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Part IIB – Industry Empirical Studies Lecture 2: NEIO and Industry Models of Market PowerPowerPoint Presentation

Part IIB – Industry Empirical Studies Lecture 2: NEIO and Industry Models of Market Power

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### Part IIB – IndustryEmpirical StudiesLecture 2: NEIO and Industry Models of Market Power

Dr Christos Genakos

- NEIO and the Structural Approach
- Identification
- Estimation and Hypothesis Testing
- Examples: Graddy (1995); Genesove and Mullin (1998)
- Reduced form and Non-Parametric approaches

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 seprately
- 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.

- 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 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

Marginal cost

Marginal Revenue

λi is a parameter which measures conduct; λi=0 price taker, λi=1 monopolist.

Optimality Condition gives us the supply relationship:

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 λionly unbiased if underlying method is the result of a conjectural variations eq.; underestimate if firms collude

- NEIO and the Structural Approach
- Identification
- Estimation and Hypothesis Testing
- Examples: Graddy (1995); Genesove and Mullin (1998)
- Reduced form and Non-Parametric approaches

Can we identify the market power parameter λigiven 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

D(Y3)

D(Y2)

D(Y1)

S

P3

P2

P1

Q

Q2

Q3

Q1

Identification

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

S(W1)

P

S(W2)

S(W3)

P1

P2

P3

D

Q

Q2

Q3

Q1

Identification

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

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

Assume also that marginal cost is given by

Hence, supply relationship is

MCC

P

P2

MCM

P1

D(Y2)

D(Y1)

MR(Y2)

Q1

Q

Q2

MR(Y1)

Market Power is NOT Identified

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

MCC

P

MCM

P2c

P1

D(Y2)

D(Y1)

MR(Y2)

Q2c

Q2m

Q1

Q

MR(Y1)

Market Power IS Identified

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

Hence, using econometric estimates of the demand and supply parameters 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: credible instrumental variables play a crucial role in the identification.

- NEIO and the Structural Approach
- Identification
- Estimation and Hypothesis Testing
- Examples: Graddy (1995); Genesove and Mullin (1998)
- Reduced form and Non-Parametric approaches

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:
- 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)
- 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

- NEIO and the Structural Approach
- Identification
- Estimation and Hypothesis Testing
- Examples: Graddy (1995); Genesove and Mullin (1998)
- Reduced form and Non-Parametric approaches

Graddy (1995): imperfect competition in the Fulton fish market

Graddy tests for “law of one price” in sales of whiting (type of fish) in a market that has many characteristics of a perfectly competitive market

She also estimates a structural model of imperfect competition to estimate market power

Why should an industrial economist care about the answer?

The Fulton fish market and Data market

The Fulton fish market is the central fish market in NY

Open-air, wholesale person-to-person market with many dealers and main customers stores and fry shops

Author claims that entry and exit are easy, but also lots of Mafia activity

All dealers are white, whereas buyers are white, black or Asian

Graddy chose whiting cause more homogenous, more transactions (and it was the only salesman to let her watch)

Hand-collected data combined with info from inventories

Data on price, quantities, customer type, fish quality and cash or credit plus weather data

Price Discrimination and Market Power market

Graddy first estimates a reduced-form model of transaction prices on various determinants (time, location in Manhattan or Brooklyn, store dummy, Asian and black dummy, cash dummy, regular dummy, average quantity and quality)

She finds that the only consistently significant variable is the Asian dummy indicating that Asian buyers get about 7% discount compare to white customers for the same quality of fish

She then sets out to estimate the degree of market power in the market for Asian compare to white customers

Price Discrimination and Market Power market

The inverse demand function for Asian and white buyers are:

The marginal cost functions for the Asian and white market are assumed to be the same and differ only by a random term:

The optimality conditions for imperfect competition gives us:

Price Discrimination and Market Power market

Restricting the conduct parameter to be the same across time and across groups gives us the supply equation to be estimated:

Having estimates on all these parameters, we can back-up the conduct parameter θ.

Because quantity and price endogenous, need to use exogenous instruments: four measurements of weather and dummy variables for each day of the week

Results and Interpretation market

- Results from the structural model indicate that:
- Asian buyers have more elastic demand than white buyers, although difference is not statistically significant
- Conduct parameters are all between zero and one, but s.e. large, which means cannot reject hypothesis of neither competition nor collusion
- Graddy offers three interpretations for the price differences:
- Market less than competitive and third degree price discrimination between two ethnic groups
- Different firm conduct in white and Asian markets (many possible combinations)
- Different search behaviour, Asians search more

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 industry, 1890-1914

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:

Demand and Structural Model industry, 1890-1914

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

Supply Equation and Results industry, 1890-1914

- 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:
- NEIO methodology does pretty good tracking calculated price-cost margins independent of the assumed demand function, although θunderestimated
- Cost estimates sensitive to the model assumed, predictive power improved when add real info even if model misspecified
- Estimating a “free” conduct parameter improves estimates

OUTLINE industry, 1890-1914

- NEIO and the Structural Approach
- Identification
- Estimation and Hypothesis Testing
- Examples: Graddy (1995); Genesove and Mullin (1998)
- Reduced form and Non-Parametric approaches

Reduced form and Non-Parametric approaches industry, 1890-1914

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 industry, 1890-1914: 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 industry, 1890-1914

*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.

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