An economic indicator is a relatively simple or straightforward variable that, on the basis of past experience, can act as a signal for changes in a set of other, often more complex variables in the economy. To some extent a single economic indicator can act as a “proxy” for a combination of other variables. New business start-ups, for instance, can indicate a whole set of interconnected changes in the business sector and the wider economy. In the United States in the 1920s and 1930s, “freight car loadings” were reported on the business pages of newspapers, and these were avidly read by investors, business managers, politicians, and others, as the movements of raw materials and finished goods around the American railway system was, at that time, believed to be a reliable indicator of the general health of the economy.
In business terms, an economic indicator can be used as a piece of information that assists in managerial decision making. Indicators can also be used by governments in order to guide future policy, such as plans for raising revenue and the setting of priorities for the allocation of government expenditure.
Leading And Lagging Indicators
A “leading” indicator is a variable or a series of statistical data that can be expected to anticipate changes in some related areas of the economy, and which usually precedes the changes by a fairly consistent time period. A leading indicator can therefore be used to make predictions and forecasts. For example, if demographic trends show that there is likely to be a significant expansion in the relative size of the 16–25 age group among the male population in a few years’ time, then it is reasonable to predict that the country in question is going to experience a “crime wave.” This is because, in many countries, most crime is committed by young men. Such information would be of interest to, among others, government departments concerned with crime and justice, companies supplying private sector prison facilities, and the insurance industry that can expect to be called upon to underwrite and compensate for the financial costs of crime to individuals and businesses.
Leading indicators such as investment plans, orders for machine tools, and new house-building starts can be combined to construct a measure of “business confidence,” which in turn can help predict cyclical changes in gross national product. Similarly, “consumer confidence” is linked to variables such as future spending, output, and incomes, through economic mechanisms such as the multiplier principle.
A “lagging” indicator is a variable or a series of statistical data that can be expected to reflect earlier changes in some related areas of the economy, and which usually follow the changes by a fairly consistent time period. A lagging indicator can therefore be used to make an analysis of previous trends in the economy, or a diagnosis of previous problems with a view to avoiding similar problems next time around. The inflation rate, for example, is calculated using recent historic data concerning price movements within a statistically constructed “basket” of typical goods and services. If an analysis of the inflation rate shows that its causes are demand led, then an appropriate policy response (such as an adjustment of the interest rate) can be prescribed as a solution.
If, on the other hand, the diagnosis is that inflation is imported due to higher world commodity process (a cost factor rather than a demand actor), then the interest rate approach might be inappropriate or even damaging to the wider economy. Since government policy makers are not noted for their infallibility, knowledge of lagging indicators is as important as leading indicators as a piece of managerial intelligence, because this knowledge can help business people to be prepared in advance for changes in the wider business environment, some of which will come from government policy adjustments, whether appropriate or inappropriate.
It is quite possible for a particular indicator to be interpreted as being both leading and lagging simultaneously. A country’s unemployment rate, for example, tells us something about the performance of the economy in recent history, and shows the end result of many contributing factors including the efficiency of the business sector, the state of the labor market, and the efficacy or otherwise of government policies concerning both the “hard” economy of variables such as interest rates and taxation, and the “soft” economy of education, training, and investment in human capital. However, the unemployment rate can also be used to make forecasts of likely future trends in directly affected variables such as saving and consumer spending, together with more indirect knock-on effects on other variables such as investment, output, and national income.
When economists analyze “leads and lags” they are referring to the timing differences that exist between, on the one hand, the peaks and troughs of leading indicators and lagging indicators and, on the other hand, peaks and troughs in the general business cycle. If, for example an 11-year cycle can be discerned in the level of economic activity, with a peak in the growth rate of gross national product in year four and a trough in year eight, it could be that manufacturing investment acts as a leading indicator that peaks in year two and troughs in year six, while house-building acts as a lagging indicator that peaks in year six and troughs in year ten. It should be noted that indicators of this sort can work quite differently in different countries.
The state of the manufacturing and house-building sectors can have quite different significance in Germany, say, compared with the United Kingdom (UK) and United States, depending on factors such as the structure of their business sectors, and the role played by different types of industry in contributing to national output and employment.
