51-60 of 362 Results

Article

Behavioral Development Economics  

Karla Hoff and Allison Demeritt

Economics, like all behavioral sciences, incorporates premises about how people think. Behavioral economics emerged in reaction to the extreme assumption in neoclassical economics that agents have unbounded cognitive capacity and exogenous, fixed preferences. There have been two waves of behavioral economics, and both have enriched development economics. The first wave takes into account that cognitive capacity is bounded and that individuals in many situations act predictably irrationally: there are universal human biases. Behavioral development economics in this first wave has shown that low-cost interventions can be “small miracles” that increase productivity and well-being by making it easier for people to make the rational choice. The second wave of behavioral economics explicitly takes into account that humans are products of culture as well as nature. From their experience and exposure to communities, humans adopt beliefs that shape their perception, construals, and behavior. This second wave helps explain why long-run paths of economic development may diverge across countries with different histories. The second wave also suggests a new kind of intervention: Policies that give individuals new experiences or new role models may change their perceptions and preferences. New perceptions and preferences change behavior. This is a very different perspective than that of neoclassical economics, in which changing behavior requires ongoing interventions.

Article

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.

Article

The Academic Effects of United States Child Food Assistance Programs—At Home, School, and In-Between  

Michael D. Kurtz, Karen Smith Conway, and Robert D. Mohr

The primary goals of food assistance programs are to alleviate child hunger and reduce food insecurity; if successful, such programs may have the added benefit of improving child academic outcomes (e.g., test scores, attendance, behavioral outcomes). Some U.S. government programs serve children in the home, such as the Supplemental Nutritional Assistance Program (SNAP), others serve them at school, such as the National School Lunch Program (NSLP) and School Breakfast Program (SBP), and still others fall in-between, such as the Summer Food Service Program (SFSP) and the Child and Adult Care Food Program (CACFP). Most empirical research seeking to identify the causal effect of such programs on child academic outcomes addresses the endogeneity of program participation with a reduced form, intent-to-treat approach. Specifically, such studies estimate the effect of a program’s availability, timing, or other specific feature on the academic outcomes of all potentially affected children. While findings of individual studies and interventions are mixed, some general conclusions emerge. First, increasing the availability of these programs typically has beneficial effects on relatively contemporaneous academic and behavioral outcomes. The magnitudes are modest but still likely pass cost-benefit criteria, even ignoring the fact that the primary objective of such programs is alleviating hunger, not improving academic outcomes. Less is known about the dynamics of the effects, for example, whether such effects are temporary boosts that dissipate or instead accumulate and grow over time. Likewise, the effects of recent innovations to these programs, such as breakfast in the classroom or increases in SNAP benefits to compensate for reduced time in school during the pandemic, yield less clear conclusions (the former) and/or have not been studied (the latter). Finally, many smaller programs that likely target the neediest children remain under- or un-examined. Unstudied government-provided programs include SFSP and CACFP. There are also a growing number of understudied programs provided primarily by charitable organizations. Emerging evidence suggests that one such program, Weekend Feeding or “Backpack” programs, confers substantial benefits. There, too, more work needs to be done, both to confirm these early findings and to explore recent innovations such as providing food pantries or “Kids’ Cafés” on school grounds. Especially in light of the uncertain fate of many pandemic-related program expansions and innovations, current empirical evidence establishes that the additional, beneficial spillover effects to academic outcomes—beyond the primary objective of alleviating food insecurity—deserve to be considered as well.

Article

Governance by Persuasion: Hedge Fund Activism and Market-Based Shareholder Influence  

Alon Brav, Wei Jiang, and Rongchen Li

Hedge fund activism refers to the phenomenon where hedge fund investors acquire a strict minority block of shares in a target firm and then attempt to pressure management for changes in corporate policies and governance with the aim to improve firm performance. This study provides an updated empirical analysis as well as a comprehensive survey of the academic finance research on hedge fund activism. Beginning in the early 1990s, shareholder engagement by activist hedge funds has evolved to become both an investment strategy and a remedy for poor corporate governance. Hedge funds represent a group of highly incentivized, value-driven investors who are relatively free from regulatory and structural barriers that have constrained the monitoring by other external investors. While traditional institutional investors have taken actions ex-post to preserve value or contain observed damage (such as taking the “Wall Street Walk”), hedge fund activists target underperforming firms in order to unlock value and profit from the improvement. Activist hedge funds also differ from corporate raiders that operated in the 1980s, as they tend to accumulate minority equity stakes and do not seek direct control. As a result, activists must win support from fellow shareholders via persuasion and influence, representing a hybrid internal-external role in a middle-ground form of corporate governance. Research on hedge fund activism centers on how it impacts the target company, its shareholders, other stakeholders, and the capital market as a whole. Opponents of hedge fund activism argue that activists focus narrowly on short-term financial performance, and such “short-termism” may be detrimental to the long-run value of target companies. The empirical evidence, however, supports the conclusion that interventions by activist hedge funds lead to improvements in target firms, on average, in terms of both short-term metrics, such as stock value appreciation, and long-term performance, including productivity, innovation, and governance. Overall, the evidence from the full body of the literature generally supports the view that hedge fund activism constitutes an important venue of corporate governance that is both influence-based and market-driven, placing activist hedge funds in a unique position to reduce the agency costs associated with the separation of ownership and control.

