21-30 of 362 Results


Peer Effects in Education  

Andrés Barrios-Fernandez

The identification of peer effects is challenging. There are many factors not related to social influences that could explain correlations among peers. Peers have been shown to affect many important outcomes, including academic performance and educational trajectories. Confirming the existence of peer effects is important from a policy perspective. Both the cost-benefit analysis and the design of policies are likely to be affected by the existence of social spillovers. However, making general policy recommendations from the current evidence is not easy. The size of the peer effects documented in the literature varies substantially across settings and depends on how peers are defined and characterized. Understanding what is behind this heterogeneity is thus key to extract more general policy lessons. Access to better data and the ability to map social networks will likely facilitate investigating which peers and which characteristics matter the most in different contexts. Conducting more research on the mechanisms behind peer effects is also important. Understanding these drivers is key to take advantage of social spillovers in the design of new educational programs, to identify competing policies, and to gain a deeper understanding of the nature and relevance of different forms of social interactions for the youth.


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.


Economic History of the Middle East, 622–1914  

Timur Kuran

In the Middle Ages, the Middle East was an economically advanced region. Driving its successes were an essentially uniform legal system that supported intra- and interregional commerce, partnership rules that supported commerce among nonrelatives, and a form of trust known as waqf, which served as both a wealth shelter and a vehicle for endowing social services with protections against state predation. These same institutions disincentivized the institutional advances needed to generate the modern economy’s infrastructure indigenously. Home-grown innovations, such as the tradable equity known as gedik and a form of waqf used for moneylending (cash waqf), were ill-suited to large-scale and perpetual enterprises. Partnerships used to form small and ephemeral enterprises did not spawn organizational forms conducive to pooling resources on a large scale and perpetually. The waqf’s rigidities led to increasingly serious capital misallocation and misuse with changes in relative prices and the emergence of new technologies. Thus, the Middle East reached the Industrial Era institutionally unprepared. Sensing an existential threat from the West, its ruling elites launched massive economic reforms in the 1800s. These reforms involved transplanting Western economic institutions to the West in a hurry. Although the Middle East’s economic performance improved greatly in absolute terms, it remained underdeveloped in 1914, and the catch-up process has continued. Until the 1700s, the economic fortunes of the Middle East’s religious minorities generally tracked those of its Muslims. Thereafter, non-Muslims pulled ahead. As the global economy modernized, they benefited from a right that, from the early years of Islam, was denied to Muslims: choice of law. With the development of modern economic institutions by Europeans, choice of law enabled non-Muslims to increase the efficiency of their business operations. In the century preceding the Industrial Revolution, non-Muslims benefited also from international treaties that strengthened their property rights vis-à-vis those of Muslims.


Explaining the Mathematics Gender Gap: The Role of Stereotypes  

Pilar Cuevas Ruiz, Ismael Sanz, and Almudena Sevilla

Descriptive stereotypes such as “girls are not good at mathematics” or prescriptive stereotypes, that is, fixed views about women’s societal roles, can explain the persistent gender gap in mathematics. Stereotypes lower girls’ beliefs, expectations, and incentives to put forth effort, and can constrain girls’ choices in male-dominated high-paying careers that are math-intensive and that require strong math skills. This gap slows progress toward gender equality in the labor market and hinders productivity and economic growth. Policy interventions to alleviate the negative impacts of descriptive stereotypes aim to prevent girls from internalizing socially constructed behaviors aligned with prevalent gender stereotypes regarding the innate mathematical abilities of boys and girls. Boosting girls’ confidence in their math skills includes introducing them to female role models, such as women math teachers, using gender-neutral language, and providing textbooks and other teaching materials that challenge gender stereotypes. A different set of policies focuses on altering the environment in which girls learn, rather than modifying their beliefs. By adjusting the testing methods (such as reducing the level of competition) or adapting the instructional approach to better align with the learning style of girls, it is possible to create an environment that enables more girls to achieve their maximum potential and to accurately assess their math abilities and interests, rather than simply their test-taking or classroom performance. However, interventions that aim to modify the beliefs and attitudes of girls and women ex post, as well as those that seek to alter the environment, may not work in the long term because they reinforce preexisting stereotypes and operate within the constraints of those stereotypes. For instance, while modifying the testing environment may result in higher grades for girls, it may not necessarily alter the perception that girls are incapable of excelling in math. In some cases, these interventions may even have negative consequences. Encouraging girls to “lean in” and behave like boys, for example, can lead to unequal, unjust, and inefficient outcomes because the benefits (economic returns) of doing so are lower or even negative for girls in light of existing gender stereotypes. One popular and affordable approach to combating gender stereotypes involves addressing (unconscious) biases among teachers, parents, and peers through initiatives such as unconscious bias training and self-reflection on biases. The underlying premise is that by increasing awareness of their own (unconscious) biases, individuals will engage their more conscious, non-gender-stereotypical thinking processes. However, such behavioral interventions can sometimes have unintended consequences and result in backlash, and their effectiveness may vary significantly depending on the context, so that their external validity is often called into question. The recognition of the adaptable nature of both conscious and unconscious stereotypes has led to progress in economics, with the development of social learning and information-based theories. Interventions resulting from these models can effectively counteract prescriptive stereotypes that limit girls’ education to certain fields based on societal expectations of gender roles. However, prescriptive gender stereotypes are often based on biased beliefs about the innate abilities of girls and women. Overcoming deeply ingrained descriptive stereotypes about innate abilities of boys and girls is a fruitful avenue for future economics research and can help close the gender performance gap in mathematics.