When using economic variables as indicators, it is worth distinguishing between independent and dependent variables. Economic theory is largely built upon hypotheses about and observations of functional relationships between variables. If, for example, we suggest that “consumption is a function of income,” then we are saying that income is an independent variable, while consumption is a dependent variable. We are saying that what people spend depends on their income, rather than vice versa.
Economists use the following equation (1) to show a basic relationship between a large set of variables:
Y = C + I + G + (X – M) (1)
Here, Y stands for “national income,” which in economic theory is equal to the value of planned national output, which in turn is equal to planned total national expenditure on goods and services. Income, output, and expenditure are also obviously linked to employment, since higher levels of employment will tend to coexist with higher levels of income, spending, and output, whereas a fall in these three variables can be expected to reduce employment and hence increase unemployment. If it is believed that inflation is demand led, then higher levels of economic activity can be expected to coexist with higher levels of inflation, if it is the case that there is any difficulty in utilizing spare productive capacity, due to such problems as inadequate infrastructure, or the existence of skills gaps.
C stands for “consumption,” or consumer spending on goods and services. I signifies “investment,” or spending on capital goods (as opposed to consumer goods). Capital, or investment goods, such as factory buildings and manufacturing infrastructure, is used in order to produce consumer goods and services.
G stands for government spending, and (X – M) signifies the net effect of export earnings and import spending—in other words, it indicates the net effect of international trade, or the balance of trade surplus or deficit. Economists use a model known as the circular flow of income to show how the variables linked in the above equation relate to each other, and also to show that injections into the circular flow (investment, government spending, and export earnings) tend to increase the level of economic activity, while withdrawals or leakages from the circular flow (saving, taxation, and import spending) tend to reduce the level of economic activity. This also follows from the alternative way of expressing the macroeconomic equilibrium condition (equation 2):
I + G + X = S + T + M (2)
This tells us that if the sum of planned investment, government spending, and export earnings (i.e., total planned injections) equals planned saving, taxation, and import spending (i.e., total planned withdrawals), then there is no reason for economic activity either to increase or decrease.
In business, the uses to which economic indicators such as these are put can be complex and sophisticated, or they can be relatively straightforward but no less valuable for their simplicity. For example a sudden change in interest rates will have implications for many business enterprises. Those implications can be predicted by making reasonable deductions from the basic models outlined above. In equation 1, interest rates can be assumed to have a direct effect on consumption (C), since many major individual spending decisions (for example, the decision to buy a car or another major household item) are influenced by interest rates, if they tend to be bought using borrowed money. Households repaying a mortgage can also be assumed to adjust their consumption expenditure, at least to some extent, in response to changed interest rates, since a change in their monthly mortgage repayments will, in effect, alter their disposable income. Interest rates will also affect investment (I) in the equation, since it is reasonable to assume that investment decisions are sensitive to interest rate changes, depending in part on the extent to which investments are financed by borrowing, as opposed to sources of finance such as shareholding or ploughed back profits.
The same principles can be applied to equation 2, where a change in interest rates will have direct effects on saving (S) and investment (I). Similarly, changes in other indicators, such as the exchange rate, can be applied to these equations and predictions made about their likely effects on variables such as export earnings (X) and import spending (M). The effects of variables under direct government control, government spending (G) and taxation (T), can also be predicted. From experience, businesses should be able to extrapolate the knock-on effects of changes in the level of economic activity. There are, of course, differential effects on different types of business, with sectors such as tourism, house-building, and car manufacturing often acting as weather vanes, and in turn being used as indicators for the likely future level of activity in the rest of the economy.
Use And Interpretation
Companies that offer economic forecasting services will, of course, use models of the economy that are highly sophisticated and complex, and some will attempt to replicate the high-powered computer models used by government departments and agencies such as the Federal Reserve and the Bank of England; but the basic models on which these programs are based will be similar in their fundamentals to the relationships shown in the equations above. The “average” business person should not fall into the trap of believing that the implications of changes in economic indicators are too complicated to be understood and interpreted by the everyday practitioner.
