Did Highways Cause Suburbanization?

Slides:



Advertisements
Similar presentations
The Social Scientific Method An Introduction to Social Science Research Methodology.
Advertisements

REGRESSION, IV, MATCHING Treatment effect Boualem RABTA Center for World Food Studies (SOW-VU) Vrije Universiteit - Amsterdam.
Urban Economics 1 Dr. Adnan A. Alshiha.
Chapter 5 Urban Growth. Purpose This chapter explores the determinants of growth in urban income and employment.
Growth Empirics Key question in growth: What are the underlying, fundamental causes of s, q, A? How have some countries managed to attain high levels of.
Turun kauppakorkeakoulu  Turku School of Economics REGIONAL DIFFERENCES IN HOUSING PRICE DYNAMICS: PANEL DATA EVIDENCE European Real Estate Society 19th.
Omitted Variable Bias Methods of Economic Investigation Lecture 7 1.
The Rise and Decline of the American Ghetto Written by David M. Cutler., Edward L. Glaeser., and Jacob L. Vigdor Journal of Political Economy 107 (3)
Econometric Modeling More on Experimental Design.
Lecture 12 (Ch16) Simultaneous Equations Models (SEMs)
Comparative Models of Urban Systems
Outline Central Cities vs Suburbs Why Did Suburbs Grow? Decentralization of Employment and the Monocentric City Is Suburbanization Efficient?
Sector Model Hoyt.
How Much Crime Reduction Does the Marginal Prisoner Buy? Rucker Johnson Goldman School of Public Policy UC Berkeley Steven Raphael Goldman School of Public.
The Generalized IV Estimator IV estimation with a single endogenous regressor and a single instrument can be naturally generalized. Suppose that there.
Urbanization in Africa: Research confronts Reality Hugh Wenban-Smith
Class 17: Tuesday, Nov. 9 Another example of interpreting multiple regression coefficients Steps in multiple regression analysis and example analysis Omitted.
Chapter 9 Simultaneous Equations Models. What is in this Chapter? In Chapter 4 we mentioned that one of the assumptions in the basic regression model.
7.1 Chapter 7 – Empirical Demand Functions  Optimal pricing is critical to the success of any business.  Given the stakes, it is frequently worth investing.
John F. Kain Housing Segregation, Negro Employment, and Metropolitan Decentralization Quarterly Journal of Economics 82 (1968) Presentation by Aida K.
1 Research Method Lecture 11-1 (Ch15) Instrumental Variables Estimation and Two Stage Least Square ©
Development Economics ECON 4915 Lecture 2 Andreas Kotsadam Room 1038
1/L The Impact of a Temporary Help Job: An Analysis of Outcomes for Participants in Three Missouri Programs Carolyn J. Heinrich LaFollette School of Public.
1 Is Transparency Good For You? by Rachel Glennerster, Yongseok Shin Discussed by: Campbell R. Harvey Duke University National Bureau of Economic Research.
1 Innovation and Employment: Evidence from Italian Microdata Mariacristina Piva and Marco Vivarelli Università Cattolica S.Cuore - Piacenza.
State Tax Differentials, Cross-Border Commuting, and Commuting Times in Multi-State Metropolitan Areas David Agrawal and William Hoyt Discussant: Byron.
Assessing Studies Based on Multiple Regression
JDS Special program: Pre-training1 Carrying out an Empirical Project Empirical Analysis & Style Hint.
ECN741: Urban Economics The Basic Urban Model 3: Comparative Statics.
AUSTIN, TX AN ANALYSIS OF GROWTH AND DRIVING FACTORS.
 Internal Validity  Construct Validity  External Validity * In the context of a research study, i.e., not measurement validity.
Name: Angelica F. White WEMBA10. Teach students how to make sound decisions and recommendations that are based on reliable quantitative information During.
Soc 3306a Multiple Regression Testing a Model and Interpreting Coefficients.
For each question: what did you learn from the workshops? What matters are still left unanswered? 1.What are the main observations or conclusions - for.
Chapter 9 Analyzing Data Multiple Variables. Basic Directions Review page 180 for basic directions on which way to proceed with your analysis Provides.
1 Comments on Hancock, Peek, and Wilcox and Wilcox and Yasuda Sole Martínez Pería (World Bank) Presentation prepared for the World Bank, Rensselaer Polytechnic.
Correlation & Regression
Various topics Petter Mostad Overview Epidemiology Study types / data types Econometrics Time series data More about sampling –Estimation.
Is there a demographic bias in the Kansas tornado record? Dr. John Heinrichs Samuel Lane Department of Geosciences Fort Hays State University.
Methods of Economic Investigation: Lent Term Radha Iyengar Office Hour: Monday Office: R425.
1 Some Comments on “What Borders are Made of: An Analysis of Banking Integration Using European Regional Data” (M. Affinito and M. Piazza) Ron Martin Department.
Comments on “State-Owned Banks: Do They Promote or Depress Financial Development and Economic Growth?” Dani Rodrik February 25, 2005.
The Choice Between Fixed and Random Effects Models: Some Considerations For Educational Research Clarke, Crawford, Steele and Vignoles and funding from.
Does Trade Cause Growth? JEFFREY A. FRANKEL AND DAVID ROMER*
Seðlabanki Íslands Inflation control around the world: Why are some countries more successful than others? Thórarinn G. Pétursson Central Bank of Iceland.
CC-07: Atmospheric Carbon Dioxide Concentrations and Weather: Evidence from Hawaii Kevin F. Forbes The Catholic University of America Washington, DC .
Modeling Political Phenomena Using Control Variables and Gauging Validity.
Network Effects & Welfare Culture Marianne Bertrand, Erzo Luttmer, and Sendhil Mullainathan Oct. 29, 2004.
Agresti/Franklin Statistics, 1 of 88 Chapter 11 Analyzing Association Between Quantitative Variables: Regression Analysis Learn…. To use regression analysis.
Business Statistics for Managerial Decision Farideh Dehkordi-Vakil.
David Roodman (2008) Presentation by Faraharivony Rakotomamonjy and Estelle Zemmour.
Multi-Centered Metropolitan Region. The City The city is a form of development about 10,000 Years Old –High population density –Bounded –Surrounded by.
Aid, policies and Growth
Agent-Based Firmographic Models: A Simulation Framework for the City of Hamilton By Hanna Maoh and Pavlos Kanaroglou Center for Spatial Analysis (CSpA)
10-1 MGMG 522 : Session #10 Simultaneous Equations (Ch. 14 & the Appendix 14.6)
1 Empirical methods: endogeneity, instrumental variables and panel data Advanced Corporate Finance Semester
Rodent Complaints in Boston Number of rodent complaints in Boston per 2010 US census tract, Question: Is the spatial pattern of rodent complaints.
What drives banks’ geographic expansion? The role of locally non-diversifiable risk Reint Gropp, Felix Noth, Ulrich Schüwer.
R ETURN TO COMMUTING IN S WEDEN Sergii Troshchenkov PhD student L.A.S.E.R.
Urban Land-Use Theories
C O N V E N E S P R E S E N T S C O O R D I N A T E S
More on Specification and Data Issues
More on Specification and Data Issues
Complex Ownership and Capital Structure
PROXIMITY AND INVESTMENT: EVIDENCE FROM PLANT-LEVEL DATA
Introduction to Design
Econometrics Analysis
More on Specification and Data Issues
Output and Prices in the Short Run
Measuring the Wealth of Nations
Presentation transcript:

