Handbook of industrial organization. Volume 4 / Kate Ho, Ali Hortacsu and Alessandro Lizzeri.

Format:

Book

Author/Creator:

Ho, Katherine, author.

Hortaçsu, Ali, author.

Lizzeri, Alessandro, author.

Series:

Handbooks in economics.

Handbooks in economics

Language:

English

Subjects (All):

Industrial organization (Economic theory)--Handbooks, manuals, etc.

Industrial organization (Economic theory).

Industries--Economic aspects.

Industries.

Physical Description:

1 online resource (788 pages)

Edition:

1st ed.

Place of Publication:

Amsterdam, Netherlands : Elsevier, [2021]

Summary:

Handbook of Industrial Organization, Volume Four highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of expert authors.- Presents authoritative surveys and reviews of advances in theory and econometrics- Reviews recent research on capital raising methods and institutions-.

Contents:

Front Cover

Handbook of Industrial Organization, Volume 4

Copyright

Contents

Contributors

Introduction to the series

Preface

1 Foundations of demand estimation

1 Introduction

1.1 Why estimate demand?

1.2 Our focus

2 The challenges of demand estimation

2.1 The first fundamental challenge

2.2 The second fundamental challenge

2.3 Demand is not regression

2.4 A surprisingly difficult case: exogenous prices

2.5 Many common tools fall short

2.5.1 Controls, including fixed effects

2.5.2 Control function

2.5.3 Average treatment effects

2.6 Balancing flexibility and practicality

2.7 Demand or utilities?

3 Discrete choice demand

3.1 Random utility models

3.2 The canonical model

3.3 Why random coefficients?

4 Market-level data

4.1 The BLP estimator

4.2 Instruments

4.2.1 Cost shifters and their proxies

4.2.2 BLP instruments

4.2.3 Waldfogel-Fan instruments

4.2.4 Exogenous measures of market structure

4.2.5 Optimal instruments

4.2.6 Evaluating instruments

4.3 Using a supply side

4.4 Computing the BLP estimator and standard errors

5 Nonparametric identification: market-level data

5.1 Insights from parametric models

5.1.1 Multinomial logit

5.1.2 Nested logit

5.1.3 The BLP model

5.1.4 Index, inversion, and instruments

5.2 Nonparametric demand model

5.2.1 A nonparametric index

5.2.2 Inverting demand

5.3 Identification via instruments

5.4 Discussion

5.4.1 Why 2J instruments?

5.4.2 Why BLP instruments?

5.4.3 Why the index?

5.4.4 Further restrictions and tradeoffs

6 Micro data, panels, and ranked choices

6.1 Micro data

6.2 Consumer panels

6.3 Ranked choice data

6.4 Hybrids

7 Nonparametric identification with micro data

7.1 Nonparametric demand model

7.2 Identification.

7.2.1 Identification of the index function

7.2.2 Identification of demand

7.3 Discussion

8 Some directions for future work

References

2 Empirical models of demand and supply in differentiated products industries

2 A motivating example

2.1 Model

2.1.1 Supply

2.1.2 Demand

2.2 Estimation and results

2.3 Discussion

3 Demand

3.1 Background

3.2 Discrete choice demand models

3.2.1 Price elasticity and substitution patterns

3.2.2 Consumer welfare

4 Demand estimation

4.1 The estimation problem

4.2 What variation in the data can identify the parameters?

4.2.1 Intuition from individual-level data

4.2.2 The informational content of E[ξ|Z]=0

4.3 The general estimation procedure

4.3.1 Instrumental variables

BLP instruments

Hausman instruments

Waldfogel instruments

4.3.2 Additional sources of variation

Multiple markets

Micro moments and second choice data

Supply-side moments

4.3.3 Efficiency

4.3.4 Computational algorithms

Nested fixed point

Mathematical programming with equilibrium constraints (MPEC):

Approximate BLP (ABLP):

4.4 Extensions

4.4.1 Error in market shares

4.4.2 Non-parametric and flexible estimation

5 Supply

5.1 The workhorse model of horizontal competition

5.2 Distinguishing between models of competition

5.3 Adding retailers into the mix

5.4 Models of bargaining

6 Extensions of the demand model

6.1 Extensions to the static demand model

6.1.1 Multiple goods

6.1.2 General characteristics demand models

6.2 Dynamic demand

6.2.1 Storable products

6.2.2 Durable products

7 Concluding comments

3 An industrial organization perspective on productivity

1 A productivity primer

1.1 Background and focus

1.2 Productivity conceptualized.

2 Empirical facts about productivity at the producer level

2.1 Dispersion

2.2 Persistence within producers

2.3 Correlations

3 A simple model of equilibrium productivity dispersion

3.1 Demand

3.2 Supply

3.3 Equilibrium

3.4 Empirical implications

4 Measurement of output and inputs

4.1 Output measurement

4.2 Input measurement

4.3 Data sources

5 Recovering productivity from the data

5.1 Operating environment and unit of analysis

5.1.1 Market structure

5.1.2 Unit of analysis

5.1.3 Output and input data

5.1.4 Trade-offs across approaches

5.1.5 Notation and setup

5.2 Factor shares

5.3 Production function estimation (producer level)

5.3.1 Perfect competition (A.1)

Control function approach

Selection bias

Procedure

Dynamic panel

Discussion

5.3.2 Imperfect competition (B.1)

Homogeneous good

Product differentiation

Deflating revenue

Adding demand-side information

Pass-through

Beyond price data: how to compare quantities?

