Climate Policies and Skill-Biased Employment Dynamics. Dynamics: Evidence from EU countries

Transcription

1 Background Taxonomy of drivers Environmental policy and employment Climate Policies and Skill-Biased Employment Dynamics: Evidence from EU countries Giovanni Marin 1 Francesco Vona 2 1 University of Urbino Carlo Bo, Italy; SEEDS, Ferrara, Italy 2 OFCE SciencesPo; SKEMA Business School, France Green Growth and Sustainable Development (GGSD) OECD, Paris, November 2018

2 Background Taxonomy of drivers Environmental policy and employment Background A taxonomy of structural drivers Data and measures Taxonomy of structural factors Taxonomy at work Environmental policy and employment dynamics Empirical strategy Results Summing up

3 Background Taxonomy of drivers Environmental policy and employment Environmental policies and jobs Environmental policies and job destruction Opposition of pollution-intensive industries to stringent environmental regulation (Cragg et al., 2013) Generous policy exemptions to polluting industries (Ekins and Speck, 1999) Job killing argument persists despite lack of strong evidence Modestly negative impacts of US Clean Air Act, concentrated on energy-intensive industries (Greenstone, 2002; Kahn and Mansur, 2013; Walker, 2013) Positive correlation between green innovation and employment (Horbach and Rennings, 2013; Gagliardi et al., 2016) Large green job multiplier, driven by the green Obama stimulus package (Vona et al., 2018b) Our paper fills three gaps in the literature Focus on EU countries at the sector-level Distribution of the effects across skills Different policies, different impacts?

4 Background Taxonomy of drivers Environmental policy and employment Environmental policies and jobs Environmental policies and job destruction Opposition of pollution-intensive industries to stringent environmental regulation (Cragg et al., 2013) Generous policy exemptions to polluting industries (Ekins and Speck, 1999) Job killing argument persists despite lack of strong evidence Modestly negative impacts of US Clean Air Act, concentrated on energy-intensive industries (Greenstone, 2002; Kahn and Mansur, 2013; Walker, 2013) Positive correlation between green innovation and employment (Horbach and Rennings, 2013; Gagliardi et al., 2016) Large green job multiplier, driven by the green Obama stimulus package (Vona et al., 2018b) Our paper fills three gaps in the literature Focus on EU countries at the sector-level Distribution of the effects across skills Different policies, different impacts?

5 Background Taxonomy of drivers Environmental policy and employment Environmental policies and jobs Environmental policies and job destruction Opposition of pollution-intensive industries to stringent environmental regulation (Cragg et al., 2013) Generous policy exemptions to polluting industries (Ekins and Speck, 1999) Job killing argument persists despite lack of strong evidence Modestly negative impacts of US Clean Air Act, concentrated on energy-intensive industries (Greenstone, 2002; Kahn and Mansur, 2013; Walker, 2013) Positive correlation between green innovation and employment (Horbach and Rennings, 2013; Gagliardi et al., 2016) Large green job multiplier, driven by the green Obama stimulus package (Vona et al., 2018b) Our paper fills three gaps in the literature Focus on EU countries at the sector-level Distribution of the effects across skills Different policies, different impacts?

6 Background Taxonomy of drivers Environmental policy and employment Environmental policies and jobs Environmental policies and job destruction Opposition of pollution-intensive industries to stringent environmental regulation (Cragg et al., 2013) Generous policy exemptions to polluting industries (Ekins and Speck, 1999) Job killing argument persists despite lack of strong evidence Modestly negative impacts of US Clean Air Act, concentrated on energy-intensive industries (Greenstone, 2002; Kahn and Mansur, 2013; Walker, 2013) Positive correlation between green innovation and employment (Horbach and Rennings, 2013; Gagliardi et al., 2016) Large green job multiplier, driven by the green Obama stimulus package (Vona et al., 2018b) Our paper fills three gaps in the literature Focus on EU countries at the sector-level Distribution of the effects across skills Different policies, different impacts?

