Microeconomics has shot off into all sorts of areas, looking at all kinds of questions — from how people select marriage partners to whether financial markets are inherently unstable.
Here are ten areas where you can add to your knowledge of microeconomics and put it into practice, helping you to understand some of the deeper problems of technology, society and organisation.
Consider creative destruction and the problems of technology
Microeconomists use comparative static models, which means that they’re like looking at two different photographs of the world and comparing the difference: most microeconomists use this as a starting point for how to approach problems. But considering how things change over time is often important too.
One problem, however, is that technology, in the sense of inventions, never stands still: it evolves, changing what’s possible in markets, which in turn changes how consumers and producers interact.
This reality is extremely important when looking at information technology (IT). The pace of innovation is relentless in this industry, because product lifecycles tend to be short, and because products can build large networks of users in a short period of time. Therefore, some market structures — for instance, perfect competition — tend to get ruled out.
Instead, competition is often sequential — a firm gets challenged not by a competitor but by a potential entrant. As a result, economists focus more on a theory like Schumpeter’s creative destruction — that old companies are overturned when people find newer and cheaper ways of doing something. A refinement, the model of disruptive technology, points out that seemingly worse products can win in a marketplace, because they hit new groups of consumers rather than ones that firms in the market already have.
IT firms have to cope with a lot of innovation around them and change their best strategies for dealing with it. Microeconomists look at many areas within technology — from exploring the best size of a network to the problem of why some things seem to escape pressure from innovators and become ‘locked in’, such as the QWERTY keyboard users have right now.
This area is complicated because information itself is a good with a marginal cost of zero. Therefore, many microeconomists are looking at finding a balance that allows innovators to profit temporarily from their creativity, while preventing them from having permanent monopolies.
Question the rationality of politicians: Public choice
What happens if you assume that politicians making up a government are rational and play to the incentives set for them? Public choice economics — that is, applying economic principles such as rationality to political questions — looks at this area and makes some startling predictions.
For instance, in a voting system such as in the UK or US, the only vote that ever counts is the one that grants victory to a candidate. If 100 people vote and two candidates are involved, the only voter who matters is the one who determines the winning candidate.
So if parties know that, say, 40 voters on each side are sure to vote firmly with one party or the other, they put their efforts rationally into winning over the 20 undecided people. That’s why politicians seemingly spend ages courting the ‘floating voters’ who may change their minds.
Public choice economists also point out that governments can often be ‘captured’ by special interests. Even when no collective interest exists in pursuing those policies, politicians following their own private interests get persuaded to do so, for example by lobbyists. The problem is that each voter gets only a small fragment of the benefit of her preferred choice, but the costs of ending those policies that lobbyists have successfully persuaded politicians to introduce may be very high to the politician!
Economists as politically different as James Buchanan and Elinor Ostrom have used public choice economics as a lens to focus on all kinds of questions — from how best to manage a common resource to the best way to ensure that corruption doesn’t take over a political system.
Build a bridge to financial markets: Macroeconomics
As a result of the recession of recent years, which began in the financial markets, finance has received a lot of attention lately. Building on economic models, and adding a whole host of computers and data processing, financial economics looks at how financial markets work and how they connect to the wider economy. That means answering questions about the best way to value an uncertain asset or the best way to deal with risk.
Given that finance is full of uncertainty — Yogi Berra said it’s not easy to predict anything, especially not the future! — it doesn’t mean that anyone in a market is going to be right.
Looking at finance involves a number of special challenges. One of the most important is that it requires a lot of study of probability and statistics, which are needed because financial trades happen very quickly and very often. Another problem is that markets have to price on the basis of predicted values rather than accrued values — because someone will already have booked the profits or losses by the time you trade.
One famous result is Eugene Fama’s efficient market hypothesis. People have often questioned since the recession whether financial markets are efficient, but Fama’s hypothesis doesn’t use the concept of efficiency in the same way that ordinary people do. Instead the hypothesis talks about how information gets used in a market: in its weak form, for instance, it says that past performance doesn’t mean future performance, because the present price reflects all past information. It doesn’t say that the price is correct, given that things will change in the future.
