SMILE! You’re on Traffic Light Camera:

Applying Stated Choice Modeling

in TransportationW. Douglass Shaw (presenter)W. Douglass Shaw (presenter)

Dept. of Agricultural Economics and Dept. of Agricultural Economics and Recreation, Parks and Tourism Recreation, Parks and Tourism

SciencesSciencesApril 14, 2008April 14, 2008

AcknowledgmentsAcknowledgments

The graduate students in this semester’s Ag. The graduate students in this semester’s Ag. Econ. 695 (Frontiers in Natural Resource and Econ. 695 (Frontiers in Natural Resource and Environmental Economics) and RPTS 616 Environmental Economics) and RPTS 616 (Economics of Tourism and Recreation)(Economics of Tourism and Recreation)– Especially Especially Lindsey HigginsLindsey Higgins (Ag. Econ.), (Ag. Econ.), Liam CarrLiam Carr

(Geography)(Geography)

Conversations on CM with Bill Breffle (and 2 of Conversations on CM with Bill Breffle (and 2 of his slides), Barbara Kanninen, Edward Morey, his slides), Barbara Kanninen, Edward Morey, Mary RiddelMary Riddel

My Main ContributionMy Main Contributionto Economics?to Economics?

Probably…Probably…

We (Pete Feather and I) showed that people can We (Pete Feather and I) showed that people can have an opportunity cost of their time that have an opportunity cost of their time that exceeds their wage rate (see exceeds their wage rate (see Economic InquiryEconomic Inquiry 2000; 2000; J. of Environmental Economics and J. of Environmental Economics and ManagementManagement 1999) 1999)

Are there any applications to transportation of Are there any applications to transportation of that…?that…?

Links to Transportation?Links to Transportation?

Somebody apparently thought soSomebody apparently thought so

Peter is now the Chief of the Fuel Peter is now the Chief of the Fuel Economy Division at the United States Economy Division at the United States Department of Transportation…Department of Transportation…– He is an “environmental and natural He is an “environmental and natural

resource” economistresource” economist

Outline / PreviewOutline / Preview

Hope: I could present some statistical results Hope: I could present some statistical results from the graduate seminar class project on from the graduate seminar class project on “choice modeling”“choice modeling”– Not quite ready, but I’ll show you what we have so Not quite ready, but I’ll show you what we have so

far.far.

So, this talk is an overview of stated choice So, this talk is an overview of stated choice modeling method and how it can be applied to modeling method and how it can be applied to some transportation issues.some transportation issues.– What are experimental/economic choice models and What are experimental/economic choice models and

how can these be used to model transportation-how can these be used to model transportation-related preferences and behaviors?related preferences and behaviors?

Audience Knowledge?Audience Knowledge?

How many here today know about stated choice models as a tool How many here today know about stated choice models as a tool that can be used to evaluate transportation-related preferences?that can be used to evaluate transportation-related preferences?

Some big “transportation” names of people who have done this kind Some big “transportation” names of people who have done this kind of modeling include: C. Bhat, Dan McFadden, Moshe Ben Akiva, of modeling include: C. Bhat, Dan McFadden, Moshe Ben Akiva, David Hensher, S. Lerman, Jordan Louviere, Charles Manski, David Hensher, S. Lerman, Jordan Louviere, Charles Manski, Kenneth TrainKenneth Train– Not sure any of these people are exclusively “transportation” Not sure any of these people are exclusively “transportation”

researchers per se.researchers per se.

Lots of SCM papers published recently in the journals Lots of SCM papers published recently in the journals Transportation, Transportation Research, Transport Policy, Journal Transportation, Transportation Research, Transport Policy, Journal of Transportation Economics and Policy, etc.of Transportation Economics and Policy, etc.

