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)

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

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

Some related papers

• Theory– Messner (1997), Grubler and Messner (1998), Goulder &

Schneider (1999), Manne and Richels (2002), Nordhaus (2002), Van der Zwaan et al. (2002), Buonanno et 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

Data

Empirical Model

Econometrics

Robustness

Results

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

Innovation Equations

Tax-inclusive Fuel price (P): Test αC >0

Clean spillovers (stock):β1

C>0 if “path dependent”

Dirty spillovers:Ambiguous, butExpect β1

C> β2C

Own firm past clean innovations Stock: γ1

C>0 if “path dependent”Own firm past dirty innovations stock, expect γ1

C> γ2C

Clean Innovations (patents) for assignee i at time t

Other controls – GDP, fixed effects,time dummies, etc.

Innovation Equations – Cont.

Dirty Innovations (patents) for assignee i at time t

Data

Empirical Model

Econometrics

Robustness

Results

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,..)

International Patent Classification codes

“Clean”

“Dirty”

Ratio of clean to dirty patents, 1980-2007

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

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

Idea

• Companies operating mostly in Germany should focus mainly on German price

• Companies in US and Germany should look at US + German prices

Clean patents from companies mostly active in Germany

Variation in fuel price across countries

Note: we use 25 countries in the analysis; variation mainly by tax policies

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– wP

ic = % 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

FORD (1992-2002) Car sales Patent weightsUSA 0.59 0.56Canada 0.04 0.01Mexico 0.02 0.00Britain 0.08 0.08Germany 0.06 0.16Italy 0.03 0.04Spain 0.02 0.02France 0.02 0.05Australia 0.02 0.00Japan 0.01 0.05

Comparing patent-based weights and sales-based weights

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%

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

SPILL   SPILLC S Cit ic ctc

w

Data

Empirical Model

Econometrics

Robustness

Results

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)

Data

Empirical Model

Econometrics

Robustness

Results

(1) (2) (3) (4) (5) (6) (7) (8)

CLEAN PATENTS DIRTY PATENTSFuel Price 0.054** 0.032** -0.023** -0.023**

ln Pit-1 (0.010) (0.008) (0.011) (0.009)Clean spillovers 0.055** 0.030** -0.002 -0.000

ln(1+SPILLCit-1) (0.008) (0.004) (0.004) (0.004)

Dirty spillovers -0.001 -0.005 0.014** 0.010**ln(1+SPILLD

it-1) (0.010) (0.007) (0.005) (0.005)Own clean patents 0.257** 0.255** 0.057** 0.057**

ln(1+KCLEANit-1) (0.015) (0.015) (0.013) (0.013)Own dirty patents 0.033** 0.034** 0.093** 0.093**

ln(1+KDIRTYit-1) (0.007) (0.007) (0.015) (0.016)

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

Basic Results

Basic FE Poisson Add clean R&D subsidies

Clean Dirty Clean DirtyFuel Price 2.491** 0.185 1.276** -0.106

(0.647) (0.495) (0.613) (0.409)

Clean public R&D 0.324** 0.186**

(0.044) (0.030)

Clean spillovers 0.474** 0.071 -0.129 -0.418**

(0.176) (0.095) (0.248) (0.109)

Dirty spillovers -0.190 0.031 0.398 0.514**

(0.179) (0.098) (0.264) (0.114)

Own clean patents 1.422** -0.041 1.328** -0.035

(0.035) (0.033) (0.038) (0.030)

Own dirty patents -0.139** 1.379** -0.095* 1.321**

(0.069) (0.034) (0.051) (0.033)

Observations 111,520 111,520 111,520 111,520

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.

FE Poisson Models

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

• At current fuel prices it will take a long time for clean to catch up

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irty

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2010 2020 2030 2040 2050year

Clean over dirty knowledge stock

Simulations

Data

Empirical Model

Econometrics

Robustness

Results

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

Conclusions

• Clean innovation can be induced via carbon prices (& R&D subsidies)– Policy can direct innovation

• There is path dependence in type of innovation (spillovers & firm-specific history)– Need to act soon

Thank you

Back Up

Climate Change Will Produce Many More Hot Days in US

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<10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 >90

Distribution of Annual Daily Mean Temperatures (F)

1968-2002 Average Predicted Change, Hadley 3-A1FI, Error-Corrected

Source: Greenstone (2011)

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

Dirty Patents - Internal Combustion Engine, 1978-2007

Table 5: Citations much greater within technology types than

between them

Fuel Taxes across countries

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

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

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

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)

Table 4

How much does path dependence matter?

Car sales Patent weightsTOYOTA (2003-2005) Japan 0.34 0.27North America 0.31 0.39Europe 0.13 0.34VW (2002-2005) Germany 0.19 0.54 UK 0.07 0.07 Spain 0.06 0.03 Italy 0.05 0.05 France 0.05 0.08 USA 0.07 0.12 Mexico 0.03 0.00 Canada 0.02 0.00 Japan 0.01 0.02FORD (1992-2002) USA 0.59 0.56Canada 0.04 0.01Mexico 0.02 0.00Britain 0.08 0.08Germany 0.06 0.16Italy 0.03 0.04Spain 0.02 0.02France 0.02 0.05Australia 0.02 0.00Japan 0.01 0.05Peugeot (2001-2005) Western Europe 0.75 0.84 France 0.25 0.29 Other countries 0.50 0.55The Americas 0.04 0.11Asia-Pacific 0.12 0.04Honda (2004-2005) Japan 0.23 0.24North America 0.5 0.5Europe 0.08 0.25

(1) (2) (3) (4)Clean Dirty Clean Dirty

Fuel Price 2.441** 0.748*

(0.685) (0.418)Fuel tax 0.997** 0.325

(0.460) (0.213)Clean spillovers 0.048 0.199* -0.022 0.180

(0.159) (0.113) (0.169) (0.112)Dirty spillovers 0.091 -0.116 0.143 -0.102

(0.156) (0.094) (0.157) (0.092)Own stock of clean patents 1.433** -0.043 1.436** -0.043

(0.062) (0.029) (0.064) (0.030)Own stock of dirty patents 0.036 1.352** 0.040 1.354**

(0.070) (0.023) (0.070) (0.023)

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