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Industrial Organization

Industrial Organization. Product Differentiation Empirical work. 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). Differentiation: Empirical Work. Focus of empirical work: Market power: (P-MC)/P

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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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