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Aflatoxins and Health Considerations in Consumer Food Choices in Ghana

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Veröffentlicht/Copyright: 15. Februar 2018

Abstract

Food safety enjoys sustained attention among the scientific community, policymakers, and the general public due to health impacts. However, pursuing appropriate regulations for pervasive food contaminants is a challenging policy issue, particularly for naturally-occurring food toxins such as aflatoxins and other mycotoxins. This paper explores consumer preferences for quality aflatoxin-free peanuts, and how food safety concerns may impact willingness to pay more for safer foods. Incorporating ‘risky’ foods into random utility-maximization framework, we analyze contingent valuation survey data on Ghana. Model and survey results show consumers in Ghana approve of food aflatoxin regulations, and are prepared to pay price premiums as incentives to ensure supply of quality peanuts. Findings reveal that consumers prioritize food safety above prices in market decisions. People prefer introduction of aflatoxin regulations that would guarantee good health; useful information for policy makers in Ghana, Africa, and rest of the developing world.

JEL Classification: D12; D60; I12

1 Introduction

Peanut (Arachis hypogaea) is an important food crop produced and consumed across several countries (Nwokolo 1996). The peanut crop is a major source of protein in Ghana where it is predominantly grown in the northern regions (Atuahene-Amankwa, Hossain, and Assibi 1990). Food products commonly derived from peanuts include butter, confectionaries, oil, and cake. Figure 1 shows the trend of Ghana’s peanut production and trade over the past two decades. Generally, production of peanuts in the country has been rising with high domestic consumption and limited international trade.

Figure 1: 
          Peanut production trend in Ghana from 1993–2013.
          Note: Production (left axis); trade (right axis). Source: Computed from Food and Agriculture Organization of the United Nations (2016)
Figure 1:

Peanut production trend in Ghana from 1993–2013.

Note: Production (left axis); trade (right axis). Source: Computed from Food and Agriculture Organization of the United Nations (2016)

In the developing world, peanut and many other basic food staples are extremely susceptible to mycotoxin contamination, especially aflatoxins (Jolly et al. 2006). Mycotoxins are produced by a group of fungi that contaminate food crops pre- and post-harvest. Mycotoxin-producing fungi flourish in conducive environmental conditions such as high temperature and humidity, insect infestation, as well as improper hygiene (Dohlman 2003). As a result, there is high incidence of the mycotoxin problem in the tropical and sub-tropical regions of the world. Several food supply chains are severely affected by the mycotoxin contamination problem. The widespread nature of mycotoxins contamination adversely affects the health and economic welfare of large populations across several developing countries. Available studies show strong association between aflatoxins exposure and a host of negative health outcomes (Wang et al. 2001; Turner et al. 2003; Williams et al. 2004; Lewis et al. 2005; Wu 2006; Liu and Wu 2010; Wu and Khlangwiset 2010). Serious disease burdens linked to aflatoxins and other harmful mycotoxins include liver cancers, mycotoxicosis (e. g. aflatoxicosis), kwashiorkor in young children, and suppression of human immune systems leading to onset of opportunistic diseases (Montesano, Hainaut, and Wild 1997; Wild and Hall 1999; and Dash et al. 2007). Among the notable groups of mycotoxins––namely aflatoxins, fumonisins, zearalenone, and ochratoxins––this study focuses on aflatoxins due to their toxicity to humans and farm animals (Park et al. 2002; Jolly et al. 2006).

