Lecture 6: Transport Costs and Congestion Forces

Transcription

1 Lecture 6: Transport Costs and Congestion Forces WWS 538 Esteban Rossi-Hansberg Princeton University ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 1 / 43

2 Baum-Snow (2007) Between 1950 and 1990 the population of central cities declined by 17% despite population growth of 72% in MSAs Why? Paper argues that construction of limited access highways is important Lower commuting cost lead to suburbanization as demand for space increases Explains about one third of total suburbanization Central city population would have grown by 8% without the interstate highway system Other explanations? Amenity value of suburbs Tiebout sorting and racial preferences Crime and blight Desegregation of central city schools ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 2 / 43

3 Empirical Strategy Highway system designed to connect far away places not for local commuting So number of highway links varies independently from city characteristics Still, endogeneity is a possibility Instrument using 1947 highway plan The legislation stipulated that highways in the planned system should be... 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 routes of continental importance in the Dominion of Canada and the Republic of Mexico. Growth in rays between 1950 and 1990 is correlated with urban population growth in , but planned rays are not correlated Planned rays are correlated with 1940 population, so essential to control for population Count rays emanating from central cities ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 3 / 43

4 Aggregate Trends in Suburbanization DID HIGHWAYS CAUSE SUBURBANIZATION? 777 TABLE I AGGREGATE TRENDS IN SUBURBANIZATION, Percent change Panel A: Large MSAs MSA population Total CC population Constant geography CC population N for constant geog. CC population Panel B: Large Inland MSAs MSA population Total CC population Constant geography CC population N for constant geog. CC population Total U. S. population Notes: All populations are in millions. CC stands for central city. The sample includes all metropolitan areas (MSAs) of at least 100,000 people with central cities of at least 50,000 people in The sample in Panel B excludes MSAs with central cities located within 20 miles of a coast, major lake shore, or international border. MSA populations are for geography as of year Constant geography central city population uses 1950 central city geography. Census tract data are not available to build constant geography central city populations for some small cities in These cities are assigned a population of 0 for constructing the aggregates. Reported total U. S. population excludes Alaska and Hawaii. determining the provision of local public goods. The fact that constant geography central city population fell significantly ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 4 / 43 Downloaded from at Prince

5 Counting Rays 780 QUARTERLY JOURNAL OF ECONOMICS FIGURE I The Projected System of Interstate Highways in 1947 at Princeton University on March 14, 2012 Downloaded from ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 5 / 43

6 First Stage DID HIGHWAYS CAUSE SUBURBANIZATION? 783 TABLE II FIRST STAGE RESULTS LARGE MSAS IN1950 Panel A: Long difference Change in Rays Rays in the plan (.074)** (.076)** (.074)** 1950 Central city radius (.071)** (.072)** Change in simulated log income.939 (1.819) Change in log of MSA population.856 (.279)** Constant (.218)** (.247) (1.231) Observations R-squared Panel B: Full panel Rays Smoothed Rays Smoothed rays in plan (.029)** (.040)** (.023)** (.030)** Log simulated income (.234)** (.179) Log MSA population (.200) (.154)** MSA Fixed effects Yes Yes Yes Yes Groups R-squared Downloaded from at Princeton University on March 14, 2012 Notes: Panel A shows the first stage results for the regressions in Table IV. Panel B shows the first stage results for the regressions in Table VI. Listed specification numbers match those in the corresponding second stage tables. Smoothed rays is calculated by multiplying the stock of rays in 1990 in each MSA by the fraction of these rays mileage that is completed at each point in time. Smoothed rays in the plan is calculated by multiplying the number of rays in the 1947 plan by the fraction of federally funded highway mileage in the 1956 Federal Aid Highway Act completed at each point in time. All coefficients remain significant when estimated using the more selected sample in Table I Panel B. ERH (Princeton University ) importance of holding population levels constant in an evaluation of the effect Lecture of rays 6: Transport on suburbanization. Costs and Congestion Forces 6 / 43

7 The Effect of Lower Commuting Costs Basic monocentric city model implies that lower commuting costs result in lower density at the center Careful, more general theory does not Basic conclusion from the standard model: population in metropolitan areas should spread out along new highways central city population should increase with metropolitan area population and the radius of the central city central city population should decline with the number of highway rays ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 7 / 43

