Do Automotive Fuel Economy Standards Increase Rates of Technology Change?

Transcription

1 Do Automotive Fuel Economy Standards Increase Rates of Technology Change? Don MacKenzie PhD Candidate, Engineering Systems Division, MIT Presented at USAEE/IAEE North American Conference November 5,

2 Motivation If policymakers want to accelerate the diffusion of energy-saving technologies, they need to know what types of policies will achieve this. (Jaffe & Stavins, 1994) Policy debates and analyses of fuel economy standards make the assumption that standards cause changes in technology adoption. (Kleit, 2004; Fischer, Harrington & Parry, 2007; NESCAUM, 2008; Bunch et al., 2011; Shiau, Michalek, & Henderson, 2009) Do they really? 11/13/

3 Measuring Technology: Product Characteristics Approach Express technology change as the change in expected value of one attribute, holding all others constant (Newell, Jaffe, & Stavins, 1999; Knittel, 2011) 11/13/ Mention technologies as widgets hard to count, and all have different effects Technology change as IP growth, measured by patents 3

4 Knittel s application to automobiles w = weight hp = engine power tq = engine torque X = dummies for manual, diesel, and firm T t = year fixed effects, interpreted as cumulative technology change since base year. 11/13/ Essentially, Knittel is definining technological change as the change in expected fuel economy, conditional on power, weight, torque, and the fraction of cars with manual transmissions or diesel engines remaining unchanged. 4

5 CAFE Program: relevant features Corporate Average Fuel Economy (CAFE) standards are applied separately to each firm s fleet of Domestic cars Import cars Light trucks Financial penalty for missing standard but no benefit for exceeding it Different firms may react differently (Jacobsen, 2012) 11/13/ FLEETS are key, because it allows me to look at how rates have varied within a fleet over time, when that fleey is above or below the standard, my greater or smaller margins. PENALTIES are key, because they mean (1) good records and (2) different responses above and below standard Structural changes to CAFE program in recent years, but these do not affect years considered in this analysis ( ) 5

6 Empirical strategy: dependent variable Estimate technology progress separately for each fleet: W = curb weight P = engine power X = dummies for diesel, manual T jkt = fixed effect for fleet j from firm k in year t Estimate by OLS & WLS different interpretations of T Take first differences as a measure of year to year technology change within each fleet: 11/13/ OLS is interpreted as technology change among vehicles offered for sale. WLS is interpreted as technology change among vehicles actually sold. Analogous to extensive vs intensive margin Either way, end up with unbalanced panel data for 29 fleets over 30 years 6

7 Empirical strategy: independent variables Define fuel economy shortfall: Define binary constrained/unconstrained variable: 11/13/

8 Model specifications Gasoline price specification: P = gas price D = CAFE-constrained S = MPG shortfall μ 0 = fleet fixed effects μ 1, μ 2 = fleet-specific time trends X = covariates (vehicle type mix, size, weight, power ) Fixed effect specification: δ t = year fixed effects Also investigated specifications that included interaction terms between a firm HQ dummy (Detroit) and the treatment variables S and D. This allows for different responses from the Detroit firms, as reported by other authors (Jacobsen, 2012; Kleit, 2004) 11/13/ Also, investigated specifications that included interaction terms between a firm HQ dummy (Detroit) and the treatment variables S and D This allows for different responses from the Detroit firms, as reported by other authors (Jacobsen, Kleit) Covariates included average weight, average size (interior volume), average engine peak power, average fuel consumption (gallons per mile), fraction of small cars (mini-compacts, subcompacts, and two-seaters), fraction of cars with 4-wheel drive, fraction of wagons, and fraction of convertibles. The rationale for including these covariates is that the composition of a firm's fleet may plausibly affect the ease with which new technologies can be adopted. 8

9 Identification Identifying assumptions: Or 11/13/ i.e. we need there to be no unobserved, time-varying confounders!! Problems with identification assumptions: Possible SIMULTANEITY problem Standards are set, with input from firms, at levels that firms are already planning to meet This is a bigger problem for the first specification (with gas prices). With year fixed effects, average industry plans should be soaked up in fixed effects However, least capable manufacturer standard setting could still confound here Alternatively, can focus on years where standard was constant 9

