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What Extent of Welfare Loss is Caused by the Disparity between Perceived and Scientific Risks? A Case Study of Food Irradiation

  • Masahide Watanabe EMAIL logo and Yukichika Kawata
Published/Copyright: January 24, 2017
The B.E. Journal of Economic Analysis & Policy
From the journal The B.E. Journal of Economic Analysis & Policy Volume 17 Issue 1

Abstract

An individual perceived risk often differs from an objective risk based on the scientific evidence; risks about nuclear power generation and food technology including genetic modification and food irradiation are typical such cases. However, the extent to which welfare loss is caused by the disparity between perceived and scientific risks is unclear. Based on this gap in the literature, we conduct a discrete choice experiment to estimate the welfare loss. At the same time, we must tackle two issues arising in the estimation: endogeneity and ambiguity in the perceived risk. We construct an empirical model based on maxmin expected utility to consider ambiguity and apply a control function approach to alleviate endogeneity bias. The results show that 1) the disparity between perceived and scientific risks causes a significant welfare loss; 2) the ambiguity in the perceived risk exacerbates the welfare loss; and 3) endogeneity largely biases welfare measurement.

Keywords: ambiguity; choice experiments; endogeneity; subjective probability; maxmin expected utility

Funding statement: This work was supported by JSPS KAKENHI Grant Number 25780176.

Appendix A: Scientific Evidence and Consumers’ Perceptions of Food Irradiation

Scientifically, food irradiation is regarded as a safe and useful way in which to reduce pathogens in food (EFSA Panel on Biological Hazards (BIOHAZ) 2011a, EFSA Panel on Biological Hazards (BIOHAZ) 2011b, Farkas 1998, Farkas and Mohácsi-Farkasb 2011, and Lutter 1999). In addition, food irradiation has little effect on the appearance, color, or smell of food irradiated within the recommended doses (Nassar et al. 1997). WHO (1999) also notes the safety and wholesomeness of irradiated food. By contrast, some issues have cast doubt on the safety of food irradiation. For example, it was reported that 2-dodecylcyclobutanone (a unique radiolytic product suspected of being toxic to human health) is formed during the irradiation process. However, WHO (2003) states that the amounts formed are too small to damage health.

Nevertheless, many consumers still fear possible health damage from the consumption of carcinogenic substances that may be formed during irradiation (Henson 1995, Nayga, Aiew, and Nichols 2005, and Misra, Fletcher, and Huang 1995). Indeed, although food irradiation is officially approved in over 55 countries (Farkas and Mohácsi-Farkasb 2011), the market share of irradiated food remains low because of poor consumer acceptance (Gunes and Tekin 2006, Henson 1995, Nayga, Aiew, and Nichols 2005, and Misra, Fletcher, and Huang 1995). In Japan, for example, consumers are concerned about possible health damage from consuming irradiated food despite scientists’ assurances about its safety (Furuta 2004).[19]

Appendix B: Choice Sets of the Experiment

The choice sets of our experiment are shown in Table 5. It is noteworthy that our choice experiment fundamentally differs from conventional discrete choice experiments because we do not directly use the attributes provided to respondents in the survey, except for price, in the estimation. Although the objective average food poisoning probabilities are given as attributes in the choice sets, they do not directly enter our structural model; rather, the subjective probabilities are included in the model. As stated in Section 3.2, we asked respondents to state their subjective probabilities and ambiguities regarding food poisoning from normal and irradiated chicken that correspond to different objective probabilities. It is not easy for respondents to state their subjective probabilities and ambiguities, while we are also concerned about the possibility of confusion when repeatedly asking them to discuss probabilities and ambiguities regarding different objective probabilities. Thus, to ensure that respondents do not face excessive difficulties, we were careful not to repeat the choice.

Table 5:

Choice sets of the experiment.

PatternPrice of normal chicken (JPY per 100 g)Price of irradiated chicken (JPY per 100 g)Food poisoning probability from normal chickenFood poisoning probability from irradiated chicken
11001301/1,0001/2,000
21001301/1,0001/10,000
3100801/1,0001/2,000
4100801/1,0001/10,000
5100501/1,0001/2,000
6100501/1,0001/10,000
71001301/2,0001/4,000
81001301/2,0001/20,000
9100801/2,0001/4,000
10100801/2,0001/20,000
11100501/2,0001/4,000
12100501/2,0001/20,000
13801201/1,0001/5,000
14801201/1,0001/50,000
1580601/1,0001/5,000
1680601/1,0001/50,000
1780401/1,0001/5,000
1880401/1,0001/50,000
19801201/2,0001/10,000
20801201/2,0001/100,000
2180601/2,0001/10,000
2280601/2,0001/100,000
2380401/2,0001/10,000
2480401/2,0001/100,000