Did Highways Cause Suburbanization? Nate Baum-Snow Quarterly Journal of Economics, 2007 Presented to Ec-2333 on 2/28/2014 by Josh Abel

Introduction 1950 – 1990: population in center cities falls, population in MSAs rises Interstate Highway System (IHS) is constructed CAUSUAL RELATIONSHIP? Exogeneity of IHS is central to argument Results Yes, causal impact from IHS on suburbanization Additional interstate through city causes 18% CC population reduction Not much said on dynamics or mechanism Comment: Reminiscent of Michaels (2008) But Baum-Snow (2007) more “pure” economic history

Theory Alonso-Muth-Mills framework for spatial decisions Commuting costs make proximity to CC valuable IHS reduces commuting costs, spreads population out Reduction in costs increases income, raising demand for space – also pushes toward spreading out

The Interstate Highway System http://lawprofessors.typepad.com

Measuring IHS Acces A “ray” connects CC with outside Highway passing through city = 2 rays Austin, TX

Exogeneity of IHS Plausible that suburbanization actually caused placement of IHS First-order concern! Legislation: “…so located as to connect by routes as direct as practicable, the principal metropolitan areas, cities, and industrial centers, to serve the national defense, and to connect at suitable border points with the routes of continental importance in the Dominion of Canada and the Republic of Mexico…” Nothing about reducing intra-MSA commute time Michaels (2008) “arcsin” argument IHS access more likely if due-N, -S, -E, -W of CC Consistent with exogenous placement of IHS But, 1940-1950 MSA growth predictive of rays IV would be useful…

The arcsin Argument

The 1947 Map Preliminary plan developed in 1947 Not yet touched by political and legislative processes Strong predictor of actual IHS rays Cannot be predicted from 1940-1950 MSA population growth!

Preliminary Evidence For story to make sense, suburbs should develop along rays Census-tract level regressions Indicate that population was denser closer to rays Not a complete story No attempt to address reverse causality Reality check

Results – Long Differences Regression: change in CC population on change in rays (1950-1990) One observation for each MSA Marginal impact of ray: -6% to -12% Increases somewhat if exclude geographically constrained MSAs IV estimates are somewhat larger This is opposite of simple reverse causality story from above Perhaps hidden “suburban rays” developed that were missed by his measure Suburban rays negatively correlated with rays, positively with planned rays But maybe there are unobserved city effects biasing us…

Results – Panel Data Framework to correct for city fixed effects Has its own difficulties, though Is 10 years enough to see our effects? 20? Measurement error: timing within the decade is crucially important OLS effect disappears, IV results are similar to long difference results Suggests measurement error is causing problems Question (MY CONFUSION): how does the IV panel regression work? Where does the within-MSA variation come from? Does he just rescale actual rays at time t by PlannedT/ActualT?

Interesting Robustness Checks Placebo test Can rays predict 1910-1950 changes in CC population? If so, that would be worrying Either IHS was responding to previous suburbanization Or something spurious is driving them both Fortunately, it passes the placebo test: rays don’t predict 1910-1950 suburbanization One control, MSA population growth could be endogenous Not accounting for this biases estimate toward 0 Instrument with January rainfall Glaeser et al (2001) show good weather is one of the best predictors of metropolitan growth in that time period Results hardly change

Conclusion Counterfactual: without IHS, CC population growth would have been +8% instead of -17% over 1950-1990. Firm migration was more rapid than residential migration Maybe highways increased distance over which agglomeration could occur Suggests limit to usefulness of AMM, since not everyone is commuting to CC Dynamics? Who moves when? Interactions between firms and residents Do we need critical mass? If so, how do we get it?