7 Background Taxonomy of drivers Environmental policy and employment Environmental policies and jobs Environmental policies and job destruction Opposition of pollution-intensive industries to stringent environmental regulation (Cragg et al., 2013) Generous policy exemptions to polluting industries (Ekins and Speck, 1999) Job killing argument persists despite lack of strong evidence Modestly negative impacts of US Clean Air Act, concentrated on energy-intensive industries (Greenstone, 2002; Kahn and Mansur, 2013; Walker, 2013) Positive correlation between green innovation and employment (Horbach and Rennings, 2013; Gagliardi et al., 2016) Large green job multiplier, driven by the green Obama stimulus package (Vona et al., 2018b) Our paper fills three gaps in the literature Focus on EU countries at the sector-level Distribution of the effects across skills Different policies, different impacts?

8 Background Taxonomy of drivers Environmental policy and employment Environmental policies and skills The impact of environmental policies on labour market outcomes goes beyond net effects on the quantity of labour Vona et al. (2018a): a taxonomy of Green Skills using task-based approach (Autor and Acemoglu, 2011) Green skills refer to the actual activities performed on the workplace that contribute to reduce the environmental burden of economic activities Green skills identified using a data-driven approach on 900+ occupations, comparing the skill-requirement of green and similar non-green occupations Engineering and technical Operation management Monitoring Science Vona et al. (2018a) environmental regulation in the US (Clean Air Act) induced an increase in the demand for green skills (even in absence of negative effects on total employment)

9 Background Taxonomy of drivers Environmental policy and employment Environmental policies and skills The impact of environmental policies on labour market outcomes goes beyond net effects on the quantity of labour Vona et al. (2018a): a taxonomy of Green Skills using task-based approach (Autor and Acemoglu, 2011) Green skills refer to the actual activities performed on the workplace that contribute to reduce the environmental burden of economic activities Green skills identified using a data-driven approach on 900+ occupations, comparing the skill-requirement of green and similar non-green occupations Engineering and technical Operation management Monitoring Science Vona et al. (2018a) environmental regulation in the US (Clean Air Act) induced an increase in the demand for green skills (even in absence of negative effects on total employment)

10 Background Taxonomy of drivers Environmental policy and employment Environmental policies and skills The impact of environmental policies on labour market outcomes goes beyond net effects on the quantity of labour Vona et al. (2018a): a taxonomy of Green Skills using task-based approach (Autor and Acemoglu, 2011) Green skills refer to the actual activities performed on the workplace that contribute to reduce the environmental burden of economic activities Green skills identified using a data-driven approach on 900+ occupations, comparing the skill-requirement of green and similar non-green occupations Engineering and technical Operation management Monitoring Science Vona et al. (2018a) environmental regulation in the US (Clean Air Act) induced an increase in the demand for green skills (even in absence of negative effects on total employment)

11 Background Taxonomy of drivers Environmental policy and employment Environmental policies and skills The impact of environmental policies on labour market outcomes goes beyond net effects on the quantity of labour Vona et al. (2018a): a taxonomy of Green Skills using task-based approach (Autor and Acemoglu, 2011) Green skills refer to the actual activities performed on the workplace that contribute to reduce the environmental burden of economic activities Green skills identified using a data-driven approach on 900+ occupations, comparing the skill-requirement of green and similar non-green occupations Engineering and technical Operation management Monitoring Science Vona et al. (2018a) environmental regulation in the US (Clean Air Act) induced an increase in the demand for green skills (even in absence of negative effects on total employment)

12 Background Taxonomy of drivers Environmental policy and employment Overlapping structural drivers Changes in the relative demand for different skills depends on other important stuctural drivers: - Automation and routine-replacing technical change (Autor et al., 2003; Acemoglu and Restrepo, 2016) - Deindustrialization and globalization (Autor, Dorn and Hanson, 2013) affecting also polluting industries (Levinson and Taylor, 2008) - Green innovations induced by climate policies (Porter and van der Linde, 1995) It is crucial to account for these structural drivers and other mediating factors to understand the impact of environmental policy