Appendix C: Question Format for the Subjective Probabilities and Ambiguities

Health Damage:

At what probability do you expect “serious health damage” such as carcinogenicity or toxicity to occur by consuming irradiated chicken? Figure 2, which shows the probabilities of various events, is provided for your reference. Here, serious health damage includes a new risk (e. g., new toxicity) or an increase in an existing health risk (e. g., carcinogenicity) by consuming irradiated chicken. You may not be able to compare these health risks with those shown in Figure 2. When you provide the probabilities (%) that you expect, use only Figure 2 as a reference. You can also provide a probability that is not included in Figure 2. If you are not confident about the probability you have provided, please answer using an expected range.

Percentage at which serious health damage will occur

( ) %

If you are not confident with the above value, please answer the range you expect.

From ( ) % to ( ) %

Food Poisoning:

How often do you think you would suffer from food poisoning when consuming a normal chicken meal (150 g)? Please answer the frequency you expect referring to Figure 3. If you are not confident about the frequency, please answer using an expected range.

Frequency of food poisoning when consuming a chicken meal:

Once per ( ) meals

If you are not confident about the above value, please provide a range.

From once per ( ) meals to once per ( ) meals.

Appendix D: Comparison between the Sample and Population Averages

Table 6 presents the sample averages of the variables representing the individual characteristics of survey respondents and compares these averages with those of the Japan population by using statistical tests (t-test). As a result, the null hypothesis that the population and sample average are the same is rejected at the 1 % level except for income; however, we believe that the differences are not sufficiently significant to deem our results unreliable.

Table 6:

Comparison between the variable averages for the sample and population.

VariableSample averagePopulation average
Age44.040.1 (2011)
Female (%)56.051.3 (2011)
Annual income (JPY)5,936,2245,546,652 (2011)
Marital status (%)71.968.2 (2010)
Sample size588n/a

  1. Source: Statistical Research and Training Institute (2013), Japan Statistical Yearbook 2013: Table 2–7, Table 2–1, Table 19–1; Ministry of Internal Affairs and Communications (2010), National Population Census in Japan: Table 5-1.

Appendix E: CF Approach

We denote the deterministic part of the random utility model as Wxij,yij;β, where xij is a vector of the exogenous variables, yij is a vector of the endogenous variables, and β is a vector of the parameters in eq. (5). Here, yij includes the subjective probabilities. Then, eq. (5) can be written as

(9)Vij=Wxij,yij;β+ϵij.

Here, yij is endogenous, that is, yij and ϵij are correlated. Without loss of generality, we assume Eϵij=0. When the endogenous variables are included in the models, usual estimation models (e. g., conditional logit models) provide inconsistent estimators (Petrin and Train 2010 and Wooldridge 2010). The CF approach alleviates this endogeneity problem.

We first define the reduced form of yij:

(10)yij=αxi+γzi+μij,

where xi=xijfor∀j is a vector of the exogenous variables in eq. (9) for any j, zi is a vector of the other exogenous variables that do not appear in eq. (9) for any j, μij is the error term, and α and γ are the vectors of the parameters. Here, zi works as IVs that must satisfy the exclusion and relevance criteria. The CF approach maintains that ϵij and μij are independent of xi and zi, whereas ϵij and μij are not independent of each other (Petrin and Train 2010). Under these settings, we find that Eyijϵij=Eμijϵij≠0, which means that μij is a part of yij that correlates with ϵij. Next, we decompose ϵij into a part that can be explained by the function μij and residual eij, as follows:

(11)ϵij=CFμij;λ+eij.

CFμij;λ is called a CF, where λ is the parameter vector. We assume Eeij|μij=0, that is, CFμij;λ=Eϵij|μij. A polynomial approximation is often used to represent CFμij;λ; however, the order of the polynomial is not predetermined (e. g., Liu, Lovely, and Ondrich (2010) and Petrin and Train (2010) use a first-order polynomial function, while Nakajima and Tanaka (2014) employ a third-order polynomial). We specify the order of the residuals that minimize the Akaike information criterion value up to the third order of the residual in our estimation. By substituting eq. (11) into eq. (9), we obtain

(12)Vij=Wxij,yij;β+CFμij;λ+eij.

Since Eyijeij=Eαxi+γzi+μijeij=0, from the maintained assumptions, we find that including CFμij;λ eliminates the endogeneity.

