1. Jeff Lazo Societal Impacts Program WAS*IS Workshop Boulder, CO July 2006 Some Economics to Make a Wiser WAS*ISer

2. ec·o·nom·ics (ĕk'ə-nŏm'ĭks, ē'kə-) n. The social science that deals with the production, distribution, and consumption of goods and services and with the theory and management of economies or economic systems. The social science that deals with the production, distribution, and consumption of goods and services and with the theory and management of economies or economic systems.http://www.answers.com/topic/economics Lionel Robbins 1932: "the science which studies human behavior as a relation between scarce means having alternative uses." Lionel Robbins 1932: "the science which studies human behavior as a relation between scarce means having alternative uses." Lionel Robbins Lionel Robbinshttp://en.wikipedia.org/wiki/Economics study of the allocation of scarce resources in light of unlimited wants study of the allocation of scarce resources in light of unlimited wants Economics

3. Objectives exposed to basic concepts of economics exposed to basic concepts of economics exposed to basic methods of economics exposed to basic methods of economics discuss some applications discuss some applications Reality check can’t teach economics in one hour can’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 than other people – that is NOT true some people think economists think differently than other people – that is NOT true other people think differently than economists other people think differently than economists Economics

4. Why Value Forecasts? 1. program justification benefit-cost analysis benefit-cost analysis 2. program evaluation 3. guidance for research investment any cases of true comparative analysis? any cases of true comparative analysis? 4. inform users of forecast benefits 5. developing end-to-end-to-end forecast and warning system

5. What Should Be Valued? Weather impacts Weather impacts Dutton - $3T US Forecasts Forecasts Improved forecasts Improved forecasts Research to improve forecasts Research to improve forecasts How forecasts are used How forecasts are used

6. What Should Be Valued? Forecasts  Value Integrate forecasting and valuation - Meteorology - Economics

7. What Should Be Valued? Weather  Observation  Forecast  Communication  Perception  Use  Value MeteorologyRisk communicationMarketing PsychologyAnthropologySociology GeographyRisk perceptionsEconomics

8. Some Basic Economics: Value Theory and Topics in Valuation Econ 101

9. TOPICS value theory value theory consumers and producers consumers and producers supply and demand supply and demand markets and prices markets and prices consumer surplus consumer surplus producer surplus producer surplus net societal welfare net societal welfare market failures market failures Econ 101

10. What is Value? Nelson and Winter QJE Forecast Frost No Frost No Frost ActionProtect -C (lost cost) Don’t Protect -L (lost crop) 0 Why ask “What is Value?” Ensure that “economic value” is valid economics Look at broader approach to economic valuation

11. What is Value? Market Failures  Public goods  Market power  Externalities  Information

12. What is Value? 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”?

13. What is Value? Topics: Time PeriodBenefitsCosts 00.00100.00 560.000.00 1060.000.00 120.00100.00 Discounting Discounted Benefits Discounted Costs 0.00100.00 47.020.00 36.830.00 83.85100.00 Net Benefit 20.00Net Benefit -16.15 5% Rate of Time Preference

14. What is Value? Topics: 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

15. What is Value? Topics: Benefits Transfer Application of results from one study for a different analysis context. Same commodity being valued? same baseline? same outcome? No Forecast ClimatologyCurrentImprovedPerfect Adjusting for: date of study – changes in prices (inflation) changes in preference income differences availability of substitutes and complements other significant determinants of value

16. Evaluation of the Sensitivity of U.S. Economic Sectors to Weather (OUSSSA) Evaluation of the Sensitivity of U.S. Economic Sectors to Weather (OUSSSA) Jeffrey K. Lazo – NCAR Pete Larsen – NCAR / Cornell University Megan Harrod – Stratus Consulting Donald Waldman – University of Colorado Purpose: Assess sensitivity of US economic sectors to weather variability