13 Background Taxonomy of drivers Environmental policy and employment Overlapping structural drivers Changes in the relative demand for different skills depends on other important stuctural drivers: - Automation and routine-replacing technical change (Autor et al., 2003; Acemoglu and Restrepo, 2016) - Deindustrialization and globalization (Autor, Dorn and Hanson, 2013) affecting also polluting industries (Levinson and Taylor, 2008) - Green innovations induced by climate policies (Porter and van der Linde, 1995) It is crucial to account for these structural drivers and other mediating factors to understand the impact of environmental policy

14 Background Taxonomy of drivers Environmental policy and employment Overlapping structural drivers Changes in the relative demand for different skills depends on other important stuctural drivers: - Automation and routine-replacing technical change (Autor et al., 2003; Acemoglu and Restrepo, 2016) - Deindustrialization and globalization (Autor, Dorn and Hanson, 2013) affecting also polluting industries (Levinson and Taylor, 2008) - Green innovations induced by climate policies (Porter and van der Linde, 1995) It is crucial to account for these structural drivers and other mediating factors to understand the impact of environmental policy

15 Background Taxonomy of drivers Environmental policy and employment Overlapping structural drivers Changes in the relative demand for different skills depends on other important stuctural drivers: - Automation and routine-replacing technical change (Autor et al., 2003; Acemoglu and Restrepo, 2016) - Deindustrialization and globalization (Autor, Dorn and Hanson, 2013) affecting also polluting industries (Levinson and Taylor, 2008) - Green innovations induced by climate policies (Porter and van der Linde, 1995) It is crucial to account for these structural drivers and other mediating factors to understand the impact of environmental policy

16 Background Taxonomy of drivers Environmental policy and employment Overlapping structural drivers Changes in the relative demand for different skills depends on other important stuctural drivers: - Automation and routine-replacing technical change (Autor et al., 2003; Acemoglu and Restrepo, 2016) - Deindustrialization and globalization (Autor, Dorn and Hanson, 2013) affecting also polluting industries (Levinson and Taylor, 2008) - Green innovations induced by climate policies (Porter and van der Linde, 1995) It is crucial to account for these structural drivers and other mediating factors to understand the impact of environmental policy

17 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

18 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

19 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

20 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

21 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

22 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

23 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Data and measures Balanced panel of 14 EU countries and 15 industrial sectors (manufacturing + mining and utilities) Long-term changes: collapse data in 4 windows: , , , Data sources - Trade exposure: Import penetration OECD Stan Indicator - Automation and technological change: Intensity of ICT and non-ict capital input per worker EUKLEMS database - Relevance of environmental technology: Stock of climate-related patents per employees OECD REGPAT; OECD ENV-TECH Indicator; Lybbert and Zolas (2014) - Exposure to environmental regulation: Direct and indirect Greenhouse gas emission intensity of value added WIOD - Labour market outcomes Hours worked (WIOD) and skill/occupation composition of employment (EU-LFS)

24 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Correlation among structural drivers and labour market outcome (static) log(ict capital intensity) log(non-ict capital intensity) Import penetration Climate patent stock per empl log(ghg/va) log(ict capital intensity) 1 log(non-ict capital intensity) Import penetration Climate patent stock per empl log(ghg/va) Managers Professionals Technicians Manual High education Mid education Low education Stacked periods: ; ; ; , weighted by hours worked in 1995.

25 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Correlation among structural drivers and labour market outcome (dynamic) log(ict capital intensity) log(non- ICT capital intensity) Import penetration Climate patent stock per empl log(ghg/va) log(ict capital intensity) 1 log(non-ict capital intensity) Import penetration Climate patent stock per empl log(ghg/va) log(hours worked) Managers Professionals Technicians Manual High education Middle education Low education Stacked periods: ; ; ; , weighted by hours worked in 1995.

