Climate Change & Directed Innovation: Evidence from the Auto industry

1. Climate Change & Directed Innovation: Evidence from the Auto industry Antoine Dechezleprêtre (LSE) Joint work with: Philippe Aghion & David Hemous (Harvard), Ralf Martin & John Van Reenen (LSE)

2. Motivation • Tackling climate change • Stern (2008, AER): 50% chance of temp rise >50C by 2100 • Need for massive emissions reductions • Technology is key • How to induce “clean” technological change? • Acemoglu et al. (AER, forth.): carbon taxes + subsidies to clean R&D can redirect technical change • But inventors build on available knowledge: technical change is path-dependent • Need to act early

3. This paper • Do firms respond to policies by changing “direction” of innovation? • How important is path dependence in types of “clean” or “dirty” technologies? • Econometric case study: auto industry • Contributor to greenhouse gases • Distinction between dirty (internal combustion engine) & clean (e.g. electric vehicles) innovations/patents by OECD

4. Some related papers • Theory • Messner(1997), Grubler and Messner (1998), Goulder & Schneider (1999), Manne and Richels (2002), Nordhaus (2002), Van derZwaan et al. (2002),Buonannoet al (2003), Wing (2003), Popp (2004), Gerlagh (2008) • Empirics • Popp (2002, AER) U.S. patent data 1970 to 1994. Positive effect of energy prices on energy-efficient innovations. • Newell, Jaffe and Stavins (1999, QJE): air conditioning after energy price hikes

5. Empirical Model Data Econometrics Results Robustness

6. Simple model: basic idea • Firms can invest in 2 types of R&D (clean or dirty) • Previous firm/economy specialization in either clean or dirty influences direction of innovation • Path-dependence • If expected market size to grow for cars using more clean technologies (e.g. electric/hybrid) then more incentive to invest in clean (relative to dirty) • Higher fuel prices (a proxy for carbon price) increase demand for clean cars • Induces greater “clean” R&D and patenting

7. Innovation Equations Clean Innovations (patents) for assignee i at time t Dirty spillovers: Ambiguous, but Expect β1C> β2C Clean spillovers (stock): β1C>0 if “path dependent” Tax-inclusive Fuel price (P): Test αC >0 Own firm past clean innovations Stock: γ1C>0 if “path dependent” Own firm past dirty innovations stock, expect γ1C> γ2C Other controls – GDP, fixed effects, time dummies, etc.

8. Innovation Equations – Cont. Dirty Innovations (patents) for assignee i at time t

9. Empirical Model Data Econometrics Results Robustness

10. Clean & dirty innovation • World Patent Statistical Database (PATSTAT) of European Patent Office (EPO) • All patents filed from 1978 to 2007 pertaining to "clean" and "dirty" technologies in the car industry • 39,111 patents in “dirty” technologies (regular internal combustion engine). • 13,182 patents in “clean” technologies (electric vehicles, hybrid vehicles, fuel cells,..)

11. International Patent Classification codes “Clean” “Dirty”

12. Ratio of clean to dirty patents, 1980-2007

13. Patent assignees • PATSTAT data has assignee name of patent applicants • Requires cleaning (OECD HAN database, Eurostat Harmonized names, manual cleaning) • For every assignee we count clean and dirty patent applications every year • Match clean and dirty patents with 6,560 distinct patent holders: 3,861 companies & 2,699 individuals

14. How to measure firm-level fuel price? • Data on fuel prices Pct only available at country level • Use US price for firm with HQ in US? No: global industry • Solution: use weighted (wPic) average of all countries’ prices where weights depend on where firms expect to be selling cars lnPit= ΣcwPiclnPct

15. Idea • Companies operating mostly in Germany should focus mainly on Germanprice • Companies in US and Germany should look at US + Germanprices

16. Clean patents from companies mostly active in Germany

17. Variation in fuel price across countries Note: we use 25 countries in the analysis; variation mainly by tax policies

18. Fuel price variable – cont. • Firm-specific weights wPic • Based on each firm’s worldwide patent portfolio • A proxy for where it expects future markets to be • wPic= % of firm’s patents protected in country c (weighted by GDP) • Weights are time-constant and calculated on pre-sample period (1965-1990) to mitigate endogeneity • Alternative: firm current sales • Not available for small companies • Patent weights reasonably well correlated with sales

19. Comparing patent-based weights and sales-based weights

20. Knowledge stocks • Patent stock a la Griliches • Clean patent stock: • KCLEANit= CLEANit + (1-δ)KCLEANit-1 ; • CLEAN = flow of clean patents; δ =15% • Dirty patent stock: • KDIRTYit= DIRTYit + (1-δ)KDIRTYit-1 ; • DIRTY = flow of dirty patents; δ =15%

