"The man with two watches is never quite sure what time it is.”
Does anybody really know what time it is? Does anybody really care? Most of you will want to skip this. Somehow, I became interested in explaining the statistical discrepancy. As this paper notes, it’s an important issue because the growth rates of GDP and GDI, quantities that differ by the discrepancy, can give different indications about the strength of economic growth.
This paper looks into the sources of the statistical discrepancy. While it doesn’t explain the systematic pattern noted here, it does talk extensively about the measurement and productivity issues including reference to Greenspan’s use of an income based productivity measure. According to this paper, most of the discrepancy arises due to mismeasurement in a few key industries, machinery and instruments, trade, and finance and insurance:
Integrating Expenditure and Income Data: What To Do With the Statistical Discrepancy?, J. Joseph Beaulieu and Eric J. Bartelsman (SSRN paper): Abstract: The purpose of this paper is to build consistent, integrated datasets to investigate whether various disaggregated data can shed light on the possible sources of the statistical discrepancy. ... We find a few “problem” industries that appear to explain most of the statistical discrepancy. Second, we explore what combination of the expenditure data and the income data seem to produce the most sensible data according to a few economic criteria. A mixture of data that do not aggregate either to GDP or to GDI appears optimal.