This document discusses the need to go beyond aggregate productivity statistics and leverage micro-level data to better understand productivity dynamics. It provides examples of how micro-level data has helped attribute sources of productivity growth, such as the role of reallocation between more and less productive firms. The author argues Statistics Finland could provide standardized micro-based productivity statistics and analyses, like decompositions of aggregate productivity growth, to serve as benchmarks for researchers and inform policy. This would help address inconsistencies between studies that arise from individual choices in working with the rich Finnish firm and plant-level data.

1. Is the current productivity data enough for
researchers?
Paolo Fornaro (Etla Economic Research)

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. 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. 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. Boppart and Li (2021) Income per
capita
Capital intensity Human
capital TFP

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. 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. 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. 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. 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. 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. 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. 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.