Economic Indicators Essay

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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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.


  1. Bernard Baumohl, The Secrets of Economic Indicators: Hidden Clues to Future Economic Trends and Investment Opportunities  (Wharton   School,  2005);
  2. The Economist, Guide to Economic Indicators:  Making  Sense of Economics (Economist Books, 2006);
  3. Manfred Gartner, Macroeconomics (Prentice Hall, 2006);
  4. Guide to Economic Indicators: Making Sense of Economics (Bloomberg Press, 2007);
  5. John Sloman, Economics (Pearson, 2007);
  6. Pass, B. Lowes, and L. Davies, The Collins Dictionary of Economics (HarperCollins, 2005);
  7. Anne Dolganos Picker, International Economic Indicators and Central Banks (John Wiley & Sons, 2007).

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