Compatibility and Investment in the U.S. Electric Vehicle...

Compatibility and Investment in theU.S. Electric Vehicle Market

Jing LiMIT Sloan School of Management

Slides to accompany paper draft from January 27th, 2019

Introduction Electric Vehicle Market Model Estimation Mandated Compatibility Conclusion Appendix

Compatibility in product markets

• Product markets vary in compatibility with complementary goods

• Mobile phones: chargers• Checking accounts: ATM networks• Electric vehicles: charging stations

• Antitrust and policy debates over compatibility

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• Product markets vary in compatibility with complementary goods• Mobile phones: chargers

• Checking accounts: ATM networks• Electric vehicles: charging stations


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• Product markets vary in compatibility with complementary goods• Mobile phones: chargers• Checking accounts: ATM networks

• Electric vehicles: charging stations


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• Product markets vary in compatibility with complementary goods• Mobile phones: chargers• Checking accounts: ATM networks• Electric vehicles: charging stations


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Effect of compatibility theoretically ambiguous

Opposing effects:

1. Increase efficiency: holding investment fixed, consumers are better off

2. Decrease firms’ incentives to invest in complementary goods due to positive spillovers

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Electric vehicle market

• Rapidly growing segment of the automobile industry

• Large potential environmental benefits draw billions of dollars in annual subsidies.

• Charging stations incompatible across car brands MORE DETAIL

• E.U. mandate for a particular standard on all stations

• U.S. local electric utilities nationwide considering entry

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This paper

What is the impact of compatibility in electric vehicle charging standards?

• Consumer vehicle purchases• Car manufacturer investment in charging stations• Social welfare

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This paper

What is the impact of compatibility in electric vehicle charging standards?• Consumer vehicle purchases

• Car manufacturer investment in charging stations• Social welfare

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This paper

What is the impact of compatibility in electric vehicle charging standards?• Consumer vehicle purchases• Car manufacturer investment in charging stations

• Social welfare

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This paper

What is the impact of compatibility in electric vehicle charging standards?• Consumer vehicle purchases• Car manufacturer investment in charging stations• Social welfare

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Empirical Approach

1. Model and estimate electric vehicle demand

2. Model and estimate car manufacturer charging station investment

3. Counterfactual market outcomes and welfare under compatibility

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Empirical Approach

1. Model and estimate electric vehicle demand• Discrete choice with charging stations as a product characteristic• Estimate price and charging station elasticities• Identify demand parameters using federal and state subsidy variation• Preserve important spatial properties of charging stations

2. Model and estimate car manufacturer charging station investment


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Empirical Approach

1. Model and estimate electric vehicle demand• Discrete choice with charging stations as a product characteristic• Estimate price and charging station elasticities• Identify demand parameters using federal and state subsidy variation• Preserve important spatial properties of charging stations

2. Model and estimate car manufacturer charging station investment• Recover costs of building charging stations• Make counterfactual computation tractable


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Main Result

Compatibility in electric vehicle charging standards:

• Decreases number of charging stations built

• Increases number of electric vehicles purchased

• Increases consumer surplus

• Increases producer surplus in total (not all producers better off)

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Contributions to relevant literatures

1. Compatibility and standardization

Theory: Katz and Shapiro (1985, 1986a, 1986b); Simcoe and Farrell (2012)

Empirical: Gross (2016); Lee (2013); Knittel and Stango (2011, 2008); Kuhn and Van Reenen (2009);

Ishii (2007); Ho (2006)

2. Firm choices in product quality

Wollmann (2018); Crawford et al. (2015); Eizenberg (2014); Nosko (2014); Sweeting (2013); Fan (2013);

Draganska et al. (2009)

3. Electric vehicles

Environment: Holland et al. (2016); Graff Zivin et al. (2014); Michalek et al. (2011)

Consumer subsidy design and impact: Clinton and Steinberg (2016); Sheldon et al. (2017); Borenstein

and Davis (2015), Holtsmark and Skonhoft (2014)

Network effects: Li et al. (2017); Springel (2016)

4. Directed technical change

Acemoglu et al. (2016); Aghion et al. (2016); Greaker and Midttømme (2016); Jaffe et al. (2005)

