Jeff Lazo Societal Impacts Program WAS*IS Workshop Boulder, CO July 2006

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Jeff LazoJeff Lazo

Societal Impacts ProgramSocietal Impacts Program

WAS*IS WorkshopWAS*IS Workshop

Boulder, COBoulder, CO

July 2006July 2006

Some Economics Some Economics to Make a Wiser to Make a Wiser WAS*ISWAS*ISerer

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ec·o·nom·ics (ĕkec·o·nom·ics (ĕk''ə-nŏm'ĭks, ēə-nŏm'ĭks, ē''kə-) kə-) n. n. The social science that deals with the production, The social science that deals with the production,

distribution, and consumption of goods and services distribution, and consumption of goods and services and with the theory and management of economies and with the theory and management of economies or economic systems.or economic systems.

http://www.answers.com/topic/economicshttp://www.answers.com/topic/economics

Lionel RobbinsLionel Robbins 1932: "the science which studies 1932: "the science which studies human behavior as a relation between scarce means human behavior as a relation between scarce means having alternative uses."having alternative uses."

http://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Economics

study of the allocation of scarce resources in light of study of the allocation of scarce resources in light of unlimited wantsunlimited wants

EconomicsEconomics

http://en.wikipedia.org/wiki/Lionel_Robbins

0101

ObjectivesObjectives exposed to basic concepts of economicsexposed to basic concepts of economics exposed to basic methods of economicsexposed to basic methods of economics discuss some applicationsdiscuss some applications

Reality checkReality check can’t teach economics in one hourcan’t teach economics in one hour what economics do you need to know?what economics do you need to know? some people think economists think differently some people think economists think differently

than other people – that is NOT truethan other people – that is NOT true other people think differently than other people think differently than

economistseconomists

EconomicsEconomics

Why Value Forecasts?Why Value Forecasts?

1.1. program justificationprogram justification benefit-cost analysisbenefit-cost analysis

2.2. program evaluationprogram evaluation

3.3. guidance for research investmentguidance for research investment any cases of true comparative any cases of true comparative

analysis?analysis?

4.4. inform users of forecast benefitsinform users of forecast benefits

5.5. developing end-to-end-to-end developing end-to-end-to-end forecast and warning systemforecast and warning system

What Should Be Valued?What Should Be Valued?

Weather impactsWeather impacts

Dutton - $3T USDutton - $3T US ForecastsForecasts Improved forecastsImproved forecasts Research to improve forecastsResearch to improve forecasts How forecasts are usedHow forecasts are used

What Should Be Valued?What Should Be Valued?

Forecasts

Value

Integrate forecasting and valuation- Meteorology- Economics

What Should Be Valued?What Should Be Valued?Weather

Observation

Forecast

Communication

Perception

Use

Value

Meteorology Risk communication

Marketing

Psychology Anthropology Sociology

Geography Risk perceptions Economics

Some Basic Some Basic Economics: Economics:

Value Theory and Value Theory and Topics in ValuationTopics in Valuation

Econ 101Econ 101

TOPICSTOPICS value theoryvalue theory consumers and producersconsumers and producers supply and demandsupply and demand markets and pricesmarkets and prices consumer surplusconsumer surplus producer surplusproducer surplus net societal welfarenet societal welfare market failuresmarket failures

Econ 101Econ 101

0101

What is Value?What is Value?

Nelson and Winter QJE

ForecastForecast

FrostFrost No FrostNo Frost

ActioActionn

ProtectProtect -C (lost -C (lost cost)cost)

-C (lost cost)-C (lost cost)

Don’t Don’t ProtectProtect

-L (lost -L (lost crop)crop)

00

Why ask “What is Value?”Why ask “What is Value?”• Ensure that “economic value” is valid

economics• Look at broader approach to economic

valuation

What is Value?What is Value?Market FailuresMarket Failures

Public goods

Market power

Externalities

Information

What is Value? What is Value? Topics: Public Goods Topics: Public Goods

What is the price (i.e., value) of weather forecasts?

Weather forecast characteristics• Non-rival• Non-exclusive

Problems of public goods• No observable price information• No provision by private markets

Weather forecasts as “quasi-public goods”?

What is Value? What is Value? Topics: Time Topics: Time

PeriodBenefit

s Costs

0 0.00 100.00

5 60.00 0.00

10 60.00 0.00

120.00 100.00

Discounting

Discounted Benefits

Discounted Costs

0.00 100.00

47.02 0.00

36.83 0.00

83.85 100.00Net Benefit

20.00Net Benefit -16.15

5% Rate of Time Preference

What is Value?What is Value?Topics: VSLTopics: VSL

Value of Statistical Life (VSL)

• 1,000,000 people each willing to pay $50 a year for a program to reduce the chance of death by 1 in 100,000 per year (say from 20 in 100,000 to 19 in 100,000 each year)

• Means that the group is WTP $50,000,000 to prevent 10 deaths

• VSL = $50,000,000/10 deaths = $5,000,000

What is Value?What is Value?Topics: Benefits TransferTopics: Benefits Transfer

Application of results from one study for a different analysis context.

