Vol. 43 (2000) 127–139
Do as you say, say as you do: evidence on
gender differences in actual and
stated contributions to public goods
Kelly M. Brown
a,∗, Laura O. Taylor
baUS Environmental Protection Agency, 1200 Pennsylvania Ave., NW,
Mail Code 2172, Washington, DC 20460, USA
bDepartment of Economics, Andrew Young School of Policy Studies,
Georgia State University, Atlanta, GA 30303, USA
Received 10 January 2000; received in revised form 25 January 2000; accepted 9 February 2000
Abstract
Recent work on public goods contributions has examined the relationship between gender and free-riding behavior in studies using laboratory public goods. This research furthers this line of in-quiry by examining gender as a possible explanation of hypothetical bias, which occurs in valuation studies using real world public goods. Results show that gender differences exist in hypothetical valuation exercises, but not in real valuation exercises. Further, the results show that hypothetical bias is almost three times larger for males than for females, an important result for researchers investigating the source of, and solutions for, hypothetical bias. © 2000 Elsevier Science B.V. All rights reserved.
JEL classification:H41
Keywords:Public goods valuation; Gender; Contingent valuation; Experimental economics
1. Introduction
Economists have long recognized that there may be systematic differences in behavior based on observed or unobserved characteristics of individuals. While unobserved char-acteristics cannot be directly measured, most studies control for observed charchar-acteristics that may influence outcomes, such as gender, income, age, and education. The impact of
∗Corresponding author. Tel.:+1-202-260-4148; fax:+1-202-260-5732.
E-mail address:[email protected] (K.M. Brown).
various characteristics on behavior can provide valuable insight into how and why certain phenomenon occur. For example, recent work using experimental methods has examined the relationship between gender and free-riding in public goods contributions experiments (Brown-Kruse and Hummels, 1993; Nowell and Tinkler, 1994; Seguino et al., 1996; Cadsby and Maynes, 1998). Each of these studies find evidence of significant free-riding by both females and males, however, the results are contradictory as to which gender exhibits more free-riding.
In experiments such as those just described, the “public good” consists of a group fund to which subjects contribute some or all of their endowment of tokens in exchange for a return based on total contributions by the group. These types of public goods are also referred to as laboratory goods, due to their nondescriptive nature. While experiments conducted with laboratory goods enhance our understanding of behavioral differences in response to various experimental treatments, it is important, to the extent possible, to extend the lab conditions to the “field”. In the current context, an extension of the lab to the field involves the use of non-laboratory public goods (or “real world” public goods), such as public parks or reserves, in experimental valuation exercises.
In the past, the primary method for valuing non-laboratory public goods has been the contingent valuation method, a survey method that asks subjects to state their willingness to pay (WTP) for a particular public good in a contingent, or hypothetical market.1 Contingent valuation studies often control for gender, but no systematic results as related to gender have evolved from this literature.2 This is to be expected as preferences are unique to each particular public good and therefore must be evaluated on a case-by-case basis. While it is difficult to draw conclusive results regarding gender preferences for public goods across studies using non-laboratory public goods, this does not preclude an analysis of potential gender-specificbehavioraldifferences in response to various experimental treatments for a given non-laboratory public good.
A recent focus in the contingent valuation literature has been to employ experimental techniques to test the validity of this valuation method (e.g. Cummings et al., 1995; Cum-mings and Taylor, 1999), where validity is the degree to which the contingent valuation survey responses reflect behavior when monetary payments are required as a result of the valuation process. Typically, in these experiments, subjects are randomly assigned to either a hypothetical or a real treatment. In the hypothetical treatment, subjects are asked to re-port their WTP (or value) for a particular public good in a hypothetical survey. In the real treatment, subjects are asked to report their value for the same good, in the same market scenario, but where actual monetary contributions are paid for the amount stated in the survey. The responses from the two treatments are then compared. It is often, but not always, the case that significant differences are found in the responses from the two
1See Hanemann (1994) for an extensive review of the contingent valuation method and Diamond and Hausman (1994) for a critique.
markets — stated values are significantly higher in the hypothetical treatments.3 Differ-ences in responses under hypothetical and real conditions are attributed to hypothetical bias, which refers to the overstating of true values for a public good when the payment decisions are not binding.4While this literature has focused on behavioral differences as related to ex-perimental treatments, no one has examined the potential for gender-related behavioral dif-ferences in these types of experiments, i.e., for gender difdif-ferences as related to hypothetical bias.
