Overrules
The Cumulative Exposure to Exclusionary Zoning in Impoverished Neighborhoods
Matthew Mleczko
Demography, February 2026, Pages 213-239
Abstract:
In this study, I carry out dynamic modeling strategies to estimate the cumulative associations between exclusionary zoning and material hardship in impoverished neighborhoods. To do so, I create the largest nationwide panel zoning database to date by combining the National Zoning and Land Use Database covering the years 2019‒2022 with prior zoning and land use data from 2003‒2006. Accounting for posttreatment bias -- the bias generated by including time-varying confounders that are themselves affected by past treatments in a longitudinal model -- with marginal structural models, I demonstrate that exclusionary zoning is strongly associated with neighborhood disadvantage in impoverished neighborhoods, much more than would be uncovered using fixed effects or other modeling approaches. Exclusionary zoning is associated with higher median rents and higher shares of rent-burdened households in impoverished communities. Higher rents may be generated by higher housing prices as well as by a shortage of housing available to below-median income households throughout a metro area. These results suggest that exclusionary zoning policies may not only contribute to concentrated advantage in affluent areas but also have spillover effects that have negative long-run consequences for disadvantaged neighboring communities.
Quantifying Deregulation and its Economic Effects: A Large Language Model Approach
Danilo Cascaldi-Garcia & Matteo Iacoviello
Federal Reserve Working Paper, March 2026
Abstract:
We construct a news-based index of deregulation for the United States from 1960 through 2025 using AI to semantically classify newspaper articles. We distinguish articles discussing deregulation from those discussing increased regulation, assigning intensity scores that reflect both the centrality of deregulatory content and whether articles discuss advocacy, proposals, or enacted measures. Human validation confirms strong agreement between AI and human classifications. The deregulation index captures major reform episodes including transportation and telecommunications liberalization in the 1970s--1980s, financial deregulation in the 1980s-1990s, and recent deregulatory activity. We decompose the index by sector, type of deregulation, and policy stage. We validate the news-based index against a parallel index constructed using Federal Register documents: the news-based index leads the Federal Register index by nearly one year, consistent with media coverage reflecting policy intentions before formal implementation. Unlike measures based on detailed statutory coding or Federal Register counts that weigh all rules equally, our approach covers the entire economy and weighs naturally by newsworthiness, capturing regulatory shifts before they materialize in law. Positive shocks to deregulation boost investment, productivity, stock prices, profits, and GDP. Industry-specific deregulation shocks boost industry-level stock returns, consistent with our finding that deregulation involves measures that may impact incumbent profitability and operational efficiency more than competitive entry.
Regulatory Stringency and Industry Performance: Evidence from RegData U.S. 6.0
Christos Makridis & Patrick McLaughlin
Stanford Working Paper, February 2026
Abstract:
This paper studies the economic incidence of broad federal regulation across U.S. industries using RegData U.S. 6.0. We measure regulatory stringency using binding language density -- expected regulatory restrictions per 1,000 words -- constructed from the text of the Code of Federal Regulations and mapped to NAICS industries using supervised relevance weights. Using the 1970-2025 regulatory panel, we document three stylized facts: (1) binding density evolves through many small year-to-year adjustments and a recent plateauing in 2025; (2) short-run changes are across industries rather than concentrated in a small set of sectors; and (3) annual changes are weakly related to baseline industry size and productivity. We also validate the normalized measure by showing that treated industries exhibit clear and persistent increases in binding density around major legislative events. Linking within-industry variation in binding density to Bureau of Economic Analysis measures of real output and real value added for three-digit NAICS industries over 1998-2024, we find that a one-unit increase in restrictions per 1,000 words is associated with approximately a 0.7 percent decline in real output and a 1.2 percent decline in real value added. The relationship persists in distributed-lag specifications and is robust across alternative functional forms. These findings suggest that normalized regulatory text captures economically meaningful shifts in the regulatory environment and provides a scalable framework for studying their economic effects.
