Understanding the Economy: Coincident Indicators

Posted by   admin on    February 9, 2011

pdf.pngUnderstanding the Economy: Coincident Indicators

It has been almost 235 years since the signing of the Declaration of Independence and the United States of America has developed the largest economy on the planet. However, when one considers our long and proud history, the measurement of its economic performance is a relatively recent phenomenon. While as early as the 1830’s when social reformers began using statistical indicators to relate alcohol production as a cause of crime, it wasn’t until the Great Depression of the 1930’s that economists began a focus on the need to have detailed knowledge about the state of our economy. Before this, the only data compiled on a regular basis related to labor. In 1884 the U.S. Bureau of Labor was created as an official compiler of what were then considered merely social statistics.

According to the Gale Encyclopedia of US History:

“As a result of the [Great] Depression, business and government alike clamored for a more accurate measurement of economic performance.”

A group of economists at Rutgers University in New Jersey developed the first official national economic indicators in 1948. Since then, these indicators have evolved into the composite index of economic indicators in use as of the early 2000’s. The list of economic indicators was first published by the U.S. Department of Commerce, Bureau of Economic Analysis (BEA)

Although the list of economic indicators has been revised many times to reflect the changes in the American economy, within a few years of its inception reporters began regularly citing information from the index in their writing about the American economy. In an effort to improve the accuracy of reporting on the economy, the BEA began issuing explanatory press releases during the 1970’s. Considered crude gauges compared to the more complicated econometric models that have since been developed, the indexes of the BEA are still referred to by economists, the business community, and others interested in economic conditions and tendencies in the United States.

How many times have you come away confused by how economic reports are disseminated in the media? When we hear well articulated opinions representing opposing views, confusion is a very probable outcome for most listeners  especially when those views may contain an incorrect assumption or two.

For example, you may have constantly heard that employment data is among the most lagging of economic indicators. Is this an accurate statement? Can we really be sure that what we’ve heard is completely true? If we hear something said enough and the assumption goes unchallenged, it must be true  right? Not always

Each month, various governmental agencies and research associations compile and release a wide variety of information about the economy. Much of this data is after the fact information or relates to measures of confidence. In addition, quite often the data is derived from statistical sampling methods which carry a margin of error  usually, the smaller the sample, the greater the potential for sampling error. Yet despite the potential for error, these economic indicators can help guide our ability to make more confident business decisions. That is, if you take the time to understand them.

This article is the second in a series that discusses some of the more widely followed economic indicators. And, my goal is to bring context to their usefulness through summarizing them as: leading indicators, coincident indicators and lagging indicators. My prior article discussed leading indicators and this one will review coincident indicators.

As the categories indicate, leading indicators help to predict what the economy will do in the future; coincident indicators help us understand the current state of the economy; and lagging indicators help to confirm or deny the validity of the other two.

For the sake of efficiency and debate, I will describe these indicators in groupings used by The Conference Board. The Conference Board is a global, independent research association who compiles data and publishes the indices of leading, coincident and lagging indicators each month.

The Coincident Indicator Index Components

The Conference Boards coincident indicator index model is composed of four economic measures currently thought to be indicative of current economic trends. The coincident index also serves to confirm or refute earlier observed trends in the leading index three to six months ago. Each component indicator is described as follows:

Employees on nonagricultural payroll: this data, from the Bureau of Labor Statistics, is commonly referred to by the media as the non-farm payroll employment numbers. It reflects actual hiring and firing within the nation except for agricultural (farm) jobs and jobs at the smallest of businesses. Both temporary and full time workers are included and the Conference Board promotes this as one of the most closely watched measures for gauging the health of the US economy.

As a side note: In my last article, we saw that employment data played a role in the leading indicator index and it is now used again in the coincident index. This seems to refute the assertion that employment data is the most lagging of economic indicators. It is all too easy to generalize about employment measures and forget that these statistics can provide information about the future, the current and be backward looking as well.

Personal income less transfer payments: this is another employment related measure that reflects upon the level of real salaries and other income of all persons in the nation adjusted for a handful of items. The measure is normally stated in terms of a percentage increase or decrease, as the case may be, versus prior periods. And, the data is compiled by the US Bureau of Economic Analysis. The adjustments made involve excluding government transfer payments (i.e., Social Security payments) and the effects of inflation in the numbers.

In addition to being part of the coincident index, this information is also used as a key component in calculating per capita income and median income data  which offer a common-sized way of measuring the country’s prosperity. Common-sized measures are used to compare data of different sized economies (i.e., similar data from other countries) or to compare data of the same country over different time periods. By expressing data in proportion to some size-related measure, the result can reveal trends in the data that might otherwise go unnoticed.