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Outline of talk

1. Introduction

2. Electric Vehicle Market

3. Model

4. Estimation

5. Mandated Compatibility Counterfactual

6. Conclusion

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Outline of talk

1. Introduction

2. Electric Vehicle Market• Data• Electric vehicle characteristics• Charging stations and standards

3. Model

4. Estimation


6. Conclusion

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Data

1. Nationwide vehicle sales and characteristics (2011-2015)

2. Charging stations (2010-2015)

3. Government incentives (2011-2015)

4. Consumer demographics (2009-2013)

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Data

1. Nationwide vehicle sales and characteristics (2011-2015)• Vehicle registration data from IHS Automotive (formerly Polk)• Quantities by time (quarter), market (MSA), and model• MSRP, fuel efficiency, and other characteristics from EPA, MSN Auto

2. Charging stations (2010-2015)



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Data


2. Charging stations (2010-2015)• Data from National Renewable Energy Laboratory• Opening date, geographic coordinates, charging speed and standard



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Data



3. Government incentives (2011-2015)• State congressional records and IRS website• State and federal tax incentives for consumer EV purchases• State tax incentives for charging station installation


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Data



3. Government incentives (2011-2015)• State congressional records and IRS website• State and federal tax incentives for consumer EV purchases• State tax incentives for charging station installation

4. Consumer demographics (2009-2013)• County-level commuting flows from the American Community Survey (2009-2013)• Income from the Decennial Census (2010)

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Two fuel types of electric vehicles

Fuel Type New Models Modified Existing Models

All-electric Nissan LEAF Ford Focus ElectricBMW i3Tesla Model S

Plug-in hybrid Chevrolet Volt Toyota Prius Plug-in(gasoline backup) Volkswagen e-Golf

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Rapid growth in the electric vehicle market

2011 2012 2013 2014 2015

Number of EV models 3 6 15 21 26Number of EV brands 3 6 13 15 18EV unit sales 13,542 41,643 93,734 140,320 127,699

Table: Electric vehicle sales summary statistics

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Entry of all-electric vehicles, 2011-2015

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Nissan Leaf (2011)

Tesla Roadster (2011)

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Nissan Leaf (2011)


Ford Focus Electric (2012)

Mitsubishi i-Miev (2012)

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Nissan Leaf (2011)



Mitsubishi i-Miev (2012)FIAT 500e (2013)

Honda Fit EV (2013)

smart fortwo electric drive (2013)

Tesla Model S (2013)

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Nissan Leaf (2011)




FIAT 500e (2013)

Honda Fit EV (2013)



Mercedes-Benz B-class Electric Drive (2014)

Volkswagen e-Golf (2014)

BMW i3 (2014)

Kia Soul EV (2014)

Chevrolet Spark EV (2014)

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Nissan Leaf (2011)




FIAT 500e (2013)

Honda Fit EV (2013)





BMW i3 (2014)

Kia Soul EV (2014)


Tesla Model X (2015)

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Nissan Leaf (2011)




FIAT 500e (2013)

Honda Fit EV (2013)





BMW i3 (2014)

Kia Soul EV (2014)


Tesla Model X (2015)

Chevrolet Bolt EV (2017)

Nissan Leaf (2018)

Tesla Model 3 (2017)

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Charging stations sit near parking spaces.

(a) Level 2; Boston, MA (b) Level 3 Tesla; Moab, UT14 / 47


What is a charging standard?

• Two aspects

1. A physical connector2. A set of electric signals

• Cars can charge at three speed levels:• Levels 1 and 2 (slower speeds)

• Compatible across all brands and cars• Homes, employers, retailers, and government programs

• Level 3 (DC, Fast) MORE DETAIL

• Three incompatible standards• Built by car manufacturers

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• Two aspects

1. A physical connector

2. A set of electric signals





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• Two aspects






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• Two aspects


• Cars can charge at three speed levels:

• Levels 1 and 2 (slower speeds)• Compatible across all brands and cars• Homes, employers, retailers, and government programs



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• Two aspects






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Three standards for Level 3

Level 3 (DC, Fast) Charging Standards

SAE J1772 Combo Chademo Tesla

BMW: i3 Nissan: LEAF Tesla: Model S, XGM: Bolt, Spark EV Mitsubishi: i-MiEVVolkswagen: e-Golf Kia: Soul EVFord ToyotaChrysler PeugeotDaimler Citroen

Depiction of plug shapes from Alternative Fuel Data Center

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Outline of talk

1. Introduction


3. Model• Overview• Charging network description and model• Consumer utility and firm optimization problem

4. Estimation


6. Conclusion

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Overview of each period (quarter)

• Firms• Compete in static oligopoly, and• Maximize electric vehicle profits by choosing quantity and locations of charging stations.

