the e⁄ect of transport policies on car use: theory and ... · theory and evidence from latin...

The E¤ect of Transport Policies on Car Use:Theory and Evidence from Latin American Cities

Francisco Gallego Juan-Pablo Montero Christian Salas

Department of EconomicsP. Universidad Católica de Chile

May 12, 2011: PSE & SEEIDD, Paris

Gallego, Montero, Salas (PUC) Transport Policies and Car Use PSE & SEEIDD, May 2011 1 / 36

Motivation

Air pollution and congestion remains serious problems in many LatinAmerican cities because of steady increase in car use (EconomistIntellingence Unit, 2010)

Two examples of policy intervention:

Hoy-No-Circula (HNC) in Mexico-City:

driving restriction established in November 1989most drivers cannot use their vehicles one day of the weeknear universal compliance (Eskeland Feyzioglu, 1997; Davis, 2008)

TranSantiago (TS) in Santiago-Chile:

major public transportation reform in February 2007 (Muñoz et al.,2009)good idea in theory, poor implementation (Yañez et al., 2010)recognized by many as a major policy failure


Transantiago: the day after...


Table 1.1. Transport policies in Latin America: Candidates for EvaluationProgram City Start date Typea Scope in force?

Restricción Vehicular Santiago Apr 1986 DR gradual yesHoy No Circula Mexico D.F. Nov 1989 DR drastic yesOperação Rodizio Sao Paulo Aug 1996 DR gradual yesPico y Placa Bogotá Aug 1998 DR gradual yesTransmilenio Bogotá Dec 2000 PT gradual yesPico y Placa Medellín Feb 2005 DR drastic yesMetrobus México D.F. Aug 2005 PT gradual yesRestricción Vehicular San José Aug 2005 DR gradual yesb

TranSantiago Santiago Feb 2007 PT drastic yesPico y Placa Quito Quito May 2010 DR drastic yes

aDR: driving restriction; PT: public transportation reform/investment.

bThe program su¤ered a temporary interruption in June-July of 2009.

Source: Lizana (2011)


this paper

have these policies been e¤ective in moving people away from theircars? (and hence in reducing local pollution, congestion, and carbonemissions?)

hard to construct a counterfactual for policy evaluation:transportation systems remarkably complex and dynamic (Small andVerhoef, 2007)

interested in both:

short-run responses (e.g., Davis, 2008, for HNC)long-run responses (here: how long it takes for households to adjusttheir stock of vehicles). See Duranton & Turner (2011) for longer run.

we see this paper as a theoretical and empirical exploration to theextent to which we can evaluate the e¤ect of these policies on car use

HNC and TS for empirical exploration: drastic interventions like anyother


Quick look at the data and results

natural candidate por policy evaluation: hourly records of vehicletra¢ c from tra¢ c-control stations scattered across the city

but some problems....besides we have this data only for Santiago


Quick look at the data and results, cont.

alternative candidate: hourly records of carbon monoxide (CO) frommonitoring stations across the cities

vehicles are responsabile for 90% or more of CO emissions (Molina &Molina, 2002; DICTUC, 2009)

Other papers using this high frequency pollution data: Davis (2008)for HNC; Chen & Walley (2010) for Taipei; Gomez-Lobo (2011) forTS

we compare CO levels before and after policy implementation fordi¤erent hours of the day and di¤erent days of the week

empirical results


CO observations for Mexico-City


CO observations for Santiago


Summary of CO empirical results

Mexico-City (HNC)short-run long-run T (months)

peak hours (8-9 am) -11% +13% 11o¤-peak (12-2 pm) -6% +11% 10sunday (8-10 am) 0% +20% 10

Santiago (TS)short-run long-run T (months)

peak hours (7-8 am) 0% +33% 10o¤-peak ? ? ?sunday ? ? ?


Adaptation under HNC


Adaptation under TS (only for peak hours)


Theory: making sense of these empirical results

household�s problem: allocation of existing vehicle capacity tocompeting uses (peak vs o¤-peak hours) and decision to adjustcapacity in response to a policy shock

short and long-run e¤ects on car travel for peak and o¤-peak hourscan be di¤erent

same model can accomodate for both HNC and TS policies

simple model: we (partially) borrow from the non-linear pricing (orbundling) literature (e.g., McAfee et al., 1989; Armstrong andVickers, 2010)


Table 2.1. Numerical results for a HNC-type policyCase variable SR LR LR(1) LR(2)1 peak -41% 8% 8% 43%

o¤-peak -28% 0% 0% 19%∆ stock 0% 6% 21% 21%

hard to match empirical results

LR(1): households don�t return their cars because oftransaction/lemon costs (Eberly, 1994)

LR(2): LR(1) + additional cars 70% dirtier than existing stock (usedcars); consistent with Eskeland & Feyzioglu (1997) for HNC andCantillo & Ortuzar (2011) for Colombia