A Little Technical StuffA Little Technical Stuff

Choice modeling is similar to “Conjoint” Choice modeling is similar to “Conjoint” analysisanalysis– Stated Preferences/choices/rankings can be used Stated Preferences/choices/rankings can be used

(so can data from “actual” or “real” choices)(so can data from “actual” or “real” choices)– Most use “discrete” choice analysis (econometrics)Most use “discrete” choice analysis (econometrics)– Designs vary from:Designs vary from:

Paired Designs (Choose A or Choose B)Paired Designs (Choose A or Choose B)

Multiple Choice Designs (Choose one from A,B,C)Multiple Choice Designs (Choose one from A,B,C)

Rank these routes (more often done in conjoint)Rank these routes (more often done in conjoint)

A B C

Discrete Choice EconometricsDiscrete Choice Econometrics

As there are typically few choices, the error As there are typically few choices, the error terms are not continuously/normally distributed; terms are not continuously/normally distributed; rather, they relate to discrete distributesrather, they relate to discrete distributes

The old standard is to use the extreme value The old standard is to use the extreme value distribution leading to the logit or multinomial distribution leading to the logit or multinomial logitlogit

The new standard is to use the “mixed” or The new standard is to use the “mixed” or random parameters logit, or perhaps, a panel random parameters logit, or perhaps, a panel (fixed or random effects) logit model(fixed or random effects) logit model

Experiments?Experiments?

Laboratory experiments are designed to control Laboratory experiments are designed to control for every aspect that influences the outcomefor every aspect that influences the outcome

Choice experiments seek the same level of Choice experiments seek the same level of controlcontrol– Unlike using revealed preference data (e.g. data on Unlike using revealed preference data (e.g. data on

your actual trips) the researcher here constructs every your actual trips) the researcher here constructs every aspect of a choice alternative in a stated choice aspect of a choice alternative in a stated choice model (SCM)model (SCM)

Choices can be made in a computer laboratory Choices can be made in a computer laboratory settingsetting

Another Advantage of SCMsAnother Advantage of SCMs

Suppose you want to “market” a new product or idea?Suppose you want to “market” a new product or idea?– The idea is a plan, as in a planned new transportation route or The idea is a plan, as in a planned new transportation route or

alternativealternative

Define the attributes of the new route and develop an Define the attributes of the new route and develop an SCMSCM

In Transportation: New airline, bus route service; new In Transportation: New airline, bus route service; new airplane configuration (more leg room); the new Texas airplane configuration (more leg room); the new Texas highway; toll roads; new HOV lanes; congestion taxes, highway; toll roads; new HOV lanes; congestion taxes, new parking facilities; new sidewalks; new traffic lights; new parking facilities; new sidewalks; new traffic lights; remove traffic lights; rotaries (new Beaver Creek, remove traffic lights; rotaries (new Beaver Creek, Colorado), etc, etc…Colorado), etc, etc…

Essentials of Experimental DesignEssentials of Experimental Design

The AlternativesThe Alternatives

The Attributes of the alternativesThe Attributes of the alternatives

The Levels of the attributesThe Levels of the attributes

How are these designed so as to elicit the most How are these designed so as to elicit the most information possible without increasing the information possible without increasing the complexity such that individuals cannot perform complexity such that individuals cannot perform the experiment?the experiment?

A Little More JargonA Little More Jargon

A “profile” is a single alternative that is described A “profile” is a single alternative that is described by the levels of each attributeby the levels of each attribute

A “choice set” is a set of alternatives (two or A “choice set” is a set of alternatives (two or more) presented to the individual, e.g. A versus more) presented to the individual, e.g. A versus B, or A versus B versus C, where each letter is a B, or A versus B versus C, where each letter is a profile and the combination is a choice setprofile and the combination is a choice set

Researcher has to first figure out how many Researcher has to first figure out how many profiles are needed, then how many choice setsprofiles are needed, then how many choice sets

The The AlternativesAlternatives

Does a person look at Does a person look at two at once?two at once?

Or three?Or three?

Or four (or more)?Or four (or more)?