Article

Housing Policy and Affordable Housing  

Christian A.L. Hilber and Olivier Schöni

Lack of affordable housing is a growing and often primary policy concern in cities throughout the world. The main underlying cause for the “affordability crisis,” which has been mounting for decades, is a combination of strong and growing demand for housing in desirable areas in conjunction with tight long-term supply constraints—both physical and man-made regulatory ones. The affordability crisis tends to predominately affect low- and moderate-income households. Increasingly, however, middle-income households—which do not usually qualify for government support—are similarly affected. Policies that aim to tackle the housing affordability issue are numerous and differ enormously across countries. Key policies include mortgage subsidies, government equity loans, rent control, social or public housing, housing vouchers, low-income tax credits, and inclusionary zoning, among others. The overarching aim of these policies is to (a) reduce the periodic housing costs of or (b) improve access to a certain tenure mode for qualifying households. Existing evidence reveals that the effectiveness and the distributional and social welfare effects of housing policies depend not only on policy design but also on local market conditions, institutional settings, indirect (dis)incentives, and general equilibrium adjustments. Although many mainstream housing policies are ineffective, cost-inefficient, and/or have undesirable distributional effects from an equity standpoint, they tend to be politically popular. This is partly because targeted households poorly understand adverse indirect effects, which is exploited by vote-seeking politicians. Partly, it is because often the true beneficiaries of the policies are the politically powerful existing property owners (homeowners and landlords), who are not targeted but nevertheless benefit from positive policy-induced house price and rent capitalization effects. The facts that existing homeowners often have a voter majority and landlords additionally may be able to influence the political process via lobbying lead to the conundrum of ineffective yet politically popular housing policies. In addition to targeted policies for individuals most in need (e.g., via housing vouchers or by providing subsidized housing), the most effective policies to improve housing affordability in superstar cities for all income groups might be those that focus on the root causes of the problem. These are (a) the strongly and unequally growing demand for housing in desirable markets and (b) tight land use restrictions imposed by a majority of existing property owners that limit total supply of housing in these markets. Designing policies that tackle the root causes of the affordability crisis and help those in need, yet are palatable to a voter majority, is a major challenge for benevolent policymakers.

Article

International Trade and the Environment: Three Remaining Empirical Challenges  

Jevan Cherniwchan and M. Scott Taylor

Considerable progress has been made in understanding the relationship between international trade and the environment since Gene Grossman and Alan Krueger published their now seminal working paper examining the potential environmental effects of the North American Free Trade Agreement in 1991. Their work articulated a simple framework through which international trade and economic growth could affect the environment by impacting: the scale of economic activity (the scale effect), the composition of production across industries (the composition effect), or the emission intensity of individual industries (the technique effect). GK provided preliminary evidence of the relative magnitudes of the scale, composition and technique effects, and reached a striking conclusion: international trade would not necessarily harm the environment. Much of the subsequent literature examining the effects of international trade and the environment has adopted Grossman and Krueger’s simple framework and builds directly from their initial foray into the area. We now have better empirical evidence of the relationship between economic growth and environmental quality, of how environmental regulations affect international trade and investment flows, and of the relative magnitudes of the scale, composition and technique effects. Yet, the need for further progress remains along three key fronts. First, despite significant advances in our understanding of how economic growth affects environmental quality, evidence of the interaction between international trade, economic growth, and environmental outcomes remains scarce. Second, while a growing body of evidence suggests that environmental regulations significantly alter trade flows, it is still unclear if these policies have a larger or smaller effect than traditional determinants of comparative advantage. Third, although it is clear the technique effect is the primary driver of changes in pollution, evidence as to how trade has contributed to the technique effect is limited. Addressing these Three Remaining Challenges is necessary for assessing whether Grossman and Krueger’s conclusion that international trade need not necessarily harm the environment still holds today.

Article

Sparse Grids for Dynamic Economic Models  

Johannes Brumm, Christopher Krause, Andreas Schaab, and Simon Scheidegger

Solving dynamic economic models that capture salient real-world heterogeneity and nonlinearity requires the approximation of high-dimensional functions. As their dimensionality increases, compute time and storage requirements grow exponentially. Sparse grids alleviate this curse of dimensionality by substantially reducing the number of interpolation nodes, that is, grid points needed to achieve a desired level of accuracy. The construction principle of sparse grids is to extend univariate interpolation formulae to the multivariate case by choosing linear combinations of tensor products in a way that reduces the number of grid points by orders of magnitude relative to a full tensor-product grid and doing so without substantially increasing interpolation errors. The most popular versions of sparse grids used in economics are (dimension-adaptive) Smolyak sparse grids that use global polynomial basis functions, and (spatially adaptive) sparse grids with local basis functions. The former can economize on the number of interpolation nodes for sufficiently smooth functions, while the latter can also handle non-smooth functions with locally distinct behavior such as kinks. In economics, sparse grids are particularly useful for interpolating the policy and value functions of dynamic models with state spaces between two and several dozen dimensions, depending on the application. In discrete-time models, sparse grid interpolation can be embedded in standard time iteration or value function iteration algorithms. In continuous-time models, sparse grids can be embedded in finite-difference methods for solving partial differential equations like Hamilton-Jacobi-Bellman equations. In both cases, local adaptivity, as well as spatial adaptivity, can add a second layer of sparsity to the fundamental sparse-grid construction. Beyond these salient use-cases in economics, sparse grids can also accelerate other computational tasks that arise in high-dimensional settings, including regression, classification, density estimation, quadrature, and uncertainty quantification.