Numerical example 1: HNC (driving restriction)

Table 2.1. Numerical results for a HNC-type policy, cont.Case variable SR LR LR(1) LR(1)2 peak -10% 1% 3% 12%

o¤-peak -4% 3% 4% 10%∆ stock 0% 3% 7% 7%

3 peak -29% -20% -13% -3%o¤-peak -9% 4% 9% 21%∆ stock 0% -2% 9% 9%


Numerical example 2: TS (public transportation)

Table 2.2. Numerical results for a TS-type policyCase variable change in relative prices SR LR LR(1) LR(2)1 peak 40% (price pub trans ") 2% 31% - -

o¤-peak 0% -2% 19% - -∆ stock 0% 21% - -

2 peak 0% -5% 30% - -o¤-peak 230% 8% 85% - -∆ stock 0% 40% - -

3 peak 30% 1% 31% - -o¤-peak 30% 0% 35% - -∆ stock 0% 25% - -

4 peak 40% 4% 20% 21% 30%o¤-peak -40% -5% -10% 0% 3%∆ stock 0% 8% 12% 12%


Pollution Data

Mexico-City (HNC):

5 - 15 monitoring stations from Atmospheric Monitoring Network ofMexico-Cityhourly concentration measures of 5 criteria pollutants and 4 weathervariables

Santiago (TS):

7 monitoring stations from CONAMAhourly concentration measures of 7 criteria pollutants and 5 weathervariables

We use (simple) hourly average of all stations in both cities and focuson a 4-year window around policy implementation


Data for HNC: Nov 1987 to Nov 1991Series Obs Frequency Mean Std.Dev. Min Max

Carbon Monox. 33704 hour 5.102 2.110 0.644 20.78Sulfur Dioxide 33794 " 0.052 0.019 0.012 0.254Temperature 33378 " 15.94 4.786 0.467 30.77Real Humidity 32773 " 47.92 20.20 2.300 99.60Wind Speed 33671 " 4.597 2.032 0.400 17.60Wind Direction 33677 " 173.3 56.03 1.000 420Precipitation 35088 " 2.232 4.381 0.000 53.52Real Ex. Rate 48 month 7.30 0.65 6.28 9.41

Footnote: Pollutant levels are reported in parts per million, Temperature in celsius degrees, Humidity in percentage, Wind Speed

in kilometers per hour,

Wind Direction in azimut degrees, and Real Exchange Rate in Mexican Pesos.


Data for TS: Feb 2005 to Feb 2009Series Obs Frequency Mean St.Dev. Min Max

Carbon Monox. 34994 hour 0.919 1.151 0.000 9.649Sulfur Dioxide 34944 " 9.258 5.873 0.852 102.7Temperature 35064 " 14.30 5.18 0.18 31.60Real Humidity 35064 " 66.44 16.01 13.99 98.01Wind Speed 35064 " 2.68 1.40 0.20 9.02Wind Direction 35064 " 187.08 49.98 38.62 302.14Precipitation 34752 " 0.01 0.09 0.00 4.87Atm. Pressure 34719 " 970.63 14.14 718.53 1021Real Ex. Rate 48 month 95.5 6.3 81.4 108.8Gasoline Price 48 " 517.9 517.9 368.4 721.7

Footnote: Pollutants concentration is measured in micrograms per cubic meter with the exception of Carbon Monoxide which is

measured in parts per million (ppm);

Temperature in celsius degrees, Humidity in percentage, Wind Speed in kilometers per hour, Wind Direction in azimut degrees,

Precipitation in milimeters,

Atmospheric Pressure in milibars, Real Exchange Rate and Gasoline Price in Chilean Pesos.


why CO?

mobile sources are responsible for 97% of it in HNC (Molina &Molina, 2002) and 89% in TS (DICTUC, 2009)

contribution of light vehicles (passanger cars and commercial vehiclesother than buses and trucks): 72% in HNC and 85% in TS

only pollutant non-reactive on a time scale of 1 day (Schmitz, 2005)

it has a relatively short residence time (more so under windier andwarmer conditions)

all these important for identi�cation because concentration can act asa good proxy for emissions

unlike hourly tra¢ c observations, CO records are better at capturinge¤ects at the entire city level


but....

emissions vs concentration: weekday in January 2002 (Schmitz, 2005)


Empirical Strategy

Imposing adaptation process:yt = α+ φybt + [a+ b(t � tT )]At + cTt (1� At ) + θt + γxt + εt

ybt : background pollutionxt : includes �xed e¤ects (day of week, month), weather variablesdt : dummies for transition monthsTt = 1 if t > tT (time of policy adoption) and zero otherwiseAt = 1 if tT < t � tA (end of adjustment phase, endogenous) andzero otherwise

why linear trend θ?


