Skip to main content

Microeconomics: Oligopolistic firms

Definition of Oligopoly:

  • An oligopoly is a market structure where there is a limited number of firms that are allowed to enter in the market.
  • New firms face barriers to entry to access an oligopoly market:
    • Economies of scale give the already existing firms a low cost advantage over smaller entrants into the market.
    • Existing firms may work together to prevent new firms from accessing the market.
    • Older firms have name recognition that newer firms do not have.
  • A firm's best choices are influenced by one another's decisions. Thus, each firm needs to have strategy in order to make the best decision possible.
    • In an oligopoly market, firms can change their production and prices in reaction to a competitor's production (or pricing) decision.
    • Firms may be of unequal size, thus, one firm may be seen as the price leader, and the other firms set their prices in response to the leading firm.
Collusion and Cartels:
  • Oligopolistic firms have an incentive to collude, to set prices and productions.
  • If unregulated, collusion among firms may result in a cartel, which can be defined as an agreement not to compete, instead this group of firms behave like a monopoly by determining collectively the price and quantity to produce.
  • Cartels have a hard time subsisting in the long run due to the incentive to cheat. For example, one firm can decide to produce more, or lowering its price in order to increase their profit. 
Modeling Oligopolistic Behavior:
  • An oligopoly market can be modeled via a payoff matrix. A payoff matrix is simply a matrix that shows the possible outcomes when two agents make a decision (see game theory).
  • Assume that two firms form a oligopoly market and both try to maximize their profit. The payoff matrix below shows the daily profit given their choice to set a high or low price. 
Payoff Matrix: Firm A: high prices Firm A: low prices
Firm B: high pricesA: $1000
B: $1000
A: $1200
B: $500
Firm A: low pricesA: $500
B: $1200
A: $750
B: $750
  • If the firms compete, they will both earn a daily profit of $750. Low price is the dominant strategy, both firms will always choose the low price option as it is always the best response no matter what the other firm chooses.
    • Firm A is always better off by choosing a low price strategy. If firm B sets a high price then A makes an even bigger profit by choosing low price. If firm B sets a low price, then firm A still makes a bigger profit than if it had chosen to set at a high price. 
    • Choosing a low price is also the dominant strategy for firm B. This is not always the case, in some cases there can be no dominant strategy or only one firm has a dominant strategy.
  • If the firms decide to collude, they would both decide to charge a high price and earn each a $1000 daily profit. Both firms would rather earn a $1000 daily profit rather than $750. Also, notice that both have an incentive to cheat and earn $1200 by deciding to charge a low price while to other firm charges a high price. In the long run this will result in both earning a $750 daily profit.
Reference: Mayer,David. AP Microeconomics Crash Course. Research & Education Association (2014). p 119-122. 

Comments

Popular posts from this blog

Econometrics: Bivariate population model

Hello I'm finally back from my extended break. I thought that we should start studying Econometrics.  Let's begin by analyzing a simple bivariate regression. Assume that this equation describes the relationship between two variables X and Y. We say that Y is the dependent variable, whereas X is the independent variable. In other words, we assume that Y (the output) depends on X (the input). Epsilon is the error term, it represents other factors that affect Y. The error term must be uncorrelated with the variable X so that we do not need to include them in our regression, and thus the coefficient of X (beta) should not change, even though Epsilon also determines Y.  beta-0 is the constant term. It tells us what would be Y if X=0. beta-1 is the effect on Y if X changes by one unit. To see this, assume X=education is a continuous function, let's take the derivative of Y=wage with respect to X: Thus, if education goes up by one unit, we should expect, on average, wage to go up

Macroeconomics: multiplier and crowding out effects

Multiplier effect: whenever   any of the components of AD increases, the increase in GDP will be greater than the initial increase in expenditures. The impact on GDP of a particular increase in spending depends on the proportion of the new income that is taken out of the system to the proportion that continues to circulate in the economy. The multiplier effect tells us the impact a particular change in one the components of AD will have on the total income (GDP).  Let k denote the spending multiplier, which is a function of MPC and MPS. The larger the marginal propensity to consume, the larger the spending multiplier. Notice that the larger the MPC, the greater the impact a particular change in the spending variables will have on the nation's GDP. The crowding out effect: If government spending increases without an increase in taxes, the government must borrow funds from the private sector to finance its deficit, thereby increasing the interest rate. This increase in interest rate,

Econometrics: OLS estimates

  Let X and Y be column vectors and the sample has the size n: The vector beta contains both the coefficient of X as well as the coefficient for the intercept. This is equivalent to writing this equation: We will assume that the expected value of the error term is 0 (this can be done by construction), and that X is uncorrelated to epsilon. Assume the following is for population data. Let's prove that statement: Since we know that the covariance between X and epsilon is 0, it follows that the expected value of X times the error term must also be 0. Since we do not have access to the actual expected value of the distribution, let's use the sample data instead: The hat on the covariance signifies that this is a MM estimator. Let's use the fact that the sample mean of epsilon is also equal to 0, then:   Using the estimation for the covariance and that the expected value of the error term equals 0: Then we have: To get this result, you must use the properties of the summation op