Integrating Expenditure and Income Data: What To Do With the Statistical Discrepancy?

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. Our strategy is first to use disaggregated data to estimate consistent sets of input-output models that sum to either GDP or GDI and compare the two in order to see where the discrepancy resides. 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.

Sectoral Productivity in the United States: Recent Developments and the Role of it

“…3 For additional precision in a practical forecasting setting, the sectoral weights could be developed from elements of macroeconomic data and/or a forecast in conjunction with the latest information on I-O relationships and actual MFP at the sectoral level could be estimated for another year (in this case, 2005). To estimate sectoral MPF for another year, the methods described in Beaulieu and Bartelsman (2006) could be used to estimate industry output from data on final demand components, and simplified methods for estimating capital input (e.g., of Oliner and Sichel 2000) could be adapted for use in a sectoral format using the tools described in Bartelsman and Beaulieu (2003). trends during 2005, 2006, and 2007-though at lower rates than during the preceding period-remain robust and average nearly 1-3/4 percent per year.…”