Same commodity being valued?same baseline? same outcome?

No No ForecastForecast

ClimatologClimatologyy

CurrentCurrent ImprovedImproved PerfecPerfectt

Adjusting for:date of study – changes in prices (inflation)changes in preferenceincome differencesavailability of substitutes and complementsother significant determinants of value

Evaluation of the Evaluation of the Sensitivity of U.S. Sensitivity of U.S. Economic Sectors to Economic Sectors to Weather Weather ((OUSSSAOUSSSA))

Jeffrey K. Lazo – NCARPete Larsen – NCAR / Cornell UniversityMegan Harrod – Stratus ConsultingDonald Waldman – University of Colorado

Purpose: Assess sensitivity of US economic sectors to weather variability

OutlineOutline

MotivationMotivation ConceptConcept What is Economic Sensitivity? What is Economic Sensitivity? Data and ModelingData and Modeling ResultsResults ConclusionsConclusions

Dutton – BAMS – September 2002

“. . . one-third of the private industry activities, representing annual revenues of some $3 trillion, have some degree of weather and climate risk.

This represents a large market for atmospheric information . . . “

Conceptual ApproachConceptual ApproachModel Building:Model Building: using historical using historical economic and weather data, we model economic and weather data, we model the relationship between economic the relationship between economic output in 11 sectors, economic inputs, output in 11 sectors, economic inputs, and weather and weather variabilityand weather and weather variability

Capital

Labor

Energy

Temperature

Precipitation? Gross

StateProduct

Conceptual ApproachConceptual Approach

Sensitivity Analysis:Sensitivity Analysis: Using these Using these models, we then hold the economic models, we then hold the economic inputs constant, and use 70 years of inputs constant, and use 70 years of weather data to see how economic weather data to see how economic output varies as a result of variation in output varies as a result of variation in weatherweather

Capital

Labor

Energy

Temperature

Precipitation

Gross State

Product

Define “Sensitive”Define “Sensitive” No single correct definition

Characteristics of a meaningful approach consistent with economic theory amenable to empirical examination provide meaningful information

about economic impacts of Wx

What is Weather What is Weather Sensitivity?Sensitivity?

P$

Q

S(K0, L0, E0;W

0)

D(W0)

P*

Q*

D(W1)

S(K0, L0, E0; W1)

Q1

P1

Change in GSPGSP

Issues?Issues?

•Weather or climate?

•Sensitivity or something else?

Super SectorsSuper SectorsSector Billions (2000$)

Wholesale Trade 592Retail Trade 662

Transportation 302Utilities 189

Communications 458Agriculture 98

FIRE 1,931Manufacturing 1,426Construction 436

Mining 121Services 675

Total Private Sector 6,890Government 1,135

Total 8,026

Economic ModelingEconomic Modeling

ij WL K Er t

ijt ijt ij it i tijt jtQ A e L K E W

( , , )Q f L K E

ln( ) ln( ) ln( ) ln( ) ln( )

1 1ln( ) ln( ) ln( ) ln( ) ln( )

2 2ln( ) ln( ) ln( ) ln( ) ln( ) ln( )

1ln( ) ln( ) ln( ) ln

2

ijt ijt L ijt K ijt E ijt

it it it LL ijt ijtW WW

LK ijt ijt LE ijt ijt ijt itLW

KK ijt ijt KE ijt

Q A t L K E

W W W L L

L K L E L W

K K K

( )

1ln( ) ln( ) ln( ) ln( ) ln( ) ln( )

2

ijt

ijt it EE ijt ijt ijt it ijtKW EW

E

K W E E E W

Translog FunctionTranslog Function

Weather “Sensitivity”Weather “Sensitivity”

ln ln ln lnWQ A rt LL

W

lnln W

QW

n l ln ,l n , L W WQ L LL

W W

, caused b yQ WQ Q

Economic DataEconomic DataEconomic DataEconomic Data - - state x year x sector

Gross State Product Gross State Product (dependent variable)

Production InputsProduction Inputs Capital (K) - dollarsCapital (K) - dollars Labor (L) - hoursLabor (L) - hours Energy (E) – BTUsEnergy (E) – BTUs

Weather Data Weather Data - - state x year

Temperature VariabilityTemperature Variability CDD : Cooling Degree Days: (T - 65) on a given day HDD : Heating Degree Days: (65 - T) on a given day

PrecipitationPrecipitation P_Tot: Precipitation Total (per square mile)P_Tot: Precipitation Total (per square mile) P_Std: Precipitation Standard DeviationP_Std: Precipitation Standard Deviation

i = state 48j = sector 11t = year 1977-2000 = 24 years48 x 11 x 24 = 12,672 “observations”

Temperature Weather Temperature Weather InputsInputs

CDD: Defined as (T - 65) = daily CDD, where T is daily Average Temperature (F). If T is less than 65 degrees F, CDD=0.

