A study of cartel stability: the Joint Executive Committee, 1880-1886 Paper by: Robert H. Porter
Joint Executive Committee Cartels can increase profits by restricting output from competitive levels. However, members face an incentive to cheat because price is above marginal cost. Cartel needs to discipline its members to make sure cheating does not occur.
Joint Executive Committee In practice, it may be difficult to monitor competitor s output. Does an unexpected low price mean that demand was hit by a bad shock or your competitors are cheating?
Model Assumptions (Green and Porter) 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)
The Game Repeated game. Firms restrict output to increase overall profits 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.
The Cheater s (Dis)Incentive 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.
The JEC JEC controlled eastbound freight from Chicago in the 1880s. Collusion was legal (pre Sherman act) and the workings of the cartel are documented. Ulen (1978) said there are several instances where they thought cheating occurred. Dropped prices, then returned to collusive output as in Green and Porter.
The JEC Porter argues homogeous good. Grain was 73% of dead tonnage. Even though endpoints of rails differed, overseas shipping rates adjusted. Attention to grain without loss of generality.
The JEC 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 (as we shall see in our regressions).
The JEC Lake steamers and sailships were primary form of competition. As we shall see, this does not explain most of the fluctuations in prices. The breakdown of collusion is more important.
Demand Demand relationship of the industry is given to be: log Q t = a 0 + a 1 log p t + a 2 L t + U 1t Where: p t = market price in period t L t = dummy variable equal to 1 if Great Lakes are open and 0 otherwise
Supply Supply is trickier to specify since it can involve fluctuating between competition and collusion in various periods. α 1 is elasticity of market demand θ t =1 monopoly behavior Θ t =0 implies perfect competition Cournot lies in between Model parameterization allows for testing of Cournot P t (1+θ t /α 1 )=MC i
Average over the supply behavior of all firms in the industry to get market supply curve. Requires some tricky functional form assumptions. Supply relationship of the industry is given to be: log p t = B o + B 1 log Q t + B 2 S t +B 3 I t + U 2t Where: Q t = total quantity demanded S t = vector of dummies which reflect entry and acquisitions in the industry I t = regime indicator which equals 1 for cooperative and 0 otherwise
Supply The value of β 3 allows Porter to learn about θ in collusive versus non-collusive periods (mechanical). If β 3 is large, that means there are large price fluctuations from the breakdown of collusion.
Supply The probability of switching between collusive and noncollusive regime (I t =1 or 0) is λ. This is a parameter to be estimated in the model. Equations to be estimated are 1) Demand, 2) Supply and 3) λ. Switching regression model.
Data Described in Tables 1 and 2. GR is somewhat suspect- if you are cheating impacts your incentives to accurately report price. Monthly dummies to control for seasonal aspects of demand and supply.
The Variables
The Data In table 2 notice that the standard deviation of quantity is high (variable demand). Cheating on collusion, as reported by Railway Review, occurs 40 percent of the time.
The Results
Interpretations All signs are as expected. Demand slopes down, supply up. Lakes shifts (residual) demand of cartel down. Note that R 2 on demand is 0.31. Hard to predict.
Interpretation Entry drives prices down in supply. The estimate of β 3 is roughly consistent with Cournot behavior when collusion is taking place. The breakdown in collusion leads to significantly lower prices.
Interpretations (Continued...) Setting all variables equal to their sample mean (using the PN estimate), we get the numbers in Table 4. Price was 66% higher in cooperative periods and quantity 33% lower. When lakes were open, price fell 4.5% and quantity fell 33%. As a whole, the cartel could expect to earn 11% higher revenues during cooperative periods (about $11,000 per week in 1880 dollars). The opening of the lakes caused revenues to fall about 35%.
Plot of GR, PO, PN as a Function of Time
PO often reflects a price war before PN, but they normally switch back to unity together. This is consistent with GR 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.
Comparison of Studies
Significance Test Porter 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. Tests the null hypothesis that the coefficient on I t is equal to zero (no regime change). Uses a chi-squared distribution with 1 degree of freedom. Test-statistic = 554.1 - the null is overwhelmingly rejected! Conclusion: Price and quantity changes cannot be attributed solely to exogenous changes in demand and structural conditions.