8 Austin, TX 786 QUARTERLY JOURNAL OF ECONOMICS FIGURE II Development Patterns in Austin, TX. Downloaded from at Princeton University on March 14, 2012 ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 8 / 43

9 Population Density and Distance to Center 788 QUARTERLY JOURNAL OF ECONOMICS TABLE III THE SPATIAL DISTRIBUTION OF METROPOLITAN AREA POPULATIONS Panel A: 1970 and 1990 Cross-Sections Log population density Sample Large MSAs in 1950 (36,250 Distance to CBD tracts, 139 MSAs) (.001)** (.001)** Distance to highway (.002)** (.002)** Large MSAs in 1950 with Distance to CBD central cities at least 20 miles (.002)** (.001)** from a coast or border (17,336 Distance to highway tracts, 100 MSAs) (.003)** (.003)** Panel B: Evolution between 1970 and 1990 Log population Sample density Large MSAs in 1950 (36,250 Distance to CBD.021 tracts, 139 MSAs) (.000)** Distance to highway.015 (.002)** Large MSAs in 1950 with Distance to CBD.021 central cities at least 20 (.001)** miles from a coast or border Distance to highway.008 (17,336 tracts, 100 MSAs) (.003)** Notes: Each pair of entries lists coefficients and standard errors from a regression of log population density on the listed variables at the census tract level. All regressions include MSA fixed effects. Regressions in Panel B also include the distance to the nearest highway in Estimated coefficients on distance to the nearest highway in 1970 are between and Regressions using the distance to planned highways as an instrument for the distance to observed highways yield similar results. When standard errors are clustered by MSA, results for the larger sample in Panel B and results for the smaller sample in Panel A remain significant at the 5 percent level. Other results are not statistically significant with clustering. Regressions are weighted by the fraction of MSA population that is represented in the tract. Analogous unweighted regressions produce highway distance coefficients that are larger in absolute value. All distances are in miles. Downloaded from at Princeton University on March 14, 2012 estimated gradient is much greater in the sample, including more centrally located MSAs, likely because the less restricted space in ERH (Princeton University ) these MSAs Lecture generates 6: Transport equilibria Costswith and less Congestion population Forces restricted 9 / 43

10 Main Specification The main specification used is log N c i = δ 0 + δ 1 ray i + δ 2 r ci + δ 3 w i + δ 4 log N MSA i + δ 5 G i + ε i where N c represents 1950-definition central city population, r c denotes central city radius, N MSA denotes MSA population, w i represents mean log annual personal income, and G i represents the Gini coeffi cient of the income distribution for MSA i ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 10 / 43

11 Main Results: Long Differences DID HIGHWAYS CAUSE SUBURBANIZATION? 791 TABLE IV LONG-DIFFERENCE REGRESSIONS OF THE DETERMINANTS OF CONSTANT GEOGRAPHY CENTRAL CITY POPULATION GROWTH, Large MSAs in 1950 Change in log population in constant geography central cities OLS3 IV1 IV2 IV3 IV4 IV5 Change in number of rays (.014)** (.022) (.032)** (.029)** (.026)** (.046)* 1950 central city radius (.014)** (.023)** (.023)** (.023)** (.021)** Change in simulated log income (.378) (.417) (6.174) (.480) Change in log of MSA population (.082)** (.094)** (.079)** (.108)** Change in Gini coeff of simulated income (23.266) Log 1950 MSA population.062 (.062) Constant (.260)* (.078)* (.076)** (.281)* (5.091) (.265)* Observations R-squared Notes: In columns IV1 IV5, the number of rays in the 1947 plan instruments for the change in the number of rays. Standard errors are clustered by state of the MSA central city. Standard errors are in parentheses. ** indicates significant at the 1 percent level, * indicates significant at 5 percent level. Summary statistics are in the Appendix Table. First stage results are in Table II. suburbanization. Coefficients on rays in all specifications have the expected negative sign and are usually statistically significant and sizable. OLS results indicate that conditional on control variables, each additional ray is associated with about a 6 percent ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 11 / 43 Downloaded from at Princeton University on March 14,

