developmental origins of inequalities in health. On the one hand, studies exploiting quasi-random exposure to adverse events have shown long-term physical and mental health impacts of exposure to early shocks, including pandemics or maternal illness, famine, malnutrition, stress, vitamin deficiencies, maltreatment, pollution, and economic recessions. On the other hand, studies from the 20th century have shown that early interventions of various content and delivery formats improve life course health. Further, given that the most socioeconomically disadvantaged groups show the greatest gains, such measures can potentially reduce health inequalities. However, studies of long-term impacts as well as the mechanisms via which shocks or policies affect health, and the dynamic interaction among them, are still lacking. Mapping the complexities of those early event dynamics is an important avenue for future research.
Article
Disability and Economic Development
Marc Suhrcke
While definitional and measurement problems pose a challenge, there is no doubt that disability affects a noticeable share of the population, the vast majority of whom live in low- and middle-income countries (LMICs). The still comparatively scarce empirical data and evidence suggests that disability is closely associated with poverty and other indicators of economic deprivation at both the country and—if with slightly greater nuance—at the individual/household level. There is also a growing body of evidence documenting the sizeable additional costs incurred by persons with disabilities (PwDs) as a direct or indirect consequence of their disability, underlining the increased risk of PwDs (and the households they are part of) falling under the absolute poverty line in any given LMIC.
Looking ahead, there remains considerable scope for more evidence on the causal nature of the link between disability and poverty, as well as on the (cost-)effectiveness of interventions and policies attempting to improve the well-being of PwDs.
Article
Discounting and Climate Policy
Frederick van der Ploeg
The social rate of discount is a crucial driver of the social cost of carbon (SCC), that is, the expected present discounted value of marginal damages resulting from emitting one ton of carbon today. Policy makers should set carbon prices to the SCC using a carbon tax or a competitive permits market. The social discount rate is lower and the SCC higher if policy makers are more patient and if future generations are less affluent and policy makers care about intergenerational inequality. Uncertainty about the future rate of growth of the economy and emissions and the risk of macroeconomic disasters (tail risks) also depress the social discount rate and boost the SCC provided intergenerational inequality aversion is high. Various reasons (e.g., autocorrelation in the economic growth rate or the idea that a decreasing certainty-equivalent discount rate results from a discount rate with a distribution that is constant over time) are discussed for why the social discount rate is likely to decline over time. A declining social discount rate also emerges if account is taken from the relative price effects resulting from different growth rates for ecosystem services and of labor in efficiency units. The market-based asset pricing approach to carbon pricing is contrasted with a more ethical approach to policy making. Some suggestions for further research are offered.
Article
Dual Labor Markets Revisited
Samuel Bentolila, Juan J. Dolado, and Juan F. Jimeno
This article provides an overview of empirical and theoretical research on dual labor markets. It revisits the labor-market effects of dual employment protection legislation as well as the main factors behind its resilience. Characterized by a high incidence of temporary contracts, which may lead to stepping-stone or dead-end jobs, dual labor markets exhibit specific features regarding the determination of employment, unemployment, churn, training, productivity growth, wages, and labor market flows. Relying on the contrasting experiences of several OECD countries with different degrees of duality and, in particular, on the very poor employment performance of some EU countries during the Great Recession, lessons are drawn about policy-reform strategies aiming to correct the inefficiencies of dual labor markets.
Article
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.
Article
The Early Origins of the Civil Rights Movement in the United States: An Analysis of the Growth of the NAACP
Daniel Aaronson, Jala Abner, Mark Borgschulte, and Bhashkar Mazumder
A newly digitized panel of county-level branch activity of the National Association for the Advancement of Colored People (NAACP) is used to describe the potential factors underlying the expansion of political participation in the American South, with a particular emphasis on the short period from the late 1930s through the 1940s. This period has long been recognized for its significant progress in reducing sizable racial gaps in labor market outcomes. But little work in economics has considered the role of political participation in shaping that progress. As the preeminent civil rights organization prior to the 1950s, the NAACP provides a natural lens in which to explore the expansion in political activism during this crucial period. Associative evidence suggests that a few potential channels could be especially worthy of future study, including the role of demographics, increased human capital, expansion in labor demand driven by wartime efforts, reduction in racial violence, latent political activism, and expansions in political and social networks, all of which have been highlighted in a variety of history and social science literatures. However, careful causal empirical work does not currently exist on these factors. Filling in this hole is important for providing compelling evidence on the origins of the 20th century’s most important U.S. political movement, as well as adding to a growing literature in political economy and development economics which examines the role that grassroots activism has played on economic growth and income inequality around the world.
Article
Earnings Inequality in Latin America: A Three-Decade Retrospective
Manuel Fernández and Gabriela Serrano
Latin American countries have some of the highest levels of income inequality in the world. However, earnings inequality have significantly changed over time, increasing during the 1980s and 1990s, declining sharply in the 2000s, and stagnating or even increasing in some countries since 2015. Macroeconomic instability in the region in the 1980s and early 1990s, as well as the introduction of structural reforms like trade, capital, and financial liberalization, affected the patterns of relative demand and relative earnings across skill-demographic groups in the 1990s, increasing inequality. Significant gains in educational attainment, the demographic transition, and rising female labor force participation changed the skill-demographic composition of labor supply, pushing the education and experience premiums downward, but this was not enough to counteract demand-side trends. At the turn of the 21st century, improved external conditions, driven by China’s massive increase in demand for commodities, boosted economies across Latin America, which began to grow rapidly. Growth was accompanied by a positive shift in the relative demand for less-educated workers, stronger labor institutions, rising minimum wages, and declining labor informality, a confluence of factors that reduced earnings inequality. In the aftermath of the global financial crisis, particularly after the end of the commodities price boom in 2014, economic growth decelerated, and the pace of inequality decline stagnated. There is extensive literature documenting and trying to explain the causes of recent earnings inequality dynamics in Latin America. This literature is examined in terms of themes, methodological approaches, and key findings. The focus is on earnings inequality and how developments in labor markets have shaped it.
