The market microstructure of industrial ecosystems in the digital and green transitions: evidence from Estonia Louise GUILLOUET (Science, Technology and Innovation Directorate, OECD) Thanks to a unique combination of administrative and survey data matched to the Estonian VAT data, this project studies how information on transaction data can shed light on industrial policy making, through two different angles: 1/ Improving the understanding of the production network, industrial ecosystems and the relevant unit of analysis for industrial policy design and 2/ An application to the diffusion of the green and digital transitions, showing the role of production network in technology diffusion and how this can be leveraged to increase policy effectiveness. Find out more at https://oe.cd/spl-mtg

1. FIRM-LEVEL PRODUCTION NETWORKS:
EVIDENCE FROM ESTONIA
New approaches to understand business conditions and links between firms
OECD workshop – November 13th, 2023
Louise Guillouët
with Chiara Criscuolo, Antoine Dechezleprêtre, Guy Lalanne, Francesco Manaresi,
Ariana Paola Cortes Angel and Jaan Masso (University of Tartu)
Supported by the European Commission,
DG GROW

2. Why study firm-level production networks?
• Ambitious goals such as Net Zero for 2050 require overcoming coordination failures and
leveraging complementarities across all types of firms
• The sectoral approach of most industrial policies does not achieve this.
• This project: building block towards evidence base for industrial ecosystems
• Proof-of-concept with Estonian VAT data
• How is the production network structured?
• Do suppliers transmit new technologies and good practices to their clients ?
• Policy implication: leverage ongoing relationships, target key players in each ecosystem,
maximise diffusion of good practices...
2

3. An extremely rich set of data
• Quasi-universe of buyer-seller transactions in Estonia, 2015-2021
• All VAT transactions worth >1 000 euros / month
• 89k to 115k firms per year : 85% of active firms and 99% of employees in Estonia in 2020
• A wide range of complementary data sources
• For most firms: location, sector, employment, turnover, costs, capital, investment,
exposure to trade
• For a sample of firms:
• adoption of digital technologies
• energy use
3

4. HOW IS THE NETWORK
STRUCTURED?
4

5. A few extremely central firms, many non-central firms
• Local centrality is based on the number of buyers (out-degree centrality) or sellers (in-degree
centrality) that the firm directly transacts with
• Many firms have very few connections, while a few firms have a very large number of
connections
5

6. A lot of heterogeneity across sectors
• The utilities, finance and trade sectors are important sellers and important buyers
• The public sector is a very important buyer
6

7. Analytical framework – cross-section
𝑦𝑖𝑡 = 𝛼 + 𝛽𝑋𝑖𝑡 + 𝜅 𝐾𝑖𝑡 + 𝛿𝑠−𝑐 + 𝛿𝜏−𝑎 + 𝛿𝑡 + 𝜖𝑖𝑡
Where:
𝑦𝑖𝑡 is the firm’s centrality,
𝐾𝑖𝑡 is the firm’s physical capital,
𝛿𝑠−𝑐 are sector by county fixed effects, 𝛿𝜏−𝑎 are size by age fixed effects, and
𝛿𝑡 are year fixed effects
𝑋𝑖𝑡:
- Capital
- Productivity
- International trade
- Intangible assets
- Digital technology adoption
- Green technology adoption
We find that on average, firms that:
- Are more capital-intensive
- Are more productive
- Export or import
- Have intangible assets, and larger intangible assets
- Adopt more digital technologies
- Are more energy-efficient
are also predicted to be more central, all things equal
7

8. Communities, an alternative to sectors?
• Data-driven approach: the Leiden algorithm is applied to the
network data to propose “communities” of firms that transact
more within the community than outside.
• The 13 largest communities contain 95% of firms (97% of
employment)
• The communities are intuitive, and correspond to domestic
value-chains
• 75% of transactions take place within
• Allows the identification of sectors that are “specific” to
communities vs those that are “horizontal” and matter to the
entire economy.
8

9. Human health
12
Apparel, Textiles,
& Furniture
(workwear, outerwear,
blankets)
11
Wood products
& Forestry
9
Manufacturing
(metal products, electronics,
electrical equipment)
8
Civil Eng. /
Public Admin.
7
Land transport
(urban passenger & freight)
6
Construction of
buildings
(non-metallic mining products)
5
Information &
Communication
(Tallinn)
0
Construction of
buildings
(wood products)
1
Repair of motor
vehicles
(and wholesale and retail trade)
2
Education &
Real estate
3
Retail & Food
processing
4
Agriculture
(rural counties)
10
• Transaction data allow to identify value chains at a granular level
• Communities could be used as a unit of industrial policy
The 13 largest communities

10. DIFFUSION
10

11. EU Carbon market 2018 price shock
• EU ETS permit price increase starting in 2018, due to regulatory reforms and market factors
11
• The literature predicts firms become more energy-efficient (e.g. 2022 study of BC’s ETS)
• Objective: measure diffusion to the buyers and sellers of Estonian EU ETS participants
• Identification strategy: compare buyers and sellers of EU ETS participants to non-connected
firms, from 2019 onwards vs 2016-2018, excluding never treated sectors

