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IO‐De Silva
Empirical Industrial Organization
Dakshina G. De Silva
Short course
Université Paris – 1, Panthéon‐Sorbonne
March – April, 2013
These notes are mainly based on Timothy Dunne’s IO – II lecture notes collected when I was a grad student and various other sources. All errors are mine.
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Introduction A definition of industrial organization:
“Industrial organization is concerned with the workings of markets and industries, in particular the way firms compete with each other." Two branches of I.O. a) Theories of Markets and Market Structure. This branch treats the firm as a black box and focuses on how firms compete with each other.
b) Theories of the Firm. This branch investigates why some transactions are conducted through markets while others are conducted within a firm. Attempts to look inside the black box and explain things like firm size, the boundaries of the firm, and incentive schemes within the firm.
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Main question: Are the real world markets perfectly competitive? (Most of the time this is the null.) Theory – Cournot behavior
Markov perfect games
Econometric tools – Simultaneous equations Panel data techniques Discrete choice models Nonparametric methods Institutional framework Data – Often disappointing
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A Brief History of Industrial Organization 1. Harvard Tradition (1940 – 1960: Joe Bain)
Structure‐Conduct‐Performance
Structure (i.e., how sellers interact with each other, buyers, and potential entrants) is a function of number of firms, technology, existing constraints, products... CR4, HHI
Conduct (i.e., how firms behave in a given market structure) includes price setting, competition, advertising...
Performance (i.e., technological efficiency, social efficiency, dynamic efficiency) includes consumer surplus, optimal variety, profits, social welfare...
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2. Chicago School (1960 – 1980: Robert Bork … “The Antitrust Paradox")
Firms become big for particular reasons
Emphasis on price theory (markets work)
More careful application of econometric techniques
Use different market structures to understand different industry settings or markets
Monopoly is much more often alleged than confirmed; entry (or just the threat of entry) is important
When monopoly does exist, it is often transitory
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3. Game Theory (1980 ‐ 1990)
Emphasis on strategic decision making
Modeled mathematically using Nash equilibrium concept
Produces a huge proliferation of models which are often very intuitive theoretically
However, it is difficult to know which model is the right one for a real world industry
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4. New Empirical I.O. (1990 ‐ )
Combines theory and econometrics in a serious way
Sophisticated, computationally intense, complex empirical models
Not all I.O. economists think this way or use the same methods
This view of the world is constantly evolving
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Contemporary Issues in I.O.
Entry and exit
Merger analysis
Product choice: characteristics and location
Retail Markets
Price discrimination
The role of information and monitoring technologies
Advertising
Learning‐by‐doing
Technological innovation
R & D spillovers
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Evolution of Empirical IO I’m sure you have seen these before. So this is going to be quick.
Bain (1951)
, If we run and look at ⁄ 0 (This is the Lerner index.) Main weakness: Market structure is exogenous
Profits and CR (+)
They do not depend on LI
The (+) relationship may be due to one firms efficiency. Therefore, 1 does not show the correct market power.
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Comanor and Wilson (1967)
, , , … , )
This is at industry level Growth was measured in change in revenue If p increases revenues will increase. Then ‘Growthi’ and are correlated
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Orr (1974)
Examine net entry (Canadian manufacturing industries) Measured net entry as an index and lagged variables. But lagged shock is not a current year shock Dropped variables due to collinearity — leads to specification error.
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Domowitz, Hubbard, & Petersen (1987) Looked at the CR, and profit relationship. A panel of 284 industries – first to use a panel
industry effect
Used fixed effects. Therefore, looking at as CR what will happen to (time variation.)
Measure from price cost margin.
(Rev – VC)/Rev left with FC, Capital ratio, and . This is a weakness.
Nice paper to read: Caves, Richard E. (2007) “In praise of the Old I.O.” International Journal of Industrial Organization.