12 Main Results: City Fixed Effects DID HIGHWAYS CAUSE SUBURBANIZATION? 797 TABLE VI PANEL IV REGRESSIONS OF THE DETERMINANTS OF CONSTANT GEOGRAPHY CENTRAL CITY POPULATION, Large MSAs in 1950 Log central city population Number of rays (0.016)** (0.026)** (0.028)** (1990 Rays) (Fraction of Ray (0.016)** (0.012)** (0.013)** miles completed at t) Log simulated income (0.117) (0.109) (0.075)** (0.077)** Log MSA population (0.104)* (0.105)* (0.100)** (0.098)** Simulated Gini coefficient (1.106) (0.847) MSA Fixed Effects Yes Yes Yes Yes Yes Yes R-Squared Notes: The instrument used is (rays in the plan) (MSA mileage of highways running through the central city at time t)/(msa mileage of highways running through the central city in 1990). Standard errors are clustered by the state of the central city. Standard errors are in parentheses. ** indicates significant at the 1 percent level, * indicates significant at the 5 percent level. First stage results are in Table II. Each regression includes 132 MSAs with five observations each, one for each year There are fewer MSAs in this sample than that in Table IV because of lack of census tract data for seven MSAs in in the long-difference regressions because 1950-definition central city population is not available for all cities in all years. Panel OLS regressions using discrete rays as an explanatory variable produce coefficients of less than These small coefficients reflect adjustment costs and measurement problems. ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 12 / 43 Downloaded from at Princeton University on March

13 The Fundamental Law of Road Congestion, Duranton and Turner (2011) What is the effect of building more roads on the vehicle-km traveled (VKT)? Fundamental Law of Highway Congestion: VKT increases one to one with highways So building more roads does not relieve congestion Costs of congestion and transportation are large in 2001 an average American household spent 161 person-minutes per day in a passenger vehicle These minutes allowed 134 person-kilometer of auto travel at an average speed of 44 km/h Cost of transport infrastructure are large Current policy based on the idea that infrastructure relieves congestion Linked to environmental problem of carbon emissions ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 13 / 43

14 Conceptual Framework VOL. 101 NO. 6 duranton and turner: the fundamental law of road congestion 2619 P AC(R) P(Q) AC(R ) 0 Q* Q* VKT Figure 1. Supply and Demand for Road Traffic That is, willingness to pay equals average cost. Increasing the supply of road lane kilometers from R to R reduces the average cost of driving for any level of VKT. 4 It thus shifts the average cost curve to the right. With R lane kilometers of roads in the city, the demand curve intersects with the supply curve at Q *, the equilibrium VKT. With R lane kilometers of road, the ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 14 / 43

15 Empirical Framework Goal is to learn about Q (R) Or estimate ρ Q R in ln (Q it ) = A 0 + ρ Q R ln (R it ) + A 1 X it + ε it where X it denotes city characteristics and ε it unobserved contributors to driving If ε it = δ i + η ιt then using fixed effects one can estimate Or in first differences ( ) ln (Q it ) = A 0 + ρ Q R ln (R it ) + A 1 X it + δ i + η ιt ln (Q it ) = ρ Q R ln (R it ) + A 1 X it + η ιt ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 15 / 43

16 Empirical Framework Problem is still that roads could be assigned to cities in response to a contemporaneous shock to the city s traffi c So use ln (R it ) = B 0 + B 1 X it + B 2 Z it + µ it ln (Q it ) = A 0 + ρ Q R ln (R it ) + A 1 X it + δ i + η ιt where the instrument Z satisfies cov (Z, R) = 0 and cov(z, ε) = 0. ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 16 / 43