AA BB

AA BB CC

AA BB CC DD

AttributesAttributes Characterize Characterize Alternatives (the Choices)Alternatives (the Choices)

e.g.: What are the attributes of a e.g.: What are the attributes of a commutingcommuting alternative that matter to alternative that matter to people?people?– Cost (money and time)Cost (money and time)– ComfortComfort– Discretionary power (flexibility in choosing Discretionary power (flexibility in choosing

schedule)schedule)– ReliabilityReliability

LevelsLevels Determine Determine Definition of the AlternativeDefinition of the Alternative

What are the money prices?What are the money prices?– Range from “free” to calculations based on parking, toll Range from “free” to calculations based on parking, toll

roads, gasoline prices, mpg of the vehicleroads, gasoline prices, mpg of the vehicle– e.g. Local prices per trip are: {$0, $1, $2.50, $5.00, $8.00}e.g. Local prices per trip are: {$0, $1, $2.50, $5.00, $8.00}

What are the times?What are the times?– Range from few minutes to hoursRange from few minutes to hours– e.g. Local commuting times per trip (including all parts of e.g. Local commuting times per trip (including all parts of

the trip): {5 min., 10 min, 20 min, 30 min, 1 hour)the trip): {5 min., 10 min, 20 min, 30 min, 1 hour)

Comfort (low, medium, high); Reliability (very unreliable, Comfort (low, medium, high); Reliability (very unreliable, sometimes unreliable, always reliable)sometimes unreliable, always reliable)

How Many Possibilities? DesignHow Many Possibilities? Design

L levels, K attributes LL levels, K attributes LKK = possible “profiles” = possible “profiles”

Full factorial design considers all possible Full factorial design considers all possible profilesprofiles– Possible?Possible?

Example of Commuting Attributes (# of Levels)Example of Commuting Attributes (# of Levels)– Price (five), Time (five), Comfort (three), Flexibility Price (five), Time (five), Comfort (three), Flexibility

(three), Reliability (three)(three), Reliability (three)– 5522 X 3 X 333 = 25 X 27 = 675 = 25 X 27 = 675

Quickly ExplodingQuickly Exploding

I didn’t probably get all the attributes or I didn’t probably get all the attributes or levels covered. If more…levels covered. If more…

Can you “cover” 1,000 profiles?Can you “cover” 1,000 profiles?– NoNo

So, what to do? That’s the art of designSo, what to do? That’s the art of design– Fractional factorial designFractional factorial design

Key Design Components Key Design Components (Huber and Zwerina)(Huber and Zwerina)

Level Balance: Each level of each attribute Level Balance: Each level of each attribute should appear with equal frequencyshould appear with equal frequency

Orthogonality: mathematical independence to Orthogonality: mathematical independence to allow identification of parameters:allow identification of parameters:– Satisfied when joint occurrence of any 2 levels of Satisfied when joint occurrence of any 2 levels of

different attributes appear in profiles with frequencies different attributes appear in profiles with frequencies = the product of their marginal frequencies= the product of their marginal frequencies

– Simply: attributes are purposefully uncorrelated Simply: attributes are purposefully uncorrelated (makes it easier to identify variable X’s influence)(makes it easier to identify variable X’s influence)

Key Design Components Key Design Components (cont.)(cont.)

Minimal Overlap: the probability that an Minimal Overlap: the probability that an attribute level repeats itself in a choices attribute level repeats itself in a choices set is minimizedset is minimized

Utility Balance: Balance the utility Utility Balance: Balance the utility received: Avoid dominance of choices, the received: Avoid dominance of choices, the probability of choosing each alternative probability of choosing each alternative should be fairly evenshould be fairly even

Quantitative measures of efficiency:D-optimal efficiency

The D-optimal criterion seeks to maximize the determinant The D-optimal criterion seeks to maximize the determinant of the Fisher information matrixof the Fisher information matrix

Max: D = 100{1/N|(X’X)Max: D = 100{1/N|(X’X)-1-1||1/A1/A

N = number of observations; A is number of N = number of observations; A is number of attributes*levels in design; X’X is the information matrixattributes*levels in design; X’X is the information matrix

1.1. Uninformed prior – all parameters equal zeroUninformed prior – all parameters equal zero

2.2. A priori information based on pretest or other dataA priori information based on pretest or other data

3.3. Bayesian information hierarchically addedBayesian information hierarchically added

Conclusions about D-criterion

D-efficiency preferred when specification and design are D-efficiency preferred when specification and design are both correctboth correct

D-efficiency with Bayesian info preferred when D-efficiency with Bayesian info preferred when specification is incorrect but design is correctspecification is incorrect but design is correct