• Consumers• Consider only present options and characteristics (are myopic), and• Maximize utility by choosing among plug-in or non-plug-in (outside option) vehicles.

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Each period’s sequence of events

0. Station investments by firms in the previous period arrive.

Vehicle models from exogenous R&D arrive.

1. Firms choose charging station investment.

2. Consumer demand shocks realized.

3. Firms set prices given demand shocks and product characteristics.

4. Consumers choose which vehicle to purchase given existing charging stations and prices.

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Automakers begin building stations after car release.

Nissan LEAF 0

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Chademo (Nissan)

Data Source: Alternative Fuel Data Center

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Nissan LEAF Tesla Model S 0

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Manufacturers pursue different location strategies.

Figure: Data source: Alternative Fuels Data Center; September 2015

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Many possible charging locations in 1-D example

s t

s t

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Figure: Need to simplify firms’ action space

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Complementarity among stations in 1-D example

• The last station to “connect the dots” increases value of others

s t

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Reduce action space and spatial complementarity

s t

s t

Figure: Firms choose whether to build links between cities

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Costs of linking cities are linear

coeff = .28536971

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Relevant charging stations for consumers

• Model consumers as taking two types of trips

• Local: stations weighted by county-to-county commuting flows• Inter-city: number of traversable city pairs

• Tractable suppy side• Preserves relation of charging locations

• to consumers’ origins/destinations• to other charging locations

• Implicitly assumes uniform value of links

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• Model consumers as taking two types of trips• Local: stations weighted by county-to-county commuting flows

• Inter-city: number of traversable city pairs




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• Model consumers as taking two types of trips• Local: stations weighted by county-to-county commuting flows• Inter-city: number of traversable city pairs




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Consumer utility over car purchasesConsumer i maximizes utility by choosing among vehicles r and the outside good 0.

Uirmt = δrmt + εirmt

δrmt = γS f (Gt , l) + γLg(Gt , dr ) − αprmt + Xrmtβ + ξrmt

• prmt is the purchase price

• Gt is the set of existing charging stations

• f (Gt , l) is the quality of the local charging network

• g(Gt , dr ) is the quality of the national network

• Xrmt includes other characteristics, gas and electricity prices, market and time fixedeffects

• ξrmt represents factors such as targeted advertising, demand shocks

• εirmt has type I extreme value (logit), represents idiosyncratic preferences

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Firm optimization problem

• Each firm j chooses charging station investment ajt to maximize discounted flow ofprofits.

• ajt is a vector of length M + 1, for M local networks and 1 national long-distance network

πjt(Gt−1 + at) =∑m

∑r∈Jjt

(prt −mcrt + ZEVcreditrmt) srmt(Gt−1 + at , pt ; xmt , ξmt , θ)Nmt

• Objective:maxajt

πjt(Gt−1 + a−jt + ajt)− c(ajt)

where c(ajt) = κ|ajt |+ ωjt

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Outline of talk

1. Introduction


3. Model

4. Estimation• Identification• Zero shares• Estimates


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Identification

Mean utility:δrmt = γS f (Gt , l) + γLg(Gt , dr ) − αprmt + Xrmtβ + ξrmt

• Concern: prices and charging stations are correlated with ξrmt

• Use plausibly exogenous variation from government subsidies and assumptions on actiontiming

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Identification: Prices


• Instrument 1: state tax credits that vary across car, state, and time

• Instrument 2: federal tax credits that vary across car MORE DETAIL

• Identification assumption: policy variation exogenous conditional on market and timefixed effects

• Idiosyncratic months when subsidies begin and end• Range of reasons given by lawmakers

• Adding BLP instruments does not change the price parameter estimate.

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Identification: Stations


• Instrument 1: state subsidies that vary across state and time

• Instrument 2: lagged levels of charging stations• Identifying assumption:

• Stations take at least one period to arrive after firms choose investment• Arrival of stations in period t uncorrelated with

νrm,t = ξrm,t − ρξrm,t−1

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Some products have zero market shares.

• Zero market shares a common problem in empirical demand analysis

• Practical challenge: cannot invert predicted share function because log(0) is undefined.

