Travisi und Nijkamp - 2008 - Valuing environmental and health risk in agricultu

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ECOLOGICAL ECONOMICS 67 (2008) 598– 607 603 Fig. 2 – Example of choice set. alternative profiles. The first one was fixed and corresponded to the status quo scenario, i.e. the conventional scenario of agricultural practices, priced at the household's monthly grocery expenditure level, as reported by each respondent, and characterized by the current national pesticide damage levels. The other two profiles varied from card to card and corresponded to agricultural scenarios that provide lower pesticide risk levels. Internal coherence was verified. All combinations were asked in roughly equal frequencies. Respondents were instructed to select the most preferred one (see Fig. 2). 5. Empirical findings 5.1. Some basic statistics of the questionnaire Table 2 shows the survey statistics and the socio-demographics of the sample. The average respondent is 34 years old, has a household income of roughly €2100 a month, and has completed secondary school. The average household size is 3.5, with 15% of the sample having at least one person in the household who is younger than 15. The household monthly food expenditure is about €612, corresponding to about 30% of the monthly income. Moreover, 26% of the respondents had a strong environmental attitude and 12.2% were very concerned about pesticide risks. Comparing the environment with other problems in Italy, respondents ranked environment as the third important area for public investment after public health and education. 68.9% of the sample population considered public investments for environmental safety very important, compared with the 77.3 and 71.5% who found investment for, respectively, public health and education very important. 56.3% of the population believe that the media should inform the public about pesticide risks, while only 10.2% indicated that they were ‘not at all’ or ‘slightly’ informed. The main differences between the socio-demographics of our sample and those of the population of Milan concern age and income level. The average age of our sample is rather low – 34 as opposed to 44 years old – and the household income is 25% lower than the Milan average. This suggests that we should control for these individual characteristics in our statistical model of the choice responses. On the basis of the responses to the choice questions and to the control questions, we believe that the respondents had a reasonably good comprehension of the survey material and choice tasks. Only 4.4% complained that they had insufficient Table 2 – Survey statistics and socio-demographics of the sample Mean or percentage Individual characteristics Age 33.9 Female 61.6 Household size 3.5 Households with one or more persons under 15 15.1 Years of schooling 13.0 Monthly household income (in Euros) 2,098.1 Monthly household grocery expenditure (in Euros) 611.9 Attitudinal characteristics ‘Very much sensitive’ to sensitive to 26.1 environmental and health issues (⁎) Believe that public investment for environmental 68.9 safety is very important Believe that public investment for public health is 77.3 very important Believe that public investment for education is very 71.5 important ‘Very well’-informed about pesticide risks (⁎) 12.2 ‘Not at all’ or ‘slightly’-informed about 10.2 pesticide risks (⁎) Believe that media should inform public opinion 56.3 about pesticide risks Respondents debriefs Found some questions hard to understand 8.5 Found not enough information provided 4.4 Note: (⁎) Based on a 5-point Likert scale.