““Shifting” preferred to D-efficiency when the specification Shifting” preferred to D-efficiency when the specification is correct but the design is notis correct but the design is not– Most commonMost common– If design is correct, do not need a design-creating process!If design is correct, do not need a design-creating process!– Also known as cyclingAlso known as cycling

Alternatives using D-criterion

Fractional design drawn multiple times, with D-criterion Fractional design drawn multiple times, with D-criterion compared for each drawcompared for each draw

““Frequentist” model averaging: design evaluated over a Frequentist” model averaging: design evaluated over a distribution of parameter values and final design is a distribution of parameter values and final design is a weighted average (uses partial info)weighted average (uses partial info)

Class on Choice Modeling: Class on Choice Modeling: An Example ProjectAn Example Project

Agricultural Economics 695 (PhD seminar)Agricultural Economics 695 (PhD seminar)

Recreation, Parks, and Tourism Sciences Recreation, Parks, and Tourism Sciences (616 – PhD course)(616 – PhD course)

Assignment: Design a choice modeling Assignment: Design a choice modeling experiment that has something to do with experiment that has something to do with transportation issue in Bryan/College transportation issue in Bryan/College StationStation

Students’ DecisionStudents’ Decision

Identify the impact of CARES (Camera Identify the impact of CARES (Camera Advancing Red Light Enforcement Safety) on Advancing Red Light Enforcement Safety) on driver behavior, road and traffic safety, and driver behavior, road and traffic safety, and pedestrian safetypedestrian safety– Installation of red light cameras, coupled with $75 Installation of red light cameras, coupled with $75

citation for violationscitation for violations– Expansion of the program plannedExpansion of the program planned

No funding, so using convenience sample and No funding, so using convenience sample and internet surveyinternet survey

Student News ItemStudent News Item

The The BattalionBattalion (April 9, 2008) – Nathan Ball (April 9, 2008) – Nathan Ball– 3,318 citations as of April 13,318 citations as of April 1stst, generating , generating

$248,859 (four existing cameras)$248,859 (four existing cameras)– Of existing fines, 659 mailed to College Of existing fines, 659 mailed to College

Station residentsStation residents

DesignDesign

Four attributes: # of cameras; the location Four attributes: # of cameras; the location of the intersections for installation; the cost of the intersections for installation; the cost of the fine; the posted speed limit (mph)of the fine; the posted speed limit (mph)

Student in the class used SAS Optex Student in the class used SAS Optex procedureprocedure

16 “profiles” (see next slide)16 “profiles” (see next slide)

Original Profiles Original Profiles (Thanks Lindsey Higgins)(Thanks Lindsey Higgins)

Corrected ProfilesCorrected ProfilesObsObs CamerasCameras CostCost LocationLocation SpeedSpeed11

22

33

44

55

66

77

88

99

1010

1111

1212

1313

1414

1515

1616

44

44

44

44

88

88

88

88

88

88

1212

1212

1212

1212

1212

1212

5050

7575

7575

100100

100100

5050

100100

7575

5050

7575

7575

5050

100100

100100

5050

7575

CurrentCurrent

CurrentCurrent

CurrentCurrent

CurrentCurrent

High volHigh vol

MixedMixed

High PedHigh Ped

High volHigh vol

High PedHigh Ped

MixedMixed

MixedMixed

High PedHigh Ped

High vol.High vol.

High PedHigh Ped

High volHigh vol

MixedMixed

DecreaseDecrease

DecreaseDecrease

CurrentCurrent

CurrentCurrent

DecreaseDecrease

CurrentCurrent

DecreaseDecrease

CurrentCurrent

CurrentCurrent

CurrentCurrent

DecreaseDecrease

CurrentCurrent

CurrentCurrent

CurrentCurrent

DecreaseDecrease

DecreaseDecrease

Next StepNext Step

Suppose we want to create a pair of profiles to Suppose we want to create a pair of profiles to evaluate and ask the person to choose profile 1 evaluate and ask the person to choose profile 1 versus profile 2.versus profile 2.

