Journal of Urban Economics最新文献

What drives the propagation of discriminatory fake news? To answer this question, this paper focuses on India at the onset of the COVID-19 pandemic: on March 30, a Muslim convention (the Tablighi Jamaat) in New Delhi became publicly recognized as a COVID hotspot. Using Twitter data, we build a comprehensive novel dataset of georeferenced tweets to identify anti-Muslim fake news. First, we document that fake news about Muslims intentionally spreading the virus spiked after March 30. Then, we investigate the geographical and historical determinants of the spread of fake news in a difference-in-difference setting. We find that the diffusion of anti-Muslim false stories was more pronounced (i) in districts closer to New Delhi, suggesting that fake news spread spatially; and (ii) in districts exposed to historical attacks by Muslim groups, suggesting that the propensity to disseminate fake news has deep-rooted historical origins.

We use a housing search model and data on individual home listings to decompose short-run fluctuations in home sales and price growth into supply or demand factors, defined as the number of new sellers and buyers entering the housing market, respectively. We find that fluctuations in the number of buyers demanding homes explain much more of the variation in home sales and price growth than do fluctuations in the supply of homes for sale. In our preferred paramaterization, fluctuations in demand explain essentially all of the variation in home sales, and most of the variation in prices. We consider two implications of these results. First, we show that reduction of supply was a minor factor relative to an increased number of buyers in the tightening of housing markets during COVID-19. New for-sale listings would have had to expand 30 percent to keep the rate of price growth at pre-pandemic levels given the pandemic-era surge in demand. Second, we estimate that the number of buyers demanding homes is very sensitive to changes in mortgage rates, even more so than comparable estimates for home sales, suggesting that policies that affect housing demand through mortgage rates can influence housing market dynamics.

We take advantage of two spatial discontinuities in Britain's Help to Buy (HtB) scheme to explore the effectiveness and distributional implications of mortgage credit expansion policies. Employing a Difference-in-Discontinuities design, we find that HtB significantly increased house prices and had no detectable effect on construction volumes in severely supply constrained and unaffordable Greater London. Conversely, HtB did increase construction numbers without a noticeable effect on prices near the English/Welsh border, an affordable area with comparably lax supply conditions. While HtB did not help would-be-buyers in already unaffordable areas, it boosted the financial performance of developers participating in the scheme.

The subdued wage growth observed in many countries has spurred interest in monopsony views of regional labour markets. This study measures the extent and robustness of employer power and its wage implications exploiting comprehensive matched employer–employee data. We find average (employment-weighted) Herfindhal indices of 800 to 1,100, stable over the 1986–2019 period covered, and that typically less than 8% of workers are exposed to concentration levels thought to raise market power concerns. When controlling for both worker and firm heterogeneity and instrumenting for concentration, we find that wages are negatively affected by employer concentration, with elasticities of around −1.4%. We also find that several methodological choices can change significantly both the measurement of concentration and its wage effects.

I study why some cities have strict land use regulation, how regulation affects the U.S. economy, and how policymakers can mitigate its negative consequences. I develop a quantitative spatial equilibrium model where local regulation is determined endogenously, by voting. Landowners in productive cities with attractive amenities vote for strict regulation. The model accounts for 40% of the observed differences in regulation across cities. Quantitative experiments show that excessive local regulation reduces aggregate productivity, but not necessarily welfare because, unlike renters, landowners benefit from regulation. I propose federal policies that raise productivity and welfare by weakening incentives to regulate land use.

This paper provides a theoretical argument for preferential treatment of shared vehicles (SV) over private ones by municipal parking authorities. When all parked vehicles are treated equally, multiple equilibria may exist: (i) a “private” one, in which travellers are hesitant to switch to SV because the latter are hard to find, and (ii) a “shared” equilibrium, in which travellers use shared mobility because the city is saturated with vacant SV. The latter equilibrium, if it exists, is shown to yield higher welfare. Municipal parking discounts for SV reduce the amount of investment required for a “big push” towards the shared equilibrium, or even make it the only equilibrium.

This paper investigates market response to a nationwide school quality information disclosure regime. Exploiting quasi-exogenous timing of the release of information through the school year, I uncover a number of novel empirical findings. First, investigating the house price margin, the results reveal a strikingly convex hedonic price function: large effects to school quality ratings for homes located near schools serving advantaged students but little impact in poorer neighborhoods. These heterogeneous effects can help reconcile relatively modest overall mean school quality capitalization effects with, for example, the large fees wealthier families typically pay for private schools. Furthermore, the results demonstrate that the impact of credible and low frequency school quality information shocks persists through the medium term. Finally, exploring the impact of the same information intervention on the school choice margin demonstrates that uprated schools serving advantaged students experience dramatic increases in demand from local families, however response for schools serving less advantaged families is more muted.

This paper examines whether homeowner opposition to nearby housing development affects local councillors’ votes on housing bills. Homeowners benefit financially from restricted housing supply through increased housing prices. City councillors, who approve housing development applications, cater to the needs of homeowners who are often long-term resident voters with a financial stake in neighbourhood amenity levels. Using data from Toronto, Canada from 2009 to 2020, we identify housing bills through a machine learning algorithm. We find that councillors who represent more homeowners oppose more housing bills. In particular, councillors are significantly more likely to oppose large housing developments if the project is within their own ward.

How does housing policy influence the long-run distribution of population? We examine the impact on long-term population dynamics of the world’s first large-scale rural public housing scheme, specifically the case of Ireland’s Labourers Acts. We link detailed data on the location of over 45,000 heavily subsidized cottages for agricultural laborers built 1883–1915 in over 200 districts to decennial Censuses between 1841 and 2002. We examine how the density of this social housing affected subsequent population change and find significant persistence in the effect of this treatment on the population. These findings are from specifications that include other factors plausibly related to future population growth, including initial housing stock, land values and population density, as well as distance to urban centres. A causal interpretation is supported by an assessment of pre-trends, by no effect of cottages authorized but not built and by an IV approach that exploits a 1906 limit on legal costs. We also highlight the role of agglomeration in amplifying the impact of the initial investment. Mediation analysis suggests that schooling was a key factor, with districts receiving more cottages less likely to lose primary schools, thus further influencing population growth.

I examine how racial disparities in police use of force vary using new data covering every municipal police department in New Jersey. Along the intensive margin of force severity, I find disparities that disfavor Black subjects and are larger at higher force levels, even after adjusting for incident-level factors and using new techniques to address selection bias. I then extend empirical Bayes methods to estimate department-specific racial disparities and observe significant differences across and within these hundreds of departments. My findings suggest that ignoring heterogeneity in police use of force misrepresents the problem and masks the existence of both departments with very large disparities and those without apparent disparities against Black civilians, but the variation even within departments may make identifying and treating inequitable policing difficult.