Second Choice Experiment: Collective WTP

Unlike the estimation results for the first experiment, parameters for the second all show the expected signs, e.g. parameters for teachers, bus drivers and police officers per district all have a negative sign implying that consumers evaluate a reduction of public expenditure for these goods as costs, while estimated parameters for animal welfare are positive for both classes (Table 3.7).

Table 3.7: Estimated parameters of LC-Model for public goods

Attribute Class 1

Teacher -1.344***

BusDriver -0.228

PoliceOfficer -0.813***

AnimalWelfare 0.177

p < 0.10 * p < 0.05 ** p < 0.01 ***

Source: Authors’ analysis of survey data

Results for class membership show that now students belong to class 2 (Table 3.8). All other covariates have the same direction like in the hus-bandry model, meaning that class 2 can be described in the same way for both models.

We used the estimation results to calculate the trade-off between ani-mal welfare and the other public goods as the collective WTP. Therefore, we were able to evaluate the relative value of animal welfare compared to other public services relevant in daily life. Figure 3.3 shows these trade-offs as a density plot.

Based on calculations one can see that respondents are rather willing to reduce bus drivers, teachers or policy men, i.e. reallocate education

Table 3.8: Estimation results for class membership (public goods)

Variable Class 1 Class 2

(Reference)

Constant 0 -3.379 ***

IMPORTANCE_ANIMAL_WELFARE 0 0.878***

NEED_ACTION_HUSBANDRY 0 0.176***

EVALUATE_AW_LEVEL 0 -0.442***

REGULATION_MARKET 0 -0.051**

SEX 0 0.901**

STUDENT 0 0.404***

Share 0.428 0.572

p < 0.10 * p < 0.05 ** p < 0.01 ***

Source: Authors’ analysis of survey data

or security budgets in exchange for a higher animal welfare level. Sum-mary statistics for the trade-off variables can be found in appendix 3.12.

Again, there is a clear difference between both classes regarding the WTP.

No matter if teachers, bus drivers or policemen, the collective WTP is the highest in the second class (Figure 3.4). Theoretically, these empirical results reflect consumer’s different shadow prices for public transport, se-curity and education, respectively.

Table 3.9: Mean collective WTP by socio-economic characteristics Teacher Bus drivers Police officers

Overall mean 0.8 12.86 1.58

Students 0.96 15.47 1.9

Non-students 0.82 13.03 1.61

Men 0.51 7.57 0.99

Women 1.08 17.54 2.13

Source: Authors’ own calculation

Looking at the socio-economic characteristics¹⁰ again, we observe a higher collective WTP for animal welfare for students when compared to non-students (Table 3.9). As already observed for the private WTPs for husbandry components, estimations reveal a higher collective WTP for women when compared to men. Furthermore, our results imply a weak

10Again, a Mann-Whitney-Wilcoxon test confirms the significance of the mean differ-ences.

Figure 3.3: Collective WTP for increase of animal welfare in terms of jobs per district

Source: Authors’ own presentation.

but significant negative correlation of roughly -0.28 between respondent’s attitude in favor of a market-based implementation of animal welfare and the collective WTP based on education, public transport and security, re-spectively (see appendix 3.13 ).

Finally, we compared the collective WTP with the private WTP. To this end we transformed estimated collective WTPs in corresponding values measured as Euros per kg of pig meat. In particular based on average salaries for teachers, policy men and bus drivers, respectively, as well as total number of pigs in Germany and the average carcass weight we were able to transform originally estimated collective WTP into Euros per kilo-gram of carcass weight. The means of the transformed collective WTPs are presented in Table 3.10. Please note that in Table 3.10 “Private Total”

WTP has been calculated as the mean of the sum of the WTPs for the

Figure 3.4: Collective WTP for animal welfare by class membership

Source: Authors’ own presentation.

three components estimated at the first stage.

Table 3.10: Means of transformed collective WTP and private WTP

WTP e / kg carcass weight

Collective

AW_TEACHER 0.012

AW_BUSDRIVER 0.098

AW_POLICEOFFICER 0.012

Private Total 6.771

Source: Authors’ own calculation

As one can easily see from Table 3.10, there is a big gap between private and collective WTP, where the latter is much lower than the for-mer. These patterns indicate that citizens’ WTP for animal welfare is

ne-glectable, when compared to the other public goods, i.e. education, public transport or security. Furthermore, assuming the WTPs for the other public goods roughly correspond to their costs (i.e. the average salaries of teach-ers, policy men and bus drivers used in the calculations) implies that the estimated private WTP exceeds the collective WTP by the factor ranging between 69.9 (bus drivers) and 564.25 (teacher/police officers). Following our theoretical expositions, this indicates a significant bias resulting from a mismatched design of choice experiments focusing only on animal wel-fare and neglecting other public goods. However, please note that based on our second choice experiment data we are unable to identify abso-lute WTP neither for animal welfare nor for the other public goods. Thus, reported values in Table 3.10 could only be calculated assuming that ab-solute WTPs for the other public goods correspond to their costs. This is, of course, only an ad hoc assumption. Hence, we conclude that further analyses are needed.

Moreover, at a theoretical level, also other theoretical explanations for our results are conceivable. For example, the difference between the pri-vate and collective WTPs could be explained following the prospect theory of Kahneman and Tversky (1979). People tend to judge losses larger than gains (Kahneman et al., 1991). In the case of collective WTP for farm animal welfare, citizens might judge the loss of teachers, bus drivers and police officers higher than the gain of farm animal welfare, respectively the

"consumption" of farm animal welfare which is expressed in private WTP.

Thus, they refuse to pay for animal welfare through reducing for example police officers in the district.

Finally, please note that the collective WTPs differ significantly depend-ing on the assumption how animal welfare is financed. In particular, as-suming animal welfare is financed via budget reallocations cutting expendi-tures for education or security results in a significant lower collective WTP when compared to cuts in expenditure for public transport (see Tables 3.9 and 3.10). This might reflect different shadow prices for these goods. Fur-ther, it indicates that the WTP for animal welfare also depends on the way the latter is financed. Overall, we conclude from our findings that the em-pirical measurement of WTP for animal welfare remains a problem, which has not been fully solved yet. Hence, we still consider WTP analyses as a very interesting and relevant topic for future research.