17. Outline Motivation Motivation Concept Concept What is Economic Sensitivity? What is Economic Sensitivity? Data and Modeling Data and Modeling Results Results Conclusions Conclusions

18. 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... “

19. Conceptual Approach Model Building: using historical economic and weather data, we model the relationship between economic output in 11 sectors, economic inputs, and weather and weather variability Capital Labor Energy Temperature Precipitation ? Gross State Product

20. Conceptual Approach Sensitivity Analysis: Using these models, we then hold the economic inputs constant, and use 70 years of weather data to see how economic output varies as a result of variation in weather Capital Labor Energy Temperature  Precipitation  Gross State Product

21. 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

22. What is Weather Sensitivity? P$ Q S(K 0, L 0, E 0 ;W 0 ) D(W 0 ) P* Q* D(W 1 ) S(K 0, L 0, E 0 ; W 1 ) Q1Q1 P1P1 Change in GSP GSP

23. Issues? Weather or climate? Sensitivity or something else?

24. Super Sectors SectorBillions (2000$) Wholesale Trade592 Retail Trade662 Transportation302 Utilities189 Communications458 Agriculture98 FIRE1,931 Manufacturing1,426 Construction436 Mining121 Services675 Total Private Sector6,890 Government1,135 Total8,026

25. Economic Modeling Economic Modeling Translog Function

26. Weather “Sensitivity”

27. Economic Data Economic Data - Economic Data - state x year x sector Gross State Product Gross State Product (dependent variable) Production Inputs Capital (K) - dollars Capital (K) - dollars Labor (L) - hours Labor (L) - hours Energy (E) – BTUs Energy (E) – BTUs Weather Data - Weather Data - state x year Temperature Variability CDD : Cooling Degree Days: (T - 65) on a given day HDD : Heating Degree Days: (65 - T) on a given dayPrecipitation P_Tot: Precipitation Total (per square mile) P_Tot: Precipitation Total (per square mile) P_Std: Precipitation Standard Deviation P_Std: Precipitation Standard Deviation i = state48 j = sector11 t = year1977-2000 = 24 years 48 x 11 x 24 = 12,672 “observations”

28. Temperature Weather Inputs 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

29. Econometric Methods Level data versus per capita Panel data – time series – AR(1) Heteroskedasticity Fixed Effects Covariance calculations for marginal effects

30. Econometric Results ParameterEstimateSig. Intercept-12.089*** YEAR0.003** Capital0.672*** Labor0.798*** Energy0.086** Heating Degree Days-0.035ns Cooling Degree Days-0.068*** P_Tot-0.187*** P_Std0.185*** Sector: Agriculture ns = not significant at 10% * 10%, ** 5%, *** 1%

31. Parameter Estimates from Full Model Regressions Significance (* = 10%, ** = 5%, *** = 1%, ns = not signficant) DF=1068 for all models Agric.WholesRetailFIREComm.UtilitiesTransp.Manf.Constr.MiningSvcs Inter 50.46 ns -1.65 ns -2.08 ns 39.98 ns 27.08 ns -25.26 ns -28.44 ns -24.78 ns -6.13 ns 153.57 ** 55.72 *** YEAR -0.01 *** 0.02 *** -0.01 *** 0.004 ** -0.01 *** 0.01 ** 0.01 *** 0.03 *** 0.00 ns -0.03 *** 0.003 *** ln KAP -2.10 ** -0.12 ns -0.75 ns 7.70 *** 2.98 *** 9.21 *** 4.78 *** 0.51 ns -9.55 *** -5.93 *** -2.66 *** CDD 2.56 * -0.24 ns 0.24 ns -3.10 ** -0.97 ns 2.79 ns -3.52 *** 1.49 ns -1.43 ns 2.50 ns -1.05 * KAP 2 0.14 *** 0.05 *** 0.08 *** 0.02 ns 0.02 ns -0.23 *** 0.07 *** 0.04 ns 0.06 *** 0.19 *** -0.03 ns KAP x HDD -0.06 ns 0.04 ** 0.09 *** -0.07 ** 0.06 ** -0.31 *** -0.06 ns 0.05 ns 0.24 *** 0.57 *** 0.15 ***