We estimate the parameters in two stages. In the first stage, we estimate the parameters in eq. (10) by using the ordinary least squares (OLS) method and obtain the residual. Then, we use the residual to construct the CF. In the second stage, we estimate the following model with the CF:

(13)Vij=Wxij,yij;β+CFμˆij;λ+eij,

where μˆij is the residual obtained from the first-stage estimation. Here, we assume that the error term eij is iid with a type-I extreme value distribution. Then, we estimate the parameters by using the maximum likelihood method. Note that since the first-stage OLS estimate μˆij is included, we use the bootstrap method to obtain the correct standard errors.

Appendix F: First-Stage Regression Results

The results for the first-stage regression (Subsection 6.1) are presented in Table 7.

Table 7:

Results of the first-stage regression (n = 588).

MEU modelSEU model
VariableDependent:Dependent:Dependent:Dependent:
PjhRjhPjRj
j=NMj=IRj=NMj=NMj=IRj=IR
CNM-7.71e-06-2.72e-06-0.00011-5.97e-06-3.90e-07-0.000052
(7.79e-06)(5.03e-06)(0.00040)(5.26e-06)(3.96e-06)(0.00029)
CIR-2.07e-06-3.85e-076.24e-06-7.26e-07-4.76e-07-0.000023
(1.98e-06)(1.28e-06)(0.00010)(1.34e-06)(1.01e-06)(0.000076)
female-0.00021-0.000037-0.0012-0.00021-0.000036-0.0073
(0.00021)(0.00014)(0.011)(0.00014)(0.00011)(0.0080)
age-0.000012-4.41e-06-0.00082*-8.49e-06-4.27e-06-0.00069**
(9.19e-06)(5.94e-06)(0.00047)(6.21e-06)(4.67e-06)(0.00035)
income-1.55e-072.94e-08-0.000018-6.44e-08-2.32e-08-0.000017
(2.67e-07)(1.72e-07)(0.000013)(1.80e-07)(1.36e-07)(0.000010)
constant0.00240.000750.10*0.0017*0.000500.072
(0.0012)(0.00075)(0.060)(0.00078)(0.00059)(0.044)
IVs
OPNM　0.56-0.14-12.590.63**−0.16-1.39
(0.43)(0.28)(22.23)(0.29)(0.22)(16.51)
OPIR　1.261.41***2.690.620.99**20.64
(0.80)(0.82)(41.27)(0.54)(0.41)(30.64)
concern about antibiotics0.000380.00044***0.033**0.00038**0.00032**0.021**
(0.00026)(0.00017)(0.013)(0.00018)(0.00013)(0.0099)
p-value (joint significance of IVs)0.032**0.0018***0.0945*0.0033***0.0056***0.15

  1. Notes: Standard errors are in parentheses.*, **, and *** represent significance at the 10 %, 5 %, and 1 % levels, respectively.

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Published Online: 2017-1-24

© 2017 Walter de Gruyter GmbH, Berlin/Boston

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  5. Lobbying as a Guard against Extremism
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https://doi.org/10.1515/bejeap-2016-0071
Keywords for this article
ambiguity; choice experiments; endogeneity; subjective probability; maxmin expected utility

Articles in the same Issue

  1. Research Articles
  2. The Market Value of R&D in Emerging Economies: Evidence from India
  3. Are You What You Eat? Healthy Behaviour and Risk Preferences
  4. The Impacts of Rural Property Rights on Urban Unemployment, Wage Inequality, and Welfare in Developing Countries
  5. Lobbying as a Guard against Extremism
  6. Information Acquisition in Vertical Relations
  7. What Extent of Welfare Loss is Caused by the Disparity between Perceived and Scientific Risks? A Case Study of Food Irradiation
  8. Characteristics and Employment of Applicants for Social Security Disability Insurance over the Business Cycle
  9. Preferences Toward Leniency under Mandatory Criminal Sentencing Guidelines: Role-in-the-Offense Adjustments for Federal Drug Trafficking Defendants
  10. The Effect of Kinship Placement Laws on Foster Children’s Well-Being
  11. Are Immigrants in Favour of Immigration? Evidence from England and Wales
  12. Competition, Product Innovation and Licensing
  13. The Impact of Educational Mismatches on Wages: The Influence of Measurement Error and Unobserved Heterogeneity
  14. Education Outcomes of Children of Asian Intermarriages: Does Gender of the Immigrant Parent Matter?
  15. Letters
  16. Endogenous Leadership in Tax Competition: A Combination of the Effects of Market Power and Strategic Interaction
  17. Can Catastrophic Long-Term Care Insurance Policies Increase Private Insurance Coverage and Reduce Medicaid Expenditure?
  18. Getting Girls to Schools! – Assessing the Impacts of a Targeted Program on Enrollment and Academic Performance
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