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Generic models of Oligopoly – Bresnahan (1987) Homogeneous product industry Perfect competition: p = MC Monopoly: MR = MC Oligopoly framework:
q12 firms q2
mc1(q1) = mc2(q2) Q = q1 + q2 Market demand function: p(Q)
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Suppose a Cournot model
F.O.C
0
MR MC
2 2
12
1/
121
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For n firms
1 1
If you chose Bertrand
,
if12 if
0if
NE pi = pj = c , therefore LI = 0
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Data Only have p and Q Demand function: Q = D(p,y) MC function: c(Q,w)
Competitive supply
Monopoly supply
, ,
Quasi supply curve
,
If = 0 then market is perfectly competitive (= 1 then monopoly) We like to estimate but we have unobservable .
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Example – not identified 1) Market demand: (1)
2) Marginal cost: (2)
3) Quasi supply:
MC
Note that ⁄
(3)
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Two equation model (solve using eq. 1 & 3)
Q
P
MCm ( , 1
MCc ( , 0
MR
D
Q1
p1
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Take Eq 1 and put a rotational variable (z)
(4) Supply relationship with
(5)
This is identified since and comes in different forms.
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Q
P
MCm ( , 1
MCc ( , 0
MR
MR’
D
D’
Q1
p1
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Bresnahan (1987)
If you only have data on market price and quantity (no cost data) and endogenous demand and supply shifters, you can identify the degree of market power from market demand and supply relationship if the demand curve structure (in the real world) is rich enough to allow for the rotation of the demand curve.
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Appelbaum – JE (1982)
Industries (US): Rubber, Textile, Electrical machinery, & Tobacco
Industry demand: ,
Quasi‐supply: 1
Industry cost: ,
Must model , , & MC
FOC for firms:
1 ,
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Assumed MC constant and equal for all firms.
, ∗
1 ∗
This means is equal for all firms – homogeneous cost function.
Equilibrium output level at oligopoly
1 ∗
Industry demand curve:
ln ln ln
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Cost function:
c /
(Leontief cost function of the Gorman polar form)
Quasi supply curve:
1 /
MC
He estimates:
/ 1
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Then h is approximated linearly as
p & Q endogenous—simultaneous equation method. Industry ∗ ⁄Rubber .019 .056 Textile .370 .670 E. machinery .200 .196 Tobacco .402 .651 Contribution: Econometrics and Data Issues: Same demand function for all industries Industry classification—sawing machines and power generators Also check: Schroeter, John R. (1988) Estimating the Degree of Market Power in the Beef Packing Industry, REStat
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Detecting collusion
Porter (1983) –Bell
Porter (1983) analyzes the pricing behaviour of the US railroad cartel (The Joint Executive Committee –1880‐1886.) Cartel controlled freight shipment from Chicago to the Atlantic seaboard. Collusion was legal (pre Sherman act) and the workings of the cartel are very well documented.
Ulen (1978) said there are several instances where they thought cheating occurred.
Cartels can increase profits by restricting output from competitive levels. JEC agreement involved allocation of market shares.
However, members face an incentive to cheat because price is above marginal cost.
JEC collected and disseminated info to its members on total quantity and average price.
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In practice, it may be difficult to monitor competitor’s output. Firms’ market share depended not only its own price, but also prices of other firms and demand conditions.
Model Assumptions (Green and Porter—1984) Firms set their own production level Firms do not know the quantity produced by any other firms ‐ they only observe the market price
Firms’ output is homogenous (they face a common market price) Pattern of observed prices consistent with firms following trigger strategies: periods of high prices followed by periods of low prices “Cheaters” are punished by an industry‐wide switch to noncollusive (e.g. Cournot) quantities for a fixed period of time, resulting in lower revenues for all firms.
Since firms do not observe one another’s output, this switch occurs once the market price falls below a previously decided “trigger price”.
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Collusion supported by an appropriately chosen “punishment pair” (trigger price, punishment period length). To be effective, the “punishment pair” must make the cost of cheating be at least as large as the benefits on expectation. Costs go up by longer punishment period or lower trigger price. Porter wants to establish that
(i) price wars occurred, (ii) price wars were triggered by demand shocks
Econometric model is design to test whether significant switches in supplier behaviour occurred and to identify the periods in which it took place.
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More on JEC Porter argues homogenous good.
