Measuring productivity levels: is more data always better? (work in progress) Robert Inklaar and Marcel Timmer (University of Groningen) The EU KLEMS project is funded by the European Commission, Research Directorate General as part of the 6th Framework Programme, Priority 8, "Policy Support and Anticipating Scientific and Technological Needs".

Motivation: measurement matters US=1

Motivation: measurement matters US=1

But can we be so sure? “The Panel appreciated the much needed effort to improve statistical measures of productivity in Europe, but could not avoid feeling that the [productivity] data’s inability to detect sharp patterns may be due to a poor signal/noise ratio.” (Economic Policy, editor’s introduction, Jan. 2008) Are more sophisticated productivity measures noisier? Does more sophisticated measurement lead to significantly different results? What are the wider implications of noisy productivity measures?

We can be pretty sure *: difference crude-sophisticated significant at 5% level US=1

Stochastic approach to index numbers Country-product dummy (CPD) regressions: Basic information: digit output PPPs Include 19 country dummies and 29 product dummies Gross output data for weighted CPD Coefficient variance/covariance matrix for standard errors: Use same approach for (ICP) GDP PPPs (1999 basic headings) Only country variation, so use standard errors of country dummies

Little aggregate difference between expenditure and production side

But sizeable differences in standard errors of international prices

Empirical set-up Use standard errors for PPPs to simulate standard errors for MFP measures (1000 draws) Compare three set of MFP estimates based on ever more sophisticated measurement Question 1: are the standard errors larger for more sophisticated measures? Question 2: are the MFP measures significantly different at the industry level?

‘Crude’ productivity levels Use GDP PPPs to convert industry value added to a common currency Undifferentiated labour (total hours worked) Undifferentiated capital stocks Converted using GDP PPPs Intermediate case: use industry output PPPs to convert industry value added (single deflation)

‘Sophisticated’ productivity levels Covers 20 countries and 29 industries Industry output PPPs and input PPPs Symmetric IO table based on Supply-Use tables Both domestic (29x29) and imports (29x29) Use sectoral output concept (exclude intra-industry deliveries) Distinguish 30 types of labour 5 education classes x 3 age groups x gender Distinguish 8 types of capital 3 ICT assets, 5 non-ICT assets PPP for each input (exchange rate for imports) CCD aggregation

Answer 1: Standard errors are larger for more sophisticated measures

Answer 2: More sophisticated MFP measures are substantially different

Significant differences are seen across all countries

Same for all industries

Preliminary conclusions More sophisticated productivity measurement leads to a worse signal/noise ratio The productivity measures become conceptually more appropriate Both at the industry and aggregate level, the changes in productivity levels are frequently significant

Next steps Refine weighting scheme in CPD or use more detailed PPPs Judge uncertainty about capital and labour PPPs CPD of 2 capital/labour vs. 38 types? Use cross-industry variation? Application of uncertain productivity levels Convergence regressions? Is ‘more uncertainty’ the main message?