Willingness to Pay for Environmental Improvements in the Presence of Warm-Glow

1. Willingness to Pay for Environmental Improvements in the Presence of Warm-Glow Matthew G. Interis, Mississippi State University Timothy C. Haab, The Ohio State University CNREP Meeting May 28, 2010 New Orleans, LA

2. Why think about warm-glow? • Warm-glow: the private benefit a contributor to the public good gets from the act of giving itself (Andreoni 1990) – i.e. the warm fuzzy feeling one gets from doing something “good.” • In non-market valuation, the value of the good we traditionally seek, WTPA, is implicitly defined by: • v(y-WTPA, F1, w(a0)) = v(y, F0, w(a0)) • v is indirect utility • y is income • F is the level of environmental quality • w is warm-glow • a is a vector of past actions creating warm-glow

3. Why think about warm-glow? • In the presence of warm-glow, what gets reported in a contingent valuation (CV) survey is: • v(y-WTPR, F1, w(a0, WTPR)) = v(y, F0, w(a0)) • (or more usually, a yes/no response based on the above) • Questions • How much of a factor is warm-glow? • A: it’s more important for people who have engaged in fewer past activities → Diminishing marginal utility of warm-glow actions. • Can we back out WTPA from WTPR? If so, how do they differ? • A: Yes. WTPR ~ 73% greater than WTPA

4. Empirical setting • Internet Survey of Ohio Adults • Sample size 859 • 537 completed surveys • Survey had several sections, but two are important here: • Task 1) Respondents answered yes/no whether they would pay a higher per gallon gas price, p1, to lower a Fuel Index (FI) • FI attempts to aggregate effects of different emissions vectors resulting from different mixes of fuel consumption across the U.S. • A higher index is worse – greater risk to human health, greater strain on natural resources, greater threat of environmental damage, etc.

5. Empirical setting • Task 2) Respondents asked whether they would make a hypothetical contribution to a carbon offsetting organization (e.g. TerraPass) • Also: • Rated themselves 0-10, on their self-image • Rated a hypothetical other person who gives some amount to offsetting • This task came after the other task.

6. Empirical model • In task 1, respondents are willing to pay the higher price if: • v1 = v(p1, y, F1, w1) ≥ v(p0, y, F0, w0) = v0 • where: w1 = w(a0, ∆p) w0 = w(a0) F is the fuel index value p is the per gallon price of gas • estimated using standard random utility model • p, y, and F are easy to measure. w is difficult to measure. • more specifically, in RUM, we need a measure for ∆w = w1 - w0 • this is where task 2 is used

7. Empirical model • How to measure ∆w? • No obvious way, and any attempt will have its flaws • We use, from task 2: ∆w = (Rating of self – Rating of other)/(contribution of self – contribution of other) * ∆p • Interpreted as: the change in warm-glow per dollar, γ, times the change in the price of gasoline. • Obvious weaknesses: • assumes people rate others similarly to how they rate themselves • comes from a different context (contribution to carbon offsetting) • can take on a negative value (no constraint that a greater contribution must mean a higher image)

8. Empirical model • Let tAbe the actual price premium consumer is willing to pay • tRis the reported price premium • Assuming a linear in parameters indirect utility function,then: • where αp < 0and αw > 0 are the marginal utilities of gas price and warm glow, respectively. • inequality holds assuming γ≥ 0, and denominator remains positive • note: if γ = 0, then tR= tA • note also: for a good with inelastic demand (i.e. gas), WTPi= ti*q , where i = A,R, and q is quantity of gas consumed (Johannson 1996)

9. Empirical model • Survey contained questions on past environmental behavior, (vector a): whether respondent had given money to an env. organization, whether they were a member of an env. organization, whether they had performed any env. activities, etc. • Separating people by past environmental activity, a pattern emerged that those who had done less in the past had a higher marginal utility of warm-glow, αw, and, for people who had done more in the past, αw became negative. • Warm-glow measure is composed of a warm-glow per dollar, γ, and a change in price – by themselves, one would expect these to have opposite marginal effects on utility.

10. Empirical results • Probit results: Covariate Estimate Standard Error Intercept -0.42 0.61 p (low a) -4.63* 2.11 p (high a) -1.44 2.03 FI -0.06** 0.02 w (low a) 18.65* 9.13 w (high a) -6.99 6.74 Badness of ∆FI 0.29** 0.11 Conservative -0.15* 0.07 *indicates significance at 5% level, ** 1% level. N = 196. Percent Concordant = 69.70. Sample includes only respondents for whom γ ≥ 0 (196 out of 251)

11. Empirical results • LR test that there is no difference between parameters on p and w across groups is rejected at 95% level. • All signs are as expected • Signs indicate direction of marginal change in the independent variable on probability of a “yes” response • Interesting result is parameters on w • Positive and significant for those with little environmental background • Negative but insignificant for those with high environmental background • Diminishing marginal utility of warm-glow actions • Including people with γ <0, all signs and significance remain the same except that parameter on w becomes significant for the high environmental background group • Makes sense – these people get the opposite of warm-glow. Many possible explanations: don’t trust govt. management of funds, think contributing is for chumps, etc.

12. Warm-glow and WTP • Recall that the reported premium, tR is greater than actual premium, tA , by a factor of : • Using the mean value of γ and estimated values of αp and αw for the low group, the above has a value of 1.73. • i.e. for those who get more utility from the warm-glow of contributing, the reported price premium is ~ 73% greater than the premium they would be willing to pay, were they to receive no warm-glow from contributing.

13. Warm-glow and WTP • Calculating premium based on means of data for the low group: tR = $0.165 , tA = $0.095 • Not accounting for warm-glow at all: tA = $0.155 • Nunes and Schokkaert (JEEM 2003) – reported WTP 55-270% higher than “cold” WTP • Most research has focused on finding evidenceof warm-glow in decisions to contribute to a public good (e.g. Menges et al. ERE 2005, Ribar and Wilhelm JPE 2002), but hasn’t focused on determining “cold” WTP in the presence of warm-glow.

14. Conclusions • Respondents show decreasing marginal utility of warm-glow activities • Failing to account for warm-glow results in an estimate of WTP that is ~73% higher than “true” WTP • i.e. the WTP we would normally think of, that is, the monetary payment the respondent pays that makes him just as well off as before the improvement, ceteris paribus. • More research needed • Which is the appropriate measure for practical concerns of benefit cost analysis? • It most likely depends on how the environmental good or service is provided – whether people get a warm-glow.

15. Questions? Comments? Thank you.