This paper reviews recent quantitative urban models. These models are sufficiently rich to capture observed features of the data, such as many asymmetric locations and a rich geography of the transport network. Yet these models remain sufficiently tractable as to permit an analytical characterization of their theoretical properties. With only a small number of structural parameters (elasticities) to be estimated, they lend themselves to transparent identification. As they rationalize the observed spatial distribution of economic activity within cities, they can be used to undertake counterfactuals for the impact of empirically-realistic public-policy interventions on this observed distribution. Empirical applications include estimating the strength of agglomeration economies and evaluating the impact of transport infrastructure improvements (e.g., railroads, roads, Rapid Bus Transit Systems), zoning and land use regulations, place-based policies, and new technologies such as remote working.
Authors
- Acknowledgements & Disclosure
- I am grateful to Princeton University for research support. This paper was commissioned for the Handbook of Regional and Urban Economics. An accompanying Online Appendix contains the technical derivations of all results. A toolkit with the Matlab code for the baseline quantitative urban model in Section 4 is available from https://www.quantitativeurbanmodels.com/. I am grateful to Arthur Adam and Zhuokai Huang for excellent research assistance. I would like to thank Gabriel Ahlfeldt, Don Davis, Jonathan Dingel, Dave Donaldson, Gilles Duranton, Stephan Heblich, Ferdinando Monte, Esteban Rossi-Hansberg, Daniel Sturm and Felix Tintelnot for helpful comments and discussion. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.
- DOI
- https://doi.org/10.3386/w33130
- Pages
- 76
- Published in
- United States of America
Table of Contents
- NBER WORKING PAPER SERIES 1
- QUANTITATIVE URBAN ECONOMICS 1
- Stephen J. Redding 1
- Working Paper 33130 httpwww.nber.orgpapersw33130 1
- NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge MA 02138 November 2024 1
- Quantitative Urban Economics Stephen J. Redding NBER Working Paper No. 33130 November 2024 JEL No. R32 R41 R52 2
- Stephen J. Redding Department of Economics School of Public and International Affairs Princeton University Princeton NJ 08544 and CEPR and also NBER reddingsprinceton.edu 2
- 1 Introduction 3
- 2 Motivating Evidence 7
- 2.1 Land Prices 7
- 2.2 Population Density 8
- 2.3 Workers and Residents 10
- 2.4 Gravity in Commuting 11
- 3 Traditional Theoretical Literature 13
- 3.1 Alonso-Muth-Mills Monocentric Cities 13
- 3.2 Non-monocentric Cities 14
- 4 Baseline Quantitative Urban Model 14
- L L 15
- 4.1 Workplace-Residence Choices 15
- Q w κ 15
- B b ω 16
- B B 16
- B B 16
- R i η δ 16
- G b e ϵ 16
- B w κ P Q 17
- L B w κ P Q 17
- B w κ P Q 17
- L n i L 17
- L B P Q 17
- B P Q 17
- R n 17
- L w 17
- B κ P Q 17
- L i 17
- U u ϑ 17
- B w κ P Q 17
- B w κ P Q 18
- B w κ P Q 18
- 4.2 Production 18
- A w q β 18
- A q 18
- A A 18
- A A 18
- 4.3 Commuter Market Clearing 19
- 4.4 Land Market Clearing 19
- Q H α v R . 19
- 4.5 General Equilibrium 20
- B A H H τ 20
- R L w v 20
- Q q U L 20
- A B H H 21
- 5 Properties of Quantitative Urban Models 21
- 5.1 First Versus Second-Nature Geography 22
- R L 22
- Q q τ κ e 22
- R τ 22
- B U 23
- L τ 23
- A w q 23
- L Q q w v H 23
- R v H q 23
- L w 23
- H G 23
- H G H G 23
- A B G 23
- H G H G 24
- B A 24
- 5.2 Parameter Estimation 25
- L e u 25
- P Q 25
- L U u 25
- 5.3 Model Counterfactuals 28
- H H 29
- B A 29
- R R λ λ L L L λ λ L 30
- L q 30
- Q v R . 30
- L L 30
- B A 31
- H H 31
- H H Q 31
- 5.4 Sufficient Statistics 32
- L R 33
- 6 Extensions and Generalizations 34
- 6.1 Closed versus Open Cities 34
- U L 35
- B w κ P Q 35
- L L 35
- B w κ P Q 35
- L L ϵ 35
- 6.2 Floor Space Supply Elasticities 35
- H H 35
- H θ θ 35
- H G 35
- H K G µ 35
- H µ 36
- G . 36
- H G 36
- 6.3 Goods Trade Assumptions 36
- F A l j x j j 37
- L M L σF 37
- M p L τ w A 37
- L τ w A . 37
- 6.4 Spatial Sorting 38
- . . . n i 39
- L B w κ P Q 39
- B w κ P Q . 39
- 6.5 Dynamics 40
- V u βV µ ρϵ 40
- F ϵ e γ 41
- 6.6 Consumption Access 42
- 7 Empirical Applications 43
- 7.1 Estimating Agglomeration Forces 44
- 7.2 Transport Infrastructure Improvements 49
- 7.3 Optimal Transport Infrastructure 56
- 7.4 Remote Working and the Future of Cities 57
- 8 Areas for Further Research 59
- 9 Conclusions 61
- References 63