Does it matter which profiles are paired?Does it matter which profiles are paired?– YesYes

How do we match them?How do we match them?– Answer is complicated and there are many schemes Answer is complicated and there are many schemes

that try to achieve an efficient design based on the that try to achieve an efficient design based on the four goals above.four goals above.

ExampleExample

Do we want choice A to be profile 1 and choice Do we want choice A to be profile 1 and choice B to be profile 2? From the original profiles, we’d B to be profile 2? From the original profiles, we’d get:get:

Choose between A (4 cameras, citation fine is Choose between A (4 cameras, citation fine is $50, cameras at current locations, speed is $50, cameras at current locations, speed is reduced) and B (4 cameras, citation fine is $74, reduced) and B (4 cameras, citation fine is $74, cameras at current locations, speed is reduced)cameras at current locations, speed is reduced)

Only thing that varies between A and B is the Only thing that varies between A and B is the citation/fine amountcitation/fine amount

Final Choice Set - Each Gets 8Final Choice Set - Each Gets 8AA

Cameras cost loc speedCameras cost loc speed

BB

Cameras cost loc speedCameras cost loc speed

88 100 HP decrease100 HP decrease

99 50 HP current50 HP current

4 50 current decrease4 50 current decrease

4 75 current decrease4 75 current decrease

4 75 current current4 75 current current

4 100 current current4 100 current current

8 75 mixed current8 75 mixed current

8 50 mixed current8 50 mixed current

1212 50 mixed current50 mixed current

4 75 HV decrease4 75 HV decrease

4 75 mixed current4 75 mixed current

8 100 HP current8 100 HP current

8 100 HP decrease8 100 HP decrease

12 75 HV decrease12 75 HV decrease

12 100 mixed decrease12 100 mixed decrease

88 50 HP decrease50 HP decrease

See Their Survey See Their Survey

http://geography.tamu.edu/cares_survey/http://geography.tamu.edu/cares_survey/

Aside on Internet surveysAside on Internet surveys– See Knowledge Networks Inc. (webcast of See Knowledge Networks Inc. (webcast of

presentation on survey bias, April 24presentation on survey bias, April 24 thth))– Wave of the future?Wave of the future?

For each section read any instructions and each question carefully before answering. Please do not leave any answers blank. An answer of N/A is provided for questions you’d choose to not answer. Thank you again for taking the time to complete this survey.Current Residency: College Station Bryan NeitherPrior to taking this survey, of the four intersections with red light cameras, how many can you confidently name or locate?0 1 2 3 4

The map shows the location of red light cameras in the College Station CARES Program. The cameras carry a $75 citation at intersections of roads with a 40 mph speed limit.For the purposes of this survey, these four cameras will remain in use. For each question, you will be given two alternatives for changing the CARES Program. The alternatives may change the cost of the citation, the speed limit, number of additional cameras, and placing additional cameras at intersections with high pedestrian traffic, high road volume, or a mix of intersections throughout College Station. Based on the information and your own personal knowledge, select the alternative you prefer.

IntersectionsIntersections1 – S. Texas Ave. & Walton Dr. 2- Harvey Rd. & 1 – S. Texas Ave. & Walton Dr. 2- Harvey Rd. &

George Bush Dr. E.George Bush Dr. E.3 – Harvey Rd & Munson Ave. 4 – Wellborn Rd. & 3 – Harvey Rd & Munson Ave. 4 – Wellborn Rd. &

George Bush Dr.George Bush Dr.

Example ChoicesExample Choices

1. Alternative A1. Alternative A Alternative BAlternative B

4 Additional Cameras placed 4 Additional Cameras placed throughout CSthroughout CS

$75 fine/citation for running light$75 fine/citation for running light

35 mph Speed limit35 mph Speed limit

No additional camerasNo additional cameras

$125 fine/Citation for running light$125 fine/Citation for running light

35 mph Speed limit35 mph Speed limit

Which of these above do you prefer, A or B? (circle)Which of these above do you prefer, A or B? (circle)

2. Alternative C2. Alternative C Alternative DAlternative D

4 added cameras placed at high 4 added cameras placed at high traffic volume intersectionstraffic volume intersections

