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Empirical Approaches to Market Power and Product Differentiation in Industrial Organization

This document outlines empirical methodologies for analyzing market power and product differentiation in industrial organization. It emphasizes the estimation issues encountered when studying demand for numerous brands, specifically focusing on the need for dimensionality reduction techniques. Key approaches discussed include nested logit models, random coefficients logit, and continuous choice models alongside their advantages and disadvantages. The transformation methods for estimating demand and elasticity within competitive frameworks such as Bertrand and collusion are highlighted to provide robust analytical tools for empirical work in market structure analysis.

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Empirical Approaches to Market Power and Product Differentiation in Industrial Organization

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  1. Industrial Organization Product Differentiation Empirical work

  2. Announcements • Project 3 will be due on June 27 (next Thursday) • We will work on it on Monday (and perhaps at the end of the day today)

  3. Differentiation: Empirical Work • Focus of empirical work: • Market power: (P-MC)/P • Extension of conjectural variations approach to a more complex environment • Each firm f maximizes profit over its portfolio of brands • Each firm has FfFOC, where Ffis the number of brands in :

  4. Estimation Issues • To study market power, one needs to estimate demand: • But, J products implies the computation of J2 own- and cross-price coefficients • Assume a simple linear demand equation: • With 50 brands (for example): • 50 equations and 2,500 cross- and own- price coefficients. • How to reduce dimensionality?

  5. Estimation Issues: Solutions • Solutions: • Nest products into mutually exclusive categories and estimate coefficients in every nest Cereal (25 brands) Kids (8) Healthy (10) Adult (7)

  6. Estimation Issues: Solutions • Solutions: • Impose restrictions in estimation (e.g. symmetry) • Assume a discrete choice model to project J onto a lower dimensional space (namely characteristics) Assume error is distributed extreme value Logit formula (McFadden, 1978)

  7. Logit: Discrete Choice (DC) Demand • Very parsimonious: many substitution patterns recovered via few parameters: • Logit: Simplest DC model • Independence of Irrelevant Alternatives property: off-diagonal entries in a column of elasticity matrix are equal. • Substitution patterns are driven solely by market shares.

  8. Logit: Discrete Choice (DC) Demand • Nested Logit: • Products grouped into mutually exclusive sets. Cross-elasticities across different groups are not restricted. • IIA property remains within groups. • Random Coefficients Logit • Berry, Levinsohn and Pakes (BLP) • Also known as “mixed logit” (McFadden and Train) • Most general of DC models. • Allows taste parameters to have a distribution • Implication: flexible substitution patterns

  9. Other Models: Continuous Choice (CC) Demand • Also called representative consumer models • Not parsimonious in nature. Suitable to model broad categories of goods. Solutions: • Multistage Budgeting (e.g. Hausman, Leonard and Zona, 1994): • Demand estimated in stages • Bottom stage has mutually exclusive sets of products. • Problems: separability structure is difficult to test; as the number of products increases, the problem of having to estimate too many parameters arises again.

  10. Other Alternatives: Continuous Choice (CC) Models • Distance Metric (DM) Method (Pinkse, Slade, Brett, 2002) • Based on brands’ location in product space (need product characteristics data) • Intuition: cross-price effects are a function of closeness in product space to reduce dimensionality • It does not restrict the choice of CC demand model

  11. Approaches to Estimation Disadvantages of Continuous Choice: • Dimensionality is usually larger (J2 parameters) • Certain analyses are difficult (e.g. evaluating the introduction of a new brand) Disadvantages of Characteristics Space approach: • Data on characteristics may be hard to get • Dealing with non-discrete choice goods and complements is difficult • Computational burden

  12. Estimation Remarks Continuous Choice Models: • Several functional forms • Linear, log-log, etc. are convenient • Theory based: Almost Ideal Demand System; trans-log. Parameters estimated have a theoretical meaning (e.g. you can impose symmetry of Slutsky matrix) • Regardless of functional form, the ultimate goal is to obtain a measure of to conduct empirical analyses

  13. Estimation of DC models: Details Problematic unobservable • Logit model: Each consumer has an idiosyncratic shock eij Assumption: IID, distributed “extreme value”

  14. Estimation of DC models: Details • Berry (1994) transformation • Instrumenting for endogenous price is easier if we have an additively separable error: OLS (if no endogeneity), 2SLS (endogeneity)

  15. Estimation of DC models: Details • Other details With aggregate data (q1,…qJ), we approximate πjt with market shares (sjt). But s0t (approximation for π0t) is not observable! Solution:

  16. Estimation of DC models: Details • Profit function: • Nevo (2001): recover ck (and corresponding Lerner Index) based on: • Single product Bertrand Nash • Multiproduct Bertrand Nash • Full collusion

  17. Estimation of DC models: Details • Elasticities • Derivatives:

  18. Nevo’s work • Assume a model of competition (Bertrand-Nash or collusion) • Call and then compare with some rough estimate (accounting, for example) Back out marginal cost

  19. Useful for mergers • Intuition • Hence: Search for p post merger Contains new ownership info

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