604 ECOLOGICAL ECONOMICS 67 (2008) 598– 607 Table 3 – List of explanatory variables Variable PRICE Continuous; monthly household grocery expenditure BIODIV Categorical variable; takes on value 15, 9, 6, 3 GRWATER Categorical variable; takes on value 65, 45, 25, 15 HEALTH Categorical variable; takes on value 250, 150, 100, 50 AGE Continuous variable providing the respondent's age CONCERN Categorical variable based on a 5-point Likert-scale: from 1 ‘not at all’ to 5 ‘very much’ informed on environmental and health risks due to pesticides information, and the relatively low proportion of 8.5% reported that they had found some of the questions difficult to understand. 5.2. Model specifications and estimation results Data from our unlabelled experiment are examined with the following additive RUM model specifications 10 . Damage attributes are formalized as categorical variables (see Table 3). Model 1 is the simplest model that we consider in order to investigate the effect that each attribute can have on the respondents' preferences for alternative agricultural scenarios. Formally, we have: 5.2.1. Model 1 V ¼ b 1 PRICE þ b 2 BIODIV þ b 3 GRWATER þ b 4 HEALTH: In this model formulation, PRICE refers to the cost of the policy to the respondents. BIODIV denotes the variable for the level of biodiversity damage. GRWATER denotes the variable for the level of contamination of soil and groundwater. Similarly, HEALTH denotes the variable for the level of human health impacts. Ceteris paribus, β 1 can be interpreted as the coefficient of the cost of the pesticide policy to the respondent. β 2 provides the effect of a unit decrement of the biodiversity damage (for instance: from 15 to 14 endangered bird species), on the probability of choosing an agricultural scenario. β 3 provides the effect of a unit decrement of the soil and groundwater contamination (i.e. by 1%) on the probability of choosing an agricultural scenario. Finally, β 47 provides the effect of a unit decrement of human health impact (for instance from 250 to 249 cases of acute intoxication) on the probability of choosing an agricultural scenario. The estimation results are shown in Table 4. Model (1) shows a Pseudo-R 2 of 0.562, which indicates a very high fit (Hensher et al., 2005; Hensher and Johnson, 1981). All variables are highly statistically significant and have the expected sign: the sign of PRICE and of all the various pesticide damages being negative. This signals that any risk level reduction from the status quo is welcomed by the respondents. The results from Model (1), therefore, confirm the expectation that the pesticide risk reduction elasticity is positive (Florax et al., 2005). We also want to control for differences in the respondent's profile with respect to the consumer's food choice and the economic valuation of alternative agricultural scenarios. Model (2) 11 10 Note that all the indexes for the respondents and alternatives have been omitted. 11 We also explored other model specifications. Those presented show the highest goodness of fit. ð1Þ integrates information on the respondents' profile, and assess the respective effect on the valuation of the agricultural option. 5.2.2. Model 2 V ¼ b 1 PRICE þ b 2 BIODIV þ b 3 GRWATER þ b 4 HEALTH þb 5 HEALTH AGE þ b 6 GRWATER CONCERN Model (2) incorporates in the utility function the respondents' level of age and concern towards environmental and health issues (see Table 3). It involves the cross terms of HEALTH and AGE, GRWATER and CONCERN. AGE is a continuous variable providing the respondent's age; CONCERN is a categorical variable taking values from 1 ‘not at all’ to 5 ‘very much’ informed on environmental and health risks due to pesticides (see Table 3). The results are presented in Table 4. The results of log–likelihood ratio tests show that adding demographic and attitudinal variables adds significantly to Model (1). The pseudo-R 2 is again very high (0.565). All the effects of interaction of the choice attributes with AGE and CONCERN are statistically significant, meaning that individual utility is sensitive to the respondent's level of age and concern to environmental and health issues. The interaction between AGE and HEALTH is negative and highly statistically significant, illustrating a positive relationship between respondents' age level and WTP estimates for biodiversity. Similarly, the coefficient of the interaction between CONCERN and GRWATER is significant and negative, meaning that people concerned about pesticide risks are more prone to pay to reduce soil and groundwater contamination. In order to capture the empirical magnitude of the effect of these three socio-demographic factors, we provide a sensitive analysis to the valuation results in Table 5. Results refer to BIODIV, GRWATER and HEALTH, whose values are estimated for a set of different age and concern profiles. These figures are Table 4 – Estimated coefficients of nested logit models Model (1) Model (2) PRICE −0.014⁎⁎⁎ −0.015⁎⁎⁎ (0.794 − 03 ) (0.828 −03 ) BIODIV −0.068⁎⁎⁎ −0.075⁎⁎⁎ (0.016) (0.016) GRWATER −0.0263⁎⁎⁎ −0.044⁎⁎⁎ (0.004) (0.010) HEALTH −0.006⁎⁎⁎ −0.003⁎ (0.001) (0.002) HEALTH ×AGE −0.100 −03⁎⁎⁎ (0.433 −04 ) GRWATER ×CONCERN −0.004⁎ (0.003) SAMPLE 1358 1337 Log–likelihood −811.159 −792.572 Pseudo-R 2 0.562 0.565 LR test of significance of all coefficients 37.174 (pb0.001) Note: Significance is indicated by ⁎⁎⁎, ⁎⁎ and ⁎ for the 1, 5, and 10% level, respectively, with standard error in parentheses. Calculations are performed with nested logit procedures in Nlogit 4.0. ð2Þ
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