Food safety concerns have grown rapidly in the past two decades, particularly among consumers in developed countries (Grunert 2005). The general public’s rising interest in food safety issues has stimulated extensive inquiries and policy discussions (Grunert 2005). On the policy front, regulatory agencies in developed countries have responded to the credible threat from aflatoxins by setting maximum permissible standards to protect public health. For instance, the United States (US) enforces its own aflatoxins standard set at 20 parts per billion (ppb), whereas the European Union (EU) imposes 4 ppb on food produced for direct human consumption (Otsuki et al., 2001). However, enforcing acceptable aflatoxin levels for food crops has been a contentious policy issue among international trading partners. The enforcement of unilateral (non-uniform) standards often leads to trade disputes in international markets. Also, the adoption of international standards in countries where there is absence of domestic aflatoxin regulations is a cause for concern. As one would expect, debate on enforcements of strict regulations by some countries as trade barriers has motivated numerous policy evaluation studies (Nguyen and Wilson 2009; Nogueira et al. 2008; Otsuki, Wilson, and Sewadeh 2001a; 2001b; Yue, Beghin, and Jensen 2006). The general conclusion in the literature is that food standards are economically harmful to suppliers across the developing world in terms of lost revenues. Conspicuously missing in the food standards discussions, however, is consumers’ valuation of quality assurance induced by standards enforcements. This paper contributes to the ongoing debate on food standards regulation by evaluating consumer preferences in the developing world, focusing on the case of Ghana in West Africa. The majority of existing research on consumer preferences for food safety issues is concentrated on the areas of genetically modified foods, and consumer interests in synthetic chemical residues in food products (Eom 1994; Jan, Fu, and Huang 2005; Kimenju and De Groote 2008; Lin et al. 2005; Lusk and Briggeman 2009; McCluskey et al. 2003; Wang, Mao, and Gale 2008). To the best of our knowledge, the contingent valuation literature is silent on consumer behavior towards the mycotoxins problem in spite of the central role of aflatoxins in global food safety discussions. It is critical, therefore, to evaluate consumers’ behavior (and attitudes) toward food safety and how preferences for aflatoxin-free foods are affected.

2 Theoretical Foundation

The paper analyzes consumer preferences for risky foods in a random utility framework. The primary assumption is that individuals make choices that maximize their utility in the face of limited budgets (Hanemann and Kanninen 1998; Lusk and Hudson 2004; De Groote and Kimenju 2008; Gallardo et al. 2009).

2.1 Theory Underlying Consumer Food Safety Choices

This study is based on utility maximization in the context of ‘risky’ goods. We incorporate food safety into an expected utility framework (Choi and Jensen 1990; 1991). Specifically, in addition to prices and income, aflatoxin contamination is one of the important factors influencing consumer demand for ‘risky’ foods. To illustrate, consider the existence of a ‘risky’ bundle of goods with exogenous toxic content; such as basic food staples with given levels of aflatoxin concentration. This implies that consumers have no control over the amount of the toxin present in the risky food since the contamination is naturally-occurring on the supply side. However, the consumer endogenously selects quantities of the risky good to consume in order to maximize his/her utility.

The theory is presented in the context of the following simplifying assumptions:

  1. All firms are identical and produce the homogeneous risky good X; e. g. peanuts.

  2. Consumers are unable to visually identify the colorless and tasteless aflatoxin.

  3. The representative consumer lives for two periods.

  4. Consumption of the risky good affects utility only in the second period since the toxin is undetected in the initial period (i. e. before and during consumption); hence the health of the individual is impacted either positively (‘good health’) or negatively (‘poor health’) in the second period. The consumer is said to have ‘survived’ the second period if he gains in health (or his health remains unchanged), otherwise an adverse health effect is interpreted as ‘nonsurvival’. Also, the ‘invisibility’ characteristic of the toxin introduces an element of uncertainty about the consumer’s survival in the second period. This implies that the probability of survival (i. e. Ω) is less than one (0 ≤ Ω < 1), and consumers are adversely affected in various degrees by the risky good. In spite of the uncertainty facing the consumer, s(he) knows that the probability of survival in the second period is influenced by quantity of the risky food consumed (henceforth we adopt ‘he’ as a gender-neutral term).

  5. If the individual fails to survive the second period due to poor health, we assume he earns no income and attains a zero utility level (i. e. U2 = 0). In this situation, the individual is considered worse-off and social welfare decreases.

  6. The toxin is scientifically measurable and the consumer is aware of its hazardous effects when ingested through dietary exposure.

Furthermore, the individual possesses time-invariant utility functions based on two consumption goods, namely X and Y, where X represents quantity of the risky food purchased at the relative price Px whereas Y is the quantity of a composite (numeraire) good comprising of all non-food items with a normalized price of unity. Also, suppose the representative consumer survives in the second period, we can represent his preferences in the two periods by employing a monotonically increasing and concave utility function as shown below:

(1) U i = U ( X i , Y i )

The consumer maximizes the above utility subject to the following budget constraint:

(2) I i = P x i X i + Y i

where the subscript i = 1, 2 represents periods 1 and 2, respectively.

