Expert workshop on the creation and uses of combined environmental and economic performance datasets at the micro-level - 10-11 July 2018 - OECD, Paris

1. EACAI in brief Data quality and access Applications
EACEI (Enquˆete Annuelle sur le Consommations
d’Energie dans l’Industrie) with Applications
Giovanni Marin1
Francesco Vona2
1
University of Urbino ‘Carlo Bo’, Italy; SEEDS, Ferrara, Italy
2
OFCE SciencesPo, France
OECD 2018
Paris, France, July 2018
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2. EACAI in brief Data quality and access Applications
EACEI in brief
▸ Survey on quantity and expenditures for energy products (by
source: electricity, oil, coal, natural gas, steam, other)
▸ Other interesting variables: auto-production, maximal installed
capacity, tariffs from 2005
▸ Unit of analysis ⇒ establishment (SIRET)
▸ Years available: 1983-2015
▸ Stratified sample of medium-small manufacturing establishments
(20-250 employees, in some sectors-years <10) and population of big
manufacturing establishments (250+ employees)
▸ Long panel for large establishments, fair length for small ones
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3. EACAI in brief Data quality and access Applications
Link with economic and other data
▸ Employment and wage data (DADS poste for the universe of
French establishments): also at the establishment-level
▸ Balance sheets (FARE-FICUS for the universe of French
establishments): at the firm-level (identifier: SIREN)
▸ Trade data: at the firm-level
▸ Plant-level data on EU-Emission Trading Scheme (from European
Union Transaction Log, EUTL) through SIRET or name
▸ All these matches are not problematic ⇒ SIRET/SIREN
straightforward: SIREN 9-digit number is the first part of the SIRET
14-digit
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4. EACAI in brief Data quality and access Applications
Data quality: general remarks
▸ High response rate, approx. 90%
▸ Easy remote access with box and fingerprints, and excellent
support service
▸ Large samples increasing in size over time (between approx. 7000
to 13000)
▸ Classifications and definitions: small changes over time (e.g. NAF
2002, 2008)
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5. EACAI in brief Data quality and access Applications
Example: sample selection and characteristics
In a panel study, establishments observed at least two times:
Table: Characteristics of the estimation sample
Potential number of observations (2000-2015) 125480
Observations in the estimation sample 88622
Share of ‘selected’ observations 0.7663
(over total observations)
Share of energy consumption in ‘selected observations’ 0.7761
(over total energy consumption)
Share of labour in ‘selected’ observations 0.7597
(over total labour)
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6. EACAI in brief Data quality and access Applications
Obviously, large establishments are over-represented:
Table: Differences between estimation sample and overall population
Dep var log(L) log(E) log(E/L) log(pE ) log(pelectr )
Dummy: selected sample 0.463** 1.205** 0.742** -0.0110** -0.0216**
(0.0095) (0.0208) (0.0172) (0.0027) (0.0025)
log(ener consumption) -0.119**
(0.0009)
log(electr consumption) -0.0947**
(0.0008)
N 125041 125041 125041 125041 125041
OLS pooled model weighted with sampling weights. Year dummies included. Robust
standard errors in parenthesis. *p<0.05, **p<0.01.
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7. EACAI in brief Data quality and access Applications
Some issues
▸ Changes in the survey design: in 2001, changes in the minimum
thresholds (10 for some sectors) while in 2013 all minimum
thresholds set to 20; until 2001 sectors 10-12 (Manufacture of food
products, beverages and tobacco products, NACE Rev2) were not
included in the survey design
▸ Representativeness: micro vs. sector-level study
▸ Sample size increases over time but not smoothly, so in early years
very few obs for some three digit sectors ⇒ lack of reliability of cells
based on 3-digit
▸ Aggregates of energy and labor inputs look too volatile at the 2-digit
sector-level ⇒ this reflect a certain volatility in sample size from
5695 to +12000, small samples especially between 2000-2005
(except 2001)
▸ We plan to perform an external validation using WIOD or other
standard data sources
▸ Coherence: good in general (employment correlated at 99% level
with employment from DADS), but not for all variables, e.g.
maximal capacity not always greater than total consumption
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8. EACAI in brief Data quality and access Applications
Some issues
▸ Changes in the survey design: in 2001, changes in the minimum
thresholds (10 for some sectors) while in 2013 all minimum
thresholds set to 20; until 2001 sectors 10-12 (Manufacture of food
products, beverages and tobacco products, NACE Rev2) were not
included in the survey design
▸ Representativeness: micro vs. sector-level study
▸ Sample size increases over time but not smoothly, so in early years
very few obs for some three digit sectors ⇒ lack of reliability of cells
based on 3-digit
▸ Aggregates of energy and labor inputs look too volatile at the 2-digit
sector-level ⇒ this reflect a certain volatility in sample size from
5695 to +12000, small samples especially between 2000-2005
(except 2001)
▸ We plan to perform an external validation using WIOD or other
standard data sources
▸ Coherence: good in general (employment correlated at 99% level
with employment from DADS), but not for all variables, e.g.