32. Marginal Responses CapitalLaborEnergy SectorMarg EffT-StatMarg EffT-StatMarg EffT-Stat Agriculture1.1035.020.448.55-0.01-0.14 Communications1.1230.080.3112.57-0.14-6.23 Construction0.4812.401.1452.350.124.60 FIRE0.9832.490.399.82-0.20-6.84 Manufacturing0.485.760.626.980.091.71 Mining1.2011.860.609.200.101.51 Retail Trade0.9131.150.5415.94-0.04-2.02 Services0.9435.850.6418.57-0.07-5.53 Transportation0.9428.840.3312.210.071.90 Utilities1.1122.57-0.31-4.94-0.03-0.73 Wholesale Trade0.5019.990.7833.01-0.02-1.15

33. Marginal Responses HDDCDDTotal PrecipPrecip Variance Sector Marg EffT-StatMarg EffT-StatMarg EffT-StatMarg EffT-Stat Agriculture0.00-0.05-0.19-6.110.281.89-0.12-6.75 Communic.0.133.960.063.310.060.360.1716.15 Construct.-0.01-0.380.062.85-0.01-0.050.2620.84 FIRE0.153.520.062.700.543.19-0.08-5.60 Manufact.0.181.850.020.360.492.34-0.22-6.60 Mining0.251.970.040.57-3.52-9.541.1027.44 RetailTrade0.041.750.032.88-0.13-1.320.1318.20 Services0.042.070.000.290.334.01-0.05-7.72 Transport.-0.03-0.910.010.44-0.15-0.740.1512.18 Utilities0.000.040.081.91-0.59-1.42-0.28-11.59 Wholesale0.104.630.021.65-0.19-1.930.023.03

34. Wx Sensitivity Analysis Average K, L, E 1996-2000 Average K, L, E 1996-2000 Set Year to 2000 Set Year to 2000 Historical weather data 1931-2000 Historical weather data 1931-2000 Fitted GSP values by sector by state by year Fitted GSP values by sector by state by year 11 sectors 11 sectors 48 states 48 states 70 “years” fit to 2000 “economic structure” 70 “years” fit to 2000 “economic structure” 11 Sector Models: Q = f (K, L, E, W; Year, State)

35. State Sensitivity State Sensitivity (Billions $2000)

36. StateMeanMaxMinRange% RangeRank New York633.3679.6594.085.613.5%1 Alabama92.093.981.712.213.3%2 California1019.41080.5968.6111.911.0%3 Wyoming13.714.312.81.410.5%4 Ohio312.0330.6298.432.210.3%5.......................................... Delaware30.230.629.61.03.3%44 Maine27.027.426.50.93.3%45 Montana17.217.416.90.63.3%46 Louisiana109.5111.2107.63.63.3%47 Tennessee141.1142.8139.33.52.5%48

37. Sector Sensitivity Sector Sensitivity (Billions $2000)

38. SectorMeanMaxMinRange%Range Agriculture127.6134.4119.015.412.09% Wholesale trade601.5607.8594.513.32.20% Retail trade761.5771.2753.917.32.27% FIRE1,639.31,713.11,580.6132.58.08% Communications237.3243.4232.311.14.68% Utilities212.9220.8206.014.96.98% Transportation276.1280.7271.09.83.53% Manufacturing1,524.81,583.21,458.2125.18.20% Construction374.5384.0366.417.74.71% Mining102.0108.994.214.714.38% Services1,834.91,865.41,804.960.53.30%

