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Are the present data sufficient for researchers? researcher Paolo Fornaro, ETLA Economic Research

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Renewed productivity survevy - what from here on? 15.10.2021 Statistics Finland

Are the present data sufficient for researchers? researcher Paolo Fornaro, ETLA Economic Research

  1. 1. Is the current productivity data enough for researchers? Paolo Fornaro (Etla Economic Research)
  2. 2. Contact details: 2 Statistics Finland 17.10.2021 Head of Department Katri Kaaja @katri_maria1 katri.kaaja@stat.fi Senior Statistician Marja Sauli marja.sauli@stat.fi Researcher Paolo Fornaro @PaoloFornaro87 paolo.fornaro@etla.fi Senior Statistician Nata Kivari nata.kivari@stat.fi
  3. 3. Outline (and answer to title) • (Yes and no. While TK provides a lot of interesting productivity statistics, there could be something more regarding, e.g., microdynamics.) • Why do we care about productivity? • Why do we need something more than simple aggregate productivity statistics? • What can Statistics Finland provide?
  4. 4. Why do we care about productivity? • Most mainstream economic models have identified productivity growth as the main driver of long-term economic prosperity. • This is supported from empirical observations. • Boppart and Li (2021) provides a very recent example: most of the income gap difference between, e.g., Nigeria and the US is due to differences in productivity.
  5. 5. Boppart and Li (2021) Income per capita Capital intensity Human capital TFP
  6. 6. Why should we go beyond aggregate productivity? • Looking at simple labor and total factor productivity aggregate series is important and useful. • However, we are left wondering what are the causes of certain productivity dynamics. • Is it because of firms becoming more productive? Are more productive firms growing in size?
  7. 7. The role of reallocation (examples) • Micro-level data can help us disentangle sources of productivity growth. Let’s take reallocation, as an example. • Hsieh and Klenow (2009) show that allocation efficiency explains a lot of cross-country differences in productivity (30 to 50% between China and US) • Böckerman and Maliranta (2007) identify efficient reallocation as the major cause of productivity gaps between Finnish regions.
  8. 8. What can Statistics Finland do? • Many studies of productivity dynamics rely on micro-level data. • In Finland, researchers have access to excellent firm and plant-level data. • However, there is a lot of arbitrary choices to make, no real benchmark.
  9. 9. Data • We consider productivity growth for periods 2000-2005, 2006-2012 and 2013- 2018. • Let’s compare four micro-based productivity growth formula and the aggregate series produced by StatFi (labour prod, non-financial corporations). • First two micro-formulas rely on firm-level data, while the 3rd and 4th columns are based on establishments/plants. • We remove firms/plants with<1 employee and with negative value added or sales
  10. 10. Productivity changes (yearly avarages) Agg Firm-level 1 Firm-level 2 Plant-level 1 Plant-level 2 StatFi 2000-2005 1,32 0,95 1,11 0,83 2,76 2006-2012 -0,89 -0,99 -1,01 -0,73 -0,26 2013-2018 2,17 2,11 1,05 1,33 0,61
  11. 11. Problems • There are differences, sometimes substantial, between different productivity formulas and level of observation (firm vs plant). • The differences with official statistics are even more prominent. • In this case, the same the micro data was used, but each researcher might have different data-handling procedures (remove more or less industries, keep all firms, etc.). • Consequently, all micro-based analyses start from different premises.
  12. 12. Statistics Finland role • Productivity analyses, such as the productivity growth decompositions, are not only important in academics, but also in policy-oriented settings. • Simple, micro-based statistics and analyses provided by StatFi would be very welcome. • They would offer a reliable benchmark to examine alternative methodologies. • Simple statistics can be fed into further studies (e.g., study of scale ups).
  13. 13. Example of possible statistics • Decompositions of aggregate productivity growth, e.g., Olley and Pakes (1996), Maliranta (1997), or Foster, Haltiwanger and Krizan (1998), would be easy to produce. • The output of these decompositions would show how much of aggregate productivity growth can be explained by improvement within firms vs reallocation vs the role of new and exiting firms.

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