HNC Results


HNC Estimation with more structure (all hours)Peak O¤-Peak Sunday

Inmediate Impact -.114** -.064** .034(.053) (.030) (.038)

Adaptation Trend 3.03e-05*** 2.29e-05*** 2.43e-05(1.04e-05) (7.07e-06) (7.45e-06)

Impact after Adaptation .132 .106** .196***(.083) (.041) (.048)

Trend -9.13e-6* 7.13e-7 1.80e-6(4.60e-6) (3.30e-6) (4.17e-6)

ynight 0.339*** 0.153*** 0.526***(0.052) (0.044) (0.033)

SO2 0.258*** 0.334*** 0.100**(0.046) (0.030) (0.038)


Adaptation under HNC


TS Results


TS Estimation with more structure (peak hours only)(1) (2)

Inmediate Impact .045 .059(.084) (.076)

Adaptation Trend 3.87e-05* 4.14e-05**(2.08e-05) (1.7e-05)

Impact after Adaptation .310*** .339***(.067) (.080)

ynight 0.414*** 0.395***(0.026) (0.024)

ln(Pgas) -0.637*** -0.671***(0.204) (0.218)

SO2 0.503*** 0.497***(0.085) (0.086)

Obs 2004 2004R2 0.771 0.773


Adaptation under TS (only for peak hours)


Alternative short-run impact estimation: RDD

RDD estimations using Imbens and Kalyanaraman (2009) withoptimal bandwith

monthly weakly dailyHNC peak -0.057*** -0.25*** -0.64***

(0.012) (0.017) (0.170)TS peak -0.0002 -0.55*** -0.27

(0.031) (0.10) (0.45)

daily or weekly pollution data too noisy (lots of idiosyncratic volatitily)


Additional Empirical Evidence for TranSantiago

Gasoline sales

Car registrations (new cars) and transfers (used cars)

Price of taxi permits (medallions)

NOTE: Eskeland & Feyzioglu (1997) and Davis (2008) discussadditional similar data for HNC


Gasoline Sales

panel of monthly data of gasoline sales at the region level; includeregion and time �xed e¤ects and adjust for seasonal variation:

(1) (2)

TranSantiago .058*** .048**

(0.018) (0.016)

GDP growth 0.054 -0.013

(0.054) (0.200)

Log of Pgas/Pdiesel�Region 2 -0.663*** -0.457***

... (0.007) (0.004)

Region 13 -0.680*** -0.370***

(0.042) (0.039)

Obs 936 611

R2 0.945 0.957

Consistent with CO results: from pre-TS data we get that 1%increase in gasoline leads to a 0.25% increase of CO at peak


Transfers and registrations

Transfers Registration

(1) (2) (3) (4) (5) (6)

TranSantiago 2,406.6*** 1,028.5 -272.6 3,078.6*** 2,201.2** 2,421.2**

(503.1) (1,035.5) (1,230.7) (474.0) (969.5) (1,037.2)

Month 1 1,889.8*** 2,989.1***

(674.6) (568.4)

Month 2 2,594.7*** -316.3

(647.6) (540.9)

Month 3 1,032.5 -1,644.3***

(622.3) (515.8)

Month 4 2,778.7*** -560.5

(598.8) (493.3)

Month 5 1,438.1** -1,212.0**

(577.4) (474.0)

Month 6 -702.1 -1,876.9***

(558.3) (458.1)

Regional Linear Trends No Yes Yes No Yes Yes


Price of taxi permits (medallions), direct observations


all prices "collected"


Dependent Variable: Log (Price of taxi medallion)

(1) (2) (3) (4) (5) (6) (7) (8)

TS 0.709*** 0.613*** 0.612*** 0.521*** 0.623*** 0.604*** 0.517** 0.846***

(0.041) (0.057) (0.166) (0.065) (0.065) (0.088) (0.248) (0.085)

Ln(#Permits) -0.832** 0.197 -0.285 -0.541 -0.745 -0.745 -2.951***

(0.332) (0.651) (0.357) (0.449) (0.592) (0.592) (0.452)

Trends Yes No Yes No No No Yes No

Year F-E No No No Yes No Yes Yes No

Model F-E No No No No Yes Yes Yes No

Sample 430 430 430 430 430 430 430 permit-adds

Obs 430 430 430 370 370 370 370 60

R2 0.422 0.431 0.466 0.486 0.533 0.740 0.740 0.709


Conclusions & Some Policy Implications

empirical results show (i) non-trivial adaptation period and (ii)di¤erentiated e¤ects over peak and o¤-peak hours

CO good proxy for car use particularly at peak hours

people adjust rather quickly: little time to react

Mexico-City driving restriction (HNC) didn�t include, unlike others,incentives for a faster and cleaner �eet turnover

welfare evaluation for TS: how much of the increase in the stock ofvehicles was going to take place later anyway?

policy design: more market-based oriented either working assubstitute or complements (see London Congestion Charge andSigapur�s vehicle quota market).

more on policy instruments for vehicle pollution: Feng et al., (2005),Fullerton and Gan (2005)


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