maximal capacity not always greater than total consumption
7/14

9. EACAI in brief Data quality and access Applications
Some issues
▸ Changes in the survey design: in 2001, changes in the minimum
thresholds (10 for some sectors) while in 2013 all minimum
thresholds set to 20; until 2001 sectors 10-12 (Manufacture of food
products, beverages and tobacco products, NACE Rev2) were not
included in the survey design
▸ Representativeness: micro vs. sector-level study
▸ Sample size increases over time but not smoothly, so in early years
very few obs for some three digit sectors ⇒ lack of reliability of cells
based on 3-digit
▸ Aggregates of energy and labor inputs look too volatile at the 2-digit
sector-level ⇒ this reflect a certain volatility in sample size from
5695 to +12000, small samples especially between 2000-2005
(except 2001)
▸ We plan to perform an external validation using WIOD or other
standard data sources
▸ Coherence: good in general (employment correlated at 99% level
with employment from DADS), but not for all variables, e.g.
maximal capacity not always greater than total consumption
7/14

10. EACAI in brief Data quality and access Applications
Direct applications of the data
▸ Energy efficiency and technology choices: how energy mix changed
over time?
▸ Obtain CO2 impacts computing the technical CO2 emission factor
for each energy source
▸ Policy measures (besides EU-ETS): easy to compute average unit
value prices for different sources
– For instance, the following definition of energy prices:
pE
it =
N
∑
j=1
φj
it pj
it ,
where φj
it is the share of energy consumption of source j (i.e. gas,
electr, coal, oil, etc) over total energy consumption, while pj
it is the
price of energy source j paid by establishment j at time t
– This allows to evaluate the impact of source-specific shocks (e.g.
carbon prices) on economic and environmental outcomes using
shift-share IV
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11. EACAI in brief Data quality and access Applications
Direct applications of the data
▸ Energy efficiency and technology choices: how energy mix changed
over time?
▸ Obtain CO2 impacts computing the technical CO2 emission factor
for each energy source
▸ Policy measures (besides EU-ETS): easy to compute average unit
value prices for different sources
– For instance, the following definition of energy prices:
pE
it =
N
∑
j=1
φj
it pj
it ,
where φj
it is the share of energy consumption of source j (i.e. gas,
electr, coal, oil, etc) over total energy consumption, while pj
it is the
price of energy source j paid by establishment j at time t
– This allows to evaluate the impact of source-specific shocks (e.g.
carbon prices) on economic and environmental outcomes using
shift-share IV
8/14

12. EACAI in brief Data quality and access Applications
Direct applications of the data
▸ Energy efficiency and technology choices: how energy mix changed
over time?
▸ Obtain CO2 impacts computing the technical CO2 emission factor
for each energy source
▸ Policy measures (besides EU-ETS): easy to compute average unit
value prices for different sources
– For instance, the following definition of energy prices:
pE
it =
N
∑
j=1
φj
it pj
it ,
where φj
it is the share of energy consumption of source j (i.e. gas,
electr, coal, oil, etc) over total energy consumption, while pj
it is the
price of energy source j paid by establishment j at time t
– This allows to evaluate the impact of source-specific shocks (e.g.
carbon prices) on economic and environmental outcomes using
shift-share IV
8/14

13. EACAI in brief Data quality and access Applications
Direct applications of the data
▸ Energy efficiency and technology choices: how energy mix changed
over time?
▸ Obtain CO2 impacts computing the technical CO2 emission factor
for each energy source
▸ Policy measures (besides EU-ETS): easy to compute average unit
value prices for different sources
– For instance, the following definition of energy prices:
pE
it =
N
∑
j=1
φj
it pj
it ,
where φj
it is the share of energy consumption of source j (i.e. gas,
electr, coal, oil, etc) over total energy consumption, while pj
it is the
price of energy source j paid by establishment j at time t
– This allows to evaluate the impact of source-specific shocks (e.g.