39. Sector Sensitivity Sector Sensitivity (Billions $2000) SectorMeanMaxMinRange%Range Agriculture127.6134.4119.015.4 12.09% Wholesale trade601.5607.8594.513.32.20% Retail trade761.5771.2753.917.32.27% FIRE1,639.31,713.11,580.6132.58.08% Communications237.3243.4232.311.14.68% Utilities212.9220.8206.014.96.98% Transportation276.1280.7271.09.83.53% Manufacturing1,524.81,583.21,458.2125.18.20% Construction374.5384.0366.417.74.71% Mining102.0108.994.214.7 14.38% Services1,834.91,865.41,804.960.53.30%

40. Sector Sensitivity Sector Sensitivity (Billions $2000) SectorMeanMaxMinRange%Range Agriculture127.6134.4119.015.412.09% Wholesale trade601.5607.8594.513.32.20% Retail trade761.5771.2753.917.32.27% FIRE1,639.31,713.11,580.6 132.5 8.08% Communications237.3243.4232.311.14.68% Utilities212.9220.8206.014.96.98% Transportation276.1280.7271.09.83.53% Manufacturing1,524.81,583.21,458.2 125.1 8.20% Construction374.5384.0366.417.74.71% Mining102.0108.994.214.714.38% Services1,834.91,865.41,804.960.53.30%

41. National Sensitivity National Sensitivity (Billions $2000) Total National 7,692.47,554.67,813.4258.7 3.36% SectorMeanMaxMinRange%Range Agriculture127.6134.4119.015.412.09% Wholesale trade601.5607.8594.513.32.20% Retail trade761.5771.2753.917.32.27% FIRE1,639.31,713.11,580.6132.58.08% Communications237.3243.4232.311.14.68% Utilities212.9220.8206.014.96.98% Transportation276.1280.7271.09.83.53% Manufacturing1,524.81,583.21,458.2125.18.20% Construction374.5384.0366.417.74.71% Mining102.0108.994.214.714.38% Services1,834.91,865.41,804.960.53.30%

42. 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 demand split supply and demand model uncertainty model uncertainty

43. 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

44. Conclusions Economically valid analysis Significant impact of weather significant regression coefficients significant regression coefficients significant marginal effects significant marginal effects Interpretation of weather sensitivity upper-bound weather risk measure? upper-bound weather risk measure? upper-bound measure of value of weather information? upper-bound measure of value of weather information? 3.4% of annual US economic variability 3.4% of annual US economic variability $260B US economic variability related to weather variability $260B US economic variability related to weather variability

45. Benefits of Investing in Weather Forecasting Research SuperComp Benefits of Investing in Weather Forecasting Research Jeff Lazo, Jennie Rice, Marca Hagenstad SuperComp Purpose: Assess benefits of buying a new supercomputer for weather forecast research

46. TOPICS TOPICS Value of investments in research Value of investments in research Assess value chain Assess value chain Benefit-cost analysis Benefit-cost analysis Benefits transfer Benefits transfer Value of statistical life Value of statistical life Discounting Discounting Sensitivity analysis Sensitivity analysis SuperComp

47. SuperComp Study Methods 1. Determine potential impact of supercomputer on forecast quality 2. Identify potential sectors/users and of improved forecast 3. Identify existing benefit studies for sectors/users 4. Quantify probabilities and timing of impacts 5. Develop benefits model for aggregating over time 6. Conduct sensitivity analysis

48. 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 Benefits Household Benefits

49. Example: “SuperComp” Household Benefits of Short Term Weather Forecasts Stratus Consulting (2002) – stated preference study

50. Example: “SuperComp” Agriculture: Annual value of improvement to perfect information (PI) Apples, peaches, and pearsAlfalfa Winter wheat Total - these crops Value of improvement to PI per acre of farmland $1,403$75$35$65.19 Acres of farmland828,46023,541,00044,349,00068,718,460 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

51. Example: “SuperComp” Weather-related fatalities and VSL estimates (we assume 10% of weather-related fatalities preventable with perfect information) YearFatalities Fatalities — value (millions) 1996540$3,240 1997600$3,600 1998687$4,122 1999908$5,448 2000476$2,856 2001464$2,784 2002540$3,240 Average annual602$3,613 a. Calculated as $6 million per fatality. Source: NWS (1996-2002).