If the consumer ‘survives’ the first period, he maximizes utility in the second period U2(X2,Y2), subject to the corresponding budget constraint I2. Thus, the resulting demand functions are X2 (Px2, I2) and Y2 (Px2, I2), with the associated indirect utility, V (Px2, I2).

On the other hand, if the individual fails to ‘survive’ in the second period due to poor health from consuming contaminated food, then the utility level is zero, U2 = 0. Furthermore, assuming that the consumer’s utility function in each period is normalized, the second period utility can be expressed as the dichotomous random variable shown below:

(3) U 2 = { 0 , w i t h p r o b a b i l i t y 1 Ω 1 , w i t h p r o b a b i l i t y Ω

Regardless of market structure, Choi and Jensen (1991) argue that government intervention is required when the food industry produces goods with ‘hazardous content’. The intervention is necessary given that toxins are typically invisible to consumers, and producers have no incentives to reveal levels of contamination or commit resources to reduce the toxin. With this theoretical framework, we analyze a contingent valuation problem on consumers’ preferences for peanut with reduced aflatoxin contamination. Before introducing the survey, the following assumptions must be emphasized:

  1. Aflatoxin minimization efforts are costly to peanut producers; this increases production costs on the supply side.

  2. Additional production costs lead to higher retail prices for consumers.

  3. Individuals who vote ‘in favor’ of referendums seeking to introduce aflatoxin regulations into a country are willing to pay more to increase their ‘survival’ in future periods (i. e. they are stating their demand for good health). However, consumers who vote ‘Against’ such referendums are prepared to face the risks of exposure to contaminated food.

3 Consumer Valuation Survey in Ghana

The data used in this study were collected in a survey carried out in Ghana from May through July, 2012. Contingent valuation (CV) questionnaires were administered in face-to-face interviews with selected peanut consumers across the country. All activities in this valuation study––namely literature review, survey design, interviewer training, pretesting of questionnaires, and the actual field survey implementation––were carefully executed in accordance with best practices in the literature (Portney 1994; Carson et al. 2003; McCluskey et al. 2003; Gallardo et al. 2009). For instance, interviewers explained to respondents that researchers have found evidence showing strong association between aflatoxin exposure and health problems such as aflatoxicosis, immune system suppression, and liver cancer. Prior to the interviews, enumerators explained to respondents the goal of the study. Concise descriptions of the peanut aflatoxin issue and potential introduction of government regulation policy were presented to survey participants using market information scripts. Furthermore, survey participants were briefed on potential benefits of consuming peanut with zero or reduced aflatoxin contamination. Survey respondents were then offered the choice to vote either ‘in favor’ of or ‘against’ potential aflatoxin regulation policy in Ghana that would ensure availability of aflatoxin-free peanuts in local markets but at higher prices. Consumers who voted ‘in favor’ of aflatoxin regulation enforcement were subsequently asked to state the maximum price they would be willing to pay for aflatoxin-free peanuts. Thus, willingness to pay (WTP) was solicited through a combination of referendum format and open-ended elicitation questions where consumers state maximum amounts they are willing to pay relative to existing prices in their own local markets. The novelty in this study is that rather than using random bids in the follow-up elicitation questions, we asked respondents to use existing local prices (exogenously determined by local markets) as references (lower-bounds) for their own stated maximum WTP amounts. Using existing prices as anchors allowed for possible local and regional (spatial) heterogeneity in the reference prices used by respondents to state their maximum willingness to pay. Because peanut is an important food crop widely consumed in various forms in Ghana, the use of CV methodology in this study is legitimate. Previous research shows that CV methods yield useful information on preferences when survey respondents are familiar with the goods and services presented to them in experimental markets (Cummings et al. 1986; Whitehead et al. 1995; Wedgwood and Sansom 2003).