Macroprudential Policies and Global Finance  

Stijn Claessens, Aaron Mehrotra, and Ilhyock Shim

Macroprudential policy involves using mainly prudential but sometimes also monetary and fiscal tools to reduce systemic risk and achieve financial stability. It is motivated by externalities associated with the buildup of systemic risk over time due to strategic complementarities, fire sales and credit crunches related to a generalized sell-off of assets, and strong cross-institutional interconnectedness. Macroprudential tools apply to both banks and nonbank financial institutions and to different classes of borrowers to the extent that they come with systemic risks. Both advanced economies (AEs) and emerging market economies (EMEs) have steadily increased their use of macroprudential measures since the mid-1990s. Empirical evidence suggests that tighter macroprudential policy has improved banking system resilience and that tools such as lower caps on loan-to-value or debt service ratios have helped reduce housing credit and price growth. Evidence shows that while tightening macroprudential policy is generally effective, relaxing it is less so. By moderating fluctuations in general bank credit, housing/household credit, or house prices, macroprudential policy tends to reduce the severity and likelihood of future crises as well as the volatility of growth, but recent studies also show that it slows output growth. As financial globalization progresses, macroprudential policy may become less effective due to regulatory arbitrage and cross-border leakages, especially in financially more open AEs and EMEs, also given the limited scope for international coordination and the lack of an internationally agreed-upon macroprudential framework. To deal with the associated externally driven risks, many EMEs use foreign exchange (FX)-related macroprudential tools, e.g., FX liability-based reserve requirements, limits on currency mismatch or FX positions, and FX liquidity requirements. In EMEs, FX intervention can also play a macroprudential role. In contrast, AEs rarely use such instruments but use macroprudential capital buffers more actively than EMEs. Recent evidence suggests both positive and negative international spillover effects of these and other macroprudential policies. These and other findings point to the need to build formal models that have a macro-financial stability framework (MFSF) at their core. The framework incorporates the relevant policy tools, their individual effects, and interactions, and captures the channels of financial risk-taking and their implications for global and domestic financial conditions. Such an MFSF includes monetary policy to ensure macroeconomic stability and assist with financial stability, fiscal policy to guarantee fiscal sustainability and limit cyclical economic fluctuations, and macroprudential policy to complement monetary and fiscal policies by strengthening the resilience of the financial system and limiting the buildup of financial imbalances. Research is underway to develop such models, but considerable scope for further work in this area exists.


Publication Bias in Asset Pricing Research  

Andrew Y. Chen and Tom Zimmermann

Researchers are more likely to share notable findings. As a result, published findings tend to overstate the magnitude of real-world phenomena. This bias is a natural concern for asset pricing research, which has found hundreds of return predictors and little consensus on their origins. Empirical evidence on publication bias comes from large-scale metastudies. Metastudies of cross-sectional return predictability have settled on four stylized facts that demonstrate publication bias is not a dominant factor: (a) almost all findings can be replicated, (b) predictability persists out-of-sample, (c) empirical t-statistics are much larger than 2.0, and (d) predictors are weakly correlated. Each of these facts has been demonstrated in at least three metastudies. Empirical Bayes statistics turn these facts into publication bias corrections. Estimates from three metastudies find that the average correction (shrinkage) accounts for only 10%–15% of in-sample mean returns and that the risk of inference going in the wrong direction (the false discovery rate) is less than 10%. Metastudies also find that t-statistic hurdles exceed 3.0 in multiple testing algorithms and that returns are 30%–50% weaker in alternative portfolio tests. These facts are easily misinterpreted as evidence of publication bias. Other misinterpretations include the conflating of phrases such as “mostly false findings” with “many insignificant findings,” “data snooping” with “liquidity effects,” and “failed replications” with “insignificant ad-hoc trading strategies.” Cross-sectional predictability may not be representative of other fields. Metastudies of real-time equity premium prediction imply a much larger effect of publication bias, although the evidence is not nearly as abundant as it is in the cross section. Measuring publication bias in areas other than cross-sectional predictability remains an important area for future research.