The potential for gender differences in hypothetical bias is suggested by the work of Gilligan (1982) in which she found that females think about and act on moral dilemmas in a more inclusive manner, taking relationships into consideration, whereas males are more concerned with obligations and rules. The experimental literature using laboratory public goods has interpreted her work as implying that females are less likely to free-ride than males (Brown-Kruse and Hummels, 1993; Nowell and Tinkler, 1994) or that females are more likely to respond to context than males (Cadsby and Maynes, 1998). We ex-tend this notion and propose that if females are more likely to respond to context than males, then females would be more likely to respond to themarketcontext than males. Hence, females would be more likely to truthfully reveal their WTP in the hypotheti-cal treatment, where the true WTP is assumed to be represented by the responses in the real treatment. That is to say, females may be more likely to search their preferences in response to the interviewer’s request and consider the context of the valuation scenario more closely than males.5 If this is the case, then we would expect females to exhibit less hypothetical bias than males in experiments using a non-laboratory public good. To test this hypothesis, this research elicits values for a non-laboratory public good from a sample of subjects in two distinct experimental treatments: a hypothetical treatment and a real treatment. Results from these two treatments are used to test for differences in values stated by females and males within a treatment; differences in values by treatment within a gender (i.e., for hypothetical bias by gender); and differences in hypothetical bias across genders.
2. Experimental design
The experimental design used in this research consists of an in-person survey that was ad-ministered to 488 student and adult subjects in 25 experimental sessions between September
3See Cummings and Taylor (1999) or Smith and Mansfield (1998), for examples in which differences are not found between responses in hypothetical and real treatments.
4In this type of research, it could be the case that responses in the hypothetical treatments are biased upward, while responses in the real treatments are biased downward due to free-riding. It has been difficult to disentangle these effects in experiments using non-laboratory public goods. The validation literature typically assumes that differences in behavior between hypothetical and real treatments are due to hypothetical bias, not free-riding, following the results of Cummings et al. (1995) as well as Taylor (1998). Cummings et al. find evidence of hypothetical bias with a private good (where there is no incentive to free ride). Taylor uses an incentive compatible revelation mechanism for a public good and still finds evidence of hypothetical bias.
1997 and September 1998.6 The same interviewer conducted all surveys, which were si-multaneously administered to the subjects in each particular session. Student subjects were recruited through on-campus organizations at Georgia State University, such as fraternities and sororities and academic clubs. Adult subjects were recruited through church groups, computer clubs, and other community organizations in the Atlanta, GA metropolitan area. To recruit subjects, the interviewer contacted social and business groups and requested per-mission to conduct the survey at the beginning of a regularly scheduled group meeting. Participating groups were randomly assigned to either a hypothetical or a real treatment. In exchange for time to conduct the survey during the group’s meeting, the Environmental Policy Program at Georgia State University paid $5 per completed survey to each partic-ipating group’s general fund. The monetary payment served as an incentive for groups to participate, but since the money was not paid directly to individuals the payment should not have affected their responses.
The survey involves contributions to the Nature Conservancy, a national nonprofit orga-nization that developed and now sponsors the Adopt an Acre program. The Adopt an Acre program works by allowing individuals to contribute money to directly purchase, and place under protection, sensitive rainforest land. Each year the Nature Conservancy focuses their fund-raising efforts on a particular rainforest. At the conclusion of a fund-raising year a new Adopt an Acre program is initiated with another rainforest, however the previous projects remain active in that conservation and protection efforts continue through the funds raised in a given year. For this research, subjects contribute to the Nature Conservancy’s rainforest project in Costa Rica, which was the focus of the Adopt an Acre program in 1994 and 1995. While the Costa Rican project was closed to the general public for fund-raising during the time the surveys were conducted, the Nature Conservancy agreed to allow participating sub-jects a one-time opportunity to contribute to the Costa Rican project as part of this research.7
We employ two experimental treatments using this good: a hypothetical treatment where no payments are expected from the subject as a result of their responses and a real treatment where actual payments are expected if the subject reveals a positive value. Following a de-scription of the rainforest in Costa Rica and its destruction issues, the payment mechanism is implemented as follows. In the real treatment, each survey packet includes a payment form and a stamped envelope, pre-addressed to the Nature Conservancy. Subjects write their maximum willingness to pay on their survey questionnaire and on the payment form, both of which include a subject identification number. Everyone is told to take the payment form and stamped envelope home with them, regardless of whether or not they state a positive value. The interviewer instructs those who state a positive value to place a check for the correct amount in the envelope with the payment form and mail it directly to the Nature
6Student and adult categories are not necessarily mutually exclusive (students are adults, while subjects from the adult subject pool may be students). However, we use this classification to refer to the method in which the subject was recruited.