17 Data VOL. 101 NO. 6 duranton and turner: the fundamental law of road congestion 2623 Table 1 Summary Statistics for Our Main Hpms and Public Transportation Variables Year: Mean daily VKT (IH, 000 km) 7,777 11,905 15,961 (16,624) (24,251) (31,579) Mean AADT (IH) 4,832 7,174 9,361 (2,726) (3,413) (4,092) Mean lane km (IH) 1,140 1,208 1,280 (1,650) (1,729) (1,858) Mean lane km (IH, per 10,000 population) (26.9) (20.9) (16.4) Mean daily VKT (MRU, 000 km) 14,553 22,450 31,242 (36,303) (49,132) (70,692) Mean AADT (MRU) 3,146 3,646 3,934 (847) (947) (1,059) Mean lane km (MRU) 3,885 5,071 6,471 (7,926) (9,119) (12,426) Mean VKT share urbanized (IHU/IH) Mean lane km share urbanized (IHU/IH) Mean share truck AADT (IH) Peak service large buses per 10,000 population (1.02) (0.98) (0.98) Peak service large buses (563) (562) (742) Number MSAs Mean MSA population 753, , ,054 Notes: Cross MSA means and standard deviations in parentheses. IH denotes interstate highways for the entire MSA. IHU denotes interstate highways for the urbanized areas within an MSA. MRU denotes major roads for the urbanized areas within an MSA. Table 2 Vkt as a Function of Lane Kilometers, Univariate Ols by Decade ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 17 / 43

18 2624 THE AMERICAN ECONOMIC REVIEW october 2011 Basic OLS Results Table 3 Vkt as a Function of Lane Kilometers, Ols by Decade Year: (1) (2) (3) (4) (5) (6) (7) (8) (9) Panel A. Dependent variable: ln VKT for interstate highways, entire MSAs ln (IH lane km) 0.92*** 0.94*** 0.92*** 0.73*** 0.76*** 0.77*** 0.71*** 0.75*** 0.76*** (0.06) (0.06) (0.05) (0.05) (0.04) (0.04) (0.05) (0.04) (0.04) ln (population) 0.43*** 0.42*** 1.01*** 0.54*** 0.51*** 0.46* 0.53*** 0.49*** 0.39 (0.04) (0.05) (0.37) (0.04) (0.04) (0.25) (0.04) (0.04) (0.35) Elevation range (0.060) (0.054) (0.056) (0.054) (0.053) (0.048) Ruggedness 6.81* * * 3.46 (3.46) (3.24) (3.00) (3.00) (3.06) (3.11) Heating degree days 0.014*** 0.015*** 0.012*** 0.013*** 0.011*** 0.013*** (0.004) (0.01) (0.003) (0.004) (0.003) (0.004) Cooling degree days 0.019* 0.027** 0.019*** 0.022** 0.019** 0.020** (0.010) (0.012) (0.007) (0.009) (0.007) (0.009) Sprawl * * (0.0031) (0.0036) (0.0028) (0.0029) (0.0027) (0.0027) Census divisions Y Y Y Y Y Y Past populations Y Y Y Socioeconomic Y Y Y characteristics R Panel B. Dependent variable: ln VKT for interstate highways, urbanized areas within MSAs ln (IHU lane km) 1.04*** 1.05*** 1.06*** 0.95*** 0.97*** 1.00*** 0.92*** 0.94*** 0.97*** (0.03) (0.03) (0.03) (0.03) (0.03) (0.04) (0.03) (0.03) (0.04) Panel C. Dependent variable: ln VKT for major roads, urbanized areas within MSAs ln (MRU lane km) 0.90*** 0.89*** 0.88*** 0.72*** 0.78*** 0.80*** 0.66*** 0.67*** 0.70*** (0.03) (0.03) (0.03) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) Panel D. Dependent variable: ln VKT for interstate highways, outside urbanized areas within MSAs ln (IHNU lane km) 0.83*** 0.85*** 0.84*** 0.81*** 0.83*** 0.82*** 0.82*** 0.84*** 0.83*** (0.05) (0.04) (0.03) (0.04) (0.03) (0.03) (0.03) (0.03) (0.03) Notes: The same regressions for different types of roads are performed in all four panels. All regressions include a constant. Robust standard errors in parentheses; 228 observations for each regression in panel A and 192 in panels B D. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 18 / 43