Stochastic Volatility in Bayesian Vector Autoregressions  

Todd E. Clark and Elmar Mertens

Vector autoregressions with stochastic volatility (SV) are widely used in macroeconomic forecasting and structural inference. The SV component of the model conveniently allows for time variation in the variance-covariance matrix of the model’s forecast errors. In turn, that feature of the model generates time variation in predictive densities. The models are most commonly estimated with Bayesian methods, most typically Markov chain Monte Carlo methods, such as Gibbs sampling. Equation-by-equation methods developed since 2018 enable the estimation of models with large variable sets at much lower computational cost than the standard approach of estimating the model as a system of equations. The Bayesian framework also facilitates the accommodation of mixed frequency data, non-Gaussian error distributions, and nonparametric specifications. With advances made in the 21st century, researchers are also addressing some of the framework’s outstanding challenges, particularly the dependence of estimates on the ordering of variables in the model and reliable estimation of the marginal likelihood, which is the fundamental measure of model fit in Bayesian methods.


Unobserved Components Models  

Joanne Ercolani

Unobserved components models (UCMs), sometimes referred to as structural time-series models, decompose a time series into its salient time-dependent features. These typically characterize the trending behavior, seasonal variation, and (nonseasonal) cyclical properties of the time series. The components are usually specified in a stochastic way so that they can evolve over time, for example, to capture changing seasonal patterns. Among many other features, the UCM framework can incorporate explanatory variables, allowing outliers and structural breaks to be captured, and can deal easily with daily or weekly effects and calendar issues like moving holidays. UCMs are easily constructed in state space form. This enables the application of the Kalman filter algorithms, through which maximum likelihood estimation of the structural parameters are obtained, optimal predictions are made about the future state vector and the time series itself, and smoothed estimates of the unobserved components can be determined. The stylized facts of the series are then established and the components can be illustrated graphically, so that one can, for example, visualize the cyclical patterns in the time series or look at how the seasonal patterns change over time. If required, these characteristics can be removed, so that the data can be detrended, seasonally adjusted, or have business cycles extracted, without the need for ad hoc filtering techniques. Overall, UCMs have an intuitive interpretation and yield results that are simple to understand and communicate to others. Factoring in its competitive forecasting ability, the UCM framework is hugely appealing as a modeling tool.


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.


Corporate Governance Implications of the Growth in Indexing  

Alon Brav, Andrey Malenko, and Nadya Malenko

Passively managed (index) funds have grown to become among the largest shareholders in many publicly traded companies. Their large ownership stakes and voting power have attracted the attention of market participants, academics, and regulators and have sparked an active debate about their corporate governance role. While many studies explore the governance implications of passive fund growth, they often come to conflicting conclusions. To understand how the growth in indexing can affect governance, it is important to understand fund managers’ incentives to be engaged shareholders. These incentives depend on fund managers’ compensation contracts, ownership stakes, assets under management, and costs of engagement. Major passive asset managers, such as the Big Three (BlackRock, State Street, and Vanguard), may have incentives to be engaged even though they track the indices and their engagement efforts benefit all other funds that track the same indices. This is because such funds’ substantial ownership stakes in multiple firms can both increase the effectiveness of their engagement and create relatively large financial benefits from engagement despite the low fees they collect. However, there is a difference between large and small index fund families: the incentives of the latter are likely to be substantially smaller, and the empirical evidence appears to be consistent with this distinction. The governance effects of passive fund growth also depend on where flows to passive funds come from, which investors are replaced by passive funds in firms’ ownership structures, how passive funds interact with other shareholders, and how their growth affects other asset managers’ compensation structures. Considering such aggregate effects and interactions can help reconcile the seemingly conflicting findings in the empirical literature. It also suggests that policymakers should be careful in using the existing studies to understand the aggregate governance effects of passive fund growth over the past decades. Overall, the literature has made important progress in understanding and quantifying passive funds’ incentives to engage, their monitoring activities and voting practices, and their interactions with other shareholders. Based on the findings in the literature, there is yet no clear answer to whether passive fund growth has been beneficial or detrimental for governance, and there are many open questions remaining. These open questions suggest several important directions for future research in this area.