52. Example: “SuperComp” 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

53. Example: “SuperComp” 1. Contribution from NHRA – 20% 2. Contribution from computing – 33% 3. Contribution to forecast improvements over 5 year life of NHRA – 75% 20% x 33% x 75% = 5%

54. Example: “SuperComp” Financial assumptions for base case present value calculations Real social discount rate3% Decision to purchase supercomputer 2004 First year of operation2005 Number of years until benefits begin2 Number of years in which benefits accrue5 Time horizon for accrued benefitsInfinite

55. Example: “SuperComp” Summary of present value of benefits in 2003 (millions, 2002$) Household sector69 Orchards, winter wheat, alfalfa26 Avoided weather-related fatalities21

56. What Are Weather Forecasts Worth? Stated Preference Approaches to Valuing Information Storm What Are Weather Forecasts Worth? Stated Preference Approaches to Valuing Information Jeff Lazo, Rebecca Morss, Barb Brown, Stratus Consulting Storm Purpose: Assess values to households of ordinary weather forecasts

57. TOPICS Stated preference valuation Stated preference valuation Survey development Survey development Analysis Analysis Uses – between city comparison Uses – between city comparison Sources - regression Sources - regression Perceptions – between individual comparison Perceptions – between individual comparison Econometrics - regression analysis Econometrics - regression analysis Probit model Probit model Bivariate probit model Bivariate probit model Combining CV and SC data Combining CV and SC data STORM

58. Study Objective Evaluate benefits to households of improvements in weather forecasting servicesEvaluate benefits to households of improvements in weather forecasting services 104,705,000 households104,705,000 households Day-to-day weatherDay-to-day weather National Oceanic & Atmospheric AdministrationNational Oceanic & Atmospheric Administration

59. STATED PREFERENCE METHODS REVEALED PREFERENCE METHODS NON-MARKET VALUATION METHODS RATING RANKINGCHOICE Open Ended Contingent Valuation Referendum Contingent Valuation Other Choice Based Methods Attribute Based Stated Choice Adapted from Figure 1: Adamowicz, Louviere, and Swait. 1998

60. Survey Development Atmospheric Science Advisors (ASA) Atmospheric Science Advisors (ASA) attributes of weather forecasts attributes of weather forecasts current and potential level of attributes current and potential level of attributes Focus groups (15 subjects) Focus groups (15 subjects) One-on-one interviews (11 subjects) One-on-one interviews (11 subjects) Denver Pretest (84 Subjects) Denver Pretest (84 Subjects) Survey Expert Review Panel Survey Expert Review Panel North Carolina Focus Groups (23 subjects) North Carolina Focus Groups (23 subjects) Multi-site implementation (381 Subjects) Multi-site implementation (381 Subjects) National random sample (~1,400 Subjects) National random sample (~1,400 Subjects)

61. Survey Layout Introduction Introduction Sources, perceptions and uses Sources, perceptions and uses Forecast attributes Forecast attributes Value for improved weather forecasts Value for improved weather forecasts Stated choice - attributes of forecasts Stated choice - attributes of forecasts Contingent valuation – demand characteristics Contingent valuation – demand characteristics Household characteristics Household characteristics Value for Current Forecasts Value for Current Forecasts Severe Weather Severe Weather

62. Survey Implementation 9 cities – in-person self-administered 9 cities – in-person self-administered written survey - ~25-30 minutes written survey - ~25-30 minutes 381 Respondents 381 Respondents

63. Socio-demographics

64. Results Sources Sources Perceptions Perceptions Uses Uses Attributes and Levels Attributes and Levels Valuation Valuation

