Mirror, Signal, Manoeuvre – Analysis and Prediction of Economic Phenomena by Indicators
Steering a business through a volatile economy is a bit like driving a car in heavy traffic. Without a clear idea of where you are and what might happen next, things can go badly. One of the principle values of Economic Theory to businesses of all types is its ability to analyse and predict changes in the economy as a whole by reference to what’s known as the ‘business cycle’. Most of us have heard of so-called ‘boom-bust’ economics, but aside from these large scale and often catastrophic cycles, the economy naturally undergoes smaller, more or less cyclical changes, even as part of an overall pattern of growth. Analysis of the business cycle centres around the Gross Domestic Product (GDP), which is the total of all business production, trade and activity at a particular moment in time.
Although broadly cyclical, fluctuations in GDP are not regular and are frequently very hard to predict. Analysis of particular Indicators, therefore, is helpful because they can give clues to the factors preceding, representing and/or underlying a change.
Economic Indicators fall broadly into three categories and each is described in some detail below. The data may be available weekly, monthly or annually, depending on the type.
Leading indicators tend to precede a change in the business cycle and are used for prediction. The Conference Board issues information about seven leading indicators at monthly intervals throughout the year. These are:
Order Book Volume – A measure of the total trading activity on a stock exchange. Volume of Expected Output – The total quantity of goods and services expected to be produced by a particular entity, in this case, a nation. Consumer Confidence Indicator – A subjective measure of the willingness of individuals to spend. Although inexact, it does serve as a good predictor of subsequent spending, which accounts for around 70% of GDP. FTSE All-Share Index – Share prices are a good leading indicator because they change rapidly, and information is available at extremely high resolution (parts of seconds). The CB issues monthly aggregates of this data. Yield Spread – A measure of the difference in payoff between particular debt instruments (like government bonds).
Productivity, Whole Economy – A measure of efficiency calculated as unit output per unit input. Total Gross Operating Surplus of Corporations – An indirect measure of ‘value-added’ by incorporated companies within an economy. It is calculated by subtracting the wage bill and cost of intermediate services and goods from total gross output.
This composite data has successfully predicted major downturns in the business cycle since its inception in the 70s. However, critics of the Conference Board point out that, not only is the data not really leading (there is usually a time lag of two months before the composite is released – often a long time after the component reports are available), but there are frequent errors in the predictions made by the data; a fact that has led American economist Paul Samuelson to quip that ‘Economists have successfully predicted 9 out of the last 5 recessions.’
Another principle source of information is the Organisation for Economic Cooperation and Development (OECD), which collects data across all major economies and publishes monthly reports of hundreds of indicators. An exhaustive list is available here. [Link to download attached document]
Other data are available weekly and allow for short-term predictions of changes in the business cycle:
Jobless Claims report – Released weekly, this gives an estimate of the number of new benefits claims received, a leading measure of unemployment.
Money Supply – A calculation of the amount of ‘free’ money available in the economy (e.g., after the kind of ‘quantitative easing’ seen during the credit crunch). A rising money supply theoretically predicts inflation but with the advent of global electronic money transfers, the statistic is somewhat outdated.
Data from the housing and construction markets, too, can be useful leading indicators, since large construction contracts and purchases of property represent a significant portion of GDP.
Lagging indicators begin to change only after the economy has entered a particular phase. Like the rear-view mirror of a car for a driver in traffic, lagging indicators in the economy serve to confirm what has already happened, and help businesses to understand the events of previous months. Although perhaps less useful than Leading Indicators, and often doing no more than telling us what we already know, lagging indicators can be useful in confirming long-terms trends, which may aid the prediction of future events through the interpretation of other indicators in turn.
Some examples of Lagging Indicators are give below:
Interest rates – Currently controlled by the bank of England, UK interest rates typically change in response to major economic events. Low interest rates can be used to stimulate spending and increase consumer confidence; higher interest rates can be seen as a check to excessive inflation.
Unemployment tends to lag two to three quarters behind the economy as a whole, partly because of the requirement to give adequate notice to employees of redundancy. A dip in output or productivity typically stimulates companies to lay off staff to improve efficiency.
Profit – Large corporations tend to publish quarterly profit/loss statements, which will typically reflect the economic conditions prevalent in that period.
Labour Cost per Unit of Output – A measure of the efficiency of production, since labour represents the majority of the costs in most industries. Labour costs will typically rise if inflation demands a rise in compensation, for example.
Coincident Indicators demonstrate the current state of the economy, giving businesses an idea of the conditions in which they’re currently operating, and a platform for predicting further change from Leading Indicators. Combined with Lagging Indicators, Coincident Indicators give a good picture of an overall trend and, since they are based on actual data rather than projections, provide a good degree of accuracy.
Some examples of Coincident Indicators are given below:
GDP – Although complex and difficult to calculate, Gross Domestic Product (see above) gives arguably the best indicator of the state of the economy and is principally used in retrospect to identify peaks and troughs in the business cycle.
Employment – The number of people in paid work at any particular time is a measure of the strength of the economy because people in work have money to spend and are (typically) doing useful work that increases overall productivity.
Real Earnings – Real earnings are adjusted for inflation and so reflect the actual spending power of an individual, rather than a nominal value for the amount they are paid.
Retail Sales – Frequently in the news around Christmas time, the amount of money spent by consumers over a particular period gives a good indication of how much confidence there is in the economy, and how well businesses in the retail sector are doing.
Because of the nature of the economic system, and its reliance on (often irrational) human behaviour, the data used to derive indicators always contains a greater or lesser degree of ‘noise’. Here, noise refers to random fluctuations that are insignificant but serve to obscure the true pattern of the business cycle. Many organisations, like the OECD and Conference Board, supply composite data, giving a single index value representing a whole range of indicators. Combining the data-sets in this way serves to cancel out some of the randomness, often resulting in a clearer picture of the underlying pattern.
A good handle on Leading, Lagging and Coincident Indicators is essential for any forward-looking business in the modern world. The global economy is in the most volatile state in its history; and anticipating, rather than adapting to change, is now the norm. The next blog in this series [link to blog 3] takes a look at different perspectives on the economy, from the very large to the very small.
- Analysis of economic business cycles centres around the Gross Domestic Product (GDP)
- Indicators help to predict fluctuations in GDP
- There are three types of indictors: leading, lagging and coincident
- The data may be available weekly, monthly or annually
- The data used to derive indicators always contains some irregularities
- Composite data representing a range of indicators can cancel out some of this ‘noise’
Apr 16, 2015