Identifying factors influencing willingness to pay: results from regression analyses

Respondents’ willingness to pay and how much, both for ensuring long term drinking water quality for the entire aquifer (scenario 1) or no risk to connected natural ecosystems (scenario 2) vary among respondents. Some respondents (37%) refuse to pay to both scenarios, while some (20%) are willing to pay for the first scenario but not for the second scenario. Finally, some (41%) also accept to pay for both scenarii. The values chosen by respondents willing to pay are highly heterogeneous within the sample, ranging from 10 SIT to 5 000 SIT for the first scenario and from 50 SIT to 5 000 SIT for the second scenario. The present section attempts to identify the main factors explaining differences between respondents’ responses – assessing the statistical relationship between different characteristics or perceptions of respondents and their willingness to pay for groundwater protection programmes. Regression analysis was performed using in particular the variables presented in Table 5.

Table 5. Variables and coding for regression analyses

Variable Explanation – coding Values in the sample

Age Age of the respondent Average = 48 years

Low income Coding 1 if has an income lower than 200 000 SIT per month, coding 0 otherwise

The income of 37% of the respondents is below 200 000 SIT per household per month.

Above the aquifer Coding 1 if lives on top of the aquifer, coding 0 otherwise 40% of the respondents live above the aquifer.

Far from the aquifer

Coding 1 if lives far from the aquifer (between 5 and 30 km) and coding 0 otherwise

20% of the respondents live far from the aquifer.

Bill amount Monthly water bill (in thousand SIT) Average = 5 630 SIT Tap water

frequency

Drink water rarely, several times per week or every day (1) – never drink tap water (0).

7% of the respondents never drink tap water.

Well Has a well (1) or does not have a well (0) 19% of the respondents have a private well.

Environment problems

Has cited an environmental problem as one of the main problems faced by the region – 1 () or not (0)

78% of the respondents cite environment as one of the main problems in the area.

Active Active in an environmental organization – value 1 (Yes) or 0 (No) 19% of the respondents are active in an environmental organization.

Patrimony Want to contribute financially because thinks that groundwater is part of the patrimony and as such must be protected – value 1 (Yes) or not 0 (No)

61% of the respondents justify groundwater protection because of the need to protect the patrimony First program

possible

Coding 1 if the first groundwater improvement scenario is considered as implementable and coding 0 otherwise

83% of the respondents think that the first programme is reliable.

Second program possible

Coding 1 if the second groundwater improvement scenario is considered as implementable and coding 0 otherwise

55% of the respondents think that the second programme is reliable.

Different types of regressions were developed, mainly logistic regression, log-log regressions estimated through ordinary least squares or tobit (Tobit I, Tobit II, Heckman model or its

alternative: the double-hurdle model) models. The results of these regression analyses are presented below.

Which factors influencing the willingness to pay for the first groundwater improvement scenario?

a. Logistic regression

Table 6 presents the results of the logistic regression on willingness to pay for the first scenario.

Table 6. Logistic regression on willingness to pay for the first scenario Variables influencing respondents’ willingness to pay for the first

groundwater improvement scenario Coefficient Std. Error

Age -.027 (***⁸) .008

Low income -.75 (**) .266

Live above the aquifer 0.83 (***) .265

Well 0.72 (**) .345

Environment cited in one .64 (**) .303

First programm possible 1.53 (***) .319

Constante 0.08 0.508

Number of obs = 354 Log likelihood = -195.01 LR chi2(6) = 68.50

Prob > chi2 = 0.0000 Pseudo R2 = 0.1494

*** = 1% significance level

** = 5% significance level

* = 10% significance level

The table stresses that six variables have been found as statistically significant in the logistic regression: age, earning an income smaller than 200 000 SIT per month per household, living on top of the aquifer, having a well, citing environmental problems as the first problem for the region, and confidence in the proposed groundwater improvement program for reaching drinking water levels.

• Two socio-economics variables only are significant in the regression, i.e. age and income level. The younger the respondent, the higher the probability to accept to pay.

Similarly, the lower the income the lower the probability to be willing to pay for the first groundwater improvement scenario.

• Localisation and owning a well also have a strong importance when deciding to pay or not. Since people living above the aquifer and people owning a well are users or potential users (if the well does not work or if their water does not come from the aquifer), they are more ready to pay than the other.

• Awareness towards environmental problems is reflected through the problems cited as the most important in the Krsko Polje region. When environmental problems are ranked as first among the one identified by respondents in the area, the probability to accept to pay becomes higher. Nevertheless being active in an environmental

organization which is also a good proof of interest in environmental problems is not significant in this regression.