HDD: Defined as (65 - T) = daily HDD, where T is daily Average Temperature (F). If T is greater than 65 degrees F, HDD=0.

Average (Mean) Temperature of the day : (High Temperature + Low Temperature) / 2 ; High and Low Temperature are whole integer values.

http://www.weather2000.com/dd_glossary.html

Econometric MethodsEconometric Methods

• Level data versus per capita

• Panel data – time series – AR(1)

• Heteroskedasticity

• Fixed Effects

• Covariance calculations for marginal effects

Econometric ResultsEconometric Results

Parameter EstimateSig

.

Intercept -12.089 ***

YEAR 0.003 **

Capital 0.672 ***

Labor 0.798 ***

Energy 0.086 **

Heating Degree Days -0.035 ns

Cooling Degree Days -0.068 ***

P_Tot -0.187 ***

P_Std 0.185 ***

Sector: Agriculture

ns = not significant at 10%* 10%, ** 5%, *** 1%

Parameter Estimates from Full Model Parameter Estimates from Full Model RegressionsRegressions

Significance (* = 10%, ** = 5%, *** = 1%, ns = not signficant)DF=1068 for all models

Agric. Wholes Retail FIRE Comm. Utilities Transp. Manf. Constr. Mining Svcs

Inter50.46

ns-1.65ns

-2.08ns

39.98ns

27.08ns

-25.26ns

-28.44ns

-24.78ns

-6.13ns

153.57**

55.72***

YEAR-0.01

***0.02***

-0.01***

0.004**

-0.01***

0.01**

0.01***

0.03***

0.00ns

-0.03***

0.003***

lnKAP

-2.10**

-0.12ns

-0.75ns

7.70***

2.98***

9.21***

4.78***

0.51ns

-9.55***

-5.93***

-2.66***

CDD2.56

*-0.24ns

0.24ns

-3.10**

-0.97ns

2.79ns

-3.52***

1.49ns

-1.43ns

2.50ns

-1.05*

KAP20.14***

0.05***

0.08***

0.02ns

0.02ns

-0.23***

0.07***

0.04ns

0.06***

0.19***

-0.03ns

KAP x HDD

-0.06ns

0.04**

0.09***

-0.07**

0.06**

-0.31***

-0.06ns

0.05ns

0.24***

0.57***

0.15***

Marginal ResponsesMarginal Responses Capital Labor Energy

Sector Marg Eff T-Stat Marg Eff T-Stat Marg Eff T-Stat

Agriculture 1.10 35.02 0.44 8.55 -0.01 -0.14

Communications 1.12 30.08 0.31 12.57 -0.14 -6.23

Construction 0.48 12.40 1.14 52.35 0.12 4.60

FIRE 0.98 32.49 0.39 9.82 -0.20 -6.84

Manufacturing 0.48 5.76 0.62 6.98 0.09 1.71

Mining 1.20 11.86 0.60 9.20 0.10 1.51

Retail Trade 0.91 31.15 0.54 15.94 -0.04 -2.02

Services 0.94 35.85 0.64 18.57 -0.07 -5.53

Transportation 0.94 28.84 0.33 12.21 0.07 1.90

Utilities 1.11 22.57 -0.31 -4.94 -0.03 -0.73

Wholesale Trade 0.50 19.99 0.78 33.01 -0.02 -1.15

Marginal ResponsesMarginal Responses HDD CDD Total Precip Precip

Variance

Sector Marg Eff T-Stat Marg Eff T-Stat Marg Eff T-Stat Marg Eff T-Stat

Agriculture 0.00 -0.05 -0.19 -6.11 0.28 1.89 -0.12 -6.75

Communic. 0.13 3.96 0.06 3.31 0.06 0.36 0.17 16.15

Construct. -0.01 -0.38 0.06 2.85 -0.01 -0.05 0.26 20.84

FIRE 0.15 3.52 0.06 2.70 0.54 3.19 -0.08 -5.60

Manufact. 0.18 1.85 0.02 0.36 0.49 2.34 -0.22 -6.60

Mining 0.25 1.97 0.04 0.57 -3.52 -9.54 1.10 27.44

RetailTrade 0.04 1.75 0.03 2.88 -0.13 -1.32 0.13 18.20

Services 0.04 2.07 0.00 0.29 0.33 4.01 -0.05 -7.72

Transport. -0.03 -0.91 0.01 0.44 -0.15 -0.74 0.15 12.18

Utilities 0.00 0.04 0.08 1.91 -0.59 -1.42 -0.28 -11.59

Wholesale 0.10 4.63 0.02 1.65 -0.19 -1.93 0.02 3.03

Wx Sensitivity AnalysisWx Sensitivity Analysis

Average K, L, E 1996-2000Average K, L, E 1996-2000 Set Year to 2000Set Year to 2000 Historical weather data 1931-2000Historical weather data 1931-2000 Fitted GSP values by sector by state by Fitted GSP values by sector by state by

yearyear 11 sectors11 sectors 48 states48 states 70 “years” fit to 2000 “economic structure”70 “years” fit to 2000 “economic structure”