5.3.3 Impact on the coefficients of interest

5.4 Multi-product production

5.4.1 Allocation of inputs to products

Explicit aggregation from product to producer level

5.4.2 Estimate transformation function (A.2)

5.4.3 Product differentiation and imperfect competition (B.2.2)

Illustration

5.5 Cost versus production functions

5.6 Measurement and specification errors

5.6.1 Measurement error

5.6.2 Model misspecification

Productivity process

Technology heterogeneity

Functional form

6 Productivity analysis

6.1 Producer-level productivity analysis

6.1.1 Identifying producer-level drivers

Exogenous drivers

Endogenous drivers

6.1.2 Sources of productivity differences

Managerial practices

Unobservable input quality

Intangible capital

Firm structure

Product-side differences.

6.2 Aggregate analysis: resource (re/mis)allocation

6.2.1 What does theory predict?

6.2.2 Empirical work

Decomposing industry aggregate productivity

6.2.3 Exogenous drivers: reallocation

Deregulation

Technology

6.2.4 Endogenous drivers and aggregation: market power

6.3 Misallocation

7 Looking ahead

7.1 Market power and productivity data

7.1.1 Measuring market power using production data

Applications

7.1.2 Integrating product and factor markets using productivity data

Vertical linkages

Labor market power

7.2 Technological change and market-level outcomes

7.2.1 Factor-biased technological change

7.2.2 Endogenous productivity growth

8 Conclusion

4 Dynamic games in empirical industrial organization

1.1 Role of dynamic games in empirical industrial organization

1.2 Organization of this chapter

2 Models

2.1 Basic framework

2.2 Markov perfect Nash equilibrium

2.2.1 Definition

2.2.2 Equilibrium existence

2.2.3 Incomplete information

2.2.4 Multiple equilibria

2.3 Examples

2.4 Extensions of the basic framework

2.4.1 Continuous time

2.4.2 Oblivious equilibrium

2.4.3 Large state spaces

2.4.4 Persistent asymmetric information

2.4.5 Firms' biased beliefs

3 Identification and estimation

3.1 Data

3.2 Identification

3.2.1 Non-identification result

3.2.2 A set of sufficient conditions for identification

3.2.3 Relaxing restrictions (ID.1) to (ID.8)

3.2.4 Identification of mixed continuous-discrete choice models

3.3 Estimation methods

3.3.1 Full solution methods

3.3.2 Two-step CCP methods

3.3.3 Bajari-Benkard-Levin (BBL) method

3.3.4 Large state space and finite dependence

3.3.5 Unobserved market heterogeneity

3.4 The promise of machine learning

4 Empirical applications.

4.1 Earlier empirical work on dynamic games

4.1.1 Competition in the hospital market

4.1.2 Dynamic output competition with learning by doing

4.1.3 Dynamics in auctions

4.1.4 Environmental regulations in concentrated industries

4.1.5 Demand shocks and market structure

4.1.6 Subsidizing entry

4.2 Innovation and market structure

4.2.1 Microprocessor innovation: Intel vs AMD

4.2.2 Hard drive innovation: new products and cannibalization

4.2.3 Car innovation and quality ladders

4.2.4 Data on innovation

4.3 Antitrust policy towards mergers

4.3.1 Endogenous mergers

4.3.2 Evolving market structure and mergers

4.3.3 Revealed merger efficiencies

4.4 Dynamic pricing

4.4.1 Competition with price adjustment costs

4.4.2 Limit pricing

4.4.3 Dynamic pricing with network effects

4.5 Regulation

4.5.1 Environmental regulation

4.5.2 Land use regulation

4.5.3 Product variety

4.5.4 Industrial policy

4.6 Retail

4.6.1 Economies of density and cannibalization

4.6.2 Chains

4.6.3 Unobserved heterogeneity and entry in retail

4.6.4 Effect of Walmart on rival grocers

4.6.5 Exit in declining industries

4.6.6 Repositioning

4.6.7 Advertising

4.7 Uncertainty and firms' investment decisions

4.7.1 Firm investment under uncertainty

4.7.2 Uncertainty and oil drilling in Texas

4.7.3 Uncertainty in shipping

4.8 Network competition in the airline industry

4.9 Dynamic matching

4.10 Natural resources

5 Concluding remarks

5 Moment inequalities and partial identification in industrial organization

2 Definitions and background

3 Revealed preference

3.1 Primitive assumptions

3.2 Paths to estimators

3.3 Examples

3.3.1 Richer assumptions on disturbances

4 Generalized discrete choice approaches.

4.1 Models of discrete games with complete information.

Notes:

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

9780323915144

0323915140

OCLC:

1289372090