Standard microeconomics includes a discussion on the Hicksian demand curve. This article extends a similar analysis to establish a post-Beckerian framework for the analysis of preference change.
A big shoutout to Desmos which was used to create all of the graphs this post will contain.
The series will proceed as follows:
- Overview and Motivation
- Assigning Explanation Under Simultaneous Price and Preference Shifts
- Total Quantity Explained by Factor
- From the Preference Elasticity of Demand
- An Application to Business Sensitivity Testing
Preferences are well known not only as a key determinant of demand, but indeed the chief determinant. Prices, income, and preferences are the necessary ingredients to solve a typical model, but income and prices both ultimately arise from preferences.
Despite the central importance of preferences, allowing for preferences to vary is a traditional taboo in methodology, a fact simultaneously noted and fueled by the landmark piece from Becker and Stigler, De Gustibus Non Est Disputandem.
The omission of preferences from most economic models helps to explain the inaccuracy of the discipline as it directly induces an omitted-variable bias. Empirical evidence, alongside the plain experience of every individual, demonstrates that individual preferences do change with time.
In an academic paper I would present a more thorough literature review, but here are some papers supporting my claim:
- Stigler–Becker versus Myers–Briggs: why preference-based explanations are scientifically meaningful and empirically important
- A Regret-Induced Status Quo Bias
- Is Choice-Induced Preference Change Long Lasting?
- Some Determinants of Changes in Preference over Time
Finally, while preferences have been shown to change over time, that fact need not even be established to make the present research interesting. The mere lack of inquiry would be sufficient to motivate such research, even if such inquiry were only to empirically establish the methodology of Stigler and Becker.
A separate motivation is that such research would easily be applied in industry for sensitivity testing among other things.