• Theoretical challenge: the model predicts strictly positive shares.

Variable Mean Std. Dev. 10% Median 90% # Obs. % Zeros

All vehicle sales 13,798.7 28,488.5 1,140 3,973.5 37,471 40,200 0Plug-in sales 20.4 50.2 0 1 16 40,200 35.7- 2011 plug-in sales 9.5 35.0 0 2 18 1,424 15.5- 2012 plug-in sales 10.7 49.1 0 2 17 3,910 23.0- 2013 plug-in sales 11.8 47.8 0 1 20 7,966 30.8- 2014 plug-in sales 12.0 61.1 0 1 20 11,694 33.2- 2015 plug-in sales 8.4 42.8 0 1 13 15,206 45.5

Observed market share .00085 .0019 0 .00024 .0023 40,200 35.7

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Empirical Bayes estimator of market shares

• Demand estimation uses true market share s0.

• Observe Krm purchases of product r out of Nm total purchases

• Observed market share:

sMLErm =

Krm

Nm

• Replace with empirical Bayes estimator for market share:

Krm ∼ Binomial(Nm, s0rm)

s0rm ∼ Beta(λ1rm, λ2rm)

srm =λ1rm + Krm

Nm + λ1rm + λ2rm

• Consistent with logit demand model theory (McFadden (1974))

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Empirical Bayes posteriors

• Estimate hyperparameters of the Beta-Binomial distribution.

• Priors from 50 markets closest in income per capita

Market Share Mean Std. Dev. 10% Median 90% # Obs. % Zeros

Observed .00085 .0019 0 .00024 .0023 40,200 35.7Posterior .00082 .0015 .000027 .00035 .0020 40,200 0

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Demand estimation results

OLS

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Model estimates are close to industry estimates.

• Demand parameters• Tesla adapter retail price: $450• Predicted consumer surplus change: $426 average ($932 for Tesla purchasers)

• Charging station costs• Engineering estimates of Level 3 charging station costs: $150,000• Costs from firm profit first-order condition: $96,540 (Chademo) and $132,960 (Combo)

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Outline of talk

1. Introduction


3. Model

4. Estimation


6. Conclusion

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Overview

• Held fixed:• car prices• other vehicle characteristics, such as driving range.• arrival of car models

• Three steps

1. Demand response to unified standard, holding stations fixed in quantity and location2. Firms re-optimize station locations, holding quantity fixed3. Firms re-optimize both locations and quantities of stations

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Traversability of national network under unified standard

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Figure: Traversability is defined as the number of city pairs that can be reached using charging stationsbetween them, normalized by the total number of city pairs.

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Step 1: Charging station investment fixed

Units Sold in MSAs (2011-2015)

Standard Status Quo Counterfactual ∆ Quantity % Change

Chademo 80,673 80,271 -402 -.5Combo 27,289 31,650 4,361 16.0Tesla 46,009 68,383 22,374 48.6Other Plug-Ins 262,956 259,164 -3,792 -1.44

Total Change (Level 3) 26,333 17.1Total Change (All Plug-Ins) 22,541 5.5

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Step 2: Firms choose new locations

• Two challenges resolved by demand specification:• Reduce action space• Vehicle sales due to each action spatially independent

• Location problem is equivalent to computational problem called fractional knapsack

• Greedy algorithm gives optimal placement.

• Find equilibrium in each period by simulating firms playing iterated best-response

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Step 2: Firms disperse stations more under compatibility• Incompatibility: 165 markets (out of 344) with 0 charging stations• Compatibility: 82 markets with 0 charging stations

Figure: Number of markets with charging station presence from each coalition

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Step 3: Firms and consumers re-optimize - Welfare

Simulated Counterfactual Outcomes Difference Across Regimes

Incompatible (I) Compatible (C) Social Planner (SP) (C-I) (SP-I) (SP-C)(1) (2) (3) (4) (5) (6)

A. SOCIAL WELFARE ($millions)

∆Social Welfare - - - 2289.998 4220.684 1930.686(1141.106) (638.902) (1361.262)

∆Consumer Surplus - - - 490.017 3104.464 2614.446(1146.154) (555.232) (1416.486)

Producer Vehicle Profits 6293.642 8145.766 7740.801 1852.124 1447.159 -404.965(2273.197) (3069.404) (3050.881) (287.890) (417.790) (512.623)