Preceding the choice (voting) task and as part of the market information, respondents were shown printed photographs of three peanut samples labelled as follows: ‘Sample A’, ‘Sample B’, and ‘Sample C’. Sample ‘A’ had a high proportion of moldy, broken and shriveled kernels plus other foreign materials; Sample ‘B’ consisted of moderately sorted peanuts showing a lower percentage of broken/shriveled kernels; and Sample ‘C’ contained clean and well-sorted peanuts showing no moldy, broken or shriveled kernels. This means Sample A would possess the highest probability of aflatoxin contamination, while Sample C would have the least likelihood of contamination. Respondents were then asked if they would vote for aflatoxin regulations that will ensure availability of aflatoxin-free peanuts (such as Sample C) in local markets but at higher retail prices. The survey participants were frequently prompted to make objective choices in the context of their own unique preferences, limited budgets, and food expenditure patterns. In addition to demographic and socioeconomic characteristics, the survey gathered information on important factors that possibly influence consumers’ food purchase decisions. Survey participants were asked to indicate the single most important factor considered in their household food transactions–– namely prices and food safety concerns. Questions were also asked about any adverse health experiences in the past that respondents attributed to their peanut consumption.

A sample of 652 peanut consumers was randomly selected to participate in the survey. Participants were purposively sampled from five out of ten administrative regions in Ghana, namely Ashanti, Brong Ahafo, Western, Central, and Eastern (see Table 1). Capital cities of the regions were selected since urban centers are prominent destination markets for peanuts produced in the northern part of the country. Table 1 shows proportional samples of consumers in the selected regions. Regions with large populations are weighted more given their importance as peanut markets. Sub-metropolitan areas within each capital city were identified and peanut consumers chosen from those areas. A total of 68 areas were covered. Figure 2 shows geographical distribution of the survey regions and corresponding capital cities.

Table 1:

Selected regions in Ghana and proportional sample sizes.

Region Populationa Sample Size Capital City
Ashanti 4,780,380 299 Kumasi
Eastern 2,633,154 109 Koforidua
Western 2,376,021 92 Takoradi
Brong Ahafo 2,310,983 86 Sunyani
Central 2,201,863 66 Cape Coast
Total sample size 652
  1. a Population figures obtained from Ghana Statistical Service (2012).

    Source: Authors’ Survey Data from Ghana, West Africa (2012).

Figure 2: 
          Map of Ghana showing distribution of study areas.
          Source: Adapted from Owusu (2005).
Figure 2:

Map of Ghana showing distribution of study areas.

Source: Adapted from Owusu (2005).

4 Empirical Estimation of Willingness to Pay

In this study, we estimate a random utility model consistent with the choice problem facing consumers. Although consumers know their preferences with certainty, investigators and econometricians perceive individual utility functions as consisting of systematic and random/unobservable components (Hanemann 1984; Hanemann and Kanninen 1998). Therefore, we state a peanut consumer’s utility as follows;

(4) U i = f ( y i , z i , e i )

where y is the individual’s income, z is a vector of the respondent’s socioeconomic and/or demographic characteristics, e is the random error term and i represents the consumer. Consumer utility is directly unobservable, hence probability of utility maximization is often obtained from observed behavior. With a utility maximization objective, consumers would be willing to pay more for a new product if they believe that the proposed change (such as the introduction of aflatoxin-free peanut) will increase or retain their existing utility (Hanemann 1984; Hanemann and Kanninen 1998). The preceding assumption is expressed below in probabilities;

(5) P i = P ( U i 1 ( y i B i , z i , e i 1 ) U i 0 ( y i , z i , e i 0 ) )

where Pi is the probability of a consumer’s willingness to pay a bid price of Bi for the new product; Ui1 is the final utility after acquiring the new product; Ui0is the initial utility before buying the new product; yiis the consumer’s income; zi is a vector of the individual’s socio-demographic information; and ei1 is the random component after obtaining the new product, while ei0 is the random term for the case without the new product. Notice that the bid price is paid directly from the consumer’s income. Therefore, consumers will only vote ‘in favor’ of the regulation and agree to pay a bid price when their willingness to pay equals or exceeds the offered price for aflatoxin-free peanut.

With the random utility method providing structure, we estimate probability of voting ‘in favor’ of the aflatoxin regulation using logistic regression model. In the logistic model, we express probability of voting ‘in favor’ as a function of existing prices, relevant socioeconomic factors, and characteristics demonstrating consumer concern for food safety. In addition to the logistic model, we estimate a quantile regression model expressing consumers’ stated maximum willingness to pay as a function of existing prices, relevant socioeconomic factors, and characteristics showing concern for food safety. Quantile regression models are appropriate for estimating open-ended contingent valuation data given that distributions of maximum willingness to pay are often skewed (see O’Garra and Mourato 2007; Champonnois and Chanel 2016). Aside from being robust against outliers, quantile regression also offers the opportunity to evaluate impacts of relevant covariates on the distribution of maximum willingness to pay.