Grain was 73% of dead tonnage.
Even though endpoints of rails differed, overseas shipping rates adjusted.
Attention to grain without loss of generality.
Entry occurred multiple times in this sample.
New entrants were accepted into the cartel and allocated market shares.
JEC office took weekly accounts so that the shipments could be monitored.
Demand was quite variable and hard to predict.
Lake ships were primary competition.
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Few notation change from Appelbuam*
∗ & ∗ Market demand:
ln ln Cost:
1
1 1
MC
Where ∑ /
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Take logs Quasi Supply:
ln ln 1 ln ln 1
Demand curve:
ln ln Reversionary period
ln ln 1 ln ln 1
ln ln ln 1 1 ln
ln β β ln
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Collusive period
ln ln 1 ln ln 1
ln ln ln 1 β ln ln 1 ln 1
ln ln β ln ln
(Supply shifter is generated by regime shift)
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SC
SR
D
P
Q
PC
PR
QRQC
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R: ln β β ln C: ln ln β ln β 0 if reversionary period Pr = 1 ‐
1 if collusive period Pr =
Switching model:
ln
| 1 1 | 0 SupplyC SupplyR
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Results
All signs are as expected.
Demand slopes down, supply up. Lakes shifts (residual) demand of cartel down.
Note that R2 on demand is 0.31
Hard to predict.
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Results (cont...)
Entry drives prices down in supply.
The estimate of 3 is roughly consistent with Cournot behavior when collusion is taking place.
The breakdown in collusion leads to significantly lower prices.
Setting all variables equal to their sample mean (using the PN estimate), we get the numbers in Table 4.
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Table 4 Variable Lakes 0 1Price PN 0 .1673 .1612 1 .2780 .2679 +66% Q PN 0 38680 25904 1 25775 17261 ‐33%TR PN 0 129423 83514 1 143309 92484 +11%PN: Estimated cheating dummy variable
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PO often reflects a price war before PN, but they normally switch back to unity together.
This is consistent with Green and Porter not picking up secret price cuts, so there is a lag in the PN estimate.
On average, non‐cooperative periods lasted about 10 weeks.
In this sample, price wars (using either PO or PN) were not preceded by adverse demand shocks. Normally incidents began after entry of another firm, though they were not immediate (average 40 week lag time).
This is consistent with theory, as the increase in number of participating firms leads to increased enforcement problems for the cartel.
Reversions also became more frequent as the number of firms increased.
Uses likelihood ratio tests to determine whether structural change has occurred in the industry, or if changes in price can be attributed to outside demand shocks.
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Tests the null hypothesis that the coefficient on It is equal to zero (no regime change). Uses a distribution with 1 degree of freedom.
Test‐statistic = 554.1 ‐ the null is overwhelmingly rejected!
Main conclusion: Price and quantity changes cannot be attributed solely to exogenous changes in demand and structural conditions.
JEC – led to the Sherman act
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Ellison (1994)—Rand This is a refinement for Porter. Focuses on empirical implication of trigger price strategies.
Expanding output of i‐th firm. For punishment j firms reduce prices. If we cannot observe price change then we should see an expansion of the output.
This means
High market share for one firm Low market share for other firms Low market share for one firm (other are cheating)
Two predictions
Price wars should occur Price wars should occur following demand signals used as strategies
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Model Demand
ln ln
1AR 1 Supply
ln ln β ln β β New Eq (logit): Probability of moving from one state to the next state.
Pr 1| 1
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Wt – constant – : allows for state dependence – : the trigger variables Demand shocks: and unanticipated component increases price wars decrease. Conclusion: Green and Porter: Price wars should occur
This is true But price is more alike a monopoly Regime transition probabilities—provide evidence for the existence of the trigger strategies that are central to the theory.
Serially correlated demand shocks. Therefore, such trigger effects can be distinguished from predictions of the RS model.