19 Fixed Effects Pooled 2626 THE AMERICAN ECONOMIC REVIEW october 2011 Table 4 VKT as a Function of Lane Kilometers, Pooled ols All All All All All All All w. IHU Big Small MSA sample (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Dependent variable: ln VKT for interstate highways, entire MSAs ln (IH lane km) 1.24*** 0.82*** 0.86*** 0.85*** 1.05*** 1.06*** 1.05*** 0.95*** 1.05*** 1.12*** (0.02) (0.05) (0.05) (0.04) (0.04) (0.04) (0.04) (0.03) (0.04) (0.08) ln (population) 0.48*** 0.44*** 0.32*** 0.34*** 0.39*** 0.32*** 0.44*** 0.31** (0.04) (0.04) (0.12) (0.09) (0.09) (0.09) (0.11) (0.12) Geography Y Y Census divisions Y Y Socioeconomic Y Y characteristics Past populations Y MSA fixed effects Y Y Y Y Y Y R Notes: All regressions include year effects. Robust standard errors in parentheses (clustered by MSA in columns 1 4). Complete sample of 228 MSAs (684 observations) with interstate highways in columns 1 7; 192 MSAs (576 observations) with urban interstate highways in column 8; 114 MSAs (342 observations) above the median population size in 1990 in column 9; 114 MSAs (342 observations) below the median population size in 1990 in column 10. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. Columns 5 10 of Table 4 estimate equation (3) by including MSA fixed effects in our cross-sectional regression. Because they condition out permanent determinants of VKT for each city that are potentially correlated with roadway, we prefer the specifications with MSA fixed effects to those without. In column 5 we replicate column 1 of the same table but include MSA fixed effects. In column 6, we augment ERH (Princeton University the specification ) of column Lecture 6: 2 with Transport MSA Costs fixed andeffects. Congestion In Forces column 7, we repeat this 19 / 43

20 First Differences VOL. 101 NO. 6 duranton and turner: the fundamental law of road congestion 2627 Table 5 Change in VKT as a Function of Change in Lane Kilometers All All All All All Lane Lane Lane All All MSA sample (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Panel A. Dependent variable: Δln VKT for interstate highways, entire MSAs, OLS Δln (IH lane km) 1.04*** 1.05*** 1.02*** 1.00*** 0.93*** 1.09*** 0.90*** 0.82*** 1.03*** 1.03*** (0.05) (0.05) (0.04) (0.04) (0.04) (0.06) (0.06) (0.09) (0.05) (0.05) Δln (population) 0.34*** 0.40*** 0.44*** 0.39*** 0.31* 0.45** ** (0.10) (0.10) (0.11) (0.13) (0.17) (0.21) (0.22) (0.20) ln (initial VKT) 0.047*** 0.057*** 0.12*** 0.15*** 0.13*** (0.006) (0.007) (0.02) (0.03) (0.04) Geography Y Y Y Y Census divisions Y Y Y Y Socioeconomic Y Y Y characteristics Past populations Y Y Y MSA fixed effects Y Y R Panel B. Dependent variable: Δln VKT for interstate highways, entire MSAs, TSLS Δln (IH lane km) 1.05*** 1.02*** 1.00*** 0.92*** 1.07*** 0.90*** 0.82*** 1.03*** (0.05) (0.04) (0.04) (0.04) (0.06) (0.05) (0.09) (0.03) Δln (population) ** ** * (0.18) (0.16) (0.32) (0.45) (0.29) (0.72) (1.45) (0.37) First stage statistic Notes: All regressions include a constant and decade effects. Robust standard errors clustered by MSA in parentheses. 456 observations for each regression in columns 1 5 and 9 10, 205 in columns 6 7 which consider only increases in lane kilometers of more than 5 percent, and 115 in column 8 which considers declines in lane kilometers greater than 5 percent. Instrument for Δln (population) is expected population growth based on initial composition of economic activity. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. ERH (Princeton University In column ) 3, we also Lecture control 6: for Transport initial Costs VKT. andin Congestion column 4, Forces we add physical geog- 20 / 43

21 Instrument 1: 1947 Interstate Highway Plan 780 QUARTERLY JOURNAL OF ECONOMICS FIGURE I The Projected System of Interstate Highways in 1947 at Princeton University on March 14, 2012 Downloaded from ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 21 / 43

22 Instrument 2: 1898 Railroads Figure US Interstate Highway Plan Source: Image based on US House of Representatives (1947). Source: Image based on Gray (c. 1898). Figure US Railroads For this reason, we conduct iv estimations only for interstate highways and urban ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 22 / 43

23 Instrument 3: Routes of US Major Expeditions of VOL. 101 NO. 6 duranton and turner: the fundamental law of road congestion 2631 Exploration: 1835 to 1850 Figure 4. Routes of US Major Expeditions of Exploration, 1835 to 1850 Source: Image based on US Geological Survey (1970, p. 138). ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 23 / 43