5 Results and Discussion

5.1 Consumer Characteristics

From Table 2, the monthly household income in Ghana is 578 Ghana cedis (GH₵) (as at July 2012, the currency exchange rate in Ghana was US$ 1 = GH₵ 1.8). A typical household consisted of four to five individuals. Households consume about 2 ‘margarine cups’ (i. e. 0.67 kg or 1.5lbs) of shelled peanuts every week. The average survey participant was 33 years old.

Table 2:

Summary statistics for continuous variables.

Variables definition Observations Mean Std. Dev.
Maximum stated price (GH₵/margarine cup) 652 2.1 0.5
Price premium (%) 652 42.6 37.5
Amount of peanuts consumed weekly (margarine cups) 652 1.8 1.1
Price of peanuts in existing local markets (GH₵/margarine cup) 652 1.5 0.2
Household income (GH₵/month) 652 577.6 342.8
Age of respondent (years) 652 32.9 9.6
Number of people living in household (household size) 652 4.5 2.4
  1. Exchange rate during the survey period (May-July, 2012) was 1 US$ = 1.8 GH₵.

    A ‘margarine cup’ of shelled peanuts weighs approximately 0.37 kg or 0.82 lbs.

    See Nagai (2008) for details on local measurement units for cereals and legumes in Ghana.

    Source: Authors’ Survey Data from Ghana, West Africa (2012).

Furthermore, descriptive statistics in Table 3 show that survey participants were predominantly females (about two-thirds of the sample). This is because females are primarily responsible for household decisions regarding food purchases, food handling and storage, meals preparation, among others. Therefore, in most cases where two or more members of a household were available for interviews, females were unanimously chosen by the other members to participate on behalf of the household.

Table 3:

Summary statistics for categorical variables.

Variables definition Categoriesa Percent
1. Distribution of Aflatoxin referendum voting:
Votes on potential introduction of aflatoxin referendum in Ghana In favor 78.53
(Against) 16.26
Undecided 5.21
2. Food safety aspects
Are respondents aware of the food aflatoxins problem? Yes 6.75
(No) 93.25
Self-reported awareness of food contaminants in general Aware 51.07
(Not aware) 48.93
Whether respondents have any food substitutes for peanut Yes 89.72
(No) 10.28
Main factor causing respondent to switch from peanuts to substitutes Food safety 56.29
Others 5.37
Prices 38.34
Top priority in respondent’s food purchase decisions Food safety 91.56
(Price) 8.44
Personal health problems linked to peanuts consumption (No) 73.93
Yes 26.07
Family health problems linked to peanuts consumption (No) 80.06
Yes 19.94
3. Demographic characteristics:
Formal education level attained (0–6 yrs of school) 12.42
9 yrs of school 41.26
12+ yrs of school 46.32
Sex of respondent Female 62.27
(Male) 37.73
  1. aReference categories of variables used in the models are shown in parentheses.

    Source: Authors’ Survey Data from Ghana, West Africa (2012).

On formal education, Table 3 indicates that over one-third of the survey participants have attained 9 years of schooling (at the junior-high/middle school level) while nearly half of the individuals interviewed have had 12 or more years of formal schooling.

5.2 Consumers’ Willingness to Pay for Safe and Quality Peanuts

Results of the aflatoxins regulation referendum suggest that consumers in Ghana are willing to pay more for aflatoxin-free peanuts. Over two-thirds of the survey participants voted ‘in favor’ of aflatoxin regulation interventions that would guarantee supply of aflatoxin-free peanuts in local markets. The overwhelming support for the enforcement of aflatoxin regulations was received from survey participants in spite of repeated reminders about attendant increase in food retail prices. Respondents who voted ‘in favor’ of aflatoxin regulations were apprehensive of the alternative world without food standards; as suppliers would have no incentives to voluntarily discard unwholesome food products.

In Table 2, the average market price of shelled peanuts was recorded at 1.5 Ghana cedis per cup (GH₵ 1.5/cup). Against this average reference price, survey participants were willing to pay GH₵ 2.1/cup for aflatoxin-free peanuts. This implies that consumers are willing to pay a price premium of approximately 40 % for safer peanuts (median premium is 33 %).