21. Knowledge Spillovers • =stock of clean patents filed by inventors located in country c at year t • Use all auto patents in clean back to 1950 in each of 80 individual patent offices in PATSTAT • Weight () is the proportion of firm’s inventors in country c since 1965 (who got an auto patents) • This is because spillovers are likely to be greater when inventors are geographically close together • Dirty spillovers defined in analogous way

22. Empirical Model Data Econometrics Results Robustness

23. Econometrics • Use count data models with fixed effects • Hausman et al (1984) FE Poisson needs strict exogeneity(e.g. no lagged dependent variable allowed) • Blundell et al (1999). Use long history of patents firms to control for fixed effects allowing for weak exogeneity • Compare with OLS with FE • lnCLEAN= ln(1 + PATENTSCLEAN)

24. Empirical Model Data Econometrics Results Robustness

25. Basic Results Note: Dependent variable is ln(1+CLEAN) and ln(1+Dirty) OLS estimates (SE clustered by firm) , all columns inc. GDP/capita, fixed effects, year dummies. 111,520 obs over 6,560 individuals

26. FE Poisson Models Note: Dependent variable is CLEAN or DIRTY. All estimates by Poisson with fixed effects as in Blundell et al (1999). SE clustered by firm. All columns inc. GDP/capita and year dummies.

27. Magnitudes • We find energy price elasticities of ≈22% • Higher than Popp’s (2002) 6%, but his study on: • Earlier time period • US macro only (not multi-country) • Popp’s identification solely from time series, not micro data interacted with multiple price changes and controls for path dependency

28. Simulations Clean over dirty knowledge stock • At current fuel prices it will take a long time for clean to catch up

29. Empirical Model Data Econometrics Results Robustness

30. Robustness tests • USPTO: similar price effect and path dependency • Using an alternative definition of clean patents (internal combustion fuel efficiency re-classified as clean) • Modifying the period used to calculate the weights (e.g. Use (i) years 1965-2007, (ii) 1965-1985 (and estimate 1986-2007) • Including other variables besides fuel price (GDP, population, etc.) but weighted in the same way as fuel price • Dropping the top 1% clean and dirty patent holders innovation • Placebo tests with “grey” patents • Using longer (and distributed) lags of the price • Alternative definitions of stock • Use citation weighting to correct for heterogeneous valuation

31. Conclusions • Clean innovation can be induced via carbon prices (& R&D subsidies) • Policy can directinnovation • There is path dependence in type of innovation (spillovers & firm-specific history) • Need to act soon

32. Thank you

33. Back Up

34. Climate Change Will Produce Many More Hot Days in US Source: Greenstone (2011)

35. Clean Patents - electric, hybrid & fuel cells, 1978-2007

36. Dirty Patents - Internal Combustion Engine, 1978-2007

37. Table 5: Citations much greater within technology types than between them

38. Fuel Taxes across countries

39. Table 6: Top 10 holders of clean patents, 1978-2007, EPO

40. Table 7: Top 10 holders of dirty patents, 1978-2007, EPO

44. Stern (2008, AER) • Need aggregate GHG stabilization targets of below 550 parts per million (ppm) carbon dioxide equivalent CO2e (7% change of being >50C) • Corresponds to cuts in global emissions flows of at least 30% by 2050 (~1% of world GDP p.a. annum cost to get this) • The carbon price required to achieve these reductions (up to 2030) would be around, or in excess of, $30 per ton of CO2. • Today ~430ppm and rising ~2.5ppm CO2e pa & accelerating (China). Likely to reach 750ppm by end of Century • If stabilize at this level we have 50% chance of temp rise>50C (over pre-industrial times, 1850). Disastrous transformation of planet • 3m years ago planet was 2-30C warmer (humans about 100,000 old have never experienced this). Last time Earth was 50C was 35-55m years ago (Alligators in the North Pole) • About 10-12k years ago was last Ice Age when temperatures 50C lower & ice sheets where just below NYC and London

45. Patents as an innovation indicator • We use history of firms own patenting in different types of technologies • Advantages of patents • Publicly available (no real alternative) • Comparable over time across firms • Disadvantages of patents • Not all knowledge patented • Heterogeneous values (check with future citations; screening out low value in various ways) • Patent propensity differs by industry (so focus on 1 sector - autos)

46. Table 4

47. How much does path dependence matter?

49. Table 14: USPTO, FE Poisson Models Note: Dependent variable is CLEAN or DIRTY. All estimates by Poisson with fixed effects as in Blundell et al (1999). SE clustered by firm. All columns inc. GDP/capita and year dummies. 72,558 obs over 4,031 firms