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probability of price war should not be constant but depend on whether the previous period was cooperative. Finds market power = 0.848, much closer to collusion Probability of collusion this week is 0.975 if behavior was cooperative last week, but probability of collusion is 0.067 is there was a price war last week Quite interestingly, concludes that actual cheating was rare, hence more evidence that Trigger strategies were followed as a way to sustain collusion
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Graddy (1995) – Rand Imperfect competition Price discrimination Old Fulton Fish Market: 1822‐2005 (now a restaurant)
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New Fulton Fish Market ($85 million facility) in the Bronx
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Graddy collects data from a dealer Fish: the common name whiting
There will be no difference in the cost Sales to consumers within a day Large variations in the price across days
$ .33 / lb May 1st $1.75 /lb May 8th
Systematic days of the week effects Substantial variations in prices across customers in the same day Are these price differences systematically related to customer characteristics?
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Estimates
, , , , . , i = transaction, t = day, and = day dummy Finds that prices charged for Asians are about 6‐7% lower! Empirical model: Asian
Average price on the day at time t White
are unobservable but know for one firm. Assume MC cost for all firms
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MCiAt = MCAt & MCiWt = MCWt For i
ln1
ln ln
ln ln1
ln
Same for
ln ln1
⋯
ln ln1
⋯
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Quasi supply relationship
1
1 Note that and are elasticities. Set
11
Assume
11
.598 .213 .753 .361
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.388 .640 11
. 708
. 768 .922
This shows that is about 8.5% above
≅ 1.07
Sub into (A)
11.07
1 .751 .6
.35 calculated & .388 estimated
IO data does not come easy! Sometimes you may need to get your hands dirty! Issues: ...
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Entry, Growth, and Exit A good starting point is: Dunne, Timothy, Roberts, Mark and Samuelson, Larry (1989) "The Growth and Failure of U.S. Manufacturing Plants," Quarterly Journal of Economics, November, 671‐698. Bresnahan‐Reiss (1991) – JPE They look at the increase in the number of firms in a market as market size increases. The pattern of entry should tell us about how mark‐ups decrease as market size increases. They look at entry patterns across geographically differentiated markets—these are small markets all over US (towns which are located far away from other towns in the US.)
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Industries:
Dentists Tires Car Dealers Plumbers
What can these entry data tell us about mark‐ups and competition? Demand:
, ∙
, = representative consumer & = size of the market (number of cunsumers). Z = demographic characteristics & P is price.
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Note: Consumers are separable—this demand function is good for a private goods market. Firm cost: Fixed cost: F (this is a sunk cost)
,
, = firm output
Single firm case
, , ∙
percustomervariation
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0 ⇒
Entry if 0
As F increase or v decrease you need a larger market to sustain firms. is the entry threshold for monopolist.
Duopoly case Question is if you have two firms what level of s is need to support two firms.
, , ∙ Price in duopoly but not
specific about the nature of competition
Difference in production costs for firms—think of big box stores!
Implicitly assume that the 2 firms split the market —necessary assumption but ...
Difference in fixed costs
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If 0
2
If 0
2
is the duopoly entry threshold.
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1
2
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Few notes: and differ due to
MC may differ (b2 > 0) Depend on price in monopoly vs. price in duopoly.
If no cost differences B2 = b2 = 0 Perfect cartel case
P2 = P1
v2 = v1
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* Per firm level demand supports 2 firms or firms are identical. Then get the 2nd firm when the market doubles.
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* B2 = b2 = 0 & perfect competition. Slope gets flatter as P = MC
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* B2 = b2 = 0 & Cournot competition.
1
2
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B & R:
∙ 1
If 0 then this ratio is about completion and/or MC change as N increase. Per customer fall as N increase and if no cost differences then it reflects a competitive effect. More generally:
∙
Observable Unobservable
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Empirical work: 202 geographic markets Ordered probit
The profits are the latent variable
, , , , ,
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Y = Population variable (market size)
Z, W = Market demand and cost shifters (County level variables) WL = fixed cost shifters (farm land prices) Note that can be estimated since this is interacted with pop variables. This breaks the orthogonal condition. This allow for separability.
∑∑
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These differences are due to entry cost, mark‐ups, and cost differences. Key interpretation: After the 1st and 2nd entry the mark‐up does not differ. Few issues to think about:
When looking at service industry like doctors it is hard to identify when they add a new doctor to staff
Market definition: people drive to find a doctor from one city to the other.