26 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Classifying sectors using cluster analysis We classify sector-country pairs depending on their exposure to different structural changes Six clusters favourite aggregation according to different criteria Cluster Name ICT K intensity Non-ICT K intensity Import penetration Climate patent stock per empl GHG/VA n Empl share 1 Brown Global Low-tech (0.169) (1.069) (0.582) (0.023) (1.121) 2 Brown Medium-tech (0.239) (2.452) (0.215) (0.104) (1.627) 3 Green Global High-tech (1.031) (3.328) (0.726) (0.779) (0.315) 4 Exposed to Automation (0.901) (4.844) (0.301) (0.085) (0.881) 5 Black and Multiple Exposure (1.212) (10.590) (0.660) (1.655) (3.249) 6 Black High-tech (1.662) (19.493) (0.182) (4.794) (11.170) Total (0.575) (3.819) (0.436) (0.290) (1.265) Each cell contains the average percentile of the weighted distribution of the variable (weights=hours worked in the first year of each time window). Median values of the original variable in parenthesis.

27 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Composition of clusters - sectors 1 Brown Global Low-tech 2 Brown Mediumtech 3 Green Global High-tech 4 Exposed to Automation 5 Black and Multiple Exposure 6 Black High-tech C - Mining and Quarrying Food, Beverages and Tobacco Textiles, Leather and Footwear Wood and Products of Wood Pulp, Paper, Printing, Publishing Coke, Refined Petroleum Chemicals Rubber and Plastics Other Non-Metallic Mineral Basic Metals, Fabricated Metal Machinery, Nec Electrical-Optical Equipment Transport Equipment Manufacturing, Nec; Recycling E - Electricity, Gas and Water Supply

28 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Composition of clusters - countries 1 Brown Global Low-tech 2 Brown Mediumtech 3 Green Global High-tech 4 Exposed to Automation 5 Black and Exposed to Multiple Shocks 6 Black High-tech Austria Belgium Czech Republic Germany Denmark Spain Finland France Hungary Ireland Italy Netherlands Sweden United Kingdom

29 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Labour market characteristics of clusters (static) Managers Professionals Technicians Manual 1 Brown Global Low-tech Brown Medium-tech Green Global High-tech Exposed to Automation Black and Exposed to Multiple Shocks Black High-tech Total Higheducation Mideducation Loweducation 1 Brown Global Low-tech Brown Medium-tech Green Global High-tech Exposed to Automation Black and Exposed to Multiple Shocks Black High-tech Total

30 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Figure: Labour market characteristics of clusters (dynamic)

31 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Summing up The taxonomy does not fully overlap with the country and sectoral dimensions Heterogeneous (unconditional) labour market outcomes across different clusters, but no clear patterns here Clusters appear to be rather stable in time structural factors are persistent here

32 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Summing up The taxonomy does not fully overlap with the country and sectoral dimensions Heterogeneous (unconditional) labour market outcomes across different clusters, but no clear patterns here Clusters appear to be rather stable in time structural factors are persistent here

33 Background Taxonomy of drivers Environmental policy and employment Data Taxonomy of structural factors Taxonomy at work Summing up The taxonomy does not fully overlap with the country and sectoral dimensions Heterogeneous (unconditional) labour market outcomes across different clusters, but no clear patterns here Clusters appear to be rather stable in time structural factors are persistent here

34 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Empirical strategy Accounting for multiple (and overlapping) exposure to structural transformations crucial to identify the possible effect of environmental policies on labour market outcomes The stringency of environmental policies may be correlated with these transformations Endogeneity issues: both skill composition and policy stringency correlated with structural factors As starting point to evaluate the role played by environmental policy given the exposure to other structural drivers, we estimate the following equation in stacked long differences (4-years): Y ijt = βets string ij,t,t 4 + γen price ij,t,t 4 + δlog(ghg/va) ij, ηlog(ghg/va) ij,1995 EPS i,t,t 4 + c φ c ij, µ it + θ j + ε ijt where Y ijt is the change in (log) employment or in the share of occupation k

