Air quality

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Willingness to Pay for Air Quality Improvements
in S
tofia, Sn

People in Sofia are willing to pay 4.2 percent of their income or more for a

to improveair

Public Disclosure Authorized

Public Disclosure Authorized

Hua Wang DaleWhittington

The World Bank Development Research Group Infrastructure and Environment January 2000


Summary findings
Through a survev, Wang and Whittington study willingness to pav for improvermentsin air quality in Sofia, Bulgaria. Using a stochastic pavmrcn card approach -- asking respondents the likelihood that they would agree to pay a series of prices- they estimate the distribution ot willingniessto pay various prices. T hey find that people in Sofia are willing to pay up to for about 4 7.percent of their inconme a program to improve air quality. The income elasticity of willingness to pay for air quality improvements is aboLut percent. 27 For comparison, they also used the referendum contingent valuation approach. Results from that approachyielded a higher estimate of willingness to pay.

This paper - a product of inifrastructure and Environment, Development Research Group -is part of a larger effort in the group to understand the economics of pollution control in developing countries. Copies of thc paper are available fr( e from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contactRoulaYazigi, roomIMC2-533,telephone 202-473-7176, fax 202-522-3230, email address ryazigi( V(Y, P, Eo, Z, s) }
Pr {V(Y-t, P, E, Z, )> V(Y-WTP, P, El, Z, c)}


Pr {WTP>t}

1- F(t)

where again V is an indirect utility function; Y is income; P is a price vector; Z is a vector of individual's socioeconomic characteristics; E. is the initial environmental quality, which would be improved to E, if the air qualitymanagement plan is implemented; t is the price offered to obtain the environmental quality change; and WTP is the individual's value for the certain change. The likelihood matrix obtained with the stochastic payment card is a record of an individual's probabilities of accepting different proffered payments. The cumulative valuation distribution function F(.), the valuation probability densityfunction, the mean and the variance of the probability function can be estimated with the likelihood matrix data. The estimation of the valuation distribution is straight

It is possiblethat introducinguncertainty explicitlyin the CV questionscould createconfusionfor the respondent rather than enablinga respondent give a more complete to valuationresponse. This concernis especiallypertinentincountrieswherepeopledo not have a long experience with democraticvoting procedures,such as Bulgaria. However,the resultsof our casestudyandthe findingsof WelshandPoe (1998)would appearto indicatethat this threatis perhapsnot as seriousas one mightfear. 7

forward. From (4), we have Pi,= I-F,(tj), where Pij is individual i's probability (the number circled by respondent i on the stochastic paymentcard) of voting for the referendum at the jth payment point tj; F,(.) is the person i's CDF. By assuming a specific functional form for Fi(.), standard statistical software can be used to estimate the parameters in Fi(.). and the mean 1L, standard variance vi of individual i's valuation and distribution. For example, if a normal distribution is assumed for F(.), we have,

Pi = I - (X(8u t) ti = +o-I (1- Pi)

With a set of ti's and Pi's, a simple regression can be used to estimate jL and a. For cases where numeric likelihood values cannot be obtained for estimating a valuation distribution, it might be possible to estimate an upper bound, a lower bound, and a mean value with some reasonable assumptions about the meaning of the verbal likelihood data. An estimate of the upper bound of an...
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