The importance of getting the statistics right

This posting follows on my previous posting on interesting insights from the OECD Knowledge-Based Capital conference. In his presentation at the opening session, Steve Landefeld, Director of the US Bureau of Economic Analysis (BEA) raised a core question: does R&D (S&T) raise growth? Or put another way, do we know what drives economic growth?
Landefeld points out that the original formulation of the “technology” or “knowledge” portion of the growth equation was Solow’s residual. It was what was left over after capital and labor were accounted for. The label “technology” was hung on that residual. In reality, the residual includes many items not just S&T. After years of economic and statistical analysis, there is still 40% of the residual that we don’t understand (a point that Chuck Hulten also made in a later presentation). (For an overview, see also my 1998 paper, Technology and Economic Growth)
Landefeld pointed out the importance of trying to understand what drives growth — that 40% we don’t understand. Without that understanding, we can get policy (technology policy, tax policy, macroeconomic policy) wrong. One example is monetary targeting. He noted that if the monetary target is one half of 1% lower growth than is could be over the next ten years, this results in a cumulative reduction of $5.8 trillion in GDP, roughly the size of the hit on the value of household real estate assets during the housing crisis.
Let me explain that point further. The Federal Reserve is charged with setting monetary policy balancing two factors: economic growth/employment and inflation. The trade-off between the two was at center stage at the recent Congressional testimony by Fed Chairman Bernanke. Monetary targets are based on how fast the economy can growth without triggering inflation. That the potential non-inflationary growth rate of the economy — the “speed limit” as Alan Blinder called it a few decades ago — is based on productivity growth. If the productivity growth projections are off because we don’t understand what all the factors that drive productivity growth, then our monetary targets will be off. Nor — as I pointed out in that 1998 paper — can we design effective policies to raise productivity growth if we don’t understand all the factors.
Those factors are simple. They are all the investments in intangible assets that we have not been able to measure — either as inputs, outputs or impacts. Hence, getting the statistic right on intangible is not just an academic exercise. Without the right data, we are trying to navigate by looking in the rear view mirror. Always a dangerous activity.

One thought on “The importance of getting the statistics right”

  1. The importance of getting the statistics right – 2

    Apropos my earlier posting (and the recent news that GDP grew rather than declined in the 1Q), the Upjohn Institute is hosting a conference today and tomorrow on another measure issue: Measuring the Effects of Globalization. Papers range from how…


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