11 Sector Models:11 Sector Models:Q = Q = f f (K, L, E, W; Year, State)(K, L, E, W; Year, State)

State SensitivityState Sensitivity(Billions $2000)

State SensitivityState Sensitivity(Billions $2000)

State Mean Max Min Range % Range Rank

New York 633.3 679.6 594.0 85.6 13.5% 1

Alabama 92.0 93.9 81.7 12.2 13.3% 2

California 1019.4 1080.5 968.6 111.9 11.0% 3

Wyoming 13.7 14.3 12.8 1.4 10.5% 4

Ohio 312.0 330.6 298.4 32.2 10.3% 5...

......

......

......

Delaware 30.2 30.6 29.6 1.0 3.3% 44

Maine 27.0 27.4 26.5 0.9 3.3% 45

Montana 17.2 17.4 16.9 0.6 3.3% 46

Louisiana 109.5 111.2 107.6 3.6 3.3% 47

Tennessee 141.1 142.8 139.3 3.5 2.5% 48

Sector Sector SensitivitySensitivity (Billions $2000)

Sector SensitivitySector Sensitivity (Billions $2000)

Sector Mean Max Min Range %Range

Agriculture 127.6 134.4 119.0 15.4 12.09%

Wholesale trade 601.5 607.8 594.5 13.3 2.20%

Retail trade 761.5 771.2 753.9 17.3 2.27%

FIRE 1,639.3 1,713.1 1,580.6 132.5 8.08%

Communications 237.3 243.4 232.3 11.1 4.68%

Utilities 212.9 220.8 206.0 14.9 6.98%

Transportation 276.1 280.7 271.0 9.8 3.53%

Manufacturing 1,524.8 1,583.2 1,458.2 125.1 8.20%

Construction 374.5 384.0 366.4 17.7 4.71%

Mining 102.0 108.9 94.2 14.7 14.38%

Services 1,834.9 1,865.4 1,804.9 60.5 3.30%



Agriculture 127.6 134.4 119.0 15.4 12.09%Wholesale trade 601.5 607.8 594.5 13.3 2.20%

Retail trade 761.5 771.2 753.9 17.3 2.27%

FIRE 1,639.3 1,713.1 1,580.6 132.5 8.08%

Communications 237.3 243.4 232.3 11.1 4.68%

Utilities 212.9 220.8 206.0 14.9 6.98%

Transportation 276.1 280.7 271.0 9.8 3.53%

Manufacturing 1,524.8 1,583.2 1,458.2 125.1 8.20%

Construction 374.5 384.0 366.4 17.7 4.71%

Mining 102.0 108.9 94.2 14.7 14.38%Services 1,834.9 1,865.4 1,804.9 60.5 3.30%



Agriculture 127.6 134.4 119.0 15.4 12.09%

Wholesale trade 601.5 607.8 594.5 13.3 2.20%

Retail trade 761.5 771.2 753.9 17.3 2.27%

FIRE 1,639.3 1,713.1 1,580.6 132.5 8.08%

Communications 237.3 243.4 232.3 11.1 4.68%

Utilities 212.9 220.8 206.0 14.9 6.98%

Transportation 276.1 280.7 271.0 9.8 3.53%

Manufacturing 1,524.8 1,583.2 1,458.2 125.1 8.20%

Construction 374.5 384.0 366.4 17.7 4.71%

Mining 102.0 108.9 94.2 14.7 14.38%

Services 1,834.9 1,865.4 1,804.9 60.5 3.30%

National SensitivityNational Sensitivity (Billions $2000)

Total National 7,692.4 7,554.6 7,813.4 258.7 3.36%


Agriculture 127.6 134.4 119.0 15.4 12.09%

Wholesale trade 601.5 607.8 594.5 13.3 2.20%

Retail trade 761.5 771.2 753.9 17.3 2.27%

FIRE 1,639.3 1,713.1 1,580.6 132.5 8.08%

Communications 237.3 243.4 232.3 11.1 4.68%

Utilities 212.9 220.8 206.0 14.9 6.98%

Transportation 276.1 280.7 271.0 9.8 3.53%

Manufacturing 1,524.8 1,583.2 1,458.2 125.1 8.20%

Construction 374.5 384.0 366.4 17.7 4.71%

Mining 102.0 108.9 94.2 14.7 14.38%

Services 1,834.9 1,865.4 1,804.9 60.5 3.30%

Future Research (1)Future Research (1)

extend data past 2000 better capital and energy data include “storms” data include forecast skill measure

value of weather forecasts?value of weather forecasts? split supply and demandsplit supply and demand model uncertaintymodel uncertainty

