December, 2001, Vol. 124, No. 12
Précis
NBER: expansion ended in March
Old dogs and new tricks
Why use so many statistics?
NBER: expansion ended in March
The National Bureau of Economic Research (NBER) Business Cycle Dating Committee, a group of six distinguished academics, determined that a peak in U.S. business activity occurred in March 2001. The committee examined a range of broad-based monthly economic indicators to support their designation.
The broadest monthly indicator was nonfarm payroll employment. In addition, the committee refers to two indicators heavily influenced by manufacturing: industrial production and real sales of the manufacturing and trade sectors. The committee also used another monthly indicator of economy-wide activity, personal income less transfer payments, in real terms.
According to the NBER announcement, "Employment reached a peak in March 2001 and declined subsequently. The figure for October is the first to reflect the effects of the September 11 attacks. Through October, the decline in employment has been similar to the average over the first 7 months of recessions. The cumulative decline is now about 0.7 percent, about two-thirds of the total decline in the average recession."
The announcement continued, "A peak [in industrial production] occurred in September 2000, and the index declined over the next 12 months by close to 6 percent, surpassing the average decline in the earlier recessions of 4.6 percent." The measure of real manufacturing and trade sales peaked almost a year before the NBER report was released in November, while the measure of real personal income less transfers had not yet reached a peak and had continued to rise.
The NBER is a private, nonprofit, nonpartisan research organization dedicated to promoting a greater understanding of how the economy works. Our research is conducted by more than 600 university professors around the country, including the members of the Business Cycle Dating Committee.
In the long, just-ended expansion of the 1990s, the unemployment rate went from a high of 7.8 percent in mid-1992 to a low of 3.9 percent in the early autumn of 2000. Abbigail J. Chiodo and Michael Y. Owyang explore what lay behind the decade’s ability to "continually reduce unemployment without succumbing to inflationary pressures" in an article in the October issue of The Regional Economist from the Federal Reserve Bank of St. Louis. They propose two basic forces influencing the trend: the new tricks of technological innovation and the increasing labor force share of the mature, job-stable old dogs of the baby-boom generation.
After regressing the unemployment rate on median age of the labor force, the level of technology lagged 15 years, and the current rate of technological innovation as measured by patent applications, Chiodo and Owyang were able to decompose the 3.5-percentage point decline in annual average unemployment between 1992–2000. Their results indicated that 1.8 percentage points could be associated with the rise in the median age of the labor force and that historical technological changes accounted for 1.6 percentage points of the decline. Offsetting these was a 2.1-percentage point increase that could be attributed to the current rate of technological innovation. Thus, they conclude, "past improvements in technology and the rising median age of the labor force have played roughly similar roles in reducing the unemployment rate between 1992 and 2000."
Economists have always been almost insatiable in their appetite for numbers. In fact, in the February 1976 issue of this Review, BLS Commissioner Julius Shiskin urged analysts to consume both "the doughnut and the hole" of employment and unemployment data as he introduced an array of new measures. Sharon Kozicki, writing in the Economic Review published by the Federal Reserve Bank of Kansas City, gives us more empirical evidence of why it is generally a good idea to look at more than one economic indicator.
Kozicki’s interest was in finding an indicator that would reliably predict inflation under a broad range of economic conditions and varying economic structures. To attempt this, she evaluated 20 interest rate, money, foreign exchange, unemployment and output measures using the root mean square errors (RMSE) of their inflation forecasts as a criterion and the naïve forecast of unchanged inflation as a benchmark. She found that no single indicator works best in all 11 countries she included in the study. "Furthermore," she reported, "indicators that perform well in some countries often perform poorly in others."
Based on this finding, and that only one indicator provided even a small average improvement over the naïve forecast, Kozicki suggests using GDP growth, exchange rates, and changes in unemployment rates to provide forecasts that are reliable and have lower RMSE than the naïve forecast. "In fact," says Kozicki, "monitoring signals from a wide range of indicators is likely worthwhile precisely because most indicators were good predictors of inflation in at least one economic environment. Differences across countries in economic structure, economic experiences, and monetary policy procedures may help explain why in a given period an indicator might predict inflation well in some countries but be largely useless in others."
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