Conservancy; those who state a zero value are told to throw away the payment form and en-velope in the privacy of their own home. Once the Nature Conservancy receives a check, they verify that the amount on the check matches the amount written on the payment form and then mails the payment form back to Georgia State University so that it can be matched by iden-tification number to the subject’s questionnaire (subjects are also told this information).8
The hypothetical treatment consists of an identical description of the payment mecha-nism with the exception that subjunctive language is used and subjects are not provided with payment forms or envelopes. In addition, subjects are reminded several times in the hypo-thetical treatment that they are participating in a hypohypo-thetical survey and are not actually being given the opportunity to send money to the Nature Conservancy.
This experimental design allows us to test several hypotheses related to gender. First, we test for gender differences in stated values in the hypothetical treatment and then repeat this test for values elicited in the real treatment. These comparisons allow us to examine the preferences of females versus males for the public good offered in these experiments. While we have no expectations as to whether females or males might report higher values for this good, ceteris paribus, we do expect the results to be consistent across treatments if there are no behavioral effects due to the valuation mechanism. In other words, if males state higher WTP than females in the hypothetical treatment, then we might also expect males to state higher WTP than females in the real treatment. Second, and related to the previous tests, we conduct out-of-sample tests to examine whether or not hypothetical bias exists for females and males and whether or not it is differentiated according to gender. Following Gilligan, and the interpretation of her work by Cadsby and Maynes, we might expect hypothetical bias to be smaller for females than for males if females are more likely to respond to the market context.
3. Empirical results
Surveys are conducted with 488 subjects.9 WTP responses and several relevant summary statistics are presented in Table 1 by treatment and gender. As indicated in Table 1, there
8It is important to note that this design essentially requires subjects in the real treatment to pledge a contribution during the experiment and then send an actual payment once they leave the experiment. This design serves two important purposes. First, it allows as much anonymity as possible. Other on-the-spot payment mechanisms would reveal who stated a positive value and who did not, which could invite peer-pressure effects. Second, this payment mechanism does not require subjects to have cash or checks available at the time of the survey, which is important since they would not have anticipated the need for these items. Results show that 52 percent of subjects who stated a positive value in the real treatment mailed in their contribution — thus, stated values in the real treatment were different than actual payments. In our analysis, we treat stated values in the real treatment as equivalent to actual values had payments been binding (say, had we employed follow-up requests). Our overall conclusions do not change if we include the “follow-up” behavior as part of the analysis (as discussed in Footnote 14). Therefore, for clarity and succinctness we do not focus on this aspect of the experimental design.
Table 1
Descriptive statistics
Variable Hypothetical treatmenta Real treatmenta
Female Male Female Male
Stating a positive WTP (%) 63.76 64.95 22.46 28.85
Mean willingness to pay 27.97 72.22 3.23 6.14
(52.75) (163.82) (7.66) (13.37)
[0–500] [0–1000] [0–50] [0–50]
Household incomeb(×103) 59.64 70.68 48.75 49.06
(38.74) (37.31) (32.22) (34.35)
[2.5–125] [2.50–125] [2.5–125] [2.5–125]
AGE 33.21 42.10 35.90 38.29
(14.37) (16.60) (12.76) (14.30)
[20–77] [20–81] [20–68] [21–84]
N 149 97 138 104
aStandard deviations are in parentheses and the range of values are in brackets.
bIncome is based on the mid-point of responses to an interval question asking the annual pre-tax income. The intervals are $5000 or less, $5001–15 000, $15 001–30 000, $30 001–45 000, $45 001–60 000, $60 001–75 000, $75 001–90 001, $90 001–100 000, and over $100 001.
are 149 females and 97 males in the hypothetical treatment and 138 females and 104 males in the real treatment. There is no significant difference in the percentage of females and males stating a positive value in both the hypothetical treatment (approximately 64 percent) and the real treatment (approximately 25 percent).10 However, the mean willingness to pay for females is $27.97, while the mean willingness to pay for males is $72.22, in the hypothetical treatment, and these differences are significant (t=2.57). Similarly, in the real treatment, the mean willingness to pay for females is $3.23 and the mean willingness to pay for males is $6.14 and these are just significantly different at the 95 percent level of confidence (t=1.99). Although the “raw” mean willingness to pay responses suggest that males state higher values in both hypothetical and real treatments — suggesting that males may have stronger preferences for the public good than females — these results also suggest that hypothetical bias may be stronger for males than for females. To test this hypothesis more formally, we next present models estimating WTP responses while controlling for other factors (such as income) that may influence behavior.