5.3 Consumer Prioritization of Food Safety

The survey revealed that consumers in Ghana place high importance on food safety in their market transactions. For instance, nearly all the survey participants (9 out of every 10) stated that food safety issues (including hygiene) rank supreme among lists of factors they consider before making food purchase decisions (see Table 3). The majority of consumers prioritize food safety above other important factors such as prices. Furthermore, when asked about factors that would make them switch away from their peanut consumption pattern, only one-third of the respondents chose price as the strongest candidate. Thus, consistently, more than one-half of the survey participants selected food safety concerns as the most paramount determinant of their food purchase decisions.

To study health considerations in consumer food choices, the survey also gathered information on respondents’ history of adverse health experiences that they linked to their peanut consumption habits. Table 3 indicates that about one-quarter of the consumers had experienced poor health in the past which they attributed to peanut consumption. Similarly, about 20 % of survey participants were familiar with cases where close relatives had complained about health problems believed to have developed after consumption of meals prepared from peanuts.

5.4 Determinants of Consumer Preferences for Safer Peanuts

Probability of consumer willingness to support aflatoxin regulations, and pay more for aflatoxin-free peanuts, was estimated using a logistic model, whereas quantile regression model was estimated on the distribution of stated maximum willingness to pay. Prior to estimation of the final model, we estimated a Heckman selection model (Heckman 1976) to explore potential sample selection process in the apparent two-stage choice problem since respondents were asked to state the maximum prices they were willing to pay following their vote ‘in favor’ of aflatoxins regulation. The Heckman model estimation (results not reported here) did not reveal any selection issues, and as a result we used both logistic and quantile regression models to estimate the choice data. The logistic model results are shown in Table 4 while results from the quantile regression are provided in Table 5. Generally, the model results are consistent with expectations. We first discuss the logistic model and follow-up with the quantile regression results.

Table 4:

Determinants of support for the introduction of aflatoxins regulation for peanut.

(Dep. variable = in favor of aflatoxin regulation and willing to pay more for aflatoxin-free peanut)
Variables Odds Ratio
Intercept 0.6350
(0.8574)
Aware of food contaminants in general 1.5472*
(0.3619)
Respondents aware of the food aflatoxins problem 1.1529
(0.6301)
Price of peanuts in existing local markets (GH₵/margarine cup) 0.7892
(0.6100)
Respondents who have food substitutes for peanut 1.3966
(0.4906)
Food safety is top priority in food purchase decisions 10.2545***
(3.5792)
Suffered personal health problems linked to peanuts 1.1675
(0.3153)
Family suffered health problems linked to peanuts 1.0630
(0.2991)
Females 0.6659*
(0.1583)
Young age group 1.4953*
(0.3566)
Number of people living in household 0.7495***
(0.0413)
Income of household 1.003***
(0.0005)
Formal education level attained:
9 yrs of school 0.7822
(0.2763)
12+ yrs of school 0.4834*
(0.1788)
Region of respondent’s residence in Ghana:
Brong Ahafo Region 0.6340
(0.2389)
Central Region 0.2092***
(0.0745)
Eastern Region 0.2765***
(0.0885)
Western Region 0.3806***
(0.1313)
Likelihood Ratio 137.49***
Number of observations 652
  1. *** Significant at 1 %; ** Significant at 5 %; and * Significant at 10 %. Standard errors are in parentheses.

    Source: Authors’ Survey Data from Ghana, West Africa (2012).

On variables illustrating concern for health and food safety, the logistic model results in Table 4 show that individuals who are aware of food contaminants in general are more likely to vote ‘in favor’ of aflatoxin regulations. Similarly, people who prioritize food safety in their market transactions are 10 times more likely to support aflatoxin regulations to guarantee safer food products. These results are not surprising given that majority of the survey participants ranked food safety as the most important factor in their purchase decisions.

