We've never in history been so sophisticated in our use of benchmark metrics, economic statistics and trending indices as we are now. But we desperately need to learn how the twin game-changers of the 21st century -- globalization and information technology -- and new management practices, such as lean, have changed how we should interpret these numbers -- or whether we're collecting and analyzing the right ones. The data may not look different on the surface, but once-familiar and easily interpreted trend lines increasingly result in unexpected outcomes. Rising productivity is supposed to lift personal income and create jobs, but it hasn't. Raising short-term interest rates is supposed to lead to higher long-term interest rates, but they haven't. Even the venerable Alan Greenspan, Chairman of the Federal Reserve Board for 18 years, has been reduced to admitting that results of recent monetary policy are a conundrum.
Further complicating our understanding of the data is the growing cadre of analysts who are challenging long-held assumptions about what the traditional indicators mean. With such reinterpretation, rising trade deficits, factory closings and layoffs, and currency imbalances have become overnight signs of economic strength where they once meant weakness.
Finally, as if all of the above weren't enough, we're reacting to statistics at an ever-increasing speed. Now each month, as we await each of a long list of figures as well as the revisions of the previous months' figures, we parse the predictions of what next months' results will be. Then we deal with the market shake-up that occurs when the actuals come in short or ahead of the predictions. Often I wonder if the monthly reports provide more cause for confusion, rather than useful information, and whether we need to smooth the data as we do the unemployment statistics.
I realize that changing the metrics and methods by which we monitor the U.S. economy is not a radical proposition. Over the years we've made many changes to our data collection efforts to better mirror economic changes -- we've changed from SIC codes to NAICS codes and, before that, started to emphasize GDP rather than GNP as the primary measure of U.S. production. Still, I fear our data collection, analysis and reporting efforts have yet to catch up to our fast-changing economy. Among some possible changes that come immediately to mind are the following:
- The U.S. Census Bureau conducts the Census of Manufacturers every five years, a lifetime in the current economic climate. It should be conducted yearly or at least every two years and published rapidly.
- The addition of a "business services" sector in U.S. economic statistics was laudable in its attempt to capture the influence of new, fast-growing industries, but it seems to downplay the jobs-creating capacity of manufacturers. We should begin collecting data on which industries are responsible for creating those service jobs.
- Lean manufacturing practices have turned traditional accounting measures and investment patterns upside down, causing companies to tap all avenues to squeeze more value from existing assets, before investing in additional equipment or facilities. We should reconsider using capital investment as a metric that foretells future productivity improvements.
- Manufacturing is global, yet, with few exceptions, economic statistics from other countries are reported separately if at all. Those data should be integrated.
Most business and public policy leaders agree that the manufacturing sector is undergoing (or has undergone) structural change. But we continue to rely on old methods and metrics to measure progress and success -- and to set national policy and management strategy and to provide insight into future trends. The quicker we're able to adjust how we collect and interpret the data about the new economic reality, the sooner we'll be able to capitalize on it. Let's get started.
Patricia Panchak is IW's editor-in-chief. She is based in Cleveland.