35 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Empirical strategy Accounting for multiple (and overlapping) exposure to structural transformations crucial to identify the possible effect of environmental policies on labour market outcomes The stringency of environmental policies may be correlated with these transformations Endogeneity issues: both skill composition and policy stringency correlated with structural factors As starting point to evaluate the role played by environmental policy given the exposure to other structural drivers, we estimate the following equation in stacked long differences (4-years): Y ijt = βets string ij,t,t 4 + γen price ij,t,t 4 + δlog(ghg/va) ij, ηlog(ghg/va) ij,1995 EPS i,t,t 4 + c φ c ij, µ it + θ j + ε ijt where Y ijt is the change in (log) employment or in the share of occupation k

36 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Empirical strategy Accounting for multiple (and overlapping) exposure to structural transformations crucial to identify the possible effect of environmental policies on labour market outcomes The stringency of environmental policies may be correlated with these transformations Endogeneity issues: both skill composition and policy stringency correlated with structural factors As starting point to evaluate the role played by environmental policy given the exposure to other structural drivers, we estimate the following equation in stacked long differences (4-years): Y ijt = βets string ij,t,t 4 + γen price ij,t,t 4 + δlog(ghg/va) ij, ηlog(ghg/va) ij,1995 EPS i,t,t 4 + c φ c ij, µ it + θ j + ε ijt where Y ijt is the change in (log) employment or in the share of occupation k

37 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Empirical strategy Accounting for multiple (and overlapping) exposure to structural transformations crucial to identify the possible effect of environmental policies on labour market outcomes The stringency of environmental policies may be correlated with these transformations Endogeneity issues: both skill composition and policy stringency correlated with structural factors As starting point to evaluate the role played by environmental policy given the exposure to other structural drivers, we estimate the following equation in stacked long differences (4-years): Y ijt = βets string ij,t,t 4 + γen price ij,t,t 4 + δlog(ghg/va) ij, ηlog(ghg/va) ij,1995 EPS i,t,t 4 + c φ c ij, µ it + θ j + ε ijt where Y ijt is the change in (log) employment or in the share of occupation k

38 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Definition of variables ETS string ij,t,t 4 - Average stringency of the EU-ETS meaured as the ratio between verified emissions (within the EU ETS) of establishments in a sector-country and total GHG emissions of all establishments in the same sector-country - Source: EU ETS Registry; WIOD - We take the average value within each time window En price ij,t,t 4 - Energy prices measured by combining national source-specific energy prices with country-sector-year specific energy mix (Sato et al., 2015) - Source: IEA Energy Prices Database; WIOD - We take the average value within each time window EPS i,t,t 4 - Environmental Policy Stringency indicator of the OECD from which we excluded the EU-ETS component not sector specific needs to be interacted with the (initial) exposure to environmental regulatory stringency (GHG intensity) - Source: OECD - We take the average value within each time window

39 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Definition of variables ETS string ij,t,t 4 - Average stringency of the EU-ETS meaured as the ratio between verified emissions (within the EU ETS) of establishments in a sector-country and total GHG emissions of all establishments in the same sector-country - Source: EU ETS Registry; WIOD - We take the average value within each time window En price ij,t,t 4 - Energy prices measured by combining national source-specific energy prices with country-sector-year specific energy mix (Sato et al., 2015) - Source: IEA Energy Prices Database; WIOD - We take the average value within each time window EPS i,t,t 4 - Environmental Policy Stringency indicator of the OECD from which we excluded the EU-ETS component not sector specific needs to be interacted with the (initial) exposure to environmental regulatory stringency (GHG intensity) - Source: OECD - We take the average value within each time window

40 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Definition of variables ETS string ij,t,t 4 - Average stringency of the EU-ETS meaured as the ratio between verified emissions (within the EU ETS) of establishments in a sector-country and total GHG emissions of all establishments in the same sector-country - Source: EU ETS Registry; WIOD - We take the average value within each time window En price ij,t,t 4 - Energy prices measured by combining national source-specific energy prices with country-sector-year specific energy mix (Sato et al., 2015) - Source: IEA Energy Prices Database; WIOD - We take the average value within each time window EPS i,t,t 4 - Environmental Policy Stringency indicator of the OECD from which we excluded the EU-ETS component not sector specific needs to be interacted with the (initial) exposure to environmental regulatory stringency (GHG intensity) - Source: OECD - We take the average value within each time window