$75 citation$75 citation

40 mph speed limit40 mph speed limit

No additional camerasNo additional cameras

$75 citation$75 citation

40 mph speed limit40 mph speed limit

Which of these above do you prefer, C or D? (circle)Which of these above do you prefer, C or D? (circle)

Few Preliminary Results N = 38Few Preliminary Results N = 38Cameras make it saferCameras make it safer 1 strongly disagree (N = 7)1 strongly disagree (N = 7)

2 (2)2 (2)

3 (13)3 (13)

4 (6)4 (6)

5 strongly agree (10)5 strongly agree (10)

Additional cameras will Additional cameras will improve traffic safetyimprove traffic safety

1 (7)1 (7)

2 (3)2 (3)

3 (9)3 (9)

4 (10)4 (10)

5 (9)5 (9)

Additional cameras will Additional cameras will improve pedestrian/bike improve pedestrian/bike safetysafety

1 (6)1 (6)

2 (2)2 (2)

3 (14)3 (14)

4 (10)4 (10)

5 (6)5 (6)

Cameras are primarily to Cameras are primarily to get revenue for townget revenue for town

1 (6)1 (6)

2 (6)2 (6)

3 (13)3 (13)

4 (3)4 (3)

5 (10)5 (10)

Preliminary Responses (N = 38, all drivers except 4 who take the bus) 1.1A 4 HP cameras, $100 fine, speed down by 5 mph Number who chose this: 13

1.1B 8 mixed cameras, $50 fine, speed current Chosen by 25

1.2A 4 HP cameras, $50 fine, speed current Chosen by 32

1.2B No cameras, $75 fine, speed down by 5 mph Chosen by 6

1.3A No cameras, $50 fine, speed down by 5 mph Chosen by 14

1.3B No cameras, $75 fine, speed current Chosen by 24

1.4A No cameras, $75 fine, speed down by 5 mph Chosen by 15

4 more HP cameras, $100 fine, speed current Chosen by 23

1.5A No cameras, $75 fine, speed current Chosen by 24

4 more HP cameras, $100 fine, speed down 5 mph Chosen by 14

1.6A No cameras, $100 fine, speed current Chosen by 14

8 more HV cameras, $75 fine, speed down 5 Chosen by 24

1.7A 4 mixed cameras, $75 fine, speed current Chosen by 30

8 more mixed cameras, $100 fine, speed down by 5 mph Chosen by 8

1.8 4 mixed cameras, $50 fine, speed current Chosen by 24

4 more HP cameras, $50 fine, speed down by 5 Mph Chosen by 14

Initial ThoughtInitial Thought

Not everyone thinks cameras are “good” in Not everyone thinks cameras are “good” in safety – may be revenue for townsafety – may be revenue for town

They might focus on speed limitThey might focus on speed limit

Had hoped to have at least some more Had hoped to have at least some more preliminary statistical results on this onepreliminary statistical results on this one– In the “field” giving surveysIn the “field” giving surveys– No complete data set yetNo complete data set yet

Thoughts on CM Applications to Some Thoughts on CM Applications to Some Other Texas Transportation IssuesOther Texas Transportation Issues

Hurricane evacuation behavior and risk Hurricane evacuation behavior and risk perceptionsperceptions– What is the risk that a hurricane will hit?What is the risk that a hurricane will hit?– Given this, will you evacuate? Perhaps add, how long Given this, will you evacuate? Perhaps add, how long

before you do? (could add the risks of getting caught before you do? (could add the risks of getting caught in a traffic jam, which are function of when you leave)in a traffic jam, which are function of when you leave)

““New” routes through rural and other areas?New” routes through rural and other areas?

Biking v. Driving as the cost of gasoline Biking v. Driving as the cost of gasoline increases…increases…

ApplicationApplication(UTCM project w/ Mark Burris)(UTCM project w/ Mark Burris)

Managed Lanes (less congestion, but pay Managed Lanes (less congestion, but pay a toll for this)a toll for this)

Katy FreewayKaty Freeway– Will people use it/the ML’s?Will people use it/the ML’s?– What will they willing to pay in tolls?What will they willing to pay in tolls?– What is the value of ML’s?What is the value of ML’s?