Future Research (2)Future Research (2)

finer spatial scales county level data for a state

finer temporal scales quarterly / monthly economic data

finer sectoral scales 2, 3, or 4 digit sector study

other regions / countries

ConclusionsConclusions

Economically valid analysis Significant impact of weather

• significant regression coefficientssignificant regression coefficients• significant marginal effectssignificant marginal effects

Interpretation of weather sensitivity• upper-bound weather risk measure?upper-bound weather risk measure?• upper-bound measure of value of weather upper-bound measure of value of weather

information?information? 3.4% of annual US economic variability3.4% of annual US economic variability $260B US economic variability related $260B US economic variability related

to weather variabilityto weather variability

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Benefits of Investing Benefits of Investing in Weather in Weather Forecasting ResearchForecasting ResearchJeff Lazo, Jennie Rice, Marca Hagenstad

SuperCompSuperComp

Purpose: Assess benefits of buying a new supercomputer for weather forecast research

TOPICSTOPICS Value of investments in Value of investments in

researchresearch Assess value chainAssess value chain Benefit-cost analysisBenefit-cost analysis Benefits transferBenefits transfer Value of statistical lifeValue of statistical life DiscountingDiscounting Sensitivity analysisSensitivity analysis

SuperCompSuperComp

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SuperComp Study MethodsSuperComp Study Methods

1.1. Determine potential impact of Determine potential impact of supercomputer on forecast qualitysupercomputer on forecast quality

2.2. Identify potential sectors/users and of Identify potential sectors/users and of improved forecastimproved forecast

3.3. Identify existing benefit studies for Identify existing benefit studies for sectors/userssectors/users

4.4. Quantify probabilities and timing of Quantify probabilities and timing of impactsimpacts

5.5. Develop benefits model for Develop benefits model for aggregating over timeaggregating over time

6.6. Conduct sensitivity analysisConduct sensitivity analysis

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Example: “SuperComp”Example: “SuperComp”

New Supercomputer

Improved Environmental

Modeling

Air Force Benefits

DOE Benefits (wind)

Marine Resource Mgt. Benefits

Private Sector Benefits (e.g., highways)

International Benefits

Improved Operational Forecasts (NWS Benefits)

Army Benefits

Aviation Benefits

Retail Benefits

Energy Benefits (temps, wind)

Marine Transportation Benefits

Agriculture Benefits

Total

BenefitsHousehold Benefits

Example: “SuperComp” Example: “SuperComp” Household Benefits of Short Term Weather Household Benefits of Short Term Weather

ForecastsForecasts

Short TermWeatherModeling

Severe WeatherForecasts

TemperatureForecasts

PrecipitationForecasts

WindForecasts

Home ImprovementDecisions

Preparation forSevere Weather

ShoppingDecisions

Commuting /Travel Decisions

Total

Benefits

RecreationDecisions

Cloud CoverForecasts

Stratus Consulting (2002) – stated preference study

Example: “SuperComp”Example: “SuperComp”Agriculture: Annual value of improvement to

perfect information (PI)Apples,

peaches, and pears Alfalfa

Winter wheat

Total - these crops

Value of improvement to

PI per acre of farmland $1,403 $75 $35 $65.19

Acres of farmland 828,460 23,541,000

44,349,000

68,718,460

Value of PI - 100% of land

$1.16 B $1.77 B $1.55 B $4.48 B

Value of PI - 5% of land

$58 M $89 M $77 M $224 M

Example: “SuperComp”Example: “SuperComp”Weather-related fatalities and VSL

estimates (we assume 10% of weather-related fatalities preventable

with perfect information)

Year Fatalities

Fatalities — value

(millions)

1996 540 $3,240

1997 600 $3,600

1998 687 $4,122

1999 908 $5,448

2000 476 $2,856

2001 464 $2,784

2002 540 $3,240

Average annual 602 $3,613a. Calculated as $6 million per fatality.Source: NWS (1996-2002).


Observation

Understanding

Computing

Improvements in Weather Forecasts

NCEP Supercomputing

NHRA

GFDL Supercomputing

HPCS – double current computing capabilities - enable doubling of the spatial and temporal resolutions of environmental models currently run by NOAA, including finite-difference models of the atmosphere and ocean


1.1. Contribution from NHRA – 20%Contribution from NHRA – 20%

2.2. Contribution from computing – 33%Contribution from computing – 33%

3.3. Contribution to forecast improvements Contribution to forecast improvements over 5 year life of NHRA – 75%over 5 year life of NHRA – 75%

20% x 33% x 75% = 5%20% x 33% x 75% = 5%


Financial assumptions for base case present value calculations

Real social discount rate 3%

Decision to purchase supercomputer 2004

First year of operation 2005

Number of years until benefits begin 2

Number of years in which benefits accrue

5

Time horizon for accrued benefits Infinite


Summary of present value of benefits in 2003 (millions, 2002$)

Household sector 69

Orchards, winter wheat, alfalfa 26

Avoided weather-related fatalities 21

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What Are Weather What Are Weather Forecasts Worth? Forecasts Worth? Stated Preference Stated Preference Approaches to Approaches to Valuing InformationValuing InformationJeff Lazo, Rebecca Morss, Barb Brown, Stratus Consulting

