Backcasting National Accounts Data Examples from United States Experience Brent Moulton Advisory Expert Group on National Accounts Washington DC 9 September 2014

2 Why backcast economic data? ▪ Provide a service to data customers ▪ Maintain time-series consistency ▪ Produce longer time series to study changes in the economy over time ▪ Understand sources of economic growth and productivity over time

When is backcasting used? ▪ Changes in classification  Industry and other classification systems ▪ Changes in concepts  Newly recognized asset or redefined activity ▪ Expanded detail  Sub-aggregate breakouts ▪ When data are not available to directly measure the economic variables 3

Approaches ▪ Microdata approaches  Detailed reclassification of micro units ▪ Macrodata approaches  Concordance tables  Proportional splicing  Interpolation/Backward extrapolation with or without indicator 4

Examples in the US national accounts ▪ GDP-by-industry estimates  North American Industry Classification System (NAICS) ▪ Reclassifications of exports and imports  For example, new treatment of merchandising in BPM6 ▪ Recognition of R&D as fixed assets  Newly constructed measures of R&D investment 5

GDP by industry and NAICS ▪ U.S. statistical agencies implemented new classification system in different years  Economic Census data  Tax data  Employment and earnings data  Prices ▪ Prior to 1998, GDP by industry was based on Standard Industrial Classification (SIC) ▪ Users urged BEA to provide NAICS time series ▪ Not feasible to convert source data to NAICS 6

Backcasting GDP by industry ▪ Designed a backcasting technique  1997 concordance of detailed SIC to NAIC data  Backward extrapolate concordance with SIC source data  Create published level SIC – NAICS conversion matrices  Convert published SIC estimates to NAICS  Conversion matrices for had less SIC detail  For , 1977 matrix held constant  V k i, t-p = V k i, t-p · (n k i, t-p / n k i, t-p+1 ) Where: 7 i = industry t = 1997 p = 1,…,10 k = VA component (output, intermediate inputs, compensation, GOS) n = conversion coefficient V = dollar value of VA component

Evaluating results 8 ▪ Reasonableness and consistency checks  Growth rates compared to published SIC industries  Aggregation of industry level real value added compared against expenditure-based real GDP

Recognition of R&D as fixed asset ▪ 2013 NIPA comprehensive revision ▪ New estimates of R&D output and investment ▪ Less available and reliable data further back in time * Prior to aggregate estimates deemed more reliable than detailed industry data – proportionally scaled detail to hit aggregates 9 Time periodSource dataComments presentR&D expenditure surveys and economic census data Detailed costs by industry (business, academic, government); relatively consistent across time *R&D expenditure surveysLess consistency of surveys across time Insufficient dataGeometric interpolation Various research studies of R&D costs Selected years; straight line interpolation between data points

Summary ▪ Many different reasons to backcast ▪ Each instance has unique requirements ▪ Necessitates resourcefulness and inventiveness ▪ Need to weigh the benefit of backcasting against the resources required and the resulting quality of the estimates ▪ Need a strong evaluation process 10