Industry Empirical Studies NEIO and Industry Models of Market Power Based on the lectures of Dr Christos Genakos (University of Cambridge)

IndustryEmpirical Studies

NEIO and Industry Models of Market Power

Based on the lectures of Dr Christos Genakos (University of Cambridge)

1. NEIO and the Structural Approach

2. Identification

3. Estimation and Hypothesis Testing

4. Examples: Porter (1983); Genesove and Mullin (1998)

5. Reduced form and Non-Parametric approaches

OUTLINE

New Empirical Industrial Organization (NEIO)

Most important weakness of the SCP paradigm was the lack of feedback mechanisms emphasized by game theory

Structure, Conduct and Performance are jointly determined by underlying primitives, institutional details and equilibrium assumptions

Two important lessons during the 70-80’s: every industry has many potentially important idiosyncrasies and these details matter a lot for the predicted conduct and performance

Perhaps we should abandon the hope of finding common patterns across industries and instead look at each industry more carefully

New Empirical Industrial Organization (NEIO)

Key features of NEIO:

No use of accounting data for costs and price-cost margins

Estimate market power fore each industry separately

Behavior of firms is estimated based on theoretical oligopoly models. This allows for explicit hypothesis testing on the degree of market power.

The degree of market power is identified and estimated. The inference of market power is based on the conduct of firms.

The Structural Approach

Suppose you had data on the following homogeneous goods market:

•P industry price

•qi output for each firm and Q the whole industry

•Y variables that shift the demand curve (income, weather, price of substitutes)

•W variables that shift the supply curve (price of inputs, weather, technology)

Could you uncover the extent of market power?

YES! Use the data to simultaneously estimate the elasticity of demand, marginal costs and firm conduct!

The Structural Approach

The key aspect of this approach is that it uses theory to specify the structure of demand and supply and in the process firm conduct is identified (pure magic!)

Let’s see how:

Demand function

Supply function

Profit function

),,( YQPP

),,( WqCC i

),,(),,( WqCqYQP iii

The Structural Approach

Marginal cost

Marginal Revenue

λi is a parameter which measures conduct; λi=0 price taker, λi=1 Cournot, λi=1/si Monopoly.

Optimality Condition gives us the supply relationship:

i

ii dq

WqdCWqMC

),,(),,(

iii qdQ

dPPYMR ),,(

i

iii

iii

s

P

WqMCP

qdQ

dPWqMCP

),,(

),,(

The Structural Approach

Two interpretations of λi parameter: (i) measures the gap between price and marginal cost, and (ii) an “aggregate conjectural variation”

Problem with interpretation (i): can justify only few values, not a continuous index

Problem with interpretation (ii): Corts (1999) critique that estimation of λi only unbiased if underlying method is the result of a conjectural variations eq.; underestimate if firms collude

Mkt Structure λ L

Competition/Bertrand

0 0

Cournot 1 -si/ε

Monopoly (collusion) 1/si -1/ε

1. NEIO and the Structural Approach

2. Identification

3. Estimation and Hypothesis Testing

4. Examples

5. Reduced form and Non-Parametric approaches

OUTLINE

Identification

Can we identify the market power parameter λi given only market level data on P, Q, Y and W?

Remember our supply function is:

Identification Problem is that Q and P are equilibrium values, simultaneously determined by the interaction of consumers and firms

QdQ

YQdPWQMCP

),,(),,(

Identification

To trace the supply equation we need variables that shift the demand curve (like income) but not the supply relationship

P1

Q1

P2

Q2

P3

Q3

D(Y2)D(Y3)

D(Y1) S

P

Q

Identification

Similarly, to trace out the demand curve we need variables that shift the supply (like wages) but not the demand relationship

P1

Q1

P2

Q2

P3

Q3

D

S(W1)

S(W2)

S(W3)

P

Q

Market Power Identification

Hence to identify demand (supply) function, we need at least one exogenous variable in the supply (demand) relationship that does not enter the demand (supply) function.

What about the market power?

Assume demand is given by

(1)

Assume also that marginal cost (not observable) is given by

(2)

Hence, supply relationship is

(3)

24131210 YQYYQP

WQMC 210

WQYQP 213110 )(

Market Power is NOT Identified

Shifting only the intercept of the demand curve does not identify market power

P1

Q1 Q

PMCC

MCMP2

Q2

D(Y2)

MR(Y2)D(Y1)

MR(Y1)

Market Power IS Identified

Shifting ΒΟΤΗ the intercept and the slope of the demand curve identifies market power

P1

Q1 Q

PMCC

MCM

D(Y2)

MR(Y2)D(Y1)

MR(Y1)

P2c

Q2c Q2

m

Market Power Identification

Hence, using econometric estimates of the demand and supply parameters (equations 1 and 3) we can obtain an estimate of the degree of market power, in our example here:

Note: identification is based on (arbitrary?) assumptions on the functional form of both the demand and marginal cost functions.

