Choice modelling

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically "utility" in economics and various related fields). Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically "utility" in economics and various related fields). Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions. A strength of DCEs and conjoint analyses is that they typically present a subset of the full factorial. For example, a phone with two brands, three shapes, three sizes and four amounts of memory has 2x3x3x4=72 possible configurations. This is the full factorial and in most cases is too large to administer to respondents. Subsets of the full factorial can be produced in a variety of ways but in general they have the following aim: to enable estimation of a certain limited number of parameters describing the good: main effects (for example the value associated with brand, holding all else equal), two-way interactions (for example the value associated with this brand and the smallest size, that brand and the smallest size), etc. This is typically achieved by deliberately confounding higher order interactions with lower order interactions. For example, two-way and three-way interactions may be confounded with main effects. This has the following consequences: The number of profiles (configurations) is significantly reduced; A regression coefficient for a given main effect is unbiased if and only if the confounded terms (higher order interactions) are zero; A regression coefficient is biased in an unknown direction and with an unknown magnitude if the confounded interaction terms are non-zero; No correction can be made at the analysis to solve the problem, should the confounded terms be non-zero. Thus, researchers have repeatedly been warned that design involves critical decisions to be made concerning whether two-way and higher order interactions are likely to be non-zero; making a mistake at the design stage effectively invalidates the results since the hypothesis of higher order interactions being non-zero is untestable. Designs are available from catalogues and statistical programs. Traditionally they had the property of Orthogonality where all attribute levels can be estimated independently of each other. This ensures zero collinearity and can be explained using the following example. Imagine a car dealership that sells both luxury cars and used low-end vehicles. Using the utility maximisation principle and assuming an MNL model, we hypothesise that the decision to buy a car from this dealership is the sum of the individual contribution of each of the following to the total utility. Price Marque (BMW, Chrysler, Mitsubishi) Origin (German, American) Performance Using multinomial regression on the sales data however will not tell us what we want to know. The reason is that much of the data is collinear since cars at this dealership are either: high performance, expensive German cars low performance, cheap American cars There is not enough information, nor will there ever be enough, to tell us whether people are buying cars because they are European, because they are a BMW or because they are high performance. This is a fundamental reason why RP data are often unsuitable and why SP data are required. In RP data these three attributes always co-occur and in this case are perfectly correlated. That is: all BMWs are made in Germany and are of high performance. These three attributes: origin, marque and performance are said to be collinear or non-orthogonal. Only in experimental conditions, via SP data, can performance and price be varied independently – have their effects decomposed. An experimental design (below) in a Choice Experiment is a strict scheme for controlling and presenting hypothetical scenarios, or choice sets to respondents. For the same experiment, different designs could be used, each with different properties. The best design depends on the objectives of the exercise. It is the experimental design that drives the experiment and the ultimate capabilities of the model. Many very efficient designs exist in the public domain that allow near optimal experiments to be performed. For example the Latin square 16 17 design allows the estimation of all main effects of a product that could have up to 16 17 (approximately 295 followed by eighteen zeros) configurations. Furthermore this could be achieved within a sample frame of only around 256 respondents. Below is an example of a much smaller design. This is 3 4 main effects design. This design would allow the estimation of main effects utilities from 81 (3 4 ) possible product configurations assuming all higher order interactions are zero. A sample of around 20 respondents could model the main effects of all 81 possible product configurations with statistically significant results. Some examples of other experimental designs commonly used: Balanced incomplete block designs (BIBD) Random designs Main effects Higher order interaction designs Full factorial More recently, efficient designs have been produced. These typically minimise functions of the variance of the (unknown but estimated) parameters. A common function is the D-efficiency of the parameters. The aim of these designs is to reduce the sample size required to achieve statistical significance of the estimated utility parameters. Such designs have often incorporated Bayesian priors for the parameters, to further improve statistical precision. Highly efficient designs have become extremely popular, given the costs of recruiting larger numbers of respondents. However, key figures in the development of these designs have warned of possible limitations, most notably the following. Design efficiency is typically maximised when good A and good B are as different as possible: for instance every attribute (feature) defining the phone differs across A and B. This forces the respondent to trade across price, brand, size, memory, etc; no attribute has the same level in both A and B. This may impose cognitive burden on the respondent, leading him/her to use simplifying heuristics ("always choose the cheapest phone") that do not reflect his/her true utility function (decision rule). Recent empirical work has confirmed that respondents do indeed have different decision rules when answering a less efficient design compared to a highly efficient design. More information on experimental designs may be found here. It is worth reiterating, however, that small designs that estimate main effects typically do so by deliberately confounding higher order interactions with the main effects. This means that unless those interactions are zero in practice, the analyst will obtain biased estimates of the main effects. Furthermore (s)he has (1) no way of testing this, and (2) no way of correcting it in analysis. This emphasises the crucial role of design in DCEs.

Strengths

Forces respondents to consider trade-offs between attributes; Makes the frame of reference explicit to respondents via the inclusion of an array of attributes and product alternatives; Enables implicit prices to be estimated for attributes; Enables welfare impacts to be estimated for multiple scenarios; Can be used to estimate the level of customer demand for alternative 'service product' in non-monetary terms; and Potentially reduces the incentive for respondents to behave strategically.

Occupational choice model

In economics, an occupational choice model is a model that seeks to answer why people enter into different occupations . In the model, in each moment, the person decides whether to work in the previous occupation, in some other occupation, or not to be employed. In some versions of the model, an individual chooses that occupation for which the present value of his expected income is a maximum. However, in other versions, risk aversion may drive people to work in the same occupation as before.

External links

Curated bibliography at IDEAS/RePEc

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