Under the (Neighbor)Hood: Understanding Interactions Among Zoning Regulations
Amrita Kulka, Aradhya Sood & Nicholas Chiumenti
Review of Economics and Statistics, forthcoming
Abstract:
We study how various zoning regulations combine to affect housing supply, prices, and rents of single- and multifamily homes using novel lot-level zoning data from Greater Boston and a cross-sectional boundary discontinuity design at regulation boundaries. Looser density restrictions, alone or with other less restrictive regulations, are most effective in increasing supply and reducing per-housing-unit rents and prices. We theoretically and empirically show that restrictive zoning regulations shift housing stock towards larger units, increasing prices per housing unit. Counterfactuals imply that a recent Massachusetts law increasing building density near transit can reduce long-run rents and prices, particularly in suburbs.
Limiting Accessibility: How Targeting Consumers with Disabilities Constrains Acceptable Prices for Innovations
Musa Essa, Johannes Boegershausen & Gabriele Paolacci
Journal of Consumer Research, forthcoming
Abstract:
People with disabilities constitute 15% of the world’s population with a total disposable income of more than $2.6 trillion. However, few companies offer products tailored to the needs of this segment, making it important to understand how mass-market consumers react to innovations that target people with disabilities. Nine studies and six supplementary studies (twelve preregistered) reveal that innovations targeting consumers with disabilities are subject to comparatively greater scrutiny by mass-market consumers. Specifically, consumers find charging price premiums for innovative products less acceptable when they are targeted at people with disabilities. The aversion to targeting this segment occurs only when firms charge a price premium and persists even when firms provide cost justifications for the relatively higher prices. Drawing on research on disability stereotypes, we identify pity for people with disabilities as a critical driver of these reactions. Variations in pity across disabilities are related to the acceptability of a price premium for adaptive innovations. These findings are suggestive of a novel form of paternalism against consumers with disabilities. Paradoxically, this view may render the marketplace less inclusive for consumers with disabilities, as it could penalize companies that provide more options for this underserved segment.
Identifying Uncertainty, Learning about Productivity, and Human Capital Acquisition: A Reassessment of Labor Market Sorting and Firm Monopsony Power
Cristina Gualdani et al.
NBER Working Paper, March 2026
Abstract:
We examine the empirical content of a large class of dynamic matching models of the labor market with ex-ante heterogeneous firms and workers, symmetric uncertainty and learning about workers’ productivity, and firms’ monopsony power. We allow workers’ human capital, acquired before and after entry into the labor market, to be general across firms to varying degrees. Such a framework nests and extends known models of worker turnover across firms, occupational choice, wage growth, wage differentials across occupations, firms, and industries, and wage dispersion across workers and over the life cycle. We establish intuitive conditions under which the model primitives are semiparametrically identified solely from data on workers’ wages and jobs, despite the dynamics of these models giving rise to complex patterns of selection based on endogenously time-varying observable and unobservable characteristics of workers and firms. By relying on this identification argument, we develop a constructive estimator of the model primitives, which builds on common methods for mixture and extremal quantile regression models and displays standard properties. Through the lens of this framework, we investigate how well typical empirical wage measures of matching assortativeness and firms’ wage-setting power detect the degrees of sorting and monopsony power in the labor market, respectively. We show that usual measures of sorting severely understate its importance because they ignore the option value of worker human capital and the information about worker productivity acquired through employment, in terms of higher future wages and improved future sorting, which is priced into current wages thus depressing them. We also demonstrate how the markdown of wages relative to output largely overstates firms’ labor market power by ignoring that this option value, which captures future returns from acquired human capital and information, generally lowers wages. We find evidence of both of these features in U.S. data by documenting a strong degree of labor market sorting once appropriately measured and, correspondingly, a lower degree of firm monopsony power than typically documented.