• The trust in the first scenario is another explanation of willingness to pay. Reliability of the scenario and point of view of respondents about its feasibility must not be neglected because thinking the first programme is possible to implement, makes the probability of accepting to pay increase.

b. Log-log regression - ols

Table 7 presents the results of the log-log regression on the amount respondents are willing to pay for the first scenario.

Table 7. Regression on the amount respondents are willing to pay for the first scenario

Variables influencing the Log of the WTP amount Coefficient Std. Error

Constante 6.79 (***) .266

Low income -.297 (**) .116

Log (Bill amount) .33 (**) .126

Tap water frequency -.57 (**) .203

Live above the aquifer .18 (*) .103

Live far from the aquifer -.48 (***) .168

Active in an environmental organization .26 (**) .125

Patrimony reason .25 (**) .099

Number of obs.=230 F(8,221)=6.19 Prob > F=0.00 R-squared=0.2099

*** = 1% significance level

** = 5% significance level

* = 10% significance level

For this regression all zeros are excluded, and only positive bids are considered. As for the logistic regression, earning a low income influences negatively the amount respondents are willing to pay for the first scenario. However, once the decision to pay has been taken, age does not influence anymore the amount people are willing to contribute. The different signs before other variables are in line with expectations:

• The higher the water bill paid by respondents, the higher their financial contribution to the first scenario. An increase in the water bill by one thousand SIT leads to an increase by 33% of the financial contribution respondents are ready to make for the first scenario. Indeed, water quality improvements mean smaller treatment costs for water users at the end of the day, an element that is seen as more important when water bills are already high.

• Respondents who never drink tap water are ready to contribute financially more than other respondents. One explanation might be their current defiance towards tap water that might make them more sensitive to water quality issues at least from an organo-leptic point of view. Among people who never drink tap water, most of them (58%) do not trust its quality, and 29% think it has a bad taste, is too chlorinated or is “too hard”.

• The location where respondent live has an important influence on the willingness to pay since respondents living on top of the aquifer declare significantly higher amounts than those living far away from the aquifer (between 5 and 30 km).

• The positive coefficient for people active in an environmental organization shows that ensuring in the longer term drinking water quality for the entire aquifer is also an issue that environmentalists consider as relevant. Respondents being members of environmental organizations, supporting financially their activities or participating actively to their activities state amounts that are 26% higher than other respondents on average (other variables being constant).

• Accepting to pay because of the existence value of the aquifer indicates higher values, as amounts of respondents who refer to existence value is 25% higher than amounts stated by other respondents. Such non-use considerations come from people having a real interest toward environment protection, not only for financial reasons but because they consider nature in its own right and that it should not be destroyed or depleted by humans.

c. Treatment of Zero values

This regression presented above excluded respondents refusing to pay. Thus, the amounts which are explained are always strictly positive. Another alternative is to integrate respondents saying they do not want to pay thanks to Tobit models and consider their contribution as equal to zero SIT. The main challenge in doing that is to be able to differenciate between two types of refusals. The first type is considered as “true” refusals, i.e.

an improvement in the aquifer quality is not worth any sum of money for the respondent. The second type of refusals comes from so-called “protesters”. Protesters do not want to pay because they consider taxes as too high already, because they want the polluter-pay principle to be applied, because they think the state has to pay for it….. In the questionnaire, several reasons were proposed to respondents for explaining why they refused to pay for groundwater improvements although they might have stressed that groundwater quality improvements was important. Table 8 presents the relative importance of the different reasons identified by respondents⁹, making the distinction between so called “true” refusals and “false” zero or protest answers.

Table 8. Reasons explaining why respondents refuse to pay for the first scenario What are the main reasons explaining that you do not want to

contribute financially to the first groundwater protection program?

% Type

Cleaning groundwater from its pollution is clearly not a priority problem 7.7% Zero bidder

My income is too low 42.9% Zero bidder

Groundwater has high value for me but I do no want to pay by principle 19.2% Protest I prefer to buy bottled water instead if groundwater quality becomes

problematic 2.6% Zero bidder

I find other things on which I can spend my money more important

than groundwater quality improvement 3.2% Zero bidder

Other reason(s) – Please specify 32.1% Protest¹⁰

d. Tobit I

Regressions were made separately (i) to explain why respondents agree or disagreed to pay and (ii) to explain levels of positive amounts. The Tobit models was also applied as it is designed to deal with both zero bids and positive bids at the same time. The simplest Tobit model is called Tobit I and is applied for truncated data: bids can not be smaller than zero and are thus cut down at zero. Indeed, the willingness to pay of respondents is at least equal to zero and it cannot be negative. This truncation results in a high concentration of bids equal

9 Sum of percentage is higher than zero since respondents may give several explanations. This possibility was rarely used.