Nissan 1131.595 936.056 852.920 -195.539 -278.675 -83.136(922.561) (602.007) (601.986) (56.665) (127.097) (98.641)

BMW 268.010 936.716 1028.590 668.706 760.580 91.874(79.694) (605.593) (904.527) (81.246) (127.553) (143.222)

Tesla 2161.018 3416.890 2982.082 1255.872 821.064 -434.808(1359.788) (1700.059) (1582.061) (227.337) (229.020) (281.513)

• W (Social Planner) >W F (Compatible) >W F (Incompatible)

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Step 3: Firms and consumers re-optimize - Stations



B. NUMBER OF CHARGING LOCATIONS

Total No. Locations 1868.510 1816.367 1537.571 -52.143 -330.939 -278.796(150.628) (192.070) (382.786) (14.675) (53.401) (52.595)

built by Nissan 1237.510 1185.367 - -52.143 - -(150.628) (192.070) (14.675)

built by BMW 380.000 380.000 - 0.000 - -(0.000) (0.000) (0.000)

built by Tesla 251.000 251.000 - 0.000 - -(0.000) (0.000) (0.000)

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Step 3: Firms and consumers re-optimize - EV sales



C. EV UNITS SOLD (thousands)

Total EV Units 488.173 589.649 573.303 101.476 85.130 -16.346(67.840) (144.214) (194.166) (15.950) (29.176) (33.967)

Chademo 127.564 109.711 102.773 -17.853 -24.791 -6.938(66.454) (45.403) (68.634) (4.286) (12.294) (10.773)

SAE Combo 15.670 83.672 86.828 68.002 71.158 3.156(3.991) (58.325) (79.452) (8.217) (11.395) (13.075)

Tesla 83.953 138.351 122.420 54.398 38.467 -15.931(41.254) (63.269) (66.109) (9.640) (9.845) (12.187)

Other Plug-In 260.986 257.914 261.282 -3.071 0.296 3.368(3.502) (9.154) (4.250) (0.884) (0.689) (1.323)

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Conclusion

• Mandating compatibility in charging standards improves social welfare.

• Electric vehicle charging compatibility will become more important as US governmentand local utilities consider building charging stations.

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Electric vehicle charging adapter

• Adapter must overcome two challenges

1. Physical connection2. Electric communication signals

• Currently only a single adapter exists• Tesla-to-Chademo one-way adapter released in 2015• 2-3 years of engineering effort• Other directions incompatible

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Tesla-to-Chademo adapter

BACK TO INTRO BACK TO EV MKT

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State tax credits as price variation

Figure: Data source: National Conference of State Legislatures and state legislative records. State taxcredits available in September 2015, $1,500 to $7,500 per car.

BACK

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Federal tax credits as price variation

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0 20 40 60Battery Size (kWh)

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Demand estimation results(1) (2)

VARIABLES OLS IV

log(Price) -2.316*** -2.732***(0.0787) (0.625)

log(Local Level 2) × PHEV 0.0931*** 0.129***(0.0177) (0.0295)

log(Local Level 2) × BEV 0.0614** 0.0912***(0.0245) (0.0339)

log(Local Level 3) × PHEV -0.00300 0.0236**(0.00904) (0.0114)

log(Local Level 3) × BEV 0.0580*** 0.0671***(0.00776) (0.00867)

# City pairs × PHEV -0.234 -0.902*(0.300) (0.509)

# City pairs × BEV 0.00552*** 0.00524**(0.00155) (0.00267)

BEV dummy -1.889*** -2.276***(0.133) (0.217)

Battery range 0.00760*** 0.00915***(0.00104) (0.00174)

.

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.Observations 40,200 35,418R-squared 0.376 0.388Minimum Eigvalue Stat (IV F-stat) 59.42

Standard errors clusterd by model-market; market and MSA FE included.

BACK TO BLP 52 / 47

References

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Borenstein, S. and L. W. Davis (2015). The Distributional Effects of U.S. Clean Energy TaxCredits. In J. R. Brown (Ed.), Tax Policy and the Economy, Volume 30, Chapter 6, pp.191–234. University of Chicago Press.

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Draganska, M., M. Mazzeo, and K. Seim (2009). Beyond Plain Vanilla: Modeling JointProduct Assortment and Pricing Decisions. Quantitative Marketing and Economics 7,105–146.

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