Key Objectives

This chapter outlines the methodology used in the consumer survey that addresses farm animal welfare issues specific to the Canadian pork sector. Additionally, it describes the estimation methods used in the analysis of the survey data. The survey enables an in-depth assessment of consumers attitudes toward farm animal welfare (FAW) and the means by which FAW quality verification can be credibly signalled to consumers. Specifically, survey data enable an assessment of 1) consumers‘ perceptions of the current status of FAW in Canada, 2) consumers‘

willingness-to-pay (WTP) for alternative pig production methods, 3) consumers‘ WTP for FAW quality assurances provided by different stakeholders involved in the Canadian Pork sector (i.e., by government, by agricultural producers, producer associations, downstream food firms, or a third-party enterprise), 4) whether declared trust in verifying organizations relates to WTP for the assurances provided for FAW attributes, and 5) the extent to which different groups of consumers exist with different preferences for FAW measures. This chapter begins by positioning the examination of consumer preferences for FAW within a utility maximization framework. Following this, a review of consumer studies in the area of FAW that used stated preference method is presented. Then, the chapter outlines data collection and choice experiment design methods used in the survey. Lastly, the econometric models used to estimate the utility consumers derive from FAW attributes are described.

93 4.2. Farm Animal Welfare and Consumer Demand

Economic theory states that utility maximization (U) drives consumers‘ behaviour. This is expressed in the relationship U = f(Z1, Z2, ...., Zm) where the [Zi] are different goods in the consumption bundle, among which one can identify livestock and poultry products with welfare attributes [Zj] (McInerney, 2004).⁵² The magnitude of the marginal utility, ∂U/∂Zj, shows the relative value placed on product Zj, and hence the price that consumers would be prepared to pay to obtain it. Utility maximization also depends on the income constraint as well as the prices of the products to be purchased. The consumer‘s decision about how much livestock and poultry products with FAW to buy is reflected by his/her demand function, typically represented as:



 



F

Q^D_j  _j, _o , , , (4.1)

Where Q , the quantity of commodity j that will be bought, is directly determined by the ^D_j product‘s price (Pj), the prices of all other products in the consumption set [P₀], the level of income (I), personal tastes and preferences (T), and a host of socio-economic ‗positioning‘

factors (S) relating to culture, education, experience, social group, etc. (McInerney, 2004). In other words, consumer preferences for livestock and poultry products with FAW attributes are derived from ethical principles, personal values and feelings of concern for animals. McInerney (2004) draws two major conclusions from this assertion. First, he suggests that for some types of consumers, their response is very price inelastic when they see livestock and poultry products with FAW characteristics in the grocery stores.⁵³ Second, he implies that income elasticity of demand for the FAW quality attributes of these products is high.⁵⁴

52 McInerney (2004) points out that to the extent consumers with strong perceptions of FAW are representative of society at large, then a society‘s attitudes toward FAW and the value placed on it can be embedded within consumer demand.

53 ―This implies that there are no effective economic substitutes for the products that offer a clear welfare value – and the absence of substitutes is the major factor which leads to less response to price‖ (McInerney, 2004, p.14).

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Lancaster (1966) proposed an alternative approach to modelling consumer demand, suggesting that a good is composed of intrinsic and extrinsic characteristics such as shape, colour, style, size, taste, comfort, ease of use, nutritive value, etc. Accordingly, these characteristics are the ones from which consumers obtain utility. In other, words, utility functions are defined not in terms of products consumed but by the characteristics [xi] that give value in consumption, i.e., U

= f(x1, x2, x3,..., xn). In the context of credence goods, food product quality may include a range of attributes, such as food safety attributes, nutrition attributes, and process attributes like FAW environmental attributes. In turn, livestock and poultry products with higher levels of farm animal welfare may be characterized by a bundle of specific attributes. For example, free run eggs may be sourced from hens that have continuous access to the floor of the barn where they are free to roam, roost, nest, and perch.

Drawing upon Lancaster‘s (1966) theory, various methods have tried to assess the determinants of consumer utility. Both stated and revealed preference elicitation methods have been used widely. Revealed preference data represent information on actual purchases by individuals and households which are tracked over time. In other words, revealed preference data reflect what consumers did in a situation that had economic consequences (Norwood and Lusk, 2008).

Combined with demographic data provided by the household, if available, this information can provide input for econometric analysis of actual purchase decisions (Goddard et al., 2007).

However, with this kind of analysis it is difficult to identify whether the actual purchase decision was driven by product availability at a particular store which may be different than that in another community or at another store. As well, we are unable to determine whether the purchase

54 ―As incomes rise, consumers may not necessarily buy substantially more cars/clothes/houses/holidays/

cameras/etc., but they certainly buy higher and higher quality versions of those goods‖ (McInerney, 2004, p.15).

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was motivated by the FAW attribute or some other product attribute. The revealed preference method is limited because it can only capture preferences for AW products actually available on the market and it does not reveal the latent demand for higher FAW standards. Accordingly, to obtain this information, other methods are needed.

Stated preference data are those derived by research methods that ―ask consumers, hypothetically, what they would do in a given situation. They are most often obtained from mail and phone surveys by asking hypothetical willingness-to-pay or purchase intentions‖ (Norwood and Lusk, 2008, p.354). Stated preference method allows researchers to examine hypothetical products and attributes combinations. A first advantage of stated preference data is that

―consumers can be asked to evaluate any potential problem or situation – even products that have not actually been developed in situations that have never occurred‖ (Norwood and Lusk, 2008, p.354). Thus, they are frequently used in the environmental, marketing, and transportation literature to predict consumer choice by determining the relative importance of various attributes in consumers' choice processes (Adamowicz et al., 1997, 1998a; Louviere et al., 2000). This means that stated preference research is very flexible in the types of preferences that can be measured.

Thus, SP experiments can be used to assess consumer preferences for specific FAW attributes not widely available in the market. SP methods are particularly useful for evaluating the contribution of individual product attributes to overall consumer utility from product or to examine trade-offs between attributes (e.g., price vs. higher FAW or methods of assuring FAW).

A second advantage of this approach is that stated preference data can be relatively easy to

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obtain from a large number of consumers (Norwood and Lusk, 2008). The primary drawback to stated preference data is that they are hypothetical, since they are stated. That is, a consumer can give any answer and suffer no adverse consequences; hence, the answer may not truly represent his/her preferences. As well, consumers might answer questions in such a way as to try to benefit themselves later (Norwood and Lusk, 2008). For example, if they think that under-representing their WTP might lead to lower prices in the future.