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.
The Economic Effect of Vocational Education on Student Outcomes
Shaun M. Dougherty and Walter G. Ecton
The Effect of Immigration on Education
Does a higher share of immigrants affect the school performance of both immigrants and natives? Do desegregation policies improve efficiency? The existing evidence suggests that a higher share of immigrants has a negative (and often sizable) effect on the school performance of immigrants and a negative but probably small effect on the performance of natives. When average school performance is considered, this asymmetry generates concave peer effects, a key condition for the efficiency of desegregating policies. The broad message from the empirical literature is that these policies are not only equitable, in that they provide better opportunities to individuals with relatively low parental background, but also efficient.
Assessments in Education
Hans Henrik Sievertsen
Assessments like standardized tests and teacher evaluations are central elements of educational systems. Assessments affect the behaviour of students, teachers, parents, schools, and policymakers through at least two channels: The information channel and the incentive channel. Students use the information to adjust study effort and to guide their course selection. Schools and teachers use information from assessments to evaluate teaching quality and the effectiveness of the applied methods. Educational programs use information from assessment results to sort students in educational programs and employers use the results as signals of productivity in their hiring decisions. Finally, policymakers use assessments in accountability systems to reward or penalize schools, and parents use information from assessment results to select schools. The incentive channel is a natural consequence of the information channel: Students are incentivized to work hard and do well in assessments to get access to educational programs and jobs. Teachers and schools are incentivized to do well to receive rewards or avoid punishments in accountability systems. The information channel is important for ensuring the most efficient human capital investments: students learn about the returns and costs of effort investments and about their abilities and comparative advantages. Teachers and schools learn about the most effective teaching methods. However, because of the strong incentives linked to assessments, both students and teachers might focus on optimizing assessment results at the cost of learning. Students might for example select tracks that maximize their grades instead of selecting tracks aligned with their interests and comparative advantages. Understanding the implications of assessments for the behaviour of students, parents, teachers, and schools is therefore necessary to achieve the overall goals of the educational system. Because education affects lifetime earnings, health, and well-being and assessments play an important role in individuals’ educational careers, assessments are also important for efficiency and equity across domains. Biases in assessments and the heterogeneity in access to assessments are sources of inequality in education according to gender, origin, and socioeconomic background. Finally, because assessment results also carry important consequences for individuals’ educational opportunities and in the labor market, they are a source of stress and reduced well-being.
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.
The Contribution of Vocational Education and Training to Innovation and Growth
Uschi Backes-Gellner and Patrick Lehnert
Despite the common view that innovation requires academically educated workers, some countries that strongly emphasize vocational education and training (VET) in their education systems—such as Switzerland and Germany—are highly competitive internationally in terms of innovation. These countries have dual VET programs, that is, upper-secondary-level apprenticeship programs, that combine about three quarters of workplace training with about one quarter of vocational schooling, and design them in such a way that their graduates (i.e., dual apprenticeship-graduates) play crucial roles in innovation processes. Regular updates of VET curricula incorporate the latest technological developments into these curricula, thereby ensuring that dual apprenticeship-graduates possess up-to-date, high-level skills in their chosen occupation. This process allows these graduates to contribute to innovation in firms. Moreover, these graduates acquire broad sets of technical and soft skills that enhance their job mobility and flexibility. Therefore, conventional wisdom notwithstanding, dual apprenticeship-graduates in such countries not only have broad skill sets that accelerate innovation in firms, but also willingly participate in innovation because of their high flexibility and employability. Moreover, Switzerland and Germany have tertiary-level VET institutions that foster innovation. These are universities of applied sciences (UASs), which teach and conduct applied research, thereby helping build a bridge between different types of knowledge (vocational and academic). UAS students have prior vocational knowledge through their dual apprenticeship and acquire applied research skills from UAS professors who usually have both work experience and a doctoral degree from an academic university. Thus UAS graduates combine sound occupational knowledge with applied research knowledge inspired by input from the academic research frontier and from practical research and development (R & D) in firms. Firms employ UAS graduates with their knowledge combination as an important input for R & D. Consequently, regions with a UAS have higher levels of innovation than regions without one. This effect is particularly strong for regions outside major innovation centers and for regions with larger percentages of smaller firms.