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Mazzeo (2002)—Rand Use ‘Motel industry’ to extend B&R 1991 paper. Extend the model by allowing firms to chose which type of firms they enters as. Firms can differentiate between each other by choosing to enter in different areas of the product space (for example one might build a French restaurant if another decides to build an Italian restaurant.) For this example Mazzeo chose high (h) and low (l) quality (i) Motels. Market is defined as near highway exits.
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Questions:
He wants to understand how firms decide the “location” of the products that they produce. This is driven in large part by the incentive to differentiate ‘my’ product from those of ‘my rivals.’ Allow for different types of producers to compete with each other: not just a homogenous good. Profit in reduce form:
, , ,
Demand characteristics that affect profits
Competitor effects that influence profits
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Equilibrium concept: Follow Sutton (1998) – Consider firms in each market to be playing a generic two‐stage game. In the first stage (the "investment" stage), firms decide whether to enter and choose to offer either low‐quality or high‐quality services. In the second stage (the "competition" stage) ensues in which payoffs are determined. Assume that there are infinite number of potential identical entrants in each market (that is, for a given market structure, the same product‐type choice gives the same profits for every firm.) In other words they get the same shocks (this means you don’t have to specify the number of potential entrants in a market). Thus the equilibrium conditions in this market are: Firms that are in the market make positive profits:
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, , 0
, , 0 If an additional firm entered, it would make negative profits:
, 1, 0
1, , 0
To close the model, he needs to impose and additional assumption, that the effect of competition is higher for firms of your own type that the other type:
, 1, , 1,
1, , 1, ,
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Estimation: Equilibrium is unique and therefore, the sum of the probabilities for all market configurations always equals one. Maximum likelihood selects the profit function parameters that maximize the probability of the observed market configurations across the dataset. The model is estimated using simulated maximum likelihood. Industry and data set: 492 oligopoly markets Motels began to prosper during the first half of the 20th century.
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System of Interstate and Defense Highways, a 42,500‐mile network of freeways (1956‐present) In the early years, most motel properties were independent‐often a single family designed, built, managed, and operated the motel. Last several decades, more motel owners have affiliated themselves with regional and national franchises and chains. Consumers were attracted to chain‐affiliated motels, known to have a consistent and predictable level of quality. Generally motels provide the same basic services but differ in the level of service quality they have chosen to supply.
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American Automobile Association (AAA) has established ratings systems to provide consumers with accurate information about the quality of motel services. This is what Mazzeo use for quality index. The nature of demand for highway motel services complicates the selection and definition of markets to analyze empirically. Highway motels serve both visitors of residents and businesses in the town nearby each exit, as well as long‐distance travellers. Observe geographically isolated clusters of motels along most interstates—this limits the extent to which motels at one exit compete with motels at other exits. Also collect data only from small, rural exits.
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Results: Operating a high‐quality motel is on average more profitable than operating a low‐quality motel ( CH = 2.5252 versus CL = 1.6254) With no competitors, the payoffs to operating a low‐quality motel are on average higher (L = 1.6254+ ( ‐2.303) * (.2711) = 1.001) than those to operating a high‐quality motel (H = 2.5252 + ( ‐2.303) * (0.6768) = .9668) Issues: An issue with the Mazzeo (2002) model is that it is difficult to estimate the model when there are multiple “types” of firms, since the number of inequalities which need to be satisfied rises exponentially.
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Seim (2006)—Rand To overcome the issue discussed in Mazzeo’s paper Seim introduces ‘private information’ into the model. This means firms have information about the payoffs of entering a market that other firms do not see. This is a very common strategy in empirical I.O—especially in auctions! But ... Private information shocks will lead to “mistakes” in the sense that two firms enter when only one firm would make positive profits. This could also happen in the perfect information case if firms are playing a mixed strategy equilibria.