41 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Results (1) (2) (3) (4) (5) log(hours Managers Professionals Technicians Manual worked) Log of GHG intensity (1995) ** ** ** (0.0168) ( ) ( ) ( ) ( ) Average (t, t-4) energy price *** * ** (0.0749) (0.0116) (0.0125) (0.0226) (0.0256) Average (t, t-4) ETS stringency * ** (0.0425) ( ) (0.0109) (0.0131) ( ) Average (t, t-4) EPS *** ** ** x Log of GHG intensity (1995) (0.0219) ( ) ( ) ( ) ( ) R squared N OLS regressions on stacked differences ( ; ; ; ) weighted by hours worked in the first year of each time window. Standard errors clustered by sector-country in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. All regressions include country-specific time dummies, sector dummies and initial (1995) cluster dummies.

42 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Results - Additional dependent variables (1) (2) (3) (4) (5) log(output/l) log(va/l) High-skilled Mediumskilled Low-skilled Log of GHG intensity (1995) *** *** (0.0183) (0.0203) ( ) ( ) ( ) Average (t, t-4) energy price (0.137) (0.151) (0.0143) (0.0170) (0.0168) Average (t, t-4) ETS stringency * ** (0.0765) (0.0720) ( ) ( ) ( ) Average (t, t-4) EPS *** *** x Log of GHG intensity (1995) (0.0282) (0.0239) ( ) ( ) ( ) R squared N OLS regressions on stacked differences ( ; ; ; ) weighted by hours worked in the first year of each time window. Standard errors clustered by sector-country in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. All regressions include country-specific time dummies, sector dummies and initial (1995) cluster dummies.

43 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Endogeneity concerns and IVs Sources of endogeneity Lobbying efforts have warranted generous policy exemptions in sectors in which emission reductions were most needed also correlated with labour market outcomes relevant for EU-ETS and EPS Energy prices are correlated with the error term because quantity discounts render the price of energy lower for large consumers and because changes in the energy mix are likely to be correlated with changes in the input mix, including the skill mix Instrumental Variables IVs Interaction between EPS (inclusive of ETS stringency) end emission intensity emission intensity of the same sector in non-european high-income OECD countries (as in Albrizio et al., 2017 JEEM) multiplied by EPS in other European countries (excluding the country of reference) Energy price shift-share IV using year-specific national source prices weighted by initial sector-country specific energy mix

44 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Endogeneity concerns and IVs Sources of endogeneity Lobbying efforts have warranted generous policy exemptions in sectors in which emission reductions were most needed also correlated with labour market outcomes relevant for EU-ETS and EPS Energy prices are correlated with the error term because quantity discounts render the price of energy lower for large consumers and because changes in the energy mix are likely to be correlated with changes in the input mix, including the skill mix Instrumental Variables IVs Interaction between EPS (inclusive of ETS stringency) end emission intensity emission intensity of the same sector in non-european high-income OECD countries (as in Albrizio et al., 2017 JEEM) multiplied by EPS in other European countries (excluding the country of reference) Energy price shift-share IV using year-specific national source prices weighted by initial sector-country specific energy mix

45 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Results with IV (1) (2) (3) (4) (5) log(hours Managers Professionals Technicians Manual worked) Log of GHG intensity (1995) *** *** (0.0236) ( ) ( ) ( ) ( ) Average (t, t-4) energy price *** ** *** (0.0916) (0.0123) (0.0132) (0.0233) (0.0278) Average (t, t-4) EPS (with ETS) 0.124*** ** *** x Log of GHG intensity (1995) (0.0392) ( ) ( ) ( ) (0.0102) R squared N IV regressions on stacked differences ( ; ; ; ) weighted by hours worked in the first year of each time window. Standard errors clustered by sector-country in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. All regressions include country-specific time dummies, sector dummies and initial (1995) cluster dummies. F test of excluded IV: Note that, using a control function approach to endogeneity, we fail to detect a significant difference between the IV and the OLS model - So we can infer that that endogeneity is not a serious issue after controlling for sector, country-by-year and cluster dummies