On impacts of socio-demographic characteristics, we observe that household income, size of a household, age, gender, and formal education status are important determinants of consumers’ preferences for aflatoxin-free peanuts. Specifically, respondents from high-income households are more willing to pay price premiums for safer peanuts. Furthermore, younger consumers (i. e. 33 years and below) are 1.5 times more willing to offer price premiums for safer peanuts. On the contrary, females are less likely to pay price premiums than males. The odds of females’ willingness to pay is approximately 33 % less than that of males. Also, the addition of a member to a household decreases the odds of willingness to pay by 25 %. On formal education, it is interesting that people who have attained 12 or more years of schooling are less likely to support aflatoxin regulations compared to those with 0–6 years of schooling. We also find evidence of regional influences on preferences for safer foods as the geographic location of a consumer appears to affect his/her willingness to pay for aflatoxin-free peanuts. Unlike people living in the Ashanti and Brong Ahafo regions of Ghana, individuals located in the Central, Eastern, and Western regions are less likely to support aflatoxin regulations.

The quantile regression results shown in Table 5 are consistent with the logistic model results discussed above. Also, as expected, the model results indicate that existing prices are key determinants of maximum amounts individuals are willing to pay for aflatoxin-free peanuts. It must be noted that the median (50th) quantile regression model fairly represents the influences of the socioeconomic variables on maximum WTP at both lower and upper quantiles. In fact, hypotheses testing conducted on equality of coefficients across quantiles revealed no statistically significant differences concerning impacts of the covariates along the quantiles of maximum WTP except for one. The only covariate that showed some heterogeneous impacts across quantiles was ‘Food safety is top priority in food purchase decisions’; the observed differences were found between the upper quantile (75th) and the two lower quantiles (no differences were found between the 25th and 50th quantiles).

Table 5:

Factors driving amount people are willing to pay for aflatoxin-free peanuts.

(Dep. variable = maximum WTP amount (GH₵/margarine cup) stated by respondents)
Variables Quantile (25) Quantile (50) Quantile (75)
Intercept −1.4066*** −1.2223** 0.4620*
(0.3876) (0.4913) (0.2584)
Aware of food contaminants in general 0.1807 0.0932** 0.2135***
(0.1113) (0.0395) (0.0417)
Respondents aware of the food aflatoxins problem 0.0368 −0.0274 −0.05091
(0.1575) (0.1006) (0.1255)
Price of peanuts in existing local markets (GH₵/cup) 0.8822*** 0.9000*** 0.6211***
(0.2302) (0.1032) (0.1113)
Respondents who have food substitutes for peanut 0.3068** 0.0961 0.1365**
(0.1010) (0.0605) (0.0604)
Food safety is top priority in food purchase decisions 1.4543*** 1.6390*** 0.3838**
(0.1661) (0.4558) (0.1764)
Suffered personal health problems linked to peanuts −0.0399 −0.0446 −0.0737*
(0.0997) (0.0425) (0.0423)
Family suffered health problems linked to peanuts 0.0442 −0.0181 −0.0843*
(0.1032) (0.0483) (0.0438)
Females −0.1171 −0.0517 −0.0480
(0.0819) (0.0340) (0.0427)
Young age group 0.0853 0.0527 0.0223
(0.0847) (0.0416) (0.0405)
Number of people living in household −0.0807*** −0.0456*** −0.0514***
(0.0188) (0.0078) (0.0098)
Income of household 0.0006*** 0.0004*** 0.0007***
(0.0001) (0.0001) (0.0001)
Formal education level attained:
9 yrs of school −0.0334 −0.0148 0.0571
(0.0824) (0.0545) (0.0444)
12+ yrs of school −0.1110 −0.0127 0.0588
(0.0940) (0.0557) (0.0442)
Region of respondent’s residence in Ghana:
Brong Ahafo Region 0.1510 0.4267** 0.7214***
(0.2329) (0.1937) (0.0725)
Central Region −1.4143*** −0.1565* −0.1669**
(0.0997) (0.0848) (0.0567)
Eastern Region −0.2063 0.0033 0.3303*
(0.3185) (0.0476) (0.1972)
Western Region −0.2496 −0.0812** −0.1306**
(0.3930) (0.0387) (0.0408)
Number of observations 652 652 652
Predicted WTP across quantiles (GH₵/cup) 1.5 1.9 2.2
  1. *** Significant at 1 %; ** Significant at 5 %; and * Significant at 10 %. Robust standard errors are in parentheses.

    Interquartile range t-tests showed predicted WTP values differ significantly across quantiles (at the 5 % level).