Article

Score-Driven Models: Methodology and Theory  

Mariia Artemova, Francisco Blasques, Janneke van Brummelen, and Siem Jan Koopman

Score-driven models belong to a wider class of observation-driven time series models that are used intensively in empirical studies in economics and finance. A defining feature of the score-driven model is its mechanism of updating time-varying parameters by means of the score function of the predictive likelihood function. The class of score-driven models contains many other well-known observation-driven models as special cases, and many new models have been developed based on the score-driven principle. Score-driven models provide a general way of parameter updating, or filtering, in which all relevant features of the observation density function are considered. In models with fat-tailed observation densities, the score-driven updates are robust to large observations in time series. This kind of robustness is a convenient feature of score-driven models and makes them suitable for applications in finance and economics, where noisy data sets are regularly encountered. Parameter estimation for score-driven models is straightforward when the method of maximum likelihood is used. In many cases, theoretical results are available under rather general conditions.

Article

Score-Driven Models: Methods and Applications  

Mariia Artemova, Francisco Blasques, Janneke van Brummelen, and Siem Jan Koopman

The flexibility, generality, and feasibility of score-driven models have contributed much to the impact of score-driven models in both research and policy. Score-driven models provide a unified framework for modeling the time-varying features in parametric models for time series. The predictive likelihood function is used as the driving mechanism for updating the time-varying parameters. It leads to a flexible, general, and intuitive way of modeling the dynamic features in the time series while the estimation and inference remain relatively simple. These properties remain valid when models rely on non-Gaussian densities and nonlinear dynamic structures. The class of score-driven models has become even more appealing since the developments in theory and methodology have progressed rapidly. Furthermore, new formulations of empirical dynamic models in this class have shown their relevance in economics and finance. In the context of macroeconomic studies, the key examples are nonlinear autoregressive, dynamic factor, dynamic spatial, and Markov-switching models. In the context of finance studies, the major examples are models for integer-valued time series, multivariate scale, and dynamic copula models. In finance applications, score-driven models are especially important because they provide particular updating mechanisms for time-varying parameters that limit the effect of the influential observations and outliers that are often present in financial time series.

Article

The Costs of Bankruptcy Restructuring  

Wei Wang

Financially distressed and insolvent firms file for bankruptcy to either reorganize or liquidate under court supervision. Fundamentally, bankruptcy law is designed to resolve creditor coordination and holdout problems. It not only sets up rules and guidelines to allow firms to restructure their debt claims but also provides means for firms to reallocate their assets to other users. Although an efficient bankruptcy system can help mitigate bargaining frictions and maximize asset value and thus creditor recovery by avoiding inefficient liquidation or excess continuation, the bankruptcy process itself can be costly. Understanding and quantifying the costs of bankruptcy restructuring are important not only to financially distressed firms but also to the capital structure decisions and the pricing of securities of healthy firms. More broadly, efficient bankruptcy mechanisms are important for economic growth, the productivity of firms in an economy, and the resiliency of the economy to adverse shocks. From the 1990s through the 2020s, the literature has flourished, with a growing number of empirical studies investigating the efficiency of the bankruptcy system and different aspects of bankruptcy costs. Bankruptcy costs are typically classified as either direct or indirect costs. The former refers to out-of-pocket expenses associated with the retention of professionals, while the latter refers to opportunity costs incurred as a result of the adverse effect of a bankruptcy filing on business operations, human capital, and investments. Indirect costs are typically larger and more difficult to measure and quantify than direct costs, which studies show to be a small fraction of a bankrupt firm’s assets. Because of significant economic frictions such as conflicts of interest, information asymmetry, and judicial biases presented in the system, bankruptcy can be a lengthy process. Since delay allows both direct and indirect costs to accumulate, a number of studies show that shortening the bargaining process can effectively help preserve firm value. Besides delay, bankruptcy costs can be manifested in inefficient liquidation, excess continuation, fire sales, loss of human capital, and managerial turnover, which impose real costs on bankrupt firms. How to mitigate frictions and minimize costs has been the central theme of bankruptcy research from the 1990s through the 2020s, a time that has also witnessed several notable changes to the U.S. bankruptcy system, including the rise of specialized distressed investors, the strengthening of secured creditor control rights, and the increasing intensity of asset sales. These changes have important implications for the restructuring landscape.