1. GEF Session 9B Valuing Reduced Morbidity: A Case Study of the Persian Gulf Environmental Damages Morteza Rahmatian California State University, Fullerton mrahmatian@fullerton.edu Ashgabad, November, 2005

2. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity During the 1991 Gulf War, 700 oil wells were set on fire by Iraq’s troops. These fires burned for 10 months creating the most disastrous environmental problem ever recorded. The propose of this report is to estimate the health effects from the air pollution caused by this disaster. Contingent Valuation Method (CVM) is employed to estimate the monetary values.

3. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Based on our experience with focus groups and pre-testing, we chose to target valuing seven symptoms: coughing spells, shortness of breath, eye irritation, sore throat, headache, chest pain and asthma. Values presented here are “one-day” willingness to pay (WTP) estimates for one less day of symptom occurrence.

4. GEF The Utility Model U = U(X, L, I, N; Z) Where: X:Consumption goods L:Leisure I:Illness adjusted for its severity N:Nature of illness Z:Vector of individual characteristics

5. GEF The Utility Model I = (D)(S)where: D:is the disutility from illness. S:is the severity of the illness. Z:is a vector of individual characteristics such as health history, age, etc.

6. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Utility Model I = (D)(S) I(P, N, M, E) = [D(P, N, E)][S(M, E)] P:Air pollution N:Nature of the illness E:Severity of air pollution M:Mitigating behavior (i.e. Medication)

7. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Utility Model Individuals’ Utility maximization subject to the Budget Constraint: Y + W(T – L – I) = P X X + P M MWhere Y;Non-wage income W;Real wage rate T;Total time P;Price

8. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Utility Model Willingness to pay for a change in D necessary to achieve U 0 at the original duration of illness, D 0, minus the expenditure necessary to achieve U 0 at the new (lower) duration of illness D 1 : WTP = E(P X, P M, Y, W, N, S, Z, D 0, U 0 ) - E(P X, P M, Y, W, N, S, Z, D 1, U 0 )

9. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Data and Health Impact Valuation Residents of Busheher and Hormozghan were surveyed. First, respondent’s health background and the frequency of which they experienced any of the health symptoms. Second, Maximum Willingness to Pay, per symptom avoided, per day was asked Third, socio-economic questions was asked

10. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Data and Health Impact Valuation Number of observation200 Smokers37% Sports46% Diet53% Male59% Female51% Age34.26 Years Education14.31 Years Household size3.92 Head of household55% Average income903,580 Rials

11. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Data and Health Impact Valuation SymptomsMean ValueMedian Cough per day18,39012,000 Shortness of breath21,80017,500 Eye irritation16,05011,000 Sore throat20,54010,000 Headache32,37020,000 Chest pain31,02020,000 Asthma Attack40,51030,000

12. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity The Data and Health Impact Valuation Due to large discrepancy between mean and medium avoidance bids, median bids were bids were used in this study. Majority of the indicated socio-economic variables displayed the expected relationship with bids providing for the survey instrument used in this report.

13. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Population at Risk This report attempts to place a monetary value on avoiding seven health symptoms, which restricts daily activities. Many other elements are missing such as, loss of human life, pain and suffering, ecological degradation,…….

14. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Population at Risk To estimate the health effects of the pollutants due to impact of the Gulf War, the following steps were taken: 1.An estimate of an exposure-response and or dose-response function specific to the local pollutant mix was derived. 2.Age and gender distributions were obtained through Iran’s national statistics to estimate the total population at risk

15. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Population at Risk 3. Time-activity profiles for the population is used to determine the percentage of time the specific population spends outdoors relative to the time spent indoors. 4. Ambient air quality data for all pollutants of interest needs to be collected. 5. An emission source inventory is identified. Here, the inventory source of pollution was the 700 oil wells set on fire.

16. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Population at Risk Total population exposed in the 8 counties under study is: MaleFemaleTotal 7,636,4647,179,95114,816,415 MaleFemale Outdoor 3,619,684299,490 Indoor4,016,7806,880,461

17. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Dose response function Number of symptom per month SymptomsMeanMedianAd. Median Cough 12.55139 Sh. of breath 9.98107 Eye irritation 8.6685 Sore throat 5.7154 Headache 13.721512 Chest pain 1.6300 Asthma Att. 0.3800

18. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Dose response function Nearly 45% of the population was exposed to levels of pollution above the first stage alert levels The relationship between air quality, the amount of pollution, the health effects of breathing the pollution, and the economic benefits of preventing those effects is quantified

19. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Sensitive Population in the Southern Part of Iran Infants and the elderly experienced the lowest exposures per capita because they spent less time outdoors. School age children, college students, and adults experienced the highest exposure per capita. This group constitute 28% of the population, yet they experienced 40% of the symptoms.

20. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity WTP Adjustment Function The value placed on, the first day of reduced symptoms would not be expected to be the same as that for the tenth day due to simple economic theory of diminishing marginal utility. WTP to reduce N days of a symptom is significantly less than N times the WTP to reduce 1 day of a symptom.

21. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Adjusted WTP for Multiple Days of Symptom # Days reducedAll Days ValuedAdj. WTPMult Factor 1 11,0001,0001.00 22,0001,4100.705 33,0001,7000.566 44,0001,9900.497 55,0002,2400.448 66,0002,4900.415 7 7,000 2,690 0.384 88,0002,8700.358 99,0003,0300.336 101,00003,1600.316

22. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity To estimate the indoor/outdoor total economic values (Cough for example), the outdoor population, the frequencies of symptoms (9), the unit values (WTP = 12,000), and the multiple days adjusting factors were utilized (0.336). Outdoor Total Value = 768,000,000,000 Indoor Total Value = 1,620,000,000,000

23. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Indoor Outdoor The main distinction between indoor and outdoor is the fact that for the indoor population the frequencies of symptom occurrence adjusted by the 0.625 indoor - outdoor factor.

24. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation The bids offered for five symptom combinations (cough, shortness of breath, eye irritation, sore throat and headache) is valued at 55% of the summed symptoms separately because of the diminishing marginal utility. Note that chest pain and asthma attacks were eliminated from the analysis due to zero median frequencies for the period in question.

25. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation The total population at risk was estimated at 45% of the general population. This is based on population density, distance to the source of pollution, spatial distribution, the unemployment rate and population concentration in villages vs. major metropolitan areas.

26. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation Thus the final monetary value assigned for reducing pollution must be adjusted twice. Once by 55% for multiple symptom days and the second time by 45% to capture the general population at risk from such pollution. Therefore, the total adjusting factor: 0.2475 = [(0.55)(0.45)]

27. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation 1.The final adjusting factor for the general population at risk is: (Multiple Symptom Factor)(Percent Population Exposed) = Adjusting Factor (55%)(45%) = 0.2475

28. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation 2.The final monthly monetary is: (Total Monthly Value)(Adjusting Factor) = Final Monthly Value (2,380,000,000,000)(0.2475) = 590,000,000,000 Rials

29. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation 3.Using the exchange rate of $1 = 8,000 Rials, this total monthly value can be exchanged into Dollars. (Final Monthly Value)(Exchange Rate) = Final Value in Dollars (590,000,000,000)(1/8000) = $73,750,000

30. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation Thus an average of 5 months is used to compute the total value lost in health benefits (Final Monthly Value)(5 Months) =Total Value Lost (590,000,000,000)(5) = 2,950,000,000,000 Rials The same value presented in Dollars is, ($73,750,000)(5) = $ 368,750,000

31. GEF Caspian EVE 2005/UNDP and WBI Morteza Rahmatian, Valuing Morbidity Overall Valuation These estimates are the lower – bound, estimate of the benefits. Comparing this value to the cost of reducing ambient pollution can provide policy makers with a guide to the net benefits of reducing air pollution in terms of reduced incidence of health related illnesses. Of course, a more comprehensive analysis would need to include the other benefits of reducing air pollution, such as mortality and damages to agricultural and agricultural goods.