Index of industrial production: Industrial Production, along with Capacity Utilization data is compiled and released monthly by the Federal Reserve Board. The IP index measures all of the physical output in the US’s manufacturing, mining, as well as the gas and utility industries. This index, along with other monthly industrial statistics is used for analyzing short-term and long-term changes of production activities, estimating Gross Domestic Product and for gauging labor productivity. And while the industrial sector is only a fraction of the US’s total economy, the index has historically captured a majority of the fluctuations in the nations total output.

Manufacturing and trade sales: Sales at the manufacturing, wholesale and retail levels are considered to be very pro-cyclical and this data is inflation adjusted to represent real total spending. In business cycle theory and finance, an economic measure that has a strong mutual relationship with the overall state of the economy is deemed pro-cyclical.

Which Indicators are Considered Important?

Coincident indicators are thought to be useful for helping investors, business managers and consumers understand the current state of the economy. For example, these statistics can help us assess whether and when the US has emerged or is emerging from the deep recession we’ve been clawing our way back from.

While in last months article, I reviewed that leading indicators are useful to help predict turning points in the economy, coincident indicators help us compare where we are versus where we’ve been or to confirm our assumptions about the health of the economy. In either event, these measures assist us in making more informed decisions and afford us the ability to operate with greater confidence.

In constructing the coincident indicator index, the Conference Board assigns weightings and averages to the individual components listed above in order to smooth out any volatility in the readings. The weightings given to the individual components have been recently revised and are summarized as:

  • Non-farm Payroll Employment is the single-most influential component, accounting for 48.8% of the index.
  • The remaining measures comprise the balance: Personal Income 26.2%; Industrial Production 13.7%; and Manufacturing and Trade Sales 11.3%.

It is interesting to see that Non-farm Payroll Employment and Personal Income, taken together, comprise more than 70% of the Coincident Indicator Index. If anything should be clear from these weightings, it is the emphasis put on the health of consumers.

I don’t know about you but when I hear the words 70% and consumers used together, I think of the generalization that Consumer Spending accounts for approximately 70% of GDP (Gross Domestic Product). This should come as no surprise as GDP, in theory, is probably the best suited measure to represent the current state of the economy as it is the broadest statistical measure of economic activity.

So, why not just use GDP as a current economic indicator instead of the Conference Boards coincident indicator index?

The problem with using GDP, as in virtually most countries, is that it is compiled and made available only on a quarterly basis after a substantial time has lagged. In other words, this measure isn’t produced frequently enough or released timely enough to provide a current economic picture. Therefore, the Conference Boards coincident indicator index can provide us a more timely reference series for the state of the economy.

ELF's Outlook and Performance

During January, we started to see less correlation among asset classes which may be signaling a return to normalcy. During the recessions melt down and the first year of recovery melting up, most asset classes seemed to be trading in unison. Going down, except for holding cash and US Treasury bonds there seemed to be no place to hide; and in the first melt up, it almost didn’t matter what you bought  everything went up. Now that asset classes are becoming less correlated again, we’ve moved our portfolio allocations to be more diversified into US, developed country and emerging market stocks, as well as, maintaining modest fixed income exposure and roughly 10% in cash that can be put to work when an opportunity avails.

Looking back to 2010, those who weren’t afraid to take all-in risk were the only ones rewarded. Now, with January 2011 behind us, heres the recap of the months winners and losers: Broad based commodity ETFs, US large company stocks and developed country non-US stock markets were the months greatest overall winners; corporate bonds, US small and mid cap stocks performed relatively flat; gold and emerging market stocks were the months losers with gold off more than 6% for the month.

Compared to last November, there was little to no significant media events that roiled the markets. In the first half of the month, we were hearing from media pundits that we shouldn’t become complacent as market volatility abates and that we should expect municipal bond defaults on the horizon. Maybe so, but other than municipal bond prices suffering some, the overall markets seemed to shrug off these opinions. Then, we learned of political unrest occurring in Yemen, Tunisia and Egypt which sparked a brief flight to quality in the markets. While the unrest is ongoing and unresolved, the markets have shrugged off any near term threat to investors and the focus has turned back to positive corporate earnings reports.

Tying in the Conference Boards coincident indicator index, the last release on January 20th indicated that the CEI for December continued to show increased economic activity with all its components advancing. The largest positive contributor was Industrial Production followed by Non-farm Payroll Employment. Although we’ve recently heard in the media that the most recent non-farm payroll numbers were bad, they were only below expectations and the trend is still up for this measure.

Our portfolio clients ended the month of January up 1.41%. Here are some comparative numbers for you to review:


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