Note: If we assume constant marginal cost, we can estimate the degree of market power! Without shift in slope!!!

3

3

01 1

1

1. NEIO and the Structural Approach

2. Identification

3. Estimation and Hypothesis Testing

4. Examples: Joint Ex Committee; Genesove and Mullin

5. Reduced form and Non-Parametric approaches

OUTLINE

Estimation and Hypothesis Testing

Given a set of credible instruments, the econometrician estimates the demand and optimality condition either separately (2SLS) or as a system (3SLS, GMM) of equations

Two ways to estimate the market power parameter:

1) Estimate it as a “free” continuous variable. One then tests whether λ equals a value associated with a well-known model of competition (Bertrand, Cournot, collusion)

2) Estimate separate models corresponding to the various well-known models by imposing the particular value of λ and then use non-nested tests to choose among them

1. NEIO and the Structural Approach

2. Identification

3. Estimation and Hypothesis Testing

4. Examples: Joint Ex Committee; Genesove and Mullin (1998)

5. Reduced form and Non-Parametric approaches

OUTLINE

Genesove and Mullin (1998): conduct and cost in the sugar industry, 1890-1914

Genesove and Mullin’s aim is to test the validity of the NEIO methodology by comparing the estimated conduct parameter from a structural model to the calculated price-cost margins in the sugar industry

The simple production function together with its volatile history of high concentration, price wars and court cases at the beginning of the century make this industry the ideal test ground

Why should an industrial economist care about the answer?

The Sugar Industry and Production Technology

The industry during period of study is characterized by high levels of concentration, episodes of entry and price wars and later acquisition by or accommodation with ASRC

Refined sugar is a homogenous good with common technology:

RAWRAW PPkcc 075.126.0

Demand and Structural Model

The postulate a general demand formula

that encompass as special cases the quadratic, linear, log-linear and exponential

Optimality condition for a constant marginal cost, c, and conduct parameter, θ, is given by:

Instruments used: Cuban raw sugar imports, which are driven by harvest cycle, weather conditions, Cuban Revolution, Spanish-American War

)()( PPQ

c

cP )(

Supply Equation and Results

Substituting marginal cost function into pricing rule gives us:

Genesove and Mullin estimate different versions of their model depending on the demand function but also cost information availability

Results:

1) NEIO methodology does pretty good tracking calculated price-cost margins independent of the assumed demand function, although θ underestimated

2) Cost estimates sensitive to the model assumed, predictive power improved when add real info even if model misspecified

3) Estimating a “free” conduct parameter improves estimates

RAWkPc

P

0

1. NEIO and the Structural Approach

2. Identification

3. Estimation and Hypothesis Testing

4. Examples

5. Reduced form and Non-Parametric approaches

OUTLINE

Reduced form and Non-Parametric approaches

An alternative method to a full structural model is to use comparative statics and be able to distinguish firm behaviour

Good alternatives if important concerns on specification of structural model or data limitations

Basic idea: suppose that firms face a constant marginal cost; a shock causes the marginal cost to rise. If the market is competitive, the price will increase by the same amount as mc. If the market is oligopolistic, price will not change by the same amount.

Again we need to specify a demand function and functional form will matter for the results, but in principle we require less info than a full structural model

However, by imposing less structure we are able JUST to test whether the market is competitive or not, cannot measure the degree of market power

NEIO and Industry Models of Market Power:

References

*Bresnahan, T. (1982) “The Oligopoly Solution is Identified”, Economic Letters, 10: 87-92.

*Bresnahan, T. (1989) “Empirical Studies of Industries with Market Power”, Handbook of Industrial Organization, 1011-1057.

Corts, K. (1999) “Conduct Parameters and the Measurement of Market Power”, Journal of Econometrics, 88:227-250.

Genesove, D. and Mullin, W. (1998) “Testing Static Oligopoly Models: Conduct and Cost in the Sugar Industry, 1890-1914”, Rand Journal of Economics, 29:355-377.

Graddy K. (1995) “Testing for imperfect competition at the Fulton Fish Market”, Rand Journal of Economics, 26:75-92.

Next time: Differentiated Products Structural Models

*Berry, S (1994) “Estimating Discrete-Choice Models of Product Differentiation”, Rand Journal of Economics, 25:242-262.

*Hausman, J. (1997) “Valuation of New Goods Under Perfect and Imperfect Competition”, in Bresnahan and Gordon eds., The Economics of New Goods, NBER.

Nevo (2001) “Measuring Market Power in the Ready-to-Eat Cereal Industry”, Econometrica, 69:307-342.

*Nevo (2000) “A Practitioner’s Guide to Estimation of Random-Coefficients Logit Models of Demand”, Journal of Economics and Management Strategy, 9:513-548.

Berry, S., Levinsohn J. and Pakes, A. (1995) “Automobile Prices in Market Equilibrium”, Econometrica, 63:841-890.