Education and Social Mobility
Helena Holmlund and Martin Nybom
Family background is a strong determinant of an individual’s educational achievement and labor market success. Using an economics framework, intergenerational persistence in socioeconomic status can be explained by a variety of factors, including parental investment behavior, credit constraints, and the degree of inequality in society. Genetic transmission from parents to children may also play a role. In addition, the skill formation process is governed by dynamics between different stages of a child’s life, such as complementarities between early and late investments or between informal and formal education. Education policy holds the promise of breaking the strong ties between family background and socioeconomic position by providing publicly accessible education for children of all backgrounds. However, the education system may also perpetuate social inequalities if well-off families are able to protect their children from downward mobility by, for example, moving to neighborhoods with high-quality schools and by providing networks that offer opportunities to succeed. However, a growing number of studies show that educational interventions can have long-lasting effects on students’ outcomes, in particular for disadvantaged students, and that they can be cost-effective. For example, reducing class size, increasing general education spending, tutoring, and improving teacher quality are policy levers that are shown to be successful in this regard. Shifting from selective to comprehensive school systems is also a policy that enhances equality of opportunity. While the evidence on credit constraints and their role for access to higher education is evolving, but still mostly U.S. focused and largely inconclusive, it is a key domain for shaping social mobility given the life-changing impacts that a university degree can have.
The Effect of Education on Health and Mortality: A Review of Experimental and Quasi-Experimental Evidence
Titus Galama, Adriana Lleras-Muney, and Hans van Kippersluis
Education is strongly associated with better health and longer lives. However, the extent to which education causes health and longevity is widely debated. We develop a human capital framework to structure the interpretation of the empirical evidence and review evidence on the causal effects of education on mortality and its two most common preventable causes: smoking and obesity. We focus attention on evidence from randomized controlled trials, twin studies, and quasi-experiments. There is no convincing evidence of an effect of education on obesity, and the effects on smoking are only apparent when schooling reforms affect individuals’ track or their peer group, but not when they simply increase the duration of schooling. An effect of education on mortality exists in some contexts but not in others and seems to depend on (i) gender, (ii) the labor market returns to education, (iii) the quality of education, and (iv) whether education affects the quality of individuals’ peers.
Human Capital in a Historical Perspective
Gabriele Cappelli, Leonardo Ridolfi, and Michelangelo Vasta
Human capital can be defined as the set of knowledge and skills that individuals accumulate over time. These range from basic competences to more sophisticated forms of knowledge (intermediate and upper-tail human capital). All of them entail complex measurement problems in historical perspective as sources are often too scarce, problematic, and unreliable to allow proper measurement. Human capital is usually measured relying on the extensive margin of education or the quantity of education, that is, how many people are able to read or count or how many people have a certain degree of schooling. Less is known about the effective acquisition of skills, for example, the quality of education. Human capital can affect labor productivity and innovative capacity and it is generally regarded as one of the most important determinants of economic growth, figuring prominently in debates on the origin of the Industrial Revolution and the transition from preindustrial to modern economic growth. The determinants of education are several and vary widely over time and across space, including economic, institutional, cultural, and social factors. Historically, the acquisition of skills has deeply changed in nature, passing from the largely decentralized and fragmented systems of the preindustrial period to the 19th-century systems of mass education, where education was more and more universal and free, and the accumulation of skills was largely coordinated by states and other public authorities. In several regards, literature on human capital is still limited. Few efforts, for instance, have been made to harmonize data, integrate them in a comparative and regional perspective, explore the potential of individual-level information, and assess if and to what extent different dimensions of human capital such as technical and higher education have affected long-term patterns in economic growth and development. Other aspects have long been neglected or remain virtually unexplored, such as gender differences in education, the efficiency of education systems and its determinants, and the analysis of human capital in developing countries.
