Cities, matching and the productivity gains of agglomeration
By Fredrick Andersson, Simon Burgess, J. Lane. 2004
185 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/1b1e7e7f875e22ddb762bd31080434d0eb272860
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Gilles Duranton
- W. Strange
- Enrico Moretti
- Guido Blasio
- Elisabet Viladecans-Marsal
- Jordi Jofre-Monseny
- Raquel Marín-López
- Bernardo S. Blum
In Sociology and Political Science
- W. Strange
- Elisabet Viladecans-Marsal
- Enrico Moretti
- Guido Blasio
- Gilles Duranton
- Lance Lochner
- Michael Grossman
- Eric A. Hanushek
Citations in the corpus
- Personnel Economics: Hiring and Incentives (2010, unknown): “Andersson et al. (2007) put this same basic idea to somewhat more rigorous scrutiny.”
- Local Labor Markets (2010, background): “45 Andersson et al. (2007) show that thicker urban labor markets are associated with more assortative matching between”
- The mechanisms of agglomeration: Evidence from the effect of inter-industry relations on the location of new firms (2011, background): “Costa and Khan (2000) and Andersson et al (2007) have shown that employee-employer matches are better in densely populated areas.”
- Marshallian Labour Market Pooling: Evidence From Italy (2013, background): “With regard to matching, Andersson et al. (2007) find evidence of stronger positive assortative matching in larger markets, while Di Addario (2011), using Italian data, finds a greater rate of transitions from unemployment to employment.”
- Skills in the city (2009, background): “Skills also influence the matching of workers to jobs (e.g., Andersson et al., 2007) and turnover (e.g., Fallick et al., 2006). jobs.”
- The Empirics of Agglomeration Economies (2014, unknown): “Andersson et al. (2007) use matched worker-firm panel data on California and Florida to estimate a wage equation involving worker and firm fixed effects. They then compute for each county the correlation across firms between the firm fixed effect and the average worker fixed effect within the firm. ”