The data are characterized by a “spike” at zero and a right-skewed distribution, therefore the normal distribution is likely to be inappropriate for our estimation models. Because alternative distributions, such as the Gamma, log-normal, and Weibull, are only supported in the positive quadrant, we employ the hurdle model (see, e.g., Gurmu, 1997; Brown et al., 1999). The hurdle model handles a non-negligible portion of zero responses and a skewed distribution in the data by maximizing a likelihood function that estimates the probability of a spike at zero as well as the factors impacting only the positive responses.
Similar to the Tobit model, the hurdle model is a mixture of discrete and continuous parts. The discrete component of the hurdle model estimates the probability that a subject will state a positive value (called the “participation decision”) as a function of attitudes and demographic characteristics; the continuous component of the hurdle model is estimated for only those subjects stating a positive value (called the “contribution decision”) as a function of attitudes and demographic characteristics.11 The participation decision is estimated with a probit model and the contribution decision is estimated by maximum likelihood, assuming a log-normal distribution.12
Table 2 presents the results of hurdle models estimating the factors that influence the participation and contribution decisions in a pooled model (i.e., the full sample pooled across females and males, as well as hypothetical and real treatments) and models with only the observations in the hypothetical treatment and the real treatment, separately. In addition to the coefficient estimates, Table 2 presents the marginal effects for the gender, treatment, and income variables. The models control for gender (GENDER=1 if male and GENDER=0 if female), as well as the following demographic and attitudinal variables:
REAL=
1 if treatment is a real survey,
0 if hypothetical,
ADULT=
1 if subject is not recruited through a University group,
0 otherwise,
AGE=the age of the subject,
EMPLOY=
1 if subject is employed full-time,
0 otherwise,
HHINCOME=household income(see Table 1,footnote b for a complete definition),
BUDGET=
1 if subject is primarily in control of the household budget,
0 otherwise,
RACE=
1 if subject is Caucasian,not of Hispanic decent,
0 otherwise,
MARRIED=
1 if subject is married,
0 otherwise,
EDUC=
1 if subject has a college degree,
0 otherwise,
11Gurmu (1997) demonstrates that estimating these two parts of the hurdle model (i.e., maximizing separate likelihood functions) is equivalent to the maximization of the joint likelihood function for both the discrete and continuous components.
RANK=
1 if subject characterizes the Adopt an Acre program as very unimportant to the goal of saving rainforests,
2 if moderately unimportant,
1 if subject rates the Adopt an Acre program as
much less favorable to other programs he or she might support,
2 if less favorable,
3 if about the same,
4 if moderately favorable,
5 if much more favorable,
AWARE=
1 if the subject was previously aware of the Adopt an Acre program,
0 otherwise.
Results for the pooled model indicate that gender is nota significant predictor of the participation decision (column 1a), but is a significant predictor of the contribution decision (column 1b), after controlling for treatment, attitudes, and other demographic characteristics of the subjects. In other words, although females and males choose to contribute at the same rate (the participation decision), once they decide to contribute, males state higher values than females (the contribution decision). Specifically, of those who decide to contribute to the Nature Conservancy, males state values that are $13.34 higher than females, according to the marginal effects. This model also indicates that being in the real treatment (REAL) reduces the probability that a subject states a positive value by almost 50 percent (see column 1a), and of those who state a positive value, being in the real treatment reduces WTP almost $40 as compared to those in the hypothetical treatment. Attitudes towards the Adopt an Acre program (RANK) and the Nature Conservancy (RATE) are significant predictors of the participation decision, while demographic factors such as income, marital status, and gender are significant predictors of the contribution decision.
While the pooled model indicates that gender is a significant predictor of behavior after controlling for treatment, this model restricts gender effects to be linearly related. To relax this assumption, we also report hurdle models estimated for the hypothetical treatment separately from the real treatment in columns (2a, b) and (3a, b) of Table 2, respectively. These models indicate that gender is not a significant predictor of the participation decision in either the hypothetical or real treatment (see columns 2a and 3a), just as suggested by the model pooled across treatments. However, when considering the contribution decision for those who state a positive value, males state statistically higher values than females in the hypothetical treatment (column 2b), but not in the real treatment (column 3b).13 In the hypothetical treatment, males state values that are $28 higher, on average, than females.
