In a recent speech titled, “Trucks and Terabytes: Integrating the ‘Old’ and ‘New’ Economies,” at the 61st Annual Meeting of the National Association for Business Economics, Federal Reserve Chairman Jerome H. Powell challenged the underlying data concerning measurements of economic growth. He asks: “with terabytes of data increasingly competing with truckloads of goods in economic importance, what are the best ways to measure output and productivity? Put more provocatively, might the recent productivity slowdown be an artifact of antiquated measurement?” In considering the question, here are his comments:
How Should We Measure Output and Productivity?
Let’s now turn to the second question of how to best measure output and productivity. While there are some subtleties in measuring oil output, we know how to count barrels of oil. Measuring the overall level of goods and services produced in the economy is fundamentally messier, because it requires adding apples and oranges—and automobiles and myriad other goods and services. The hard-working statisticians creating the official statistics regularly adapt the data sources and methods so that, insofar as possible, the measured data provide accurate indicators of the state of the economy. Periods of rapid change present particular challenges, and it can take time for the measurement system to adapt to fully and accurately reflect the changes in the economy.
The advance of technology has long presented measurement challenges. In 1987, Nobel Prize–winning economist Robert Solow quipped that “you can see the computer age everywhere but in the productivity statistics.”6 In the second half of the 1990s, this measurement puzzle was at the heart of monetary policymaking.7 Chairman Alan Greenspan famously argued that the United States was experiencing the dawn of a new economy, and that potential and actual output were likely understated in official statistics. Where others saw capacity constraints and incipient inflation, Greenspan saw a productivity boom that would leave room for very low unemployment without inflation pressures. In light of the uncertainty it faced, the Federal Open Market Committee (FOMC) judged that the appropriate risk‑management approach called for refraining from interest rate increases unless and until there were clearer signs of rising inflation. Under this policy, unemployment fell near record lows without rising inflation, and later revisions to GDP measurement showed appreciably faster productivity growth.8
This episode illustrates a key challenge to making data-dependent policy in real time: Good decisions require good data, but the data in hand are seldom as good as we would like. Sound decisionmaking therefore requires the application of good judgment and a healthy dose of risk management.
Productivity is again presenting a puzzle. Official statistics currently show productivity growth slowing significantly in recent years, with the growth in output per hour worked falling from more than 3 percent a year from 1995 to 2003 to less than half that pace since then.9 Analysts are actively debating three alternative explanations for this apparent slowdown: First, the slowdown may be real and may persist indefinitely as productivity growth returns to more‑normal levels after a brief golden age.10 Second, the slowdown may instead be a pause of the sort that often accompanies fundamental technological change, so that productivity gains from recent technology advances will appear over time as society adjusts.11 Third, the slowdown may be overstated, perhaps greatly, because of measurement issues akin to those at work in the 1990s.12 At this point, we cannot know which of these views may gain widespread acceptance, and monetary policy will play no significant role in how this puzzle is resolved. As in the late 1990s, however, we are carefully assessing the implications of possibly mismeasured productivity gains. Moreover, productivity growth seems to have moved up over the past year after a long period at very low levels; we do not know whether that welcome trend will be sustained. Recent research suggests that current official statistics may understate productivity growth by missing a significant part of the growing value we derive from fast internet connections and smartphones. These technologies, which were just emerging 15 years ago, are now ubiquitous (figure 3). We can now be constantly connected to the accumulated knowledge of humankind and receive near instantaneous updates on the lives of friends far and wide. And, adding to the measurement challenge, many of these services are free, which is to say, not explicitly priced. How should we value the luxury of never needing to ask for directions? Or the peace and tranquility afforded by speedy resolution of those contentious arguments over the trivia of the moment? Researchers have tried to answer these questions in various ways.13 For example, Fed researchers have recently proposed a novel approach to measuring the value of services consumers derive from cellphones and other devices based on the volume of data flowing over those connections.14 Taking their accounting at face value, GDP growth would have been about 1/2 percentage point higher since 2007, which is an appreciable change and would be very good news. Growth over the previous couple of decades would also have been about 1/4 percentage point higher as well, implying that measurement issues of this sort likely account for only part of the productivity slowdown in current statistics. Research in this area is at an early stage, but this example illustrates the depth of analysis supporting our data-dependent decisionmaking.
View the original article here: A Greater Appreciation for the Contribution and Value of Some Intangibles (particularly "free" intangibles)?
The article was originally posted on IP Finance.
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