Childcare and Children’s Development: Features of Effective Programs
Jo Blanden and Birgitta Rabe
Governments around the world are increasingly investing resources for young children, and universal provision of early childhood education and care (ECEC) has become widespread. Children’s development is affected by the investments they receive both within and outside the household. A simple theoretical framework predicts that the provision of public childcare will improve children’s development if it offers more stimulation than the care it replaces. Generally, carefully designed studies show that the provision of early childcare is beneficial, especially for children from disadvantaged backgrounds. This is in line with expectations that the alternative care experienced by children from less affluent, less educated, and immigrant backgrounds is likely to be of lower quality. Interestingly, however, studies show that the children who would benefit the most are least likely to receive care, providing a challenge for policy makers. Some programs, such as the $5-per-day childcare in Quebec, have negative effects and therefore may be of poor quality. However, comparing results across programs that vary in several dimensions makes it difficult to separate out the ingredients that are most important for success. Studies that focus on identifying the factors in ECEC that lead to the greatest benefit indicate that some standard measures such as staff qualifications are weakly linked to children’s outcomes, whereas larger staff–child ratios and researcher-measured process quality are beneficial. Spending more time in high-quality childcare from around age 3 has proved to be beneficial, whereas the effect of an increase in childcare for younger children is particularly sensitive to each program’s features and context.
The Role of Incentives for Improving Students’ Motivation and Performance
Although the returns to education can be substantial, many students underperform in school, for example, by not putting in sufficient effort. To mitigate this underinvestment problem, policymakers are often eager to try to motivate students using extrinsic incentives, such as cash payments and merit scholarships, stricter grading standards, and more competitive admission processes. The design, scope, and implementation of such incentive policies with the goal of affecting student motivation and study habits have been a fruitful area of economic research over the last 30 years. However, the evidence on their potency for improving student performance is mixed. In particular, the use of extrinsic incentives often elicit strategic responses from students, resulting in behavior that might improve performance metrics, but are not productive in terms of learning and skill development. Many incentive policies have therefore ended up producing unintended consequences that goes contrary to the policy objective. As incentives are everywhere in any school system, economists should pursue a better understanding of how they affect which outcomes students focus on, the choices they make, and how these effects differ across groups of students. Broadening the scope of outcomes considered when assessing the effects of incentives, in particular a greater focus on what student’s choose not to do, could provide a fruitful foundation for future research.
An Analysis of COVID-19 Student Learning Loss
Harry Patrinos, Emiliana Vegas, and Rohan Carter-Rau
The coronavirus disease 2019 (COVID-19) pandemic led to school closures around the world, affecting almost 1.6 billion students. This caused significant disruption to the global education system. Even short interruptions in a child’s schooling have significant negative effects on their learning and can be long lasting. The capacities of education systems to respond to the crisis by delivering remote learning and support to children and families have been diverse and uneven. In response to this disruption, education researchers are beginning to analyze the impact of these school closures on student learning loss. The term learning loss is commonly used in the literature to describe declines in student knowledge and skills. Early reviews of the first wave of lockdowns and school closures suggested significant learning loss in a few countries. A more recent and thorough analysis of recorded learning loss evidence documented since the beginning of the school closures between March 2020 and March 2022 found even more evidence of learning loss. In 36 identified robust studies, the majority identified learning losses that amount to, on average, 0.17 of a standard deviation (SD), equivalent to roughly a one-half school year’s worth of learning. This confirms that learning loss is real and significant and has continued to grow after the first year of the COVID-19 pandemic. Most studies observed increases in inequality where certain demographics of students experienced more significant learning losses than others. The longer the schools remained closed, the greater were the learning losses. For the 19 countries for which there are robust learning loss data, average school closures were 15 weeks, leading to average learning losses of 0.18 SD. Put another way, for every week that schools were closed, learning declined by an average of 0.01 SD. However, there are also outliers—countries that managed to limit the amount of loss. In Nara City, Japan, for example, the initial closures had brought down test scores, but responsive policies largely overcame this decline. In addition, a decreased summer vacation helped. In Denmark, children received good home support and their reading behavior improved significantly. In Sweden, where primary schools did not close during the pandemic, there were no reported learning losses. Further work is needed to increase the quantity of studies produced, particularly in low- and middle-income countries, and to ascertain the reasons for learning loss. Finally, the few cases where learning loss was mitigated should be further investigated to inform continued and future pandemic responses.