Understanding labour markets
Is a minimum wage a good thing? How do trade unions affect wages and employment? Labour economics looks at these types of questions.
In a labour market, typically the roles of supply and demand are different to ones in other markets: individuals supply their labour and firms demand it. Labour economics looks at these markets — using economic tools, adding in statistical analysis and using specialised models. This field analyses how skills affect the choices of hirers, how people choose to get those skills and how different types of labour market affect outcomes — asking, for example, whether making it difficult to fire people makes it harder to consider hiring them.
One consideration, for instance, is that paying higher wages than your competitors may be rational, if workers are motivated by the fact that they won’t get as high a wage at competitors. Thus, you get better productivity — output per unit of work — than rivals, as Henry Ford found when he did just that.
Investigating the importance of institutions
Institutions, such as government departments, universities or central banks, are important structures in an economy. They have a role in shaping what happens in the economy, for good or ill, in many ways — from how they behave as institutions in their own right to how they affect other people’s decisions. Institutional economics is a diverse approach to looking at the role of these institutions in an economy.
This definition seems self-explanatory, but pinning down institutional economics further is almost impossible, because researchers in the field have looked at such diverse problems. One approach grafts on marginalism and becomes the economic analysis of law. Another approach borrows tools from sociology to describe the roles of institutions.
After a long period in the shadows, interest in institutional economics has grown recently, because people want to focus on the role that various institutions played in the financial crash and its aftermath. Most recently, Daron Acemoðlu’s work on the role of elites in developing economies has been a big talking point. In his analysis, one reason for the failure of development efforts in some countries is the role of ‘extractive’ elites — who syphon off the gains to development rather than allowing them to spread around the economy for everyone’s good.
Studying foxes and bunnies: The complex systems view
A famous model in biology examines what happens when foxes and rabbits share a field. How many of each species exist depends on how good they are at reproducing, how good they are at eating — or avoiding being eaten by — each other and the size of field. When you plot the populations of both animals against time, you get two nice smooth curves:
For foxes: If they’re too good at eating rabbits, they get too successful. As a result, too few rabbits exist to support that number of foxes, and their population falls and the rabbit population rises.
For rabbits: If they’re too good at avoiding being eaten by foxes, they get too successful and foxes find rabbits more plentiful. As a result, they eat more rabbits and the rabbit population falls again.
Interestingly, when assuming nothing about the intelligence of the two species, you always get the same type of curve as long as they’re rivals.
This simple example illustrates a complex system — complex in this sense means interconnected, not necessarily complicated! Economics is full of interconnected systems like this one, for example between a hardware manufacturer and a software producer.
The complex systems approach to economics uses a mix of models — like the foxes and rabbits one — simulations and game theory to answer questions about the development of economic organisation. The tools are quite difficult, but the intuition is simple: economic development has a lot to do with how complex your systems are and how complex the things you make with that system are. Many of these techniques have been imported into economics from biology or physics, and take a slightly different tack on how to look at economic questions.
One question that complex systems theory tries to answer is why some places in the world have developed advanced economies and others fail to, no matter how many times people attempt to do just that. One answer suggests that it isn’t related to what people can make on their own, but how they link to one another. For instance, in a peasant economy, what’s the point valuing a cow as an asset you can sell if no market exists where you can sell it.
You can also adapt complex systems to look at product complexity. The computers on which most people use in their daily lives are extremely complex products with parts built from thousands of components, involving many different companies all over the world. The complex systems view tries to model how all this comes together to make the final computer — as you can imagine, it’s a tough task.
Learning from the past: Economic history
One of the interesting things for economists in the UK is that public records go back nearly a thousand years! Economists can pore over all kinds of information to discover all manner of things about the differences in production and consumption between the past and now. Many of the insights are useful: for example, movements in tulip bulb prices during the great tulip boom and bust of the 17th century tells you a lot about how financial systems today behave during a crisis.
Economic historians are always analysing available data, or finding or estimating data to give a clearer picture of how the economy came to be. Along the way, they’ve found ways to convert data into prices comparable with today — a surprising number of products have very old roots! — and built data series that can be explored to tell you a fascinating story about the differences between today and the past. (The Bank of England, for example, has an amazing set of data on all kinds of things.)