24 IV Results VOL. 101 NO. 6 duranton and turner: the fundamental law of road congestion 2633 Table 6 VKT as a Function of Lane Kilometers, IV (1) (2) (3) (4) (5) Panel A (TSLS). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1835 exploration routes, ln 1898 railroads, and ln 1947 planned interstates ln (IH lane km) 1.32*** 0.92*** 1.03*** 1.01*** 1.04*** (0.04) (0.10) (0.11) (0.12) (0.13) ln (population) 0.40*** 0.30*** 0.34*** 0.23* (0.07) (0.09) (0.10) (0.12) Geography Y Y Y Census divisions Y Y Y Socioeconomic characteristics Y Y Past populations Y Overidentification p-value First-stage statistic Panel B (LIML). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1835 exploration routes, ln 1898 railroads, and ln 1947 planned interstates ln (IH lane km) 1.32*** 0.94*** 1.05*** 1.02*** 1.06*** (0.04) (0.11) (0.12) (0.13) (0.15) Overidentification p-value Panel C (TSLS). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1947 planned interstates ln (IH lane km) 1.33*** 1.00*** 1.10*** 1.08*** 1.12*** (0.05) (0.11) (0.13) (0.13) (0.15) First-stage statistic Panel D (TSLS). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1898 railroads ln (IH lane km) 1.31*** 0.83*** 1.03*** 1.00*** 1.02*** (0.06) (0.15) (0.18) (0.18) (0.22) First-stage statistic Panel E (TSLS). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1835 exploration routes ln (IH lane km) 1.25*** 0.63*** 0.75*** 0.68*** 0.72*** (0.08) (0.17) (0.18) (0.21) (0.22) First-stage statistic Panel F (LIML). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1898 railroads, and ln 1947 planned interstates ln (IH lane km) 1.39*** 1.09*** 1.18*** 1.15*** 1.20*** (0.04) (0.10) (0.11) (0.13) (0.16) Overidentification p-value First-stage statistic Panel G (LIML). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1898 railroads, and ln 1947 planned interstates ln (IH lane km) 1.33*** 0.98*** 1.13*** 1.08*** 1.13*** (0.05) (0.13) (0.16) (0.15) (0.17) Overidentification p-value First-stage statistic Panel H (LIML). Dependent variable: ln VKT for interstate highways, entire MSAs Instruments: ln 1898 railroads, and ln 1947 planned interstates ln (IH lane km) 1.26*** 0.82*** 0.93*** 0.92*** 0.97*** (0.05) (0.11) (0.13) (0.13) (0.16) Overidentification p-value First-stage statistic Notes: All regressions include a constant (and year effects for panels A E). Robust standard errors in parentheses (clustered by MSA in panels A E); 684 observations corresponding to 228 MSAs for each regression for panels A E and 228 observations for panels F H. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 24 / 43

25 Commuting Today (MRRH 2015) Counties become more open over time Density Share of Residents that Work in the County Where They Live Commuting links are sizeable and heterogeneous Min p5 p10 p25 p50 p75 p90 p95 Max Mean Commuters from Residence County Commuters to Workplace County County Employment/Residents Commuters from Residence CZ Commuters to Workplace CZ CZ Employment/Residents Tabulations on 3,111 counties and 709 CZ after eliminating business trips (trips longer than 120km). ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 25 / 43

26 Gravity in Goods Trade Across CFS Regions Slope: (after removing origin and destination fixed-effects) Log Trade Flows (Residuals) Log Distance (Residuals) Dashed line: linear fit; slope: ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 26 / 43

27 Gravity in Commuting Flows Slope: (after removing origin and destination fixed-effects) Log Commuting Flows (Residuals) Log Distance (Residuals) Dashed line: linear fit; slope: ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 27 / 43

28 Elasticity of Local Employment to Productivity 5% productivity shocks Density S. Diego (CA) dlnlm/da: 0.63 New Haven (CT) dlnlm/da: 1.47 Arlington (VA) dlnlm/da: Elasticity of Employment to Productivity Eliminating bottom and top 0.5%; gray area: 95% boostrapped CI ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 28 / 43