Housing, Neighborhoods, and Education
Sarah A. Cordes, Jeehee Han, and Amy Schwartz
Children’s educational outcomes are determined not only by school inputs, such as teachers, curriculum, or classroom peers, but also by a broad range of resources and experiences outside the classroom. Housing and neighborhoods—where children live—are likely where students spend most of their time when not in school and can play a crucial role in children’s development. Housing may influence children’s K–12 educational outcomes through three key channels. First, unit quality (i.e., size, ventilation, etc.) may affect student performance through sleep, ability to concentrate, or health. Second, affordability and tenure may shape student outcomes by affecting disposable income or wealth accumulation, which could be used for complementary educational inputs or could influence outcomes by affecting parental stress and housing stability. Third, housing stability/mobility may itself result in better or worse academic outcomes depending on whether moves are made to access better opportunities or are disruptive. Neighborhoods may also play an important role in education by shaping the peers and adult role models to whom children are exposed, through levels of exposure to crime and violence, and access to opportunities, such as the quality of local schools. A growing body of research points to the importance of both housing and neighborhoods in shaping educational outcomes, suggesting investments in housing or neighborhoods may pay an educational dividend and such investments may be leveraged to improve children’s educational outcomes. Yet there is still work to be done to better understand the roles that housing and neighborhoods play in shaping educational outcomes. In particular, future research should focus on examining how the physical aspects of housing may shape children’s outcomes, disentangling the effects of residential mobility under different circumstances (i.e., forced moves due to job losses versus voluntary moves), and estimating the effects of specific neighborhood changes — or improvements — on academic outcomes.
Economics of Rural–Urban Migration
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.
The Economics of Childhood and Adolescent Obesity
Obesity is widely recognized as a chronic disease characterized by an elevated risk of adverse health conditions in association with excess body fat accumulation. Obesity prevalence reached epidemic proportions among adults in the developed world during the second half of the 20th century, and it has since become a major public health concern around the world, particularly among children and adolescents. The economics of childhood and adolescent obesity is a multi-faceted field of study that considers the numerous determinants, consequences, and interventions related to obesity in those populations. The central economic framework for studying obesity is a life-cycle decision-making model of health investment. Health-promoting investments, such as nutritional food, healthcare, and physical activity, interact with genetic structure and risky health behaviors, such as unhealthy food consumption, to generate an accumulation or decumulation of excess body fat over time. Childhood and adolescence are the primary phases of physical and cognitive growth, so researchers study how obesity contributes to, and is affected by, the growth processes. The subdiscipline of behavioral economics offers an important complementary perspective on health investment decision processes, particularly for children and adolescents, because health investments and participation in risky health behaviors are not always undertaken rationally or consistently over time. In addition to examining the proximate causes of obesity over the life cycle, economists study obesity’s economic context and resulting economic burden. For example, economists study how educational attainment, income, and labor market features, such as wage and work hours, affect childhood and adolescent obesity in a household. Once obesity has developed, its economic burden is typically measured in terms of excess healthcare costs associated with increased health risks due to higher obesity prevalence, such as earlier onset of, and more severe, diabetes. Obesity among children and adolescents can lead to even higher healthcare costs because of its early influence on the lifetime trajectory of health and its potential disruption of healthy development. The formulation of effective policy responses to the obesity epidemic is informed by economic research. Economists evaluate whether steps to address childhood and adolescent obesity represent investments in health and well-being that yield private and social benefits, and they study whether existing market structures fail to appropriately motivate such investments. Potential policy interventions include taxation of, or restricting access to, obesogenic foods and other products, subsidization of educational programs about healthy foods and physical activity inside and outside of schools, ensuring health insurance coverage for obesity-related preventive and curative healthcare services, and investment in the development of new treatments and medical technologies.
Value-Added Estimates of Teacher Effectiveness: Measurement, Uses, and Limitations
Jessalynn James and Susanna Loeb
Since the turn of the 21st century, an abundant body of research has demonstrated that teachers meaningfully contribute to their students’ learning but that teachers vary widely in their effectiveness. Measures of teachers’ “value added” to student achievement have become common, and sometimes controversial, tools for researchers and policymakers hoping to identify and differentiate teachers’ individual contributions to student learning. Value-added measures aim to identify how much more a given teacher’s students learn than what would be expected based on how much other, similar students learn with other teachers. The question of how to measure value added without substantial measurement error and without incorrectly capturing other factors outside of teachers’ control is complex and sometime illusory, and the advantages and drawbacks to any particular method of estimating teachers’ value added depend on the specific context and purpose for their use. Traditionally, researchers have calculated value-added scores only for the subset of teachers with students in tested grades and subjects—a relatively small proportion of the teaching force, in a narrow set of the many domains on which teachers may influence their students. More recently, researchers have created value-added estimates for a range of other student outcomes, including measures of students’ engagement and social-emotional learning such as attendance and behavioral incidences, which may be available for more teachers. Overall, teacher value-added measures can be useful tools for understanding and improving teaching and learning, but they have substantial limitations for many uses and contexts.