carbon prices) on economic and environmental outcomes using
shift-share IV
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14. EACAI in brief Data quality and access Applications
Example: Evolution of energy price structure as in Davis et
al (2014)
Figure: SD of log energy prices.3.35.4.45.5.55
1995 2000 2005 2010 2015
SD of log total energy prices
SD of log electricity prices
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15. EACAI in brief Data quality and access Applications
Example: Environmental policies and energy prices
▸ Establishment-specific exposure to Product Market Reforms
(PMR) for electricity and gas:
PMRit = PMRgas
t × ψgas
i,0 + PMRelectr
t × ψelec
i,0 ;
▸ Dummies for establishments subjected to the EU-ETS (specific to
different ETS phases);
▸ Average (establishment-specific) new taxes per MWh of electricity
on electricity consumption to finance renewable energy (so-called
CSPE), based on exemption thresholds
(1) (2) (3) (4) (5) (6)
log(pE ) log(pE ) log(pE ) log(pE ) Electr sh Gas sh
PMR (1: fully regulated; 0: unregulated) 0.0818** 0.0850**
(0.0368) (0.0383)
Establishment-specific CSPE (e/MWh) 4.536** 4.489** -0.272 5.049***
(1.786) (2.008) (1.124) (1.632)
ETS x D(2001-2004) -0.0122 -0.0121 -0.0103 0.0187
(0.0273) (0.0265) (0.00972) (0.0141)
ETS x D(2005-2007) -0.00954 -0.0124 -0.0239 0.0310
(0.00433) (0.0433) (0.0193) (0.0248)
ETS x D(2008-2012) 0.0272 0.0258 -0.0256 0.0312
(0.0452) (0.0454) (0.0204) (0.0276)
ETS x D(2013-2015) -0.0713*** -0.0674*** -0.00251 0.00587
(0.0222) (0.0220) (0.00759) (0.0131)
N 145425 145425 145425 145425 145425 145425
Fixed effect model. Robust standard errors in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. Additional control variables: year-sector (2-digit
NACE rev 2), year-region (NUTS2) dummies, year-peak (>Q3) dummies, year-size (initial size classes) dummies. Sample: establishment that
are observed in EACEI for at least two years
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16. EACAI in brief Data quality and access Applications
Example: Environmental policies and energy prices
▸ Establishment-specific exposure to Product Market Reforms
(PMR) for electricity and gas:
PMRit = PMRgas
t × ψgas
i,0 + PMRelectr
t × ψelec
i,0 ;
▸ Dummies for establishments subjected to the EU-ETS (specific to
different ETS phases);
▸ Average (establishment-specific) new taxes per MWh of electricity
on electricity consumption to finance renewable energy (so-called
CSPE), based on exemption thresholds
(1) (2) (3) (4) (5) (6)
log(pE ) log(pE ) log(pE ) log(pE ) Electr sh Gas sh
PMR (1: fully regulated; 0: unregulated) 0.0818** 0.0850**
(0.0368) (0.0383)
Establishment-specific CSPE (e/MWh) 4.536** 4.489** -0.272 5.049***
(1.786) (2.008) (1.124) (1.632)
ETS x D(2001-2004) -0.0122 -0.0121 -0.0103 0.0187
(0.0273) (0.0265) (0.00972) (0.0141)
ETS x D(2005-2007) -0.00954 -0.0124 -0.0239 0.0310
(0.00433) (0.0433) (0.0193) (0.0248)
ETS x D(2008-2012) 0.0272 0.0258 -0.0256 0.0312
(0.0452) (0.0454) (0.0204) (0.0276)
ETS x D(2013-2015) -0.0713*** -0.0674*** -0.00251 0.00587
(0.0222) (0.0220) (0.00759) (0.0131)
N 145425 145425 145425 145425 145425 145425
Fixed effect model. Robust standard errors in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. Additional control variables: year-sector (2-digit
NACE rev 2), year-region (NUTS2) dummies, year-peak (>Q3) dummies, year-size (initial size classes) dummies. Sample: establishment that
are observed in EACEI for at least two years
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17. EACAI in brief Data quality and access Applications
Example: Simulated impact of a carbon tax on energy
prices
Figure: Predicted impact on average energy prices of policy changes
0 .1 .2 .3 .4
Big establishments
Small establishments
Non-trade intensive
Trade-intensive
Non-energy intensive
Energy intensive
Total
Relative change in energy prices wrt 2015
Carbon tax: 56 euro per ton of CO2
Carbon tax: 100 euro per ton of CO2
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18. EACAI in brief Data quality and access Applications
Example: Simulated impact of a carbon tax
We regress an outcome variable on energy prices plus rich set of controls
and plant FE; shift-share IV for energy prices
Figure: Predicted impact on outcome variables of policy changes