10 “Other reason” is considered as a protest answer, since it was used to give answers such as “I am already paying too much taxes”. For each respondent ticking this box, it was checked whether it is really a protest or not. When not, it was recoded.

to zero, a situation common to contingent valuation studies and that can be well tackled by Tobit models. As discussed before, there are two types of zero, “true” zeros and “false”

zeros. Two solutions can be applied for integrating zeros¹¹. The first solution, presented below, is to omit “false” zero since the true willingness to pay is not revealed as a sign of protest. Such protest answers represent 17,5% of the total sample. The other solution, not presented here, is to mix the two types of zeros and consider them as a whole without any distinction¹². Reported marginal effects, which can be interpreted as elasticity, are calculated at the means of explanatory variables and presented in Table 9 for the Tobit I regression excluding protest answers.

Table 9. Result of the Tobit I regression excluding protest answers

Variables influencing Log (WTP Amount) Coefficient Std Error Marginal effects

Constante 8.58 (***) 2.327 8.23 (***)

Log (Age) -1.29 (**) .529 -1.24 (**)

Low income -2.272 (***) .440 -2.15(***)

Log (Bill amount) .51 .430 .49

Tap water frequency -2.81 (***) .776 -2.76 (***)

Live above the aquifer 1.02 (**) .441 0.98 (**)

Live far from the aquifer -.645 .564 -0.61 (**)

Well .65 .489 .62

Environment cited as main problem 1.17 (**) .481 1.11 (**) Active in an environmental organization 1.13 (**) .471 1.09 (**)

First programme possible 2.96 (***) .619 2.69 (***)

Number of obs. = 199 (including 52 left-truncated) Log likelihood=-540.87

Pseudo-R2 =0.0757

*** = 1% significance level

** = 5% significance level

* = 10% significance level

e. The Heckman model

Overall, the Tobit I model does not model selection, i.e. why some people accept to reveal their true value and others not. Protest answers come from respondents who care of the aquifer quality. But since they do not reveal their “true” willingness to pay, their responses are censored. Indeed, willingness to pay can be assessed only if respondent say they are willing to pay. Censorship must not be confused with truncation, which is a model characteristic specific to the Tobit I model.

Modelling taking into account censored data due to selection can be dealt with the Heckman model (also named Tobit II) that aims at modelling the decision of revealing willingness to pay. The underlying assumption is that the dependant variable is not always observed, since some respondents decide to protest and not give the amount even if they attach a certain value to the environmental good. The overall principle of the Heckman model is to integrate two regressions in one, the first one dealing with the decision to reveal willingness to pay and

11 See for example : Bateman I.J. and Al. Comparing contingent valuation and contingent ranking : a case study considering the benefits of urban river water quality improvements. Journal of Environmental Management 79, 221-231 (2006)

Cho S. and Al. Measuring rural homeowners’ willingness to pay for land conservation easements. Forest Policy and Economics 7, 757-770 (2005).

12 See Annex 1

the second explaining the amount, if given. For the purpose of the analysis, zero bidders are excluded. The results of the Heckman model are provided in Table 10 below.

Table 10. Result of the Heckman regression excluding zero bidders

Log (WTP Amount) Coefficient Std Error

Constante 7.25 (***) .329

Low income -.245 (*) .133

Log (Bill amount) .25 (**) .123

Tap water frequency -.65 (**) .209

Live above the aquifer .13 .133

Live far from the aquifer -.35 (**) .168

Active in an environmental organization .23 (*) .128

Patrimony reason .23 (**) .118

Refuse to contribute as a protest

Constante .68 (*) .353

Age -.014 (***) .005

Low income -.02 .20

Live above the aquifer .41 (**) .181

Well .53 (**) .235

Environment as main problem .12 .210

First programme possible .49 (**) .251

Rho -.8 .16

LR test of indep. Eqns. (rho = 0): chi2(1) = 2.61 Prob > chi2 = 0.1059 Number of obs. = 261 (including 62 censored observation)

Log likelihood = -358.01 Wald chi2(7) = 36.80 Prob > chi2 = 0.0000

*** = 1% significance level

** = 5% significance level

* = 10% significance level

This model is considered as better than two separate regressions only if the residuals of both equations are correlated. Here, the ratio likelihood test states that the nullity of

ρ

can not be rejected. The equations (on amount and refusal) are thus independent since their residuals are not correlated. Thus, selection does not have to be taken into account.