12. Definition of the treatment and control groups
12
ETS
Seller
Seller Buyer
Buyer
Control firms are outside the circle and operate in sectors that have at least one firm in the circle.
Treated firms
Control firms Control firms
Excluded firms
Control firms
Excluded firms
Control firms

13. Analytical framework
Difference-in-Differences (with two-way fixed effects)
𝑦𝑖𝑡 = 𝛼 + 𝛽𝐷𝑖 × 𝑃𝑜𝑠𝑡𝑡 + 𝛿𝑖 + 𝛿𝑡 + 𝜖𝑖𝑡
𝑦𝑖𝑡 is the firm i’s outcome level in year t: log production per unit of energy consumed
𝑫𝒊 is the “network treatment” dummy:
• Exclude firms that are actually in the EU-ETS market
• Very few of them
• The shock, or at least its timing, may be endogenous
• Buyer or Seller of EU-ETS participants
• Shock is arguable exogenous for them!
• Control firms are firms that are further away in the network, and in the same sectors.
𝑃𝑜𝑠𝑡𝑡 is a dummy that turns on after the shock has taken place, 𝛿𝑖 firm fixed effects and
𝛿𝑡 are year fixed effect
Require at least two observations per firm before treatment and one observation after treatment:
~1 200 firms, ~6 000 observations 13

14. Checking for parallel trends
14

15. Carbon-market “adjacent” firms become more energy-
efficient following the 2018 price shock
15

16. • Policy implications
– Is centrality the new productivity?
– Are ecosystems or communities the new sectors?
– How can policymakers leverage “superstar” firms?
• What’s next?
– Further work on the Estonian VAT data
• Firm-level stress tests
– Extend to other countries
– Develop a framework for incorporating firm-to-firm transaction data into
policymaking
16
Conclusion

17. THANK YOU
We look forward to your questions!
17

18. APPENDIX
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19. A lot of heterogeneity across age and size
19
employees

20. • We keep only firms that were “born small”. The result is robust to controlling for size
Firms grow their network over their life-cycle
20
𝐿𝑜𝑔 𝑐𝑒𝑛𝑡𝑟𝑎𝑙𝑖𝑡𝑦𝑖𝑠𝑐𝑡 = ෍
𝑘
𝛽𝑘𝐴𝑔𝑒𝑖
𝑘
+ 𝛾𝑠𝑐 + 𝜃𝑡 + 𝛿 𝑆𝑖𝑧𝑒𝑖 + 𝐶𝑜ℎ𝑜𝑟𝑡𝑖 + 𝜀𝑖𝑠𝑐𝑡

21. Importers are more (out-)central for the domestic
network
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22. Exporters are more (in-)central for the domestic
network
22

23. Firms who are more “electricity-efficient” are more
central
23
Electricity-efficiency measure based on Lyubich, Shapiro and Walker (2018). Energy sector excluded.

24. More intense ICT users are more central
24

25. Big data users are more central
25

26. Parallel trends for buyers vs sellers
26

27. • Colmer et al. estimate that the early phases of the ETS (average price: EUR 13) lead
to a +20% increase in energy- efficiency of ETS participants
• Ahmadi et al. estimate that the introduction of the BC carbon tax (average price:
EUR 15, only applied to fuel consumption, not process emissions) lead to a +6%
increase in energy-efficiency of taxed firms
• The shock studied here is much larger (change in price +EUR 25) so we can expect a
much larger direct effect – the 10% effect identified is therefore below the direct
effect we could expect
• We find that the diffusion mostly takes place downstream, so apriori through the
price signal
• The literature estimates pass-through rates of almost 100% for electricity and
anywhere between 20 and 100% for other goods
• Our estimates are therefore plausible!
27
Discussion of the estimate

28. Covid-19’s “big push” for digital technologies
28
• The Covid-19 induced lockdowns increased the returns to adopting digital technologies, and
many firms adopted between 2019 and 2020
• However, the literature has documented a lot of heterogeneity: larger, more productive and ex-ante
more digital firms were the most likely to adopt additional technologies during Covid-19
• What is the effect of being connected to digital leaders on adoption during Covid-19?

29. Analytical framework 2
Continuous Difference-in-Differences (with two-way fixed effects)
𝑦𝑖𝑡 = 𝛼 + 𝛽𝐷𝑖 × 𝑃𝑜𝑠𝑡𝑡 + 𝛾𝐷𝑖 + 𝛿𝑃𝑜𝑠𝑡𝑡 + 𝜖𝑖𝑡
Where:
𝑦𝑖𝑡 is the firm i’s outcome level in year t
𝑫𝒊 describes the baseline digital-savviness of firm i’s partners
➢ Compare the post-2020 outcomes of firms whose partners were very digital-savvy in 2019
and before, to firms whose partners were not digital-savvy in 2019 and before
𝑃𝑜𝑠𝑡𝑡 is a dummy that turns on in 2020 and 2021
~1 200 firms, ~6 000 observations
29

30. Checking for pre-trends
30

31. Firms with more digital partners in 2015 adopted
digital technologies faster during Covid-19
31