Business Cycles and Apprenticeships
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.
The Economics of Gender and Educational Achievement: Stylized Facts and Causal Evidence
Judith M. Delaney and Paul J. Devereux
There are two well-established gender gaps in education. First, females tend to have higher educational attainment and achievement than males, and this is particularly the case for children from less advantaged backgrounds. Second, there are large differences in the fields of specialization chosen by males and females in college and even prior to college, and females disproportionately enter less highly paid fields. Gender differences in noncognitive traits, behavior, and interests have been shown to relate to differences in educational outcomes; however, this evidence cannot generally be given a causal interpretation. In contrast, the literature has been creative in estimating causal impacts of a wide range of factors using experimental and quasiexperimental variation. While the approaches are compelling, the findings vary widely across studies and are often contradictory. This may partly reflect methodological differences across studies, but it also may result from substantial true heterogeneity across educational systems and time periods. Lower educational achievement of males has been linked to gender differences in attitudes, behaviors, and educational aspirations as well as the tendency of males to mature at older ages. Differential field choices by gender are related to differences in comparative advantage by gender and gender differences in preferences for types of study and work and for nonpecuniary aspects of jobs, such as their flexibility and gender mix. There are reasons to believe that policy should address the two gender gaps, and many possible policy approaches exist. However, their effectiveness is unclear, and there is scope for further work to determine which policies are likely to be effective and in which circumstances.
The Implications of Pupil Rank for Achievement
Richard Murphy and Felix Weinhardt
The significance of social interaction has become an increasingly important part of economic thought and models through the work on peer effects, social norms, and networks. Within this literature, a novel focus of ranking within groups has emerged. The rank of an individual is usually defined as the ordinal position within a specific group. This could be the work environment or a classroom, and much of this literature focuses on rank effects in education settings. The literature studies rank effects for various age groups. There is evidence that a rank position even during early life phases, such as in elementary education, has lasting effects on education outcomes such as test scores or subject specializations, choices during college, and wages. A first-order challenge in the study of rank effects is to separate them from other highly correlated effects. For example, individuals with a high rank academic rank in a group will likely have high academic ability in absolute terms. Papers in this field directly account for measured ability, and so rely on the variation in rank that exists across groups for any given ability measure, that is, a score of 80 in one group would rank the student top, while near the bottom in another. The comparability of achievement measures across settings is key; one commonly employed solution is to account for level differences across settings. While the literature has now established the importance of rank, there are several—potentially non-competing—ideas for the precise behavioral mechanisms of why rank matters so much. Future work will most likely focus on integrating rank effects into the literature on social interactions to discuss implications for optimal group formation.
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.
Education and Economic Growth
Eric A. Hanushek and Ludger Woessmann
Economic growth determines the future well-being of society, but finding ways to influence it has eluded many nations. Empirical analysis of differences in growth rates reaches a simple conclusion: long-run growth in gross domestic product (GDP) is largely determined by the skills of a nation’s population. Moreover, the relevant skills can be readily gauged by standardized tests of cognitive achievement. Over the period 1960–2000, three-quarters of the variation in growth of GDP per capita across countries can be accounted for by international measures of math and science skills. The relationship between aggregate cognitive skills, called the knowledge capital of a nation, and the long-run growth rate is extraordinarily strong. There are natural questions about whether the knowledge capital–growth relationship is causal. While it is impossible to provide conclusive proof of causality, the existing evidence makes a strong prima facie case that changing the skills of the population will lead to higher growth rates. If future GDP is projected based on the historical growth relationship, the results indicate that modest efforts to bring all students to minimal levels will produce huge economic gains. Improvements in the quality of schools have strong long-term benefits. The best way to improve the quality of schools is unclear from existing research. On the other hand, a number of developed and developing countries have shown that improvement is possible.