Another solution is to take into account zero bidders and protesters as a whole and to model the decision of refusing to pay without any distinction between “true” and “false” zeros instead of modelling the decision of revealing true willingness to pay. Indeed, respondents first consider whether they are willing to pay or not and, in a second step and only if willing to pay, they mention how much they are willing to pay. This solution, was implemented by Anxo and Al. (2002) as well as Cho and Al. (2005). However; concerning selection, results are similar to the previous one since both equations are not independent.

Which factors influencing the willingness to pay for the second groundwater improvement scenario?

Similar regressions as those developed and tested for the willingness to pay for the first scenario were developed for the willingness to pay for the second scenario. The results of the different statistical models developed are summarised in the following paragraphs.

a. Logistic regression

The results of the logistic regression on respondents’ willingness to pay or the second groundwater improvement scenario are presented in Table 11.

Table 11. Logistic regression on willingness to pay for the second scenario Variables influencing respondents’ willingness to pay for the second

groundwater improvement scenario

Coefficient Std.

Error

Age .001 .01

Low income -.67 (**) .344

Live above the aquifer -.25 .323

Well .39 .390

Environment cited as one key problems for the are -.24 .390

Second programm possible 1.78 (***) .317

Constante .24 .552

Log likelihood = -123.56 Number of obs. = 228 LR chi2(6) = 40.29 Prob > chi2 = 0.0000 Pseudo-R2 = 0.1402

*** = 1% significance level

** = 5% significance level

* = 10% significance level

Age, identifying environmental issues as important for the area or location do not have statistically significant influence on the whether respondents are willing or not to pay for the second groundwater improvement scenario. Indeed, these variables have already explained by the willingness to pay for the first groundwater improvement scenario. Two variables only were found as been significant in this logistic regression:

• Respondents with a low income are less incline to accept to pay for the second groundwater improvement scenario. The magnitude of the coefficient, however, shows that low income influences slightly less people’s willingness to pay for the second scenario (coefficient equal to -.67 before the variable “low income”, see Table 18) than for the first scenario (coefficient equal to -.75 before the variable “low income”, see Table 11).

• Thinking that the second groundwater improvement program is feasible and could be implemented has a positive influence on the willingness to pay. The feasibility of the scenario is more important for the second scenario that is clearly more ambitious (coefficient of 1.78 before the variable “second programme possible”, see Table 18) than for the first scenario (coefficient of 1.53 before the variable “first programme possible”, see Table 11).

b. Treatment of zeros

The relative importance of the different reasons explaining why respondents who agreed to pay for the first groundwater improvement scenario were not willing to pay for the second more ambitious groundwater improvement scenario are summarised in Table 12.

Table 12. Main reasons explaining respondents’ refusal to pay for the second scenario What are the main reasons explaining why you are not

willing to pay for the second groundwater protection program?

% of

respondents Type

Cleaning groundwater more than its drinking water quality level is

not my priority. 1.15% Zero bidder

I am already willing to pay for the first scenario and this is

sufficient. 72.41%

First willingness to pay value is considered as total willingness to pay value.

My income is too low. 8.05% Zero bidder

I find other things on which I can spend my money more

important. 0% Zero bidder

Other reason(s) – Please specify 19.54% Protest

Respondents saying they are already paying enough for the first scenario are not considered as refusing to pay. Indeed, it is assumed that the value they proposed for the first scenario already included some part of what they were ready to pay for the first and second scenario.

This is in line with the fact that some respondents who were willing to pay for the first scenario decided to modify their value for this first scenario when asked whether they were willing to pay for the second scenario.

c. Log-log regression : Ols¹³

As a result, a regression was done for the total willingness to pay value for both the first and the second scenario (see Table 13 below). Indeed, this is what respondents are at the end willing to pay for bringing groundwater quality as close as possible to natural conditions. For respondents saying that their contribution for the first groundwater improvement scenario is sufficient, a value of zero is given for the willingness to pay value for the second groundwater improvement scenario so their total willingness to pay is equal to their willingness to pay for

As a result, a regression was done for the total willingness to pay value for both the first and the second scenario (see Table 13 below). Indeed, this is what respondents are at the end willing to pay for bringing groundwater quality as close as possible to natural conditions. For respondents saying that their contribution for the first groundwater improvement scenario is sufficient, a value of zero is given for the willingness to pay value for the second groundwater improvement scenario so their total willingness to pay is equal to their willingness to pay for

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