Managed Lanes (ML) offer travelers the option of congestion free travel in corridors where the general purpose lanes (GPL) are congested. To ensure the MLs do not become congested (and often to help pay for the construction of the lanes) travelers have to pay a toll to use the MLs. This toll varies by time of day or by congestion level, increasing as demand for the lane increases. Thus travelers have to make a decision, often at the spur of the moment, on use.

If Time Allows: Another ExampleIf Time Allows: Another Example

NSF Hurricane ProjectNSF Hurricane Project– Small Exploratory Grants Research (SGER) Small Exploratory Grants Research (SGER)

ProgramProgram– Look at victims from Katrina/Rita who had Look at victims from Katrina/Rita who had

relocated here or in Houstonrelocated here or in Houston– Examine their Examine their locationlocation preferences for moving preferences for moving

back or elsewhere using a choice modelback or elsewhere using a choice model– Also look at their subjective perceptions of risk Also look at their subjective perceptions of risk

(just after the hurricanes in 2005, and over one (just after the hurricanes in 2005, and over one year later)year later)

Two RoundsTwo Rounds

Round I – mostly from B/CS – living here Round I – mostly from B/CS – living here “temporarily”“temporarily”

Round II – from B/CS and from Houston Round II – from B/CS and from Houston (we lost many from Round I – no one (we lost many from Round I – no one knows where they went)knows where they went)

Compare risks and behaviors in model of Compare risks and behaviors in model of all subjectsall subjects

Empirical ApproachEmpirical Approach

Tried two panel logit specifications (Tried two panel logit specifications (ηη is is normally distributed individual-specific normally distributed individual-specific component, T is # of observations per component, T is # of observations per person i). Log likelihood (random effects):person i). Log likelihood (random effects):

1 1

ln ( | , ) [ { }ln[ ( )]

(1 { }ln[1 ( )] ( )

iTI

it iti t

it it

L Y Y A X

Y A X d

Round I (N = 72; 508 responses)Round I (N = 72; 508 responses)VariableVariable Fixed Effects Fixed Effects

ModelModel

*** significant at *** significant at the 1% levelthe 1% level

Random Effects Random Effects ModelModel

Risk Level:Risk Level:

NoneNone

LowLow

MediumMedium

Net IncomeNet Income

ConstantConstant

HoustonHouston

New OrleansNew Orleans

1.09***1.09***

0.976***0.976***

0.696***0.696***

0.00011***0.00011***

----

-.004-.004

0.1230.123

1.01***1.01***

0.940***0.940***

0.655***0.655***

0.0001***0.0001***

-1.09***-1.09***

-.009-.009

0.1140.114

Round II (N = 45; 206 responses)Round II (N = 45; 206 responses)

VariableVariable Fixed Effects Fixed Effects ModelModel*** significant at the 1% *** significant at the 1% level,** 5%, * 10%level,** 5%, * 10%

Random Effects Random Effects ModelModel

Risk Level:Risk Level:

NoneNone

LowLow

MediumMedium

Net IncomeNet Income

ConstantConstant

HoustonHouston

New OrleansNew Orleans

0.830***0.830***

0.945***0.945***

0.659**0.659**

0.00011***0.00011***

----

-.831*-.831*

-.489*-.489*

.768**.768**

0.939***0.939***

0.553*0.553*

0.0001***0.0001***

-0.792**-0.792**

-.777**-.777**

-.475*-.475*

Marginal WTP (One time)Marginal WTP (One time)

High Risk to NoneHigh Risk to None– Round I $10,100Round I $10,100– Round II $4,800Round II $4,800

High Risk to Medium RiskHigh Risk to Medium Risk– Round I $6,550Round I $6,550– Round II $3,456Round II $3,456

Results from Hurricane StudyResults from Hurricane Study(in words)(in words)

Key:Key:– Risks matter: higher risks, less likely to Risks matter: higher risks, less likely to

choose that locationchoose that location– Risks still matter to both groups, but Risks still matter to both groups, but matter matter

lessless a year later a year later– People do NOT want to all go back to New People do NOT want to all go back to New

OrleansOrleans– Net income (income less housing costs) Net income (income less housing costs)

increases chance of picking a locationincreases chance of picking a location