-.4 -.3 -.2 -.1 0
Big establishments
Small establishments
Non-trade intensive
Trade-intensive
Non-energy intensive
Energy intensive
Total
Energy consumption
Carbon tax: 56 euro per ton of CO2
Carbon tax: 100 euro per ton of CO2
-.4 -.3 -.2 -.1 0
Big establishments
Small establishments
Non-trade intensive
Trade-intensive
Non-energy intensive
Energy intensive
Total
CO2 emissions
Carbon tax: 56 euro per ton of CO2
Carbon tax: 100 euro per ton of CO2
-.4 -.3 -.2 -.1 0
Big establishments
Small establishments
Non-trade intensive
Trade-intensive
Non-energy intensive
Energy intensive
Total
Employment
Carbon tax: 56 euro per ton of CO2
Carbon tax: 100 euro per ton of CO2
-.4 -.3 -.2 -.1 0
Big establishments
Small establishments
Non-trade intensive
Trade-intensive
Non-energy intensive
Energy intensive
Total
Wages
Carbon tax: 56 euro per ton of CO2
Carbon tax: 100 euro per ton of CO2
Relative change with respect to 2015
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19. EACAI in brief Data quality and access Applications
Less obvious applications of the data
▸ Another survey Antipol (1996-2015) tracks green investments by
environmental domain (used and underground water, air and GHG
emissions, radioactive). Plant-level, similar sample design of EACEI
▸ Merge Antipol and EACEI to account for the company’s green
behaviour along different dimensions
Table: Comparing EACEI and Antipol
Year Either EACEI
or Antipol
Both Antipol
and EACEI
Share Emp in
both
Share Energy
in both
1997 10,179 6,658 0.647 0.853
(0.605) (0.395)
2001 10,115 6,451 0.682 0.849
(0.611) (0.389)
2005 6,607 4,257 0.893 0.937
(0.608) (0.392)
2009 9,354 5,215 0.804 0.925
(0.642) (0.358)
2013 9,739 4,938 0.847 0.897
(0.664) (0.336)
2015 11,512 7,065 0.784 0.78
0.620 0.380
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20. EACAI in brief Data quality and access Applications
Less obvious applications of the data
▸ Another survey Antipol (1996-2015) tracks green investments by
environmental domain (used and underground water, air and GHG
emissions, radioactive). Plant-level, similar sample design of EACEI
▸ Merge Antipol and EACEI to account for the company’s green
behaviour along different dimensions
Table: Comparing EACEI and Antipol
Year Either EACEI
or Antipol
Both Antipol
and EACEI
Share Emp in
both
Share Energy
in both
1997 10,179 6,658 0.647 0.853
(0.605) (0.395)
2001 10,115 6,451 0.682 0.849
(0.611) (0.389)
2005 6,607 4,257 0.893 0.937
(0.608) (0.392)
2009 9,354 5,215 0.804 0.925
(0.642) (0.358)
2013 9,739 4,938 0.847 0.897
(0.664) (0.336)
2015 11,512 7,065 0.784 0.78
0.620 0.380
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21. EACAI in brief Data quality and access Applications
Less obvious applications of the data
▸ Another survey Antipol (1996-2015) tracks green investments by
environmental domain (used and underground water, air and GHG
emissions, radioactive). Plant-level, similar sample design of EACEI
▸ Merge Antipol and EACEI to account for the company’s green
behaviour along different dimensions
Table: Comparing EACEI and Antipol
Year Either EACEI
or Antipol
Both Antipol
and EACEI
Share Emp in
both
Share Energy
in both
1997 10,179 6,658 0.647 0.853
(0.605) (0.395)
2001 10,115 6,451 0.682 0.849
(0.611) (0.389)
2005 6,607 4,257 0.893 0.937
(0.608) (0.392)
2009 9,354 5,215 0.804 0.925
(0.642) (0.358)
2013 9,739 4,938 0.847 0.897
(0.664) (0.336)
2015 11,512 7,065 0.784 0.78
0.620 0.380
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22. EACAI in brief Data quality and access Applications
Some final thoughts and suggestions for improvement
▸ In general, exceptionally good and easy to use databases
▸ Four main suggestions to improve data quality for policy evaluation:
– Sector vs. firm level: aggregating (with weights) at the sectoral level
produces (but we have to double check) data that are not fully
comparable with those provided directly by statistical offices
– Firm vs. establishment level: to study input’s reallocation across
establishments, it would be extremely useful to extend the survey
also to non-sampled establishments of a surveyed company
– Explicit questions on technology-adoption and on changes in
organizational practices
– Explicit questions on emissions and pollution impacts in Antipol
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