29 Local Employment vs. Resident Elasticity to Productivity 5% productivity shocks Density S. Diego (CA) dlnlm/da: 0.63 λnn n:.996 New Haven (CT) dlnlm/da: 1.47 λnn n:.746 Arlington (VA) dlnlm/da: 2.35 λnn n: Elasticity of Employment and Residents to Productivity Employment Residents Eliminating bottom and top 0.5%; gray area: 95% boostrapped CI ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 29 / 43

30 The Role of Commuting in Local Labor Demand Shocks Announcements of Million Dollar Plants (MDP) Compare winning county where new firm locates to runner-up counties 82 MDP announcements from Greenstone and Moretti (2004) GHM(2010) use subset of 47 MDP openings in (confidential) Census data We generalize GHM(2010) with commuting interactions ) + γ ln L it = κi j τ + θ ( I j τ W i ) +β (I j τ λ R ii i +α i +η j +µ t +ɛ it ( I j τ W i λ R ii i ) + i: counties; j: cases; t: calendar year; τ: treatment year index; Lit : employment in county i, t years after announcement; Ijτ : 1 for case j starting in treatment year; Wi : indicator for winner county; λ R ii i : residence own-commuting share in 1990 (experiment with more) αi, η j, µ t : counties, cases, calendar years fixed effects. ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 30 / 43

31 The Role of Commuting in Local Labor Demand Shocks Variable Coeffi cient (1) (2) (3) (4) (5) (6) (7) (8) Ijτ Wi θ (0.018) (0.078) (0.065) (0.068) (0.078) (0.078) (0.060) (0.066) Ijτ Wi λ R ii i γ (0.096) (0.096) (0.077) (0.066) Ijτ Wi λ L ii i γ (0.087) Ijτ Wi λ ARL ii i γ (0.088) Ijτ Wi λ MRL ii i γ (0.110) Ijτ λ R ii i β (0.135) (0.108) (0.075) (0.082) Ijτ λ L ii i β (0.129) Ijτ λ ARL ii i β (0.160) Ijτ λ MRL β ii i (0.145) Ijτ κ (0.008) (0.109) (0.096) (0.125) (0.106) (0.086) (0.060) (0.066) County Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Case Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Industry-year Fixed Effects Yes Census-region-year Fixed Effects Yes State-year Fixed Effects Yes Observations 4,431 4,431 4,431 4,431 4,431 4,431 4,431 4,431 R-squared County observations are weighted by population at the beginning of the sample period. Standard errors are clustered by state. p-value 0.05l; p-value p-value 0.1; ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 31 / 43

32 NY Area Employment/Residents in the Data P E N N S Y LVA N I A N E W Y O R K C O N N E C T I C U T N E W Y O R K N E W J E R S E Y P E N N S Y LVA N I A ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 32 / 43

33 NY Area Employment Change Shutting down commuting P E N N S Y LVA N I A N E W Y O R K C O N N E C T I C U T N E W Y O R K N E W J E R S E Y P E N N S Y LVA N I A ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 33 / 43

34 NY Area Residents Change Shutting down commuting P E N N S Y LVA N I A N E W Y O R K C O N N E C T I C U T N E W Y O R K N E W J E R S E Y P E N N S Y LVA N I A ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 34 / 43

35 NY Area Real Income Change Shutting down commuting P E N N S Y LVA N I A N E W Y O R K C O N N E C T I C U T N E W Y O R K N E W J E R S E Y P E N N S Y LVA N I A ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 35 / 43

36 Berlin Subway Extension Public Transport Network ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 36 / / 81

37 Berlin Subway Extension Public Transport Network Zoom ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 37 / / 81

38 Berlin Subway Extension Mean Relative Travel Time Reduction ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 38 / / 81

39 Berlin Subway Extension Relative Increase Floor Prices ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 39 / / 81

40 Berlin Subway Extension Relative Increase Residence Employment ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 40 / / 81

41 Berlin Subway Extension Relative Increase Real Expected Residential Income ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 41 / / 81

42 Berlin Subway Extension Relative Increase Workplace Employment ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 42 / / 81

43 Berlin Subway Extension Relative Reduction Wages ERH (Princeton University ) Lecture 6: Transport Costs and Congestion Forces 43 / / 81