The promise and perils of the sharing economy
The impact of Airbnb lettings on crime
Datos Bibliográficos
| ID | 9680392 |
|---|---|
| Autores | Charles C Lanfear (0000-0001-5712-757X, Institute of Criminology University of Cambridge Cambridge UK, autor de correspondencia), David S Kirk (0000-0003-0037-4291, Department of Criminology University of Pennsylvania Philadelphia Pennsylvania USA) |
| Año | 2024 |
| Volumen | 62 |
| Número | 4 |
| Páginas | 769-798 |
| Fecha de publicación | 2024-11-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Criminology (JOURNAL) |
| Identificadores de la revista | ISSN: 0011-1384 • E-ISSN: 1745-9125 |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/1745-9125.12383 |
| OpenAlex | W4403739848 |
| Idioma | EN |
| Citas recibidas | 3 |
| Referencias citadas | 57 |
Private short‐term letting via Airbnb has exploded in the last decade, yet little is known about how this affects neighborhood crime rates. We estimate the association between Airbnb short‐term letting activity and six types of police‐reported crime in London, as well as an intervening mechanism, collective efficacy. We estimate these associations with maximum likelihood dynamic panel models with fixed effects (ML‐SEM) using data on Airbnb lettings in 4,835 London neighborhoods observed for 13 calendar quarters. We explore mechanisms for the observed effects using multiple lag specifications and by disaggregating lettings into entire properties and spare rooms. We find that Airbnb activity is positively related to robbery, burglary, theft, and violence. These associations are attributable to lettings for entire properties rather than for rooms. Furthermore, associations are contemporaneous, as is consistent with an opportunity mechanism, rather than delayed, as would be consistent with a social control mechanism. Similarly, we find that the association between Airbnb activity and crime is not mediated by collective efficacy. Overall, these results suggest short‐term letting contributes to neighborhood crime and these effects are more likely to be attributable to changes in criminal opportunity than erosion of neighborhood social control
Business · Criminology · Economic geography · Economics · Economy · Gig economy · Political science · Sharing economy · Sociology · Gambling Behavior and Treatments · Law · Sexuality, Behavior, and Technology · Sharing Economy and Platforms
Social sources of delinquency
Armed Robbers in Action
No Place Like Home
Criminality of place
Commercial Density, Residential Concentration, and Crime
The Effect of Home-Sharing on House Prices and Rents
Cutoff criteria for fit indexes in covariance structure analysis
Some Tests of Specification for Panel Data
Another look at the instrumental variable estimation of error-components models
Hierarchical linear models
Who ‘Tweets’ Where and When, and How Does it Help Understand Crime Rates at Places? Measuring the Presence of Tourists and Commuters in Ambient Populations
Assessing the impacts of Airbnb listings on London house prices
The local structures of human mobility in Chicago
Pockets of Crime
Robberies in Chicago
Gentrification, Land Use, and Crime
Broken Windows, Informal Social Control, and Crime
UK open source crime data
A Note on the Theme of Too Many Instruments
Neighborhood Change and Crime in the Modern Metropolis
Collective efficacy
Urban Revitalization and Seattle Crime, 1982−2000
How Collective Is Collective Efficacy? The Importance of Consensus in Judgments About Community Cohesion and Willingness to Intervene
Collective efficacy and the built environment
Collective Efficacy, Deprivation and Violence in London
Bridging Structure and Perception
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data
Social Change and Crime Rate Trends
Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects
How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study
Measuring Collective Efficacy
What You Can-and Can't-Do With Three-Wave Panel Data
Systematic Social Observation of Public Spaces
Growing pains or appreciable gains? Latent classes of neighborhood change, and consequences for crime in Southern California neighborhoods
| Obras citantes distintas | 3 |
|---|---|
| Citas por año | 3 |
| Intervalo de citas | 2025 - 2026 (2) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 2 |