61-70 of 368 Results

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.

Article

Applications of Web Scraping in Economics and Finance  

Piotr Śpiewanowski, Oleksandr Talavera, and Linh Vi

The 21st-century economy is increasingly built around data. Firms and individuals upload and store enormous amount of data. Most of the produced data is stored on private servers, but a considerable part is made publicly available across the 1.83 billion websites available online. These data can be accessed by researchers using web-scraping techniques. Web scraping refers to the process of collecting data from web pages either manually or using automation tools or specialized software. Web scraping is possible and relatively simple thanks to the regular structure of the code used for websites designed to be displayed in web browsers. Websites built with HTML can be scraped using standard text-mining tools, either scripts in popular (statistical) programming languages such as Python, Stata, R, or stand-alone dedicated web-scraping tools. Some of those tools do not even require any prior programming skills. Since about 2010, with the omnipresence of social and economic activities on the Internet, web scraping has become increasingly more popular among academic researchers. In contrast to proprietary data, which might not be feasible due to substantial costs, web scraping can make interesting data sources accessible to everyone. Thanks to web scraping, the data are now available in real time and with significantly more details than what has been traditionally offered by statistical offices or commercial data vendors. In fact, many statistical offices have started using web-scraped data, for example, for calculating price indices. Data collected through web scraping has been used in numerous economic and finance projects and can easily complement traditional data sources.

Article

Gene–Environment Interplay in the Social Sciences  

Rita Dias Pereira, Pietro Biroli, Titus Galama, Stephanie von Hinke, Hans van Kippersluis, Cornelius A. Rietveld, and Kevin Thom

Nature (one’s genes) and nurture (one’s environment) jointly contribute to the formation and evolution of health and human capital over the life cycle. This complex interplay between genes and environment can be estimated and quantified using genetic information readily available in a growing number of social science data sets. Using genetic data to improve our understanding of individual decision making, inequality, and to guide public policy is possible and promising, but requires a grounding in essential genetic terminology, knowledge of the literature in economics and social-science genetics, and a careful discussion of the policy implications and prospects of the use of genetic data in the social sciences and economics.

Article

Maternity Leave and Paternity Leave: Evidence on the Economic Impact of Legislative Changes in High-Income Countries  

Serena Canaan, Anne Sophie Lassen, Philip Rosenbaum, and Herdis Steingrimsdottir

Labor market policies for expecting and new mothers emerged at the turn of the 19th century. The main motivation for these policies was to ensure the health of mothers and their newborn children. With increased female labor market participation, the focus has gradually shifted to the effects that parental leave policies have on women’s labor market outcomes and gender equality. Proponents of extending parental leave rights for mothers in terms of duration, benefits, and job protection have argued that this will support mothers’ labor market attachment and allow them to take time off from work after childbirth and then safely return to their pre-birth jobs. Others have noted that extended maternity leave can work as a double-edged sword for mothers: If young women are likely to spend months, or even years, on leave, employers are likely to take that into consideration when hiring and promoting their employees. These policies may therefore end up adversely affecting women’s labor market outcomes. This has led to an increased focus on activating fathers to take parental leave, and in 2019, the European Parliament approved a directive requiring member states to ensure at least 2 months of earmarked paternity leave. The literature on parental leave has proliferated during the past two decades. The increased number of studies on the topic has brought forth some consistent findings. First, the introduction of short maternity leave is beneficial for both maternal and child health and for mothers’ labor market outcomes. Second, there appear to be negligible benefits from a leave extending beyond 6 months in terms of health outcomes and children’s long-term outcomes. Furthermore, longer leaves have little, or even adverse, influence on mothers’ labor market outcomes. However, evidence suggests that there may be underlying heterogeneous effects from extended leave among different socioeconomic groups. The literature on the effect of earmarked paternity leave indicates that these policies are effective in increasing fathers’ leave-taking and involvement in child care. However, the evidence on the influence of paternity leave on gender equality in the labor market remains scarce and is somewhat mixed. Finally, recent studies that focus on the effect of parental leave policies for firms find that in general, firms are able to compensate for lost labor when their employees go on leave. However, if firms face constraints when replacing employees, it could negatively influence their performance.

Article

Trade Shocks and Labor-Market Adjustment  

John McLaren

When international trade increases, either because of a country’s lowering its trade barriers, a trade agreement, or productivity surges in a trade partner, the surge of imports can cause dislocation and lowered incomes for workers in the import-competing industry or the surrounding local economy. Trade economists long used static approaches to analyze these effects on workers, assuming either that workers can adjust instantly and costlessly, or (less often) that they cannot adjust at all. In practice, however, workers incur costs to adjust, and the adjustment takes time. An explosion of research, mostly since about 2008, has explored dynamic worker adjustment through change of industry, change of occupation, change of location, change of labor-force participation, adjustment to change in income, and change in marital status or family structure. Some of these studies estimate rich structural models of worker behavior, allowing for such factors as sector-specific or occupation-specific human capital to accrue over time, which can be imperfectly transferable across industries or occupations. Some allow for unobserved heterogeneity across workers, which creates substantial technical challenges. Some allow for life-cycle effects, where adjustment costs vary with age, and others allow adjustment costs to vary by gender. Others simplify the worker’s problem to embed it in a rich general equilibrium framework. Some key results include: (a) Switching either industry or occupation tends to be very costly; usually more than a year’s average wages on average. (b) Given that moving costs change over time and workers are able to time their moves, realized costs are much lower, but the result is gradual adjustment, with a move to a new steady state that typically takes several years. (c) Idiosyncratic shocks to moving costs are quantitatively important, so that otherwise-identical workers often are seen moving in opposite directions at the same time. These shocks create a large role for option value, so that even if real wages in an industry are permanently lowered by a trade shock, a worker initially in that industry can benefit. This softens or reverses estimates of worker losses from, for example, the China shock. (d) Switching costs vary greatly by occupation, and can be very different for blue-collar and white-collar workers, for young and old workers, and for men and women. (e) Simple theories suggest that a shock results in wage overshooting, where the gap in wages between highly affected industries and others opens up and then shrinks over time, but evidence from Brazil shows that at least in some cases the wage differentials widen over time. (f) Some workers adjust through family changes. Evidence from Denmark shows that some women workers hit by import shocks withdraw from the labor market at least temporarily to marry and have children, unlike men. Promising directions at the frontier include more work on longitudinal data; the role of capital adjustment; savings, risk aversion and the adjustment of trade deficits; responses in educational attainment; and much more exploration of the effects on family.