How much you can learn from the past fascinates economists. In theory, the past should be a sunk cost, and people should learn from their mistakes. In practice, patterns re-emerge throughout history — you can see some of the same runaway behaviour in the Japanese property crash of the 1990s as in the South Seas bubble that gripped London in 1720 or the tulip mania of the 1630s!
In each case investors piled into the market in expectation of rising returns, making lots of money until some event made the expectation of profit unlikely and the market crashed. (If you’re interested, the ‘Tulip Mania’ case informed a famous book by Charles Mackay called Extraordinary Popular Delusions and the Madness of Crowds.)
Reflecting behaviour in the real world: Bounded rationality
The rational consumer model has many advantages. But it also has some problems if you want to use it for prediction — not every decision can be explained when you assume rational consumers. Perhaps people have limited foresight, limited time or limited ability to understand what offers are put to them; or they just believe that the optimum is unavailable. These limitations change the way people make their decisions and mean that some of the equilibrium results may not be as robust in reality as they are in the model.
In contrast, bounded rationality models often emphasise that because the world is complex, and people limited, the best outcome is often ‘good enough’ given all the constraints on a person. As a result, people’s methods for making choices matter — bounded rationality calls these heuristics.
For instance, supermarkets stock many types of tomato ketchup, but buyers don’t go through them all to choose which one they want. Instead they probably form a preference and then follow a heuristic that says ‘look for that tomato ketchup and if it’s there, buy it’.
This behaviour doesn’t mean that people are stupid, not at all! It means that instead of going for what the rational choice model thinks is optimal, people go for what they can get given the overall difficulty of constantly making choices in the world. Bounded rationality adds to the analysis an understanding that sometimes acting in a way that microeconomics considers irrational is actually rational. Going to the gym every week just because you paid a subscription may not be rational, but your friend who does exactly that is likely to get fitter than you (feel the burn!).
You can to some extent say that bounded rationality models and traditional economic rationality can be the same thing under certain circumstances. Imagine you want to optimise not only the utility you receive from buying something, but also the time and calculation effort that went into choosing what to buy. Under those circumstances, you can also say that bounded rationality and traditional rationality may get the same result when you include the time, effort and emotion that goes into choosing as a cost to the consumer.
Exploring statistical relationships: Econometrics
Econometrics is the branch of economics that looks at what happens in the real world by building models and testing them using statistical techniques to see whether they work. Economics as a whole uses a lot of data these days, partly because so much gets collected. Having the data available means that you can perform better quality tests in all kinds of areas.
Econometrics does two things:
Develops sophisticated statistical techniques for looking at economic data: Such data often has issues — it’s never perfect and doesn’t always follow the same distributions that you expect statistical data to follow. Econometricians come up with techniques for dealing with these problems — most of which are now quite simple to implement on a computer.
Tests models using various techniques that have different advantages and disadvantages: Applied economists test in several different ways to ensure that they have the best chance of weeding out false correlations — where data seem to be connected — while unearthing real ones.
Finding lags and leads: Time series analysis
Most data in economics concerns changes over time — for instance, growth is a measure of how much gross domestic product changes over a year. A particular problem exists, however, because the past is often completely different from today — what determined growth in 1950 may not be same as today. Time series analysis tries to develop models that are robust to those changes where possible, in order to get a view of how things change over time and what relationships — for example, between earnings and dividends that companies pay shareholders — stay reasonably consistent.
Many issues exist with time series — not just the problem of the past being unlike the present. Nobel laureate Clive Granger picked up a particular one — spurious regression. If you have two series that are both increasing, and you run a time series model to find out how they’re correlated, the measure of correlation goes up as the number of points in each series does — so that the longer the run of the model, the more likely you’ll find a relationship. That’s why statisticians found that when the Danish stork population increased, so did the birth rate.
Economists are often interested in finding lagging and leading relationships, because some time is usually required for economic adjustments to happen. If you change tax rates this year, you may not see an effect on consumer spending until next year — in other words, the tax change leads the change in consumer spending. Leading indicators are important because they give you an estimate of what will happen in the future, and forewarned is often forearmed. For this reason, financial markets in particular pay lots of attention to them!