46 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Summing up Different dimensions of environmental policy influence different labour market outcomes The long-term decline in employment in most polluting sectors is unrelated to environmental policy stringency However, environmental policy stringency have strong and heterogeneous distributional impacts - Increase the demand for employees with engineering (energy prices) and technical (EPS) skills, while both reduce the demand for manual skills - Results robust using a shift-share IV strategy

47 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Summing up Different dimensions of environmental policy influence different labour market outcomes The long-term decline in employment in most polluting sectors is unrelated to environmental policy stringency However, environmental policy stringency have strong and heterogeneous distributional impacts - Increase the demand for employees with engineering (energy prices) and technical (EPS) skills, while both reduce the demand for manual skills - Results robust using a shift-share IV strategy

48 Background Taxonomy of drivers Environmental policy and employment Empirical strategy Results Summing up Summing up Different dimensions of environmental policy influence different labour market outcomes The long-term decline in employment in most polluting sectors is unrelated to environmental policy stringency However, environmental policy stringency have strong and heterogeneous distributional impacts - Increase the demand for employees with engineering (energy prices) and technical (EPS) skills, while both reduce the demand for manual skills - Results robust using a shift-share IV strategy

49 Background Taxonomy of drivers Environmental policy and employment THANK YOU FOR YOUR ATTENTION

50 Background Taxonomy of drivers Environmental policy and employment Stability of clusters - period-to-period transition matrix 1 Brown Global Low-tech 2 Brown Mediumtech 3 Green Global High-tech 4 Exposed to Automation 5 Black and Exposed to Multiple Shocks 6 Black High-tech 1 Brown Global Low-tech Brown Medium-tech Green Global High-tech Exposed to Automation Black and Exposed to Multiple Shocks Black High-tech Total Back to Back

51 Background Taxonomy of drivers Environmental policy and employment Predictive power of cluster dummies vs. clustering variables Back to Back Dep. var: (1) (2) (3) (4) (5) (6) log(hours worked) 1 Brown Global Low-tech *** *** (0.0315) (0.0267) (0.0151) 2 Brown Medium-tech (0.0148) (0.0141) (0.0138) 3 Green Global High-tech (0.0203) (0.0127) (0.0238) 4 Exposed to Automation [base cat] [base cat] [base cat] 5 Black Multiple Exposure (0.0146) (0.0153) (0.0224) 6 Black High-tech * (0.0187) (0.0222) (0.0290) Import penetration ( ) ( ) log(ict K intensity) * (0.0135) (0.0119) log(non-ict K intensity) (0.0122) (0.0111) Climate patent stock pc *** *** *** ( ) ( ) log(ghg/va) * ** ( ) ( ) Controls Year dummies Country, sector and year dummies Year-specific country and sector dummies Year dummies Country, sector and year dummies Year-specific country and sector dummies R squared N OLS regressions on stacked differences ( ; ; ; ) weighted by hours worked in the first year of each time window. Standard errors clustered by sector-country in parenthesis. * p<0.1, ** p<0.05, *** p<0.01.

52 Background Taxonomy of drivers Environmental policy and employment Results with IV - Additional dependent variables (1) (2) (3) (4) (5) log(output/l) log(va/l) High-skilled Mediumskilled Low-skilled Log of GHG intensity (1995) *** * (0.0365) (0.0386) ( ) ( ) ( ) Average (t, t-4) energy price (0.172) (0.175) (0.0160) (0.0226) (0.0223) Average (t, t-4) EPS (with ETS) x Log of GHG intensity (1995) (0.0652) (0.0668) ( ) ( ) ( ) R squared N IV regressions on stacked differences ( ; ; ; ) weighted by hours worked in the first year of each time window. Standard errors clustered by sector-country in parenthesis. * p<0.1, ** p<0.05, *** p<0.01. All regressions include country-specific time dummies, sector dummies and initial (1995) cluster dummies. F test of excluded IV: 33.35