Table 2
Regression resultsa
Variable Pooled model Hypothetical model Real model
(1a)
Partici-(0.46) (0.56) (0.67) (0.68) (0.77) (1.01)
GENDER 0.12 0.32∗∗ 0.07 0.50∗∗ 0.13 −0.08
(0.14) (0.16) (0.22) (0.21) (0.20) (0.24)
[0.049] [13.34] [0.026] [27.54] [0.39] [1.34]
REAL −1.21∗∗∗ −0.95∗∗∗
(0.14) (0.17) [−0.478] [−38.98]
ADULT 0.19 0.06 0.68 0.42 −0.05 −0.22
(0.24) (0.24) (0.43) (0.35) (0.31) (0.31)
AGE −0.008 −0.002 −0.01 −0.009 −0.005 0.004 (0.007) (0.008) (0.01) (0.009) (0.011) (0.013)
EMPLOY 0.29 0.29 −0.08 −0.15 0.45∗ 0.84∗∗∗
(0.19) (0.20) (0.32) (0.27) (0.25) (0.28)
HH INCOME 0.002 0.005∗∗ 0.006∗∗ 0.006∗∗ −0.005 0.002
(0.002) (0.002) (0.003) (0.003) (0.003) (0.006)
[0.20] [0.31] [0.03]
BUDGET −0.002 −0.07 −0.17 0.04 0.04 −0.33
(0.17) (0.19) (0.25) (0.23) (0.25) (0.35)
RACE 0.08 0.09 0.22 0.02 0.01 0.25
(0.17) (0.19) (0.27) (0.27) (0.22) (0.27)
MARRIED 0.06 0.39∗∗ 0.11 0.36∗ 0.09 0.57∗∗
(0.16) (0.17) (0.24) (0.22) (0.23) (0.29)
EDUC −0.24 0.01 −0.43∗ 0.14 −0.03 −0.25
(0.16) (0.18) (0.25) (0.22) (0.21) (0.27)
RANK 0.52∗∗∗ 0.16 0.53∗∗∗ 0.33∗∗ 0.47∗∗∗ −0.20
(0.11) (0.14) (0.15) (0.16) (0.18) (0.24)
RATE 0.40∗∗∗ 0.14 0.69∗∗∗ 0.07 0.17 0.24
(0.08) (0.09) (0.14) (0.11) (0.12) (0.15)
AWARE 0.18 −0.13 −0.23 −0.02 0.55∗∗ −0.24
(0.17) (0.17) (0.25) (0.22) (0.24) (0.29)
Scale 1.01∗∗∗ 1.02∗∗∗ 0.85∗∗∗
(0.05) (0.06) (0.08)
L −239.52 −300.87 −112.42 −218.91 −114.65 −73.81
N 461 211 237 152 224 59
aIn each model the participation decision is a probit model where the dependent variable is equal to one if the subject states a positive value and 0 otherwise, and the contribution model is a maximum likelihood model for those who state a positive value, assuming a log-normal error distribution. Standard errors are in parentheses and marginal effects are reported in brackets, for select variables. Marginal effects are evaluated at the mean of the independent variables.
Table 3
Regression results by gendera
Variable Females only Males only
(1a) Participation (1b) Contribution (2a) Participation (2b) Contribution
decision decision Decision decision
Intercept −2.54∗∗∗ 1.55∗∗ −2.67∗∗∗ 2.69∗∗∗
(0.62) (0.64) (0.73) (0.96)
REAL −1.28∗∗∗ −0.68∗∗∗ −1.15∗∗∗ −1.22∗∗∗
(0.19) (0.20) (0.23) (0.29)
[−0.50] [−20.36] [−0.51] [−69.00]
ADULT −0.07 −0.02 0.30 −0.04
(0.32) (0.29) (0.38) (0.39)
AGE −0.012 0.019∗∗ 0.0004 −0.014
(0.010) (0.009) (0.010) (0.011)
EMPLOY 0.09 −0.14 0.58∗∗ 0.61∗∗
(0.27) (0.26) (0.29) (0.30)
HH INCOME 0.003 0.005∗ 0.0002 0.004
(0.003) (0.003) (0.003) (0.004)
[0.15] [0.25]
BUDGET 0.10 0.22 −0.16 −0.40
(0.23) (0.22) (0.27) (0.32)
RACE −0.04 0.37 0.15 0.12
(0.23) (0.26) (0.25) (0.29)
MARRIED 0.23 0.37∗ −0.30 0.26
(0.22) (0.20) (0.26) (0.29)
EDUC −0.15 −0.15 −0.26 0.20
(0.21) (0.21) (0.24) (0.29)
RANK 0.52∗∗∗ 0.13 0.54∗∗∗ 0.15
(0.15) (0.16) (0.16) (0.22)
RATE 0.42∗∗∗ −0.03 0.36∗∗∗ 0.15
(0.11) (0.11) (0.14) (0.15)
AWARE 0.25 −0.26 0.01 −0.12
(0.22) (0.20) (0.27) (0.29)
Scale 0.86∗∗∗ 1.08∗∗∗
(0.05) (0.08)
L −138.36 −154.26 −97.49 −133.14
N 269 122 194 89
aParticipation and contribution decisions are described in footnote a of Table 2. Standard errors are in paren-theses and marginal effects for selected variables are reported in brackets. Marginal effects are evaluated at the mean of the independent variables.
