What did you expect? Lessons from the French environmental Bonus/Malus

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1 What did you expect? Lessons from the French environmental Bonus/Malus X. d Haultfœuille, I. Durrmeyer, P. Février

2 Environmental bonus/malus policy Main objectives of the policy Reduce CO 2 emissions related to automobiles Bonus/malus : instrument to encourage the purchase of green cars Financially neutral on the budget (taxes subsidize bonuses) Class of Emissions emissions (in g/km) Rebate A (60-100] +1000e B ( ] +700e C+ ( ] +200e C- ( ] 0e D ( ] 0e E+ ( ] -200e E- ( ] -750e F ( ] -1600e G > e

3 Consequences 1st objective reached : Average CO 2 emissions decreased by 9g (vs. trend of 3g/year) Average CO2 of new vehicles 130 Trend before december Zero-cost objective NOT reached : 2008 cost = 225Me Bonus/malus schedule changed in 2010 and 2011

4 Consequences Shares of vehicles according to the bonus/malus Important reactions : successful policy and large cost

5 Consequences Detailed shares of vehicles according to the bonus/malus Class Fee Market share Average price (in e) Average ratio of emission (in e) fee /price A ,02% 0,06% ,3% B ,9% 38,4% ,7% C ,3% 9,5% ,2% C- 0 19,7% 18,6% % D 0 26,9% 21,7% % E ,9% 2,0% ,2% E ,1% 7,8% ,8% F ,7% 1,4% ,0% G ,4% 0,54% ,7%

6 Presentation of the problem Main question Was the effect of the environmental bonus/malus predictable? An ex-post analysis of ex-ante evaluation of the policy Main results : Prediction of agents behavior is difficult since the reactions to bonus can not be interpreted only as price reactions Bonus/malus sensitivity estimated to be three times the price sensitivity. Evidence of a growing environmental concern? Direct consequence : cost of the policy is underestimated

7 Outline Introduction

8 Framework Main assumptions : Automobiles are (exogenously) differentiated : imperfect substitutes Each consumer purchases a car within the set available or chooses not to buy any (outside option) Consumers have preferences for vehicles attributes : the utility is linearly dependant on attributes Nested logit with observed heterogeneity : consumers with same demographic characteristics have same preferences Structural approach Utility function is modeled and its parameters estimated

9 Nested logit model Utility of consumer i, with demographic characteristics d purchasing good k Utility function : U ik,d = α d β d p k + γ d X k + ξ k + ζ ig,d + (1 σ d )ε ik,d X k and ξ k : observable and unobservable characteristics ζ ig : group-specific error term ε ik : individual error term

10 Reduced form Equation to estimate ln s k,d ln s 0,d = α d β d p k + γ d X k + σ ln s k/g,d + ξ k,d Estimation technique : linear model, IV method Endogenous variables : price and intra-group market-share Simultaneity problem Correlated with unobservable characteristics (e.g. quality)

11 Outline Introduction

12 Description of the data Source : CCFA (Syndicate of French automobile manufacturers) Purchases of new cars by French households between Jan and Dec Characteristics of the cars : Brand, model, CO 2 emissions, horsepower, weight, nb of seats, type of gas, nb of doors Prices : retail prices (transaction prices better, not available) Individual characteristics : Age Precise town Income proxy added from external source

13 Specification of utility Equation to estimate ln s k,d ln s 0,d = α d β d p k + γ d X k + σ ln s k/g,d + ξ k,d We make the following choices : Products analyzed : brand, model, car body style, fuel type, class of CO 2 emissions and number of doors Accurate level : 1000 products Relevant market : consumers with same demographic characteristics (2 age, income and city density classes) 8 indep. groups Potential market : number of households in each market (estimated from INSEE data) Segmentation of the automobile market : according to the use 8 segments

14 Selection of variables Continuous characteristics Price (in constant e) Horsepower and weight Km/e (fuel efficiency) Discrete characteristics Car body style (Station-wagon, coupe/convertible, classic) Fuel type (gasoline vs. diesel) Number of doors (4/5 vs. 3) Segment, brand and time fixed effects Instruments Sum of characteristics of other brands products Sum of characteristics of other models of the same brand Sum of characteristics of other brands products in the same segment

15 Outline Introduction

16 Estimated parameters I Estimated parameters with the sample (before the policy is in application) : Variable Coeff. Std. Err. Price ( β) (0.008) ln( s) (σ) (0.018) Charact. ( γ) Weight (0.110) Horsepower (0.001) Km/e (0.004) Cylinder capacity (0.034) Station wagon (0.017) Convert./coupe (0.038) 3 doors (0.014) Diesel (0.025) Nb. of obs Significance levels : : 10% : 5% : 1% Concl. : Parameters are significant and have expected signs

17 Estimation of price elasticities are coherent with the literature - de hab. + de hab Revenu\ Age < < <

18 Estimated parameters II Price 2008 = Price net bonus/malus Bonus/malus effect constrained to be a price effect 2008 excluded Whole sample Variable Coeff. Std. Err. Coeff. Std. Err. Price ( β) (0.008) (0.009) ln( s) (σ) (0.018) (0.018) Charact. ( γ) Weight (0.110) (0.112) Horsepower (0.001) (0.001) Km/e (0.004) (0.004) Cylinder capacity (0.034) (0.034) Station wagon (0.017) (0.017) Convert./coupe (0.038) (0.037) 3 doors (0.014) (0.015) Diesel (0.025) (0.024) Nb. of obs Concl. : Overestimation of price sensitivity

19 Bonus/malus sensitivity 2008 Bonus Asymmetric excluded independent effects Price ( β) (0.008) (0.008) (0.008) Bonus/malus (0.015) Bonus (0.048) Malus (0.018) Bonus/malus effect is almost 3 times larger than price effect Bonus sensitivity greater than malus sensitivity

20 Predicted cost Market shares and cost predicted, ex-ante : 1 Bonus/malus effect is considered a price effect 2 Real bonus/malus effect known Observed shares Predicted shares Class Without manufacturers With manufacturer of emission reaction reaction A 0,02% 0,06% 0,02% 0,02% B 19,6% 38,1% 22,0% 21,7% C+ 10,4% 9,4% 10,6% 10,8% C- 20,3% 18,6% 19,9% 20,1% D 27,4% 21,6% 27,2% 27,0% E+ 2,9% 2,1% 2,9% 2,9% E- 14,7% 8,0% 13,5% 13,7% F 3,4% 1,4% 3,0% 3,0% G 1,3% 0,57% 0,97% 0,92% Cost +135 Me +3,12 Me -3,8 Me

21 Outline Introduction

22 Main results Structural model of demand can be used in general for ex-ante policy evaluation Even if, for the Bonus/malus policy, the reaction to the bonus/malus not predictable The policy seems to have modified not only the prices but also the consumers utility. Is it a growing environmental concern? The decrease in average CO2 emissions can hide an increase in total emissions, once the market size is taken into account

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