65. Perceptions Importance of Weather Forecast Characteristic

67. Sources

68. Sources (regression analysis)

69. Use

70. Use Outdoor v. Indoor

71. Adequacy of Current Levels of Forecast Attributes

72. Stated Choice: Attributes and Attribute Levels 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

73. Stated Choice Quest- ion

74. A-B Probit Model

75. Stated Choice Question

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

77. Stated Value: Valuation Question

78. Stated Value (WTP) Model

79. Model Estimates (t-ratios in parentheses)

80. National Valuation Estimate

81. Next Steps THORPEX Grant THORPEX Grant Re-defining attribute sets and levels Re-defining attribute sets and levels Temperature: 0-2 days 3-6 days 7-14 days Temperature: 0-2 days 3-6 days 7-14 days Precipitation : 0-2 days 3-6 days 7-14 days Precipitation : 0-2 days 3-6 days 7-14 days Geographic Specificity Geographic Specificity National sample - ~1400 completes National sample - ~1400 completes Internet based implementation Internet based implementation Probablistic forecast Information Probablistic forecast Information Modeling and analysis Modeling and analysis non-linear in attribute levels non-linear in attribute levels random parameters random parameters socio-demographic characteristics socio-demographic characteristics

82. Any Questions?

83. THE REST OF THE SLIDES HERE ARE EXTRA WE WON’T MAKE YOU SUFFER THROUGH THE REST (RIGHT NOW AT LEAST) THE REST OF THE SLIDES HERE ARE EXTRA WE WON’T MAKE YOU SUFFER THROUGH THE REST (RIGHT NOW AT LEAST)

84. What is Value? Neoclassical versus other approaches? Economic values and societal impacts, e.g., lives savedlives saved time savedtime saved environmental valuesenvironmental values impact on vulnerable populationsimpact on vulnerable populations

85. What is Value? Economic agents Economic agents Consumers Consumers Producers Producers Government Government Assumptions Assumptions 1. People have rational preferences 2. Individuals maximize utility 3. Firms maximize profits 4. Agents act independently using full 4. Agents act independently using full information Neoclassical theory includes or extends to Neoclassical theory includes or extends to Competitive equilibrium Competitive equilibrium Non-market and intrinsic values Non-market and intrinsic values Social welfare theory (incl. benefit-cost analysis) Social welfare theory (incl. benefit-cost analysis) Value of information (VOI) Value of information (VOI)

86. What is Value? 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

87. 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

88. 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

89. What is Value? P$ Q S D P* Q* CS PS Total social benefit = CS + PS

90. What is Value? P$ Q S0S0 D0D0 P* Q* CS PS D1D1 S1S1 Q1Q1  CS  PS

91. What is Value? Neo-classical economics – utility theory Neo-classical economics – utility theory Willingness to pay - WTP Willingness to pay - WTP

92. Q S0S0 D0D0 P* Q* CS PS D1D1 S1S1 Q1Q1  CS 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 What is Value?

93. Q S0S0 D0D0 P* Q* CS PS D1D1 S1S1 Q1Q1  PS What is Value?

94. How Are Values Measured? Murphy, A.H., 1994: Assessing The Economic Value Of Weather Forecasts: An Overview Of Methods, Results And Issues. Meteorological Applications, 1(2), 69-73. Anaman, K.A., D.J. Thampapillai, A. Henderson- Sellers, P.F. Noar, And P.J. Sullivan, 1995: Methods For Assessing The Benefits Of Meteorological Services In Australia. Meteorological Applications, 2(1), 17-29 Macauley, M.K., 1997: Some Dimensions Of The Value Of Weather Information: General Principles And A Taxonomy Of Empirical Approaches. Report Of Workshop On The Social And Economic Impacts Of Weather, Boulder, Co.

95. How Are Values Measured? Revealed preference Revealed preference market prices market prices hedonic methods hedonic methods Stated preference Stated preference stated value (contingent valuation) stated value (contingent valuation) stated choice stated choice Prescriptive Studies Prescriptive Studies Descriptive Studies Descriptive Studies