StormStormPurpose: Assess values to households of ordinary weather forecasts

TOPICSTOPICS Stated preference valuationStated preference valuation Survey developmentSurvey development AnalysisAnalysis

Uses – between city comparisonUses – between city comparison Sources - regressionSources - regression Perceptions – between individual comparisonPerceptions – between individual comparison

Econometrics - regression analysisEconometrics - regression analysis Probit modelProbit model Bivariate probit modelBivariate probit model Combining CV and SC dataCombining CV and SC data

STORMSTORM

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Study ObjectiveStudy Objective

• Evaluate benefits to households of Evaluate benefits to households of improvements in weather forecasting improvements in weather forecasting servicesservices

• 104,705,000 households104,705,000 households

• Day-to-day weatherDay-to-day weather

• National Oceanic & Atmospheric National Oceanic & Atmospheric AdministrationAdministration

STATED PREFERENCE

METHODS

REVEALED PREFERENCE

METHODS

NON-MARKET VALUATION METHODS

RATING RANKING CHOICE

Open Ended Contingent Valuation

Referendum Contingent Valuation

Other Choice Based

Methods

Attribute Based Stated ChoiceAdapted from Figure 1: Adamowicz,

Louviere, and Swait. 1998

Survey DevelopmentSurvey Development Atmospheric Science Advisors (ASA)Atmospheric Science Advisors (ASA)

attributes of weather forecastsattributes of weather forecasts current and potential level of attributescurrent and potential level of attributes

Focus groups Focus groups (15 subjects)(15 subjects) One-on-one interviews One-on-one interviews (11 subjects)(11 subjects) Denver Pretest Denver Pretest (84 Subjects)(84 Subjects)

Survey Expert Review PanelSurvey Expert Review Panel North Carolina Focus Groups North Carolina Focus Groups (23 subjects)(23 subjects) Multi-site implementation Multi-site implementation (381 Subjects)(381 Subjects) National random sample National random sample (~1,400 Subjects)(~1,400 Subjects)

Survey LayoutSurvey Layout

IntroductionIntroduction Sources, perceptions and usesSources, perceptions and uses Forecast attributesForecast attributes Value for improved weather forecastsValue for improved weather forecasts

Stated choice - attributes of forecastsStated choice - attributes of forecasts Contingent valuation – demand characteristicsContingent valuation – demand characteristics

Household characteristicsHousehold characteristics Value for Current ForecastsValue for Current Forecasts Severe WeatherSevere Weather

Survey ImplementationSurvey Implementation 9 cities – in-person self-administered9 cities – in-person self-administered written survey - ~25-30 minuteswritten survey - ~25-30 minutes 381 Respondents381 Respondents

Socio-demographicsSocio-demographics

Characteristic Mean

Kruskal-Wallis Test 2

Income (2001$) $49,934 18.84 ** Age 43.7 yrs 13.78 * Education 14.9 yrs 15.12 * Gender 43% males 10.27 How long lived in the area 19.8 yrs 18.29 ** Household size 2.7 6.25 ***, **, * Significant at the 1%, 5%, and 10% respectively

ResultsResults

SourcesSources PerceptionsPerceptions UsesUses Attributes and LevelsAttributes and Levels ValuationValuation

PerceptionsPerceptionsImportance of Weather Forecast Importance of Weather Forecast

CharacteristicCharacteristic

PerceptionsPerceptionsImportance of Weather Forecast Importance of Weather Forecast

CharacteristicCharacteristic

Characteristic Mean SD Kruskal-Wallis

Test, 2 (prob Ho)

Chance of rain, snow, or hail 4.30 0.82 12.44 (0.13)

Amount of rain, snow, or hail 4.02 0.96 21.73 (0.01)

High temperature 3.85 1.01 9.77 (0.28)

Low temperature 3.74 1.06 10.69 (0.22)

How windy it will be 3.28 1.08 7.60 (0.47)

How cloudy it will be 2.74 1.08 14.38 (0.07)

Air pressure 2.21 1.13 10.81 (0.21)

SourcesSources

SourcesSources(regression analysis)(regression analysis)

Local TV Newscasts (Q3A)

Parameter Est. (SE)

Intercept 3.445***

Gender 0.237***

Age (years) 0.018***

Income (1,000s) 0.001

Education (years) -0.043

Percent time work outdoors 0.002

Percent time leisure outdoors 0.008***

AdjRSq 0.088

ProbF 0.000 *, **, *** significant at 10%, 5%, and 1% respectively

UseUse

UseUseOutdoor v. IndoorOutdoor v. Indoor

Planning

< 50% of Leisure Time Outdoors

(n = 187) Mean (SD)

50% + Leisure Time Outdoors (n = 194)

Mean (SD) Wilcoxon Z

Dress for the day 3.86 4.07 1.77 *

Planning for the weekend 3.41 4.09 5.98 ***

Vacation or travel 3.31 3.89 4.84 ***

Social activities 2.97 3.43 3.97 ***

House or yardwork 2.94 3.26 2.50 **

How to get to work/school/store

2.73 3.02 2.08 **

Job or business 2.46 3.06 4.19 *** 1 = “never”; 2 = “rarely”; 3 = “half the time”; 4 = “often”; 5 = “most of the time.”