    Also, tests for equality of coefficients showed limited differential impacts across the WTP quantiles.

    Source: Authors’ Survey Data from Ghana, West Africa (2012).

To gain additional insights from the quantile regression models, we computed predictions of WTP values for the three quantiles (see Table 5). The predicted WTP values represent distribution of the true WTP for aflatoxin-free peanuts in Ghana. For example, the 50th quantile regression predicts that the price that half of the population in support of aflatoxin-free peanut is willing to pay is GH₵ 1.9 per cup. Interquartile range regressions and tests for equality of the predicted WTP values found that willingness to pay differs significantly across quantiles especially at the upper quantile.

6 Conclusion and Policy Relevance

A hot area of debate around mycotoxins (particularly aflatoxins) and food safety is the enforcement of appropriate regulatory standards that would not be welfare decreasing for market participants. Against this background, the primary objective of this paper was to study consumer preferences for aflatoxin-free peanuts, and how food safety concerns impact willingness to pay price premiums for aflatoxin-free peanuts. We implemented a contingent valuation survey in Ghana from May through July, 2012. The survey data was analyzed using a random utility maximization framework in the context of ‘risky’ foods. Logistic and quantile regression models were estimated to determine probability of consumer willingness to pay, and how important socioeconomic and food safety factors influence people's preferences.

Survey results indicate that any efforts to introduce and enforce aflatoxins regulation in Ghana would receive strong support from consumers. Relative to prevailing market prices for ordinary peanuts, the majority of survey participants offered to pay substantial price premiums for aflatoxin-free peanuts. We find that most consumers in Ghana prioritize food safety above prices in their market transactions. Further, model results indicate heterogeneity in willingness-to-pay between individuals who selected food safety as the paramount factor in their purchase decisions, and those who indicated price was the most important determinant.

Also, individuals who exhibit the following socio-demographic characteristics are more likely to pay price premiums for safer food products: persons aged 33 years and below, members of high-income households, as well as people from small households. In addition, we found some evidence suggesting that gender and formal education influence consumer preferences for aflatoxin-free peanuts ––females and people with advanced formal education are less willing to support aflatoxin regulations that would ensure availability of aflatoxin-free peanuts on local markets. Moreover, we have shown evidence of spatial disparity among consumers from different geographical regions in Ghana as far as preferences for quality aflatoxin-free peanuts are concerned. We also show that the population is segmented along the distribution of willingness to pay; underscoring the need for targeted policies and marketing strategies for peanut and other food industries affected by aflatoxins and other mycotoxins.

The findings from this study have valuable implications for policymakers as well as food suppliers in Ghana and other developing countries with similar socioeconomic structure, especially in Sub-Saharan Africa. First, prior to implementation of domestic aflatoxin regulations, food suppliers could take advantage of the expressed demand for aflatoxin-free peanuts by differentiating their produce to meet consumers’ safety needs. Peanuts offered for sale in local markets can be differentiated into grades through effective aflatoxin-reduction strategies such as proper drying, sorting of clean kernels, and applying sanitary food handling practices. Grading of peanuts would attract premium prices from consumers who value safe foods for themselves and their households and are willing to compensate producers for investments in supplying aflatoxin-free foods. Second, this study helps inform policymakers in Ghana as the country prepares to introduce and enforce food aflatoxin standards through its regulatory authority (the Ghana Standards Authority and the Ghana Food and Drugs Authority). Before introducing aflatoxin regulations, policy makers must consider educating the population about aflatoxins contamination and the need for people to avoid contaminated foods as they are known to cause serious illnesses. If the population is well informed about aflatoxins contamination, consumers would be willing to pay price premiums to incentivize food suppliers to invest in aflatoxin-minimization efforts along supply chains. Finally, the information and policy implications of this study are relevant for major food staples such as maize, processed cassava, and yam products/chips in Africa that are susceptible to the aflatoxins contamination problem.

Acknowledgements

We are grateful to Dr. Alan Seals (Auburn University), Dr. Cynthia Donovan (Michigan State University), and anonymous journal reviewers for providing insightful comments that helped to improve earlier drafts of the paper. The study was funded by the Peanut Collaborative Research Support Program/University of Georgia/Auburn University, USAID Grant no. LAG-G-00-96-90013-00.

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Published Online: 2018-02-15

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