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Does Public Funding Affect Preferred Tradeoffs and Crowd-In or Crowd-Out Willingness to Pay? A Watershed Management Case

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Abstract

In discrete choice experiments, survey participants are often asked to consider stated cost, to themselves, as a source of funding of an environmental project. An open question remains whether participants would consider an additional source of funding, such as public or federal support. We examine the impact of federal funding availability on the marginal utility of management attributes and on respondents’ private willingness to pay (WTP) for watershed management plans. Our results suggest that availability of public funding does not significantly alter the preferred tradeoffs among management attributes for active management plans, but alters the utility difference, and therefore the WTP, between an active plan and the status quo alternative. A latent class model further suggests that classes with relatively similar preferences may nonetheless show heterogeneity in how availability of public funds affects WTP for management plans against the status quo, depending on individuals’ sociodemographic profiles and environmental attitudes. Public funding affects WTP through both crowding-in and crowding-out effects. Our results suggest that private responses to public funds may be more complex than previous studies on public goods have suggested, as public funds may neither attract contributions nor crowd out private support uniformly.

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Notes

  1. Previous PV literature focused on evaluating alternative forms of the payment vehicle and whether these alternative forms yield consistent valuation outcomes. The general consensus in the PV literature has been to select a PV that has a plausible connection with the environmental resource being valued, is coercive and possesses a sense of “fair or equitable” share among respondents to minimize or avoid bias in valuation outcomes (Brookshire et al. 1980; Rowe et al. 1980; Greenley et al. 1981; Bateman et al. 1995; Jakobsson and Dragun 1996; Garrod and Willis 2000; Morrisson et al. 2000; Bateman et al. 2002; Carson and Groves 2007).

  2. To incorporate heterogeneity in preferences, researchers have focused mostly on two approaches: the random parameters logit (RPL) model, also called mixed logit, and the latent class model (LCM). A RPL model assumes a continuous distribution for the random parameters across the sampled population and requires a behavioral assumption in terms of preference distribution. A latent class model (LCM) approach has a semi-parametric structure as it assumes a discrete distribution in which preference heterogeneity is captured by membership in distinct classes of the utility description and does not require any assumption on the distribution of parameters.

  3. Management actions are listed in Table 1 and refer to those variables that set a level of “effort” to address watershed management concerns.

  4. NRCS motivated and funded the initial study as part of a Rapid Watershed Assessment program for pre-screening funding priorities to address resource concerns across the state.

  5. We note that the D-error for the design with federal funds was 0.11791, while for the same design with federal funds deleted the D-error was similar, at 0.11743.

  6. The approach of financing public projects with partial funding from the federal government is familiar in the USA. Therefore, the authors present the results of the study on the assumption that adding federal funding as an additional attribute may be inconsequential to the complexity respondents faced as opposed to adding less familiar watershed management attributes. We are unable to differentiate any effect from complexity as distinct from an effect due to the federal funding attribute. However, complexity would be expected to affect most or all attribute coefficients, and we found no such wide-spread effect in preliminary models or those discussed below.

  7. As applied here, this method involves sending: (a) an initial letter explaining the purpose of the survey and informing recipients that the survey will be delivered in a few days; (b) a cover letter with an initial copy of the survey; (c) a reminder postcard sent after a week to non-respondents; and (d) a second cover letter and another copy of the survey sent after two additional weeks to non-respondents.

  8. We did not match zip codes with the exact boundary of the watershed using spatial variables because the company providing the mailing list did not have this facility at the time, but rather used zip codes that were at least 50 % in the watershed.

  9. The weight for each stratum \(s = (\hbox {n}_{\mathrm{s}} /\hbox {N}) / (\hbox {r}_{\mathrm{s}} / \hbox {R})\), where \(\hbox {n}_{\mathrm{s}}\) is population in stratum s, N is total population from all strata, \(\hbox {r}_{\mathrm{s}}\) is number of completed surveys returned from stratum s, and R is total number of surveys returned from all strata.

  10. Estimating the models separately using each subsample is equivalent to estimating the model with the pooled data but including utility parameters of interest interacted with the dummy for one of the split subsamples (DF in our study) because the sum of the log-likelihood of a model estimated with each subsample separately equals the log-likelihood of the model estimated with pooled data as we have done here.

  11. Each participant faced a total of eight choice sets. We accounted for non-independence of responses to the eight choice sets from a single survey participant while estimating the latent class models by using the “cluster” option in NLOGIT 5.0 program (Greene 2012) to model econometric error.

  12. It can be important to consider the difference, if any, in underlying error variances when comparing utility parameters between two subsamples, as the heteroskedasticity between the subsamples may confound any test of differences between the utility parameters and could mislead our conclusions (Hole 2006; DeShazo and Fermo 2002; Hensher et al. 1999; Swait and Louviere 1993). Preliminary models using Eq. (5) (and models based on Table 6 as well) would not converge, so we used the Swait and Louviere (1993) method to explore heteroskedasticity. Our explorations suggest that modeling heteroskedasticity does not affect our findings, particularly our qualitative conclusions reported below.