*, **, *** significant at 10%, 5%, and 1% respectively

Adequacy of Current Levels Adequacy of Current Levels of Forecast Attributesof Forecast Attributes

Attribute Mean SD

Adequacy of updates 4 times a day 3.30 0.68

Adequacy of weather forecasts 5 days in advance

2.89 0.84

Adequacy of 80% correctness of one-day forecasts

2.88 0.81

Adequacy of geography detail to 30 miles by 30 miles

2.74 0.88

Stated Choice:Stated Choice:Attributes and Attribute Attributes and Attribute

LevelsLevels

•Dollars per year per household of $3, $8, $15, $24

•Budget constraint reminder

•20 versions of survey

•9 Stated Choice and 1 Stated Value question

Frequency One-Day Multiday Accuracy

Attribute Improvement

Level

Frequency of Updates (times

per day)

Accuracy of One-Day

Forecasts

Accuracy of Multiday

Forecasts Geographic

Detail

Baseline 4 80% 5 days 30 miles

Minimal 6 85% 7 days 15 miles

Medium 9 90% 10 days 7 miles

Maximum 12 95% 14 days 3 miles

StateStated d

ChoicChoicee

QuestQuest--

ionion

A-B Probit ModelA-B Probit Model

A B

Y-C Fr , One , Multi , Geog

Y-C Fr , One , Multi , Geog

Random Utility Model: x

Choose A if utility from U U

x x

1 1 1 2 2

2 1 2 1

,

,

A A A A A A

B B B B B B

k k k

A A B B

ij ij ij ij ij

ij ij ij ij

U

U

U

P P x x

P x x

univariate standard normal dist.function

x x

2 1

1 2

1 1

2

, 1, . . . , , , ij

ij ij

n Jk

ij ij ij iji j

x x

L k i J , P

Stated Choice QuestionStated Choice Question

A-B-Status Quo Model A-B-Status Quo Model (Conditional Probit)(Conditional Probit)

Random Utility Model (RUM) x

Example-Choose A over B and then stay with A over Status Quo

x x x x

x x x x

0

0 0

0 2 22 0

,

( ), ( )

2 , ;

k k

A B A

B A B A A A

B A A

U

P U U U U

P

Stated Value: Valuation Stated Value: Valuation QuestionQuestion

Stated Value (WTP) ModelStated Value (WTP) Model

Fr , One , Multi , Geog

x

Y-WTP Fr , One , Multi , Geog Y Fr , One , Multi , Geog

let

* * * *

* * *

0 * * * * * 0 0 0 0

* * 0 * 0

2 2* * 0 0

2

22 0

, ,

1( ) . . . ( )

~ . . . ,

fry

fr

y y

WTP f

U

U

WTP Fr Fr

WTP N Fr Fr

2

2y

Model EstimatesModel Estimates(t-ratios in parentheses)(t-ratios in parentheses)

ABO Biv.

Probit WTP Tobit Combined Frequency -0.049

(-10.0) 0.199 (0.8)

-0.067 (-16.4)

One Day 0.062 (16.4)

0.572 (4.0)

0.041 (13.3)

Multi-day 0.031 (6.5)

0.284 (1.2)

0.004 (1.1)

Geographic -0.007 (-4.8)

-0.272 (-4.0)

-0.031 (-25.6)

Cost -0.092 (-17.7)

-0.083 (-22.5)

Est. WTP (Est. std. err.)

$15.27 ($1.05)

$18.49 ($2.08)

$17.88 ($0.96)

N 3429 381 381

National Valuation National Valuation EstimateEstimate

Estimated household WTP $17.88

Number of Households 104,705,000

National WTP $1.872 B

Next StepsNext Steps THORPEX GrantTHORPEX Grant

Re-defining attribute sets and levelsRe-defining attribute sets and levels Temperature: 0-2 days 3-6 days 7-14 daysTemperature: 0-2 days 3-6 days 7-14 days Precipitation : 0-2 days 3-6 days 7-14 daysPrecipitation : 0-2 days 3-6 days 7-14 days Geographic SpecificityGeographic Specificity

National sample - ~1400 completesNational sample - ~1400 completes Internet based implementationInternet based implementation Probablistic forecast InformationProbablistic forecast Information Modeling and analysisModeling and analysis

non-linear in attribute levelsnon-linear in attribute levels random parameters random parameters socio-demographic characteristicssocio-demographic characteristics

Any Any Questions?Questions?

