Principles of Economics - Perfect Competition

Perfect Competition is a market structure that follows these assumptions:

• Firms are price takers - each firm has no impact on the price in the market, they take the price the market forces set.
• Freedom of entry into the market - there are low barriers to entry so anyone could potentially set up in this market.
• Firms produce identical products - the taxi market for example, each taxi firm offers an identical product.
• Producers and consumers have perfect knowledge - both producers and consumers know everything there is to be known about the market.

However, few, if any, industries are actually perfectly competitive.

In the short run, the number of firms is fixed. In the long run, if supernormal profits are being made then new firms will enter the industry. If losses are being made, firms will leave the industry. 

Short run equilibrium of the firm:

This is what the market looks like in the short run in perfect competition. The price is Pe, and is set by the demand and supply forces. It is horizontal because firms are price takers. Due to price being constant, the red dotted line is also the average revenue, the marginal revenue and the demand for the firm as they're all the same. Qe is the amount produced by the firm because this is the amount at which profits are maximised (MC = MR). There is slight profit being made because the average revenue is higher than the average cost at the production point.

This is where the long run can be introduced. In the long run, firms see these profits being made and enter the industry. These means the industry supply increases, shifting the supply curve to the right on the left hand diagram above. Price falls, which means each firms demand falls until the point it is equal to the average cost. At this point, firms break even and make no profit. Firms will stop entering the industry now.

As far as the public interest goes with perfect competition, it has its benefits and drawbacks. The benefits are as follows:

• Firms produce at the least cost output.
• Firms that are inefficient will be forced out.
• Prices are minimised.
• Consumers determine what and how much is produced.

The drawbacks are:

• There us very little incentive to invest in new technology.
• Goods are all the same, lack of variety for consumers.

That ties up this post about perfect competition. Thank you for reading, keep checking back and sharing. Have a good day!

Sam.

Common Agricultural Policy Part 3 - Buffer Stocks

A tool at the EU's disposal within the CAP is buffer stocks. They can use these to either stabilise the prices of farm produce or to stabilise farmers income.

First case we'll analyse is the case of buffer stocks being used to stabilise prices of farm produce.

We have a market for a crop here, Q1 and P1 being the equilibrium quantity and price respectively. Lets assume one year there is a good harvest, supply increases to S1. We notice that this would create a fall in price, however as the policy is aiming to stabilise prices this isn't what we want. So, in order for this supply increase to come with stable prices, the governments need to buy up the difference between Q1 and Q2 and put them into buffer stocks. This means, the quantity available to the public is the original level of Q1, and therefore price won't change. 

Alternatively, if there is a bad harvest and supply falls to S2, a price rise would occur. The governments would have to intervene here and sell the difference between Q1 and Q3 to the market, releasing them from buffer stocks so the quantity available is the same and therefore the price remains stable. 

The areas on the diagram represent a few different things. Area a is an income that the farmers are guaranteed, even in the worst times. Area a + b is the normal income for a farmer, assuming that the harvest is a normal one. Area c is extra income the farmer would earn given a good harvest. Notice this policy of stabilising the farming prices has created more fluctuation in the farmers incomes, something the CAP aims to eradicate. Controversial.

Now, onto how buffer stocks can be used to stabilise a farms income. This involves using the elasticity formula. If elasticity of the good equals to 1, then the percentage increase in quantity is the same as the percentage fall in price. Therefore, if these are the same then the income of the farmer will remain constant. 

This diagram shows the principle of stabilising a farmers income using buffer stocks. We have an initial equilibrium of P1 and Q1, and supply increases because of a good harvest. This essentially means that a new equilibrium will be formed at P2 and Q2. However, at this point the farmers income has changed because demand doesn't have unitary elasticity. Therefore, the government needs to intervene. Using the curve above, we can see where the price and quantity should be for farmers income to remain stable: P2' and Q2'. So, what the government needs to do is buy up the difference between Q2 and Q2' and put them into buffer stocks. This means that the quantity now available will mean that price is at P2' and therefore farmers income will be stable. We can see this visually, the farmer has lost area c in terms of income due to the price fall,but gained area a + b due to the increase in quantity. These areas should be identical and therefore the farmers income has remained constant. 

This concept also works the other way if supply were to fall. Just in this case the governments would be releasing from the buffer stocks in order to regulate the price and quantity so that the farmers income remains stable. 

Buffer stocks is one method the government has to try and stop price fluctuations or income fluctuations, however it cannot be used to control both at the same time. Next up will be the use of subsidies for the same reasons. 

Sam.

Statistics - Sampling Methods and Estimation

In statistics we have to use samples because it's normally near on impossible to get data for the entire population. As long as the sampling is done well, the results will usually be good enough. Logic would tell you that the larger the sample, the better.. and this is true. There are two concepts we need to understand here, those are random sampling and sampling distribution.

• Random sampling - The goal of this is representativeness, we aim to get an equal probability of selection to every member of the population. There are a few methods:
• Simple random sampling - A sample so that every item or person in a population has the same chance of being included.
• Systematic random sampling - Items or individuals are arranged in some sort of order. A random starting point is selected and then every nth member is selected. Alphabetic order for example. 
• Stratified random sampling - A population is divided into sub groups (strata) and a sample is selected from each strata.
• Cluster sampling - A population is divided up into primary units and then samples are selected from the primary units.
• Non-probability sampling - Inclusion in the sample is based on the judgement of the person selecting the sample. (Eeek!)

• Sampling Distribution - This is the theoretical distribution of a statistic for all possible samples of a certain sample size, N. It's a device to link the samples characteristics to the population.
• If repeated sample sizes of size N are drawn from a normal population with a mean of mew and a standard deviation, σ, then the sampling distribution of sample means will be normal with a mean of mew and a standard deviation of σ / SqrRoot(N).
• The 'Central Limit Theorem' states that if repeated samples of size N are drawn from a population, as N becomes large the sampling distribution or sample means will approach normality.
• Or, in easier terms: Large samples are more reliable!

The more basic method of estimation is confidence intervals. From a sample we don't know the population mean, but we would like to estimate this with maximum efficiency. To do this we use a range, and say how certain we are that this range includes the population mean. We give a confidence interval in the form of a percentage, for example we could say that at a 99% confidence interval, between 33% and 39% of adults will vote for Labour in the next election (Made up!). A bigger confidence interval is more likely to contain the true population mean.

The next post will go further into the concept of confidence intervals and we will introduce such things as error margins. Stay tuned, thanks guys!

Sam.