Understanding US Economic Data
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US economic data, from a wide variety of sources (both government and private), are published almost every day. Market participants use this data to estimate the growth of the economy and the implications for the valuation of particular assets. Strong economic growth probably means strong corporate sales and profits, but may mean that the Fed will raise interest rates.
Economic calendars that tell you which data will be released when are widely available (see, for example, this calendar). Investors who may not necessarily analyze the data themselves can learn when data will be released, and get an idea about what the market expects. Market prices tend to be very volatile at the time of important data releases; sophisticated traders will be aware of these releases and incorporate them into their trading strategy.
This section will describe some of the most important releases in terms of how markets are affected. US Economic numbers are typically constructed from surveys of businesses and households. With a population of 300 million, it is difficult to get accurate data quickly. There are a number of government agencies (and some private companies) that calculate economic data and publish it on a regular basis.
Many economists attempt to forecast the data
, and some of these forecasts are widely available. An economic calendar (such as the this one) often includes a market expectation as well as a forecast by a particular forecaster. Further, there are nonpublic forecasts, the so called “whisper numbers” that circulate among traders before an announcement.
The numbers themselves are not quite as important as how they compare to the expected number. For example, consider a typical employment report. The number that gets the most attention, the “headline number” is the number of new jobs (from the establishment survey; see the essay about the employment report for more). In a typical month, the number of new jobs will increase by around 100,000 or so. So perhaps the average market expectation is 100,000. A higher number would cause participants to think that firms are hiring more workers than normal and the economy is growing more strongly, a lower number that it is growing less strongly. Then, close to the date of release, there might be a “whisper number”, an estimate by a well-known forecaster who might have more credibility or better information, of 150,000. If the number comes below 150,000 (even if it is above 100,000) it may be then viewed as a weak number.
To further complicate matters, the employment report also revises the data of the last few months. If the current month is a bit weaker than expected, but the last few months were revised up, traders may view the current number as stronger than expected. Finally, there are many more numbers in any given report besides the headline number. The employment report contains around 20 sections filled with tables and charts (see here), and a clever analyst might find evidence in one of these sections that provides a very different view of the labor market than would be provided by the headline number alone. All this usually results in a banal article in the newspaper the next day along the lines of “Market soars on job growth” or “Market soars despite weak job growth”.
There is another important distinction, between survey data, such as the Michigan survey of consumer confidence or the Purchasing Managers Index or ISM (where individuals or businessmen indicate what they intend to do) and actual data such as retail sales or durable goods shipments (which measures what individuals or businessmen actually do). In general, survey data is easier to collect, can be released more rapidly and may indicate what will happen in the future, while actual data is hard to collect, is released more slowly and only indicates what has happened in the past. But it is important to note that people often say things on surveys that are not consistent with what they actually do. The problems of polling and forecasting elections is well known, but sometimes the same people who criticize presidential election polls put much more faith in the index of supply managers consumer confidence numbers. Survey numbers can be useful, but should be viewed as at best a crude approximation to the underlying reality.
Some might argue that this distinction is somewhat arbitrary, as the actual data are surveys themselves. The government does not calculate all retail sales in a given month, but instead estimates the amount of retail sales from a survey of some retailers. But the important point is that this is a survey of what has actually happened at those stores, and not the opinions of the store owners about how business has been.
One further issue is that some data releases are inputs into other data releases. The Department of Commerce published monthly consumption numbers and a quarterly GDP report that includes quarterly consumption (the sum of the three monthly consumption numbers). Careful data analysts can closely replicate the work that is done at the Department of Commerce and get a fairly good estimate of quarterly GDP numbers before they are officially published. Putting together the pieces of the puzzle is both an art and a science; careful consumers of economic research should be able to distinguish among those who have done the work to interpret the numbers and those who have not.