0101

THE REST OF THE SLIDES THE REST OF THE SLIDES HERE ARE EXTRAHERE ARE EXTRA

WE WON’T MAKE YOU WE WON’T MAKE YOU SUFFER THROUGH THE SUFFER THROUGH THE

REST REST (RIGHT NOW AT LEAST)(RIGHT NOW AT LEAST)

0101


Neoclassical versus other approaches?Neoclassical versus other approaches?

Economic values and societal impacts, Economic values and societal impacts, e.g.,e.g.,

• lives savedlives saved• time savedtime saved• environmental valuesenvironmental values• impact on vulnerable populationsimpact on vulnerable populations

0101

What is Value?What is Value? Economic agentsEconomic agents

ConsumersConsumers ProducersProducers GovernmentGovernment

AssumptionsAssumptions1.1. People have rational preferencesPeople have rational preferences2.2. Individuals maximize utilityIndividuals maximize utility3.3. Firms maximize profitsFirms maximize profits4.4. Agents act independently using full Agents act independently using full

information Neoclassical theory includes or extends toNeoclassical theory includes or extends to

Competitive equilibriumCompetitive equilibrium Non-market and intrinsic valuesNon-market and intrinsic values Social welfare theory (incl. benefit-cost Social welfare theory (incl. benefit-cost

analysis)analysis) Value of information (VOI)Value of information (VOI)


P$

Q

S

D

P*

Q*

Producers maximizing profits

by offering quantities for sale at different prices.

Consumers maximizing utility by buying

quantities at different prices.Equilibrium price (P*)

and quantity (Q*) determined by

interaction of Supply and Demand

What is Value?What is Value? PRODUCER PERSPECTIVE

P$

Q

S

D

P*

Q*

Producer Surplus =

Total Revenues

minus

Total Marginal Costs

PS

Total revenues = price x quantity

Total costs = sum of marginal

costs

What is Value?What is Value? CONSUMER PERSPECTIVE

P$

Q

S

D

P*

Q*

Consumer Surplus =

Total Benefits

minus

Total Expenditures

CS

Total benefits = sum of marginal

benefits

Total expenditures = price x quantity


P$

Q

S

D

P*

Q*

CS

PS

Total social benefit = CS + PS


P$

Q

S0

D0

P*

Q*

CS

PS D

1

S1

Q1

CS

PS

What is Value?What is Value? Neo-classical economics – utility theoryNeo-classical economics – utility theory Willingness to pay - WTPWillingness to pay - WTP

1

2 1

1 1 1, ; , ;

where implies "preferred"

2-WTP wV Y P w U V Y P

w w

11 1 , ;U V Y P w

1

1 2

11 2 1 1 2 2

,, ; s.t.

X XU U X X w P X P X Y

Q

S0

D0

P*

Q*

CS

PSD

1

S1

Q1

CS

11 1 1, ; , ; 2-WTP wV Y P w U V Y P

WTP: How much income could

be taken away from the

individual who receives

improved weather

forecasts while keeping him at the same level

of utility


Q

S0

D0

P*

Q*

CS

PSD

1

S1

Q1

PS


11 1 1, ; , ; 2-WT wP P+ SV Y P w U V Y P

PPS =WT

How Are Values Measured?How Are Values Measured?Murphy, A.H., 1994: Assessing The Economic Murphy, A.H., 1994: Assessing The Economic

Value Of Weather Forecasts: An Overview Of Value Of Weather Forecasts: An Overview Of Methods, Results And Issues. Methods, Results And Issues. Meteorological Applications, 1(2), 69-73.Meteorological Applications, 1(2), 69-73.

Anaman, K.A., D.J. Thampapillai, A. Henderson-Anaman, K.A., D.J. Thampapillai, A. Henderson-Sellers, P.F. Noar, And P.J. Sullivan, 1995: Sellers, P.F. Noar, And P.J. Sullivan, 1995: Methods For Assessing The Benefits Of Methods For Assessing The Benefits Of Meteorological Services In Australia. Meteorological Services In Australia. Meteorological Applications, 2(1), 17-29 Meteorological Applications, 2(1), 17-29

Macauley, M.K., 1997: Some Dimensions Of Macauley, M.K., 1997: Some Dimensions Of The Value Of Weather Information: General The Value Of Weather Information: General Principles And A Taxonomy Of Empirical Principles And A Taxonomy Of Empirical Approaches. Report Of Workshop On The Approaches. Report Of Workshop On The Social And Economic Impacts Of Weather, Social And Economic Impacts Of Weather, Boulder, Co.Boulder, Co.

How Are Values Measured?How Are Values Measured? Revealed preferenceRevealed preference

market pricesmarket prices hedonic methodshedonic methods

Stated preferenceStated preference stated value (contingent valuation)stated value (contingent valuation) stated choicestated choice

Prescriptive StudiesPrescriptive Studies

Descriptive StudiesDescriptive Studies

Documents

Jeff Lazo Societal Impacts Program WAS*IS Workshop Boulder, CO July 2006