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Questions: How do firms choose their geographic locations: trade‐off between density of demand (lots of consumers) versus competition. Choice of different type of modems technologies The model Simultaneous Moves. Asymmetric Information. Firm f’s payoff in location l in market m:
Π Γ , n ε
Market shock Private shock
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Few more assumptions
Assumption 2: Γ , n ∑
Assumption 3: if ,
Where and denotes cutoffs for the distance band. Assumption 3 is okay—US Census tract level data.
So the payoff function is
Π
Where is the impact of competitors in distance band b.
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Each firm decides to locate in a location (l) that maximizes it’s payoff given its own type. So expected profits in location l is:
Π
Π
Assume that the private information shocks are distributed as i.i.d. logit draws.
This results in a multinomial logit probabilities for firms’ beliefs conditional on entry.
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So the entry probability with private information shocks distributed as i.i.d. logit draws is:
Prexp ∑ exp Π , , ,
1 exp ∑ exp Π , , ,
Data:
Video rental industry Homogenous good. Relatively inexpensive $2‐$4. Store heterogeneity – inventory, rental period, drop‐off convenience, and mainly spatial location. Average consumer travels about 3.2 miles to rent a movie!
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Medium size cities – 40 to 150K pop. Include cities where the largest city outside of the market within 10 miles has a pop of less than 10K and the pop of the largest city within 20 miles does not exceed 25K people.
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Figure 2 – Sample market – Greater Falls, MT
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Readings: Bresnahan, T. F. and P. C. Reiss (1991). “Entry and Competition in Concentrated Markets.” Journal of Political Economy, 99(5): 33. Dunne, Timothy, Roberts, Mark and Samuelson, Larry (1989) “The Growth and Failure of U.S. Manufacturing Plants,” Quarterly Journal of Economics, November, 671‐698. Mazzeo, M. J. (2002). “Product choice and oligopoly market structure.” Rand Journal of Economics, 33(2): 221‐242. Seim, K. (2006). “An Empirical Model of Firm Entry with Endogenous Product‐Type Choices.” RAND Journal of Economics, 37(3): 619‐640.
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Auctions: Milgrom, Paul R.; Weber, Robert J., (1982) “A Theory of Auctions and Competitive Bidding” Econometrica, 50(5): 1089‐112
De Silva, Dakshina, G., Timothy Dunne, Anuruddha Kankanamge, and Georgia Kosmopoulou. “The Impact of Public Information on Bidding in Highway Procurement Auctions” European Economic Review, 2008 (http://www.lancs.ac.uk/staff/desilvad/EER%20‐%202008%202065.pdf)
De Silva, Dakshina, G., Georgia Kosmopoulou and Carlos Lamarche “The Effect of Information on the Bidding and Survival of Entrants in Procurement Auctions,” Journal of Public Economics, 2009, 93(1‐2): 56‐72. (http://www.lancs.ac.uk/staff/desilvad/JPubE%202008.pdf)
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De Silva, Dakshina, G., Timothy Dunne, Georgia Kosmopoulou, and Carlos Lamarche “Disadvantaged Business Enterprise Goals in Government Procurement Contracting: An Analysis of Bidding Behavior and Costs”, forthcoming in International Journal of Industrial Organization. (http://www.lancs.ac.uk/staff/desilvad/IJIO%20‐%202012_DBE.pdf) Guerre, E., Perrigne, I., Vuong, Q., 2000. “Optimal nonparametric estimation offirst‐price auctions.” Econometrica 68 (3), 525–574. Haile, P.A., Hong, H., Shum, M., 2006. “Nonparametric Tests for Common Values in First Price Sealed‐bid Auctions,” Working Paper R & D and Spillovers: Abramovsky, L., Harrison, R., and Simpson, H. (2007). "University Research and the Location of Business R & D", The Economic Journal, 117: C114.C141.
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De Silva and McComb. (2012) “Research Universities and Regional High‐Tech Firm Start‐up and Exit,” (with Robert McComb.) Economic Inquiry, 50(1): 112–130. George Deltas, Dakshina G. De Silva, and Robert McComb. “Agglomeration Spillovers and Industry Dynamics: Firm Entry, Growth, and Exit in the Software Publishing Industry,” (I’ll give a copy)