1. A good measure of productivity Eric Bartelsman Vrije Universiteit Amsterdam and Tinbergen Institute Washington, World Bank, October 31, 2005

2. Overview  Who Cares about Productivity? Why? Which measures?  Sources of Productivity Data  Designing a Research Agenda  Statistical Roadmap  Policy Questions

3. Information for Policy  Appropriate indicators depend on policy problems  System of National Accounts (SNA) developed by Stone, provides information to track Keynesian problem of demand shortfall  Schumpeterian view on growth (and cycles) lacks systematic framework of data collection and presentation

4. Information for Policy  Monetary policy requires:  Indicators of potential output  Supply side: labor, capital services, TFP  Indicators of resource bottlenecks  Unit labor costs  Indicators of state of business cycle  Cyclicality of productivity, prices, wages  Research to understand economic mechanisms  Many uses of productivity information

5. Information for Policy  Structural Economic Policy  Competition, Privatization, Deregulation  Innovation, R&D  Resource markets (unemployment, labor quality, financial markets, imported inputs)  …needs performance indicators  International/sectoral productivity comparisons  Comparisons over time  Proper ‘evaluation’: treatment/control groups

6. Which measure of productivity  This is a practical issue and depends on question at hand and on availability/reliability of data  Some findings are robust across measures, while other depend crucially on measure used

7. Importance of Measurement  Productivity is output/input  Measurement errors do not cancel  Output or input measures: apples and oranges problem (across variables, across units, over time)  Practical problems:  collection of data often not ‘consistent’  Data often not integrated, or integrated in different ways over time or across units

8. Overview  Who Cares about Productivity? Why? Which measures?  Sources of Productivity Data  Designing a Research Agenda  Statistical Roadmap  Policy Questions

9. Data Sources  Level of aggregation  Macro: National Accounts and Labor Force Survey  Micro: firm or establishment census or surveys  Sectoral: Top-down, bottom-up, integrated  Ideally, data would be consistent and integrated at each level of aggregation

10. INPUT-OUTPUT TABLE INDUSTRIESFINALDEMN.GRS.OUT. IND VALADD GDP or GDI

11. Consistent, Integrated Dataset  See Beaulieu and Bartelsman (2005)  Dataset  detailed information on the gross output, value added, final demand expenditures, and use of intermediate inputs by industry  Consistent  dataset where the underlying components are based on the same definitions and industry classifications  Integrated  despite the numerous data sources employed, the estimates conform to the accounting identities linking production, income, and expenditures

12. Comparisons of NA vs micro sources

14. Sources of labor data  Labor input not in SNA (sometimes in satellite acct)  Main source: household survey  Employment and earnings surveys (excl small firms, often mfg, mng, util)  Business Statistics (survey or census)  Business register  Social Insurance register

15. Comparisons of sources of labor data Source: Productivity growth in Jamaica 1991-2000, Eric Bartelsman, report for WB

16. Sources of price data  Ideal: deflators for each cell of IO; deflators for outputs and inputs at micro level  Aggregate or sectoral deflators: ‘Schreyer method’  Expenditure PPPs vs Ind-of-origin PPP  For micro level: assume constant markup and use nominal sales

17. Overview  Who Cares about Productivity? Why? Which measures?  Sources of Productivity Data  Designing a Research Agenda  Statistical Roadmap  Policy Questions

18. Statistical Roadmap  For EU countries: EU KLEMS  STAN for OECD, multiple sources for US  Other countries: stimulate similar work, but process directly from original datasource  Try to harmonize types of original sources available, and methods used to create consistent integrated data

19. Provision of metadata. Approval of access. Disclosure analysis of cross-country tables. Disclosure analysis of Publication Researcher Policy Question Research Design Program Code Publication Network Metadata Network members Cross-country Tables NSOs Distributed micro data research

20. Recent Productivity Research: does it answer policy questions?  Labour Productivity Levels  cross-country, sectoral timeseries  Growth Accounting  Contribution of ICT, other capital, TFP  Statistical Analysis  Cross-country convergence (million regressions)  Sectoral and micro-level datasets

21. Convergence in Labor Productivity Levels  What drives convergence process?  Many factors are significant in cross-country growth regressions  Do we really know if 10% above or below is meaningful  international price comparisons  definitions of output and input  Why converge to ‘average’ of best country?  But: we learn who belongs to which ‘convergence clubs’

22. Growth Accounting Source: van Ark & O’Mahony 1995-2000: contribution in pct-points to average annual growth

23. What was the question?  No role for policy environment or spillovers  Only factors that are explicitly purchased can contribute to output in the framework  No indication of market failures  It is assumed that representative firm makes optimizing decisions  No accounting of source of higher quality inputs  Contribution of quality of capital or labor to output can be computed  Does contribution of input X (e.g. ICT, or foreign- owned capital) differ across countries/sectors/time?  And: most of output growth is accounted for

24. Sectoral Comparisons Source: van Ark & O’Mahony 1995-2002: contribution in pct-points to average annual growth

25. Sectoral and micro data  Econometric evidence in panel data setting  R&D  Shifting of R&D based on tax-incentives  Spillover benefits of R&D done abroad  ICT  Firm-level evidence on return to ICT investment  Differences by Ownership status  Corporate Taxes  Cross-country ‘productivity’ response to relative tax- rate shifts  Policy evaluation of ‘innovation vouchers’  Many other studies: trade-openness, competition, training