However, in the real treatment gender is not a significant predictor of either the participation or contribution decision (columns 3a and 3b).14
These results indicate that females and males respond to therealtreatment in a similar manner, suggesting similar preferences for the good, after controlling for demographic and attitudinal variables. However, this is not the case for the females and males participating in the hypothetical treatment. There are two possible explanations for this result. First, the sample of males in the hypothetical treatment may have had stronger preferences for the public good (after controlling for demographic and attitudinal variables). Alternatively, the males may have responded to the hypothetical treatment differently, in a behavioral sense, than females. We find the first explanation less plausible, given the results of the real treatment in which there is no evidence of differences in preferences for the public good. We examine the second possibility next (gender-related behavioral differences in response to hypothetical treatments).
Table 3 presents hurdle models with subjects from both the hypothetical and real treat-ments disaggregated by gender. Results indicate that being in a real treatment (REAL) is a significant predictor of behavior for both females and males, and in both the participation and contribution decisions.15 The marginal effect of being in the real treatment for the participation decision reduces the probability of a positive value by 50 percent for both females and males (see 1a and 2a). Similar to the models reported in Table 2, gender does not appear to affect the rate at which subjects choose to contribute to the good. However, as columns 1b and 2b indicate, being in a real treatment reduces WTP by $20, on average, for females and $69, on average, for males. Thus, after controlling for demographic and attitu-dinal differences across gender, our models provide evidence supporting the “raw data” that hypothetical bias for the males in our sample is larger than for the females in our sample.
4. Conclusions
The evidence regarding gender effects in public goods provision experiments is mixed. While Brown-Kruse and Hummels find that males contribute significantly more than fe-males, Cadsby and Maynes replicate their experiments with randomly selected subjects from a broadly-based population and find no significant difference in the contribution be-havior of females and males. We conduct valuation experiments for a “real world” public good on a sample of randomly selected adults and students and, supporting Cadsby and Maynes, findno significant differencein the contribution behavior of females and males where payments are expected based on stated values. However, we do find significant
differ-14As discussed in an earlier footnote, subjects who state a positive value in the real treatment were asked to mail in their contribution to the Nature Conservancy after the experiment ended. Of this group, 52 percent of the subjects actually mailed in their contribution. Probability models indicated that gender didnotinfluence the probability that a subject would follow-up and mail in their payment. In addition, there was no significant difference by gender in the amount actually mailed to the Nature Conservancy.
ences in responses across gender in hypothetical valuation experiments (i.e., in contingent valuation experiments) for the same public good. Specifically, hypothetical bias exhibited by males in our sample is three times larger than the hypothetical bias exhibited by the females in our sample.
Gilligan’s research suggests that females pay more attention to the particular context of a problem, while males are more concerned with abstract rights and duties. If this is the case, one might expect females to respond better to the market context in a hypothetical valuation exercise than males and state values more similar to their revealed WTP in a corresponding “real market.” While our evidence is consistent with this hypothesis, we recognize that one set of experiments cannot be used to establish unequivocal conclusions. This research does, however, offer important insights into how and why certain phenomenon, such as hypothetical bias, can occur. These results provide an important direction for future inquiries as researchers continue to develop methods for valuing public goods.
Acknowledgements
Funding for these experiments was provided by the Environmental Policy Program, An-drew Young School of Policy Studies, Georgia State University. The authors thank Ronald G. Cummings and Melonie Williams for their helpful comments. The views expressed in this paper are those of the authors and do not necessarily represent those of the institutions with which they are affiliated.
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