If business people are to use economic indicators to help in managerial decision making, however, it is important that they take care not to react to changes that might be one-off “blips.” Some indicators are more volatile than others, and in general terms the more volatile ones reflect what might be called the “virtual” economy rather than the “real” economy.
Medium to long-term trends in share prices are more reliable indicators of economic trends than day-to-day prices of monetary instruments such as “futures” and “options” for the simple reason that these variables are not indicating the fundamental activities of wealth creation; rather, they are reflecting short-term profit making based on the hopes and fears of speculators. During the oil price hike of 2008, for example, some experts estimated that the activities of speculative traders added as much as 25 percent to the price of a barrel of oil and the vast majority of these traders were, in effect, gambling on a rise or fall in future oil prices, rather than having any intention of ever possessing an actual barrel of oil. Similarly, it is possible that well over 90 percent of the transactions that take place on the currency exchanges of the world are led by speculation, rather than being connected to the desire to actually exchange a sum of money in one currency for real notes and coins in another denomination.
In the mid-1990s, British unemployment statistics underwent an interesting and relatively sudden change. The main reason given for individuals being long-term unemployed due to chronic health problems and claiming welfare benefits changed from “muscular-skeletal” to “mental health” problems, or to put it more simply, from “back pain” to “stress.” This could have been ignored by employers and policy makers as a statistical blip, but in fact it turned out to signal a sustained trend, which is likely to continue to be a feature of the UK labor market for some time to come, as well as being reflected in many other economies, especially those adopting the Anglo-Saxon model of “flexible labor markets.” As a long-run trend rather than a blip, it requires a policy response, in terms of occupational health and welfare benefit strategies. This particular indicator reflects wide-ranging changes in industry and the economy, including the trend in employment from secondary (manufacturing) toward tertiary (service) industries, and employment conditions leading toward a more “flexible” and inherently more insecure workforce.
Economic indicators extracted from time-series data can show four basic types of variation that can be used for the purposes of forecasting:
- Secular trends, which show relatively smooth development over the long term, e.g., the growth of gross national product in established economies, and the tendency for economic development to result in a decline in the proportion of national income and employment accounted for by the primary sector, initially to be replaced by an expanding secondary sector, with the tertiary sector ultimately becoming the major source of economic activity.
- Cyclical patterns, e.g., the short-term variations in actual growth around the long-term trend rate of economic growth, with “output gaps” (overstretched capacity) occurring during an “upturn” phase and “negative output gaps” during a “downturn.” This is the classic “sine-wave” pattern that is associated with the trade cycle or business cycle. Output gaps are used as a major indicator in the anti-inflation regime that has been adopted in various forms in the UK and the Eurozone, for example, where interest rate setting has been delegated to a quasi-independent central bank.
- Seasonal variations, which are short term but regular and reasonably predictable, e.g., the low season activity in large parts of the tourist industry in winter; retail sales in Western economies prior to Christmas.
- Exogenous shocks, which tend to be unpredictable, wrongly predicted, or unexpected, and which result in irregular changes to established trends. An example would be the ramifications of the collapse of the U.S. subprime market in 2008 and the resulting global credit crunch; or rapid increases in food prices arising partly as an unintended consequence of the use of land for biofuels, which in turn was a response to faster-than-expected increases in energy prices and commodity costs, especially the price of oil.
- Bernard Baumohl, The Secrets of Economic Indicators: Hidden Clues to Future Economic Trends and Investment Opportunities (Wharton School, 2005);
- The Economist, Guide to Economic Indicators: Making Sense of Economics (Economist Books, 2006);
- Manfred Gartner, Macroeconomics (Prentice Hall, 2006);
- Guide to Economic Indicators: Making Sense of Economics (Bloomberg Press, 2007);
- John Sloman, Economics (Pearson, 2007);
- Pass, B. Lowes, and L. Davies, The Collins Dictionary of Economics (HarperCollins, 2005);
- Anne Dolganos Picker, International Economic Indicators and Central Banks (John Wiley & Sons, 2007).
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