Part IIB – Industry Empirical Studies Lecture 2: NEIO and Industry Models of Market Power. Dr Christos Genakos. OUTLINE. NEIO and the Structural Approach Identification Estimation and Hypothesis Testing Examples: Graddy (1995); Genesove and Mullin (1998)
Dr Christos Genakos
Key features of NEIO:
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:
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
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
To trace the supply equation we need variables that shift the demand curve (like income) but not the supply relationship
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
Market Power is NOT Identified
Shifting only the intercept of the demand curve does not identify market power
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.
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 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
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
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:
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
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
OUTLINE industry, 1890-1914
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.