  13. We initially examined a model which allowed only the status quo alternative, marginal utility of income, and all watershed management attributes (\(\hbox {SQ}_{\mathrm{p}}, \hbox {Cost}_{\mathrm{p}}\), and \(\hbox {X}_{\mathrm{mlp}}\)) to vary across the two subsamples (DF = 1,0) and we conducted a LR test and rejected the null hypothesis of no significant effect of the availability of federal dollars on the marginal utility of watershed management attributes. However, this result did not hold after including socio-demographic characteristics and environmental attitudes interactions with the status quo alternative in the model. The authors argue that such a specification that fails to account for heterogeneity in utility of the status quo suffers from omitted variables bias, and therefore we do not present results based on that simpler model.

  14. Statistical significance was found for three variables, namely \(\hbox {Nonnative}_{\mathrm{H}}\,(\hbox {p} < 0.0895), \hbox {Bikepath}_{\mathrm{M}}\,(\hbox {p} < 0.0925)\), and \(\hbox {Trout}_{\mathrm{H}}\,(\hbox {p} < 0.0887)\) from among a total of 30 variables from 10 effects-coded management attributes. Three of 30 variables is consistent with the outcome expected by chance when using a 10 % significance level.

  15. In the preliminary analysis, the authors used available income categories in the estimation process, which were not found to be significantly affecting the models. But the authors discovered that whether or not participants reported income significantly affected the models yielding the variable Incomemissing (see Table 1).

  16. The marginal utility of a federal dollar is constant in Table 6 and of similar magnitude in both classes, and \(\hbox {FedFunds}_{\mathrm{p}}\) did not interact with \(\hbox {Cost}_{\mathrm{p}}\). Therefore, and considering space, we focus on the fixed crowding-in or crowding-out effects and Table 7 holds \(\hbox {FedFunds}_{\mathrm{p}}\) to zero. Using Table 6, readers can easily calculate how WTP would change for the two classes. For example, for federal funds of $200,000, the class WTPs would increase by $16.55 and $7.48, respectively.

  17. We conducted a Wald test of equality of WTP values i.e., Ho: \(\hbox {WTP}^{\mathrm{F}}- \hbox {WTP}^{\mathrm{NF}} =0\) where \(\hbox {WTP}^{\mathrm{F}}\) is WTP value for a management plan relative to status quo when public funding is possible (DF = 1 and \(\hbox {FedFunds}_{\mathrm{p}}=0\)) and \(\hbox {WTP}^{\mathrm{NF}}\) is the corresponding WTP value when federal funding is not possible (DF = 0). Increased WTP indicates that there is a fixed crowding-in effect.

  18. For profiles in Table 8, class probabilities are close to the average probability of class membership. Therefore, we omit discussion of details related to the class equation in Table 6.

  19. Of course, this reduction in WTP could be offset by a sufficiently large federal grant through the effect of the \(\hbox {FedFunds}_{\mathrm{p}}\) coefficient because, at the margin, an additional federal dollar does increase WTP.

  20. We are mindful of the fact that the presence of the public funding attribute is part of the active plan, such that an econometrically equivalent model could have involved interactions between the amount of public support for a plan p (\(\hbox {FedFunds}_{\mathrm{p}}\)) and (1-SQ) where SQ represents the dummy for the status quo alternative. Thus, it is not so much that the utility of the status quo changes, but rather that a discrete effect on the utility of the active plans relative to the status quo changes. Our exposition follows more directly from the econometric model presented, which is standard within the literature.

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Acknowledgments

The U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) provided partial funding, with additional funding from the Agricultural Experiment Station at the University of Rhode Island, the DelFavero Faculty Fellowship at University of Connecticut, and the UConn-Storrs Agricultural Experiment Station. The authors are grateful to Christian Vossler and anonymous referees for their invaluable critiques of the paper.

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Authors and Affiliations

  1. Department of Environmental and Natural Resource Economics (ENRE), University of Rhode Island, 1 Green House Road, Kingston, RI, 02881, USA

    Achyut Kafle

  2. Department of Agricultural and Resource Economics and Center for Environmental Sciences and Engineering, University of Connecticut, 1376 Storrs Road, Unit 4021, Storrs, CT, 06269, USA

    Stephen K. Swallow

  3. The Nature Conservancy on Long Island, Uplands Farm, 250 Lawrence Hill Road, Cold Spring, Harbor, NY, 11724, USA

    Elizabeth C. Smith

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  1. Achyut Kafle
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Kafle, A., Swallow, S.K. & Smith, E.C. Does Public Funding Affect Preferred Tradeoffs and Crowd-In or Crowd-Out Willingness to Pay? A Watershed Management Case. Environ Resource Econ 60, 471–495 (2015). https://doi.org/10.1007/s10640-014-9782-z

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