Upzoning with strings attached: Evidence from Seattle’s affordable housing mandate
Jacob Krimmel & Betty Wang
Regional Science and Urban Economics, June 2026
Abstract:
This paper evaluates the effects of Seattle’s Mandatory Housing Affordability (MHA) program, a large-scale 2019 land use reform that simultaneously increased allowable housing density and required new developments to include or fund affordable housing. Covering more than one-quarter of Seattle’s residential land, MHA represents one of the largest U.S. efforts to link upzoning with inclusionary requirements. Using a difference-in-differences framework comparing parcels just inside versus just outside MHA boundaries, we find that permitting activity declined within MHA zones as developers shifted projects to avoid affordability mandates. Declines were concentrated among low-intensity rezonings, indicating modest increases in allowable density did not offset added costs. By contrast, the small share of areas subject to high-intensity upzonings saw increased permitting, achieving the intended effect of the policy change. Together, our findings highlight the tension between promoting density and mandating affordable housing, underscoring the need to calibrate inclusionary upzoning so that it expands rather than constrains housing supply.
Eminent Domain Takings and Economic Development: The Effect of State Restrictions on Metropolitan Area Economic Development
Paul Byrne
Economic Development Quarterly, forthcoming
Abstract:
The Supreme Court's Kelo decision upheld local governments’ right to use eminent domain in furtherance of an economic development plan under the premise that their public purpose includes the jobs and tax revenue generated by such developments. Following Kelo, 21 states effectively banned economic development as a justification for eminent domain condemnations. Whereas previous research examined the impact of eminent domain restrictions at the state level, this paper's focus is at the metropolitan-area level, where the inefficient underassembly of property that eminent domain is meant to correct is most acute. Difference-in-differences methods found that metropolitan areas in states that restrict the use of eminent domain experience statistically significant negative treatment effects on employment and earnings following the restrictions. As four Supreme Court justices have indicated a willingness to reconsider Kelo, these findings provide further insight into the economic assumptions supporting the ruling.
Copyright Policy Options for Generative Artificial Intelligence
Joshua Gans
Journal of Law and Economics, February 2026, Pages 1-19
Abstract:
New generative artificial intelligence (AI) models have created new challenges for copyright policy as such models may be trained on data that include copy-protected content. This paper examines this issue from an economic perspective and analyzes how different copyright regimes for generative AI will impact the quality of content generated and AI training. Because of transaction costs (for example, because of the large amount of content being used to train generative AI models), it is not possible for copyright holders and AI providers to engage in negotiations. The result is a characterization of the factors that would favor full copyright and no copyright protections, balancing the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex post mechanism like fair use can lead to higher expected social welfare than traditional rights regimes.
Specialization in a Knowledge Economy
Yueyuan Ma
Journal of Political Economy Macroeconomics, March 2026, Pages 48-96
Abstract:
Using US census data, this paper exhibits novel specialization patterns of US firms in the 1980s and 1990s. (1) Firms, especially innovating ones, decreased their production scope. (2) Small firms specialized in innovation and large firms in production. An endogenous growth model is developed, with potential mismatches between innovation and production. Calibrating the model suggests that higher patent trading efficiency and stronger patent protection explain 20% of the production scope decrease, 108% of the innovation-production separation, and a 0.64 percentage point increase in the annual growth rate. Empirical analyses suggest that propatent reforms have contributed to the two specialization patterns.
Pricing Protection: Credit Scores, Disaster Risk, and Home Insurance Affordability
Joshua Blonz et al.
NBER Working Paper, February 2026
Abstract:
We use 70 million policies linked to mortgages and property-level disaster risk to show that credit scores impact homeowners insurance premiums as much as disaster risk. Homeowners with low credit pay 24% more for identical coverage than high–credit score homeowners. Leveraging a natural experiment in Washington State, we find that banning the use of credit information considerably weakens the relationship between credit score and pricing. We discuss the role of credit information in pricing and show that, although insurance is often overlooked in discussions of home affordability, a low credit score increases premiums roughly as much as it raises mortgage rates.