10 Results: Standards No significant effect of standards on rate of technology change among vehicles offered for sale Standards may affect the direction, but not the rate, of technological change (Newell, Jaffe, & Stavins, 1999; Greene, 1990) Limited evidence that standards increase technology change measured across mix of vehicles sold 1 MPG shortfall additional % annual change Average rate = 1.5% per year No evidence of different responses by Detroit firms 11/13/ Some specifications showed a significant effect of shortfall among vehicle mix actually sold, but volatile and not robust to covariates Newell Jaffe Stavins reported that standards affected the direction, but not the rate, of technology change in air conditioners. Greene has shown that CAFE affects fuel economy. Combined with current results, this may suggest that standards also affect the direction, but not the rate, of technology change in autos. 10

11 Results: Gas Prices Some evidence that higher gas prices increase rate of technology change among vehicles offered for sale $1 / gallon additional % annual change Average rate = 1.7% per year Stronger evidence that higher gas prices increase rate of technology change among mix of vehicles sold $1 / gallon additional % annual change Average rate = 1.5% per year Estimates are sensitive to model specification and not significant in all cases 11/13/ Borderline significance in some specifications, no significance in others, for effect on vehicles offered for sale Statistical significance in most specifications for effect on vehicle mix sold (except when we limit data set to post-1989 or Detroit co s only) 11

12 Conclusions & Caveats Main contribution Exploited fleet-specific data on standards Tested within-fleet effects of gas prices & standards Banking & borrowing provisions expected to dampen response on a year to year basis Response may play out only over longer term Limited sample size Though as big as we can get in the U.S. Absence of proof not proof of absence 11/13/ Fixed effects & first differences to reduce autocorrelation issues 12

13 Thank You Questions? 11/13/

14 References Crabb, J. & Johnson, D. (2010), `Fueling innovation: The impact of oil prices and cafe standards on energyefficient automotive technology', The Energy Journal 31(1), Fischer, C., Harrington, W. & Parry, I. (2007), `Should automobile fuel economy standards be tightened?', The Energy Journal 28, Greene, D. (1990), `CAFE or price?: An analysis of the effects of federal fuel economy regulations and gasoline price on new car mpg, ', The Energy Journal 11(3), Jacobsen, M. (2012), Evaluating us fuel economy standards in a model with producer and household heterogeneity. Jaffe, A. & Stavins, R. (1994), `Energy efficiency investments and public policy', The Energy Journal 15(2), 43. Kleit, A. (2004), `Impacts of long-range increases in the fuel economy (cafe) standard', Economic Inquiry 42(2), Knittel, C. R. (2011), `Automobiles on steroids: Product attribute trade-offs and technological progress in the automobile sector', American Economic Review 101(7), NESCAUM (2008), `Northeast States for Coordinated Air Use Management: Submission to federal docket id no. NHTSA ', Newell, R., Jaffe, A. & Stavins, R. (1999), `The induced innovation hypothesis and energy-saving technological change', The Quarterly Journal of Economics 114(3), NHTSA (2006), `Average fuel economy standards for light trucks model years : Final rule', Federal Register, 71, 66 / Thursday, April 6, 2006 / Rules and Regulations. Popp, D. (2002), `Induced innovation and energy prices', The American Economic Review 92(1), Shiau, C., Michalek, J. & Hendrickson, C. (2009), `A structural analysis of vehicle design responses to corporate average fuel economy policy', Transportation Research Part A: Policy and Practice 43(9), /13/

15 Results: Cars offered for sale, incl. gas prices 11/13/ Marginal evidence for significance of gas price effect on cars offered for sale. 15

16 Results: Cars actually sold, incl. gas prices 11/13/ Greater evidence for an effect of gas price on cars sold, compared with cars offered for sale. Some evidence that a larger CAFE shortfall increased rate of technology change. BUT, estimates are volatile and not robust to alternative specifications 16

17 Results: Cars offered for sale, year fixed effects 11/13/ Basically no effect of CAFE constraint on tech change, notwithstanding one significant coefficient, which is not robust 17

18 Results: Cars actually sold, year fixed effects 11/13/ Some evidence for effect of CAFE shortfall, though not robust to inclusion of covariates or restriction to later years. 18

19 Results: cars over 3-year intervals 11/13/

20 Measuring Technology Devices & design tweaks that reduce energy needs Creation of new IP via patents (Crabb & Johnson, 2010) Reduction in energy consumption at vehicle level Challenge: Technology can be applied to increase fuel economy, or to offset changes in other attributes. Average fuel economy, power, and weight of new U.S. cars (U.S. EPA) Attributes normalized to 1975 MPG Power Weight 11/13/