Article
Econometric Methods for Business Cycle Dating
Máximo Camacho Alonso and Lola Gadea
Over time, the reference cycle of an economy is determined by a sequence of non-observable business cycle turning points involving a partition of the time calendar into non-overlapping episodes of expansions and recessions. Dating these turning points helps develop economic analysis and is useful for economic agents, whether policymakers, investors, or academics.
Aiming to be transparent and reproducible, determining the reference cycle with statistical frameworks that automatically date turning points from a set of coincident economic indicators has been the source of remarkable advances in this research context. These methods can be classified into different broad sets of categories. Depending on the assumptions made in the data-generating process, the dating methods are parametric and non-parametric. There are two main approaches to dealing with multivariate data sets: average then date and date then average. The former approach focuses on computing a reference series of the aggregate economy, usually by averaging the indicators across the cross-sectional dimension. Then, the global turning points are dated on the aggregate indicator using one of the business cycle dating models available in the literature. The latter approach consists of dating the peaks and troughs in a set of coincident business cycle indicators separately, assessing the reference cycle itself in those periods where the individual turning points cohere.
In the early 21st century, literature has shown that future work on dating the reference cycle will require dealing with a set of challenges. First, new tools have become available, which, being increasingly sophisticated, may enlarge the existing academic–practitioner gap. Compiling the codes that implement the dating methods and facilitating their practical implementation may reduce this gap. Second, the pandemic shock hitting worldwide economies led most industrialized countries to record 2020’s most significant fall and the largest rebound in national economic indicators since records began. Under these influential observations, the outcomes of dating methods could misrepresent the actual reference cycle, especially in the case of parametric approaches. Exploring non-parametric approaches, big data sources, and the classification ability offered by machine learning methods could help improve dating analyses’ performance.
Article
Econometrics for Modelling Climate Change
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
Econometrics of Stated Preferences
Denzil G. Fiebig and Hong Il Yoo
Stated preference methods are used to collect individual-level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article is data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.
Article
The Economic Benefits of Education for the Reduction of Crime
Joel Carr, Olivier Marie, and Sunčica Vujić
Historically, social observers have repeatedly noted a correlation between education and crime, observing that individuals with lower levels of education are more likely to commit crime. However, the relationship between education and crime is complex, and it is important to clearly establish causality to determine if investing in education can effectively reduce crime. Merely observing persistent educational-attainment inequalities between offenders and non-offenders is not sufficient to make any causal claims about the underlying relationship between education and crime. Many other factors can influence an individual’s decision to stay in school or commit a crime, and these factors need to be accounted for when estimating the relationship between education and crime. Economists theoretically predicted in the late 1960s that education, via its positive effect on future earnings, would reduce the probability of criminal participation. Empirical studies have since used various econometric methods to establish that, on average, education has a strong causal crime-reducing effect. One strand of this literature has established in various contexts that individuals from cohorts forced by law to stay longer in school were much less likely to end up in court or prison. There is, however, still much to be discovered about the effect of education on crime, such as the underlying mechanisms related to income or non-cognitive effects, and heterogeneities by context, education level and quality, and individual characteristics. Overall, economists widely agree that investing in education is an efficient public-spending strategy to effectively reduce crime.
Article
Economic Development in Spain, 1815–2017
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
The Economic Effect of Vocational Education on Student Outcomes
Shaun M. Dougherty and Walter G. Ecton
As long as formal education has existed, there has been a clear connection between education and preparation for employment. In much of the world, formal educational systems have come to include vocational education and training (VET) as part of secondary education. In these spaces, individuals can receive continued training in general skills related to reading, writing, and mathematics while also pursuing specific skills in prescribed vocational or technical programs (e.g., skilled trades, culinary arts, information technology, health services). Across all countries and associated educational systems, a tension exists between whether to invest educational dollars in general versus specific skill development. On the one hand, general skills allow for transferability and likely support adaptability across workplace settings and in response to changes in employment conditions. On the other hand, secondary school completion is not universal, even in rich countries, and there are often large penalties or social costs to not completing secondary education. Furthermore, across countries of varying GDP levels, the question about how to best prepare individuals for entry into and success in the workforce is a persistent one. Evidence suggests that the payoff to investments in VET vary considerably, and that context and the characteristics of participants likely inform the expected returns to such investments. For instance, there is strong evidence across contexts that male participants in VET are likely to benefit in the short- to medium-term with respect to employment and earnings, and possibly also engage in less crime. Unresolved, however, is whether these payoffs persist in the longer term. In contrast, for women the estimated returns appear to be more context dependent. Some research shows reduced fertility and greater financial independence of women participating in VET programs in less-developed countries, but evidence is mixed in other settings. All evidence underscores that the payoff to VET is likely tied to the extent to which it adapts to contemporary economic needs, including extending the amount of total formal education that participants might otherwise receive.
